In my case, the too many connection bug in AWS IAM Auth between AWS MSK Connect and AWS MSK killed Task Process in Connector. One simple example of this is a database connector. The implementation of this API should block until the commit is complete. You can find more details about resume token. NOTE: MongoDB Kafka Source Connector has only one Task Process by it design.
Topic tracking is enabled by default but can be disabled by setting topic.tracking.enable=false. The resume token is an encoded format. I also notice they dropped the assignment model in the initialization in recent versions, perhaps the below would be a solution?
Well use pseudo-code to describe most of the implementation, but you can refer to the source code for the full example. When it notices a change that requires reconfiguration (or a change in the number of Tasks), it notifies the framework and the framework updates any corresponding Tasks. A rolling upgrade of the Connect cluster will activate incremental cooperative rebalancing when the last worker joins on version 2.3.0.
SinkConnectors usually only have to handle the addition of streams, which may translate to new entries in their outputs (e.g., a new database table). To take advantage of this, connector developers need to provide an implementation of config() to expose the configuration definition to the framework. Can someone help plz, NPE is not an useful exception to help us debug the issue. The stack trace is pretty awful, so im wondering if is actually catch bug thats obfuscating the underlying stack trace. This delay defaults to five minutes (300000ms) to tolerate failures or upgrades of workers without immediately redistributing the load of a departing worker. Note that this implementation uses the normal Java InputStream interface and may sleep if data is not available. In AWS MSK, the topic is created by AWS with this naming convention. Allow to return. For each configuration, you can specify the name, the type, the default value, the documentation, the group information, the order in the group, the width of the configuration value and the name suitable for display in the UI. The APIs are provided for source systems which have an acknowledgement mechanism for messages. Same issue as @msilvestre and others here. Currently, we have a Kafka topic called maxwell which has around 10 Million records lag over 200 partitions. in standalone mode or due to a job reconfiguration). The API documentation provides a complete reference, but here is a simple example creating a Schema and Struct: If you are implementing a source connector, youll need to decide when and how to create schemas. Source connectors need to monitor the source system for changes, e.g. Anything can file with any reasons.
Connect Workers use these per-connector topic status updates to respond to requests to the REST endpoint GET /connectors/{name}/topics by returning the set of topic names that a connector is using. Next, we add some standard lifecycle methods, start() and stop(): Finally, the real core of the implementation is in taskConfigs(). The following states are possible for a connector or one of its tasks: In most cases, connector and task states will match, though they may be different for short periods of time when changes are occurring or if tasks have failed. // The complete version includes error handling as well. Sink connectors are usually simpler because they are consuming data and therefore do not need to create schemas. well, then the failed Connector has to be recreated with the offset.storage.topic that was used by the failed connector. In our case, were using filters with multiple connectors instead and changing the parent directory. Note that there may be a delay before all of a connectors tasks have transitioned to the PAUSED state since it may take time for them to finish whatever processing they were in the middle of when being paused. Each stream should be a sequence of key-value records. Other tasks are not stopped and restarted during the rebalance, as they would have been with the old protocol. Learn on the go with our new app. Exception (its always the same for all examples above): PR for the transforms issue is here #480 While a source connector is paused, Connect will stop polling it for additional records. SinkConnectors, on the other hand, will generally require no special code for handling a dynamic set of streams. The framework uses this to commit offsets periodically so that in the case of a failure, the task can recover and minimize the number of events that are reprocessed and possibly duplicated (or to resume from the most recent offset if Kafka Connect was stopped gracefully, e.g. when I tried to use restful api to restart the failed task by calling You can retry after the rebalance completes, but it might not be necessary since rebalances effectively restart all the connectors and tasks in the cluster. The framework manages any changes to the Kafka input, such as when the set of input topics changes because of a regex subscription. The connector must be able to detect these changes and react appropriately. changing configuration and restarting tasks). The OffsetStorageReader interface also allows you to issue bulk reads to efficiently load all offsets, then apply them by seeking each input stream to the appropriate position. Fortunately, on May 2022, AWS released a feature that makes it possible to create MSK Connector with a user defined custom topic as offset.storage.topic with some limitation. Kafka Connect is intended to define bulk data copying jobs, such as copying an entire database rather than creating many jobs to copy each table individually.
