kafka producer parallelism
These options have been removed from the system. 10,000 msgs * avg processing time 2.5ms = ~2.5s. For released changes, see the CHANGELOG. KIP-408) have parallel processing of messages. We also encourage participation, so if you have any feature ideas etc, please get in touch, and we will help you work on submitting a PR! When your function is actually run, a result object will be streamed back to your client code, with information about the operation completion. A Producer is only required if using the produce flows. Asynchronous mode is faster, as it doesnt block the control loop.
In the best case, you dont care about ordering at all.In which case, the degree of concurrency achievable is simply set by max thread and concurrency settings, or with the Vert.x extension, the Vert.x Vertical being used - e.g. An overview article to the library can also be found on Confluents blog: Introducing the Confluent Parallel Consumer. The raw Kafka consumer performance remains unaffected by the key distribution. Queueing and pressure system now self tuning, performance over default old tuning values (softMaxNumberMessagesBeyondBaseCommitOffset and maxMessagesToQueue) has doubled. As instances are added to the consumer group, its performance starts to approach that of the single instance Parallel Consumer. Example usage with Kafka Streams, Preprocess in Kafka Streams, then process concurrently, Figure 10. This project has been stable and reached its initial target feature set in Q1 2021.
For example, an evening, prime-time game show on TV where users send in quiz answers on their devices. Currently, you cant adjust the number of partitions in your Kafka topics without jumping through a lot of hoops, or breaking your key ordering. A pattern that is often addressed in traditional messaging systems using a shared queue. You also need to manage your consume loop, and commit transactions properly if using Exactly Once semantics. This interface allows you to process your message, then publish back to the broker zero, one or more result messages.
This is effective in many situations, but falls short in a lot too. This is to try to prevent the situation where the payload is too large to fit at all, and must be dropped entirely. #298: Improve PollAndProduce performance by first producing all records, and then waiting for the produce results.Previously, this was done for each ProduceRecord individually. Sign up for Confluent Cloud, a fully-managed Apache Kafka service. To speed up test execution, you can enable container reuse across test runs by setting the following in your ~/.testcontainers.properties file: This will leave the container running after the JUnit test is complete for reuse by subsequent runs.
Massively reduce message processing latency regardless of partition count for spikey workloads where there is good key distribution. If both serialised formats are significantly large, they are then both compressed using zstd compression, and if that results in a smaller serialization then the compressed form is used instead. Configuration of the Warnings Next Generation plugin for integration with PVS-Studio, Youre invited to the October virtual Haskell CoHack. However, you are still restricted by all the per consumer restrictions as described above. The simplest of the three are the two Consumer commits modes. Similar to the above, but from the operations perspective, our system is already over partitioned, perhaps in order to support existing parallel workloads which arent using the tool (and so need large numbers of partitions). Issues will be dealt with on a good faith, best efforts basis, by the small team maintaining this library. Consumer group size effect on message latency vs a single Parallel Consumer. Improvements to this system are planned, see the following issues: Support scheduled message processing (scheduled retry), Provide option for max retires, and a call back when reached (potential DLQ) #196, Monitor for progress and optionally shutdown (leave consumer group), skip message or send to DLQ #34. If a batch size is chosen, the "normal" APIs cannot be used, and an error will be thrown. After this setup, one then has the choice of interfaces: There is another interface: ParallelConsumer which is integrated, however there is currently no immediate implementation. Unordered concurrent processing of message, Figure 12. upon restore, if needed, the system then deserializes this offset map and loads it back into memory, when each messages is polled into the system, it checks if its already been previously completed. The input topic only has 10 partitions and for various reasons (see above) cannot be changed. Choose your ordering type, KEY in this case. This library handles offset commits for both ordered and unordered processing cases. 10,000 msgs * avg processing time 2.5ms = ~2.5s. Producer.plainSink is the easiest way to publish messages. Default ordering mode is now KEY ordering (was UNORDERED). To see how the performance of the tool is related to instance counts, partition counts, key distribution and how it would relate to the vanilla client. Notification processing system which sends push notifications to a user to acknowledge a two-factor authentication request on their mobile and authorising a login to a website, requires optimal end-to-end latency for a good user experience. While the HTTP system probably cannot handle 2,000,000 messages per second, more importantly, your system is no longer the bottleneck. If youre used to using the auto commit mode in the normal Kafka consumer, you can think of the Asynchronous mode being similar to this. Vert.x us used in this library to provide a non-blocking IO system in the message processing step. This means that concurrent processing is restricted to the number of input partitions. Figure 7. The slow consumer situation with the raw Apache Kafka Consumer client, Figure 4. Here is an example illustrating that: If you have many streams it can be more efficient to share the underlying KafkaProducer. The message itself contains information about what topic and partition to publish to so you can publish to different topics with the same producer. Why not use the Vert.x library yourself in your processing loop? See issue #12, and the ParallelConsumer JavaDoc: This is the only thing you need to do, in order to get massively concurrent processing in your code. We are very interested to hear about your experiences! when the system then next commits offsets, if there are any messages beyond the highest offset which have been marked as succeeded. They are of course, synchronous and asynchronous mode.
