This tutorial is the continuation of Talend Input component for Hazelcast tutorial. We will not walk through the project creation again, So please start from there before taking this one. This tutorial shows how to create a complete working output component for Hazelcast As seen before, in Hazelcast there is multiple data source type. You can find queues, topics, cache, maps… In this tutorials we will stick with the Map dataset and all what we will see here is applicable to the other types. Let’s assume that our Hazelcast output component will be responsible of inserting data into a distributed Map. For that, we will need to know which attribute from the incoming data is to be used as a key in the map. The value will be the hole record encoded into a json format. Bu that in mind, we can design our output configuration as: the same Datastore and Dataset from the input component and an additional configuration that will define the key attribute. Let’s create our Output configuration class. Let’s add the i18n properties of our configuration into the Messages.properties file The skeleton of the output component looks as follows: @Version annotation indicates the version of the component. It is used to migrate the component configuration if needed. @Icon annotation indicates the icon of the component. Here, the icon is a custom icon that needs to be bundled in the component JAR under resources/icons. @Processor annotation indicates that this class is the processor (output) and defines the name of the component. constructor of the processor is responsible for injecting the component configuration and services. Configuration parameters are annotated with @Option. The other parameters are considered as services and are injected by the component framework. Services can be local (class annotated with @Service) or provided by the component framework. The method annotated with @PostConstruct is executed once by instance and can be used for initialization. The method annotated with @PreDestroy is used to clean resources at the end of the execution of the output. Data is passed to the method annotated with @ElementListener. That method is responsible for handling the data output. You can define all the related logic in this method. If you need to bulk write the updates accordingly to groups, see Processors and batch processing. Now, we will need to add the display name of the Output to the i18n resources file Messages.properties Let’s implement all of those methods We will create the outpu contructor to inject the component configuration and some additional local and built in services. Built in services are services provided by TCK. Here we find: configuration is the component configuration class hazelcastService is the service that we have implemented in the input component tutorial. it will be responsible of creating a hazelcast client instance. jsonb is a built in service provided by tck to handle json object serialization and deserialization. We will use it to convert the incoming record to json format before inseting them into the map. Nothing to do in the post construct method. but we could for example initialize a hazle cast instance there. but we will do it in a lazy way on the first call in the @ElementListener method Shut down the Hazelcast client instance and thus free the Hazelcast map reference. We get the key attribute from the incoming record and then convert the hole record to a json string. Then we insert the key/value into the hazelcast map. Let’s create a unit test for our output component. The idea will be to create a job that will insert the data using this output implementation. So, let’s create out test class. Here we start by creating a hazelcast test instance, and we initialize the map. we also shutdown the instance after all the test are executed. Now let’s create our output test. Here we start preparing the emitter test component provided bt TCK that we use in our test job to generate random data for our output. Then, we use the output component to fill the hazelcast map. By the end we test that the map contains the exact amount of data inserted by the job. Run the test and check that it’s working. Congratulation you just finished your output component.

This tutorial walks you through the creation, from scratch, of a complete Talend input component for Hazelcast using the Talend Component Kit (TCK) framework. Hazelcast is an in-memory distributed system that can store data, which makes it a good example of input component for distributed systems. This is enough for you to get started with this tutorial, but you can find more information about it here: hazelcast.org/. A TCK project is a simple Java project with specific configurations and dependencies. You can choose your preferred build tool from Maven or Gradle as TCK supports both. In this tutorial, Maven is used. The first step consists in generating the project structure using Talend Starter Toolkit . Go to starter-toolkit.talend.io/ and fill in the project information as shown in the screenshots below, then click Finish and Download as ZIP. image::tutorial_hazelcast_generateproject_1.png[] image::tutorial_hazelcast_generateproject_2.png[] Extract the ZIP file into your workspace and import it to your preferred IDE. This tutorial uses Intellij IDE, but you can use Eclipse or any other IDE that you are comfortable with. You can use the Starter Toolkit to define the full configuration of the component, but in this tutorial some parts are configured manually to explain key concepts of TCK. The generated pom.xml file of the project looks as follows: Change the name tag to a more relevant value, for example: Component Hazelcast. The component-api dependency provides the necessary API to develop the components. talend-component-maven-plugin provides build and validation tools for the component development. The Java compiler also needs a Talend specific configuration for the components to work correctly. The most important is the -parameters option that preserves the parameter names needed for introspection features that TCK relies on. Download the mvn dependencies declared in the pom.xml file: You should get a BUILD SUCCESS at this point: Create the project structure: Create the component Java packages. Packages are mandatory in the component model and you cannot use the default one (no package). It is recommended to create a unique package per component to be able to reuse it as dependency in other components, for example to guarantee isolation while writing unit tests. The project is now correctly set up. The next steps consist in registering the component family and setting up some properties. Registering every component family allows the component server to properly load the components and to ensure they are available in Talend Studio. The family registration happens via a package-info.java file that you have to create. Move to the src/main/java/org/talend/components/hazelcast package and create a package-info.java file: @Components: Declares the family name and the categories to which the component belongs. @Icon: Defines the component family icon. This icon is visible in the Studio metadata tree. Talend Component Kit supports internationalization (i18n) via Java properties files. Using these files, you can customize and translate the display name of properties such as the name of a component family or, as shown later in this tutorial, labels displayed in the component configuration. Go to src/main/resources/org/talend/components/hazelcast and create an i18n Messages.properties file as below: You can define the component family icon in the package-info.java file. The icon image must exist in the resources/icons folder. TCK supports both SVG and PNG formats for the icons. Create the icons folder and add an icon image for the Hazelcast family. This tutorial uses the Hazelcast icon from the official GitHub repository that you can get from: avatars3.githubusercontent.com/u/1453152?s=200&v=4 Download the image and rename it to Hazelcast_icon32.png. The name syntax is important and should match _icon.32.png. The component registration is now complete. The next step consists in defining the component configuration. All Input and Output (I/O) components follow a predefined model of configuration. The configuration requires two parts: Datastore: Defines all properties that let the component connect to the targeted system. Dataset: Defines the data to be read or written from/to the targeted system. Connecting to the Hazelcast cluster requires the IP address, group name and password of the targeted cluster. In the component, the datastore is represented by a simple POJO. Create a HazelcastDatastore.java class file in the src/main/java/org/talend/components/hazelcast folder. Define the i18n properties of the datastore. In the Messages.properties file let add the following lines: The Hazelcast datastore is now defined. Hazelcast includes different types of datastores. You can manipulate maps, lists, sets, caches, locks, queues, topics and so on. This tutorial focuses on maps but still applies to the other data structures. Reading/writing from a map requires the map name. Create the dataset class by creating a HazelcastDataset.java file in src/main/java/org/talend/components/hazelcast. The @Dataset annotation marks the class as a dataset. Note that it also references a datastore, as required by the components model. Just how it was done for the datastore, define the i18n properties of the dataset. To do that, add the following lines to the Messages.properties file. The component configuration is now ready. The next step consists in creating the Source that will read the data from the Hazelcast map. The Source is the class responsible for reading the data from the configured dataset. A source gets the configuration instance injected by TCK at runtime and uses it to connect to the targeted system and read the data. Create a new class as follows. The source also needs i18n properties to provide a readable display name. Add the following line to the Messages.properties file. At this point, it is already possible to see the result in the Talend Component Web Tester to check how the configuration looks like and validate the layout visually. To do that, execute the following command in the project folder. This command starts the Component Web Tester and deploys the component there. Access localhost:8080/. The source is set up. It is now time to start creating some Hazelcast specific code to connect to a cluster and read values for a map. Add the hazelcast-client Maven dependency to the pom.xml of the project, in the dependencies node. Add a Hazelcast instance to the @PostConstruct method. Declare a HazelcastInstance attribute in the source class. Any non-serializable attribute needs to be marked as transient to avoid serialization issues. implement the post construct method. The component configuration is mapped to the Hazelcast client configuration to create a Hazelcast instance. This instance will be used later to get the map from its name and read the map data. Only the required configuration in the component is exposed to keep the code as simple as possible. implement the code responsible for reading the data from the Hazelcast map through the Producer method. The Producer implements the following logic: Check if the map iterator is already initialized. If not, get the map from its name and initialize the map iterator. This is done in the @Producer method to ensure the map is initialized only if the next() method is called (lazy initialization). It also avoids the map initialization in the PostConstruct method as the Hazelcast map is not serializable. All the objects initialized in the PostConstruct method need to be serializable as the source can be serialized and sent to another worker in a distributed cluster. From the map, create an iterator on the map keys that will read from the map. Transform every key/value pair into a Talend Record with a "key, value" object on every call to next(). The RecordBuilderFactory class used above is a built-in service in TCK injected via the Source constructor. This service is a factory to create Talend Records. Now, the next() method will produce a Record every time it is called. The method will return "null" if there is no more data in the map. implement the @PreDestroy annotated method, responsible for releasing all resources used by the Source. The method needs to shut the Hazelcast client instance down to release any connection between the component and the Hazelcast cluster. The Hazelcast Source is completed. The next section shows how to write a simple unit test to check that it works properly. TCK provides a set of APIs and tools that makes the testing straightforward. The test of the Hazelcast Source consists in creating an embedded Hazelcast instance with only one member and initializing it with some data, and then in creating a test Job to read the data from it using the implemented Source. Add the required Maven test dependencies to the project. Initialize a Hazelcast test instance and create a map with some test data. To do that, create the HazelcastSourceTest.java test class in the src/test/java folder. Create the folder if it does not exist. The above example creates a Hazelcast instance for the test and creates the MY-DISTRIBUTED-MAP map. The getMap creates the map if it does not already exist. Some keys and values uses in the test are added. Then, a simple test checks that the data is correctly initialized. Finally, the Hazelcast test instance is shut down. Run the test and check in the logs that a Hazelcast cluster of one member has been created and that the test has passed. To be able to test components, TCK provides the @WithComponents annotation which enables component testing. Add this annotation to the test. The annotation takes the component Java package as a value parameter. Create the test Job that configures the Hazelcast instance and link it to an output that collects the data produced by the Source. Execute the unit test and check that it passes, meaning that the Source is reading the data correctly from Hazelcast. The Source is now completed and tested. The next section shows how to implement the Partition Mapper for the Source. In this case, the Partition Mapper will split the work (data reading) between the available cluster members to distribute the workload. The Partition Mapper calculates the number of Sources that can be created and executed in parallel on the available workers of a distributed system. For Hazelcast, it corresponds to the cluster member count. To fully illustrate this concept, this section also shows how to enhance the test environment to add more Hazelcast cluster members and initialize it with more data. Instantiate more Hazelcast instances, as every Hazelcast instance corresponds to one member in a cluster. In the test, it is reflected as follows: The above code sample creates two Hazelcast instances, leading to the creation of two Hazelcast members. Having a cluster of two members (nodes) will allow to distribute the data. The above code also adds more data to the test map and updates the shutdown method and the test. Run the test on the multi-nodes cluster. The Source is a simple implementation that does not distribute the workload and reads the data in a classic way, without distributing the read action to different cluster members. Start implementing the Partition Mapper class by creating a HazelcastPartitionMapper.java class file. When coupling a Partition Mapper with a Source, the Partition Mapper becomes responsible for injecting parameters and creating source instances. This way, all the attribute initialization part moves from the Source to the Partition Mapper class. The configuration also sets an instance name to make it easy to find the client instance in the logs or while debugging. The Partition Mapper class is composed of the following: constructor: Handles configuration and service injections Assessor: This annotation indicates that the method is responsible for assessing the dataset size. The underlying runner uses the estimated dataset size to compute the optimal bundle size to distribute the workload efficiently. Split: This annotation indicates that the method is responsible for creating Partition Mapper instances based on the bundle size requested by the underlying runner and the size of the dataset. It creates as much partitions as possible to parallelize and distribute the workload efficiently on the available workers (known as members in the Hazelcast case). Emitter: This annotation indicates that the method is responsible for creating the Source instance with an adapted configuration allowing to handle the amount of records it will produce and the required services. I adapts the configuration to let the Source read only the requested bundle of data. The Assessor method computes the memory size of every member of the cluster. implementing it requires submitting a calculation task to the members through a serializable task that is aware of the Hazelcast instance. Create the serializable task. The purpose of this class is to submit any task to the Hazelcast cluster. Use the created task to estimate the dataset size in the Assessor method. The Assessor method calculates the memory size that the map occupies for all members. In Hazelcast, distributing a task to all members can be achieved using an execution service initialized in the getExecutorService() method. The size of the map is requested on every available member. By summing up the results, the total size of the map in the distributed cluster is computed. The Split method calculates the heap size of the map on every member of the cluster. Then, it calculates how many members a source can handle. If a member contains less data than the requested bundle size, the method tries to combine it with another member. That combination can only happen if the combined data size is still less or equal to the requested bundle size. The following code illustrates the logic described above. The next step consists in adapting the source to take the Split into account. The following sample shows how to adapt the Source to the Split carried out previously. The next method reads the data from the members received from the Partition Mapper. A Big Data runner like Spark will get multiple Source instances. Every source instance will be responsible for reading data from a specific set of members already calculated by the Partition Mapper. The data is fetched only when the next method is called. This logic allows to stream the data from members without loading it all into the memory. implement the method annotated with @Emitter in the HazelcastPartitionMapper class. The createSource() method creates the source instance and passes the required services and the selected Hazelcast members to the source instance. Run the test and check that it works as intended. The component implementation is now done. It is able to read data and to distribute the workload to available members in a Big Data execution environment. Refactor the component by introducing a service to make some pieces of code reusable and avoid code duplication. Refactor the Hazelcast instance creation into a service. Inject this service to the Partition Mapper to reuse it. Adapt the Source class to reuse the service. Run the test one last time to ensure everything still works as expected. Thank you for following this tutorial. Use the logic and approach presented here to create any input component for any system.

The following tutorials are designed to help you understand the main principles of component development using Talend Component Kit. With this set of tutorials, get your hands on project creation using the Component Kit Starter and implement the logic of different types of components. Creating your first component Generating a project from the starter Creating a Hazelcast input component Creating a Hazelcast output component Creating a Zendesk REST API connector Handling component version migration With this set of tutorials, learn the different approaches to test the components created in the previous tutorials. Testing a Zendesk REST API connector Testing a Hazelcast component Testing in a continuous integration environment

A Processor is a component that converts incoming data to a different model. A processor must have a method decorated with @ElementListener taking an incoming data and returning the processed data: Processors must be Serializable because they are distributed components. If you just need to access data on a map-based ruleset, you can use Record or JsonObject as parameter type. From there, Talend Component Kit wraps the data to allow you to access it as a map. The parameter type is not enforced. This means that if you know you will get a SuperCustomDto, then you can use it as parameter type. But for generic components that are reusable in any chain, it is highly encouraged to use Record until you have an evaluation language-based processor that has its own way to access components. For example: A processor also supports @BeforeGroup and @AfterGroup methods, which must not have any parameter and return void values. Any other result would be ignored. These methods are used by the runtime to mark a chunk of the data in a way which is estimated good for the execution flow size. Because the size is estimated, the size of a group can vary. It is even possible to have groups of size 1. It is recommended to batch records, for performance reasons: You can optimize the data batch processing by using the maxBatchSize parameter. This parameter is automatically implemented on the component when it is deployed to a Talend application. Only the logic needs to be implemented. You can however customize its value setting in your LocalConfiguration the property _maxBatchSize.value - for the family - or ${component simple class name}._maxBatchSize.value - for a particular component, otherwise its default will be 1000. If you replace value by active, you can also configure if this feature is enabled or not. This is useful when you don’t want to use it at all. Learn how to implement chunking/bulking in this document. In some cases, you may need to split the output of a processor in two or more connections. A common example is to have "main" and "reject" output connections where part of the incoming data are passed to a specific bucket and processed later. Talend Component Kit supports two types of output connections: Flow and Reject. Flow is the main and standard output connection. The Reject connection handles records rejected during the processing. A component can only have one reject connection, if any. Its name must be REJECT to be processed correctly in Talend applications. You can also define the different output connections of your component in the Starter. To define an output connection, you can use @Output as replacement of the returned value in the @ElementListener: Alternatively, you can pass a string that represents the new branch: Having multiple inputs is similar to having multiple outputs, except that an OutputEmitter wrapper is not needed: @Input takes the input name as parameter. If no name is set, it defaults to the "main (default)" input branch. It is recommended to use the default branch when possible and to avoid naming branches according to the component semantic. Batch processing refers to the way execution environments process batches of data handled by a component using a grouping mechanism. By default, the execution environment of a component automatically decides how to process groups of records and estimates an optimal group size depending on the system capacity. With this default behavior, the size of each group could sometimes be optimized for the system to handle the load more effectively or to match business requirements. For example, real-time or near real-time processing needs often imply processing smaller batches of data, but more often. On the other hand, a one-time processing without business contraints is more effectively handled with a batch size based on the system capacity. Final users of a component developed with the Talend Component Kit that integrates the batch processing logic described in this document can override this automatic size. To do that, a maxBatchSize option is available in the component settings and allows to set the maximum size of each group of data to process. A component processes batch data as follows: Case 1 - No maxBatchSize is specified in the component configuration. The execution environment estimates a group size of 4. Records are processed by groups of 4. Case 2 - The runtime estimates a group size of 4 but a maxBatchSize of 3 is specified in the component configuration. The system adapts the group size to 3. Records are processed by groups of 3. Batch processing relies on the sequence of three methods: @BeforeGroup, @ElementListener, @AfterGroup, that you can customize to your needs as a component Developer. The group size automatic estimation logic is automatically implemented when a component is deployed to a Talend application. Each group is processed as follows until there is no record left: The @BeforeGroup method resets a record buffer at the beginning of each group. The records of the group are assessed one by one and placed in the buffer as follows: The @ElementListener method tests if the buffer size is greater or equal to the defined maxBatchSize. If it is, the records are processed. If not, then the current record is buffered. The previous step happens for all records of the group. Then the @AfterGroup method tests if the buffer is empty. You can define the following logic in the processor configuration: You can also use the condensed syntax for this kind of processor: When writing tests for components, you can force the maxBatchSize parameter value by setting it with the following syntax: .$maxBatchSize=10. You can learn more about processors in this document. Defining a processor/output logic General component execution logic implementing bulk processing Best practices For the case of output components (not emitting any data) using bulking you can pass the list of records to the after group method:

Processors and output components are the components in charge of reading, processing and transforming data in a Talend job, as well as passing it to its required destination. Before implementing the component logic and defining its layout and configurable fields, make sure you have specified its basic metadata, as detailed in this document. A Processor is a component that converts incoming data to a different model. A processor must have a method decorated with @ElementListener taking an incoming data and returning the processed data: Processors must be Serializable because they are distributed components. If you just need to access data on a map-based ruleset, you can use Record or JsonObject as parameter type. From there, Talend Component Kit wraps the data to allow you to access it as a map. The parameter type is not enforced. This means that if you know you will get a SuperCustomDto, then you can use it as parameter type. But for generic components that are reusable in any chain, it is highly encouraged to use Record until you have an evaluation language-based processor that has its own way to access components. For example: A processor also supports @BeforeGroup and @AfterGroup methods, which must not have any parameter and return void values. Any other result would be ignored. These methods are used by the runtime to mark a chunk of the data in a way which is estimated good for the execution flow size. Because the size is estimated, the size of a group can vary. It is even possible to have groups of size 1. It is recommended to batch records, for performance reasons: You can optimize the data batch processing by using the maxBatchSize parameter. This parameter is automatically implemented on the component when it is deployed to a Talend application. Only the logic needs to be implemented. You can however customize its value setting in your LocalConfiguration the property _maxBatchSize.value - for the family - or ${component simple class name}._maxBatchSize.value - for a particular component, otherwise its default will be 1000. If you replace value by active, you can also configure if this feature is enabled or not. This is useful when you don’t want to use it at all. Learn how to implement chunking/bulking in this document. In some cases, you may need to split the output of a processor in two or more connections. A common example is to have "main" and "reject" output connections where part of the incoming data are passed to a specific bucket and processed later. Talend Component Kit supports two types of output connections: Flow and Reject. Flow is the main and standard output connection. The Reject connection handles records rejected during the processing. A component can only have one reject connection, if any. Its name must be REJECT to be processed correctly in Talend applications. You can also define the different output connections of your component in the Starter. To define an output connection, you can use @Output as replacement of the returned value in the @ElementListener: Alternatively, you can pass a string that represents the new branch: Having multiple inputs is similar to having multiple outputs, except that an OutputEmitter wrapper is not needed: @Input takes the input name as parameter. If no name is set, it defaults to the "main (default)" input branch. It is recommended to use the default branch when possible and to avoid naming branches according to the component semantic. Batch processing refers to the way execution environments process batches of data handled by a component using a grouping mechanism. By default, the execution environment of a component automatically decides how to process groups of records and estimates an optimal group size depending on the system capacity. With this default behavior, the size of each group could sometimes be optimized for the system to handle the load more effectively or to match business requirements. For example, real-time or near real-time processing needs often imply processing smaller batches of data, but more often. On the other hand, a one-time processing without business contraints is more effectively handled with a batch size based on the system capacity. Final users of a component developed with the Talend Component Kit that integrates the batch processing logic described in this document can override this automatic size. To do that, a maxBatchSize option is available in the component settings and allows to set the maximum size of each group of data to process. A component processes batch data as follows: Case 1 - No maxBatchSize is specified in the component configuration. The execution environment estimates a group size of 4. Records are processed by groups of 4. Case 2 - The runtime estimates a group size of 4 but a maxBatchSize of 3 is specified in the component configuration. The system adapts the group size to 3. Records are processed by groups of 3. Batch processing relies on the sequence of three methods: @BeforeGroup, @ElementListener, @AfterGroup, that you can customize to your needs as a component Developer. The group size automatic estimation logic is automatically implemented when a component is deployed to a Talend application. Each group is processed as follows until there is no record left: The @BeforeGroup method resets a record buffer at the beginning of each group. The records of the group are assessed one by one and placed in the buffer as follows: The @ElementListener method tests if the buffer size is greater or equal to the defined maxBatchSize. If it is, the records are processed. If not, then the current record is buffered. The previous step happens for all records of the group. Then the @AfterGroup method tests if the buffer is empty. You can define the following logic in the processor configuration: You can also use the condensed syntax for this kind of processor: When writing tests for components, you can force the maxBatchSize parameter value by setting it with the following syntax: .$maxBatchSize=10. You can learn more about processors in this document. Defining a processor/output logic General component execution logic implementing bulk processing Best practices For the case of output components (not emitting any data) using bulking you can pass the list of records to the after group method: An Output is a Processor that does not return any data. Conceptually, an output is a data listener. It matches the concept of processor. Being the last component of the execution chain or returning no data makes your processor an output component: Currently, Talend Component Kit does not allow you to define a Combiner. A combiner is the symmetric part of a partition mapper. It allows to aggregate results in a single partition.

