kafka stream join performance

The input streams are combined using the merge function, which creates a new stream that represents all of the events of its inputs. Only events arriving on the stream side trigger downstream updates and produce join output. Few millions of records are consumed/produced every hour. KStream< String, SongEvent> rockSongs = builder.stream (rockTopic); KStream< String, SongEvent . We will begin with a brief walkthrough of some core concepts. Each message contains a key and a payload that is serialized to JSON. Benchmarking akka-stream-kafka - SoftwareMill Kafka vs Redis Pub-Sub, Differences which you should know Streamlio Claims Pulsar Performance Advantages Over Kafka More specifically, I will conduct two types of join, in a similar pattern of an RDBMS world. spark_kafka_streams_join.py is spark script to read data from kafka sources and implement join transformations to observe and track campaign performance by matching click event with impression event. As the reactive-kafka library got more and more popular, Akka Team has joined in to make it an official part of the ecosystem (and renamed the lib to akka-stream-kafka).This collaboration resulted in a groundbreaking recent 0.11 release, which brings new API and documentation. Kafka Streams offers a feature called a window. Its used for high-performance data pipelines, and streaming analytics. Kafka Streams vs. Kafka Consumer | Baeldung We took a closer look at Confluent's benchmark and found some issues. 1. In our case, the order-service application generates test data. In order to provide the community a more accurate picture, we decided to address these issues and repeat the test. Streaming Kafka topic to Delta table (S3) with Spark ... For comparison, we benchmark a P2P stream processing framework, HarmonicIO, developed in-house. Read here for more details. Failure to optimize results in slow streaming and laggy performance. We process millions of video views each day. Bill Bejeck Integration Architect (Course Author) Joins Kafka Streams provides join operations for streams and tables, enabling you to augment one dataset with another. Spring Cloud Stream's Apache Kafka support also includes a binder implementation designed explicitly for Apache Kafka Streams binding. The first thing to create a streaming app is to create a SparkSession: 1 import org.apache.spark.sql.SparkSession 2 3 val spark = SparkSession 4 .builder 5 .appName ("StructuredConsumerWindowing") 6 .getOrCreate () To avoid all the INFO logs from Spark appearing in the Console, set the log level as ERROR: Kafka Streams binder implementation builds on the foundation provided by the Kafka Streams in Spring Kafka . Since Kafka's client library (Kafka Streams) was written by software engineers for ultimate performance, they naturally wrote it in a high-performance software engineering language — Java. The test result shows that Pulsar significantly outperformed Kafka in scenarios that more closely resembled real-world workloads and matched Kafka's performance in the basic scenario Confluent used. Latency measures mean how long it takes to process one event, and similarly, how many events arrive within a specific amount of time, that means throughput measures. For the sake of this article, you need to be aware of 4 main Kafka concepts. However, when compared to the others, Spark Streaming has more performance problems and its process is through time windows instead of event by event, resulting in delay. Hi @srujanakuntumalla Currently the kafka streams binder does not expose a way to reset the offset per binding target as the regular MessageChannel based binder does. Ensure the Delta table. Crossing the Streams - Joins in Apache Kafka | Confluent A subsequent article will show using this realtime stream of data from a RDBMS and join it to data originating from other sources, using KSQL. A consumer can join a group, called a consumer group. As of Kafka 0.10.0.0 Kafka Streams offers three types of joins (with multiple variants): The Streams API allows an application to act as a stream processor , consuming an input stream from one or more topics and producing an output stream to one or more output topics, effectively transforming the input . Streaming Data from MySQL into Kafka with Kafka Connect ... You can use Kafka Connect to stream data from a source system (such as a database) into a Kafka topic, which could then be the foundation . Starting in 0.10.0.0, a light-weight but powerful stream processing library called Kafka Streams is available in Apache Kafka to perform such data processing as described above. Performance Tuning of an Apache Kafka/Spark Streaming ... https://cnfl.io/kafka-streams-101-module-5 | Kafka Streams offers three types of joins: stream-stream, stream-table, and table-table. In short, Spark Streaming supports Kafka but there are still some rough edges. Check out the below link.https://www.kite.com/get-kite/?utm_medium=ref. Stream-Stream Stream-stream joins combine two event streams into a new stream. However, you can do this for the entire application by using this global property: spring.cloud.stream.kafka.streams.binder.configuration.auto.offset.reset: earliest.The only problem is that if you have multiple input topics . fig 6: Broadcasting of the user details The idea is simple. below). Kafka Streams is also a distributed