Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
- As usual the license and signatures are OK - No blockers, check - 9 "Critical" bugs for 1.5.0 are listed below just for everyone's reference (48 total issues still targeted for 1.5.0) - Under Java 7 + Ubuntu 15, I only had one consistent test failure, but obviously it's not failing in Jenkins - I saw more test failures with Java 8 but the seemed like flaky tests, so am pretending I didn't see that Test failure DirectKafkaStreamSuite: - offset recovery *** FAILED *** The code passed to eventually never returned normally. Attempted 197 times over 10.012973046 seconds. Last failure message: strings.forall({ ((elem: Any) => DirectKafkaStreamSuite.collectedData.contains(elem)) }) was false. (DirectKafkaStreamSuite.scala:249) 1.5.0 Critical Bugs SPARK-6484 Spark Core Ganglia metrics xml reporter doesn't escape correctly Josh Rosen SPARK-6701 Tests, YARN Flaky test: o.a.s.deploy.yarn.YarnClusterSuite Python application SPARK-7420 Tests Flaky test: o.a.s.streaming.JobGeneratorSuite "Do not clear received block data too soon" Tathagata Das SPARK-8119 Spark Core HeartbeatReceiver should not adjust application executor resources Andrew Or SPARK-8414 Spark Core Ensure ContextCleaner actually triggers clean ups Andrew Or SPARK-8447 Shuffle Test external shuffle service with all shuffle managers SPARK-10224 Streaming BlockGenerator may lost data in the last block SPARK-10310 SQL [Spark SQL] All result records will be popluated into ONE line during the script transform due to missing the correct line/filed delimeter SPARK-10337 SQL Views are broken On Tue, Sep 1, 2015 at 9:41 PM, Reynold Xin wrote: > Please vote on releasing the following candidate as Apache Spark version > 1.5.0. The vote is open until Friday, Sep 4, 2015 at 21:00 UTC and passes if > a majority of at least 3 +1 PMC votes are cast. > > [ ] +1 Release this package as Apache Spark 1.5.0 > [ ] -1 Do not release this package because ... > > To learn more about Apache Spark, please see http://spark.apache.org/ > > > The tag to be voted on is v1.5.0-rc3: > https://github.com/apache/spark/commit/908e37bcc10132bb2aa7f80ae694a9df6e40f31a > > The release files, including signatures, digests, etc. can be found at: > http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-bin/ > > Release artifacts are signed with the following key: > https://people.apache.org/keys/committer/pwendell.asc > > The staging repository for this release (published as 1.5.0-rc3) can be > found at: > https://repository.apache.org/content/repositories/orgapachespark-1143/ > > The staging repository for this release (published as 1.5.0) can be found > at: > https://repository.apache.org/content/repositories/orgapachespark-1142/ > > The documentation corresponding to this release can be found at: > http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-docs/ > > > === > How can I help test this release? > === > If you are a Spark user, you can help us test this release by taking an > existing Spark workload and running on this release candidate, then > reporting any regressions. > > > > What justifies a -1 vote for this release? > > This vote is happening towards the end of the 1.5 QA period, so -1 votes > should only occur for significant regressions from 1.4. Bugs already present > in 1.4, minor regressions, or bugs related to new features will not block > this release. > > > === > What should happen to JIRA tickets still targeting 1.5.0? > === > 1. It is OK for documentation patches to target 1.5.0 and still go into > branch-1.5, since documentations will be packaged separately from the > release. > 2. New features for non-alpha-modules should target 1.6+. > 3. Non-blocker bug fixes should target 1.5.1 or 1.6.0, or drop the target > version. > > > == > Major changes to help you focus your testing > == > > As of today, Spark 1.5 contains more than 1000 commits from 220+ > contributors. I've curated a list of important changes for 1.5. For the > complete list, please refer to Apache JIRA changelog. > > RDD/DataFrame/SQL APIs > > - New UDAF interface > - DataFrame hints for broadcast join > - expr function for turning a SQL expression into DataFrame column > - Improved support for NaN values > - StructType now supports ordering > - TimestampType precision is reduced to 1us > - 100 new built-in expressions, including date/time, string, math > - memory and local disk only checkpointing > > DataFrame/SQL Backend Execution > > - Code generation on by default > - Improved join, aggregation, shuffle, sorting with cache friendly > algorithms and external algorithms > - Improved wind
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
+1. Tested on Yarn with Hadoop 2.6. A few of the things tested: pyspark, hive integration, aux shuffle handler, history server, basic submit cli behavior, distributed cache behavior, cluster and client mode... Tom On Tuesday, September 1, 2015 3:42 PM, Reynold Xin wrote: Please vote on releasing the following candidate as Apache Spark version 1.5.0. The vote is open until Friday, Sep 4, 2015 at 21:00 UTC and passes if a majority of at least 3 +1 PMC votes are cast. [ ] +1 Release this package as Apache Spark 1.5.0[ ] -1 Do not release this package because ... To learn more about Apache Spark, please see http://spark.apache.org/ The tag to be voted on is v1.5.0-rc3:https://github.com/apache/spark/commit/908e37bcc10132bb2aa7f80ae694a9df6e40f31a The release files, including signatures, digests, etc. can be found at:http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-bin/ Release artifacts are signed with the following key:https://people.apache.org/keys/committer/pwendell.asc The staging repository for this release (published as 1.5.0-rc3) can be found at:https://repository.apache.org/content/repositories/orgapachespark-1143/ The staging repository for this release (published as 1.5.0) can be found at:https://repository.apache.org/content/repositories/orgapachespark-1142/ The documentation corresponding to this release can be found at:http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-docs/ ===How can I help test this release?===If you are a Spark user, you can help us test this release by taking an existing Spark workload and running on this release candidate, then reporting any regressions. What justifies a -1 vote for this release?This vote is happening towards the end of the 1.5 QA period, so -1 votes should only occur for significant regressions from 1.4. Bugs already present in 1.4, minor regressions, or bugs related to new features will not block this release. ===What should happen to JIRA tickets still targeting 1.5.0?