The component configuration is defined in the Configuration.java file of the package. It consists in defining the configurable part of the component that will be displayed in the UI. To do that, you can specify parameters. When you import the project in your IDE, the parameters that you have specified in the starter are already present. All input and output components must reference a dataset in their configuration. Refer to Defining datasets and datastores. Components are configured using their constructor parameters. All parameters can be marked with the @Option property, which lets you give a name to them. For the name to be correct, you must follow these guidelines: Use a valid Java name. Do not include any . character in it. Do not start the name with a $. Defining a name is optional. If you don’t set a specific name, it defaults to the bytecode name. This can require you to compile with a -parameter flag to avoid ending up with names such as arg0, arg1, and so on. Examples of option name: Option name Valid myName my_name my.name $myName Parameter types can be primitives or complex objects with fields decorated with @Option exactly like method parameters. It is recommended to use simple models which can be serialized in order to ease serialized component implementations. For example: Using this kind of API makes the configuration extensible and component-oriented, which allows you to define all you need. The instantiation of the parameters is done from the properties passed to the component. A primitive is a class which can be directly converted from a String to the expected type. It includes all Java primitives, like the String type itself, but also all types with a org.apache.xbean.propertyeditor.Converter: BigDecimal BigInteger File InetAddress ObjectName URI URL Pattern LocalDateTime ZonedDateTime The conversion from property to object uses the Dot notation. For example, assuming the method parameter was configured with @Option("file"): matches Lists rely on an indexed syntax to define their elements. For example, assuming that the list parameter is named files and that the elements are of the FileOptions type, you can define a list of two elements as follows: if you desire to override a config to truncate an array, use the index length, for example to truncate previous example to only CSV, you can set: Similarly to the list case, the map uses .key[index] and .value[index] to represent its keys and values: Avoid using the Map type. Instead, prefer configuring your component with an object if this is possible. You can use metadata to specify that a field is required or has a minimum size, and so on. This is done using the validation metadata in the org.talend.sdk.component.api.configuration.constraint package: Ensure the decorated option size is validated with a higher bound. API: @org.talend.sdk.component.api.configuration.constraint.Max Name: maxLength Parameter Type: double Supported Types: — java.lang.CharSequence Sample: Ensure the decorated option size is validated with a lower bound. API: @org.talend.sdk.component.api.configuration.constraint.Min Name: minLength Parameter Type: double Supported Types: — java.lang.CharSequence Sample: Validate the decorated string with a javascript pattern (even into the Studio). API: @org.talend.sdk.component.api.configuration.constraint.Pattern Name: pattern Parameter Type: java.lang.string Supported Types: — java.lang.CharSequence Sample: Ensure the decorated option size is validated with a higher bound. API: @org.talend.sdk.component.api.configuration.constraint.Max Name: max Parameter Type: double Supported Types: — java.lang.Number — int — short — byte — long — double — float Sample: Ensure the decorated option size is validated with a lower bound. API: @org.talend.sdk.component.api.configuration.constraint.Min Name: min Parameter Type: double Supported Types: — java.lang.Number — int — short — byte — long — double — float Sample: Mark the field as being mandatory. API: @org.talend.sdk.component.api.configuration.constraint.Required Name: required Parameter Type: - Supported Types: — java.lang.Object Sample: Ensure the decorated option size is validated with a higher bound. API: @org.talend.sdk.component.api.configuration.constraint.Max Name: maxItems Parameter Type: double Supported Types: — java.util.Collection Sample: Ensure the decorated option size is validated with a lower bound. API: @org.talend.sdk.component.api.configuration.constraint.Min Name: minItems Parameter Type: double Supported Types: — java.util.Collection Sample: Ensure the elements of the collection must be distinct (kind of set). API: @org.talend.sdk.component.api.configuration.constraint.Uniques Name: uniqueItems Parameter Type: - Supported Types: — java.util.Collection Sample: When using the programmatic API, metadata is prefixed by tcomp::. This prefix is stripped in the web for convenience, and the table above uses the web keys. Also note that these validations are executed before the runtime is started (when loading the component instance) and that the execution will fail if they don’t pass. If it breaks your application, you can disable that validation on the JVM by setting the system property talend.component.configuration.validation.skip to true. Datasets and datastores are configuration types that define how and where to pull the data from. They are used at design time to create shared configurations that can be stored and used at runtime. All connectors (input and output components) created using Talend Component Kit must reference a valid dataset. Each dataset must reference a datastore. Datastore: The data you need to connect to the backend. Dataset: A datastore coupled with the data you need to execute an action. Make sure that: a datastore is used in each dataset. each dataset has a corresponding input component (mapper or emitter). This input component must be able to work with only the dataset part filled by final users. Any other property implemented for that component must be optional. These rules are enforced by the validateDataSet validation. If the conditions are not met, the component builds will fail. Make sure that: a datastore is used in each dataset. each dataset has a corresponding input component (mapper or emitter). This input component must be able to work with only the dataset part filled by final users. Any other property implemented for that component must be optional. These rules are enforced by the validateDataSet validation. If the conditions are not met, the component builds will fail. A datastore defines the information required to connect to a data source. For example, it can be made of: a URL a username a password. You can specify a datastore and its context of use (in which dataset, etc.) from the Component Kit Starter. Make sure to modelize the data your components are designed to handle before defining datasets and datastores in the Component Kit Starter. Once you generate and import the project into an IDE, you can find datastores under a specific datastore node. Example of datastore: A dataset represents the inbound data. It is generally made of: A datastore that defines the connection information needed to access the data. A query. You can specify a dataset and its context of use (in which input and output component it is used) from the Component Kit Starter. Make sure to modelize the data your components are designed to handle before defining datasets and datastores in the Component Kit Starter. Once you generate and import the project into an IDE, you can find datasets under a specific dataset node. Example of dataset referencing the datastore shown above: The display name of each dataset and datastore must be referenced in the message.properties file of the family package. The key for dataset and datastore display names follows a defined pattern: ${family}.${configurationType}.${name}._displayName. For example: These keys are automatically added for datasets and datastores defined from the Component Kit Starter. When deploying a component or set of components that include datasets and datastores to Talend Studio, a new node is created under Metadata. This node has the name of the component family that was deployed. It allows users to create reusable configurations for datastores and datasets. With predefined datasets and datastores, users can then quickly fill the component configuration in their jobs. They can do so by selecting Repository as Property Type and by browsing to the predefined dataset or datastore. Studio will generate connection and close components auto for reusing connection function in input and output components, just need to do like this example: Then the runtime mapper and processor only need to use @Connection to get the connection like this: The component server scans all configuration types and returns a configuration type index. This index can be used for the integration into the targeted platforms (Studio, web applications, and so on). Mark a model (complex object) as being a dataset. API: @org.talend.sdk.component.api.configuration.type.DataSet Sample: Mark a model (complex object) as being a datastore (connection to a backend). API: @org.talend.sdk.component.api.configuration.type.DataStore Sample: Mark a model (complex object) as being a dataset discovery configuration. API: @org.talend.sdk.component.api.configuration.type.DatasetDiscovery Sample: The component family associated with a configuration type (datastore/dataset) is always the one related to the component using that configuration. The configuration type index is represented as a flat tree that contains all the configuration types, which themselves are represented as nodes and indexed by ID. Every node can point to other nodes. This relation is represented as an array of edges that provides the child IDs. As an illustration, a configuration type index for the example above can be defined as follows: If you need to define a binding between properties, you can use a set of annotations: If the evaluation of the element at the location matches value then the element is considered active, otherwise it is deactivated. API: @org.talend.sdk.component.api.configuration.condition.ActiveIf Type: if Sample: Allows to set multiple visibility conditions on the same property. API: @org.talend.sdk.component.api.configuration.condition.ActiveIfs Type: ifs Sample: Where: target is the element to evaluate. value is the value to compare against. strategy (optional) is the evaluation criteria. Possible values are: CONTAINS: Checks if a string or list of strings contains the defined value. DEFAULT: Compares against the raw value. LENGTH: For an array or string, evaluates the size of the value instead of the value itself. negate (optional) defines if the test must be positive (default, set to false) or negative (set to true). operator (optional) is the comparison operator used to combine several conditions, if applicable. Possible values are AND and OR. The target element location is specified as a relative path to the current location, using Unix path characters. The configuration class delimiter is /. The parent configuration class is specified by ... Thus, ../targetProperty denotes a property, which is located in the parent configuration class and is named targetProperty. When using the programmatic API, metadata is prefixed with tcomp::. This prefix is stripped in the web for convenience, and the previous table uses the web keys. For more details, refer to the related Javadocs. A common use of the ActiveIf condition consists in testing if a target property has a value. To do that, it is possible to test if the length of the property value is different from 0: target: foo - the path to the property to evaluate. strategy: LENGTH - the strategy consists here in testing the length of the property value. value: 0 - the length of the property value is compared to 0. negate: true - setting negate to true means that the strategy of the target must be different from the value defined. In this case, the LENGTH of the value of the foo property must be different from 0. The following example shows how to implement visibility conditions on several fields based on several checkbox configurations: If the first checkbox is selected, an additional input field is displayed. if the second or the third checkbox is selected, an additional input field is displayed. if both second and third checkboxes are selected, an additional input field is displayed. In some cases, you may need to add metadata about the configuration to let the UI render that configuration properly. For example, a password value that must be hidden and not a simple clear input box. For these cases - if you want to change the UI rendering - you can use a particular set of annotations: Provide a default value the UI can use - only for primitive fields. API: @org.talend.sdk.component.api.configuration.ui.DefaultValue Allows to sort a class properties. API: @org.talend.sdk.component.api.configuration.ui.OptionsOrder Request the rendered to do what it thinks is best. API: @org.talend.sdk.component.api.configuration.ui.layout.AutoLayout Advanced layout to place properties by row, this is exclusive with @OptionsOrder. the logic to handle forms (gridlayout names) is to use the only layout if there is only one defined, else to check if there are Main and Advanced and if at least Main exists, use them, else use all available layouts. API: @org.talend.sdk.component.api.configuration.ui.layout.GridLayout Allow to configure multiple grid layouts on the same class, qualified with a classifier (name) API: @org.talend.sdk.component.api.configuration.ui.layout.GridLayouts Put on a configuration class it notifies the UI an horizontal layout is preferred. API: @org.talend.sdk.component.api.configuration.ui.layout.HorizontalLayout Put on a configuration class it notifies the UI a vertical layout is preferred. API: @org.talend.sdk.component.api.configuration.ui.layout.VerticalLayout Mark a field as being represented by some code widget (vs textarea for instance). API: @org.talend.sdk.component.api.configuration.ui.widget.Code Mark a field as being a credential. It is typically used to hide the value in the UI. API: @org.talend.sdk.component.api.configuration.ui.widget.Credential Mark a field as being a date. It supports and is implicit - which means you don’t need to put that annotation on the option - for java.time.ZonedDateTime, java.time.LocalDate and java.time.LocalDateTime and is unspecified for other types. API: @org.talend.sdk.component.api.configuration.ui.widget.DateTime Mark a List or List

Batch processing refers to the way execution environments process batches of data handled by a component using a grouping mechanism. By default, the execution environment of a component automatically decides how to process groups of records and estimates an optimal group size depending on the system capacity. With this default behavior, the size of each group could sometimes be optimized for the system to handle the load more effectively or to match business requirements. For example, real-time or near real-time processing needs often imply processing smaller batches of data, but more often. On the other hand, a one-time processing without business contraints is more effectively handled with a batch size based on the system capacity. Final users of a component developed with the Talend Component Kit that integrates the batch processing logic described in this document can override this automatic size. To do that, a maxBatchSize option is available in the component settings and allows to set the maximum size of each group of data to process. A component processes batch data as follows: Case 1 - No maxBatchSize is specified in the component configuration. The execution environment estimates a group size of 4. Records are processed by groups of 4. Case 2 - The runtime estimates a group size of 4 but a maxBatchSize of 3 is specified in the component configuration. The system adapts the group size to 3. Records are processed by groups of 3. Batch processing relies on the sequence of three methods: @BeforeGroup, @ElementListener, @AfterGroup, that you can customize to your needs as a component Developer. The group size automatic estimation logic is automatically implemented when a component is deployed to a Talend application. Each group is processed as follows until there is no record left: The @BeforeGroup method resets a record buffer at the beginning of each group. The records of the group are assessed one by one and placed in the buffer as follows: The @ElementListener method tests if the buffer size is greater or equal to the defined maxBatchSize. If it is, the records are processed. If not, then the current record is buffered. The previous step happens for all records of the group. Then the @AfterGroup method tests if the buffer is empty. You can define the following logic in the processor configuration: You can also use the condensed syntax for this kind of processor: When writing tests for components, you can force the maxBatchSize parameter value by setting it with the following syntax: .$maxBatchSize=10. You can learn more about processors in this document.

This tutorial shows how to create components that consume a REST API. The component developed as example in this tutorial is an input component that provides a search functionality for Zendesk using its Search API. Lombok is used to avoid writing getter, setter and constructor methods. You can generate a project using the Talend Components Kit starter, as described in this tutorial. The input component relies on Zendesk Search API and requires an HTTP client to consume it. The Zendesk Search API takes the following parameters on the /api/v2/search.json endpoint. query : The search query. sort_by : The sorting type of the query result. Possible values are updated_at, created_at, priority, status, ticket_type, or relevance. It defaults to relevance. sort_order: The sorting order of the query result. Possible values are asc (for ascending) or desc (for descending). It defaults to desc. Talend Component Kit provides a built-in service to create an easy-to-use HTTP client in a declarative manner, using Java annotations. No additional implementation is needed for the interface, as it is provided by the component framework, according to what is defined above. This HTTP client can be injected into a mapper or a processor to perform HTTP requests. This example uses the basic authentication that supported by the API. The first step is to set up the configuration for the basic authentication. To be able to consume the Search API, the Zendesk instance URL, the username and the password are needed. The data store is now configured. It provides a basic authentication token. Once the data store is configured, you can define the dataset by configuring the search query. It is that query that defines the records processed by the input component. Your component is configured. You can now create the component logic. Mappers defined with this tutorial don’t implement the split part because HTTP calls are not split on many workers in this case. Once the component logic implemented, you can create the source in charge of performing the HTTP request to the search API and converting the result to JsonObject records. You now have created a simple Talend component that consumes a REST API. To learn how to test this component, refer to this tutorial.

In common cases, you can store messages using a properties file in your component module to use internationalization. This properties file must be stored in the same package as the related components and named Messages. For example, org.talend.demo.MyComponent uses org.talend.demo.Messages[locale].properties. This file already exists when you import a project generated from the starter. Out of the box components are internationalized using the same location logic for the resource bundle. The supported keys are: Name Pattern Description ${family}._displayName Display name of the family ${family}.${category}._category Display name of the category ${category} in the family ${family}. ${family}.${configurationType}.${name}._displayName Display name of a configuration type (dataStore or dataSet). Important: this key is read from the family package (not the class package), to unify the localization of the metadata. ${family}.actions.${actionType}.${actionName}._displayName Display name of an action of the family. Specifying it is optional and will default on the action name if not set. ${family}.${component_name}._displayName Display name of the component (used by the GUIs) ${property_path}._displayName Display name of the option. ${property_path}._documentation Equivalent to @Documentation("…") but supporting internationalization (see Maven/Gradle documentation goal/task). ${property_path}._placeholder Placeholder of the option. ${simple_class_name}.${property_name}._displayName Display name of the option using its class name. ${simple_class_name}.${property_name}._documentation See ${property_path}._documentation. ${simple_class_name}.${property_name}._placeholder See ${property_path}._placeholder. ${enum_simple_class_name}.${enum_name}._displayName Display name of the enum_name value of the enum_simple_class_name enumeration. ${property_path or simple_class_name}._gridlayout.${layout_name}._displayName Display name of tab corresponding to the layout (tab). Note that this requires the server talend.component.server.gridlayout.translation.support option to be set to true and it is not yet supported by the Studio. Example of configuration for a component named list and belonging to the memory family (@Emitter(family = "memory", name = "list")): Configuration classes can be translated using the simple class name in the messages properties file. This is useful in case of common configurations shared by multiple components. For example, if you have a configuration class as follows : You can give it a translatable display name by adding ${simple_class_name}.${property_name}._displayName to Messages.properties under the same package as the configuration class. If you have a display name using the property path, it overrides the display name defined using the simple class name. This rule also applies to placeholders.

