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So we have shown that under various failure scenarios, exactly-once semantics in your stream processing applications are quite hard to achieve.
The next question then is, can we really make it easier with Kafka Streams? Now we will describe what was implemented behind the scenes within Kafka Streams library to guarantee exactly-once when users turn on this config.
As we have mentioned in the previous section, a read-process-write stream processing application built around Kafka can be abstracted as a function F that is triggered for each message A read from the input Kafka topics.
F is composed of three key steps:. Then in order to guarantee exactly-once, we need to make sure these three steps will be executed atomically : either all of them get executed, or none of them does.
While achieving atomicity for all these three steps is generally very hard for a stream processing technology, with Kafka in the picture we can actually map this hard problem into a simpler one.
So the third step above: committing offsets on the source topics, can be straightforwardly translated as just another message write to a specific Kafka topic.
Second of all, in Kafka Streams state updates can also be translated as a sequence of change capture messages.
Here is why: in the Kafka Streams library, all state stores capture their updates by default into some special Kafka topics called the changelog topics.
Each store keeps its updates in a separate changelog topic: whenever an update is applied to that store, a new record capturing this update will be sent to the corresponding changelog topic.
This topic can hence be used to bootstrap a replica of the state store in another processor upon load balancing, fault recovery, etc.
As a result, we can translate all the above three steps into a number of records sent to different topics:. With the transactions API, we can enable producer clients to atomically send to multiple Kafka topic partitions.
Messages written to multiple Kafka topics within the same transaction will be readable by consumers as a whole when the transaction is successfully committed , or none of them will not be readable at all if the transaction is aborted.
By using this mechanism, Kafka Streams can ensure that records are sent to to the sink topics, the changelog topics, and the offset topics atomically.
More specifically, when processing. Upon starting up the application, the embedded transaction producer will start the first transaction right away after the stream task has been initialized and is ready to begin processing.
And whenever the application is going to commit the current processing state, it will use the embedded transactional producer to send the fetched position offsets from its consumer as part of its transaction, and then try to commit the current transaction and start a new one.
As we have learned from the previous blog , all messages sent by the same producer are idempotent i. In addition, if any fatal error is encountered during normal processing or during the committing phase, Kafka Streams will let its producer abort the ongoing transaction before throwing the exception.
By doing this, if the stream application task ever gets restarted with the same transaction id, we are assured that the last transaction from this id has been completed either committed or aborted , and all the committed messages to the changelog topic, the offset topic and the output topics are from the same transaction the golden bars shown in the Fig below.
Therefore after the processor state has been restored and the task is ready to resume processing, its state will be in a consistent snapshot with the committed offsets and the output messages, hence guaranteeing exactly once semantics.
As we have mentioned in the previous blog post , the write amplification cost of a transaction is constant and independent of the number of messages written within the partition.
Thus, the larger the transaction in terms of messages, the smaller the amortized cost. However, larger transactions will also result in longer end-to-end processing latency because the consumer in read-committed mode can fetch the messages of a transaction only when the transaction has been committed.
In Kafka Streams, because a new transaction is created whenever commit is called, the average transaction size is determined by the commit interval: with the same incoming traffic, a shorter commit interval will result in smaller transactions.
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