stream processing system, meaning that we have designed it with the ability to scale up by adding more computers. Upgrade to the latest version of Kafka. Apart from Kafka Streams, alternative open source stream processing tools include Apache Storm and Apache Samza. When I read this code, however, there were still a couple of open questions left. ; This example currently uses GenericAvroSerde and not SpecificAvroSerde for a specific reason. The foreign-key join is an advancement in the KTable abstraction. Stateful Stream Processing with Kafka and Go. The message key is the order's id. A beacon is a collection of data representing details about the video playback experience. Kafka developed Kafka Streams with the goal of providing a full-fledged stream processing engine. Failure to optimize results in slow streaming and laggy performance. The CloudKarafka team finally put together a Best Practice blog post to guide you into how to best tune your Kafka Cluster in order to meet your high-performance needs. Running. The result is a KStream. Processing a stream of events is much more complex than processing a fixed set of records. Integrating Kafka with Spark Streaming Overview. In this post, I will explain how to implement tumbling time windows in Scala, and how to tune RocksDB accordingly. A consumer can join a group, called a consumer group. The good thing is that the window during which the late event arrived (window 1535402400000) does not include the late event. Introduction. The experiments focus on system throughput and system latency, as these are the primary performance metrics for event streaming systems in production. ; For each record in the games-sessions, reshape the records to remove everything from the key beside pid. We will be aggregating: employee_dictionary: messages contain the name, surname and employee id; contact_info: messages contain the email and other contact information; address: message contain address details; The events are streamed into Kafka from an external database, and the goal is to . Apache Kafka is the most popular open-source distributed and fault-tolerant stream processing system. When you use ksqlDB to join streaming data, you must ensure that your streams and tables are co-partitioned, which means that input records on both sides of the join have the same configuration settings for partitions.The only exception is foreign-key table-table joins, which do not have any co-partitioning requirement. Performance tuning involves two important metrics: Latency measures how long it takes to process one event. Of course, while preparing streams before joining, I will need some transformation, such as re-key, group by . Tuning Kafka for Optimal Performance. Get the tuning right, and even a small adjustment to your producer configuration can make a significant improvement to the way your . Our study reveals a complex interplay of performance trade-offs, revealing the boundaries of good performance for each framework and integration over a wide domain of application loads. What I want to discuss is another feature of Kafka Stream, which is joining streams. If you want to use a system as a central data hub it has to be fast, predictable, and easy to scale so you can dump all your . Additionally, Kafka will often capture the type of data that lends itself to exploratory analysis - such as application logs, clickstream and sensor . Consumers are allowed to read from any offset point they choose. The majority of those views will transmit multiple beacons. i.e., only to write records of Kafka topic that match the set of Unique IDs I have to another topic. Conclusion. Client application reads from the Kafka topic using GenericAvroSerde for the value and then the map function to convert the stream of messages to have Long keys and custom class values. To be more specific, tuning involves two important metrics: Latency measures and throughput measures. In this blog post, we summarize the notable improvements for Spark Streaming in the latest 3.1 release, including a new streaming table API, support for stream-stream join and multiple UI enhancements. You can perform table lookups against a table when a new record arrives on the stream. We'll cover stream processors and stream architectures throughout this tutorial. Topic: All Kafka messages pass through topics. Also, schema validation and improvements to the Apache Kafka data source deliver better usability. Kafka is a distributed system consisting of servers and clients. Join records of this stream with GlobalKTable's records using non-windowed left equi join. Join records of this stream with GlobalKTable's records using non-windowed inner equi join. the technology stack selected for this project is centered around kafka 0.8 for streaming the data into the system, apache spark 1.6 for the etl operations (essentially a bit of filter and transformation of the input, then a join), and the use of apache ignite 1.6 as an in-memory shared cache to make it easy to connect the streaming input part of … Let's imagine that, given the above data, we are given the following requirements: For each country in the games-sessions, create a record with the count of games played in from that country.Write the results to the games-per-country topic. Start the Producer by invoking the following command from the mykafkaproducerplanet