Introducing job queuing to scale your AWS Glue workloads
Big Data Blog
This article discusses a new AWS Glue job queuing feature that helps scale data integration workloads by automatically managing concurrency and retrying jobs when service limits are reached.
Specifically, the article covers:
- Use cases where job queuing is beneficial, such as processing many data sources in parallel or handling event-driven workloads with spikes
- How to enable job queuing for an AWS Glue job via the AWS Glue console
- How job queuing works, moving jobs to a "Waiting" state and retrying them when resources become available, rather than failing immediately
- The service limits covered by job queuing (concurrent job runs, DPUs, IP address exhaustion)
- Considerations like lack of support for Flex jobs and MaxRetries being non-configurable when queuing is enabled
- Conclusion highlighting how job queuing simplifies workload scaling and improves success rates
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