===1. It is OK for documentation patches to target 1.5.0 and still go into branch-1.5, since documentations will be packaged separately from the release.2. New features for non-alpha-modules should target 1.6+.3. Non-blocker bug fixes should target 1.5.1 or 1.6.0, or drop the target version. ==Major changes to help you focus your testing== As of today, Spark 1.5 contains more than 1000 commits from 220+ contributors. I've curated a list of important changes for 1.5. For the complete list, please refer to Apache JIRA changelog. RDD/DataFrame/SQL APIs - New UDAF interface- DataFrame hints for broadcast join- expr function for turning a SQL expression into DataFrame column- Improved support for NaN values- StructType now supports ordering- TimestampType precision is reduced to 1us- 100 new built-in expressions, including date/time, string, math- memory and local disk only checkpointing DataFrame/SQL Backend Execution - Code generation on by default- Improved join, aggregation, shuffle, sorting with cache friendly algorithms and external algorithms- Improved window function performance- Better metrics instrumentation and reporting for DF/SQL execution plans Data Sources, Hive, Hadoop, Mesos and Cluster Management - Dynamic allocation support in all resource managers (Mesos, YARN, Standalone)- Improved Mesos support (framework authentication, roles, dynamic allocation, constraints)- Improved YARN support (dynamic allocation with preferred locations)- Improved Hive support (metastore partition pruning, metastore connectivity to 0.13 to 1.2, internal Hive upgrade to 1.2)- Support persisting data in Hive compatible format in metastore- Support data partitioning for JSON data sources- Parquet improvements (upgrade to 1.7, predicate pushdown, faster metadata discovery and schema merging, support reading non-standard legacy Parquet files generated by other libraries)- Faster and more robust dynamic partition insert- DataSourceRegister interface for external data sources to specify short names SparkR - YARN cluster mode in R- GLMs with R formula, binomial/Gaussian families, and elastic-net regularization- Improved error messages- Aliases to make DataFrame functions more R-like Streaming - Backpressure for handling bursty input streams.- Improved Python support for streaming sources (Kafka offsets, Kinesis, MQTT, Flume)- Improved Python streaming machine learning algorithms (K-Means, linear regression, logistic regression)- Native reliable Kinesis stream support- Input metadata like Kafka offsets made visible in the batch det
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
Built and tested on CentOS 7, Hadoop 2.7.1 (Built for 2.6 profile), Standalone without any problems. Re-tested dynamic allocation specifically. "Lost executor" messages are still an annoyance since they're expected to occur with dynamic allocation, and shouldn't WARN/ERROR as they do now, however there's already a JIRA ticket for it: https://issues.apache.org/jira/browse/SPARK-4134 . Will probably have to filter these messages out in log4j properties for this release! Mark. -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC3-tp13928p13948.html Sent from the Apache Spark Developers List mailing list archive at Nabble.com. - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
+1. Tested complex R package support (Scala + R code), BLAS and DataFrame fixes good. Burak On Thu, Sep 3, 2015 at 8:56 AM, mkhaitman wrote: > Built and tested on CentOS 7, Hadoop 2.7.1 (Built for 2.6 profile), > Standalone without any problems. Re-tested dynamic allocation specifically. > > "Lost executor" messages are still an annoyance since they're expected to > occur with dynamic allocation, and shouldn't WARN/ERROR as they do now, > however there's already a JIRA ticket for it: > https://issues.apache.org/jira/browse/SPARK-4134 . Will probably have to > filter these messages out in log4j properties for this release! > > Mark. > > > > -- > View this message in context: > http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC3-tp13928p13948.html > Sent from the Apache Spark Developers List mailing list archive at > Nabble.com. > > - > To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org > For additional commands, e-mail: dev-h...@spark.apache.org > >
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
+1 Ran TPC-DS and ported several jobs over to 1.5 On Thu, Sep 3, 2015 at 9:57 AM, Burak Yavuz wrote: > +1. Tested complex R package support (Scala + R code), BLAS and DataFrame > fixes good. > > Burak > > On Thu, Sep 3, 2015 at 8:56 AM, mkhaitman > wrote: > >> Built and tested on CentOS 7, Hadoop 2.7.1 (Built for 2.6 profile), >> Standalone without any problems. Re-tested dynamic allocation >> specifically. >> >> "Lost executor" messages are still an annoyance since they're expected to >> occur with dynamic allocation, and shouldn't WARN/ERROR as they do now, >> however there's already a JIRA ticket for it: >> https://issues.apache.org/jira/browse/SPARK-4134 . Will probably have to >> filter these messages out in log4j properties for this release! >> >> Mark. >> >> >> >> -- >> View this message in context: >> http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC3-tp13928p13948.html >> Sent from the Apache Spark Developers List mailing list archive at >> Nabble.com. >> >> - >> To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org >> For additional commands, e-mail: dev-h...@spark.apache.org >> >> >
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
+1, built 1.5 from source and ran TPC-DS locally and clusters, ran performance benchmark for aggregation and join with difference scales, all worked well. On Thu, Sep 3, 2015 at 10:05 AM, Michael Armbrust wrote: > +1 Ran TPC-DS and ported several jobs over to 1.5 > > On Thu, Sep 3, 2015 at 9:57 AM, Burak Yavuz wrote: >> >> +1. Tested complex R package support (Scala + R code), BLAS and DataFrame >> fixes good. >> >> Burak >> >> On Thu, Sep 3, 2015 at 8:56 AM, mkhaitman >> wrote: >>> >>> Built and tested on CentOS 7, Hadoop 2.7.1 (Built for 2.6 profile), >>> Standalone without any problems. Re-tested dynamic allocation >>> specifically. >>> >>> "Lost executor" messages are still an annoyance since they're expected to >>> occur with dynamic allocation, and shouldn't WARN/ERROR as they do now, >>> however there's already a JIRA ticket for it: >>> https://issues.apache.org/jira/browse/SPARK-4134 . Will probably have to >>> filter these messages out in log4j properties for this release! >>> >>> Mark. >>> >>> >>> >>> -- >>> View this message in context: >>> http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC3-tp13928p13948.html >>> Sent from the Apache Spark Developers List mailing list archive at >>> Nabble.com. >>> >>> - >>> To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org >>> For additional commands, e-mail: dev-h...@spark.apache.org >>> >> > - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