directory: We are now ready to increase the load and scale the number of Kafka Connector tasks and demonstrate the scalability of the stream data . Your stream processing application doesn't run inside a broker. Event sourcing. Finally, various enhancements were made for . When you join a stream and a table, you get a new stream, but you must be explicit about the value of that stream—the combination between the value in the stream and the associated value in the table. High Performance 11 The Data Ecosystem 11 . Kafka optimization is a broad topic that can be very deep and granular, but here are four highly utilized Kafka best practices to get you started: 1. Partitioning requirements. The amount of local state required for a stream-stream join is directly proportional to the width of the join window. Streaming Data Sources • File Source • Reads files as a stream of data • Supports text, csv, json, orc parquet • Files must be atomically placed • Kafka Source • Reads from Kafka Topic • Supports Kafka broker > 0.10.x • Socket Source (for testing) • Reads UTF8 text from socket connection • Rate Source (for testing . Which is better in terms of performance and other factors ? The join is a primary key table lookup join with join attribute keyValueMapper.map(stream.keyValue) == table.key. $ ./kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic my-kafka-stream-stream-inner-join-out --property print.key=true --property print.timestamp=true Time to put everything together. JDBC source connector currently doesn't set a namespace when it generates a schema name for the data it is . One of the major factors taken into account was performance. This page describes how to benchmark Kafka's performance on the latest hardware in the cloud, in a repeatable and fully automated manner, and it documents the results from running these tests. Apache Kafka is the most popular open-source distributed and fault-tolerant stream processing system. Difference Between Redis and Kafka. While KStream-KTable join will create 1 internal topic + 1 table. Kafka Consumer provides the basic functionalities to handle messages. Since we introduced Structured Streaming in Apache Spark 2.0, it has supported joins (inner join and some type of outer joins) between a streaming and a static DataFrame/Dataset.With the release of Apache Spark 2.3.0, now available in Databricks Runtime 4.0 as part of Databricks Unified Analytics Platform, we now support stream-stream joins.In this post, we will explore a canonical case of how . Kafka Streams is a client library for processing and analyzing data stored in Kafka. Kafka optimization is a broad topic that can be very deep and granular, but here are four highly utilized Kafka best practices to get you started: 1. In order to generate and send events continuously with Spring Cloud Stream Kafka, we need to define a Supplier bean. In this tutorial, we'll explain the features of Kafka Streams to . The merged stream is forwarded to a combined topic via the to method, which accepts the topic as a parameter. Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. Downloads Documentation Join Us Blog. Stream-table joins are always non-windowed joins. In this blog post, we take a deep dive into the Apache Kafka Brokers. With this, you can process new data as its generated at high speeds and additionally can save it to some database as well. Redis: Redis is an in-memory, key-value data store which is also open source.It is extremely fast one can use it for caching session management, high-performance database and a message broker. ETL pipelines for Apache Kafka are uniquely challenging in that in addition to the basic task of transforming the data, we need to account for the unique characteristics of event stream data. Kafka Streams offers the KStream abstraction for describing stream operations and the KTable for describing table operations. Throughput measures how many events arrive within a specific amount of time. The streams are joined based on a common key, so keys are necessary. Apache Kafka Consumer Consumers can read log messages from the broker, starting from a specific offset. Then we will take a look at the kinds of joins that the Streams API permits. A Kafka Stream abstraction is here to help us join these two types of streams without touching any of the partitions: The GlobalKTable. It shouldn't come as a surprise that Mux Data works with large amounts of data. Now that we have a (streaming) dataframe of our Kafka topic, we need to write it to a Delta table. The join is a primary key table lookup join with join attribute keyValueMapper.map(stream.keyValue) == table.key. The first thing to create a streaming app is to create a SparkSession: 1 import org.apache.spark.sql.SparkSession 2 3 val spark = SparkSession 4 .builder 5 .appName ("StructuredConsumerWindowing") 6 .getOrCreate () To avoid all the INFO logs from Spark appearing in the Console, set the log level as ERROR: In order to do performance testing or benchmarking Kafka cluster, we need to consider the two aspects: Performance at Producer End; Performance at Consumer End; We need to do the testing of both i.e Producer and Consumer so that we can make sure how many messages producer can produce and a consumer can consume in a given time. sparkConf.set("spark.streaming.kafka.maxRatePerPartition", "25") So with batch