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From Maxim Khutornenko <>
Subject Re: Review Request 51759: Batching writes - Part 1 (of 3): Introducing BatchWorker and task event batching.
Date Wed, 14 Sep 2016 17:25:24 GMT

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(Updated Sept. 14, 2016, 5:25 p.m.)

Review request for Aurora, Joshua Cohen, Stephan Erb, and Zameer Manji.


Renaming `readyItems` to `batch`.

Repository: aurora


This is the first (out of 3) patches intending to reduce storage write lock contention and
as such improve overall system write throughput. It introduces the `BatchWorker` and migrates
the majority of storage writes due to task status change events to use `TaskEventBatchWorker`.

Our current storage system writes effectively behave as `SERIALIZABLE` transaction isolation
level in SQL terms. This means all writes require exclusive access to the storage and no two
transactions can happen in parallel [1]. While it certainly simplifies our implementation,
it creates a single hotspot where multiple threads are competing for the storage write access.
This type of contention only worsens as the cluster size grows, more tasks are scheduled,
more status updates are processed, more subscribers are listening to status updates and etc.
Eventually, the scheduler throughput (and especially task scheduling) becomes degraded to
the extent that certain operations wait much longer (4x and more) for the lock acquisition
than it takes to process their payload when inside the transaction. Some ops (like event processing)
are generally tolerant of these types of delays. Others - not as much. The task scheduling
suffers the most as backing up the scheduling queue directly affects t
 he Median Time To Assigned (MTTA).

Given the above, it's natural to assume that reducing the number of write transactions should
help reducing the lock contention. This patch introduces a generic `BatchWorker` service that
delivers a "best effort" batching approach by redirecting multiple individual write requests
into a single FIFO queue served non-stop by a single dedicated thread. Every batch shares
a single write transaction thus reducing the number of potential write lock requests. To minimize
wait-in-queue time, items are dispatched immediately and the max number of items is bounded.
There are a few `BatchWorker` instances specialized on particular workload types: task even
processing, cron scheduling and task scheduling. Every instance can be tuned independently
(max batch size) and provides specialized metrics helping to monitor each workload type perf.

The proposed approach has been heavily tested in production and delivered the best results.
The lock contention latencies got down between 2x and 5x depending on the cluster load. A
number of other approaches tried but discarded as not performing well or even performing much
worse than the current master:
- Clock-driven batch execution - every batch is dispatched on a time schedule
- Max batch with a deadline - a batch is dispatched when max size is reached OR a timeout
- Various combinations of the above - some `BatchWorkers` are using clock-driven execution
while others are using max batch with a deadline
- Completely non-blocking (event-based) completion notification - all call sites are notified
of item completion via a `BatchWorkCompleted` event

Happy to provide more details on the above if interested.

The introduction of the `BatchWorker` by itself was not enough to substantially improve the
MTTA. It, however, paves the way for the next phase of scheduling perf improvement - taking
more than 1 task from a given `TaskGroup` in a single scheduling round (coming soon). That
improvement wouldn't deliver without decreasing the lock contention first. 

Note: it wasn't easy to have a clean diff split, so some functionality in `BatchWorker` (e.g.:
`executeWithReplay`) appears to be unused in the current patch but will become obvious in
the part 2 (coming out shortly).  

[1] -

Diffs (updated)

  src/main/java/org/apache/aurora/scheduler/ PRE-CREATION 
  src/main/java/org/apache/aurora/scheduler/ 4a7ef0b1b90607f68d89fe8e207f42c42a8c56a0

  src/main/java/org/apache/aurora/scheduler/pruning/ f07746c2b990c1c2235e99f9e4775fc84f9c27b1

  src/main/java/org/apache/aurora/scheduler/scheduling/ bbd971a2aa8a96cf79edd879ad60e1bebd933d79

  src/main/java/org/apache/aurora/scheduler/state/ 3c7cda09ab292d696070ca4d9dfedb1f6f71b0fe

  src/main/java/org/apache/aurora/scheduler/updater/ 594bb6219294dcc77d48dcad14e2a6f9caa0c534

  src/test/java/org/apache/aurora/scheduler/ PRE-CREATION 
  src/test/java/org/apache/aurora/scheduler/pruning/ 99c27e8012f10a67ce5f1b84d258e7a5608995c7

  src/test/java/org/apache/aurora/scheduler/scheduling/ 7d104aa2ea4a4d99be4711f666d18beca238284e

  src/test/java/org/apache/aurora/scheduler/state/ 94f5ca565476f62d72879837a0e7dafabcf30432

  src/test/java/org/apache/aurora/scheduler/testing/ PRE-CREATION 
  src/test/java/org/apache/aurora/scheduler/updater/ 196df4754b553f05e50b66ad2f84271901bc9eba



All types of testing including deploying to test and production clusters.


Maxim Khutornenko

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