TCOMP-2327: Upgrade cxf to 3.4.10 TCOMP-2328: Upgrade woodstox-core to 6.4.0 TCOMP-2294: Upgrade batik to 1.16 TCOMP-2295: Upgrade tomcat to 9.0.68 TCOMP-2045: Pass and read meta information about columns. studio-integration TCOMP-2096: Support BigDecimal type in DI integration schema-record studio studio-integration TCOMP-2070: Upgrade TSBI to 2.9.18-20220104141654 build component-server component-server-vault-proxy tsbi TCOMP-2105: Upgrade Tomcat to 9.0.60 component-server maven-plugin starter TCOMP-2030: Upgrade Tomcat to 9.0.54 due to CVE-2021-42340 TCOMP-2053: Migration failing when using custom java code in configuration TCOMP-2054: Upgrade log4j2 to 2.16.0 due to CVE-2021-44228 TCOMP-2048: RowstructVisitor should respect case in member not java convention TCOMP-2047: RecordBuilder in RowstructVisitor keeps values TCOMP-2046: Rowstruct visitor recreates schema at each incoming row TCOMP-1963: Missing IMetaDataColumn fields in guess schema TCOMP-1987: Avro record : Array of Array of records issue TCOMP-1988: Unable to run component-runtime connectors in Studio with JDK 17 TCOMP-2005: Non defined columns appear in schema TCOMP-2006: Support empty values for Numbers case TCOMP-2010: Error on Documentation build on "less" usage TCOMP-2020: talend-component-kit-intellij-plugin module build fails using Bintray (decomissioned) TCOMP-1900: Create jenkins release process for component-runtime TCOMP-1997: Enable plugins reloading according criteria TCOMP-2000: Upgrade netty to 4.1.68.Final TCOMP-2001: Upgrade Beam to 2.32.0 TCOMP-2007: connectors as a json object in Environment TCOMP-2009: Upgrade dockerfile-maven-plugin to 1.4.13 TCOMP-2016: UiSchema can’t hold advanced titleMap for more advanded datalist widgets TCOMP-2007: connectors as a json object in Environment TCOMP-1957: Avro schema builder issue TCOMP-1994: WebSocketClient$ClientException when executing action in Studio TCOMP-1923: Record : add metadata TCOMP-1990: Update jsoup to 1.14.2 due to CVE-2021-37714 TCOMP-1991: Update groovy to 3.0.9 due to CVE-2021-36373 / CVE-2021-36374 TCOMP-1992: Update lombok to 1.18.20 TCOMP-1993: Update TSBI to 2.9.0-20210907155713 TCOMP-1995: Expose the connectors (global) version in the "Environment" response TCOMP-1996: BaseService must not define equals & hashcode TCOMP-1994: WebSocketClient$ClientException when executing action in Studio TCOMP-1904: Delegate Avro record in AvroRecord seems to be invalid TCOMP-1967: goal uispec generation failure TCOMP-1983: fix module inclusion in dependencies.txt when build is java9+ TCOMP-1981: Allow to filter artifacts in car file generation TCOMP-1982: Allow to include extra artifacts in car file generation TCOMP-1876: Make schemaImpl immutable TCOMP-1885: Service Serializable TCOMP-1906: Redefine equals on RecordImpl TCOMP-1955: Upgrade cxf to 3.4.4 due to CVE-2021-30468 TCOMP-1966: Upgrade Tomcat to 9.0.50 due to CVE-2021-33037 TCOMP-1968: Upgrade maven to 3.8.1 TCOMP-1969: Upgrade Beam to 2.31.0 TCOMP-1970: Upgrade jackson to 2.12.1 TCOMP-1971: Upgrade Junit to 5.8.0-M1 TCOMP-1972: Upgrade slf4j to 1.7.32 TCOMP-1973: Upgrade log4j to 2.14.1 TCOMP-1974: Upgrade commons-compress to 1.21 due to CVE-2021-36090 TCOMP-1975: Upgrade TSBI to 2.8.2-20210722144648 TCOMP-1976: Upgrade meecrowave to 1.2.11 TCOMP-1977: Upgrade OpenWebBeans to 2.0.23 TCOMP-1978: Upgrade tomcat to 9.0.44 TCOMP-1979: Upgrade xbean to 4.20 TCOMP-1980: Upgrade meecrowave to 1.2.12 TCOMP-1967: goal uispec generation failure TCOMP-1935: After Variables doesn’t support custom object types TCOMP-1941: Maven goal talend-component:web fails on startup TCOMP-1947: implement a retry strategy on failure in vault-client TCOMP-1948: Raised exception in component-server(s) should be serialized in json TCOMP-1952: IllegalArgumentException when the http response return duplicated header. TCOMP-1939: Upgrade TSBI to Talend 2.7.2-20210616074048 TCOMP-1940: Upgrade Beam to 2.30.0 TCOMP-1941: Maven goal talend-component:web fails on startup TCOMP-1939: Upgrade TSBI to Talend 2.7.2-20210616074048 TCOMP-1919: Sanitize must force encoding file TCOMP-1925: Incorrect mapping of the parameters after arrays TCOMP-1937: Classpath not fully parsed in TSBI images TCOMP-1917: Add DatasetDiscovery annotation TCOMP-1707: Upgrade Geronimo :: Simple JCache to 1.0.5 TCOMP-1850: component-server with vault feature TCOMP-1907: Service monitor implementation & cleaning of grafana dashboard TCOMP-1921: Upgrade TSBI to 2.7.0-20210527090437 TCOMP-1930: Remove jsoup 1.7.x transitive dependency due to CVE-2015-6748 TCOMP-1936: Extend properties in Schema to use JsonValue TCOMP-1938: Add the german locale in the locale mapping TCOMP-1938: Add the german locale in the locale mapping TCOMP-1937: Classpath not fully parsed in TSBI images TCOMP-1919: Sanitize must force encoding file TCOMP-1886: Errors on Schema.sanitizeConnectionName TCOMP-1905: component-runtime fails to build with Java 11 TCOMP-1893: Upgrade to Beam 2.29.0 and use Beam’s Spark 3 specific module TCOMP-705: Support After variables TCOMP-1898: Add method to Record.Builder TCOMP-1910: Upgrade commons-io to 2.8.0 due to CVE-2021-29425 TCOMP-1911: Upgrade cxf to 3.4.3 due to CVE-2021-22696 TCOMP-1912: Upgrade TSBI to 2.6.7-20210503202416 TCOMP-1938: Add the german locale in the locale mapping TCOMP-1937: Classpath not fully parsed in TSBI images TCOMP-1880: Engine Server returns binary data instead of json (aka does not respect the compressed header) TCOMP-1886: Errors on Schema.sanitizeConnectionName TCOMP-1815: Support of ComponentException in migration TCOMP-1873: Add method getEntry on TCK Record Schema class TCOMP-1892: Upgrade Spark to 3.0.1 TCOMP-1888: Remove/change validation of ComponentException TCOMP-1894: Uniformize docker images entrypoints TCOMP-1895: Enhance coercion in RecordConverters TCOMP-1896: Upgrade TSBI to 2.6.4-20210331133410 TCOMP-1806: Double values are rounded to 5 decimal places in studio TCOMP-1851: HttpClient implementation class is a Service with State TCOMP-1864: JsonSchemaConverter and johnzon-jsonschema 1.2.9+ look incompatible TCOMP-1866: Invalid number coercion on primitive type TCOMP-1869: byte[] handling is incorrect in dynamic column TCOMP-1871: Dynamic metadata name is not sanitized TCOMP-1861: Add a 'props' property in the Schema TCOMP-1863: Upgrade batik-codec to 1.14 due to CVE-2020-11988 TCOMP-1865: Upgrade cxf to 3.4.2 TCOMP-1867: Upgrade Apache Beam to 2.28.0 TCOMP-1878: Upgrade TSBI to 2.6.3-20210304090015 TCOMP-1688: Rewrite JsonSchema required rules to reflect component’s validation rules TCOMP-1857: Pojo conversion don’t support nested Objects TCOMP-1841: Add a SPI that would allow to add metadata to components TCOMP-1847: Upgrade Apache Beam to 2.27.0 TCOMP-1848: Upgrade bouncycastle to 1.68 due to CVE 2020-28052 TCOMP-1849: Proxify metrics component-server’s endpoint TCOMP-1852: Upgrade netty to v4.1.58.Final and ensure default http testing module is java 11 friendly over ssl TCOMP-1854: Upgrade netty to 4.1.59.Final due to CVE-2021-21290 TCOMP-1855: Upgrade johnzon to 1.2.10 TCOMP-1856: Upgrade tomcat to 9.0.43 TCOMP-1841: Add a SPI that would allow to add metadata to components TCOMP-1852: Upgrade netty to v4.1.58.Final and ensure default http testing module is java 11 friendly over ssl TCOMP-1854: Upgrade netty to 4.1.59.Final due to CVE-2021-21290 TCOMP-1848: Upgrade bouncycastle to 1.68 due to CVE 2020-28052 TCOMP-1839: Tomcat websocket server fails to start after tomcat 9.0.40 and meecrowave 1.2.10 TCOMP-1836: Upgrade OpenWebBeans to 2.0.20 TCOMP-1837: Upgrade xbean to 4.18 TCOMP-1838: Upgrade cxf to 3.4.1 TCOMP-1840: Upgrade tomcat to 9.0.41 TCOMP-1842: Upgrade jgit to 5.10.0.202012080955-r TCOMP-1844: Upgrade johnzon to 1.2.9 TCOMP-1845: Upgrade guava to 30.1-jre due to CVE-2020-8908 TCOMP-1848: Upgrade bouncycastle to 1.68 due to CVE 2020-28052 TCOMP-1839: Tomcat websocket server fails to start after tomcat 9.0.40 and meecrowave 1.2.10 TCOMP-1836: Upgrade OpenWebBeans to 2.0.20 TCOMP-1837: Upgrade xbean to 4.18 TCOMP-1827: Upgrade lombok to 1.18.16 TCOMP-1828: Change project’s versioning scheme TCOMP-1829: Upgrade TSBI to 2.5.3-20201201131449 TCOMP-1830: Upgrade Apache Beam to 2.26.0 TCOMP-1832: Upgrade httpclient to 4.5.13 due to CVE-2020-13956 TCOMP-1833: Upgrade spark to 2.4.7 TCOMP-1834: Upgrade groovy to 3.0.7 due to CVE-2020-17521 TCOMP-1787: ComponentManager can’t be re-created after it’s been closed TCOMP-1788: Invalid properties validation TCOMP-1801: Can’t look for resources in the classpath on Windows TCOMP-1761: Support of complete schema definition TCOMP-1725: Upgrade Tomcat to 9.0.40 TCOMP-1792: Uniform error message on component validation TCOMP-1808: Upgrade log4j2 to 2.14.0 TCOMP-1809: Update CXF to 3.3.8 due to CVE-2020-13954 TCOMP-1812: Upgrade junit to 4.13.1 due to CVE-2020-15250 TCOMP-1813: Upgrade jupiter to 5.7.0 TCOMP-1816: Apache Maven Shared Utils: OS Command Injection in Talend/component-runtime (master) and Talend/cloud-components TCOMP-1817: Upgrade gmavenplus-plugin to 1.11.0 TCOMP-1722: REST - Last / in endpoint is removed TCOMP-1757: Studio - context not set when call a @suggestable service TCOMP-1772: Code widget doesn’t allow multiline text TCOMP-1726: Update logos and colors TCOMP-1771: Record builder optimization (with static schema) TCOMP-1773: Upgrade log4j2 to 2.13.3 TCOMP-1774: Upgrade johnzon to 1.2.8 TCOMP-1775: Upgrade commons-lang3 to 3.11 TCOMP-1776: Upgrade commons-codec to 1.15 TCOMP-1777: Upgrade jgit to 5.9.0.202009080501-r TCOMP-1778: Upgrade jib-core to 0.15.0 TCOMP-1779: Upgrade batik to 1.13 TCOMP-1780: Upgrade TSBI to 2.4.0-20200925092052 TCOMP-1781: Upgrade asciidoctorj to 2.4.1 TCOMP-1782: Upgrade rrd4j to 3.7 TCOMP-1783: Upgrade netty to 5.0.0.Alpha2 TCOMP-1784: Upgrade ziplock to 8.0.4 TCOMP-1785: Upgrade JRuby to 9.2.13.0 TCOMP-1786: Upgrade to Apache Beam 2.24.0 TCOMP-1804: Upgrade to Apache Beam 2.25.0 TCOMP-1805: Upgrade TSBI to 2.5.0-20201030171201 TCOMP-1770: Performance loss on Ouput components in Studio TCOMP-1750: Deadlock at TPD job startup using the Component SDK and using the Workday component TCOMP-1759: Guess schema mixes columns returned by tck service TCOMP-1752: Make component-runtime class loader find classes in RemoteEngine JobServer TCOMP-1764: Upgrade to Apache Beam 2.23.0 TCOMP-1719: Header responses for icon not propagated correctly from Component-server-vault-proxy TCOMP-1733: NPE in Studio metadata connection with activeif on different layouts TCOMP-1734: Studio froze when installing a patch with azure-dls-gen2-1.10.0-component.car TCOMP-1736: JobImpl retrieves more than streaming.maxRecords parameter TCOMP-1739: Use scala version defined on parent for Spark related components TCOMP-1695: Support List type in Studio TCOMP-1737: Allow to force installation of an already existing component with the car bundle TCOMP-1728: Enforce use of the defined error contract in connectors TCOMP-1731: Make connectors docker image TSBI compliant TCOMP-1738: Upgrade to Apache Beam 2.22.0 TCOMP-1742: Upgrade johnzon to 1.2.7 TCOMP-1727: WebSocketContainer not present in ServletContext TCOMP-1696: Definition of an error contract to handle expected errors TCOMP-1729: Upgrade to Apache Beam 2.21.0 TCOMP-1730: Upgrade johnzon to 1.2.6 TCOMP-1719: Header responses for icon not propagated correctly from Component-server-vault-proxy TCOMP-1649: Tomcat bump to 9.0.31 broke talend-component:web goal TCOMP-1676: Starter-toolkit mvn package throws error when running for the first time TCOMP-1677: Using other types than String in Studio’s context values causes compilation error TCOMP-1679: Combination of @Required and @Suggestable on a field creates strange behaviour TCOMP-1682: Remove key attribute in UISchema for containers TCOMP-1686: antora helper function relativize corrupts documentation TCOMP-1694: [MAVEN PLUGIN] validateSvg argument is ineffective TCOMP-1698: UiSpecService injects a wrong property for suggestions and dynamic_values TCOMP-1718: Duplicated code in RecordConverters TCOMP-1702: Improve columns name TCOMP-1655: Upgrade jib-core to 0.13.1 TCOMP-1656: Upgrade log4j2 to 2.13.1 TCOMP-1657: Upgrade maven to 3.6.3 TCOMP-1658: Upgrade groovy to 3.0.2 TCOMP-1659: Upgrade lombok to 1.18.12 TCOMP-1660: Upgrade commons-compress to 1.20 TCOMP-1661: Upgrade commons-codec to 1.14 TCOMP-1662: Upgrade guava to 28.2-jre TCOMP-1663: Upgrade ziplock to 8.0.1 TCOMP-1664: Upgrade asciidoctorj to 2.2.0 and its dependencies TCOMP-1665: Upgrade jackson to 2.10.3 TCOMP-1666: Upgrade batik-codec to 1.12 TCOMP-1667: Upgrade jgit to 5.6.1.202002131546-r TCOMP-1668: Upgrade junit to 4.13 TCOMP-1669: Upgrade bouncycastle to 1.64 TCOMP-1670: Upgrade spark-core_2.11 to 2.4.5 TCOMP-1671: Upgrade maven-shade-plugin to 3.2.2 TCOMP-1672: Upgrade httpclient to 4.5.12 TCOMP-1673: Upgrade component-runtime-testing dependencies TCOMP-1674: Upgrade tomitribe-crest to 0.14 TCOMP-1678: Upgrade jgit to 5.7.0.202003090808-r TCOMP-1685: Provide docker images based on TSBI TCOMP-1687: More explicit exception messsage on reflection for findField TCOMP-1690: Upgrade netty to 4.1.48.Final TCOMP-1692: Update CXF to 3.3.6 due to CVE-2020-1954 TCOMP-1697: Update BouncyCastle to 1.65 TCOMP-1703: Upgrade log4j-2 to 2.13.2 TCOMP-1705: Upgrade to Apache Beam 2.20.0 TCOMP-1706: Upgrade OpenWebBeans to 2.0.16 TCOMP-1708: Upgrade groovy to 3.0.3 TCOMP-1710: Upgrade johnzon to 1.2.5 TCOMP-1711: Upgrade guava to 29.0-jre TCOMP-1712: Upgrade commons-lang3 to 3.10 TCOMP-1713: Upgrade jackson to 2.11.0 TCOMP-1714: Upgrade junit to 5.7.0-M1 TCOMP-1716: Upgrade maven shade plugin to 3.2.3 and misc libs TCOMP-1639: component-server incorrect response set in request TCOMP-1640: Ensure Intellij plugin works with Intellij Idea IU-201 TCOMP-1641: Upgrade OpenWebBeans to 2.0.15 TCOMP-1642: Upgrade Groovy to 3.0.1 TCOMP-1643: Add automatic scheduling eviction system on LocalCache TCOMP-1644: Upgrade log4j to 2.13.0 TCOMP-1645: Ensure correct wording is used in @Documentation TCOMP-1647: Upgrade netty to 4.1.45.Final TCOMP-1648: Unsafe Dependancy Resolution on jcommander TCOMP-1638: Inject services to delegate in proxy TCOMP-1619: Handle correctly DATETIME field type on AvroRecord TCOMP-1622: [DOC] @Icon is not supported on datastore/dataset TCOMP-1623: Change scheme for maven repos TCOMP-1628: Manage BigDecimal in RecordConverter TCOMP-1629: Ensure LocalConfiguration environment source replace dot with _ TCOMP-1630: Avoid NPE when configurationByExample() is called in a list of primitive without values TCOMP-1631: int attribute in pojo is transformed to double in a Record TCOMP-1632: Add a way to evict cached data from LocalCache TCOMP-1616: Upgrade OpenWebBeans to 2.0.14 in component-server and component-server-vault-proxy TCOMP-1617: Move mocked api results to github pages TCOMP-1618: Upgrade Junit to 5.6.0 TCOMP-1620: Upgrade to Apache Beam 2.18.0 TCOMP-1621: Upgrade to Johnzon 1.2.3 TCOMP-1624: @Service does not support list injections TCOMP-1625: Upgrade to xbean 4.16 TCOMP-1626: Ensure ContainerListenerExtensions can be sorted TCOMP-1627: Upgrade to Apache Beam 2.19.0 TCOMP-1633: Upgrade Groovy to 3.0.0 TCOMP-1634: Upgrade tomcat to 9.0.31 TCOMP-1596: Windows URI are broken TCOMP-1597: Httpclient does not support multi query parameters TCOMP-1598: validator task uses ENGLISH locale to validate instead of root one TCOMP-1612: Starter toolkit shouldn’t use the default 'STAR' icon in demo component TCOMP-1585: Upgrade netty to 4.1.43.Final TCOMP-1586: Upgrade ziplock to v8.0.0 TCOMP-1587: Upgrade jib to v0.12.0 TCOMP-1588: Upgrade JRuby to v9.2.9.0 TCOMP-1589: Upgrade crest to v0.11.0 TCOMP-1591: Update to Tomcat 9.0.29 TCOMP-1592: Update to Johnzon 1.2.2 TCOMP-1593: Update to OpenWebBeans 2.0.13 TCOMP-1595: Infinite partitionmapper shouldn’t require assesor TCOMP-1599: More unsafe usage tolerance on JVM versions TCOMP-1600: Upgrade to Tomcat 9.0.30 TCOMP-1606: Ensure job dsl can stop infinite inputs TCOMP-1608: Upgrade geronimo openapi to 1.0.12 TCOMP-1609: Ensure Intellij plugin works with Intellij Idea 2019 TCOMP-1611: Upgrade to Apache Beam 2.17.0 TCOMP-1613: Upgrade cxf to 3.3.5 TCOMP-1614: Upgrade groovy to 3.0.0-rc3 TCOMP-1615: Upgrade OpenWebBeans to 2.0.14 TCOMP-1560: Min and Max error message during configuration validation are reversed TCOMP-1563: Web Tester does not work anymore (maven/gradle goal/task) TCOMP-1573: Body encoder is called twice for each query TCOMP-1582: Deploy to Nexus 3.15 caused "Provided url doesn’t respond neither to Nexus 2 nor to Nexus 3 endpoints" TCOMP-1576: Add the possibility to desactivate http client redirection in HTTP Configurer TCOMP-1559: Support configuration of the maxBatchSize enablement TCOMP-1561: Custom action type shouldn’t need to be enforced to define a family method TCOMP-1562: Support JsonObject type in actions TCOMP-1564: Move to java.nio.Path instead of java.io.File in component-runtime-manager stack where possible TCOMP-1565: Upgade to Junit Jupiter 5.6.0-M1 TCOMP-1566: Don’t compute jvmMarkers per component module but once for all TCOMP-1567: Cache Artifact path in case of reuse TCOMP-1568: Lazily create the container services TCOMP-1569: Upgrade starter to gradle 6.0-rc1 TCOMP-1570: Ensure starter adds _placeholder entries in Messages.properties TCOMP-1571: Support [length] syntax to change array configuration TCOMP-1572: Validate that @Option is not used on final fields TCOMP-1574: Upgrade to CXF 3.3.4 TCOMP-1575: Upgrade to Spark 2.4.4 TCOMP-1577: Upgrade to xbean 4.15 TCOMP-1578: Upgrade asciidoctor-pdf to v1.5.0-beta.7 TCOMP-1581: Support JUnit5 meta annotations for our extensions TCOMP-1752: Make component-runtime class loader find classes in RemoteEngine JobServer TCOMP-1702: Improve columns name TCOMP-1685: Provide docker images based on TSBI TCOMP-1558: org.talend.sdk.component.api.service.record.RecordService must be serializable TCOMP-1548: Basic Remote Engine Customizer TCOMP-1550: Component configuration instantiation can be slow for complex configurations TCOMP-1551: ObjectFactory should default to fieldproperties when field injection is activated TCOMP-1553: Simplify and widden excluded classes for with transformer support TCOMP-1555: Upgrade to Tomcat 9.0.27 TCOMP-1556: Studio short, byte, BigDecimal and char types are wrong handled TCOMP-1557: Upgrade to Beam 2.16.0 TCOMP-1509: Intellij plugin does not declare java module preventing the plugin to run under last versions TCOMP-1526: Upgrade talend UI bundle (js) to 4.6.0 TCOMP-1533: JSON-B API does not enable to combine multiple adapters or (de)serializers in JsonbConfig TCOMP-1536: @DefaultValue ignored in documentation generation TCOMP-1541: Studio integration enforces JSON<→Record conversion instead of relying on rowStruct making number precision lost TCOMP-1542: Validator plugin uses family instead of pluginId (artifactId) to validate local-configuration TCOMP-1508: Don’t let Talend Starter Toolkit loose state on Enter in intellij TCOMP-1543: Add a uispec mapper TCOMP-1544: Update Geronimo JSON-P spec bundle to v1.3 TCOMP-1545: Update OpenWebBeans to version 2.0.12 TCOMP-1546: Update Meecrowave to 1.2.9 TCOMP-1547: Update Johnzon to 1.2.1 TCOMP-1279: Rewrite the pojo <→ record mapping to keep number types TCOMP-1504: Apache Beam 2.14.0 upgrade TCOMP-1505: Upgrade jackson-databind to 2.9.9.3 TCOMP-1506: Enable actions in bulk endpoint TCOMP-1507: Upgrade to johnzon 1.1.13 TCOMP-1511: Upgrade cxf to v3.3.3 TCOMP-1513: Upgrade to Tomcat 9.0.24 TCOMP-1514: Provide a RecordService to simplify record enrichment coding in processors TCOMP-1515: Record visitor API TCOMP-1517: Use netty 4.1.39.Final in junit http tools TCOMP-1518: Upgrade to slf4j 1.7.28 TCOMP-1519: Upgrade to jib-core 0.10.1 TCOMP-1520: Don’t use JsonNode with Avro Fields anymore TCOMP-1521: Upgrade to Beam 2.15.0 TCOMP-1522: Basic singer/tap/stitch integration with kit components TCOMP-1523: Upgrade Apache Geronimo OpenAPI to v1.0.11 TCOMP-1524: Upgrade starter to gradle 5.6 TCOMP-1525: Upgrade commons-compress to v1.19 TCOMP-1527: Remove beam Mapper/Processor wrapping support TCOMP-1528: Upgrade to maven 3.6.2 TCOMP-1529: Asciidoctor 2.1.0 upgrade TCOMP-1530: geronimo-annotation 1.2 upgrade TCOMP-1532: Upgrade to Junit 5.5.2 TCOMP-1535: Upgrade to johnzon 1.2.0 TCOMP-1537: Upgrade to Tomcat 9.0.26 TCOMP-1538: Upgrade to jackson 2.9.10 TCOMP-1539: Rework default direct runner/spark classloader rules TCOMP-1540: Ensure Asciidoctor documentation rendering releases properly JRuby threads (main usage only) TCOMP-1478: /documentation/component/{id} internationalization does not work when embedded TCOMP-1479: When generating the documentation, it can happen the lang is wrong due to ResourceBundle usage TCOMP-1480: Servers docker images don’t have curl or wget available TCOMP-1497: POJO to Record mapping is not supported in processors TCOMP-1498: SVG2Mojo wrongly log the source file as being created TCOMP-1499: component-form does not support array of object of object if 2 levels use the same field name TCOMP-1500: Ensure component-form button have a key to have an id and propagate errors in the front TCOMP-1503: EnvironmentSecuredFilter not working on /environment/ TCOMP-1482: Enable web tester to switch the language TCOMP-1483: Enable to expose the documentation through the web tester TCOMP-1485: Asciidoctor documentation does not enable titles (component name and configuration ones) to be translated TCOMP-1486: Ensure locale mapping is configurable in component-server TCOMP-1484: Junit 5.5.0 upgrade TCOMP-1487: AsciidocMojo should only use ROOT locale by default TCOMP-1488: Enable to translate gridlayout names TCOMP-1489: Upgrade Tomcat to v9.0.22 TCOMP-1491: Upgrade JIB to v1.4.0 TCOMP-1492: Upgrade jackson-databind to 2.9.9.1 TCOMP-1493: Rewrite component exception to ensure they can be loaded after a serialization TCOMP-1494: Upgrade to junit jupiter 5.5.1 TCOMP-1495: Upgrade to Geronimo OpenAPI 1.0.10 TCOMP-1496: [testing tool] MainInputFactory does not support Record TCOMP-1501: Remove generate mojo TCOMP-1502: [maven plugin] upgrade jib-core to 0.10.0 TCOMP-1469: Studio maven repository not found OOTB TCOMP-1472: Connectors maven goal does not work in 1.1.10 TCOMP-1473: Docker image text log setup should use ISO8601 and not HH:mm:ss.SSS TCOMP-1470: Upgrade Tomcat to v9.0.21 TCOMP-1471: Upgrade Geronimo OpenAPI to v1.0.9 TCOMP-1474: Ensure proxies definition are java >=11 friendly TCOMP-1425: Spark classes not excluded anymore in component-runtime-beam leading to classloading issues TCOMP-1427: dependencies.txt mojo uses timestamped versions for snapshots instead of just -SNAPSHOT TCOMP-1431: [maven] Asciidoctor files should be attached with adoc extension and not jar one TCOMP-1433: [form-model] itemwidget ignored from uischema builder TCOMP-1438: Index cache can lead to invalid index list of component TCOMP-1440: Bulk components without @ElementListener when used with component-extension (default in the server) TCOMP-1441: Missing parameter init in the UiSchema Trigger builder TCOMP-1446: Rework gradle lifecycle TCOMP-1419: Upgrade build to groovy 2.5.7 TCOMP-1420: Upgrade maven compiler to 3.1.2 TCOMP-1422: Filter allowed beam classes in component-server image TCOMP-1423: Enable to customize studio maven repository for deploy-studio maven and gradle goal/task TCOMP-1426: Ensure Spark rule and @WithSpark uses a default version consistent with the runtime TCOMP-1430: Deprecate built-in icons in favor of vendor specific icons TCOMP-1432: basic dita generation for the component documentation TCOMP-1434: [form-model] Add withCondition to UISchema builder TCOMP-1435: Dont use beam_sdks_java_core shaded libraries TCOMP-1437: Add infinite metadata to ComponentDetail TCOMP-1444: Remove KnownJarsFilter since it is no more used to discover components TCOMP-1445: Icon must support SVG TCOMP-1448: [starter] provide a basic OpenAPI integration TCOMP-1449: Upgrade XBean to v4.14 TCOMP-1450: Add a read-only bulk endpoint in component-server TCOMP-1451: [upgrade] Johnzon 1.1.12 TCOMP-1452: [upgrade] Meecrowave 1.2.8 TCOMP-1453: Upgrade to CXF 3.3.2 TCOMP-1455: Prepare DateTime support in configurations TCOMP-1457: Upgrade to Apache Beam 2.13.0 TCOMP-1458: Ensure _placeholder presence is encouraged and validated TCOMP-1459: Experimental way to patch a component dependency TCOMP-1461: Extension API for the validator plugin TCOMP-1462: Validate through the corresponding build task provided SVG TCOMP-1464: Upgrade to OpenWebBeans 2.0.11 TCOMP-1465: Upgrade to JUnit 5.5.0-RC1 TCOMP-1466: Upgrade to ziplock 8.0.0-M2 TCOMP-1467: Upgrade mock server (testing tool) to netty 5.0.0.Alpha2 TCOMP-1468: Support docker-compose >= 1.23 in vault-proxy TCOMP-1374: ensure Utf8 avro strings don’t leak in AvroRecord API, even using get(Object.class, …) TCOMP-1375: When two sources use the same dataset and one source has additional required parameter the validation fails TCOMP-1384: Enhance studio guess schema algorithm to find implicitly the action to call if needed TCOMP-1388: Can’t change the dataset name in starter TCOMP-1389: Intellij starter fails to generate a project TCOMP-1398: Using after option of @updateable can lead to a null pointer exception in component-form TCOMP-1401: Documentation table is broken TCOMP-1407: Databricks: interface javax.json.stream.JsonGeneratorFactory is not visible from class loader TCOMP-1386: Add withRecord(String,Record) in Record.Builder TCOMP-1387: Use icon bundle version 3.1.0 TCOMP-1412: Add rest and couchbase icon to component api TCOMP-1376: Upgrade jupiter to 5.4.2 TCOMP-1385: talend.component.server.component.registry must be a list TCOMP-1390: Move component-api to component-runtime repository TCOMP-1392: Tomcat 9.0.19 upgrade TCOMP-1402: Provide a placeholder for classpath extensions in docker images TCOMP-1403: Upgrade asciidoctor to 2.0.0 and asciidoctor-pdf to alpha17 TCOMP-1404: Upgrade to Apache Beam 2.12.0 TCOMP-1408: Starter does not support types starting with a lowercase TCOMP-1411: ComponentManager relies on beam jar name. This is unlikely and should move to beam integration module. TCOMP-1417: Upgrade to Geronimo OpenAPI 1.0.8 TCOMP-1326: Avro Schema is not serializable as JSON so guess schema action does not work when compoennt-runtime-beam is present TCOMP-1330: Shade extensions don’t inherit from pluginrepositories TCOMP-1340: Tools webapp (talend-component:web) does not support changing the locale anymore TCOMP-1343: Use LogicalTypes.timestampMillis() on DATETIME for avro record builder TCOMP-1360: Renaming an option (@Option("custom")) does not work on fields of type object TCOMP-1370: ImageM2Mojo does not set timestamp in the docker image leading to component-server having a wrong