interval of 10 sec, the above parameter with value 25 will allow a partition to have maximum 25*10=250 messages. The value '5' is the batch interval. df = read_stream_kafka_topic(topic, topic_schema) 4. They are one-to-many (1:N) and many-to-one (N:1) relations. Spark Streaming is one of the most widely used frameworks for real time processing in the world with Apache Flink, Apache Storm and Kafka Streams. Avoid unnecessarily wide join windows¶ Stream-stream joins require that you specify a window over which to perform the join. I encourage architects to look at this difference. Apache Kafka is a distributed streaming platform. Therefore, users can achieve better performance by sending messages to many Kafka steams either via many topics, topics created with multiple partitions, or both. Updates on the table side don't produce updated join output. The Kafka Producer parallelizes the sending of data to different Kafka streams. Send events to Kafka with Spring Cloud Stream. A good starting point for me has been the KafkaWordCount example in the Spark code base (Update 2015-03-31: see also DirectKafkaWordCount). Kafka Streams rightly applied the event time semantics to perform the aggregation! Create kafka topics. First, we need to make sure the Delta table is present. Introduction. Records on each side of the join match only if they both occur within the specified window. I am making KStream-KStream join which creates 2 internal topics. Kafka Streams also provides real-time stream processing on top of the Kafka Consumer client. This allows consumers to join the cluster at any point in time. Run kafka-console producer A stream processing application is any program that makes use of the Kafka Streams library. There is a big price difference too. Delta table. Natural to Aiven services, we evaluated . Step 2: Initialize streaming context. A Kafka stream is a discrete Kafka topic and partition. "Table lookup join" means, that results are only computed if KStream records are processed. In practice, this means it is probably "your" application. That long-term storage should be an S3 or HDFS. Kafka Performance Tuning. Each data record in a stream maps to a Kafka message from the topic. In this case, I am getting records from Kafka. Interface KStream<K, V> is an abstraction of record. I need to, for each record in the stream, check if the stream's ID is present in the set of unique IDs I have. Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. Optimizing Kafka producers. Kafka Streams partitions data for processing—enabling scalability, high performance, and fault tolerance. With this native integration, a Spring Cloud Stream "processor" application can directly use the Apache Kafka Streams APIs in the core business logic. In this post, we shall look at the top differences and performance between Redis vs Kafka. Performing Kafka Streams Joins presents interesting design options when implementing streaming processor architecture patterns. It may define its computational logic through one or more processor topologies. Kafka allows us to build and manage real-time data streaming pipelines. Joins require that the . Stream-Table Join 259 Streaming Join 261 . Back in 2017, we published a performance benchmark to showcase the vast volumes of events Apache Kafka can process. Here is where we can use the schema of the dataframe to make an empty dataframe. In Kafka, each record has a key . Pulsar integrates with Flink and Spark, two mature, full-fledged stream processing frameworks, for more complex stream processing needs and developed Pulsar Functions to focus on lightweight computation. In this article we'll see how to set it up and examine the format of the data. Consumers are allowed to read from any offset point they choose. Kafka Configuration: 5 kafka brokers Kafka Topics - 15 partitions and 3 replication factor. Streamlio, a startup created a real-time streaming analytics platform on top of Apache Pulsar and Apache Heron, today published results of stream processing benchmark that claims Pulsar has up to a 150% performance improvement over Apache Kafka. Kafka is balanced for both. Kafka Streams offers the follow join operators (operators in bold font were added in current trunk, compared to 0.10.1.x and older): KStream-KStream Join This is a sliding window join, ie, all tuples that are "close" to each other with regard to time (ie, time difference up to window size) are joined. Developers use the Kafka Streams library to build stream processor applications when both the stream input and stream output are Kafka topic (s). It can be deployed on bare-metal . As a result, Kafka Streams is more complex. streaming_spark_context = StreamingContext (spark_context, 5) This is the entry point to the Spark streaming functionality which is used to create Dstream from various input sources. There are numerous applicable scenarios, but let's consider an application might need to access multiple database tables or REST APIs in order to enrich a topic's event record with context information. There is a significant performance difference between a filesystem and Kafka. Kafka is a really poor place to store your data forever. Kafka Streams also provides real-time stream processing on top of the Kafka Consumer client. The company also unveiled a new processing framework called Pulsar Functions. A stream partition is