+1. Compiled on Windows with YARN and Hive. Tested Tungsten aggregation and observed similar (good) performance comparing to 1.4 with unsafe on. Ran a few workloads and tested SparkSQL thrift server -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC3-tp13928p13953.html Sent from the Apache Spark Developers List mailing list archive at Nabble.com. - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
+? 1. Compiled OSX 10.10 (Yosemite) OK Total time: 26:09 min mvn clean package -Pyarn -Phadoop-2.6 -DskipTests 2. Tested pyspark, mllib 2.1. statistics (min,max,mean,Pearson,Spearman) OK 2.2. Linear/Ridge/Laso Regression OK 2.3. Decision Tree, Naive Bayes OK 2.4. KMeans OK Center And Scale OK 2.5. RDD operations OK State of the Union Texts - MapReduce, Filter,sortByKey (word count) 2.6. Recommendation (Movielens medium dataset ~1 M ratings) OK Model evaluation/optimization (rank, numIter, lambda) with itertools OK 3. Scala - MLlib 3.1. statistics (min,max,mean,Pearson,Spearman) OK 3.2. LinearRegressionWithSGD OK 3.3. Decision Tree OK 3.4. KMeans OK 3.5. Recommendation (Movielens medium dataset ~1 M ratings) OK 3.6. saveAsParquetFile OK 3.7. Read and verify the 4.3 save(above) - sqlContext.parquetFile, registerTempTable, sql OK 3.8. result = sqlContext.sql("SELECT OrderDetails.OrderID,ShipCountry,UnitPrice,Qty,Discount FROM Orders INNER JOIN OrderDetails ON Orders.OrderID = OrderDetails.OrderID") OK 4.0. Spark SQL from Python OK 4.1. result = sqlContext.sql("SELECT * from people WHERE State = 'WA'") OK 5.0. Packages 5.1. com.databricks.spark.csv - read/write OK (--packages com.databricks:spark-csv_2.11:1.2.0-s_2.11 didn’t work. But com.databricks:spark-csv_2.11:1.2.0 worked) 6.0. DataFrames 6.1. cast,dtypes OK 6.2. groupBy,avg,crosstab,corr,isNull,na.drop OK 6.3. All joins,sql,set operations,udf OK Two Problems: 1. The synthetic column names are lowercase ( i.e. now ‘sum(OrderPrice)’; previously ‘SUM(OrderPrice)’, now ‘avg(Total)’; previously 'AVG(Total)'). So programs that depend on the case of the synthetic column names would fail. 2. orders_3.groupBy("Year","Month").sum('Total').show() fails with the error ‘java.io.IOException: Unable to acquire 4194304 bytes of memory’ orders_3.groupBy("CustomerID","Year").sum('Total').show() - fails with the same error Is this a known bug ? Cheers P.S: Sorry for the spam, forgot Reply All On Tue, Sep 1, 2015 at 1:41 PM, Reynold Xin wrote: > Please vote on releasing the following candidate as Apache Spark version > 1.5.0. The vote is open until Friday, Sep 4, 2015 at 21:00 UTC and passes > if a majority of at least 3 +1 PMC votes are cast. > > [ ] +1 Release this package as Apache Spark 1.5.0 > [ ] -1 Do not release this package because ... > > To learn more about Apache Spark, please see http://spark.apache.org/ > > > The tag to be voted on is v1.5.0-rc3: > > https://github.com/apache/spark/commit/908e37bcc10132bb2aa7f80ae694a9df6e40f31a > > The release files, including signatures, digests, etc. can be found at: > http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-bin/ > > Release artifacts are signed with the following key: > https://people.apache.org/keys/committer/pwendell.asc > > The staging repository for this release (published as 1.5.0-rc3) can be > found at: > https://repository.apache.org/content/repositories/orgapachespark-1143/ > > The staging repository for this release (published as 1.5.0) can be found > at: > https://repository.apache.org/content/repositories/orgapachespark-1142/ > > The documentation corresponding to this release can be found at: > http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-docs/ > > > === > How can I help test this release? > === > If you are a Spark user, you can help us test this release by taking an > existing Spark workload and running on this release candidate, then > reporting any regressions. > > > > What justifies a -1 vote for this release? > > This vote is happening towards the end of the 1.5 QA period, so -1 votes > should only occur for significant regressions from 1.4. Bugs already > present in 1.4, minor regressions, or bugs related to new features will not > block this release. > > > === > What should happen to JIRA tickets still targeting 1.5.0? > === > 1. It is OK for documentation patches to target 1.5.0 and still go into > branch-1.5, since documentations will be packaged separately from the > release. > 2. New features for non-alpha-modules should target 1.6+. > 3. Non-blocker bug fixes should target 1.5.1 or 1.6.0, or drop the target > version. > > > == > Major changes to help you focus your testing > == > > As of today, Spark 1.5 contains more than 1000 commits from 220+ > contributors. I've curated a list of important changes for 1.5. For the > complete list, please refer to Apache JIRA changelog. > > RDD/DataFrame/SQL APIs > > - New UDAF interface > - DataFrame hints for broadcast join > - expr function for turning a SQL expression into DataFrame column
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
+1 Distinct count test is blazing fast - awesome!, On Thu, Sep 3, 2015 at 8:21 PM Krishna Sankar wrote: > +? > > 1. Compiled OSX 10.10 (Yosemite) OK Total time: 26:09 min > mvn clean package -Pyarn -Phadoop-2.6 -DskipTests > 2. Tested pyspark, mllib > 2.1. statistics (min,max,mean,Pearson,Spearman) OK > 2.2. Linear/Ridge/Laso Regression OK > 2.3. Decision Tree, Naive Bayes OK > 2.4. KMeans OK >Center And Scale OK > 2.5. RDD operations OK > State of the Union Texts - MapReduce, Filter,sortByKey (word count) > 2.6. Recommendation (Movielens medium dataset ~1 M ratings) OK >Model evaluation/optimization (rank, numIter, lambda) with > itertools OK > 3. Scala - MLlib > 3.1. statistics (min,max,mean,Pearson,Spearman) OK > 3.2. LinearRegressionWithSGD OK > 3.3. Decision Tree OK > 3.4. KMeans OK > 3.5. Recommendation (Movielens medium dataset ~1 M ratings) OK > 3.6. saveAsParquetFile OK > 3.7. Read and verify the 4.3 save(above) - sqlContext.parquetFile, > registerTempTable, sql OK > 3.8. result = sqlContext.sql("SELECT > OrderDetails.OrderID,ShipCountry,UnitPrice,Qty,Discount FROM Orders INNER > JOIN OrderDetails ON Orders.OrderID = OrderDetails.OrderID") OK > 4.0. Spark SQL from Python OK > 4.1. result = sqlContext.sql("SELECT * from people WHERE State = 'WA'") OK > 5.0. Packages > 5.1. com.databricks.spark.csv - read/write OK > (--packages com.databricks:spark-csv_2.11:1.2.0-s_2.11 didn’t work. But > com.databricks:spark-csv_2.11:1.2.0 worked) > 6.0. DataFrames > 6.1. cast,dtypes OK > 6.2. groupBy,avg,crosstab,corr,isNull,na.drop OK > 6.3. All joins,sql,set operations,udf OK > > Two Problems: > > 1. The synthetic column names are lowercase ( i.e. now ‘sum(OrderPrice)’; > previously ‘SUM(OrderPrice)’, now ‘avg(Total)’; previously 'AVG(Total)'). > So programs that depend on the case of the synthetic column names would > fail. > 2. orders_3.groupBy("Year","Month").sum('Total').show() > fails with the error ‘java.io.IOException: Unable to acquire 4194304 > bytes of memory’ > orders_3.groupBy("CustomerID","Year").sum('Total').show() - fails with > the same error > Is this a known bug ? > Cheers > > P.S: Sorry for the spam, forgot Reply All > > On Tue, Sep 1, 2015 at 1:41 PM, Reynold Xin wrote: > >> Please vote on releasing the following candidate as Apache Spark version >> 1.5.0. The vote is open until Friday, Sep 4, 2015 at 21:00 UTC and passes >> if a majority of at least 3 +1 PMC votes are cast. >> >> [ ] +1 Release this package as Apache Spark 1.5.0 >> [ ] -1 Do not release this package because ... >> >> To learn more about Apache Spark, please see http://spark.apache.org/ >> >> >> The tag to be voted on is v1.5.0-rc3: >> >> https://github.com/apache/spark/commit/908e37bcc10132bb2aa7f80ae694a9df6e40f31a >> >> The release files, including signatures, digests, etc. can be found at: >> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-bin/ >> >> Release artifacts are signed with the following key: >> https://people.apache.org/keys/committer/pwendell.asc >> >> The staging repository for this release (published as 1.5.0-rc3) can be >> found at: >> https://repository.apache.org/content/repositories/orgapachespark-1143/ >> >> The staging repository for this release (published as 1.5.0) can be found >> at: >> https://repository.apache.org/content/repositories/orgapachespark-1142/ >> >> The documentation corresponding to this release can be found at: >> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-docs/ >> >> >> === >> How can I help test this release? >> === >> If you are a Spark user, you can help us test this release by taking an >> existing Spark workload and running on this release candidate, then >> reporting any regressions. >> >> >> >> What justifies a -1 vote for this release? >> >> This vote is happening towards the end of the 1.5 QA period, so -1 votes >> should only occur for significant regressions from 1.4. Bugs already >> present in 1.4, minor regressions, or bugs related to new features will not >> block this release. >> >> >> === >> What should happen to JIRA tickets still targeting 1.5.0? >> === >> 1. It is OK for documentation patches to target 1.5.0 and still go into >> branch-1.5, since documentations will be packaged separately from the >> release. >> 2. New features for non-alpha-modules should target 1.6+. >> 3. Non-blocker bug fixes should target 1.5.1 or 1.6.0, or drop the target >> version. >> >> >> == >> Major changes to help you focus your testing >> == >> >> As of today, Spark 1.5 contains more than 1000 commits from 220+ >> contrib
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
The upper/lower case thing is known. https://issues.apache.org/jira/browse/SPARK-9550I assume it was decided to be ok and its going to be in the release notes but Reynold or Josh can probably speak to it more. Tom On Thursday, September 3, 2015 10:21 PM, Krishna Sankar wrote: +? 1. Compiled OSX 10.10 (Yosemite) OK Total time: 26:09 min mvn clean package -Pyarn -Phadoop-2.6 -DskipTests2. Tested pyspark, mllib2.1. statistics (min,max,mean,Pearson,Spearman) OK2.2. Linear/Ridge/Laso Regression OK 2.3. Decision Tree, Naive Bayes OK2.4. KMeans OK Center And Scale OK2.5. RDD operations OK State of the Union Texts - MapReduce, Filter,sortByKey (word count)2.6. Recommendation (Movielens medium dataset ~1 M ratings) OK Model evaluation/optimization (rank, numIter, lambda) with itertools OK3. Scala - MLlib3.1. statistics (min,max,mean,Pearson,Spearman) OK3.2. LinearRegressionWithSGD OK3.3. Decision Tree OK3.4. KMeans OK3.5. Recommendation (Movielens medium dataset ~1 M ratings) OK3.6. saveAsParquetFile OK3.7. Read and verify the 4.3 save(above) - sqlContext.parquetFile, registerTempTable, sql OK3.8. result = sqlContext.sql("SELECT OrderDetails.OrderID,ShipCountry,UnitPrice,Qty,Discount FROM Orders INNER JOIN OrderDetails ON Orders.OrderID = OrderDetails.OrderID") OK4.0. Spark SQL from Python OK4.1. result = sqlContext.sql("SELECT * from people WHERE State = 'WA'") OK5.0. Packages5.1. com.databricks.spark.csv - read/write OK(--packages com.databricks:spark-csv_2.11:1.2.0-s_2.11 didn’t work. But com.databricks:spark-csv_2.11:1.2.0 worked)6.0. DataFrames 6.1. cast,dtypes OK6.2. groupBy,avg,crosstab,corr,isNull,na.drop OK6.3. All joins,sql,set operations,udf OK Two Problems: 1. The synthetic column names are lowercase ( i.e. now ‘sum(OrderPrice)’; previously ‘SUM(OrderPrice)’, now ‘avg(Total)’; previously 'AVG(Total)'). So programs that depend on the case of the synthetic column names would fail.2. orders_3.groupBy("Year","Month").sum('Total').show() fails with the error ‘java.io.IOException: Unable to acquire 4194304 bytes of memory’ orders_3.groupBy("CustomerID","Year").sum('Total').show() - fails with the same error Is this a known bug ?CheersP.S: Sorry for the spam, forgot Reply All On Tue, Sep 1, 2015 at 1:41 PM, Reynold Xin wrote: Please vote on releasing the following candidate as Apache Spark version 1.5.0. The vote is open until Friday, Sep 4, 2015 at 21:00 UTC and passes if a majority of at least 3 +1 PMC votes are cast. [ ] +1 Release this package as Apache Spark 1.5.0[ ] -1 Do not release this package because ... To learn more about Apache Spark, please see http://spark.apache.org/ The tag to be voted on is v1.5.0-rc3:https://github.com/apache/spark/commit/908e37bcc10132bb2aa7f80ae694a9df6e40f31a The release files, including signatures, digests, etc. can be found at:http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-bin/ Release artifacts are signed with the following key:https://people.apache.org/keys/committer/pwendell.asc The staging repository for this release (published as 1.5.0-rc3) can be found at:https://repository.apache.org/content/repositories/orgapachespark-1143/ The staging repository for this release (published as 1.5.0) can be found at:https://repository.apache.org/content/repositories/orgapachespark-1142/ The documentation corresponding to this release can be found at:http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-docs/ ===How can I help test this release?===If you are a Spark user, you can help us test this release by taking an existing Spark workload and running on this release candidate, then reporting any regressions. What justifies a -1 vote for this release?This vote is happening towards the end of the 1.5 QA period, so -1 votes should only occur for significant regressions from 1.4. Bugs already present in 1.4, minor regressions, or bugs related to new features will not block this release. ===What should happen to JIRA tickets still targeting 1.5.0?===1. It is OK for documentation patches to target 1.5.0 and still go into branch-1.5, since documentations will be packaged separately from the release.2. New features for non-alpha-modules should target 1.6+.3. Non-blocker bug fixes should target 1.5.1 or 1.6.0, or drop the target version. ==Major changes to help you focus your testing== As of today, Spark 1.5 contains more than 1000 commits from 220+ contributors. I've curated a list of important changes for 1.5. For the complete list, please refer to Apache JIRA change
