CODEWITHSUNDEEP
javaapache-kafkaapache-flinkflink-streaming
Tartartartaglia
Tartartartaglia

Reputation: 21

Interesting Flink Problem - How to restore state in Flink if the task manager fails in order to gurantee exactly once processing?

Hi I am new to Flink and trying to figure out some best practices with the following scenerio:

I am playing around with a Flink job that reads unique data from multiple CSV files. Each row in the CSV is composed of three columns: userId, appId, name. I have to do some processing on each of these records (capitalize the name) and post the record to a Kafka Topic.

The goal is to filter out any duplicate records that exist so we do not have duplicate messages in the output Kafka Topic.

I am doing a keyBy(userId, appId) on the stream and keeping a boolean value state "Processed" to filter out duplicate records.

The issue is when I cancel the Task Manager in the middle of processing a file, to simulate a failure, it will start processing the file from the beginning once it restarts.

This is a problem because the "Processed" State in the Flink job is also wiped clean after the Task Manager fails!

This leads to duplicate messages in the output Kafka topic.

How can I prevent this?

I need to restore the "Processed" Flink state to what it was prior to the Task Manager failing. What is the best practice to do this?

Would Flink's checkpointed function https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/fault-tolerance/state/#checkpointedfunction help? I think not because this is a keyed stream.

Things to consider:

  1. Flink Checkpointing is already enabled.
  2. K8 pod for Task Manager (can be scaled very fast) and Parallelism is always > 1.
  3. Files can have millions of rows and need to be processed in parallel.

Thank you for the help!

Upvotes: 2

Views: 805

Answers (2)

David Anderson
David Anderson

Reputation: 43439

You need to use flink's managed keyed state, which Flink will maintain in a manner that is consistent with the sources and sinks, and Flink will guarantee exactly-once behavior provided you setup checkpointing and configure the sinks properly.

There's a tutorial on this topic in the Flink documentation, and it happens to use deduplication as the example use case.

For more info on how to configure Kafka for exactly-once operation, see Exactly-once with Apache Kafka.

Disclaimer: I wrote that part of the Flink documentation, and I work for Immerok.

Upvotes: 0

Martijn Visser
Martijn Visser

Reputation: 2068

I would recommend to read up on Flink's fault tolerance mechanisms, checkpointing & savepointing. https://nightlies.apache.org/flink/flink-docs-master/docs/learn-flink/fault_tolerance/ and https://nightlies.apache.org/flink/flink-docs-master/docs/ops/state/checkpoints/ are good places to start.

I think you could also achieve your deduplication easier by using Table API/SQL. See https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/deduplication/

Upvotes: 0

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