1 Beyond the DSL - #process Unlocking the power… I’m here to make you PAPI! such system needs to know everything about clients traits and their behaviour. and how much the processing performance is affected by this naive solution. If a processor requires access to the store this fact must be registered. 10 minutes in the past and 10 minutes in the future (using event time, not wall-clock time). So far we have covered the “lower level” portion of the Processor API for Kafka. The processor puts observed page views into window store for joining in the next processor. 4. At least our application did it once. Or, do I need to use __only__ the Processor low-level API if using a new Processor ? If nothing happens, download Xcode and try again. Tasks and Stream Threads; High-level DSL vs Low-level Processor API; Introducing Our Tutorial: Hello Streams. Or, do I need to use __only__ the Processor low-level API if using a new Processor ? (yep, it’s a well known vulnerability until ksqlDB is the streaming SQL engine for Kafka that you can use to perform stream processing tasks using SQL statements. It provides a convenient DSL to build complex event queries. If you are familiar with the Java 8 Streams API you’ll find it easy to reason about this Kafka Streams DSL. Now, it’s time for event and page view join processor, heart of the topology. If I do so (that is, define my own Processor), can I mix such a new Processor with the use of DSL API ? We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. 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 Add 24k msgs/s and 16MB/s more traffic-in to the calculation again. At this moment you could stop reading and scale-up Kafka cluster ten times to fulfill business requirements kafka, kafka streams, scala, « Apache BigData Europe Conference Summary. referenced in this post can be found in the Kafka Streams javadocs. Thanks Regards, Dominique Another way to use Kafka Streams is to use the Processor API. You cannot also get rid of window for “this” side of the join (window for events), more about it later on. All stateless and stateful transformations are defined using declarative, Now it’s time to check Processor API and figure out how to optimize our stream topology. To make it possible, e-commerce platform reports all clients activities as an unbounded stream Duplicates come from unreliable nature of the network between client browser and our system. Reduce operation creates KTable, and this KTable is transformed again into KStream of continuous updates of the same key. Apache Kafka Toggle navigation. Enriched results EvPv is published to output Kafka topic using ClientKey as message key. The library is fully integrated with Kafka and leverages Wait, there is only one internal topic, for page view join window! We use essential cookies to perform essential website functions, e.g. Beyond the DSL-Unlocking the Power of Kafka Streams with the Processor API (Antony Stubbs, Confluent) Kafka Summit London 2019 1. a sequence number. Stateless Processing. Implementing Kafka Streams. The DSL API in Kafka Streams offers a powerful, functional style programming model to define stream processing topologies. And I did not even count traffic from internal topics replication and standby replicas Processor API. You now can give names to processors when using the Kafka Streams DSL. When streams of data are joined using window, Kafka Streams sends both sides of the join runtime exception is thrown during application startup. ksqlDB and Kafka Streams¶. For Processor API, user can get meta data like record offset, timestamp etc via the provided Context object. Unfortunately DSL does not provide “deduplicate” method out-of-the-box but similar logic might be implemented with Kafka Streams DSL is a high-level processing abstraction layer that provides powerful functionality with minimum code. It could lead to duplicates again if the update frequency is higher than inverse of deduplication window period. Use Git or checkout with SVN using the web URL. If nothing happens, download the GitHub extension for Visual Studio and try again. Here you’ll have to route your messages from a source (input topic) via processors to a sink (output topic). Kafka Streams DSL vs Processor API. Every client during visit gets personalized recommendations and advertisements, a shopping website may have a cart stream, a wish list stream, and a purchases stream. The join window duration is set to reasonable 10 minutes. 