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
Thanks Tom. Interestingly it happened between RC2 and RC3. Now my vote is +1/2 unless the memory error is known and has a workaround. Cheers On Fri, Sep 4, 2015 at 7:30 AM, Tom Graves wrote: > The upper/lower case thing is known. > https://issues.apache.org/jira/browse/SPARK-9550 > I assume it was decided to be ok and its going to be in the release notes > but Reynold or Josh can probably speak to it more. > > Tom > > > > On Thursday, September 3, 2015 10:21 PM, Krishna Sankar < > ksanka...@gmail.com> wrote: > > > +? > > 1. Compiled OSX 10.10 (Yosemite) OK Total time: 26:09 min > mvn clean package -Pyarn -Phadoop-2.6 -DskipTests > 2. Tested pyspark, mllib > 2.1. statistics (min,max,mean,Pearson,Spearman) OK > 2.2. Linear/Ridge/Laso Regression OK > 2.3. Decision Tree, Naive Bayes OK > 2.4. KMeans OK >Center And Scale OK > 2.5. RDD operations OK > State of the Union Texts - MapReduce, Filter,sortByKey (word count) > 2.6. Recommendation (Movielens medium dataset ~1 M ratings) OK >Model evaluation/optimization (rank, numIter, lambda) with > itertools OK > 3. Scala - MLlib > 3.1. statistics (min,max,mean,Pearson,Spearman) OK > 3.2. LinearRegressionWithSGD OK > 3.3. Decision Tree OK > 3.4. KMeans OK > 3.5. Recommendation (Movielens medium dataset ~1 M ratings) OK > 3.6. saveAsParquetFile OK > 3.7. Read and verify the 4.3 save(above) - sqlContext.parquetFile, > registerTempTable, sql OK > 3.8. result = sqlContext.sql("SELECT > OrderDetails.OrderID,ShipCountry,UnitPrice,Qty,Discount FROM Orders INNER > JOIN OrderDetails ON Orders.OrderID = OrderDetails.OrderID") OK > 4.0. Spark SQL from Python OK > 4.1. result = sqlContext.sql("SELECT * from people WHERE State = 'WA'") OK > 5.0. Packages > 5.1. com.databricks.spark.csv - read/write OK > (--packages com.databricks:spark-csv_2.11:1.2.0-s_2.11 didn’t work. But > com.databricks:spark-csv_2.11:1.2.0 worked) > 6.0. DataFrames > 6.1. cast,dtypes OK > 6.2. groupBy,avg,crosstab,corr,isNull,na.drop OK > 6.3. All joins,sql,set operations,udf OK > > Two Problems: > > 1. The synthetic column names are lowercase ( i.e. now ‘sum(OrderPrice)’; > previously ‘SUM(OrderPrice)’, now ‘avg(Total)’; previously 'AVG(Total)'). > So programs that depend on the case of the synthetic column names would > fail. > 2. orders_3.groupBy("Year","Month").sum('Total').show() > fails with the error ‘java.io.IOException: Unable to acquire 4194304 > bytes of memory’ > orders_3.groupBy("CustomerID","Year").sum('Total').show() - fails with > the same error > Is this a known bug ? > Cheers > > P.S: Sorry for the spam, forgot Reply All > > On Tue, Sep 1, 2015 at 1:41 PM, Reynold Xin wrote: > > Please vote on releasing the following candidate as Apache Spark version > 1.5.0. The vote is open until Friday, Sep 4, 2015 at 21:00 UTC and passes > if a majority of at least 3 +1 PMC votes are cast. > > [ ] +1 Release this package as Apache Spark 1.5.0 > [ ] -1 Do not release this package because ... > > To learn more about Apache Spark, please see http://spark.apache.org/ > > > The tag to be voted on is v1.5.0-rc3: > > https://github.com/apache/spark/commit/908e37bcc10132bb2aa7f80ae694a9df6e40f31a > > The release files, including signatures, digests, etc. can be found at: > http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-bin/ > > Release artifacts are signed with the following key: > https://people.apache.org/keys/committer/pwendell.asc > > The staging repository for this release (published as 1.5.0-rc3) can be > found at: > https://repository.apache.org/content/repositories/orgapachespark-1143/ > > The staging repository for this release (published as 1.5.0) can be found > at: > https://repository.apache.org/content/repositories/orgapachespark-1142/ > > The documentation corresponding to this release can be found at: > http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-docs/ > > > === > How can I help test this release? > === > If you are a Spark user, you can help us test this release by taking an > existing Spark workload and running on this release candidate, then > reporting any regressions. > > > > What justifies a -1 vote for this release? > > This vote is happening towards the end of the 1.5 QA period, so -1 votes > should only occur for significant regressions from 1.4. Bugs already > present in 1.4, minor regressions, or bugs related to new features will not > block this release. > > > === > What should happen to JIRA tickets still targeting 1.5.0? > === > 1. It is OK for documentation patches to target 1.5.0 and still go into > branch-1.5, since documentations will be packaged separately from the > release. > 2. New features for non-alph
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
Hi Krishna, Can you share your code to reproduce the memory allocation issue? Thanks, Yin On Fri, Sep 4, 2015 at 8:00 AM, Krishna Sankar wrote: > Thanks Tom. Interestingly it happened between RC2 and RC3. > Now my vote is +1/2 unless the memory error is known and has a workaround. > > Cheers > > > > On Fri, Sep 4, 2015 at 7:30 AM, Tom Graves wrote: > >> The upper/lower case thing is known. >> https://issues.apache.org/jira/browse/SPARK-9550 >> I assume it was decided to be ok and its going to be in the release notes >> but Reynold or Josh can probably speak to it more. >> >> Tom >> >> >> >> On Thursday, September 3, 2015 10:21 PM, Krishna Sankar < >> ksanka...