an ordered sequence of data records that maps to a Kafka topic partition. In this example, we will show how to aggregate three Kafka topics by using Streaming SQL processors. Our current application is based on Kafka Streams. Kite is a free AI-powered coding assistant that will help you code faster and smarter. Most systems are optimized for either latency or throughput. A well-tuned Kafka system has just enough . In this tutorial, we'll explain the features of Kafka Streams to . Upgrade to the latest version of Kafka. Basically, this should serve as a filter for my Kafka Streams app. You can fine-tune Kafka producers using configuration properties to optimize the streaming of data to consumers. Kafka Consumer provides the basic functionalities to handle messages. Kafka is a powerful real-time data streaming framework. The technology stack selected for this project are centered around Kafka 0.8 for streaming the data into the system, Apache Spark 1.6 for the ETL operations (essentially a bit of filter and transformation of the input, then a join), and the use of Apache Ignite 1.6 as an in-memory shared cache to make it easy to connect the streaming input part . Apache Kafka More than 80% of all Fortune 100 companies trust, and use Kafka. Multiple transformations all in one go. Kafka Streams is a client library used for building applications and microservices, where the input and output data are stored in Kafka clusters. 1. Kafka is a powerful real-time data streaming framework. Kafka Streams improved its join capabilities in Kafka 0.10.2+ with better join semantics and by adding GlobalKTables, and thus we focus on the latest and greatest joins available. Although stream-based join semantics (as used in Kafka Streams) cannot be completely consistent with join semantics in RDBMS SQL, we observed that our current join semantics can still be improved to make them more intuitive to understand. Benchmarking Kafka write throughput performance [2019 UPDATE] It's been a long time coming, but we've now have updated write throughput kafka benchmark numbers and a few extras surprises. For more complex transformations Kafka provides a fully integrated Streams API . Apache Kafka Consumer Consumers can read log messages from the broker, starting from a specific offset. Data can be ingested from many sources like Kafka, Kinesis, or TCP sockets, and can be processed using complex algorithms expressed with high-level functions like map, reduce, join and window . . We've come to think of Kafka as a streaming platform: a system that lets you publish and subscribe to streams of data, store them, and process them, and that is exactly . Debezium is a CDC tool that can stream changes from MySQL, MongoDB, and PostgreSQL into Kafka, using Kafka Connect. This allows consumers to join the cluster at any point in time. Kafka acts as a publish-subscribe messaging system. In contrast to #join(GlobalKTable,KeyValueMapper,ValueJoiner), all records from this stream will produce an output record (cf. Kafka, in a nutshell, is an open-source distributed event streaming platform by Apache. October 15, 2020 by Paul Mellor. In Part 4 of this blog series, we started exploring Kafka Connector task scalability by configuring a new scalable load generator for our real-time streaming data pipeline, discovering relevant metrics, and configuring Prometheus and Grafana monitoring. I wrote a blog post about how LinkedIn uses Apache Kafka as a central publish-subscribe log for integrating data between applications, stream processing, and Hadoop data ingestion.. To actually make this work, though, this "universal log" has to be a cheap abstraction. Data record keys determine the way data is routed to topic partitions. Apart from Kafka case, the order-service application generates test data a dive... & lt ; String, SongEvent & gt ; is the most popular open-source distributed fault-tolerant... Alternative open source stream processing on top of the join match only they! Look at the kinds of joins that the Streams API serialized to JSON ; K, &. Streams API benchmark to showcase the vast volumes of events Apache Kafka Brokers from Kafka about the playback. Internal topics I have to another topic for either latency or throughput real-time processing. The Kafka consumer client read this code, however, there were still a of... Specifically, I will need some transformation, such as re-key, group by handle messages making! The way your the value & # x27 ; t run inside a broker how many events within! Significant improvement to the Apache Kafka Brokers to build and manage real-time data streaming pipelines stream Kafka we... Such as re-key, group by serialized to JSON deep dive into the Kafka... We shall look at the kinds of joins that the Streams API permits = (! Multiple beacons dataframe to make sure the Delta table with Spark streaming supports Kafka but there are some. A group, called a consumer can join a group, called a consumer can join group! ; String, SongEvent & gt ; is an ordered sequence of data representing details about the video playback.. Takes to process one event was performance common key, so keys are.... Specified window format of the join match only if they both occur within specified. Way data is routed to topic partitions and clients a more accurate picture, we take a deep into! Picture, we need to define a Supplier bean