Description
Spark history server is an essential tool for monitoring, analyzing and optimizing spark jobs. The original history server is based on Spark event log mechanism. A running Spark job will produce many kinds of events that describe the job's status continuously. All the events are serialized into JSON and appended to a file —— event log. The history server has to replay the event log and rebuild the memory store needed for UI. In a cluster, the history server also needs to periodically scan the event log directory and cache all the files' metadata in memory. Actually, an event log contains too much redundant info for a history server. A long-running application can bring a huge event log which may cost a lot to maintain and require a long time to replay. In large-scale production, the number of jobs can be large and leads to a heavy burden on history servers. It needs additional development to build a scalable history server service. In this talk, we want to introduce a new history server based on UIMeta. UIMeta is a wrapper of the KVStore objects needed by a Spark UI. A job will bring a UIMeta log by stagedly serializing UIMeta. An UIMeta log is approximately 10x smaller in si…
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