kafka streams dsl vs processor api

Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Because join window is typically quite long (minutes) the store should be fault tolerant (logging enabled). We need to add 24k msgs/s and 16MB/s more traffic-in to the calculation. Apache Kafka Toggle navigation. Enriched results EvPv is published to output Kafka topic using ClientKey as message key. connected to “clickstream.events” and “clickstream.page_views” Kafka topics. With this blog post I would like to demonstrate that hand-crafted stream processors might be a magnitude more efficient With the release of Apache Kafka® 2.1.0, Kafka Streams introduced the processor topology optimization framework at the Kafka Streams DSL layer. Beyond the DSL-Unlocking the Power of Kafka Streams with the Processor API (Antony Stubbs, Confluent) Kafka Summit London 2019 1. 28k instead of 112k messages per second and 20MB instead of 152MB traffic-in in total. 1 Beyond the DSL - #process Unlocking the power… I’m here to make you PAPI! In the PAPI there are Processors and State Stores and you are required to explicitly name each one. Everything is still within given client context without the need for any repartitioning. antony@confluent.io. ksqlDB is the streaming SQL engine for Kafka that you can use to perform stream processing tasks using SQL statements. that the key is a client id not page view id (retainDuplicates parameter). I hope so :). Processor API version is up to 10 times more efficient than DSL version. Now we are ready to implement above use case with recommended Kafka Streams DSL. A side-by-side comparison of ksqlDB and Kafka Streams. provides a low level, imperative way to define stream processing logic. With Kafka Streams, we get a convenient way to process continuous data using Kafka Streams’ DSL and processor API. Project Setup; Creating a New Project; Adding the Kafka Streams Dependency; DSL; Processor API; Streams and Tables. Because we are not interested in late events out of defined window, deduplicated with reduce function, where first observed event wins. KIP-13 is open). This is the first in a series of blog posts on Kafka Streams and its APIs. For client “bob” the following page views and events are collected by the system. Stream / Table Duality; KStream, KTable, GlobalKTable; Summary; 3. I did not present any Kafka Streams test (what’s the shame – I’m sorry) Kafka DSL looks great at first, functional and declarative API sells the product, no doubts. Page view and event structures are different so messages are published to separate Kafka topics Duplicates come from unreliable nature of the network between client browser and our system. Is it possible to access a KTable (or GlobalKTable) created with DSL from within the Processor API (even if read-only)? Let’s imagine a web based e-commerce platform with fabulous recommendation and advertisement systems. mapping is done by the processor without the need for further repartitioning. The processor puts observed page views into window store for joining in the next processor. It could lead to duplicates again if the update frequency is higher than inverse of deduplication window period. Work fast with our official CLI. Another interface, however, is a low-level Processor API. Build applications and microservices using Kafka Streams and ksqlDB. EvPv without page view details is forwarded to the downstream. Next, try to match page view to event using simple filter pv.pvId == ev.pvId. There you’ll find the KStreams, KTables, filter, map, flatMap etc. logging into Kafka cannot be disabled using DSL. For Processor API, user can get meta data like record offset, timestamp etc via the provided Context object. Kafka Processor API KSQL Additionally, the Processor API can be used to implement custom operators for a more low-level development approach. Apache Kafka Toggle navigation. If a processor requires access to the store this fact must be registered. Create the sources from input topics “clickstream.events” and “clickstream.page_views”. kafka, kafka streams, scala, « Apache BigData Europe Conference Summary. The DSL is built on top of this Processor API. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. The Domain Specific Language (DSL) is an obvious place from which to start, but not all requirements fit the DSL model. AFAIU Kafka streams DSL lacks some feature I want to use (1) It looks like I am able to define my own Processor using low-level API. such system needs to know everything about clients traits and their behaviour. Application was started after some time of inactivity and processed 3 hours of retention in 5 minutes There is no need for a separate cluster. This framework opens the door for various optimization techniques from the existing data stream management system (DSMS) and data stream … And I did not even count traffic from internal topics replication and standby replicas Next, the stream is grouped by selected key into KGroupedStream and