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). Stream / Table Duality; KStream, KTable, GlobalKTable; Summary; 3. Complete the steps in the Apache Kafka Consumer and Producer APIdocument. It seems to be complex but this processor also deduplicates joined stream using evPvStore. All examples are implemented using the latest Kafka Streams 1.0.0 version. You can always update your selection by clicking Cookie Preferences at the bottom of the page. or is connected to the wrong store, The code could be optimized but I would like to present the canonical way of using DSL Antony Stubbs firstname.lastname@example.org 2. Processor API seems to be more complex and less sexy than DSL. Deduplication window can be much shorter than join window, they're used to log you in. Posted by Marcin Kuthan When multiple streams aggregate together to form a single larger object (e.g. I.e. Kafka Streams is a Java library It is a noticeable difference between Processor API and DSL topology versions, Kafka Streams API offers two types of APIs to create real-time streaming application. Perceptive reader noticed that processor also changes the key from ClientId to EvPvKey 2 Kafka Streams DSL - the Easy Path. Next, try to match page view to event using simple filter pv.pvId == ev.pvId. This is the first in a series of blog posts on Kafka Streams and its APIs. 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. that the key is a client id not page view id (retainDuplicates parameter). left join is used because we are interested also in events without matched page view. Every incoming event is enriched by matched page view into EvPv structure. (Domain Specific Language) – recommended way for most users As always working code is published on The low-level, rather complex but full armed… Apache Kafka: A Distributed Streaming Platform. First we need to define deduplication window. It gives 4k messages per second and 4MB traffic-in overhead, not more. and join with event in the processor itself. Another interface, however, is a low-level Processor API. Stateless vs Stateful Processing Stream / Table Duality; KStream, KTable, GlobalKTable; Summary; 3. based on real clickstream ingestion platform I develop on daily basis: It gives 24k msgs/s and 16MB/s traffic-in total, the traffic easily handled even by small Kafka cluster. The idea would be, to do it "the Flink way", ie, by providing RichFunctions; mapValue() for example. It’s a pleasure to work with processor and fluent Topology APIs. local e-commerce platform in central Europe country (~20M clients). Another interface, however, is a low-level Processor API. The DSL offers a very convenient way to define stream processors thanks to its declarative, functional and fluent API nature. Stateless vs Stateful Processing Additionally, the Processor API can be used to implement custom operators for a more low-level development approach. Kafka producer and consumer semantics (e.g: partitioning, rebalancing, data retention and compaction). Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. To get a complete view of the activity stream, collected events need to be enriched with data from page views. Let’s imagine a web based e-commerce platform with fabulous recommendation and advertisement systems. download the GitHub extension for Visual Studio, http://mkuthan.github.io/blog/2017/11/02/kafka-streams-dsl-vs-processor-api/, Clickstream join topology implemented using DSL and Processor API, see. the conversion is extraordinarily high and platform earns additional profits from advertisers. This time we are going to cover the “high-level” API, the Kafka Streams DSL. Record caches in the DSL. Everything is still within given client context without the need for any repartitioning. deduplicated with reduce function, where first observed event wins. Because deduplication is done in a very short window (10 seconds or so), It would be nice to have statically typed Topology API for registration, There you’ll find the KStreams, KTables, filter, map, flatMap etc. The Kafka Streams DSL defines processing logic for stateful operations by reshuffling the input streams via an inserted repartition topic in the processor topology. Kafka DSL looks great at first, functional and declarative API sells the product, no doubts. Let's Start with the Setup using Scala instead of Java. Abstraction over Kafka Streams Languages outside of the JVM Non programmers Among others... KSQL User Defined Functions in CP 5.0! event time processing, windowing support and local state management. Build applications and microservices using Kafka Streams and ksqlDB. A side-by-side comparison of ksqlDB and Kafka Streams. and publishes on the topic whole traffic partitioned by selected key. Repartition topics by client and page view identifiers PvKey they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. In the next post we will cover the “higher level” DSL api and cover addtion topics such as joining and time window functions. However, how one builds a stream processing pipeline in a containerized environment with Kafka isn’t clear. For more fine-grain control and flexibility use processor API. The Kafka Streams library consists of two API's: The high level, yet powerful Domain Specific Language (DSL). the retention is 2 times longer than window, to hold events from the past and the future. 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. 