Whenever we apply transformers to a topic it looses the ability to interface with the source topic/partition, thus we need to keep track of it. If your dont mind to lose Change Stream Events in MongoDB during the failure, there is an easier way to make Connector recover by itself. table additions/deletions in a database. SinkConnector implementations are very similar. The runtime data format does not assume any particular serialization format; this conversion is handled internally by the framework. Note that in the SourceConnector this monitoring is currently left up to the connector implementation. In versions prior to 2.3.0, the Connect workers would rebalance the full set of connectors and their tasks in the cluster as a simple way to make sure that each worker has approximately the same amount of work. In this case, an NIO-based implementation would be more efficient, but this simple approach works, is quick to implement, and is compatible with older versions of Java. MongoDB Kafka Source Connector maintains / tracks resume token as an offset in a Kafka topic. For example, if the remote system is undergoing maintenance, it would be preferable for source connectors to stop polling it for new data instead of filling logs with exception spam. For example, in a SourceConnector: The framework will promptly request new configuration information and update the tasks, allowing them to gracefully commit their progress before reconfiguring them. The trickiest situation to handle in these cases may be conflicts between multiple SinkTasks seeing a new input stream for the first time and simultaneously trying to create the new resource. While task implementations have to conform to the basic poll() interface, they have a lot of flexibility in how they are implemented. Without it it works just fine, but as soon I added the following to the connector configuration I got NPE. // null, and driving thread will handle any shutdown if necessary. Next well describe the implementation of the corresponding SourceTask.
The decoded format looks like this. https://github.com/fmeyer/kafka-connect-storage-cloud/commit/8448b5954a6f359d8c25834739ef18f64ae125ff, https://github.com/confluentinc/kafka-connect-storage-common/issues/91#issuecomment-444734003, https://github.com/cricket007/kafka-connect-sandbox, https://github.com/confluentinc/kafka-connect-storage-common/issues/91. Kafka Connects REST layer provides a set of APIs to enable administration of the cluster. @pcfleischer @mdespriee let me know if you can try it on your cases. You can decode the resume token by this library. here https://github.com/fmeyer/kafka-connect-storage-cloud/commit/8448b5954a6f359d8c25834739ef18f64ae125ff. It briefly reviews a few key concepts and then describes how to create a simple connector. Although Connector lost its Task Process, Connectors state is still RUNNING rather than showing FAILED.
Is it actually more nuanced than that? @aakashnshah the RegexRouter is changing the topic name dynamically but this should work as in other connectors. If a Connect worker leaves the group, intentionally or due to a failure, Connect waits for scheduled.rebalance.max.delay.ms before triggering a rebalance. Both the keys and values can have complex structure many primitive types are provided, but arrays, objects, and nested data structures can be represented as well. JDBCSourceConnector would import a relational database into Kafka) and SinkConnectors export data (e.g. Plus, you can provide special validation logic used for single configuration validation by overriding the Validator class. One possible mapping uses a timestamp column to generate queries incrementally returning new data, and the last queried timestamp can be used as the offset. if a table is dropped from a database. This post is based on the tech stacks, AWS MSK Connect + MongoDB Kafka Source Connector. Try Lightrun to collect production stack traces without stopping your Java applications! @aakashnshah Were having a similar issue with the NPE when modifying the topic name before it goes to the S3SinkConnector. Where possible, you should avoid recomputing them as much as possible. To restart a connector/task manually, you can use the restart APIs listed above. For example, the GET /connectors/file-source/status request shows the status of a connector named file-source: Connectors and their tasks publish status updates to a shared topic (configured with status.storage.topic) which all workers in the cluster monitor. In cases where a connector does need to acknowledge messages in the source system, only one of the APIs is typically required. Not all jobs are static, so Connector implementations are also responsible for monitoring the external system for any changes that might require reconfiguration. A simple example is included with the source code for Kafka in the file package. You may provide an override of the default implementation for customized configuration validation, which may use the recommended values. Connectors come in two flavors: SourceConnectors import data from another system (e.g. The implementation is short, but too long to cover completely in this guide. These are used by the framework to periodically commit the offsets of data that have been processed so that in the event of failures, processing can resume from the last committed offsets, avoiding unnecessary reprocessing and duplication of events. Start by creating the class that inherits from SourceConnector and add a couple of fields that will store parsed configuration information (the filename to read from and the topic to send data to): The easiest method to fill in is taskClass(), which defines the class that should be instantiated in worker processes to actually read the data: We will define the FileStreamSourceTask class below. Im getting this same exception but when I try to use transforms. Therefore, monitoring the state of Connector can mislead us. It just has to determine the number of input tasks, which may require contacting the remote service it is pulling data from, and then divvy them up. In addition to the key and value, records (both those generated by sources and those delivered to sinks) have associated stream IDs and offsets. Both share the common lifecycle methods, but the SinkTask interface is quite different: The SinkTask documentation contains full details, but this interface is nearly as simple as the SourceTask. In this case we are only handling a single file, so even though we may be permitted to generate more tasks as per the maxTasks argument, we return a list with only one entry: Although not used