@gmail.com> wrote: >> >> >> +? >> >> 1. Compiled OSX 10.10 (Yosemite) OK Total time: 26:09 min >> mvn clean package -Pyarn -Phadoop-2.6 -DskipTests >> 2. Tested pyspark, mllib >> 2.1. statistics (min,max,mean,Pearson,Spearman) OK >> 2.2. Linear/Ridge/Laso Regression OK >> 2.3. Decision Tree, Naive Bayes OK >> 2.4. KMeans OK >>Center And Scale OK >> 2.5. RDD operations OK >> State of the Union Texts - MapReduce, Filter,sortByKey (word count) >> 2.6. Recommendation (Movielens medium dataset ~1 M ratings) OK >>Model evaluation/optimization (rank, numIter, lambda) with >> itertools OK >> 3. Scala - MLlib >> 3.1. statistics (min,max,mean,Pearson,Spearman) OK >> 3.2. LinearRegressionWithSGD OK >> 3.3. Decision Tree OK >> 3.4. KMeans OK >> 3.5. Recommendation (Movielens medium dataset ~1 M ratings) OK >> 3.6. saveAsParquetFile OK >> 3.7. Read and verify the 4.3 save(above) - sqlContext.parquetFile, >> registerTempTable, sql OK >> 3.8. result = sqlContext.sql("SELECT >> OrderDetails.OrderID,ShipCountry,UnitPrice,Qty,Discount FROM Orders INNER >> JOIN OrderDetails ON Orders.OrderID = OrderDetails.OrderID") OK >> 4.0. Spark SQL from Python OK >> 4.1. result = sqlContext.sql("SELECT * from people WHERE State = 'WA'") OK >> 5.0. Packages >> 5.1. com.databricks.spark.csv - read/write OK >> (--packages com.databricks:spark-csv_2.11:1.2.0-s_2.11 didn’t work. But >> com.databricks:spark-csv_2.11:1.2.0 worked) >> 6.0. DataFrames >> 6.1. cast,dtypes OK >> 6.2. groupBy,avg,crosstab,corr,isNull,na.drop OK >> 6.3. All joins,sql,set operations,udf OK >> >> Two Problems: >> >> 1. The synthetic column names are lowercase ( i.e. now ‘sum(OrderPrice)’; >> previously ‘SUM(OrderPrice)’, now ‘avg(Total)’; previously 'AVG(Total)'). >> So programs that depend on the case of the synthetic column names would >> fail. >> 2. orders_3.groupBy("Year","Month").sum('Total').show() >> fails with the error ‘java.io.IOException: Unable to acquire 4194304 >> bytes of memory’ >> orders_3.groupBy("CustomerID","Year").sum('Total').show() - fails >> with the same error >> Is this a known bug ? >> Cheers >> >> P.S: Sorry for the spam, forgot Reply All >> >> On Tue, Sep 1, 2015 at 1:41 PM, Reynold Xin wrote: >> >> Please vote on releasing the following candidate as Apache Spark version >> 1.5.0. The vote is open until Friday, Sep 4, 2015 at 21:00 UTC and passes >> if a majority of at least 3 +1 PMC votes are cast. >> >> [ ] +1 Release this package as Apache Spark 1.5.0 >> [ ] -1 Do not release this package because ... >> >> To learn more about Apache Spark, please see http://spark.apache.org/ >> >> >> The tag to be voted on is v1.5.0-rc3: >> >> https://github.com/apache/spark/commit/908e37bcc10132bb2aa7f80ae694a9df6e40f31a >> >> The release files, including signatures, digests, etc. can be found at: >> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-bin/ >> >> Release artifacts are signed with the following key: >> https://people.apache.org/keys/committer/pwendell.asc >> >> The staging repository for this release (published as 1.5.0-rc3) can be >> found at: >> https://repository.apache.org/content/repositories/orgapachespark-1143/ >> >> The staging repository for this release (published as 1.5.0) can be found >> at: >> https://repository.apache.org/content/repositories/orgapachespark-1142/ >> >> The documentation corresponding to this release can be found at: >> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-docs/ >> >> >> === >> How can I help test this release? >> === >> If you are a Spark user, you can help us test this release by taking an >> existing Spark workload and running on this release candidate, then >> reporting any regressions. >> >> >> >> What justifies a -1 vote for this release? >> >> This vote is happening towards the end of the 1.5 QA period, so -1 votes >> should only occur for significant regressions from 1.4. Bugs already >> present in 1.4, minor regressions, or bugs related to new features will not >> block this release. >> >> >> === >> What should happen to JIRA
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
Krishna - I think the rename happened before rc1 actually. Was done couple months ago. On Fri, Sep 4, 2015 at 5:00 AM, Krishna Sankar wrote: > Thanks Tom. Interestingly it happened between RC2 and RC3. > Now my vote is +1/2 unless the memory error is known and has a workaround. > > Cheers > > > > On Fri, Sep 4, 2015 at 7:30 AM, Tom Graves wrote: > >> The upper/lower case thing is known. >> https://issues.apache.org/jira/browse/SPARK-9550 >> I assume it was decided to be ok and its going to be in the release notes >> but Reynold or Josh can probably speak to it more. >> >> Tom >> >> >> >> On Thursday, September 3, 2015 10:21 PM, Krishna Sankar < >> ksanka...@gmail.com> wrote: >> >> >> +? >> >> 1. Compiled OSX 10.10 (Yosemite) OK Total time: 26:09 min >> mvn clean package -Pyarn -Phadoop-2.6 -DskipTests >> 2. Tested pyspark, mllib >> 2.1. statistics (min,max,mean,Pearson,Spearman) OK >> 2.2. Linear/Ridge/Laso Regression OK >> 2.3. Decision Tree, Naive Bayes OK >> 2.4. KMeans OK >>Center And Scale OK >> 2.5. RDD operations OK >> State of the Union Texts - MapReduce, Filter,sortByKey (word count) >> 2.6. Recommendation (Movielens medium dataset ~1 M ratings) OK >>Model evaluation/optimization (rank, numIter, lambda) with >> itertools OK >> 3. Scala - MLlib >> 3.1. statistics (min,max,mean,Pearson,Spearman) OK >> 3.2. LinearRegressionWithSGD OK >> 3.3. Decision Tree OK >> 3.4. KMeans OK >> 3.5. Recommendation (Movielens medium dataset ~1 M ratings) OK >> 3.6. saveAsParquetFile OK >> 3.7. Read and verify the 4.3 save(above) - sqlContext.parquetFile, >> registerTempTable, sql OK >> 3.8. result = sqlContext.sql("SELECT >> OrderDetails.OrderID,ShipCountry,UnitPrice,Qty,Discount FROM Orders INNER >> JOIN OrderDetails ON Orders.OrderID = OrderDetails.OrderID") OK >> 4.0. Spark SQL from Python OK >> 4.1. result = sqlContext.sql("SELECT * from people WHERE State = 'WA'") OK >> 5.0. Packages >> 5.1. com.databricks.spark.csv - read/write OK >> (--packages com.databricks:spark-csv_2.11:1.2.0-s_2.11 didn’t work. But >> com.databricks:spark-csv_2.11:1.2.0 worked) >> 6.0. DataFrames >> 6.1. cast,dtypes OK >> 6.2. groupBy,avg,crosstab,corr,isNull,na.drop OK >> 6.3. All joins,sql,set operations,udf OK >> >> Two Problems: >> >> 1. The synthetic column names are lowercase ( i.e. now ‘sum(OrderPrice)’; >> previously ‘SUM(OrderPrice)’, now ‘avg(Total)’; previously 'AVG(Total)'). >> So programs that depend on the case of the synthetic column names would >> fail. >> 2. orders_3.groupBy("Year","Month").sum('Total').show() >> fails with the error ‘java.io.IOException: Unable to acquire 4194304 >> bytes of memory’ >> orders_3.groupBy("CustomerID","Year").sum('Total').show() - fails >> with the same error >> Is this a known bug ? >> Cheers >> >> P.S: Sorry for the spam, forgot Reply All >> >> On Tue, Sep 1, 2015 at 1:41 PM, Reynold Xin wrote: >> >> Please vote on releasing the following candidate as Apache Spark version >> 1.5.0. The vote is open until Friday, Sep 4, 2015 at 21:00 UTC and passes >> if a majority of at