Xuefu Zhang created SPARK-22765:
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Summary: Create a new executor allocation scheme based on that of
MR
Key: SPARK-22765
URL: https://issues.apache.org/jira/browse/SPARK-22765
Project: Spark
Issue Type: Improvement
Components: Scheduler
Affects Versions: 1.6.0
Reporter: Xuefu Zhang
Many users migrating their workload from MR to Spark find a significant
resource consumption hike (i.e, SPARK-22683). While this might not be a concern
for users that are more performance centric, for others conscious about cost,
such hike creates a migration obstacle. This situation can get worse as more
users are moving to cloud.
Dynamic allocation make it possible for Spark to be deployed in multi-tenant
environment. With its performance-centric design, its inefficiency has also
unfortunately shown up, especially when compared with MR. Thus, it's believed
that MR-styled scheduler still has its merit. Based on our research, the
inefficiency associated with dynamic allocation comes in many aspects such as
executor idling out, bigger executors, many stages (rather than 2 stages only
in MR) in a spark job, etc.
Rather than fine tuning dynamic allocation for efficiency, the proposal here is
to add a new, efficiency-centric scheduling scheme based on that of MR. Such a
MR-based scheme can be further enhanced and be more adapted to Spark execution
model. This alternative is expected to offer good performance improvement
(compared to MR) still with similar to or even better efficiency than MR.
Inputs are greatly welcome!
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