[ https://issues.apache.org/jira/browse/FLINK-9061?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Steven Zhen Wu reassigned FLINK-9061: ------------------------------------- Assignee: Indrajit Roychoudhury Summary: add entropy to s3 path for better scalability (was: S3 checkpoint data not partitioned well -- causes errors and poor performance) > add entropy to s3 path for better scalability > --------------------------------------------- > > Key: FLINK-9061 > URL: https://issues.apache.org/jira/browse/FLINK-9061 > Project: Flink > Issue Type: Bug > Components: FileSystem, State Backends, Checkpointing > Affects Versions: 1.4.2 > Reporter: Jamie Grier > Assignee: Indrajit Roychoudhury > Priority: Critical > > I think we need to modify the way we write checkpoints to S3 for high-scale > jobs (those with many total tasks). The issue is that we are writing all the > checkpoint data under a common key prefix. This is the worst case scenario > for S3 performance since the key is used as a partition key. > > In the worst case checkpoints fail with a 500 status code coming back from S3 > and an internal error type of TooBusyException. > > One possible solution would be to add a hook in the Flink filesystem code > that allows me to "rewrite" paths. For example say I have the checkpoint > directory set to: > > s3://bucket/flink/checkpoints > > I would hook that and rewrite that path to: > > s3://bucket/[HASH]/flink/checkpoints, where HASH is the hash of the original > path > > This would distribute the checkpoint write load around the S3 cluster evenly. > > For reference: > https://aws.amazon.com/premiumsupport/knowledge-center/s3-bucket-performance-improve/ > > Any other people hit this issue? Any other ideas for solutions? This is a > pretty serious problem for people trying to checkpoint to S3. > > -Jamie > -- This message was sent by Atlassian JIRA (v7.6.3#76005)