Frederick Reiss created SPARK-21084:
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Summary: Improvements to dynamic allocation for notebook use cases
Key: SPARK-21084
URL: https://issues.apache.org/jira/browse/SPARK-21084
Project: Spark
Issue Type: Umbrella
Components: Spark Core
Affects Versions: 2.2.0
Reporter: Frederick Reiss
One important application of Spark is to support many notebook users with a
single YARN or Spark Standalone cluster. We at IBM have seen this requirement
across multiple deployments of Spark: on-premises and private cloud deployments
at our clients, as well as on the IBM cloud. The scenario goes something like
this: "Every morning at 9am, 500 analysts log into their computers and start
running Spark notebooks intermittently for the next 8 hours." I'm sure that
many other members of the community are interested in making similar scenarios
work.
Dynamic allocation is supposed to support these kinds of use cases by shifting
cluster resources towards users who are currently executing scalable code. In
our own testing, we have encountered a number of issues with using the current
implementation of dynamic allocation for this purpose:
*Issue #1: Starvation.* A Spark job acquires all available YARN containers,
preventing other jobs or applications from starting.
*Issue #2: Request latency.* Jobs that would normally finish in less than 30
seconds take 2-4x longer than normal with dynamic allocation.
*Issue #3: Unfair resource allocation due to cached data.* Applications that
have cached RDD partitions hold onto executors indefinitely, denying those
resources to other applications.
*Issue #4: Loss of cached data leads to thrashing.* Applications repeatedly
lose partitions of cached RDDs because the underlying executors are removed;
the applications then need to rerun expensive computations.
This umbrella JIRA covers efforts to address these issues by making
enhancements to Spark.
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