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SKIPME Release csd 1.1 cdh 5.3.2 #44
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markhamstra
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SKIPME Release csd 1.1 cdh 5.3.2
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Apr 18, 2015
…ussian Similarity Function Add single pseudo-eigenvector PIC Including documentations and updated pom.xml with the following codes: mllib/src/main/scala/org/apache/spark/mllib/clustering/PIClustering.scala mllib/src/test/scala/org/apache/spark/mllib/clustering/PIClusteringSuite.scala Author: sboeschhuawei <stephen.boesch@huawei.com> Author: Fan Jiang <fanjiang.sc@huawei.com> Author: Jiang Fan <fjiang6@gmail.com> Author: Stephen Boesch <stephen.boesch@huawei.com> Author: Xiangrui Meng <meng@databricks.com> Closes apache#4254 from fjiang6/PIC and squashes the following commits: 4550850 [sboeschhuawei] Removed pic test data f292f31 [Stephen Boesch] Merge pull request #44 from mengxr/SPARK-4259 4b78aaf [Xiangrui Meng] refactor PIC 24fbf52 [sboeschhuawei] Updated API to be similar to KMeans plus other changes requested by Xiangrui on the PR c12dfc8 [sboeschhuawei] Removed examples files and added pic_data.txt. Revamped testcases yet to come 92d4752 [sboeschhuawei] Move the Guassian/ Affinity matrix calcs out of PIC. Presently in the test suite 7ebd149 [sboeschhuawei] Incorporate Xiangrui's first set of PR comments except restructure PIC.run to take Graph but do not remove Gaussian 121e4d5 [sboeschhuawei] Remove unused testing data files 1c3a62e [sboeschhuawei] removed matplot.py and reordered all private methods to bottom of PIC 218a49d [sboeschhuawei] Applied Xiangrui's comments - especially removing RDD/PICLinalg classes and making noncritical methods private 43ab10b [sboeschhuawei] Change last two println's to log4j logger 88aacc8 [sboeschhuawei] Add assert to testcase on cluster sizes 24f438e [sboeschhuawei] fixed incorrect markdown in clustering doc 060e6bf [sboeschhuawei] Added link to PIC doc from the main clustering md doc be659e3 [sboeschhuawei] Added mllib specific log4j 90e7fa4 [sboeschhuawei] Converted from custom Linalg routines to Breeze: added JavaDoc comments; added Markdown documentation bea48ea [sboeschhuawei] Converted custom Linear Algebra datatypes/routines to use Breeze. b29c0db [Fan Jiang] Update PIClustering.scala ace9749 [Fan Jiang] Update PIClustering.scala a112f38 [sboeschhuawei] Added graphx main and test jars as dependencies to mllib/pom.xml f656c34 [sboeschhuawei] Added iris dataset b7dbcbe [sboeschhuawei] Added axes and combined into single plot for matplotlib a2b1e57 [sboeschhuawei] Revert inadvertent update to KMeans 9294263 [sboeschhuawei] Added visualization/plotting of input/output data e5df2b8 [sboeschhuawei] First end to end working PIC 0700335 [sboeschhuawei] First end to end working version: but has bad performance issue 32a90dc [sboeschhuawei] Update circles test data values 0ef163f [sboeschhuawei] Added ConcentricCircles data generation and KMeans clustering 3fd5bc8 [sboeschhuawei] PIClustering is running in new branch (up to the pseudo-eigenvector convergence step) d5aae20 [Jiang Fan] Adding Power Iteration Clustering and Suite test a3c5fbe [Jiang Fan] Adding Power Iteration Clustering
markhamstra
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Apr 27, 2016
…w queries ## What changes were proposed in this pull request? This PR aims to implement decimal aggregation optimization for window queries by improving existing `DecimalAggregates`. Historically, `DecimalAggregates` optimizer is designed to transform general `sum/avg(decimal)`, but it breaks recently added windows queries like the followings. The following queries work well without the current `DecimalAggregates` optimizer. **Sum** ```scala scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").head java.lang.RuntimeException: Unsupported window function: MakeDecimal((sum(UnscaledValue(a#31)),mode=Complete,isDistinct=false),12,1) scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").explain() == Physical Plan == WholeStageCodegen : +- Project [sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)alteryx#23] : +- INPUT +- Window [MakeDecimal((sum(UnscaledValue(a#21)),mode=Complete,isDistinct=false),12,1) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)alteryx#23] +- Exchange SinglePartition, None +- Generate explode([1.0,2.0]), false, false, [a#21] +- Scan OneRowRelation[] ``` **Average** ```scala scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").head java.lang.RuntimeException: Unsupported window function: cast(((avg(UnscaledValue(a#40)),mode=Complete,isDistinct=false) / 10.0) as decimal(6,5)) scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").explain() == Physical Plan == WholeStageCodegen : +- Project [avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)alteryx#44] : +- INPUT +- Window [cast(((avg(UnscaledValue(a#42)),mode=Complete,isDistinct=false) / 10.0) as decimal(6,5)) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)alteryx#44] +- Exchange SinglePartition, None +- Generate explode([1.0,2.0]), false, false, [a#42] +- Scan OneRowRelation[] ``` After this PR, those queries work fine and new optimized physical plans look like the followings. **Sum** ```scala scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").explain() == Physical Plan == WholeStageCodegen : +- Project [sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)alteryx#35] : +- INPUT +- Window [MakeDecimal((sum(UnscaledValue(a#33)),mode=Complete,isDistinct=false) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING),12,1) AS sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)alteryx#35] +- Exchange SinglePartition, None +- Generate explode([1.0,2.0]), false, false, [a#33] +- Scan OneRowRelation[] ``` **Average** ```scala scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").explain() == Physical Plan == WholeStageCodegen : +- Project [avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)alteryx#47] : +- INPUT +- Window [cast(((avg(UnscaledValue(a#45)),mode=Complete,isDistinct=false) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) / 10.0) as decimal(6,5)) AS avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)alteryx#47] +- Exchange SinglePartition, None +- Generate explode([1.0,2.0]), false, false, [a#45] +- Scan OneRowRelation[] ``` In this PR, *SUM over window* pattern matching is based on the code of hvanhovell ; he should be credited for the work he did. ## How was this patch tested? Pass the Jenkins tests (with newly added testcases) Author: Dongjoon Hyun <dongjoon@apache.org> Closes apache#12421 from dongjoon-hyun/SPARK-14664.
markhamstra
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Nov 7, 2017
* Fixed k8s integration test - Enable spark ui explicitly for in-process submit - Fixed some broken assertions in integration tests - Fixed a scalastyle error in SparkDockerImageBuilder.scala - Log into target/integration-tests.log like other modules * Fixed line length. * CR
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@markhamstra merge the release branch back