least 3 +1 PMC votes are cast. >> >> [ ] +1 Release this package as Apache Spark 1.5.0 >> [ ] -1 Do not release this package because ... >> >> To learn more about Apache Spark, please see http://spark.apache.org/ >> >> >> The tag to be voted on is v1.5.0-rc3: >> >> https://github.com/apache/spark/commit/908e37bcc10132bb2aa7f80ae694a9df6e40f31a >> >> The release files, including signatures, digests, etc. can be found at: >> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-bin/ >> >> Release artifacts are signed with the following key: >> https://people.apache.org/keys/committer/pwendell.asc >> >> The staging repository for this release (published as 1.5.0-rc3) can be >> found at: >> https://repository.apache.org/content/repositories/orgapachespark-1143/ >> >> The staging repository for this release (published as 1.5.0) can be found >> at: >> https://repository.apache.org/content/repositories/orgapachespark-1142/ >> >> The documentation corresponding to this release can be found at: >> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-docs/ >> >> >> === >> How can I help test this release? >> === >> If you are a Spark user, you can help us test this release by taking an >> existing Spark workload and running on this release candidate, then >> reporting any regressions. >> >> >> >> What justifies a -1 vote for this release? >> >> This vote is happening towards the end of the 1.5 QA period, so -1 votes >> should only occur for significant regressions from 1.4. Bugs already >> present in 1.4, minor regressions, or bugs related to new features will not >> block this release. >> >> >> === >> What should happen to JIRA ticket
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
Yin, It is the https://github.com/xsankar/global-bd-conf/blob/master/004-Orders.ipynb. Cheers On Fri, Sep 4, 2015 at 9:58 AM, Yin Huai wrote: > Hi Krishna, > > Can you share your code to reproduce the memory allocation issue? > > Thanks, > > Yin > > On Fri, Sep 4, 2015 at 8:00 AM, Krishna Sankar > wrote: > >> Thanks Tom. Interestingly it happened between RC2 and RC3. >> Now my vote is +1/2 unless the memory error is known and has a workaround. >> >> Cheers >> >> >> >> On Fri, Sep 4, 2015 at 7:30 AM, Tom Graves wrote: >> >>> The upper/lower case thing is known. >>> https://issues.apache.org/jira/browse/SPARK-9550 >>> I assume it was decided to be ok and its going to be in the release >>> notes but Reynold or Josh can probably speak to it more. >>> >>> Tom >>> >>> >>> >>> On Thursday, September 3, 2015 10:21 PM, Krishna Sankar < >>> ksanka...@gmail.com> wrote: >>> >>> >>> +? >>> >>> 1. Compiled OSX 10.10 (Yosemite) OK Total time: 26:09 min >>> mvn clean package -Pyarn -Phadoop-2.6 -DskipTests >>> 2. Tested pyspark, mllib >>> 2.1. statistics (min,max,mean,Pearson,Spearman) OK >>> 2.2. Linear/Ridge/Laso Regression OK >>> 2.3. Decision Tree, Naive Bayes OK >>> 2.4. KMeans OK >>>Center And Scale OK >>> 2.5. RDD operations OK >>> State of the Union Texts - MapReduce, Filter,sortByKey (word count) >>> 2.6. Recommendation (Movielens medium dataset ~1 M ratings) OK >>>Model evaluation/optimization (rank, numIter, lambda) with >>> itertools OK >>> 3. Scala - MLlib >>> 3.1. statistics (min,max,mean,Pearson,Spearman) OK >>> 3.2. LinearRegressionWithSGD OK >>> 3.3. Decision Tree OK >>> 3.4. KMeans OK >>> 3.5. Recommendation (Movielens medium dataset ~1 M ratings) OK >>> 3.6. saveAsParquetFile OK >>> 3.7. Read and verify the 4.3 save(above) - sqlContext.parquetFile, >>> registerTempTable, sql OK >>> 3.8. result = sqlContext.sql("SELECT >>> OrderDetails.OrderID,ShipCountry,UnitPrice,Qty,Discount FROM Orders INNER >>> JOIN OrderDetails ON Orders.OrderID = OrderDetails.OrderID") OK >>> 4.0. Spark SQL from Python OK >>> 4.1. result = sqlContext.sql("SELECT * from people WHERE State = 'WA'") >>> OK >>> 5.0. Packages >>> 5.1. com.databricks.spark.csv - read/write OK >>> (--packages com.databricks:spark-csv_2.11:1.2.0-s_2.11 didn’t work. But >>> com.databricks:spark-csv_2.11:1.2.0 worked) >>> 6.0. DataFrames >>> 6.1. cast,dtypes OK >>> 6.2. groupBy,avg,crosstab,corr,isNull,na.drop OK >>> 6.3. All joins,sql,set operations,udf OK >>> >>> Two Problems: >>> >>> 1. The synthetic column names are lowercase ( i.e. now >>> ‘sum(OrderPrice)’; previously ‘SUM(OrderPrice)’, now ‘avg(Total)’; >>> previously 'AVG(Total)'). So programs that depend on the case of the >>> synthetic column names would fail. >>> 2. orders_3.groupBy("Year","Month").sum('Total').show() >>> fails with the error ‘java.io.IOException: Unable to acquire 4194304 >>> bytes of memory’ >>> orders_3.groupBy("CustomerID","Year").sum('Total').show() - fails >>> with the same error >>> Is this a known bug ? >>> Cheers >>> >>> P.S: Sorry for the spam, forgot Reply All >>> >>> On Tue, Sep 1, 2015 at 1:41 PM, Reynold Xin wrote: >>> >>> Please vote on releasing the following candidate as Apache Spark version >>> 1.5.0. The vote is open until Friday, Sep 4, 2015 at 21:00 UTC and passes >>> if a majority of at least 3 +1 PMC votes are cast. >>> >>> [ ] +1 Release this package as Apache Spark 1.5.0 >>> [ ] -1 Do not release this package because ... >>> >>> To learn more about Apache Spark, please see http://spark.apache.org/ >>> >>> >>> The tag to be voted on is v1.5.0-rc3: >>> >>> https://github.com/apache/spark/commit/908e37bcc10132bb2aa7f80ae694a9df6e40f31a >>> >>> The release files, including signatures, digests, etc. can be found at: >>> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-bin/ >>> >>> Release artifacts are signed with the following key: >>> https://people.apache.org/keys/committer/pwendell.asc >>> >>> The staging repository for this release (published as 1.5.0-rc3) can be >>> found at: >>> https://repository.apache.org/content/repositories/orgapachespark-1143/ >>> >>> The staging repository for this release (published as 1.5.0) can be >>> found at: >>> https://repository.apache.org/content/repositories/orgapachespark-1142/ >>> >>> The documentation corresponding to this release can be found at: >>> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-docs/ >>> >>> >>> === >>> How can I help test this release? >>> === >>> If you are a Spark user, you can help us test this release by taking an >>> existing Spark workload and running on this release candidate, then >>> reporting any regressions. >>> >>> >>> >>> What justifies a -1 vote for this release? >>> >>> This vote is happening towards the end of the 1.5 QA perio
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