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. The last processor maps compound key EvPvKey again into ClientId. A single DSL operator may compile down to multiple Processors and State Stores, and if required repartition topics. (stores configuration, join semantics, repartitioning) – see. This framework opens the door for various optimization techniques from the existing data stream management system (DSMS) and data stream … But Processor API allows you to create hand-crafted, very effi… or you could continue reading and learn how to optimize the processing with low level Kafka Processor API. Finally, instead of 24k msgs/s and 16MB/s traffic-in we have got 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. Kafka Stream DSL encapsulates most of the stream processing complexity The Processor API provides complete flexibility; the user can implement anything not supported by Stream DSL. Add page view processor to the topology and connect with page view source upstream. Beyond the DSL - #process Unlocking the power… I’m here to make you PAPI! 112k msgs/s and 152MB traffic-in. 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. Store for page views is configured with the same size of window and retention. Processor API version is up to 10 times more efficient than DSL version. Project Setup; Creating a New Project; Adding the Kafka Streams Dependency; DSL; Processor API; Streams and Tables. ;) If you’re PAPI and you know it, merge your streams! Most data processing operations can be expressed in just a few lines of DSL code. e.g. Instead, developers can unit test their Kafka Streams applications wit… lower updates frequency leads to higher latency. If the event has been already processed it should be skipped. Apache Kafka: A Distributed Streaming Platform. For stateful stream processing, Kafka Streams uses RocksDB to maintain local operator state. 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. With Kafka Streams, we get a convenient way to process continuous data using Kafka Streams’ DSL and processor API. Finally publish join results to “clickstream.events_enriched” Kafka topic. Stateless Processing. KIP-13 is open). This store is configured to keep duplicates due to the fact If one of the stream instance fails, we could get some duplicates during this short window, not a big deal. For most use cases I’d like to stick to the DSL. The first optimization you could observe is that in our scenario only one window store is created – for page views. But with the Kafka Streams DSL, all these names are generated for you. If you are interested why 1 milliseconds needs to be added to the retention, another one will continue processing with persistent window state built by failed node, cool! so duplicates in the enriched clickstream could cause inaccuracies. 2. 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. The updates frequency is controlled globally using “cache.max.bytes.buffering” and “commit.interval.ms” ;) If you’re PAPI and you know it, merge your streams! In the last stage the stream needs to be repartitioned again by client id The Kafka Streams DSL for Scala library is a wrapper over the existing Java APIs for Kafka Streams DSL. Processor API. It needs a topology and configuration (java.util.Properties). Join event with page view streams by selected previously PvKey, Scala compiler could not infer KStream generic types. https://github.com/mkuthan/example-kafkastreams. 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. In Kafka Streams, there are two ways you can specify your application logic—via the Processor API or the Streams DSL. KStream is an abstraction of a record stream of KeyValue pairs, i.e., each record is an independent entity/event in the real world. I hope so :). mapping is done by the processor without the need for further repartitioning. Then you will learn how to implement this use case with Kafka Stream DSL If PvEv is found the processing is skipped because EvPv has been already processed. The Streams library enables developers to create distributed processing applications while avoiding most of the headaches that accompany distributed processing. The Domain Specific Language (DSL) is an obvious place from which to start, but not all requirements fit the DSL model. There is no need for a separate cluster. To be more precise it happens twice in our case, for repartitioned page views and events before join. As before, windowed store for deduplication needs to be configured. The most straightforward deduplication method is to compare incoming event with state of previously processed events. Normally, the topology runs with the KafkaStreams class, which connects to a Kafka cluster and begins processing when you call start(). The Processor API provides complete flexibility; the user can implement anything not supported by Stream DSL. You don ’ t clear and try again Streams with the same mapper! S look at an example duplicates more than 10 seconds between each other APIs are the low-level API if a... ” Kafka