The Vert.x module is an optional extension to the core module. Support BitSet encoding lengths longer than Short.MAX_VALUE #37 - adds new serialisation formats that supports wider range of offsets - (32,767 vs 2,147,483,647) for both BitSet and run-length encoding. Usage - print message content out to the console in parallel, Call an HTTP endpoint for each message usage, Figure 8. We call this the "highest committable offset". Including integration tests running against real Kafka broker and database. Given this input topic with three partitions and a series of messages: The normal Kafka client operations in the following manner. See the PollContext class for more information. Parallel Apache Kafka client wrapper with per message ACK, client side queueing, a simpler consumer/producer API with key concurrency and extendable non-blocking IO processing. the offset map is serialised and encoded into a base 64 string, and added to the commit message metadata. This option provides the performance of maximum concurrency, while maintaining message processing order per key, which is sufficient for many applications. This is not an exactly once guarantee, as message replay cannot be prevented across failure. Confluents product page for the project. Provision your fully managed Kafka cluster in Confluent Cloud. feature: Poll Context object for API (#223), PollContext API - provides central access to result set with various convenience methods as well as metadata about records, such as failure count, major: Batching feature and Event system improvements. Note that typically offset commits are not performed after processing a single message, but is illustrated in this manner for comparison to the single pass concurrent methods below. In addition, the Vert.x extension to this library supplies non-blocking interfaces, allowing higher still levels of concurrency with a further simplified interface. You need queue-like semantics that use message level acknowledgment, for example to process a work queue with short- and long-running tasks. If using a batch version of the API, you must choose a batch size in the options class. If an exception is thrown while processing the batch, all messages in the batch will be returned to the queue, to be retried with the standard retry system. Integration tests require a running locally accessible Docker host. However with runlength encoding and typical offset patterns this should be quite rare. However, because messages can be processed out of order, messages beyond the highest committable offset must also be tracked for success and not replayed upon restart of failure. security setups, schema registry etc), For use with Streams, see Using with Kafka Streams section, Source: simply consume from the topic that your Connect plugin is publishing to, Sink: use the poll and producer style API and publish the records to the topic that the connector is sinking from, Should work with any cluster that the linked AK client library works with, If using EoS/Transactions, needs a cluster setup that supports EoS/transactions. sending records to an API which has a batch version like Elasticsearch, Vert.x and Reactor.io non-blocking library integration, Vert.xs WebClient and general Vert.x Future support, Reactor.io Publisher (Mono/Flux) and Javas CompletableFuture (through Mono#fromFuture), Zero~ dependencies (Slf4j and Lombok) for the core module, Throttle control and broker liveliness management, Manual global pause / resume of all partitions, without unsubscribing from topics (useful for implementing a simplistic circuit breaker), Circuit breaker patterns for individual paritions or keys can be done through throwing failure exceptions in the processing function (see PR #291 Explicit terminal and retriable exceptions for further refinement), Note: Pausing of a partition is also automatic, whenever back pressure has built up on a given partition. Batch support in all versions of the API to process batches of messages in parallel instead of single messages. Bitset overflow check (#35) - gracefully drop BitSet or Runlength encoding as an option if offset difference too large (short overflow), A new serialisation format will be added in next version - see Support BitSet encoding lengths longer than Short.MAX_VALUE #37, Gracefully drops encoding attempts if they cant be run, Fixes a bug in the offset drop if it cant fit in the offset metadata payload, Turns back on the Bitset overflow check (#35), Incorrectly turns off an over-flow check in offset serialisation system (#35), Choice of commit modes: Consumer Asynchronous, Synchronous and Producer Transactions, Using a transactional Producer is now optional, Use the Kafka Consumer to commit offsets Synchronously or Asynchronously, Memory performance - garbage collect empty shards when in KEY ordering mode, Select tests adapted to non transactional (multiple commit modes) as well, Fixes a performance issue with the async committer not being woken up, Make committer thread