lastUpdated value TCOMP-1372: Nested components don’t expose their doc deterministicly until it is overriden TCOMP-1341: Register deploy in studio task OOTB in gradle extension TCOMP-1325: Upgrade CXF to 3.3.1 TCOMP-1327: /environment iterates over deployed plugin for each call, this is not needed TCOMP-1328: Upgrade to Beam 2.11.0 TCOMP-1329: Lazy initialize parameter model to have a quicker cold start in plain main(String[]) TCOMP-1331: Use java 8u191 as base docker image TCOMP-1332: Provide a simple way to filter configurations and component on /index endpoints TCOMP-1334: Add a mojo to generate the list of components/services classes TCOMP-1335: Add in doc mojo table the type of configuration the parameter belongs to TCOMP-1336: Allow output processors to only have an @AfterGroup taking the list of record of the group in parameter TCOMP-1346: Upgrade to Tomcat 9.0.17 TCOMP-1347: Upgrade to Slf4j 1.7.26 TCOMP-1348: [form-core] Ensure suggestions trigger is bound to "change" event too TCOMP-1349: [form-core] When a tab is empty, don’t show it TCOMP-1350: talend.component.server.component.registry should support glob pattern TCOMP-1351: Upgrade jsoup for Spark Cluster Testing module TCOMP-1353: component-server must not use TALEND-INF/dependencies.txt but another path TCOMP-1354: Enforce services to belong to the delcaring service class TCOMP-1361: Upgrade to asciidoctorj 2.0.0-RC.1 TCOMP-1362: Beam Wrapped Components should throw shared exception types TCOMP-1366: Upgrade to XBean 4.13 to not track all classes scanned TCOMP-1371: Upgrade to Apache Geronimo OpenAPI 1.0.7 TCOMP-1307: support char and character types in configuration. TCOMP-1312: Component-form-core shouldn’t trigger validation of object due to conditional visibility (only individual fields are validable) TCOMP-1314: category field of the starter is broken TCOMP-1316: [build] Ensure snapshot use timestamped versions in dependencies.txt TCOMP-1306: Add RecordPointerFactory to enable to extract data from Record using json pointer spec TCOMP-1315: Ensure @Internationalized can use shortnames too in Messages.properties TCOMP-1303: Support docker configs/secrets in docker images TCOMP-1304: Vault proxy should support token configuration TCOMP-1305: Upgrade to beam 2.10.0 TCOMP-1308: Upgrade to Talend UI 2.6.0 TCOMP-1309: Upgrade to Component API 1.1.5 TCOMP-1310: Ensure there is a basic secured mecanism to store configuration data TCOMP-1317: Use Apache Geronimo Microprofile Config extensions (docker and secured string) TCOMP-1318: Upgrade to Apache Meecrowave 1.2.7 TCOMP-1319: Upgrade Apache Geronimo Metrics to 1.0.3 TCOMP-1320: Upgrade to Apache Geronimo OpenAPI 1.0.6 TCOMP-1321: Upgrade to Apache Geronimo OpenTracing 1.0.2 TCOMP-1322: Upgrade to Apache Geronimo Config 1.2.2 TCOMP-1263: When using @Updateable(after=xxx) the visibility condition (@ActiveIf) of the after field shouldn’t be inherited TCOMP-1264: AvroSchema does not unwrap null(able types) to map to Schema model TCOMP-1265: dataset / datastore cloud validation : allow nested configuration types TCOMP-1267: /documentation does not filter properly component TCOMP-1281: Add jackson-mapper-asl in docker image of the server TCOMP-1298: Support restricted lists for @Proposable TCOMP-1297: make max batch size property configurable for family and components through LocalConfiguration TCOMP-1266: Enhance starter to support dataset and datastore TCOMP-1268: Ensure /environment is not callable if not local or secured TCOMP-1269: Ensure ErrorReportValve does not leak Tomcat version OOTB TCOMP-1271: Upgrade to talend UI 2.3.0 TCOMP-1272: Move multiSelectTag to multiSelect for web environment TCOMP-1273: [build/dev plugin] Automatically open the browser for talend-component:web task/goal TCOMP-1276: Exclude xerces from component loadable resources for XMLReaderFactory TCOMP-1282: Upgrade meecrowave to 1.2.6 TCOMP-1283: Upgrade cxf to 3.3.0 TCOMP-1284: Upgrade to johnzon 1.1.11 TCOMP-1292: Provide a vault friendly integration for the server TCOMP-1293: Upgrade to Tomcat 9.0.16 TCOMP-1295: Ensure local-configuration.properties of a container are merged TCOMP-1296: Ensure user can enrich families with custom jar+configuration TCOMP-1245: Provided services (SPI) by tacokit not available TCOMP-1246: Rework docker image setup to use jib TCOMP-1247: Upgrade geronimo metrics to 1.0.2 TCOMP-1248: Upgrade to geronimo opentracing 1.0.3 TCOMP-1249: Provide segment extractor for doc endpoint TCOMP-1250: Make component documentation (@Documentation on component) i18n friendly TCOMP-1251: cache avrocoders used in SchemaRegistryCoder TCOMP-1252: Remove html support in documentation endpoint TCOMP-1253: Refine OpenAPI documentation TCOMP-1256: Add mapDescriptorToClassLoader to create a classloader from a list of gav TCOMP-1258: Support to build a Record from a provided Schema TCOMP-1259: Add getOptional to Record TCOMP-1223: byte[] not supported in AvroRecord (beam) TCOMP-1222: Ensure @WithComponents and @Environment are compatible TCOMP-1234: Upgrade to beam 2.9.0 TCOMP-1235: Upgrade to antora 2 TCOMP-1237: Upgrade component-api to 1.1.2 TCOMP-1238: Upgrade metrics and opentracing microprofile libraries in docker image to use Geronimo extensions TCOMP-1239: OpenWebBeans 2.0.9 upgrade TCOMP-1240: Johnzon 1.1.11 upgrade TCOMP-1242: Runtime validation error message wrongly interpolated TCOMP-1243: Ensure component classloader isolates the system classloader resources except for the JVM ones TCOMP-1170: [regression] http testing module pom imports netty and jsonb stack TCOMP-1181: tacokit can’t pass the long type field from ui rightly TCOMP-1187: Job DSL does not support correctly parameters when they are URI/URL TCOMP-1189: Ensure primitive are not nullable in Record model (builder) TCOMP-1191: [beam] BeamIOTransformer does not support serialization of complex objects correctly TCOMP-1192: Ensure Avro schema union is interpreted as nullable in Record Schema model TCOMP-1194: [testing] Ensure BeamEnvironment adds component-runtime-beam TCOMP-1196: Nested maven repository not used for component module TCOMP-1197: Tacokit beam tests. NPE when creating the schema with RECORD type. TCOMP-1198: Tacokit beam tests. SchemaParseException ⇒ drop unsupported characters TCOMP-1200: Packages not defined from nested repository classes TCOMP-1201: includeTransitiveDependencies option of nested-maven-repository does not work TCOMP-1202: Refine avro classloading exclusion to accept hadoop and mapred packages TCOMP-1205: Empty JSon object lead to NPE TCOMP-1209: Ensure SerializableCoder is replaced with a contextual version to support Talend Component Kit classloading model TCOMP-1210: BeamComponentExtension should let the exception go back to the caller when the transform fails TCOMP-1215: Nested maven repository in jars don’t go through transformers TCOMP-1218: Record entries order shouldn’t be sorted by the runtime TCOMP-1185: Support maxBatchSize in Job test runner for standalone mode TCOMP-1171: Remove component proxy server from the project TCOMP-1182: Ensure the property editor for the configuration registers the default converters TCOMP-1183: Upgrade JRuby to 9.2.4.0 TCOMP-1184: Avoid to do a group by key in BeamExecutor (job DSL) when not needed TCOMP-1188: Tolerate null for dates in Records TCOMP-1190: Enable secure processing for DocumentBuilderFactory instances TCOMP-1193: Add injectable ContainerInfo with the containerId (plugin) in services TCOMP-1195: Enable user to extend BeamEnvironment test tempalte more easily TCOMP-1199: Nested repository not used when the classpath is not composed of a single jar TCOMP-1204: [dependency upgrade] XBean 4.12 TCOMP-1207: [beam] add ContextualSerializableCoder TCOMP-1213: Upgrade guava to v27 for testing modules TCOMP-1216: Take into account the visibility for the parameter validation TCOMP-1217: Add JVM system property talend.component.runtime.serialization.java.inputstream.whitelist for our custom object input stream TCOMP-1219: Upgrade starter to gradle 5 TCOMP-1220: Upgrade Maven to 3.6.0 in starter TCOMP-1121: [tacokit proxy] suggestion trigger creation issue TCOMP-1122: [tacokit proxy] slefRefrence filter configuration type by name, type and family TCOMP-1123: Processor component onNext duplicate columns in record for rowStructs TCOMP-1126: UiSpecService shouldn’t show the documentation by default TCOMP-1129: form core - $selfReference breaks triggers TCOMP-1130: component form - default value of maxBatchSize prop loose it type. TCOMP-1131: [beam integration] Ensure Coder is contextual (classloader) TCOMP-1132: Ensure beam custom Coders implement equals.hashCode for beam contract TCOMP-1148: Asciidoctor documentation fails for collection of objects TCOMP-1149: [testing] BeamEnvironment does not reset PipelineOptionsFactory properly for beam > 2.4 TCOMP-1155: [proxy server] arrays not supporting null values in ConfigurationFormatter TCOMP-1159: AvroSchema does not support DATETTIME type (beam module) TCOMP-1168: Avro record implementation ignores nullable/union TCOMP-1143: Ensure icons are validated and fail the build if a custom one is missing (validate mojo) TCOMP-1112: Let beam PTransform define an @ElementListener method to set the component design (inputs/outputs) TCOMP-1113: Simplify the scanning by assuming there is a TALEND-INF/dependencies.txt in components TCOMP-1120: BeamMapperImpl.isStream not accurate for UnboundedSource TCOMP-1124: Add /metrics endpoint TCOMP-1125: Extend CustomPropertyConverter to pass the convertion context TCOMP-1127: Record doesn’t support null values TCOMP-1133: CXF 3.2.7 upgrade TCOMP-1134: Ensure any input/output have a dataset TCOMP-1135: Ensure any dataset has a datastore TCOMP-1136: deprecate "generate" mojo TCOMP-1145: [dependency upgrade] Beam 2.8.0 TCOMP-1146: implement infinite=true in PartitionMapper/Input TCOMP-1150: Upgrade rat plugin to 0.13 TCOMP-1154: Required validation at runtime ignores lists and nested objects TCOMP-1157: [dependency upgrade] Tomcat 9.0.13 TCOMP-1158: Enable JUnit test collector to use a static storage instead of thread related one TCOMP-1160: Upgrade spark to 2.4.0 TCOMP-1161: Upgrade shade plugin to 3.2.1 TCOMP-1162: Upgrade nested-maven-repository shade transformers to support last maven versions TCOMP-1163: Upgrade openwebbeans to 2.0.8 TCOMP-1164: Validate mojo does not log any success information TCOMP-1165: Dependency mojo does not log any success information TCOMP-1166: Documentation mojo does not log generated files properly TCOMP-1167: Beam-Avro record name generation should use avro fingerprint to be more unique than current logic TCOMP-1086: Fix documentation about DiscoverSchema TCOMP-1064: Update action can’t receive List parameter TCOMP-1110: When a configuration has no layout and uses @AfterGroup the configuration is lost TCOMP-1111: Move to PropertyEditorRegistry from xbean instead of using the deprecated static class TCOMP-1000: @Option name value is not respected on fields TCOMP-1008: Enum order is lost TCOMP-1009: (web) OptionsOrder ignored for tables (List), fields located in random order TCOMP-1028: [tools-webapp] submit button no more functional TCOMP-1031: DiscoverSchema parameters are not correctly mapped in Studio GuessSchema runtime TCOMP-1044: Fix java.lang.ClassCastException in TableActionParameter TCOMP-1046: String option can’t set default value from a file TCOMP-1056: ActiveIf doesn’t work in advanced settings TCOMP-1072: Metadata migration issues TCOMP-1074: talend-component mvn plugin : deploy-in-studio need to rise an error when component is already installed TCOMP-1075: component reload file on windows after deploying a modified jar TCOMP-1076: component starter - fix mapper generation (Record integration) TCOMP-1077: component starter - ensure kit version are updated atomically. TCOMP-1078: Guess Schema button is not shown on Basic Settings view TCOMP-1082: Fix Exception during HealthCheck parameter deserialization TCOMP-1085: [classloader] com.sun is too wide as exclusion TCOMP-1104: Fix drag and drop issue for dataset/datastore metadata TCOMP-779: Drop down list Java type in configuration class TCOMP-819: Processor doesn’t produce more than 1 row on each iteration TCOMP-917: Migration handler need only to receive component configuration TCOMP-941: Default and init values are ignored in connection wizzard (datastore/dataset) TCOMP-968: Trigger AsyncValidation call only when option annotated with Validable is changed TCOMP-970: Add support for complex parameter types for AsyncValidation methods TCOMP-973: component migration - the configuration version need to be serialized in addition to the version of the component TCOMP-984: Integrate ParameterizedTest with component-runtime-http-junit capture mode TCOMP-988: component migration - fix nested configuration migration TCOMP-989: .car studio install command breaks config.ini of the studio TCOMP-991: metadat : ignore activations from config not being part of the form while creating metadata TCOMP-996: metadata : migration issues TCOMP-1001: [proxy] ConfigurationClient should expose a migrate method TCOMP-1011: Ensure datastore/dataset i18n names are validated by the maven/gradle plugins TCOMP-1013: Add an operator support in @ActiveIfs (OR/AND switch) TCOMP-1014: Ensure a dataset has a source which has no other required parameters in the validator TCOMP-1029: Extend ActiveIf EvaluationStrategy with CONTAINS strategy TCOMP-1063: Integrate Record API to the studio TCOMP-1069: restrict input branches for output components to only one. TCOMP-1071: support actions i18n display name TCOMP-1092: Ensure @Configuration POJO are injectable as Supplier in services TCOMP-1094: Add FullSerializationRecordCoder coder for Record in beam module TCOMP-1095: Ensure all configuration type models root entries are named "configuration" TCOMP-993: [proxy] Propagate UiSpecContext in referenceservice#findByTypeAndName TCOMP-994: [dependency upgrade] CXF 3.2.6 TCOMP-1003: [dependency upgrade] Tomcat 9.0.12 TCOMP-1004: [dependency upgrade] Log4j2 2.11.1 TCOMP-1015: Upgrade icons to 1.0.0 TCOMP-1019: (form) enum should lead to restricted datalist TCOMP-1037: [dependency upgrade] Johnzon 1.1.9 TCOMP-1038: Drop spring client from component-form-core TCOMP-1041: HttpClient should enable to process InputStream directly TCOMP-1042: Upgrade to JUnit 5.3.1 TCOMP-1045: Add documentation in metadata and enable to use it in the UI on configuration TCOMP-1047: Make Suggestable text field editable (align with web) TCOMP-1048: Add update API for configuration TCOMP-1049: Add completion support for actions displayname in intellij plugin TCOMP-1050: Provide simple OAuth1 integration TCOMP-1051: Remove brave and move to geronimo-opentracing TCOMP-1054: Introduce @Configuration API TCOMP-1055: remove the ExecutionResource TCOMP-1057: Add ActiveIf on @Proposable test-case TCOMP-1058: Add DefaultValue on proposable/dynamicValue testcase TCOMP-1059: Rework generic record format TCOMP-1073: [maven/gradle plugin] Add configuration support in web goal TCOMP-1079: Document new Record structure TCOMP-1080: [dependency upgrade] Meecrowave 1.2.4 TCOMP-1081: ComponentManager should ignore engine classes in its filtering TCOMP-1087: Jsonb service should serialize byte[] as BASE64 TCOMP-1089: [starter] Upgrade gradle to 4.10.2 TCOMP-1090: [form] Main/Advanced order not respected when some remote action are involved TCOMP-1091: Ensure main component is preferred over test ones in a maven project TCOMP-1093: [dependency upgrade] netty 4.1.30.Final for junit http testing module TCOMP-1096: [dependency upgrade] xbean 4.10 TCOMP-1097: [dependency upgrade] Beam 2.7.0 TCOMP-1099: Upgrade web ui bundle to 1.0.2 TCOMP-1101: Add conditional rendering in the generated documentation TCOMP-1102: Reflect in documentation that Validable/AsyncValidation doesn’t support Object types TCOMP-1106: Enable to generate the component documentation in multiple languages TCOMP-1107: ConfigurableClassLoader does not priviledges container classloader for getResourceAsStream TCOMP-877: [documentation] Sample implementation of bulk/batch/commit-interval using groups TCOMP-980: Provide a ValidationService in server-proxy TCOMP-985: Align docker git metada on out Standard TCOMP-998: [dependency upgrade] Apache Commons Compress 1.18 TCOMP-911: Suggestions callback doesn’t support Configuration parameters TCOMP-921: String cannot be cast to Boolean when adding table with checkboxes TCOMP-922: component manager : support loading dependencies from job lib folder. TCOMP-924: component-kit.js errors are not sent to the error handler TCOMP-927: talend-component:web errors are not always unwrapped and understandable TCOMP-934: Ensure Studio rely on category and doesn’t append family name TCOMP-960: Suggestions parameters are not correctly resolved in Studio TCOMP-961: Default value of Suggestions method parameter is ignored TCOMP-964: ClassCastException is thrown when non-string values are used as Suggestions method parameter TCOMP-825: Provide component server proxy TCOMP-928: Add negate and evaluation strategy to @ActiveIf TCOMP-929: Ensure category contains the family TCOMP-816: Check migration feature and implement missing use-cases TCOMP-918: create a mvn bom with tacokit stack to keep some dependencies aligned between component-runtime and it’s studio integration TCOMP-932: Avoid Kafka recursive logging for component server TCOMP-933: Drop component-kit.js module TCOMP-935: Component server should log application and service in kafka mode TCOMP-938: Add a builtin::http trigger in the server proxy TCOMP-939: Ensure the proxy server can lookup references with a SPI TCOMP-943: (web) Grand parent references for triggers not well resolved TCOMP-944: (proxy server) Ensure the trigger are well resolved for references TCOMP-947: (maven/gradle) ensure web task logs there is a UI TCOMP-953: Upgrade to ziplock 7.0.5 TCOMP-954: Upgrade netty to 4.1.28.Final for the test stack TCOMP-958: Componentvalidator error message in case of an unsupported type is misleading TCOMP-959: [dependency upgrade] Upgrade to icon bundle 0.202.0 TCOMP-962: .car deploy-in-studio command (CarMain) should support to override an existing version TCOMP-965: [dependency upgrade] Apache Beam 2.6.0 TCOMP-966: Ensure Studio integration renames HTTP threads to identify them more explicitly TCOMP-967: Ensure parameter index is in metadata for services and constructors TCOMP-919: Starter doesn’t synchronize correctly with central versions TCOMP-920: Use Meecrowave 1.2.3 TCOMP-888: Designer pipeline records counter are wrong for tacokit components with multiples outputs TCOMP-899: Update Beam 2.5.0 compatibility TCOMP-903: [tacokit studio integration] - Guess schema - better handling of number types recognition TCOMP-904: [tacokit studio integration] - fix job classpath generation TCOMP-913: Fix absolute path resolution for child of child use-case TCOMP-900: [tacokit studio integration] - Handle conditional outputs TCOMP-898: Ensure starter will be able to auto update its versions to avoid redeployments TCOMP-905: Enrich scanning exclusion set TCOMP-906: Minimalist JsonObject to IndexeredRecord utilities for beam TCOMP-907: Support maxBatchSize as in the studio in Beam TCOMP-910: Add maxbatchsize as built in parameter to Processor meta model TCOMP-915: Upgrade Apache Meecrowave to 1.2.2 TCOMP-822: [Windows] deploy-in-studio & car copy jar command in mvn plugin - don’t work if the studio is running TCOMP-844: Service default method forwarded to interface method instead of implementation one if exists TCOMP-848: [junit5] implicit mock collector and emitter are not resetted per method TCOMP-851: [form] UiSchema shouldn’t have a JsonSchema TCOMP-858: @OptionsOrder not respected by form-core TCOMP-862: [form-core] ".." path is not correctly resolved TCOMP-863: Job DSL doesn’t support multiple outputs TCOMP-873: Fix shade junit-http module : remove shaded dependencies from generated artifact TCOMP-889: [form] arrays are lost in trigger paths TCOMP-890: Merge the component outputs (by name) from @AfterGroup and @ElementListener TCOMP-893: Don’t log a warning for services when parameters don’t have i18n support TCOMP-834: Ensure that component has only one configuration argument. TCOMP-845: [junit] ComponentsHandler misses findService TCOMP-846: [junit] allow to inject current plugin services in test class TCOMP-847: Support gzip in JUnit HTTP tooling TCOMP-849: [junit http] support to match the request payload TCOMP-850: MavenDecrypter should tolerate ${env.xxx} syntax TCOMP-861: Ensure Car Mojo can be skipped TCOMP-887: [studio] add chunk size advanced common param for processors & output TCOMP-892: Validate runtime configuration before executing the runtime TCOMP-829: Configuration Type tree is not correctly computed TCOMP-830: Move all configuration to Microprofile Config instead of DeltaSpike TCOMP-832: Provide a way to access lastUpdatedTimestamp in rest api TCOMP-833: Upgrade gradle+maven for the starter TCOMP-839: Add an API to load lazily the potential values of a list TCOMP-840: Upgrade icon bundle to 0.190.2 TCOMP-841: Add validation of option names in the validator TCOMP-852: [dependency upgrade] Upgrade shrinkwrap-resolver-impl-maven to 3.1.3 TCOMP-855: Support service injections in services TCOMP-856: [dependency upgrade] OpenWebBeans 2.0.6 TCOMP-857: SimpleCollector must not depend on junit 4 TCOMP-864: Mojo should be thread safe for car/dependencies.txt generation TCOMP-867: Expose Injector service TCOMP-868: Create an ObjectFactory service TCOMP-869: Ensure actions can get injected the requested lang TCOMP-870: Provide Beam DoFn to simplify the migration from IndexedRecord to JsonObject TCOMP-876: Allow custom converters in form-core TCOMP-878: Add beam in the docker image OOTB TCOMP-879: CarMojo doesn’t use car extension to attach the artifact TCOMP-880: [dependency upgrade] Maven 3.5.4 TCOMP-881: [dependency upgrade] CXF 3.2.5 TCOMP-882: [dependency upgrade] Tomcat 9.0.10 TCOMP-883: [dependency upgrade] Beam 2.5.0 TCOMP-884: [dependency upgrade] Upgrade to icon bundle 0.197.0 TCOMP-894: [dependency upgrade] Johnzon 1.1.8 TCOMP-895: [dependency upgrade] xbean 4.9 TCOMP-827: Fix Automatic-Module-Name TCOMP-811: Upgrade to tomcat 9.0.8 TCOMP-826: Extract component model from component server to a new artifact TCOMP-763: Add a dev mode in the studio for tacokit TCOMP-802: Add method to upload dependencies from .car to nexus TCOMP-808: Upgrade to JUnit 5.2.0 TCOMP-809: compress js and css for the starter TCOMP-810: ui spec service uses a multiselecttag for a proposable on a string field TCOMP-804: Idea plugin doesn’t render properly configuration inputs TCOMP-798: intellij plugin - add official starter url TCOMP-799: @Checkable expects the datastore name to match the validation name TCOMP-806: Ensure server and starter support gzip TCOMP-643: Deployment TCOMP-770: Removing component from web UI causes wrong number of components in summary TCOMP-775: Starter - Fix properties keys generation TCOMP-776: component-kit.js ignore credentials TCOMP-783: ActiveIfs doesn’t make option visible TCOMP-796: Datastore check (@Checkable) should default meta parameters to "datastore" if none is found TCOMP-773: Extend the http client api to handle more generic use cases TCOMP-771: ConfigurableClassLoader should skip scala.* classes TCOMP-772: Upgrade icon set to ui/icons 0.179.0 TCOMP-774: Upgrade xbean to 4.8 TCOMP-768: More tolerance of configuration prefix for implicit migration of configuration node in form core library TCOMP-756: Setup maven clirr plugin for component-api +testing TCOMP-762: Starter should only propose a single category level in the ui TCOMP-767: Ensure the configurationtype endpoints have matching name/path values TCOMP-761: Merge component-runtime-manager and component-runtime-standalone TCOMP-764: Clean up component-form-core dependencies TCOMP-765: Upgrade to batik 1.9.1 TCOMP-752: Fix Advanced settings and Test connection button appearance in repository wizard TCOMP-757: Duplicate method name "writeReplace" with signature "()Ljava.lang.Object;" in class file TCOMP-751: Support gzip compression on component-server TCOMP-753: Make classpath scanning to find component configurable TCOMP-758: Support component-server server configuration from system properties TCOMP-759: Enum must be i18n TCOMP-738: Component Server should respect ~/.m2/settings.xml local repository if it exists TCOMP-739: SerializationTransformer shouldn’t use ComponentManager to avoid ClassNotFoundException TCOMP-740: UISpecService should be reactive and use a CompletionStage based API TCOMP-741: UISpecService configuration support TCOMP-742: Configuration Type properties should be rooted TCOMP-744: Ensure wrapped BeamIO uses the right TCCL TCOMP-745: [Dependency Upgrade] CXF 3.2.4 TCOMP-746: [Dependency Upgrade] Tomcat 9.0.6 TCOMP-747: [Dependency Upgrade] Log4j2 2.11.0 TCOMP-748: Make configurationtype index endpoint lighter OOTB TCOMP-749: Intellij Idea plugin TCOMP-750: Unify @Pattern using javascript regex instead of a mixed mode TCOMP-734: Add support for context and globalMap values in Tacokit component settings TCOMP-733: support to use a beam pipeline under the hood for beam components in di TCOMP-693: Integrate Migration API TCOMP-737: upgrade to beam 2.4.0 TCOMP-731: Configuration Type migration handler skipped TCOMP-725: MavenDecrypter doesn’t support comments in settings.xml TCOMP-726: When a component is not found the error message can be misleading TCOMP-728: Http client doesn’t ignore empty query parameters TCOMP-722: WebSocket connection fails with a NPE when the endpoint doesn’t exists TCOMP-723: Adding configurationByExample utility to create query string for Job DSL TCOMP-724: Documentation endpoint doesn’t support HTML TCOMP-446: Support Embedded Documentation TCOMP-650: Ensure component can be executed in beam pipelines TCOMP-651: Ensure beam components can be wrapped and used through the Talend Component Kit Framework TCOMP-653: Web Form metamodel service TCOMP-655: Catalog service TCOMP-656: UISpec compatibility TCOMP-658: Add test Source/Sink collectors in JUnit integration TCOMP-659: Basic job builder API to simplify JUnit tests TCOMP-662: Validation Mojo TCOMP-664: Local testing server for dev TCOMP-675: Select a communication solution for Talend Component Kit server TCOMP-680: Register components into the Studio Palette TCOMP-681: Studio parameters form integration TCOMP-682: Studio Metadata integration TCOMP-683: Studio Runtime integration TCOMP-691: Create context menu for Tacokit node in repository panel TCOMP-719: Support Input Definition TCOMP-720: Support Output Definition TCOMP-721: Initial Widget Definitions