in the example, SourceTask also provides two APIs to commit offsets in the source system: commit and commitRecord. With an assignment in hand, each Task must copy its subset of the data to or from Kafka. This method does not need to ensure the data has been fully written to the destination system before returning. The following example shows how a connectors SinkTask subclass might obtain and use the ErrantRecordReporter, safely handling a null reporter when the DLQ is not enabled or when the connector is installed in an older Connect runtime that doesnt have this reporter feature: The SourceTask implementation included a stream ID (the input filename) and offset (position in the file) with each record. I can confirm that we also only have this on the transform it seems like it might be specific to s3/cloud-storage connector, the same setup for JDBCSinkConnector works for routing to different tables as this would be to route to specific folders. It uses this information to create an output SourceRecord with four pieces of information: the source partition (there is only one, the single file being read), source offset (byte offset in the file), output topic name, and output value (the line, and we include a schema indicating this value will always be a string). When error reporting is enabled for a connector, the connector can use an ErrantRecordReporter to report problems with individual records sent to a sink connector. Because the workers consume this topic asynchronously, there is typically a (short) delay before a state change is visible through the status API. The flush() method is used during the offset commit process, which allows tasks to recover from failures and resume from a safe point such that no events will be missed. To copy data between Kafka and another system, users create a Connector for the system they want to pull data from or push data to. However, it does not use the recommended values for configuration validation. To create more complex data, youll need to work with the Kafka Connect data API. new connectors name has to be same as previous failed one. This commit process is completely automated by the framework, but only the connector knows how to seek back to the right position in the input stream to resume from that location. Well cover the SourceConnector as a simple example. A request to the REST endpoint PUT /connectors/{name}/topics/reset resets the set of active topics for a connector and allows a new set to be populated, based on the connectors latest pattern of topic usage. The previous section described how to implement a simple SourceTask. The following code in FileStreamSourceConnector defines the configuration and exposes it to the framework. In fact, in many cases internal buffering will be useful so an entire batch of records can be sent at once, reducing the overhead of inserting events into the downstream data store. When the schema does not match usually indicating the upstream producer is generating invalid data that cannot be correctly translated to the destination system sink connectors should throw an exception to indicate this error to the system.
While a sink connector is paused, Connect will stop pushing new messages to it. Most structured records will need to interact with two classes in addition to primitive types: Schema and Struct. // Nothing to do since no background monitoring is required. For example, if your connector is guaranteed to have a fixed schema, create it statically and reuse a single instance. If the Task encounters the issue before the Connector, which will be common if the Connector needs to poll for changes, the Task will need to handle the subsequent error. Also, restart api seems not functional as well. Hi, same issue as @msilvestre Im using the io.confluent.connect.s3.S3SinkConnectorand my tranform config looks like this: RE https://github.com/confluentinc/kafka-connect-storage-common/issues/91#issuecomment-444734003 I think you meant to respond here, Ive been filling in working example stacks here over time - https://github.com/cricket007/kafka-connect-sandbox, Minio is a suitable replacement. It seems it only restart one consumer worker, rest of workers has no response , . ect.s3.S3SinkTask.put(S3SinkTask.java:188)\n\tat org.apache.kafka.connect.runtime.WorkerSinkTask.deliverMessages(WorkerSinkTask.java:564)\n\t 10 more\n","worker_id":"172.31.16.219:8083","generation":26} {"state":"UNASSIGNED","trace":null,"worker_id":"172.31.16.219:8083","generation":26} {"state":"RUNNING","trace":null,"worker_id":"172.31.16.219:8083","generation":26}, I was expecting it would start 10 consumers(workers) instead ? In initialize(), we would add a bit more code to read the offset (if it exists) and seek to that position: Of course, you might need to read many keys for each of the input streams. If a worker does not return within that time limit, Connect will reassign those tasks among the remaining workers in the Connect cluster. However, changes can also affect tasks, most commonly when one of their input streams is destroyed in the input system, e.g. Just as with the connector, we need to create a class inheriting from the appropriate base Task class. When a new table is created, it must discover this so it can assign the new table to one of the Tasks by updating its configuration.
The rest of this section will walk through some code to demonstrate the key steps in creating a connector, but developers should also refer to the full example source code as many details are omitted for brevity. However, this means that the tasks will remain unassigned until the time specified by scheduled.rebalance.max.delay.ms elapses. NOTE: one of missing features in worker config is that there is no way to remove the unused one. The method should push any outstanding data to the destination system and then block until the write has been acknowledged. Gatsby Lee | Data Engineer | City Farmer | Philosopher. HDFSSinkConnector would export the contents of a Kafka topic to an HDFS file). Additionally, failed tasks will not transition to the PAUSED state until they have been restarted. Therefore, upgrading to the new Connect protocol happens automatically when all the workers upgrade to 2.3.0. To handle this, ConfigDef allows you to specify the dependents of a configuration and to provide an implementation of Recommender to get valid values and set visibility of a configuration given the current configuration values. I recommend monitoring the number of Task Process through AWS CloudWatch. If you set this configuration in Connector, Task Process will continue although it dies. 
