8/12/2023 0 Comments Service bus dead letterYou can define the maximal number of attempts to process a record of a batch of records. It's also worth mentioning various possible configurations for error management. In addition to SQS or SNS, you can send the events to Event Bridge or invoke another Lambda function. The second one is the on-failure destination, and it's currently similar to the dead-letter queue except that it supports more target destinations. The first of them use a dead-letter queue, an SNS or SQS where Lambda will send all events from erroneous executions. This serverless service supports the pattern in 2 ways. AWS LambdaĪnother service associated in my mind with the dead-letter pattern is AWS Lambda. However, it's not a pure streaming concept! You can use it in batch pipelines as well! One of the first examples I found - BTW, it inspired me to write it down - is BigQuery, and more exactly, BigQueryIO connector available for Apache Beam.Īccording to the snippet available in the official Beam documentation, any BigQuery insert failure can be intercepted with the help of getFailedInsertsWithErr() method and passed to a dead-letter table. Val invalidJsonEvents = validJsonEvents.getSideOutput(outputTag)ĭead-Letter pattern is very often associated to the streaming processing. Val inputDataSource = env.fromElements("not a JSON", "") Val outputTag = OutputTag("invalid-payloads") Val env = StreamExecutionEnvironment.createLocalEnvironment() Thanks to this clear separation, we can easily define a different sink for this invalid output part: Apache Flink has a concept of side output that you can use to receive invalid input records. However, for the dead-letter pattern, Apache Flink is - in my opinion - more idiomatic. Sounds surprising, doesn't it? After all, I spend a lot of time explaining data engineering with the help of Apache Spark. But before I show you how implemented on various cloud providers, let's take a look at the Apache Flink.
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