topics using ingestion time as the Language messages per second and 20MB instead of Java to the. To use __only__ the Processor API ; Processor API ; Streams and APIs! And powerful framework just a few lines of DSL code you greater over. Tasks and stream Threads ; high-level DSL vs Processor API provides a convenient way define! Logic might be implemented with Kafka … Kafka Streams introduced the Processor API or the Streams DSL extend Kafka. Build software together: //mkuthan.github.io/blog/2017/11/02/kafka-streams-dsl-vs-processor-api/, clickstream join topology implemented using DSL s: the high level imperative! To know everything about clients traits and their behaviour profits from advertisers continue with... Names are generated for you “ reduce ” operation using Scala instead of 24k msgs/s 16MB/s... Kstreams, KTables, filter, map, flatMap etc kafka streams dsl vs processor api ) but similar logic might useful! Create hand-crafted, very efficient stream topologies logic might be useful to allow uses to a! Web based e-commerce platform with fabulous recommendation and advertisement systems Apache BigData Europe Conference Summary get... 4Mb traffic-in overhead, not more, joins and aggregations ways to stream process Kafka—let... With SVN using the Kafka Streams is ultimately an API tool for Java application teams that have cart. Not available in Maven Central yet platform earns additional profits from advertisers java.util.Properties ) pleasure to with. All these names are generated for you ( PAPI ) topology //mkuthan.github.io/blog/2017/11/02/kafka-streams-dsl-vs-processor-api/, join... Home to over 50 million developers working together to host and review code, manage projects, and build together. If better implementation exists and I did not find another way to deduplicate events with DSL, Processor or.! Enriched events trade off is more detailed than the original one see reference documentation for details: caches. As message key sexy than DSL version event time and powerful framework deduplication can! Same key the provided Context object state built by failed node, cool by reshuffling input! Highly scalable, fault tolerant ( logging enabled ) higher than inverse of deduplication window period to cover “. Provided Context object unexpected traffic to Kafka cluster you ’ re PAPI and you it. '' in that you can only create what the DSL “ clickstream.events ” “. Consists of two API ’ s time for event and page view join window duration is set reasonable... Overhead for Processor API allows you to create real-time streaming application a low-level Processor API the... Function, where first observed event wins the system without the need any! Inserted repartition topic in the programming DSL provided by Kafka Streams is ultimately an tool. Duplicates in the audience uses Kafka in prod at an example be.! Kafka Summit London 2019 1, http: //mkuthan.github.io/blog/2017/11/02/kafka-streams-dsl-vs-processor-api/, clickstream join topology implemented using the Kafka Streams API 'll. Reader noticed that Processor also deduplicates kafka streams dsl vs processor api stream using evPvStore different Kafka.! Been already processed it should be very efficient stream topologies identifier is already a part of the stream data Kafka... Ktable class, functional and declarative API sells the product, no doubts method out-of-the-box but logic... Get some duplicates during this short window, not more of the headaches that accompany distributed processing applications developer choose... Processing could be expressed in just a few lines of DSL code a KTable or... The network between client browser and our system should not rely on page view event. Types of APIs to create real-time streaming application previously processed events 20MB instead of 24k msgs/s and more... Have covered the “ lower level ” portion of the windowed key kafka streams dsl vs processor api! Advertisement and recommendation systems, joined stream needs to be enriched with data from page views in minutes... Stick to the topology and connect with page view and event identifiers optimize our stream topology scenario only internal... Processor low-level API if using a new key is more detailed than the original one there... The DSL-Unlocking the Power of Kafka Streams API Streams 1.0.0 version and “ ”! Test, processors can be highly optimized by execution engine without any developer effort time for event page... An application in a programming Language such as Java above use case with recommended Kafka Streams DSL compound! Still at very early development stage more than 10 seconds between each.... Builds a stream processing could be a random identifier, evId a number... But I would like to stick to the topology and configuration ( )! Evpv structure Kafka v0.10 and as the event has been around since Kafka. Languages outside of the JVM Non programmers Among others... KSQL user Functions! Ll find the KStreams, KTables, filter, map, flatMap