functional programming style (filter, map, flatMap, reduce, aggregate operations). Learn more. Marcin Kuthan, genuine software engineer at Allegro Group. client, page view and event identifiers. The Apache Kafka project includes a Streams Domain-Specific Language (DSL) built on top of the lower-level Stream Processor API.This DSL provides developers with simple abstractions for performing data processing operations. The DSL is a high-level interface, with many details hidden underneath. create processor which stores page view in windowed store. Add 24k msgs/s and 16MB/s more traffic-in to the calculation again. It is a noticeable difference between Processor API and DSL topology versions, This time we are going to cover the “high-level” API, the Kafka Streams DSL. If the event has been already processed it should be skipped. The low-level, rather complex but full armed Processor API (PAPI) which gives you all the power of Kafka Streams. https://github.com/mkuthan/example-kafkastreams. Features. Because we need to join an incoming event with the collected page view in the past, Kafka Streams API offers two types of APIs to create real-time streaming application. Because client identifier is already a part of the compound key, Learn more. We use essential cookies to perform essential website functions, e.g. 2. We don’t need any repartitioning to do that, only get all page views from given client Beyond the DSL-Unlocking the Power of Kafka Streams with the Processor API (Antony Stubbs, Confluent) Kafka Summit London 2019 1. The join window duration is set to reasonable 10 minutes. So far we have covered the “lower level” portion of the Processor API for Kafka. The last processor maps compound key EvPvKey again into ClientId. Both of them will construct the application’s computational logic as a processor topology , which is represented as a graph of stream processor nodes that are connected by stream … Many people are unaware of the Processor API (PAPI) – or are intimidated by it because of sinks, sources, edges and stores – oh my! Use high level Stream DSL provided by Kafka streams API. e.g. They share a lot of the same operations, and can be converted back and forth just as the table/stream duality suggests, but, for example, an aggregation on a KTable will automatically handle that fact that it is made up of updates to the underlying values. When streams of data are joined using window, Kafka Streams sends both sides of the join Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. recommended for resiliency. Apache Kafka: A Distributed Streaming Platform. The integration tests use an embedded Kafka clusters, feed input data to them (using the standard Kafka producer client), process the data using Kafka Streams, and finally read and verify the output results (using the standard Kafka consumer client). Wait, there is only one internal topic, for page view join window! The updates frequency is controlled globally using “cache.max.bytes.buffering” and “commit.interval.ms” The low-level, rather complex but full armed… (yep, it’s a well known vulnerability until Scala compiler could not infer KStream generic types. This deduplication implementation is debatable, due to “continue stream” semantics of KTable/KStream. or is connected to the wrong store, If one of the stream instance fails, we could get some duplicates during this short window, not a big deal. Let's Start with the Setup using Scala instead of Java. At first sight Processor API could look hostile but finally gives much more flexibility to developer. local e-commerce platform in central Europe country (~20M clients). 1 Beyond the DSL - #process Unlocking the power… I’m here to make you PAPI! You cannot also get rid of window for “this” side of the join (window for events), more about it later on. When multiple streams aggregate together to form a single larger object (e.g. When you create a stream processing application with Kafka’s Streams API, you create a Topology either using the StreamsBuilder DSL or the low-level Processor API. You may find a need for custom state management, access to the Kafka record’s metadata, custom functions not yet in the DSL, and explicit control over if/when to forward output records to downstream Kafka topics. Even local state stores are backed by Kafka topics to make the processing fault tolerant – brilliant! The code could be optimized but I would like to present the canonical way of using DSL Join event with page view streams by selected previously PvKey, ;) If you’re PAPI and you know it, merge your streams! The DSL offers a very convenient way to define stream processors thanks to its declarative, functional and fluent API nature. Finally, Kafka Streams library is extraordinarily fast and hardware efficient, if you know what you are doing. 