A Partition Mapper (PartitionMapper) is a component able to split itself to make the execution more efficient. This concept is borrowed from big data and useful in this context only (BEAM executions). The idea is to divide the work before executing it in order to reduce the overall execution time. The process is the following: The size of the data you work on is estimated. This part can be heuristic and not very precise. From that size, the execution engine (runner for Beam) requests the mapper to split itself in N mappers with a subset of the overall work. The leaf (final) mapper is used as a Producer (actual reader) factory. This kind of component must be Serializable to be distributable. A partition mapper requires three methods marked with specific annotations: @Assessor for the evaluating method @Split for the dividing method @Emitter for the Producer factory The Assessor method returns the estimated size of the data related to the component (depending its configuration). It must return a Number and must not take any parameter. For example: The Split method returns a collection of partition mappers and can take optionally a @PartitionSize long value as parameter, which is the requested size of the dataset per sub partition mapper. For example: The Emitter method must not have any parameter and must return a producer. It uses the partition mapper configuration to instantiate and configure the producer. For example:

Talend Component Kit provides a migration mechanism between two versions of a component to let you ensure backward compatibility. For example, a new version of a component may have some new options that need to be remapped, set with a default value in the older versions, or disabled. This tutorial shows how to create a migration handler for a component that needs to be upgraded from a version 1 to a version 2. The upgrade to the newer version includes adding new options to the component. This tutorial assumes that you know the basics about component development and are familiar with component project generation and implementation. To follow this tutorial, you need: Java 8 A Talend component development environment using Talend Component Kit. Refer to this document. Have generated a project containing a simple processor component using the Talend Component Kit Starter. First, create a simple processor component configured as follows: Create a simple configuration class that represents a basic authentication and that can be used in any component requiring this kind of authentication. Create a simple output component that uses the configuration defined earlier. The component configuration is injected into the component constructor. The version of the configuration class corresponds to the component version. By configuring these two classes, the first version of the component is ready to use a simple authentication mechanism. Now, assuming that the component needs to support a new authentication mode following a new requirement, the next steps are: Creating a version 2 of the component that supports the new authentication mode. Handling migration from the first version to the new version. The second version of the component needs to support a new authentication method and let the user choose the authentication mode he wants to use using a dropdown list. Add an Oauth2 authentication mode to the component in addition to the basic mode. For example: The options of the new authentication mode are now defined. Wrap the configuration created above in a global configuration with the basic authentication mode and add an enumeration to let the user choose the mode to use. For example, create an AuthenticationConfiguration class as follows: Using the @ActiveIf annotation allows to activate the authentication type according to the selected authentication mode. Edit the component to use the new configuration that supports an additional authentication mode. Also upgrade the component version from 1 to 2 as its configuration has changed. The component now supports two authentication modes in its version 2. Once the new version is ready, you can implement the migration handler that will take care of adapting the old configuration to the new one. What can happen if an old configuration is passed to the new component version? It simply fails, as the version 2 does not recognize the old version anymore. For that reason, a migration handler that adapts the old configuration to the new one is required. It can be achieved by defining a migration handler class in the @Version annotation of the component class. An old configuration may already be persisted by an application that integrates the version 1 of the component (Studio or web application). Add a migration handler class to the component version. Create the migration handler class MyOutputMigrationHandler. the incoming version, which is the version of the configuration that we are migrating from a map (key, value) of the configuration, where the key is the configuration path and the value is the value of the configuration. You need to be familiar with the component configuration path construction to better understand this part. Refer to Defining component layout and configuration. As a reminder, the following changes were made since the version 1 of the component: The configuration BasicAuth from the version 1 is not the root configuration anymore, as it is under AuthenticationConfiguration. AuthenticationConfiguration is the new root configuration. The component supports a new authentication mode (Oauth2) which is the default mode in the version 2 of the component. To migrate the old component version to the new version and to keep backward compatibility, you need to: Remap the old configuration to the new one. Give the adequate default values to some options. In the case of this scenario, it means making all configurations based on the version 1 of the component have the authenticationMode set to basic by default and remapping the old basic authentication configuration to the new one. if a configuration has been renamed between 2 component versions, you can get the old configuration option from the configuration map by using its old path and set its value using its new path. You can now upgrade your component without losing backward compatibility.

Input components are the components generally placed at the beginning of a Talend job. They are in charge of retrieving the data that will later be processed in the job. An input component is primarily made of three distinct logics: The execution logic of the component itself, defined through a partition mapper. The configurable part of the component, defined through the mapper configuration. The source logic defined through a producer. Before implementing the component logic and defining its layout and configurable fields, make sure you have specified its basic metadata, as detailed in this document. A Partition Mapper (PartitionMapper) is a component able to split itself to make the execution more efficient. This concept is borrowed from big data and useful in this context only (BEAM executions). The idea is to divide the work before executing it in order to reduce the overall execution time. The process is the following: The size of the data you work on is estimated. This part can be heuristic and not very precise. From that size, the execution engine (runner for Beam) requests the mapper to split itself in N mappers with a subset of the overall work. The leaf (final) mapper is used as a Producer (actual reader) factory. This kind of component must be Serializable to be distributable. A partition mapper requires three methods marked with specific annotations: @Assessor for the evaluating method @Split for the dividing method @Emitter for the Producer factory The Assessor method returns the estimated size of the data related to the component (depending its configuration). It must return a Number and must not take any parameter. For example: The Split method returns a collection of partition mappers and can take optionally a @PartitionSize long value as parameter, which is the requested size of the dataset per sub partition mapper. For example: The Emitter method must not have any parameter and must return a producer. It uses the partition mapper configuration to instantiate and configure the producer. For example: The Producer defines the source logic of an input component. It handles the interaction with a physical source and produces input data for the processing flow. A producer must have a @Producer method without any parameter. It is triggered by the @Emitter method of the partition mapper and can return any data. It is defined in the Source.java file:

Once you have generated a project, you can start implementing the logic and layout of your components and iterate on it. Depending on the type of component you want to create, the logic implementation can differ. However, the layout and component metadata are defined the same way for all types of components in your project. The main steps are: Defining family and component metadata Defining an input component logic Defining a processor/output logic Defining a standalone component logic Defining component layout and configuration In some cases, you will require specific implementations to handle more advanced cases, such as: Internationalizing a component Managing component versions Masking sensitive data implementing batch processing implementing streaming on a component You can also make certain configurations reusable across your project by defining services. Using your Java IDE along with a build tool supported by the framework, you can then compile your components to test and deploy them to Talend Studio or other Talend applications: Building components with Maven Building components with Gradle Wrapping a Beam I/O In any case, follow these best practices to ensure the components you develop are optimized. You can also learn more about component loading and plugins here: Loading a component

From the version 7.0 of Talend Studio, Talend Component Kit becomes the recommended framework to use to develop components. This framework is being introduced to ensure that newly developed components can be deployed and executed both in on-premise/local and cloud/big data environments. From that new approach comes the need to provide a complete yet unique and compatible way of developing components. With the Component Kit, custom components are entirely implemented in Java. To help you get started with a new custom component development project, a Starter is available. Using it, you will be able to generate the skeleton of your project. By importing this skeleton in a development tool, you can then implement the components layout and execution logic in Java. With the previous Javajet framework, metadata, widgets and configurable parts of a custom component were specified in XML. With the Component Kit, they are now defined in the Configuration (for example, LoggerProcessorConfiguration) Java class of your development project. Note that most of this configuration is transparent if you specified the Configuration Model of your components right before generating the project from the Starter. Any undocumented feature or option is considered not supported by the Component Kit framework. You can find examples of output in Studio or Cloud environments in the Gallery. Javajet Component Kit Javajet Component Kit Javajet Component Kit Javajet Component Kit Javajet Component Kit Javajet Component Kit or Component Kit Javajet Component Kit Javajet Component Kit Javajet Component Kit Javajet Component Kit Javajet Component Kit Javajet Component Kit Javajet Component Kit Javajet Component Kit Javajet Component Kit or Previously, the execution of a custom component was described through several Javajet files: _begin.javajet, containing the code required to initialize the component. _main.javajet, containing the code required to process each line of the incoming data. _end.javajet, containing the code required to end the processing and go to the following step of the execution. With the Component Kit, the entire execution flow of a component is described through its main Java class (for example, LoggerProcessor) and through services for reusable parts. Each type of component has its own execution logic. The same basic logic is applied to all components of the same type, and is then extended to implement each component specificities. The project generated from the starter already contains the basic logic for each component. Talend Component Kit framework relies on several primitive components. All components can use @PostConstruct and @PreDestroy annotations to initialize or release some underlying resource at the beginning and the end of a processing. In distributed environments, class constructor are called on cluster manager nodes. Methods annotated with @PostConstruct and @PreDestroy are called on worker nodes. Thus, partition plan computation and pipeline tasks are performed on different nodes. All the methods managed by the framework must be public. Private methods are ignored. The framework is designed to be as declarative as possible but also to stay extensible by not using fixed interfaces or method signatures. This allows to incrementally add new features of the underlying implementations. To ensure that the Cloud-compatible approach of the Component Kit framework is respected, some changes were introduced on the implementation side, including: The File mode is no longer supported. You can still work with URIs and remote storage systems to use files. The file collection must be handled at the component implementation level. The input and output connections between two components can only be of the Flow or Reject types. Other types of connections are not supported. Every Output component must have a corresponding Input component and use a dataset. All datasets must use a datastore. To get started with the Component Kit framework, you can go through the following documents: Learn the basics about Talend Component Kit Create and deploy your first Component Kit component Learn about the Starter Start implementing components Integrate a component to Talend Studio Check some examples of components built with Talend Component Kit

