# Data skew
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A Flink application is executed on a cluster in a distributed fashion. To scale out to multiple nodes, Flink uses the concept of keyed streams, which essentially means that the events of a stream are partitioned according to a specific key, e.g., customer id, and Flink can then process different partitions on different nodes. Many of the Flink operators are then evaluated based on these partitions, e.g., [Keyed Windows](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/datastream/operators/windows/), [Process Functions](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/datastream/operators/process_function/) and [Async I/O](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/datastream/operators/asyncio/).

Choosing a partition key often depends on the business logic. At the same time, many of the best practices for, e.g., [DynamoDB](https://aws.amazon.com/dynamodb/) and Spark, equally apply to Flink, including:
+ ensuring a high cardinality of partition keys
+ avoiding skew in the event volume between partitions

 You can identify skew in the partitions by comparing the records received/sent of subtasks (i.e., instances of the same operator) in the Flink dashboard. In addition, Managed Service for Apache Flink monitoring can be configured to expose metrics for `numRecordsIn/Out` and `numRecordsInPerSecond/OutPerSecond` on a subtask level as well.