3. The DSL and Processor API can be mixed, too. If PvEv is found the processing is skipped because EvPv has been already processed. Apache Kafka includes four core APIs: the producer API, consumer API, connector API, and the streams API that enables Kafka Streams. Perceptive reader noticed that processor also changes the key from ClientId to EvPvKey (replication factor, number of partitions, etc.). Learn more. Posted by Marcin Kuthan please ask Kafka Streams authors not me ;). To build comprehensive recommendation models, Rather, Kafka Streams is ultimately an API tool for Java application teams that have a CI/CD pipeline and are comfortable with distributed computing. I’m really keen on KSQL future, it would be great to get optimized engine like. Copyright © 2017 - Marcin Kuthan - There is no need for a separate cluster. ;) If you’re PAPI and you know it, merge your streams! Even if you don’t need fault tolerance, In addition to page view all important actions are reported as custom events, e.g: search, add to cart or checkout. One final thing to keep in mind is that the Processor API/Kafka streams is a work in progress and will continue to change for a while. is a promise that stream processing could be expressed by anyone using SQL as the language. Another interface, however, is a low-level Processor API. Let’s count Kafka Streams internal topics overhead for Processor API version. Unfortunately Kafka DSL hides a lot of internals which should be exposed via the API(stores configuration, join semantics, repartitioning) – seeKIP-182. One final thing to keep in mind is that the Processor API/Kafka streams is a work in progress and will continue to change for a while. The Streams library enables developers to create distributed processing applications while avoiding most of the headaches that accompany distributed processing. but I think testing would be easier with Processor API than DSL. For better examples readability page view and event payload is defined as simplified single value field. This store is configured to keep duplicates due to the fact and does not require any additional setup. Kafka Streams DSL is a high-level processing abstraction layer that provides powerful functionality with minimum code. and join with event in the processor itself. Processor API. Kafka Streams DSL vs Processor API. The Kafka Streams library consists of two API’s: The high level, yet powerful Domain Specific Language (DSL). nodes (4CPU, 4GB RAM) almost killed Kafka cluster deployed also on 10 physical machines (32CPU, 64GB RAM, SSD). Method selectKey sets a new key for every input record, Kafka Streams is a client library for building applications and microservices, where the input and output data are stored in an Apache Kafka® cluster. Kafka Streams provides all necessary stream processing primitives like one-record-at-a-time processing, If nothing happens, download the GitHub extension for Visual Studio and try again. All stateless and stateful transformations are defined using declarative, For 10 seconds deduplication window the updates should not be emitted more often than every 10 seconds but Processor API version is up to 10 times more efficient than DSL version. In the same step mapper gets rid of the windowed key produced by windowed reduce function. Stateless Processing. When stream of data is repartitioned Kafka Streams creates additional intermediate topic Here you’ll have to route your messages from a source (input topic) via processors to a sink (output topic). Streams DSL; Processor API; Processor APIs are the low-level API, and they provide you with more flexibility than the DSL. You signed in with another tab or window. Stateless Processing. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. For most use cases I’d like to stick to the DSL. I could also easily imagine much more complex stream topology, with tens of repartitions, joins and aggregations. Is takes a ValueMapper that only has method Processor API seems to be more complex and less sexy than DSL. Apache Kafka: A Distributed Streaming Platform. The project is configured with Embedded Kafka If I do so (that is, define my own Processor), can I mix such a new Processor with the use of DSL API ? In the next post we will cover the “higher level” DSL api and cover addtion topics such as joining and time window functions. Complex Event Processing on top of Kafka Streams Processor API ! What is really unique, the only dependency to run Kafka Streams application is a running Kafka cluster. page views and events are evenly partitioned on Kafka topics by the client identifier. It is possible to achieve high-performance stream processing by simply using Apache Kafka without the Kafka Streams API, as Kafka on its own is a highly-capable streaming … Complete the steps in the Apache Kafka Consumer and Producer APIdocument. Please note that the KTable API also offers stateless functions and what's covered in this post will be applicable in that case as well (more or less) The APIs (KStream etc.) I am using a Processor API (PAPI) topology. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. A single DSL operator may compile down to multiple Processors and State Stores, and if required repartition topics. The window store for events is not needed, if page view is collected by system after event it does not trigger new join. runtime exception is thrown during application startup. 4. so duplicates in the enriched clickstream could cause inaccuracies. Tasks and Stream Threads; High-level DSL vs Low-level Processor API; Introducing Our Tutorial: Hello Streams. Kafka Stream DSL encapsulates most of the stream processing complexity It offers an easy way to express stream processing transformations as an alternative to writing Even if one of the stream instances fails, At least our application did it once. Kafka Streams DSL Kafka Streams has a defined "contract" about timestamp propagation at the Processor API level: all processors within a sub-topology, see the timestamp from the input topic record that is currently processed and this timestamp will be used for all result records when writing them to a topic, too. (stores configuration, join semantics, repartitioning) – see. the logging to backed internal Kafka topic is disabled at all. Antony Stubbs antony@confluent.io 2. the conversion is extraordinarily high and platform earns additional profits from advertisers. 112k msgs/s and 152MB traffic-in. Add page view processor to the topology and connect with page view source upstream. This topic is then consumed directly by advertisement and recommendation systems. The steps in this document use the example application and topics created in this tutorial. Every time the client enters web page, a so-called page view is sent to Kafka cluster. ;) If you’re PAPI and you know it, merge your streams! Because most of the processing logic is built within context of given client, At this moment you could stop reading and scale-up Kafka cluster ten times to fulfill business requirements Kafka Streams is a Java library Nov 2nd, 2017 To be more precise it happens twice in our case, for repartitioned page views and events before join. Just uncomment either DSL or Processor API version, run main class and observe enriched stream of events on the console. ksqlDB and Kafka Streams¶. All examples are implemented using the latest Kafka Streams 1.0.0 version. It seems to be complex but this processor also deduplicates joined stream using evPvStore. Reduce operation creates KTable, and this KTable is transformed again into KStream of continuous updates of the same key. While the High-level DSL provides inbuilt functions for performing most of the regular operations, for any custom processing, you can use low-level processor API. Abstraction over Kafka Streams Languages outside of the JVM Non programmers Among others... KSQL User Defined Functions in CP 5.0! The Kafka Streams DSL defines processing logic for stateful operations by reshuffling the input streams via an inserted repartition topic in the processor topology. Processor API In the aforementioned sample app, we have used Kafka Streams DSL, which is one of two kinds of an interface to configure your topology. and publishes on the topic whole traffic partitioned by selected key. and marks derived stream for repartitioning. Especially developers with strong functional programming skills appreciate the overall design. Or, do I need to use __only__ the Processor low-level API if using a new Processor ? Stack Overflow Public questions & answers; Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Jobs Programming & related technical career opportunities; Talent Recruit tech talent & build your employer brand; Advertising Reach developers & technologists worldwide; About the company Then you will learn how to implement this use case with Kafka Stream DSL Or, do I need to use __only__ the Processor low-level API if using a new Processor ? 2 Kafka Streams DSL - the Easy Path. The DSL offers a very convenient way to define stream processors thanks to its declarative, functional and fluent API nature. KSQL was released recently and it is still at very early development stage. Application deployed on 10 Mesos to two intermediate topics again. If you are not careful, your Kafka Streams application could easily kill your Kafka cluster. Add join processor to the topology and connect with event source upstream. Unfortunately DSL does not provide “deduplicate” method out-of-the-box but similar logic might be implemented with another one will continue processing with persistent window state built by failed node, cool! Stream / Table Duality; KStream, KTable, GlobalKTable; Summary; 3. As always working code is published on No separate cluster is required just for processing. The DSL API in Kafka Streams offers a powerful, functional style programming model to define stream processing topologies. Finally, instead of 24k msgs/s and 16MB/s traffic-in we have got However, how one builds a stream processing pipeline in a containerized environment with Kafka isn’t clear. There is a relationship between the generated processor name state store names (hence changelog topic names) and repartition topic names. - PAPI? lower updates frequency leads to higher latency. because business logic can be expressed in a few lines of code. Stack Overflow Public questions & answers; Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Jobs Programming & related technical career opportunities; Talent Recruit tech talent & build your employer brand; Advertising Reach developers & technologists worldwide; About the company kafka-streams source code for this post. without exploring DSL internals. 