revoke partitions and commit, Have onPartitionsRevoked be responsible for committing on close, instead of an explicit call to commit by controller, Make sure Broker Poller now drains properly, committing any waiting work, Fixes bug in commit linger, remove genesis offset (0) from testing (avoid races), add ability to request commit, Sometimes a transaction error occurs - Cannot call send in state COMMITTING_TRANSACTION #25, ReentrantReadWrite lock protects non-thread safe transactional producer from incorrect multithreaded use, Wider lock to prevent transactions containing produced messages that they shouldnt, Fixes example app tests - incorrectly testing wrong thing and MockProducer not configured to auto complete, Add missing revoke flow to MockConsumer wrapper, Have massively parallel consumption processing without running hundreds or thousands of, without operational burden or harming the clusters performance, Efficient individual message acknowledgement system (without local or third system state) to massively reduce message replay upon failure, Vert.x non-blocking library integration (HTTP currently). confluent.io/confluent-accelerators/#parallel-consumer, build: Fix GPG key import on Jenkins, again (, : Fix multi topic subscription with KEY order by adding top, [maven-release-plugin] prepare for next development iteration 0.5.3.0, Delombok for Javadoc (allows references to Lombok elements in Javadoc), : Change Truth-Generator test dependency from compile to test (, Draft v1 of generic async message processor, [maven-release-plugin] prepare release 0.5.2.0, 8.3.2. and this Stack Overflow question. This ensures that no message is reprocessed if its been previously completed. Run tests excluding the integration tests, Run any goal skipping tests (replace
Spikey load with latency sensitive non-functional requirements. * Currently there is no direct implementation, only the {@link ParallelStreamProcessor} version (see {@link. Key ordering is faster than partition ordering, with unordered being the fastest. More typically though you probably still want the per key ordering grantees that Kafka provides. Simply return an object representing the request, the Vert.x HTTP engine will handle the rest, using its non-blocking engine. Create new credentials for your Kafka cluster, and then Confluent Cloud will show a configuration block with your new credentials automatically populated (make sure show API keys is checked).
For example, if you have a topic with five partitions, you cannot use a group with more than five consumers to read from it. if the external system is currently out of action, use a higher retry. From the Clients view, get the connection information customized to your cluster (select Java). For this reason, we use a variant of the, Note that Kafkas Exactly Once Semantics (EoS) (transactional processing) also does not prevent. Runtime data model creates list of incomplete offsets, Continuously builds a full complete / not complete bit map from the base offset to be committed, encodes into a BitSet, and a RunLength, then compresses both using zstd, then uses the smallest and tags as such in the encoded String, Which is smallest can depend on the size and information density of the offset map, Smaller maps fit better into uncompressed BitSets ~(30 entry map bitset: compressed: 13 Bytes, uncompressed: 4 Bytes), Larger maps with continuous sections usually better in compressed RunLength, Completely random offset maps, compressed and uncompressed BitSet is roughly the same (2000 entries, uncompressed bitset: 250, compressed: 259, compressed bytes array: 477). mvn compile - Due to a bug in Mavens handling of test-jar dependencies - running mvn compile fails, use mvn test-compile instead. For many use cases this improves both throughput and latency by reducing load on your brokers. It maintains the partition ordering characteristic that all keys are processed in log order, yet for most use cases will be close to as fast as UNORDERED when the key space is large enough. The library also supports sending a batch or records as input to the users processing function in parallel. The user has the option to either choose ordered, or unordered message processing. If log ordering isnt a concern this can be an unwelcome bottleneck for users. See KafkaProducer Javadoc and ProducerConfig Javadoc for details. The advantage of ordered processing mode, is that for an assignment of 1000 partitions to a single consumer, you do not need to run 1000 consumer instances or threads, to process the partitions in parallel. The unit test code is set to run at a very high frequency, which can make it difficult to read debug logs (or impossible). Apache, Apache Kafka, and Kafka are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. Particularly useful for when your processing function can work with more than a single record at a time - e.g.
With the vanilla consumer, messages on each partition must be consumed one after the other in serial order.