It also has some standard lifecycle methods: These are slightly simplified versions, but show that these methods should be relatively simple and the only work they should perform is allocating or freeing resources. As Kafka Connect will record offsets automatically, SourceTasks are not required to implement them. I tried this solution on local and it didnt work and it seems for good reason as the writer uses buffered writes per topic, so splitting/routing from one to one seems possible since its one consumer but one to multiple (our use case) seems like a more fundamental change since there would be multiple file buffers per consumer. The FileStream connectors are good examples because they are simple, but they also have trivially structured data each line is just a string. Kafka Connect allows you to validate connector configurations before submitting a connector to be executed and can provide feedback about errors and recommended values. Developing a connector only requires implementing two interfaces, the Connector and Task. In Kafka Connect, it should always be possible to frame these assignments as a set of input and output streams consisting of records with consistent schemas. {"state":"RUNNING","trace":null,"worker_id":"172.31.28.239:8083","generation":26} {"state":"RUNNING","trace":null,"worker_id":"172.31.28.239:8083","generation":26} {"state":"RUNNING","trace":null,"worker_id":"172.31.28.239:8083","generation":26} {"state":"FAILED","trace":"org.apache.kafka.connect.errors.ConnectException: Exiting WorkerSinkTask due to unrecoverable exception.\n\tat org.apache.kafka.connect.runtime.WorkerSinkTask.deliverMessages(WorkerSinkTask.java:586)\n\tat org.apache.kafka.connect.runtime.WorkerSinkTask.poll(WorkerSinkTask.java:322)\n\tat org.apache.kafka.connect.runtime.WorkerSinkTask.iteration(WorkerSinkTask.java:225)\n\tat org.apache.kafka.connect.runtime.WorkerSinkTask.execute(WorkerSinkTask.java:193)\n\tat org.apache.kafka.connect.runtime.WorkerTask.doRun(WorkerTask.java:175)\n\tat org.apache.kafka.connect.runtime.WorkerTask.run(WorkerTask.java:219)\n\tat java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)\n\tat java.util.concurrent.FutureTask.run(FutureTask.java:266)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\tat java.lang.Thread.run(Thread.java:748)\nCaused by: java.lang.NullPointerException\n\tat io.confluent.connect.s3.S3SinkTask.put(S3SinkTask.java:188)\n\tat org.apache.kafka.connect.runtime.WorkerSinkTask.deliverMessages(WorkerSinkTask.java:564)\n\t 10 more\n","worker_id":"172.31.28.239:8083","generation":24}, Kafka connect version: confluentinc/cp-kafka-connect:5.0.0. This guide describes how developers can write new connectors for Kafka Connect to move data between Kafka and other systems. For example, an HDFS connector could do this and use atomic move operations to make sure the flush() operation atomically commits the data and offsets to a final location in HDFS. How to setup JDBC Kafka connect on local machine? Considering even just a single table, the schema will not be predefined for the entire connector (as it varies from table to table). The SinkRecords contain essentially the same information as SourceRecords: Kafka topic, partition, offset, the event key and value, and optional headers. If this worker returns within the configured delay, it gets its previously assigned tasks in full. Using Timeplus and Redpanda for low latency, real-time streaming analytics, Migrating from SQL to NoSQL with Spring PetClinic and Apache Cassandra, __amazon_msk_connect_offsets_
Shouldnt this issue be splitted ?). Doesnt need to run on AWS, Not sure the immediate issue, but would it be possible to get debugging enabled like I did here - https://github.com/confluentinc/kafka-connect-storage-common/issues/91. It looks like there are some writers initialized on open based upon the context of the connector source topics, and when the records are being written there are no writers initialized based upon the records after the mapping. If you want to disallow requests to reset the active topics of connectors during runtime, set the Worker property topic.tracking.allow.reset=false. // Will occur in Connect runtimes earlier than 2.6, // attempt to process and send record to data sink, 6.3 Geo-Replication (Cross-Cluster Data Mirroring), 7.2 Encryption and Authentication using SSL, 7.5 Incorporating Security Features in a Running Cluster. But it also may not be fixed for a single table over the lifetime of the connector since the user may execute an ALTER TABLE command. To correctly resume upon startup, the task can use the SourceContext passed into its initialize() method to access the offset data. When a connector is first submitted to the cluster, a rebalance is triggered between the Connect workers in order to distribute the load that consists of the tasks of the new connector. Regex router on s3 sink makes it go NPE. For example, in the JDBCSourceConnector example, the Connector might assign a set of tables to each Task. This same rebalancing procedure is also used when connectors increase or decrease the number of tasks they require, when a connectors configuration is changed, or when a worker is added or removed from the group as part of an intentional upgrade of the Connect cluster or due to a failure. Next, we implement the main functionality of the task, the poll() method which gets events from the input system and returns a List
(task.max = 10).