vs Kafka the &., the order-service application generates test data trigger downstream updates and produce join output are to! They choose address these issues and repeat the test the cluster at point. Stream maps to a Delta table is present generated at high speeds and can! > Integrating Kafka with Spark streaming Overview semantics to perform the aggregation, tuning involves important. Are joined based on a common key, so keys are necessary Streams binder implementation builds on the side... Get the tuning right, and how to implement tumbling time windows in,! Don & # x27 ; kafka stream join performance explain the features of Kafka connector tasks demonstrate. Details the idea is simple this code, however, there were still couple... Is better in terms of performance and other factors sequence of data representing details about the video experience... Table when a new record arrives on the stream games-sessions, reshape records. Producer parallelizes the sending of data to consumers: latency measures how long it takes to process one event //solace.com/kafka/! Event streaming systems in production the majority of those views will transmit multiple beacons event into! Internal topics producers using configuration properties to optimize the streaming of data details... Allows consumers to join the cluster at any point in time by Kafka. - consumers... < /a > Kafka Streams to Kafka can process currently GenericAvroSerde... 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Stream-Stream join is an advancement in the games-sessions, reshape the records to remove everything from the topic arrive a! Majority of those views will transmit multiple beacons S3 or HDFS source deliver usability. This example currently uses GenericAvroSerde and not SpecificAvroSerde for a stream-stream join is an abstraction of record throughout... Couple of open questions left '' https: //solace.com/kafka/ '' > Kafka performance tuning side downstream! More specifically, I will explain how to implement tumbling time windows in Scala, and to. As these are the primary performance metrics for event streaming systems in production joined based on a common,! On each side of the Kafka consumer client, schema validation and improvements to the Apache -! Complete and Comprehensive Guide... < /a > 1 tuning involves two important metrics: latency measures how many arrive... Sequence of data transformation, such as re-key, group by data record keys determine the way is... It kafka stream join performance define its computational logic through one or more processor topologies foreign-key join is a primary table... Distributed and fault-tolerant stream processing application doesn & # x27 ; ll cover stream processors and stream architectures throughout tutorial! Streams also provides real-time stream processing tools include Apache Storm and Apache Samza look at top... Topic as a result, Kafka Streams also provides real-time stream processing on top of data. Deliver better usability point they choose base ( Update 2015-03-31: see also DirectKafkaWordCount ) join. Top differences and performance between Redis vs Kafka handle messages dataframe to make an empty dataframe tutorial, we #... Of our Kafka topic that match the set of records stream is forwarded to a Delta table present... An abstraction of record local state required for a stream-stream join is a collection of.... To implement tumbling time windows in Scala, and how to implement tumbling time windows in,... Join will create 1 internal topic + 1 table set it up and the. Write records of Kafka Streams also provides real-time stream processing on top the! To generate and send events continuously with Spring Cloud stream Kafka, we decided to address these issues and the... One event ordered sequence of data to consumers Cloud stream Kafka, we need to it! Source connector currently doesn & # x27 ; ll see how to tune RocksDB accordingly metrics: latency how. Akka-Stream-Kafka - SoftwareMill < /a > Kafka Streams binder implementation builds on the foundation provided by the Kafka parallelizes. Speeds and additionally can save it to a Delta table is present more specific, tuning involves two metrics... A namespace when it generates a schema name for the sake of article! Is the order & # x27 ; t set a namespace when it generates a schema name for the of. Streaming pipelines processing engine called Pulsar Functions the kafka stream join performance a more accurate,. Kafka can process new data as its generated at high speeds and additionally can it... Basically, this means it is a distributed system consisting of servers and clients Apache. Define a kafka stream join performance bean in this post, I will explain how to RocksDB. Amounts of data to consumers popular open-source distributed and fault-tolerant stream processing application &. Important metrics: latency measures and throughput measures and how to implement tumbling time windows in Scala and! Each side of the Kafka consumer client it to a combined topic via the method... Events Apache Kafka is the order & # x27 ; s id consumer! Streaming Overview topic, we decided to address these issues and repeat the test - Kafka performance tuning involves two important:! Course, while preparing Streams before joining, I will need some transformation, such as re-key, group.. 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