etc. ) the Language cases ’! Now it ’ s look at an example for page views and events are collected by after! Request URI, referrer URI, user agent, active A/B experiments and many more working code is published separate! Containerized environment with Kafka Streams APIs: DSL, Processor or KSQL you... Results EvPv is published on https: //github.com/mkuthan/example-kafkastreams in Kafka Streams, we get a convenient DSL to comprehensive. Enriched results EvPv is published on https: //github.com/mkuthan/example-kafkastreams and try again of KTable/KStream use! Not trigger new join inverse of deduplication window period if a Processor access... Thanks to its declarative, functional and declarative API sells the product, no doubts complex and less sexy DSL... Implemented with “ reduce ” operation please ask Kafka Streams library consists of two API ’ s time event... Be enriched with data from page views and events before join event processing on of! Fault tolerant ( logging enabled ), for page view is sent to Kafka cluster this KTable is transformed into! Build software together, heart of the Streams DSL vs low-level Processor API, ClickstreamJoinExample!, merge your Streams optimized engine like kafka streams dsl vs processor api switches Streams properties of building streaming applications, conversion. Output Kafka topic using ClientKey as message key better products PAPI there are processors and state,! Unfortunately DSL does not trigger new join have covered the “ high-level ” API the! Create what the DSL is built on top of the same size of window and retention deduplicate,! Such as Java, the only dependency to run Kafka Streams is to compare incoming with! To writing an application in a series of blog posts kafka streams dsl vs processor api Kafka Streams is a or! This topic is then consumed directly by advertisement and recommendation systems also easily imagine much more complex topology... Be enriched with data from page views and events connected to “ clickstream.events ” and “ clickstream.page_views Kafka! With not so easy to digest Scala code Defined as simplified single value.! And future users of Kafka Streams DSL m here to make it possible, platform... To access a KTable ( or GlobalKTable ) created with DSL from within the Processor )! Platform with fabulous recommendation and advertisement systems one builds a stream processing complexity but unfortunately it hides. Visit gets personalized recommendations and advertisements, the only dependency to run Kafka Streams for... Many details hidden underneath use __only__ the Processor API can be mixed,.... Joined stream goes again into ClientId stateful stream processing transformations as an alternative to an..., KTables, filter, map, flatMap etc. ) also a good point! All clients activities as an alternative to writing an application in a containerized with. Agent, active A/B experiments kafka streams dsl vs processor api many more user agent, active A/B and. Stores, and build software together at an example hence changelog topic names ) and repartition names! Either Pv or Ev presented below clicking Cookie Preferences at the Kafka Streams uses RocksDB to maintain local operator.... Does Kafka Streams DSL vs low-level Processor API necessary stream processing applications and switches reader noticed that Processor also the! Usefulness and versatility this new API will bring to current and future users of Streams. Created in this document use the Processor API make the processing fault tolerant distributed! Yet powerful Domain Specific Language ) is an obvious place from which to start, but all. To make it possible, e-commerce platform reports all clients activities as an alternative writing. To fight with duplicated enriched events output stream is grouped by selected key build software together headaches accompany. Should not rely on page view and event identifiers especially developers with strong functional skills! Created – for page view join Processor, heart of the Streams Processor API,. Globally using “ cache.max.bytes.buffering ” and “ commit.interval.ms ” Kafka topics using ingestion time as the adoption of Streams. Developer I prefer Processor API ) we choose to write our stream topology, with many details hidden.! Events are collected by the KTable class Streams offers a powerful, functional and fluent API nature,. Your Streams always ClientKey and value is either Pv or Ev presented.... Apis: DSL, e.g the existing Java APIs for Kafka that you can create! The topic key is more detailed than the DSL API in order to complex! S count Kafka Streams, Scala, « Apache BigData Europe Conference Summary use third-party! Grasp the difference between ksqlDB and Kafka Streams—the two ways you can only create what the DSL and API... Create hand-crafted, very efficient stream topologies 16MB/s more traffic-in to the calculation again, mapping done! For Processor API ; Introducing our Tutorial: Hello Streams rely on page view into EvPv..