10 minutes in the past and 10 minutes in the future (using event time, not wall-clock time). For above clickstream the following enriched events output stream is expected. using ingestion time as the event time. Application developer can choose from three different Kafka Streams APIs: DSL, Processor or KSQL. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Kafka producer and consumer semantics (e.g: partitioning, rebalancing, data retention and compaction). In more advanced use cases where the Kafka Streams DSL alone won’t suffice, you might find yourself reaching for the Processor API. Kafka Stream DSL is quite descriptive, isn’t it? Warm-up Processor API exercise, see DeduplicationExample. Kafka Streams is a flexible and powerful framework. With the Processor API you have to do all the low-level work yourself to put together your topology, but you have no restrictions on what you can build. Repartition topics by client and page view identifiers PvKey For testing though, connecting to a running Kafka cluster and making sure to clean up state between tests adds a lot of complexity and time. Instead, developers can unit test their Kafka Streams applications wit… In the last stage the stream needs to be repartitioned again by client id AFAIU Kafka streams DSL lacks some feature I want to use (1) It looks like I am able to define my own Processor using low-level API. by execution engine without any developer effort. Powered by Octopress, // A few impression events collected almost immediately, // There is also single duplicated event, welcome to distributed world, // A dozen seconds later Bob clicks on one of the offers presented on the main page, // Out of order event collected before page view on the offer page, // An impression event published almost immediately after page view, // Late purchase event, Bob took short coffee break before the final decision, // Events from main page without duplicates, // Events from offer page, somehow incomplete due to streaming semantics limitations, https://github.com/mkuthan/example-kafkastreams, Long-running Spark Streaming Jobs on YARN Cluster, Spark Application Assembly for Cluster Deployments, Spark and Kafka Integration Patterns, Part 2, Spark and Kafka Integration Patterns, Part 1, Acceptance Testing Using JBehave, Spring Framework and Maven. and published to “clickstream.events_enriched” Kafka topic for downstream subscribers. Implemented with Kafka … While the Processor API gives you greater control over the details of building streaming applications, the trade off is more verbose code. First we need to define deduplication window. Kafka DSL looks great at first, functional and declarative API sells the product, no doubts. “reduce” operation. If I do so (that is, define my own Processor), can I mix such a new Processor with the use of DSL API ? "Kafka Streams, Apache Kafka’s stream processing library, allows developers to build sophisticated stateful stream processing applications which you can deploy in an environment of your choice. Now, it’s time for event and page view join processor, heart of the topology. Thanks Regards, Dominique Create two input streams for page views and events Processor API) we choose to write our stream processing applications. Finally, the internal kafka topic can be easily configured using loggingConfig map Clickstream join topology implemented using DSL and Processor API, see ClickstreamJoinExample. ksqlDB is the streaming SQL engine for Kafka that you can use to perform stream processing tasks using SQL statements. The Kafka Streams DSL (Domain Specific Language) is built on top of the Streams Processor API. I did not find another way to deduplicate events with DSL, please let me know if better implementation exists. This library can be used to extend the Kafka Streams API in order to select complex event sequences from streams. Build applications and microservices using Kafka Streams and ksqlDB. From a developer's perspective, the way we create state stores and interact with them very much depends on which of the two different Kafka Streams APIs (Streams DSL vs. Because deduplication is done in a very short window (10 seconds or so), event time processing, windowing support and local state management. Normally, the topology runs with the KafkaStreams class, which connects to a Kafka cluster and begins processing when you call start().

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