component-runtime-junit is a test library that allows you to validate simple logic based on the Talend Component Kit tooling. To import it, add the following dependency to your project: This dependency also provides mocked components that you can use with your own component to create tests. The mocked components are provided under the test family: emitter : a mock of an input component collector : a mock of an output component The collector is "per thread" by default. If you are executing a Beam (or concurrent) job, it will not work. To switch to a JVM wide storage, set the talend.component.junit.handler.state system property to static (default being thread). You can do it in a maven-surefire-plugin execution. You can define a standard JUnit test and use the SimpleComponentRule rule: The rule can also be defined as a @ClassRule to start it once per class and not per test as with @Rule. To go further, you can add the ServiceInjectionRule rule, which allows to inject all the component family services into the test class by marking test class fields with @Service: The JUnit 5 integration is very similar to JUnit 4, except that it uses the JUnit 5 extension mechanism. The entry point is the @WithComponents annotation that you add to your test class, and which takes the component package you want to test. You can use @Injected to inject an instance of ComponentsHandler - which exposes the same utilities than the JUnit 4 rule - in a test class field : If you use JUnit 5 for the first time, keep in mind that the imports changed and that you need to use org.junit.jupiter.api.Test instead of org.junit.Test. Some IDE versions and surefire versions can also require you to install either a plugin or a specific configuration. As for JUnit 4, you can go further by injecting test class fields marked with @Service, but there is no additional extension to specify in this case: Streaming components have the issue to not stop by design. The Job DSL exposes two properties to help with that issue: streaming.maxRecords: enables to request a maximum number of records streaming.maxDurationMs: enables to request a maximum duration for the execution of the input You can set them as properties on the job: Using the test://collector component as shown in the previous sample stores all records emitted by the chain (typically your source) in memory. You can then access them using theSimpleComponentRule.getCollectedData(type). Note that this method filters by type. If you don’t need any specific type, you can use Object.class. The input mocking is symmetric to the output. In this case, you provide the data you want to inject: The component configuration is a POJO (using @Option on fields) and the runtime configuration (ExecutionChainBuilder) uses a Map. To make the conversion easier, the JUnit integration provides a SimpleFactory.configurationByExample utility to get this map instance from a configuration instance. Example: The same factory provides a fluent DSL to create the configuration by calling configurationByExample without any parameter. The advantage is to be able to convert an object as a Map or as a query string in order to use it with the Job DSL: It handles the encoding of the URI to ensure it is correctly done. When writing tests for your components, you can force the maxBatchSize parameter value by setting it with the following syntax: $configuration.$maxBatchSize=10. The SimpleComponentRule also allows to test a mapper unitarily. You can get an instance from a configuration and execute this instance to collect the output. Example: As for a mapper, a processor is testable unitary. However, this case can be more complex in case of multiple inputs or outputs. Example: The rule allows you to instantiate a Processor from your code, and then to collect the output from the inputs you pass in. There are two convenient implementations of the input factory: MainInputFactory for processors using only the default input. JoinInputfactory with the withInput(branch, data) method for processors using multiple inputs. The first argument is the branch name and the second argument is the data used by the branch. If needed, you can also implement your own input representation using org.talend.sdk.component.junit.ControllableInputFactory. The following artifact allows you to test against a Spark cluster: The testing relies on a JUnit TestRule. It is recommended to use it as a @ClassRule, to make sure that a single instance of a Spark cluster is built. You can also use it as a simple @Rule, to create the Spark cluster instances per method instead of per test class. The @ClassRule takes the Spark and Scala versions to use as parameters. It then forks a master and N slaves. Finally, the submit* method allows you to send jobs either from the test classpath or from a shade if you run it as an integration test. For example: This testing methodology works with @Parameterized. You can submit several jobs with different arguments and even combine it with Beam TestPipeline if you make it transient. The integration of that Spark cluster logic with JUnit 5 is done using the @WithSpark marker for the extension. Optionally, it allows you to inject—through @SparkInject—the BaseSpark handler to access the Spark cluster meta information. For example, its host/port. Example: Currently, SparkClusterRule does not allow to know when a job execution is done, even by exposing and polling the web UI URL to check. The best solution at the moment is to make sure that the output of your job exists and contains the right value. awaitability or any equivalent library can help you to implement such logic: To wait until a file exists and check that its content (for example) is the expected one, you can use the following logic: The HTTP JUnit module allows you to mock REST API very simply. The module coordinates are: This module uses Apache Johnzon and Netty. If you have any conflict (in particular with Netty), you can add the shaded classifier to the dependency. This way, both dependencies are shaded, which avoids conflicts with your component. It supports both JUnit 4 and JUnit 5. The concept is the exact same one: the extension/rule is able to serve precomputed responses saved in the classpath. You can plug your own ResponseLocator to map a request to a response, but the default implementation - which should be sufficient in most cases - looks in talend/testing/http/_.json. Note that you can also put it in talend/testing/http/.json. JUnit 4 setup is done through two rules: JUnit4HttpApi, which is starts the server. JUnit4HttpApiPerMethodConfigurator, which configures the server per test and also handles the capture mode. If you don’t use the JUnit4HttpApiPerMethodConfigurator, the capture feature is disabled and the per test mocking is not available. For tests using SSL-based services, you need to use activeSsl() on the JUnit4HttpApi rule. You can access the client SSL socket factory through the API handler: Sometimes the query parameters are sensitive and you don’t want to store them when capturing. In such cases, you can drop them from the captured data (.json) and the mock implementation will be able to match the request ignoring the query parameters. JUnit 5 uses a JUnit 5 extension based on the HttpApi annotation that you can add to your test class. You can inject the test handler - which has some utilities for advanced cases - through @HttpApiInject: The injection is optional and the @HttpApi annotation allows you to configure several test behaviors. For tests using SSL-based services, you need to use @HttpApi(useSsl = true). You can access the client SSL socket factory through the API handler: The strength of this implementation is to run a small proxy server and to auto-configure the JVM: http[s].proxyHost, http[s].proxyPort, HttpsURLConnection#defaultSSLSocketFactory and SSLContext#default are auto-configured to work out-of-the-box with the proxy. It allows you to keep the native and real URLs in your tests. For example, the following test is valid: If you execute this test, it fails with an HTTP 400 error because the proxy does not find the mocked response. You can create it manually, as described in component-runtime-http-junit, but you can also set the talend.junit.http.capture property to the folder storing the captures. It must be the root folder and not the folder where the JSON files are located (not prefixed by talend/testing/http by default). In most cases, use src/test/resources. If new File("src/test/resources") resolves the valid folder when executing your test (Maven default), then you can just set the system property to true. Otherwise, you need to adjust accordingly the system property value. When set to false, the capture is enabled. Instead, captures are saved in a false/ directory. When the tests run with this system property, the testing framework creates the correct mock response files. After that, you can remove the system property. The tests will still pass, using google.com, even if you disconnect your machine from the Internet. If you set the talend.junit.http.passthrough system property to true, the server acts as a proxy and executes each request to the actual server - similarly to the capturing mode. With its @ParameterizedTest, you can want to customize the name of the output file for JUnit 5 based captures/mocks. Concretely you want to ensure the replay of the same method with different data lead to different mock files. By default the framework will use the display name of the test to specialize it but it is not always very friendly. If you want some more advanced control over the name you can use @HttpApiName("myCapture.json") on the test method. To parameterize the name using @HttpApiName, you can use the placeholders ${class} and ${method} which represents the declaring class and method name, and ${displayName} which represents the method name. Here is an example to use the same capture file for all repeated test: And here, the same example but using different files for each repetition:

Several data generators exist if you want to populate objects with a semantic that is more evolved than a plain random string like commons-lang3: github.com/Codearte/jfairy github.com/DiUS/java-faker github.com/andygibson/datafactory etc. Even more advanced, the following generators allow to directly bind generic data on a model. However, data quality is not always optimal: github.com/devopsfolks/podam github.com/benas/random-beans etc. There are two main kinds of implementation: implementations using a pattern and random generated data. implementations using a set of precomputed data extrapolated to create new values. Check your use case to know which one fits best. An alternative to data generation can be to import real data and use Talend Studio to sanitize the data, by removing sensitive information and replacing it with generated or anonymized data. Then you just need to inject that file into the system. If you are using JUnit 5, you can have a look at glytching.github.io/junit-extensions/randomBeans.

The HTTP API intends to expose most Talend Component Kit features over HTTP. It is a standalone Java HTTP server. The WebSocket protocol is activated for the endpoints. Endpoints then use /websocket/v1 as base instead of /api/v1. See WebSocket for more details. Browse the API description using interface. To make sure that the migration can be enabled, you need to set the version the component was created with in the execution configuration that you send to the server (component version is in component the detail endpoint). To do that, use tcomp::component::version key. Endpoints that are intended to disappear will be deprecated. A X-Talend-Warning header will be returned with a message as value. You can connect yo any endpoint by: Replacing /api with /websocket Appending / to the URL Formatting the request as: For example: The response is formatted as follows: All endpoints are logged at startup. You can then find them in the logs if you have a doubt about which one to use. If you don’t want to create a pool of connections per endpoint/verb, you can use the bus endpoint: /websocket/v1/bus. This endpoint requires that you add the destinationMethod header to each request with the verb value (GET by default): the configuration is read from system properties, environment variables, …. Default value: 1000. Maximum items a cache can store, used for index endpoints. A comma separated list of gav to locate the components Default value: ${home}/documentations. A component translation repository. This is where you put your documentation translations. Their name must follow the pattern documentation_${container-id}_language.adoc where ${container-id} is the component jar name (without the extension and version, generally the artifactId). Default value: true. Should the component extensions add required dependencies. If you deploy some extension, where they can create their dependencies if needed. Default value: 180000. Timeout for extension initialization at startup, since it ensures the startup wait extensions are ready and loaded it allows to control the latency it implies. A property file (or multiple comma separated) where the value is a gav of a component to register(complementary with coordinates). Note that the path can end up with or .properties to take into account all properties in a folder. Default value: true. Should the /documentation endpoint be activated. Note that when called on localhost the doc is always available. Default value: true. Should the /api/v1/environment endpoint be activated. It shows some internal versions and git commit which are not always desirable over the wire. Default value: false. Should the components using a @GridLayout support tab translation. Studio does not suppot that feature yet so this is not enabled by default. Default value: icons/%s.svg,icons/svg/%s.svg,icons/%s_icon32.png,icons/png/%s_icon32.png. These patterns are used to find the icons in the classpath(s). Default value: false. If set it will replace any message for exceptions. Set to false to use the actual exception message. Default value: false. Should the lastUpdated timestamp value of /environment endpoint be updated with server start time. Default value: en*=en fr*=fr zh*=zh_CN ja*=ja de*=de. For caching reasons the goal is to reduce the locales to the minimum required numbers. For instance we avoid fr and fr_FR which would lead to the same entries but x2 in terms of memory. This mapping enables that by whitelisting allowed locales, default being en. If the key ends with it means all string starting with the prefix will match. For instance fr will match fr_FR but also fr_CA. The local maven repository used to locate components and their dependencies Default value: false. Should the plugins be un-deployed and re-deployed. Default value: 600. Interval in seconds between each check if plugins re-loading is enabled. Specify a file to check its timestamp on the filesystem. This file will take precedence of the default ones provided by the talend.component.server.component.registry property (used for timestamp method). Default value: timestamp. Re-deploy method on a timestamp or connectors version change. By default, the timestamp is checked on the file pointed by talend.component.server.component.registry or talend.component.server.plugins.reloading.marker variable, otherwise we inspect the content of the CONNECTORS_VERSION file. Accepted values: timestamp, anything else defaults to connectors. Default value: false. Should the all requests/responses be logged (debug purposes - only work when running with CXF). Default value: securityNoopHandler. How to validate a command/request. Accepted values: securityNoopHandler. Default value: securityNoopHandler. How to validate a connection. Accepted values: securityNoopHandler. A folder available for the server - don’t forget to mount it in docker if you are using the image - which accepts subfolders named as component plugin id (generally the artifactId or jar name without the version, ex: jdbc). Each family folder can contain: a user-configuration.properties file which will be merged with component configuration system (see services). This properties file enables the function userJar(xxxx) to replace the jar named xxxx by its virtual gav (groupId:artifactId:version), a list of jars which will be merged with component family classpath Default value: auto. Should the implicit artifacts be provisionned to a m2. If set to auto it tries to detect if there is a m2 to provision - recommended, if set to skip it is ignored, else it uses the value as a m2 path. The configuration uses Microprofile Config for most entries. It means it can be passed through system properties and environment variables (by replacing dots with underscores and making the keys uppercase). To configure a Docker image rather than a standalone instance, Docker Config and secrets integration allows you to read the configuration from files. You can customize the configuration of these integrations through system properties. Docker integration provides a secure: support to encrypt values and system properties, when required. It is fully implemented using the Apache Geronimo Microprofile Config extensions. Using the server ZIP (or Docker image), you can configure HTTPS by adding properties to _JAVA_OPTIONS. Assuming that you have a certificate in /opt/certificates/component.p12 (don’t forget to add/mount it in the Docker image if you use it), you can activate it as follows: You can define simple queries on the configuration types and components endpoints. These two endpoints support different parameters. Queries on the configurationtype/index endpoint supports the following parameters: type id name metadata of the first configuration property as parameters. Queries on the component/index endpoint supports the following parameters: plugin name id familyId metadata of the first configuration property as parameters. In both cases, you can combine several conditions using OR and AND operators. If you combine more than two conditions, note that they are evaluated in the order they are written. Each supported parameter in a condition can be "equal to" (=) or "not equal to" (!=) a defined value (case-sensitive). For example: In this example, the query gets components that have a dataset and belong to the jdbc-component plugin, or components that are named input. The component-form library provides a way to build a component REST API facade that is compatible with React form library. for example: the Client can be created using ClientFactory.createDefault(System.getProperty("app.components.base", "http://localhost:8080/api/v1")) and the service can be a simple new UiSpecService<>(). The factory uses JAX-RS if the API is available (assuming a JSON-B provider is registered). Otherwise, it tries to use Spring. The conversion from the component model (REST API) to the uiSpec model is done through UiSpecService. It is based on the object model which is mapped to a UI model. Having a flat model in the component REST API allows to customize layers easily. You can completely control the available components, tune the rendering by switching the uiSchema, and add or remove parts of the form. You can also add custom actions and buttons for specific needs of the application. The /migrate endpoint was not shown in the previous snippet but if you need it, add it as well. This Maven dependency provides the UISpec model classes. You can use the Ui API (with or without the builders) to create UiSpec representations. For Example: The model uses the JSON-B API to define the binding. Make sure to have an implementation in your classpath. To do that, add the following dependencies: The following module enables you to define through annotations a uispec on your own models: this can’t be used in components and is only intended for web applications. org.talend.sdk.component.form.uispec.mapper.api.service.UiSpecMapper enables to create a Ui instance from a custom type annotated with org.talend.sdk.component.form.uispec.mapper.api.model.View and org.talend.sdk.component.form.uispec.mapper.api.model.View.Schema. UiSpecMapper returns a Supplier and not directly an Ui because the ui-schema is re-evaluated when `get()̀ is called. This enables to update the title maps for example. Here is an example: This API maps directly the UiSpec model (json schema and ui schema of Talend UIForm). The default implementation of the mapper is available at org.talend.sdk.component.form.uispec.mapper.impl.UiSpecMapperImpl. Here is an example: The getTitleMapProviders() method will generally lookup a set of TitleMapProvider instances in your IoC context. This API is used to fill the titleMap of the form when a reference identifier is set on the @Schema annotation. component-kit.js is no more available (previous versions stay on NPM) and is replaced by @talend/react-containers. The previous import can be replaced by import kit from '@talend/react-containers/lib/ComponentForm/kit';. Default JavaScript integration goes through the Talend UI Forms library and its Containers wrapper. Documentation is now available on the previous link. The logging uses Log4j2. You can specify a custom configuration by using the -Dlog4j.configurationFile system property or by adding a log4j2.xml file to the classpath. Here are some common configurations: Console logging: Output messages look like: JSON logging: Output messages look like: Rolling file appender: More details are available in the RollingFileAppender documentation. You can compose previous layout (message format) and appenders (where logs are written). The server image is deployed on Docker. Its version is suffixed with a timestamp to ensure images are not overridden and can break your usage. You can check the available version on Docker hub. You can run the docker image by executing this command : You can set the env variable _JAVA_OPTIONS to customize the server, by default it is installed in /opt/talend/component-kit. The maven repository is the default one of the machine, you can change it setting the system property talend.component.server.maven.repository=/path/to/your/m2. If you want to deploy some components you can configure which ones in _JAVA_OPTIONS (see server doc online) and redirect your local m2: The component server docker image comes with two log4j2 profiles: TEXT (default) and JSON. The logging profile can be changed by setting the environment variable LOGGING_LAYOUT to JSON. Note that Component Server adds to these default Talend profiles the KAFKA profile. With this profile, all logs are sent to Kafka. You can check the exact configuration in the component-runtime/images/component-server-image/src/main/resources folder. The console logging is on at INFO level by default. You can customize it by setting the CONSOLE_LOG_LEVEL environment variable to DEBUG, INFO, WARN or to any other log level supported by log4j2. Run docker image with console logging: The JSON profile does the following: Logs on the console using the CONSOLE_LOG_LEVEL configuration as the default profile. It uses the formatting shown below. If the TRACING_KAFKA_URL environment variable is set, it logs the opentracing data on the defined Kafka using the topic TRACING_KAFKA_TOPIC. This level can be customized by setting the KAFKA_LOG_LEVEL environment variable (INFO by default). Events are logged in the following format: This profile is very close to the JSON profile and also adds the LOG_KAFKA_TOPIC and LOG_KAFKA_URL configuration. The difference is that it logs the default logs on Kafka in addition to the tracing logs. The component server uses Geronimo OpenTracing to monitor request. The tracing can be activated by setting the TRACING_ON environment variable to true. The tracing rate is configurable by setting the TRACING_SAMPLING_RATE environment variable. It accepts 0 (none) and 1 (all, default) as values to ensure the consistency of the reporting. You can find all the details on the configuration in org.talend.sdk.component.server.configuration.OpenTracingConfigSource. Run docker image with tracing on: By default, Geronimo OpenTracing will log the spans in a Zipking format so you can use the Kafka profile as explained before to wire it over any OpenTracing backend. You can register component server images in Docker using these instructions in the corresponding image directory: Docker Compose allows you to deploy the server with components, by mounting the component volume into the server image. docker-compose.yml example: If you want to mount it from another image, you can use this compose configuration: To run one of the previous compose examples, you can use docker-compose -f docker-compose.yml up. Only use the configuration related to port 5005 (in ports and the -agentlib option in _JAVA_OPTIONS) to debug the server on port 5005. Don’t set it in production. You can mount a volume in /opt/talend/component-kit/custom/ and the jars in that folder which will be deployed with the server. Since the server relies on CDI (Apache OpenWebBeans) you can use that technology to enrich it, including JAX-RS endpoints, interceptors etc…or just libraries needing to be in the JVM.

Talend Component Kit is a Java framework designed to simplify the development of components at two levels: The Runtime, that injects the specific component code into a job or pipeline. The framework helps unifying as much as possible the code required to run in Data Integration (DI) and BEAM environments. The Graphical interface. The framework helps unifying the code required to render the component in a browser or in the Eclipse-based Talend Studio (SWT). Most part of the development happens as a Maven or Gradle project and requires a dedicated tool such as IntelliJ. The Component Kit is made of: A Starter, that is a graphical interface allowing you to define the skeleton of your development project. APIs to implement components UI and runtime. Development tools: Maven and Gradle wrappers, validation rules, packaging, Web preview, etc. A testing kit based on JUnit 4 and 5. By using this tooling in a development environment, you can start creating components as described below. Developing new components using the Component Kit framework includes: Creating a project using the starter or the Talend IntelliJ plugin. This step allows to build the skeleton of the project. It consists in: Defining the general configuration model for each component in your project. Generating and downloading the project archive from the starter. Compiling the project. Importing the compiled project in your IDE. This step is not required if you have generated the project using the IntelliJ plugin. implementing the components, including: Registering the components by specifying their metadata: family, categories, version, icon, type and name. Defining the layout and configurable part of the components. Defining the execution logic of the components, also called runtime. Testing the components. Deploying the components to Talend Studio or Cloud applications. Optionally, you can use services. Services are predefined or user-defined configurations that can be reused in several components. There are four types of components, each type coming with its specificities, especially on the runtime side. Input components: Retrieve the data to process from a defined source. An input component is made of: The execution logic of the component, represented by a Mapper or an Emitter class. The source logic of the component, represented by a Source class. The layout of the component and the configuration that the end-user will need to provide when using the component, defined by a Configuration class. All input components must have a dataset specified in their configuration, and every dataset must use a datastore. Processors: Process and transform the data. A processor is made of: The execution logic of the component, describing how to process each records or batches of records it receives. It also describes how to pass records to its output connections. This logic is defined in a Processor class. The layout of the component and the configuration that the end-user will need to provide when using the component, defined by a Configuration class. Output components: Send the processed data to a defined destination. An output component is made of: The execution logic of the component, describing how to process each records or batches of records it receives. This logic is defined in an Output class. Unlike processors, output components are the last components of the execution and return no data. The layout of the component and the configuration that the end-user will need to provide when using the component, defined by a Configuration class. All input components must have a dataset specified in their configuration, and every dataset must use a datastore. Standalone components: Make a call to the service or run a query on the database. A standalone component is made of: The execution logic of the component, represented by a DriverRunner class. The layout of the component and the configuration that the end-user will need to provide when using the component, defined by a Configuration class. All input components must have a datastore or dataset specified in their configuration, and every dataset must use a datastore. The following example shows the different classes of an input components in a multi-component development project: Setup your development environment Generate your first project and develop your first component

By default, input components are designed to receive a one-time batch of data to process. By enabling the streaming mode, you can instead set your component to process a continuous incoming flow of data. When streaming is enabled on an input component, the component tries to pull data from its producer. When no data is pulled, it waits for a defined period of time before trying to pull data again, and so on. This period of time between tries is defined by a strategy. This document explains how to configure this strategy and the cases where it can fit your needs. Before enabling streaming on your component, make sure that it fits the scope and requirements of your project and that regular batch processing cannot be used instead. Streaming is designed to help you dealing with real-time or near real-time data processing cases, and should be used only for such cases. Enabling streaming will impact the performance when processing batches of data. You can enable streaming right from the design phase of the project by enabling the Stream toggle in the basic configuration of your future component in the Component Kit Starter. Doing so adds a default streaming-ready configuration to your component when generating the project. This default configuration implements a constant pause duration of 500 ms between retries, with no limit of retries. If streaming was not enabled at all during the project generation or if you need to implement a more specific configuration, you can change the default settings according to your needs: Add the infinite=true parameter to your component class. Define the number of retries allowed in the component family LocalConfiguration, using the talend.input.streaming.retry.maxRetries parameter. It is set by default to Integer.MAX_VALUE. Define the pausing strategy between retries in the component family LocalConfiguration, using the talend.input.streaming.retry.strategy parameter. Possible values are: constant (default). It sets a constant pause duration between retries. exponential. It sets an exponential backoff pause duration. See the tables below for more details about each strategy. Parameter Description Default value talend.input.streaming.retry.constant.timeout Pause duration for the constant strategy, in ms. 500 Parameter Description Default value talend.input.streaming.retry.exponential.exponent Exponent of the exponential calculation. 1.5 talend.input.streaming.retry.exponential.randomizationFactor Randomization factor used in the calculation. 0.5 talend.input.streaming.retry.exponential.maxDuration Maximum pausing duration between two retries. 5*60*1000 (5 minutes) talend.input.streaming.retry.exponential.initialBackOff Initial backoff value. 1000 (1 second) The values of these parameters are then used in the following calculations to determine the exact pausing duration between two retries. For more clarity in the formulas below, parameter names have been replaced with variables. First, the current interval duration is calculated: \$A = min(B xx E^I, F)\$ Where: A: currentIntervalMillis B: initialBackOff E: exponent I: current number of retries F: maxDuration Then, from the current interval duration, the next interval duration is calculated: \$D = min(F, A + ((R xx 2-1) xx C xx A))\$ Where: D: nextBackoffMillis F: maxDuration A: currentIntervalMillis R: random C: randomizationFactor

This gallery shows how widgets and validations are rendered in both Studio and web environments, along with sample implementation code. You can also find sample working components for each of the configuration cases below: ActiveIf: Add visibility conditions on some configurations. Checkbox: Add checkboxes or toggles to your component. Code: Allow users to enter their own code. Credential: Mark a configuration as sensitive data to avoid displaying it as plain text. Datastore: Add a button allowing to check the connection to a datastore. Datalist: Two ways of implementing a dropdown list with predefined choices. Integer: Add numeric fields to your component configuration. Min/Max: Specify a minimum or a maximum value for a numeric configuration. Multiselect: Add a list and allow users to select multiple elements of that list. Pattern: Enforce rules based on a specific a pattern to prevent users from entering invalid values. Required: Make a configuration mandatory. Suggestions: Suggest possible values in a field based on what the users are entering. Table: Add a table to your component configuration. Textarea: Add a text area for configurations expecting long texts or values. Input: Add a simple text input field to the component configuration Update: Provide a button allowing to fill a part of the component configuration based on a service. Validation: Specify constraints to make sure that a URL is well formed. Widgets allow to easily implement different types of input fields to your components. Studio Rendering Web Rendering Studio Rendering Web Rendering Studio Rendering Web Rendering Studio Rendering Web Rendering Studio Rendering Web Rendering Datetime fields rely on the Java Date Time API, including LocalTime, LocalDate, LocalDateTime and ZonedDateTime classes. Studio Rendering Web Rendering or Studio Rendering Web Rendering Studio Rendering Web Rendering Studio Rendering Web Rendering Studio Rendering Web Rendering Studio Rendering Web Rendering Validations help restricting what can be entered or selected in an input field, to make sure that the value complies with the expected type of information. Studio Rendering Web Rendering Studio Rendering Web Rendering Studio Rendering Web Rendering You can also use other types of validation that are similar to @Pattern: @Min, @Max to specify a minimum and maximum value for numerical fields. @Uniques for collection values. @Required for a required configuration.

