[
https://issues.apache.org/jira/browse/SPARK-8638?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Herman van Hovell tot Westerflier updated SPARK-8638:
-----------------------------------------------------
Description:
Improve the performance of Spark Window Functions in the following cases:
# Much better performance (10x) in the running case (e.g. BETWEEN UNBOUNDED
PRECEDING AND CURRENT ROW). The current implementation in spark uses a sliding
window approach in these cases. This means that an aggregate is maintained for
every row, so space usage is N (N being the number of rows). This also means
that all these aggregates all need to be updated separately, this takes
N*(N-1)/2 updates. The running case differs from the Sliding case because we
are only adding data to an aggregate function (no reset is required), we only
need to maintain one aggregate (like in the UNBOUNDED PRECEDING AND UNBOUNDED
case), update the aggregate for each row, and get the aggregate value after
each update. This is what the new implementation does. This approach only uses
1 buffer, and only requires N updates; I am currently working on data with
window sizes of 500-1000 doing running sums and this saves a lot of time.
#. Fewer comparisons in the sliding case. The current implementation determines
frame boundaries for every input row. The new implementation makes more use of
the fact that the window is sorted, maintains the boundaries, and only moves
them when the current row order changes. This is a minor improvement.
# A single Window node is able to process all types of Frames for the same
Partitioning/Ordering. This saves a little time/memory spent buffering and
managing partitions. This will be enabled in a follow-up PR.
# A lot of the staging code is moved from the execution phase to the
initialization phase. Minor performance improvement, and improves readability
of the execution code.
The attached perf_test.scala file contains s number of queries which can be
used to measure the differences between the current and the proposed window
function implementation. In the tests the new implementation outperforms the
current implementation by a factor 7x in sliding window cases, and by a factor
14x in the running window cases.
was:
Improve the performance of Spark Window Functions in the following cases:
# Much better performance (10x) in running cases (e.g. BETWEEN UNBOUNDED
PRECEDING AND CURRENT ROW) and UNBOUDED FOLLOWING cases. The current
implementation in spark uses a sliding window approach in these cases. This
means that an aggregate is maintained for every row, so space usage is N (N
being the number of rows). This also means that all these aggregates all need
to be updated separately, this takes N*(N-1)/2 updates. The running case
differs from the Sliding case because we are only adding data to an aggregate
function (no reset is required), we only need to maintain one aggregate (like
in the UNBOUNDED PRECEDING AND UNBOUNDED case), update the aggregate for each
row, and get the aggregate value after each update. This is what the new
implementation does. This approach only uses 1 buffer, and only requires N
updates; I am currently working on data with window sizes of 500-1000 doing
running sums and this saves a lot of time. The CURRENT ROW AND UNBOUNDED
FOLLOWING case also uses this approach and the fact that aggregate operations
are communitative, there is one twist though it will process the input buffer
in reverse.
#. Fewer comparisons in the sliding case. The current implementation determines
frame boundaries for every input row. The new implementation makes more use of
the fact that the window is sorted, maintains the boundaries, and only moves
them when the current row order changes. This is a minor improvement.
# A single Window node is able to process all types of Frames for the same
Partitioning/Ordering. This saves a little time/memory spent buffering and
managing partitions. This will be enabled in a follow-up PR.
# A lot of the staging code is moved from the execution phase to the
initialization phase. Minor performance improvement, and improves readability
of the execution code.
The attached perf_test.scala file contains s number of queries which can be
used to measure the differences between the current and the proposed window
function implementation. In the tests the new implementation outperforms the
current implementation by a factor 7x in sliding window cases, and by a factor
14x in the running window cases.
> Window Function Performance Improvements
> ----------------------------------------
>
> Key: SPARK-8638
> URL: https://issues.apache.org/jira/browse/SPARK-8638
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Reporter: Herman van Hovell tot Westerflier
> Attachments: perf_test.scala
>
>
> Improve the performance of Spark Window Functions in the following cases:
> # Much better performance (10x) in the running case (e.g. BETWEEN UNBOUNDED
> PRECEDING AND CURRENT ROW). The current implementation in spark uses a
> sliding window approach in these cases. This means that an aggregate is
> maintained for every row, so space usage is N (N being the number of rows).
> This also means that all these aggregates all need to be updated separately,
> this takes N*(N-1)/2 updates. The running case differs from the Sliding case
> because we are only adding data to an aggregate function (no reset is
> required), we only need to maintain one aggregate (like in the UNBOUNDED
> PRECEDING AND UNBOUNDED case), update the aggregate for each row, and get the
> aggregate value after each update. This is what the new implementation does.
> This approach only uses 1 buffer, and only requires N updates; I am currently
> working on data with window sizes of 500-1000 doing running sums and this
> saves a lot of time.
> #. Fewer comparisons in the sliding case. The current implementation
> determines frame boundaries for every input row. The new implementation makes
> more use of the fact that the window is sorted, maintains the boundaries, and
> only moves them when the current row order changes. This is a minor
> improvement.
> # A single Window node is able to process all types of Frames for the same
> Partitioning/Ordering. This saves a little time/memory spent buffering and
> managing partitions. This will be enabled in a follow-up PR.
> # A lot of the staging code is moved from the execution phase to the
> initialization phase. Minor performance improvement, and improves readability
> of the execution code.
> The attached perf_test.scala file contains s number of queries which can be
> used to measure the differences between the current and the proposed window
> function implementation. In the tests the new implementation outperforms the
> current implementation by a factor 7x in sliding window cases, and by a
> factor 14x in the running window cases.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
