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Join semantics in kafka streams


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With the recent adoption of the Confluent and Kafka Streams, organizations have experienced significantly improved system stability with real-time processing framework, as well as improved scalability and lower maintenance costs.
The focus of this webinar is:
~Different join operators in Kafka Streams.
~Exploring different options in Kafka Streams to join semantics, both with and without shared keys.
~How to put Application Owner in control by leveraging simplified app-centric architecture.

If you have any queries, contact Himani over mail

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Join semantics in kafka streams

  1. 1. Join Semantics in Kafka Streams Himani Arora Software Consultant Knoldus Inc.
  2. 2. Agenda ● Introduction to Apache Kafka ● Introduction to Streams API ● How to use Streams API ● Join Operations supported in Kafka Streams ● Different types of Joins
  3. 3. Apache Kafka
  4. 4. Introduction ● Apache Kafka is a distributed streaming platform where producers send messages—key-value pairs—to topics which in turn are polled and read by consumers. Each topic is partitioned, and the partitions are distributed among brokers. ● It has four core APIs: ○ Producer API ○ Consumer API ○ Streams API ○ Connector API
  5. 5. Streams API ● Kafka Streams is a client library for processing and analyzing data stored in Kafka. ● There are two main abstractions in the Streams API: ○ A KStream is a stream of key-value pairs. KStreams are stateless, but they allow for aggregation by turning them into the other core abstraction. ○ A KTable, which is often described as a “changelog stream.” A KTable holds the latest value for a given message key and reacts automatically to newly incoming messages.
  6. 6. How to install the Streams API? ● There is no installation needed - Build Apps, Not Clusters! ● It is a library and can be added to your app like any other library. <dependency> <groupId>org.apache.kafka</groupId> <artifactId>kafka-streams</artifactId> <version>1.1.0</version> </dependency>
  7. 7. Joins Kafka Streams supports 3 type of joins: ● Inner Joins ○ Gives an output when both input sources have records with same key. ● Left Joins ○ Gives an output for each record in the left or primary input source. If the other source does not have a value for a given key, it is set to null. ● Outer Joins ○ Gives an output for each record in either input source. If only one source contains a key, the other is null.
  8. 8. Type 1 Type 2 Inner Join Left Join Outer Join KStream KStream ✔ ✔ ✔ KStream KTable ✔ ✔ ✖ KStream Global KTable ✔ ✔ ✖ KTable KTable ✔ ✔ ✔
  9. 9. KStream-KStream Join ● This is a sliding window join, meaning that all tuples close to each other with regard to time are joined. Time here is the difference up to the size of the window. ● These joins are always windowed joins because otherwise, the size of the internal state store used to perform the join would grow indefinitely. ● Since KStream-KStream Join is always windowed joins, we must provide a join window. KStream<String, String> joined = left.join(right, (leftValue, rightValue) -> "left=" + leftValue + ", right=" + rightValue, /* ValueJoiner */ JoinWindows.of(TimeUnit.MINUTES.toMillis(5)), Serdes.String(), /* key */ Serdes.Long(), /* left value */ Serdes.Double() /* right value */ );
  10. 10. KTable-KTable Join ● KTable-KTable joins are designed to be consistent with their counterparts in relational databases. They are always non-windowed joins. ● The changelog streams of KTables is materialized into local state stores that represent the latest snapshot of their tables. The join result is a new KTable representing changelog stream of the join operation. KTable<String, String> joined = left.join(right, (leftValue, rightValue) -> "left=" + leftValue + ", right=" + rightValue /* ValueJoiner */ );
  11. 11. KStream-KTable Join ● KStream-KTable joins are asymmetric non-window joins. They allow you to perform table lookups against a KTable everytime a new record is received from the KStream. ● In contrast to stream-stream and table-table join which are both symmetric, a stream-table join is asymmetric. KStream<String, String> joined = left.join(right, (leftValue, rightValue) -> "left=" + leftValue + ", right=" + rightValue, /* ValueJoiner */ Serdes.String(), /* key */ Serdes.Long() /* left value */ );
  12. 12. KStream-GlobalKTable Join ● KStream-GlobalKTable joins are always non-windowed joins. ● It differs from KStream-Global KTable joins in the following manner: ○ They allow for efficient star joins, joining large scale facts stream with dimension tables. ○ They allow for joining against foreign keys ○ They are often more efficient than their partitioned KTable counterpart. KStream<String, String> joined = left.join(right, (leftKey, leftValue) -> leftKey.length(), /* derive a new key by which to lookup agianst the table */ (leftValue, rightValue) -> "left=" + leftValue + ", right=" + rightValue ); /* ValueJoiner */
  13. 13. References ● ● ●
  14. 14. Q&A Please email your queries to