For common concerns such as caching, auditing, and so on, you can use an interceptor-like API. It is enabled on services by the framework. An interceptor defines an annotation marked with @Intercepts, which defines the implementation of the interceptor (InterceptorHandler). For example: The handler is created from its constructor and can take service injections (by type). The first parameter, however, can be BiFunction, which represents the invocation chain if your interceptor can be used with others. If you make a generic interceptor, pass the invoker as first parameter. Otherwise you cannot combine interceptors at all. Here is an example of interceptor implementation for the @Logged API: This implementation is compatible with interceptor chains because it takes the invoker as first constructor parameter and it also takes a service injection. Then, the implementation simply does what is needed, which is logging the invoked method in this case. The findAnnotation annotation, inherited from InterceptorHandler, is an utility method to find an annotation on a method or class (in this order).

The Job builder lets you create a job pipeline programmatically using Talend components (Producers and Processors). The job pipeline is an acyclic graph, allowing you to build complex pipelines. Let’s take a simple use case where two data sources (employee and salary) are formatted to CSV and the result is written to a file. A job is defined based on components (nodes) and links (edges) to connect their branches together. Every component is defined by a unique id and an URI that identify the component. The URI follows the form [family]://[component][?version][&configuration], where: family is the name of the component family. component is the name of the component. version is the version of the component. It is represented in a key=value format. The key is __version and the value is a number. configuration is component configuration. It is represented in a key=value format. The key is the path of the configuration and the value is a `string' corresponding to the configuration value. URI example: configuration parameters must be URI/URL encoded. Job example: It has some starting components (components that don’t have a from connection and that need to be of the producer type). There are no cyclic connections. The job pipeline needs to be an acyclic graph. All components used in the connections are already declared. Each connection is used only once. You cannot connect a component input/output branch twice. In this version, the execution of the job is linear. Components are not executed in parallel even if some steps may be independents. Depending on the configuration, you can select the environment which you execute your job in. To select the environment, the logic is the following one: If an org.talend.sdk.component.runtime.manager.chain.Job.ExecutorBuilder class is passed through the job properties, then use it. The supported types are an ExecutionBuilder instance, a Class or a String. If an ExecutionBuilder SPI is present, use it. It is the case if component-runtime-beam is present in your classpath. Else, use a local/standalone execution. In the case of a Beam execution, you can customize the pipeline options using system properties. They have to be prefixed with talend.beam.job.. For example, to set the appName option, you need to use -Dtalend.beam.job.appName=mytest. The job builder lets you set a key provider to join your data when a component has multiple inputs. The key provider can be set contextually to a component or globally to the job. If the incoming data has different IDs, you can provide a complex global key provider that relies on the context given by the component id and the branch name. For Beam case, you need to rely on Beam pipeline definition and use the component-runtime-beam dependency, which provides Beam bridges. org.talend.sdk.component.runtime.beam.TalendIO provides a way to convert a partition mapper or a processor to an input or processor using the read or write methods. org.talend.sdk.component.runtime.beam.TalendFn provides the way to wrap a processor in a Beam PTransform and to integrate it into the pipeline. The multiple inputs and outputs are represented by a Map element in Beam case to avoid using multiple inputs and outputs. You can use ViewsMappingTransform or CoGroupByKeyResultMappingTransform to adapt the input/output format to the record format representing the multiple inputs/output, like Map>, but materialized as a Record. Input data must be of the Record type in this case. For simple inputs and outputs, you can get an automatic and transparent conversion of the Beam.io into an I/O component, if you decorated your PTransform with @PartitionMapper or @Processor. However, there are limitations: Inputs must implement PTransform> and must be a BoundedSource. Outputs must implement PTransform, PDone> and register a DoFn on the input PCollection. For more information, see the How to wrap a Beam I/O page.

The Component Kit Starter lets you design your components configuration and generates a ready-to-implement project structure. The Starter is available on the web or as an IntelliJ plugin. This tutorial shows you how to use the Component Kit Starter to generate new components for MySQL databases. Before starting, make sure that you have correctly setup your environment. See this section. When defining a project using the Starter, do not refresh the page to avoid losing your configuration. Before being able to create components, you need to define the general settings of the project: Create a folder on your local machine to store the resource files of the component you want to create. For example, C:/my_components. Open the Starter in the web browser of your choice. Select your build tool. This tutorial uses Maven, but you can select Gradle instead. Add any facet you need. For example, add the Talend Component Kit Testing facet to your project to automatically generate unit tests for the components created in the project. Enter the Component Family of the components you want to develop in the project. This name must be a valid java name and is recommended to be capitalized, for example 'MySQL'. Once you have implemented your components in the Studio, this name is displayed in the Palette to group all of the MySQL-related components you develop, and is also part of your component name. Select the Category of the components you want to create in the current project. As MySQL is a kind of database, select Databases in this tutorial. This Databases category is used and displayed as the parent family of the MySQL group in the Palette of the Studio. Complete the project metadata by entering the Group, Artifact and Package. By default, you can only create processors. If you need to create Input or Output components, select Activate IO. By doing this: Two new menu entries let you add datasets and datastores to your project, as they are required for input and output components. Input and Output components without dataset (itself containing a datastore) will not pass the validation step when building the components. Learn more about datasets and datastores in this document. An Input component and an Output component are automatically added to your project and ready to be configured. Components added to the project using Add A Component can now be processors, input or output components. A datastore represents the data needed by an input or output component to connect to a database. When building a component, the validateDataSet validation checks that each input or output (processor without output branch) component uses a dataset and that this dataset has a datastore. You can define one or several datastores if you have selected the Activate IO step. Select Datastore. The list of datastores opens. By default, a datastore is already open but not configured. You can configure it or create a new one using Add new Datastore. Specify the name of the datastore. Modify the default value to a meaningful name for your project. This name must be a valid Java name as it will represent the datastore class in your project. It is a good practice to start it with an uppercase letter. Edit the datastore configuration. Parameter names must be valid Java names. Use lower case as much as possible. A typical configuration includes connection details to a database: url username password. Save the datastore configuration. A dataset represents the data coming from or sent to a database and needed by input and output components to operate. The validateDataSet validation checks that each input or output (processor without output branch) component uses a dataset and that this dataset has a datastore. You can define one or several datasets if you have selected the Activate IO step. Select Dataset. The list of datasets opens. By default, a dataset is already open but not configured. You can configure it or create a new one using the Add new Dataset button. Specify the name of the dataset. Modify the default value to a meaningful name for your project. This name must be a valid Java name as it will represent the dataset class in your project. It is a good practice to start it with an uppercase letter. Edit the dataset configuration. Parameter names must be valid Java names. Use lower case as much as possible. A typical configuration includes details of the data to retrieve: Datastore to use (that contains the connection details to the database) table name data Save the dataset configuration. To create an input component, make sure you have selected Activate IO. When clicking Add A Component in the Starter, a new step allows you to define a new component in your project. The intent in this tutorial is to create an input component that connects to a MySQL database, executes a SQL query and gets the result. Choose the component type. Input in this case. Enter the component name. For example, MySQLInput. Click Configuration model. This button lets you specify the required configuration for the component. By default, a dataset is already specified. For each parameter that you need to add, click the (+) button on the right panel. Enter the parameter name and choose its type then click the tick button to save the changes. In this tutorial, to be able to execute a SQL query on the Input MySQL database, the configuration requires the following parameters:+ a dataset (which contains the datastore with the connection information) a timeout parameter. Closing the configuration panel on the right does not delete your configuration. However, refreshing the page resets the configuration. Specify whether the component issues a stream or not. In this tutorial, the MySQL input component created is an ordinary (non streaming) component. In this case, leave the Stream option disabled. Select the Record Type generated by the component. In this tutorial, select Generic because the component is designed to generate records in the default Record format. You can also select Custom to define a POJO that represents your records. Your input component is now defined. You can add another component or generate and download your project. When clicking Add A Component in the Starter, a new step allows you to define a new component in your project. The intent in this tutorial is to create a simple processor component that receives a record, logs it and returns it at it is. If you did not select Activate IO, all new components you add to the project are processors by default. If you selected Activate IO, you can choose the component type. In this case, to create a Processor component, you have to manually add at least one output. If required, choose the component type: Processor in this case. Enter the component name. For example, RecordLogger, as the processor created in this tutorial logs the records. Specify the Configuration Model of the component. In this tutorial, the component doesn’t need any specific configuration. Skip this step. Define the Input(s) of the component. For each input that you need to define, click Add Input. In this tutorial, only one input is needed to receive the record to log. Click the input name to access its configuration. You can change the name of the input and define its structure using a POJO. If you added several inputs, repeat this step for each one of them. The input in this tutorial is a generic record. Enable the Generic option and click Save. Define the Output(s) of the component. For each output that you need to define, click Add Output. The first output must be named MAIN. In this tutorial, only one generic output is needed to return the received record. Outputs can be configured the same way as inputs (see previous steps). You can define a reject output connection by naming it REJECT. This naming is used by Talend applications to automatically set the connection type to Reject. Your processor component is now defined. You can add another component or generate and download your project. To create an output component, make sure you have selected Activate IO. When clicking Add A Component in the Starter, a new step allows you to define a new component in your project. The intent in this tutorial is to create an output component that receives a record and inserts it into a MySQL database table. Output components are Processors without any output. In other words, the output is a processor that does not produce any records. Choose the component type. Output in this case. Enter the component name. For example, MySQLOutput. Click Configuration Model. This button lets you specify the required configuration for the component. By default, a dataset is already specified. For each parameter that you need to add, click the (+) button on the right panel. Enter the name and choose the type of the parameter, then click the tick button to save the changes. In this tutorial, to be able to insert a record in the output MySQL database, the configuration requires the following parameters:+ a dataset (which contains the datastore with the connection information) a timeout parameter. Closing the configuration panel on the right does not delete your configuration. However, refreshing the page resets the configuration. Define the Input(s) of the component. For each input that you need to define, click Add Input. In this tutorial, only one input is needed. Click the input name to access its configuration. You can change the name of the input and define its structure using a POJO. If you added several inputs, repeat this step for each one of them. The input in this tutorial is a generic record. Enable the Generic option and click Save. Do not create any output because the component does not produce any record. This is the only difference between an output an a processor component. Your output component is now defined. You can add another component or generate and download your project. Once your project is configured and all the components you need are created, you can generate and download the final project. In this tutorial, the project was configured and three components of different types (input, processor and output) have been defined. Click Finish on the left panel. You are redirected to a page that summarizes the project. On the left panel, you can also see all the components that you added to the project. Generate the project using one of the two options available: Download it locally as a ZIP file using the Download as ZIP button. Create a GitHub repository and push the project to it using the Create on Github button. In this tutorial, the project is downloaded to the local machine as a ZIP file. Once the package is available on your machine, you can compile it using the build tool selected when configuring the project. In the tutorial, Maven is the build tool selected for the project. In the project directory, execute the mvn package command. If you don’t have Maven installed on your machine, you can use the Maven wrapper provided in the generated project, by executing the ./mvnw package command. If you have created a Gradle project, you can compile it using the gradle build command or using the Gradle wrapper: ./gradlew build. The generated project code contains documentation that can guide and help you implementing the component logic. Import the project to your favorite IDE to start the implementation. The Component Kit Starter allows you to generate a component development project from an OpenAPI JSON descriptor. Open the Starter in the web browser of your choice. Enable the OpenAPI mode using the toggle in the header. Go to the API menu. Paste the OpenAPI JSON descriptor in the right part of the screen. All the described endpoints are detected. Unselect the endpoints that you do not want to use in the future components. By default, all detected endpoints are selected. Go to the Finish menu. Download the project. When exploring the project generated from an OpenAPI descriptor, you can notice the following elements: sources the API dataset an HTTP client for the API a connection folder containing the component configuration. By default, the configuration is only made of a simple datastore with a baseUrl parameter.

Developing new components using the Component Kit framework includes: Creating a project using the starter or the Talend IntelliJ plugin. This step allows to build the skeleton of the project. It consists in: Defining the general configuration model for each component in your project. Generating and downloading the project archive from the starter. Compiling the project. Importing the compiled project in your IDE. This step is not required if you have generated the project using the IntelliJ plugin. implementing the components, including: Registering the components by specifying their metadata: family, categories, version, icon, type and name. Defining the layout and configurable part of the components. Defining the execution logic of the components, also called runtime. Testing the components. Deploying the components to Talend Studio or Cloud applications. Optionally, you can use services. Services are predefined or user-defined configurations that can be reused in several components.

Before implementing a component logic and configuration, you need to specify the family and the category it belongs to, the component type and name, as well as its name and a few other generic parameters. This set of metadata, and more particularly the family, categories and component type, is mandatory to recognize and load the component to Talend Studio or Cloud applications. Some of these parameters are handled at the project generation using the starter, but can still be accessed and updated later on. The family and category of a component is automatically written in the package-info.java file of the component package, using the @Components annotation. By default, these parameters are already configured in this file when you import your project in your IDE. Their value correspond to what was defined during the project definition with the starter. Multiple components can share the same family and category value, but the family + name pair must be unique for the system. A component can belong to one family only and to one or several categories. If not specified, the category defaults to Misc. The package-info.java file also defines the component family icon, which is different from the component icon. You can learn how to customize this icon in this section. Here is a sample package-info.java: Another example with an existing component: Components can require metadata to be integrated in Talend Studio or Cloud platforms. Metadata is set on the component class and belongs to the org.talend.sdk.component.api.component package. When you generate your project and import it in your IDE, icon and version both come with a default value. @Icon: Sets an icon key used to represent the component. You can use a custom key with the custom() method but the icon may not be rendered properly. The icon defaults to Check. Replace it with a custom icon, as described in this section. @Version: Sets the component version. 1 by default. Learn how to manage different versions and migrations between your component versions in this section. For example: Every component family and component needs to have a representative icon. You have to define a custom icon as follows: For the component family the icon is defined in the package-info.java file. For the component itself, you need to declare the icon in the component class. Custom icons must comply with the following requirements: Icons must be stored in the src/main/resources/icons folder of the project. Icon file names need to match one of the following patterns: IconName.svg or IconName_icon32.png. The latter will run in degraded mode in Talend Cloud. Replace IconName by the name of your choice. Icons must be squared, even for the SVG format. Note that SVG icons are not supported by Talend Studio and can cause the deployment of the component to fail. If you aim at deploying a custom component to Talend Studio, specify PNG icons or use the Maven (or Gradle) svg2png plugin to convert SVG icons to PNG. If you want a finer control over both images, you can provide both in your component. Ultimately, you can also remove SVG parameters from the talend.component.server.icon.paths property in the HTTP server configuration. Note that SVG icons are not supported by Talend Studio and can cause the deployment of the component to fail. If you aim at deploying a custom component to Talend Studio, specify PNG icons or use the Maven (or Gradle) svg2png plugin to convert SVG icons to PNG. If you want a finer control over both images, you can provide both in your component. Ultimately, you can also remove SVG parameters from the talend.component.server.icon.paths property in the HTTP server configuration. For any purpose, you can also add user defined metadatas to your component with the @Metadatas annotation. Example: You can also use a SPI implementing org.talend.sdk.component.spi.component.ComponentMetadataEnricher. Methodology for creating components Generating a project using the starter Managing component versions Defining an input component Defining a processor or output component Defining a driver runner component Defining component layout and configuration Best practices

The first step when developing new components is to create a project that will contain the skeleton of your components and set you on the right track. The project generation can be achieved using the Talend Component Kit Starter or the Talend Component Kit plugin for IntelliJ. Through a user-friendly interface, you can define the main lines of your project and of your component(s), including their name, family, type, configuration model, and so on. Once completed, all the information filled are used to generate a project that you will use as starting point to implement the logic and layout of your components, and to iterate on them. Using the starter Using the IntelliJ plugin Once your project is generated, you can start implementing the component logic.

Components are designed to manipulate data (access, read, create). Talend Component Kit can handle several types of data, described in this document. By design, the framework must run in DI (plain standalone Java program) and in Beam pipelines. It is out of scope of the framework to handle the way the runtime serializes - if needed - the data. For that reason, it is critical not to import serialization constraints to the stack. As an example, this is one of the reasons why Record or JsonObject were preferred to Avro IndexedRecord. Any serialization concern should either be hidden in the framework runtime (outside of the component developer scope) or in the runtime integration with the framework (for example, Beam integration). Record is the default format. It offers many possibilities and can evolve depending on the Talend platform needs. Its structure is data-driven and exposes a schema that allows to browse it. Projects generated from the Talend Component Kit Starter are by default designed to handle this format of data. Record is a Java interface but never implement it yourself to ensure compatibility with the different Talend products. Follow the guidelines below. You can build records using the newRecordBuilder method of the RecordBuilderFactory (see here). For example: In the example above, the schema is dynamically computed from the data. You can also do it using a pre-built schema, as follows: The example above uses a schema that was pre-built using factory.newSchemaBuilder(Schema.Type.RECORD). When using a pre-built schema, the entries passed to the record builder are validated. It means that if you pass a null value null or an entry type that does not match the provided schema, the record creation fails. It also fails if you try to add an entry which does not exist or if you did not set a not nullable entry. Using a dynamic schema can be useful on the backend but can lead users to more issues when creating a pipeline to process the data. Using a pre-built schema is more reliable for end-users. You can access and read data by relying on the getSchema method, which provides you with the available entries (columns) of a record. The Entry exposes the type of its value, which lets you access the value through the corresponding method. For example, the Schema.Type.STRING type implies using the getString method of the record. For example: The Record format supports the following data types: String Boolean Int Long Float Double DateTime Array Bytes Record A map can always be modelized as a list (array of records with key and value entries). For example: For example, you can use the API to provide the schema. The following method needs to be implemented in a service. Manually constructing the schema without any data: Returning the schema from an already built record: MyDataset is the class that defines the dataset. Learn more about datasets and datastores in this document. Entry names for Record and JsonObject types must comply with the following rules: The name must start with a letter or with _. If not, the invalid characters are ignored until the first valid character. Following characters of the name must be a letter, a number, or . If not, the invalid character is replaced with . For example: 1foo becomes foo. f@o becomes f_o. 1234f5@o becomes ___f5_o. foo123 stays foo123. Each array uses only one schema for all of its elements. If an array contains several elements, they must be of the same data type. For example, the following array is not correct as it contains a string and an object: The runtime also supports JsonObject as input and output component type. You can rely on the JSON services (Jsonb, JsonBuilderFactory) to create new instances. This format is close to the Record format, except that it does not natively support the Datetime type and has a unique Number type to represent Int, Long, Float and Double types. It also does not provide entry metadata like nullable or comment, for example. It also inherits the Record format limitations. The runtime also accepts any POJO as input and output component type. In this case, it uses JSON-B to treat it as a JsonObject.

In some cases you can need to add some actions that are not related to the runtime. For example, enabling users of the plugin/library to test if a connection works properly. To do so, you need to define an @Action, which is a method with a name (representing the event name), in a class decorated with @Service: Services are singleton. If you need some thread safety, make sure that they match that requirement. Services should not store any status either because they can be serialized at any time. Status are held by the component. Services can be used in components as well (matched by type). They allow to reuse some shared logic, like a client. Here is a sample with a service used to access files: The service is automatically passed to the constructor. It can be used as a bean. In that case, it is only necessary to call the service method. Some common actions need a clear contract so they are defined as API first-class citizen. For example, this is the case for wizards or health checks. Here is the list of the available actions: Mark an action works for closing runtime connection, returning a close helper object which do real close action. The functionality is for the Studio only, studio will use the close object to close connection for existed connection, and no effect for cloud platform. Type: close_connection API: @org.talend.sdk.component.api.service.connection.CloseConnection Returned type: org.talend.sdk.component.api.service.connection.CloseConnectionObject Sample: Mark an action works for creating runtime connection, returning a runtime connection object like jdbc connection if database family. Its parameter MUST be a datastore. Datastore is configuration type annotated with @DataStore. The functionality is for the Studio only, studio will use the runtime connection object when use existed connection, and no effect for cloud platform. Type: create_connection API: @org.talend.sdk.component.api.service.connection.CreateConnection This class marks an action that explore a connection to retrieve potential datasets. Type: discoverdataset API: @org.talend.sdk.component.api.service.discovery.DiscoverDataset Returned type: org.talend.sdk.component.api.service.discovery.DiscoverDatasetResult Sample: Mark a method as being useful to fill potential values of a string option for a property denoted by its value. You can link a field as being completable using @Proposable(value). The resolution of the completion action is then done through the component family and value of the action. The callback doesn’t take any parameter. Type: dynamic_values API: @org.talend.sdk.component.api.service.completion.DynamicValues Returned type: org.talend.sdk.component.api.service.completion.Values Sample: This class marks an action doing a connection test Type: healthcheck API: @org.talend.sdk.component.api.service.healthcheck.HealthCheck Returned type: org.talend.sdk.component.api.service.healthcheck.HealthCheckStatus Sample: Mark an action as returning a discovered schema. Its parameter MUST be a dataset. Dataset is configuration type annotated with @DataSet. If component has multiple datasets, then dataset used as action parameter should have the same identifier as this @DiscoverSchema. Type: schema API: @org.talend.sdk.component.api.service.schema.DiscoverSchema Returned type: org.talend.sdk.component.api.record.Schema Sample: Mark a method as being useful to fill potential values of a string option. You can link a field as being completable using @Suggestable(value). The resolution of the completion action is then done when the user requests it (generally by clicking on a button or entering the field depending the environment). Type: suggestions API: @org.talend.sdk.component.api.service.completion.Suggestions Returned type: org.talend.sdk.component.api.service.completion.SuggestionValues Sample: This class marks an action returning a new instance replacing part of a form/configuration. Type: update API: @org.talend.sdk.component.api.service.update.Update Extension point for custom UI integrations and custom actions. Type: user API: @org.talend.sdk.component.api.service.Action Mark a method as being used to validate a configuration. this is a server validation so only use it if you can’t use other client side validation to implement it. Type: validation API: @org.talend.sdk.component.api.service.asyncvalidation.AsyncValidation Returned type: org.talend.sdk.component.api.service.asyncvalidation.ValidationResult Sample: These actions are provided - or not - by the application the UI runs within. always ensure you don’t require this action in your component. Mark the decorated field as supporting suggestions, i.e. dynamically get a list of valid values the user can use. It is however different from @Suggestable by looking up the implementation in the current application and not the services. Finally, it is important to note that it can do nothing in some environments too and that there is no guarantee the specified action is supported. API: @org.talend.sdk.component.api.configuration.action.BuiltInSuggestable Internationalization is supported through the injection of the $lang parameter, which allows you to get the correct locale to use with an @Internationalized service: You can combine the $lang option with the @Internationalized and @Language parameters.

The framework provides built-in services that you can inject by type in components and actions. Type Description org.talend.sdk.component.api.service.cache.LocalCache Provides a small abstraction to cache data that does not need to be recomputed very often. Commonly used by actions for UI interactions. org.talend.sdk.component.api.service.dependency.Resolver Allows to resolve a dependency from its Maven coordinates. It can either try to resolve a local file or (better) creates for you a preinitialized classloader. javax.json.bind.Jsonb A JSON-B instance. If your model is static and you don’t want to handle the serialization manually using JSON-P, you can inject that instance. javax.json.spi.JsonProvider A JSON-P instance. Prefer other JSON-P instances if you don’t exactly know why you use this one. javax.json.JsonBuilderFactory A JSON-P instance. It is recommended to use this one instead of a custom one to optimize memory usage and speed. javax.json.JsonWriterFactory A JSON-P instance. It is recommended to use this one instead of a custom one to optimize memory usage and speed. javax.json.JsonReaderFactory A JSON-P instance. It is recommended to use this one instead of a custom one to optimize memory usage and speed. javax.json.stream.JsonParserFactory A JSON-P instance. It is recommended to use this one instead of a custom one to optimize memory usage and speed. javax.json.stream.JsonGeneratorFactory A JSON-P instance. It is recommended to use this one instead of a custom one to optimize memory usage and speed. org.talend.sdk.component.api.service.dependency.Resolver Allows to resolve files from Maven coordinates (like dependencies.txt for component). Note that it assumes that the files are available in the component Maven repository. org.talend.sdk.component.api.service.injector.Injector Utility to inject services in fields marked with @Service. org.talend.sdk.component.api.service.factory.ObjectFactory Allows to instantiate an object from its class name and properties. org.talend.sdk.component.api.service.record.RecordBuilderFactory Allows to instantiate a record. org.talend.sdk.component.api.service.record.RecordPointerFactory Allows to instantiate a RecordPointer which enables to extract a data from a Record based on jsonpointer specification. org.talend.sdk.component.api.service.record.RecordService Some utilities to create records from another one. It is typically what is used when you want to add an entry in a record and passthrough the other ones. It also provides a nice RecordVisitor API for advanced cases. org.talend.sdk.component.api.service.configuration.LocalConfiguration Represents the local configuration that can be used during the design. It is not recommended to use it for the runtime because the local configuration is usually different and the instances are distinct. You can also use the local cache as an interceptor with @Cached Every interface that extends HttpClient and that contains methods annotated with @Request Lets you define an HTTP client in a declarative manner using an annotated interface. See the Using HttpClient for more details. All these injected services are serializable, which is important for big data environments. If you create the instances yourself, you cannot benefit from these features, nor from the memory optimization done by the runtime. Prefer reusing the framework instances over custom ones. The local configuration uses system properties and the environment (replacing dots per underscores) to look up the values. You can also put a TALEND-INF/local-configuration.properties file with default values. This allows to use the local_configuration: syntax in @Ui annotation. Here is an example to read the default value of a property from the configuration: Ensure your key is unique across all components to avoid global overrides on the JVM. In practice, it is strongly recommended to always use the family as a prefix. Also note that you can use @Configuration("prefix") to inject a mapping of the LocalConfiguration in a component. It uses the same rules as for any configuration object. If you prefer to inject you configuration in a service, ensure to wrap it in a Supplier to always have an up to date version. If you want to ignore the local-configuration.properties, you can set the system property: talend.component.configuration.${componentPluginId}.ignoreLocalConfiguration=true. Here a sample @Configuration model: Here is how to use it from a service: And finally, here is how to use it in a component: it is recommended to convert this configuration in a runtime model in components to avoid to transport more than desired during the job distribution. You can access the API reference in the Javadocs. The HttpClient usage is described in this section by using the REST API example below. Assuming that it requires a basic authentication header: GET /api/records/{id} - POST /api/records JSON payload to be created: {"id":"some id", "data":"some data"} To create an HTTP client that is able to consume the REST API above, you need to define an interface that extends HttpClient. The HttpClient interface lets you set the base for the HTTP address that the client will hit. The base is the part of the address that needs to be added to the request path to hit the API. It is now possible, and recommended, to use @Base annotation. Every method annotated with @Request in the interface defines an HTTP request. Every request can have a @Codec parameter that allows to encode or decode the request/response payloads. You can ignore the encoding/decoding for String and Void payloads. The interface should extend HttpClient. In the codec classes (that implement Encoder/Decoder), you can inject any of your service annotated with @Service or @Internationalized into the constructor. Internationalization services can be useful to have internationalized messages for errors handling. The interface can be injected into component classes or services to consume the defined API. By default, /+json are mapped to JSON-P and /+xml to JAX-B if the model has a @XmlRootElement annotation. For advanced cases, you can customize the Connection by directly using @UseConfigurer on the method. It calls your custom instance of Configurer. Note that you can use @ConfigurerOption in the method signature to pass some Configurer configurations. For example, if you have the following Configurer: You can then set it on a method to automatically add the basic header with this kind of API usage: The framework provides in the component-api an OAuth1.Configurer which can be used as an example of configurer implementation. It expects a single OAuth1.Configuration parameter to be passed to the request as a @ConfigurationOption. Here is a sample showing how it can be used: By default, the client loads in memory the payload. In case of big payloads, it can consume too much memory. For these cases, you can get the payload as an InputStream: You can use the Response wrapper, or not.

This part is limited to specific kinds of Beam PTransform: PTransform> for inputs. PTransform, PDone> for outputs. Outputs must use a single (composite or not) DoFn in their apply method. To illustrate the input wrapping, this procedure uses the following input as a starting point (based on existing Beam inputs): To wrap the Read in a framework component, create a transform delegating to that Read with at least a @PartitionMapper annotation and using @Option constructor injections to configure the component. Also make sure to follow the best practices and to specify @Icon and @Version. To illustrate the output wrapping, this procedure uses the following output as a starting point (based on existing Beam outputs): You can wrap this output exactly the same way you wrap an input, but using @Processor instead of: Note that the org.talend.sdk.component.runtime.beam.transform.DelegatingTransform class fully delegates the "expansion" to another transform. Therefore, you can extend it and implement the configuration mapping: In terms of classloading, when you write an I/O, the Beam SDK Java core stack is assumed as provided in Talend Component Kit runtime. This way, you don’t need to include it in the compile scope, it would be ignored anyway. If you need a JSonCoder, you can use the org.talend.sdk.component.runtime.beam.factory.service.PluginCoderFactory service, which gives you access to the JSON-P and JSON-B coders. There is also an Avro coder, which uses the FileContainer. It ensures it is self-contained for IndexedRecord and it does not require—as the default Apache Beam AvroCoder—to set the schema when creating a pipeline. It consumes more space and therefore is slightly slower, but it is fine for DoFn, since it does not rely on serialization in most cases. See org.talend.sdk.component.runtime.beam.transform.avro.IndexedRecordCoder. If your PCollection is made of JsonObject records, and you want to convert them to IndexedRecord, you can use the following PTransforms: converts an IndexedRecord to a JsonObject. converts a JsonObject to an IndexedRecord. converts a JsonObject to an IndexedRecord with AVRO schema inference. There are two main provided coder for Record: it will unwrap the record as an Avro IndexedRecord and serialize it with its schema. This can indeed have a performance impact but, due to the structure of component, it will not impact the runtime performance in general - except with direct runner - because the runners will optimize the pipeline accurately. it will serialize the Avro IndexedRecord as well but it will ensure the schema is in the SchemaRegistry to be able to deserialize it when needed. This implementation is faster but the default implementation of the registry is "in memory" so will only work with a single worker node. You can extend it using Java SPI mecanism to use a custom distributed implementation. Sample input based on Beam Kafka: Because the Beam wrapper does not respect the standard Talend Component Kit programming model ( for example, there is no @Emitter), you need to set the false property in your pom.xml file (or equivalent for Gradle) to skip the component programming model validations of the framework.

Talend Component Kit is a toolkit based on Java and designed to simplify the development of components at two levels: Runtime: Runtime is about injecting the specific component code into a job or pipeline. The framework helps unify as much as possible the code required to run in Data Integration (DI) and BEAM environments. Graphical interface: The framework helps unify the code required to be able to render the component in a browser (web) or in the Eclipse-based Studio (SWT). The Talend Component Kit framework is made of several tools designed to help you during the component development process. It allows to develop components that fit in both Java web UIs. Starter: Generate the skeleton of your development project using a user-friendly interface. The Talend Component Kit Starter is available as a web tool or as a plugin for the IntelliJ IDE. Component API: Check all classes available to implement components. Build tools: The framework comes with Maven and Gradle wrappers, which allow to always use the version of Maven or Gradle that is right for your component development environment and version. Testing tools: Test components before integrating them into Talend Studio or Cloud applications. Testing tools include the Talend Component Kit Web Tester, which allows to check the web UI of your components on your local machine. You can find more details about the framework design in this document. The Talend Component Kit project is available on GitHub in the following repository

Each type of component has its own execution logic. The same basic logic is applied to all components of the same type, and is then extended to implement each component specificities. The project generated from the starter already contains the basic logic for each component. Talend Component Kit framework relies on several primitive components. All components can use @PostConstruct and @PreDestroy annotations to initialize or release some underlying resource at the beginning and the end of a processing. In distributed environments, class constructor are called on cluster manager nodes. Methods annotated with @PostConstruct and @PreDestroy are called on worker nodes. Thus, partition plan computation and pipeline tasks are performed on different nodes. All the methods managed by the framework must be public. Private methods are ignored. The framework is designed to be as declarative as possible but also to stay extensible by not using fixed interfaces or method signatures. This allows to incrementally add new features of the underlying implementations.

Datasets and datastores are configuration types that define how and where to pull the data from. They are used at design time to create shared configurations that can be stored and used at runtime. All connectors (input and output components) created using Talend Component Kit must reference a valid dataset. Each dataset must reference a datastore. Datastore: The data you need to connect to the backend. Dataset: A datastore coupled with the data you need to execute an action. Make sure that: a datastore is used in each dataset. each dataset has a corresponding input component (mapper or emitter). This input component must be able to work with only the dataset part filled by final users. Any other property implemented for that component must be optional. These rules are enforced by the validateDataSet validation. If the conditions are not met, the component builds will fail. Make sure that: a datastore is used in each dataset. each dataset has a corresponding input component (mapper or emitter). This input component must be able to work with only the dataset part filled by final users. Any other property implemented for that component must be optional. These rules are enforced by the validateDataSet validation. If the conditions are not met, the component builds will fail. A datastore defines the information required to connect to a data source. For example, it can be made of: a URL a username a password. You can specify a datastore and its context of use (in which dataset, etc.) from the Component Kit Starter. Make sure to modelize the data your components are designed to handle before defining datasets and datastores in the Component Kit Starter. Once you generate and import the project into an IDE, you can find datastores under a specific datastore node. Example of datastore: A dataset represents the inbound data. It is generally made of: A datastore that defines the connection information needed to access the data. A query. You can specify a dataset and its context of use (in which input and output component it is used) from the Component Kit Starter. Make sure to modelize the data your components are designed to handle before defining datasets and datastores in the Component Kit Starter. Once you generate and import the project into an IDE, you can find datasets under a specific dataset node. Example of dataset referencing the datastore shown above: The display name of each dataset and datastore must be referenced in the message.properties file of the family package. The key for dataset and datastore display names follows a defined pattern: ${family}.${configurationType}.${name}._displayName. For example: These keys are automatically added for datasets and datastores defined from the Component Kit Starter. When deploying a component or set of components that include datasets and datastores to Talend Studio, a new node is created under Metadata. This node has the name of the component family that was deployed. It allows users to create reusable configurations for datastores and datasets. With predefined datasets and datastores, users can then quickly fill the component configuration in their jobs. They can do so by selecting Repository as Property Type and by browsing to the predefined dataset or datastore. Studio will generate connection and close components auto for reusing connection function in input and output components, just need to do like this example: Then the runtime mapper and processor only need to use @Connection to get the connection like this: The component server scans all configuration types and returns a configuration type index. This index can be used for the integration into the targeted platforms (Studio, web applications, and so on). Mark a model (complex object) as being a dataset. API: @org.talend.sdk.component.api.configuration.type.DataSet Sample: Mark a model (complex object) as being a datastore (connection to a backend). API: @org.talend.sdk.component.api.configuration.type.DataStore Sample: Mark a model (complex object) as being a dataset discovery configuration. API: @org.talend.sdk.component.api.configuration.type.DatasetDiscovery Sample: The component family associated with a configuration type (datastore/dataset) is always the one related to the component using that configuration. The configuration type index is represented as a flat tree that contains all the configuration types, which themselves are represented as nodes and indexed by ID. Every node can point to other nodes. This relation is represented as an array of edges that provides the child IDs. As an illustration, a configuration type index for the example above can be defined as follows:

This tutorial walks you through the most common iteration steps to create a component with Talend Component Kit and to deploy it to Talend Open Studio. The component created in this tutorial is a simple processor that reads data coming from the previous component in a job or pipeline and displays it in the console logs of the application, along with an additional information entered by the final user. The component designed in this tutorial is a processor and does not require nor show any datastore and dataset configuration. Datasets and datastores are required only for input and output components. To get your development environment ready and be able to follow this tutorial: Download and install a Java JDK 1.8 or greater. Download and install Talend Open Studio. For example, from Sourceforge. Download and install IntelliJ. Download the Talend Component Kit plugin for IntelliJ. The detailed installation steps for the plugin are available in this document. The first step in this tutorial is to generate a component skeleton using the Starter embedded in the Talend Component Kit plugin for IntelliJ. Start IntelliJ and create a new project. In the available options, you should see Talend Component. Make sure that a Project SDK is selected. Then, select Talend Component and click Next. The Talend Component Kit Starter opens. Enter the component and project metadata. Change the default values, for example as presented in the screenshot below: The Component Family and the Category will be used later in Talend Open Studio to find the new component. Project metadata is mostly used to identify the project structure. A common practice is to replace 'company' in the default value by a value of your own, like your domain name. Once the metadata is filled, select Add a component. A new screen is displayed in the Talend Component Kit Starter that lets you define the generic configuration of the component. By default, new components are processors. Enter a valid Java name for the component. For example, Logger. Select Configuration Model and add a string type field named level. This input field will be used in the component configuration for final users to enter additional information to display in the logs. In the Input(s) / Output(s) section, click the default MAIN input branch to access its detail, and make sure that the record model is set to Generic. Leave the Name of the branch with its default MAIN value. Repeat the same step for the default MAIN output branch. Because the component is a processor, it has an output branch by default. A processor without any output branch is considered an output component. You can create output components when the Activate IO option is selected. Click Next and check the name and location of the project, then click Finish to generate the project in the IDE. At this point, your component is technically already ready to be compiled and deployed to Talend Open Studio. But first, take a look at the generated project: Two classes based on the name and type of component defined in the Talend Component Kit Starter have been generated: LoggerProcessor is where the component logic is defined LoggerProcessorConfiguration is where the component layout and configurable fields are defined, including the level string field that was defined earlier in the configuration model of the component. The package-info.java file contains the component metadata defined in the Talend Component Kit Starter, such as family and category. You can notice as well that the elements in the tree structure are named after the project metadata defined in the Talend Component Kit Starter. These files are the starting point if you later need to edit the configuration, logic, and metadata of the component. There is more that you can do and configure with the Talend Component Kit Starter. This tutorial covers only the basics. You can find more information in this document. Without modifying the component code generated from the Starter, you can compile the project and deploy the component to a local instance of Talend Open Studio. The logic of the component is not yet implemented at that stage. Only the configurable part specified in the Starter will be visible. This step is useful to confirm that the basic configuration of the component renders correctly. Before starting to run any command, make sure that Talend Open Studio is not running. From the component project in IntelliJ, open a Terminal and make sure that the selected directory is the root of the project. All commands shown in this tutorial are performed from this location. Compile the project by running the following command: mvnw clean install. The mvnw command refers to the Maven wrapper that is embedded in Talend Component Kit. It allows to use the right version of Maven for your project without having to install it manually beforehand. An equivalent wrapper is available for Gradle. Once the command is executed and you see BUILD SUCCESS in the terminal, deploy the component to your local instance of Talend Open Studio using the following command: mvnw talend-component:deploy-in-studio -Dtalend.component.studioHome="". Replace the path with your own value. If the path contains spaces (for example, Program Files), enclose it with double quotes. Make sure the build is successful. Open Talend Open Studio and create a new Job: Find the new component by looking for the family and category specified in the Talend Component Kit Starter. You can add it to your job and open its settings. Notice that the level field specified in the configuration model of the component in the Talend Component Kit Starter is present. At this point, the new component is available in Talend Open Studio, and its configurable part is already set. But the component logic is still to be defined. You can now edit the component to implement its logic: reading the data coming through the input branch to display that data in the execution logs of the job. The value of the level field that final users can fill also needs to be changed to uppercase and displayed in the logs. Save the job created earlier and close Talend Open Studio. Go back to the component development project in IntelliJ and open the LoggerProcessor class. This is the class where the component logic can be defined. Look for the @ElementListener method. It is already present and references the default input branch that was defined in the Talend Component Kit Starter, but it is not complete yet. To be able to log the data in input to the console, add the following lines: The @ElementListener method now looks as follows: Open a Terminal again to compile the project and deploy the component again. To do that, run successively the two following commands: mvnw clean install `mvnw talend-component:deploy-in-studio -Dtalend.component.studioHome="" The update of the component logic should now be deployed. After restarting Talend Open Studio, you will be ready to build a job and use the component for the first time. To learn the different possibilities and methods available to develop more complex logics, refer to this document. If you want to avoid having to close and re-open Talend Open Studio every time you need to make an edit, you can enable the developer mode, as explained in this document. As the component is now ready to be used, it is time to create a job and check that it behaves as intended. Open Talend Open Studio again and go to the job created earlier. The new component is still there. Add a tRowGenerator component and connect it to the logger. Double-click the tRowGenerator to specify the data to generate: Add a first column named firstName and select the TalendDataGenerator.getFirstName() function. Add a second column named 'lastName' and select the TalendDataGenerator.getLastName() function. Set the Number of Rows for RowGenerator to 10. Validate the tRowGenerator configuration. Open the TutorialFamilyLogger component and set the level field to info. Go to the Run tab of the job and run the job. The job is executed. You can observe in the console that each of the 10 generated rows is logged, and that the info value entered in the logger is also displayed with each record, in uppercase.

If some changes impact the configuration, they can be managed through a migration handler at the component level (enabling trans-model migration support). The @Version annotation supports a migrationHandler method which migrates the incoming configuration to the current model. For example, if the filepath configuration entry from v1 changed to location in v2, you can remap the value in your MigrationHandler implementation. A best practice is to split migrations into services that you can inject in the migration handler (through constructor) rather than managing all migrations directly in the handler. For example: What is important to notice in this snippet is the fact that you can organize your migrations the way that best fits your component. If you need to apply migrations in a specific order, make sure that they are sorted. Consider this API as a migration callback rather than a migration API. Adjust the migration code structure you need behind the MigrationHandler, based on your component requirements, using service injection. A nested configuration always migrates itself with any root prefix, whereas a component configuration always roots the full configuration. For example, if your model is the following: Then the component will see the path configuration.datastore.url for the datastore url whereas the datastore will see the path url for the same property. You can see it as configuration types - @DataStore, @DataSet - being configured with an empty root path.

Standalone components are the components without input or output flows. They are designed to do actions without reading or processing any data. For example standalone components can be used to create indexes in databases. Before implementing the component logic and defining its layout and configurable fields, make sure you have specified its basic metadata, as detailed in this document. A Driver Runner (DriverRunner) is a standalone component which doesn’t process or return any data. A Driver runner must have a @RunAtDriver method without any parameter.

Services are configurations that can be reused across several classes. Talend Component Kit comes with a predefined set of services that you can easily use. You can still define your own services under the service node of your component project. By default, the Component Kit Starter generates a dedicated class in your project in which you can implement services. Built-in services Internationalizing a service Providing actions through a service Services and interceptors Defining a custom API