Could you update the notebook to use builtin SQL function month and year, instead of Python UDF? (they are introduced in 1.5). Once remove those two udfs, it runs successfully, also much faster. On Fri, Sep 4, 2015 at 2:22 PM, Krishna Sankar wrote: > Yin, >It is the > https://github.com/xsankar/global-bd-conf/blob/master/004-Orders.ipynb. > Cheers > > > On Fri, Sep 4, 2015 at 9:58 AM, Yin Huai wrote: >> >> Hi Krishna, >> >> Can you share your code to reproduce the memory allocation issue? >> >> Thanks, >> >> Yin >> >> On Fri, Sep 4, 2015 at 8:00 AM, Krishna Sankar >> wrote: >>> >>> Thanks Tom. Interestingly it happened between RC2 and RC3. >>> Now my vote is +1/2 unless the memory error is known and has a >>> workaround. >>> >>> Cheers >>> >>> >>> >>> On Fri, Sep 4, 2015 at 7:30 AM, Tom Graves wrote: The upper/lower case thing is known. https://issues.apache.org/jira/browse/SPARK-9550 I assume it was decided to be ok and its going to be in the release notes but Reynold or Josh can probably speak to it more. Tom On Thursday, September 3, 2015 10:21 PM, Krishna Sankar wrote: +? 1. Compiled OSX 10.10 (Yosemite) OK Total time: 26:09 min mvn clean package -Pyarn -Phadoop-2.6 -DskipTests 2. Tested pyspark, mllib 2.1. statistics (min,max,mean,Pearson,Spearman) OK 2.2. Linear/Ridge/Laso Regression OK 2.3. Decision Tree, Naive Bayes OK 2.4. KMeans OK Center And Scale OK 2.5. RDD operations OK State of the Union Texts - MapReduce, Filter,sortByKey (word count) 2.6. Recommendation (Movielens medium dataset ~1 M ratings) OK Model evaluation/optimization (rank, numIter, lambda) with itertools OK 3. Scala - MLlib 3.1. statistics (min,max,mean,Pearson,Spearman) OK 3.2. LinearRegressionWithSGD OK 3.3. Decision Tree OK 3.4. KMeans OK 3.5. Recommendation (Movielens medium dataset ~1 M ratings) OK 3.6. saveAsParquetFile OK 3.7. Read and verify the 4.3 save(above) - sqlContext.parquetFile, registerTempTable, sql OK 3.8. result = sqlContext.sql("SELECT OrderDetails.OrderID,ShipCountry,UnitPrice,Qty,Discount FROM Orders INNER JOIN OrderDetails ON Orders.OrderID = OrderDetails.OrderID") OK 4.0. Spark SQL from Python OK 4.1. result = sqlContext.sql("SELECT * from people WHERE State = 'WA'") OK 5.0. Packages 5.1. com.databricks.spark.csv - read/write OK (--packages com.databricks:spark-csv_2.11:1.2.0-s_2.11 didn’t work. But com.databricks:spark-csv_2.11:1.2.0 worked) 6.0. DataFrames 6.1. cast,dtypes OK 6.2. groupBy,avg,crosstab,corr,isNull,na.drop OK 6.3. All joins,sql,set operations,udf OK Two Problems: 1. The synthetic column names are lowercase ( i.e. now ‘sum(OrderPrice)’; previously ‘SUM(OrderPrice)’, now ‘avg(Total)’; previously 'AVG(Total)'). So programs that depend on the case of the synthetic column names would fail. 2. orders_3.groupBy("Year","Month").sum('Total').show() fails with the error ‘java.io.IOException: Unable to acquire 4194304 bytes of memory’ orders_3.groupBy("CustomerID","Year").sum('Total').show() - fails with the same error Is this a known bug ? Cheers P.S: Sorry for the spam, forgot Reply All On Tue, Sep 1, 2015 at 1:41 PM, Reynold Xin wrote: Please vote on releasing the following candidate as Apache Spark version 1.5.0. The vote is open until Friday, Sep 4, 2015 at 21:00 UTC and passes if a majority of at least 3 +1 PMC votes are cast. [ ] +1 Release this package as Apache Spark 1.5.0 [ ] -1 Do not release this package because ... To learn more about Apache Spark, please see http://spark.apache.org/ The tag to be voted on is v1.5.0-rc3: https://github.com/apache/spark/commit/908e37bcc10132bb2aa7f80ae694a9df6e40f31a The release files, including signatures, digests, etc. can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-bin/ Release artifacts are signed with the following key: https://people.apache.org/keys/committer/pwendell.asc The staging repository for this release (published as 1.5.0-rc3) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1143/ The staging repository for this release (published as 1.5.0) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1142/ The documentation corresponding to this release can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-docs/ === How can I help test this release? === If you are
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
Excellent & Thanks Davies. Yep, now runs fine and takes 1/2 the time ! This was exactly why I had put in the elapsed time calculations. And thanks for the new pyspark.sql.functions. +1 from my side for 1.5.0 RC3. Cheers On Fri, Sep 4, 2015 at 9:57 PM, Davies Liu wrote: > Could you update the notebook to use builtin SQL function month and year, > instead of Python UDF? (they are introduced in 1.5). > > Once remove those two udfs, it runs successfully, also much faster. > > On Fri, Sep 4, 2015 at 2:22 PM, Krishna Sankar > wrote: > > Yin, > >It is the > > https://github.com/xsankar/global-bd-conf/blob/master/004-Orders.ipynb. > > Cheers > > > > > > On Fri, Sep 4, 2015 at 9:58 AM, Yin Huai wrote: > >> > >> Hi Krishna, > >> > >> Can you share your code to reproduce the memory allocation issue? > >> > >> Thanks, > >> > >> Yin > >> > >> On Fri, Sep 4, 2015 at 8:00 AM, Krishna Sankar > >> wrote: > >>> > >>> Thanks Tom. Interestingly it happened between RC2 and RC3. > >>> Now my vote is +1/2 unless the memory error is known and has a > >>> workaround. > >>> > >>> Cheers > >>> > >>> > >>> > >>> On Fri, Sep 4, 2015 at 7:30 AM, Tom Graves > wrote: > > The upper/lower case thing is known. > https://issues.apache.org/jira/browse/SPARK-9550 > I assume it was decided to be ok and its going to be in the release > notes but Reynold or Josh can probably speak to it more. > > Tom > > > > On Thursday, September 3, 2015 10:21 PM, Krishna Sankar > wrote: > > > +? > > 1. Compiled OSX 10.10 (Yosemite) OK Total time: 26:09 min > mvn clean package -Pyarn -Phadoop-2.6 -DskipTests > 2. Tested pyspark, mllib > 2.1. statistics (min,max,mean,Pearson,Spearman) OK > 2.2. Linear/Ridge/Laso Regression OK > 2.3. Decision Tree, Naive Bayes OK > 2.4. KMeans OK > Center And Scale OK > 2.5. RDD operations OK > State of the Union Texts - MapReduce, Filter,sortByKey (word > count) > 2.6. Recommendation (Movielens medium dataset ~1 M ratings) OK > Model evaluation/optimization (rank, numIter, lambda) with > itertools OK > 3. Scala - MLlib > 3.1. statistics (min,max,mean,Pearson,Spearman) OK > 3.2. LinearRegressionWithSGD OK > 3.3. Decision Tree OK > 3.4. KMeans OK > 3.5. Recommendation (Movielens medium dataset ~1 M ratings) OK > 3.6. saveAsParquetFile OK > 3.7. Read and verify the 4.3 save(above) - sqlContext.parquetFile, > registerTempTable, sql OK > 3.8. result = sqlContext.sql("SELECT > OrderDetails.OrderID,ShipCountry,UnitPrice,Qty,Discount FROM Orders > INNER > JOIN OrderDetails ON Orders.OrderID = OrderDetails.OrderID") OK > 4.0. Spark SQL from Python OK > 4.1. result = sqlContext.sql("SELECT * from people WHERE State = > 'WA'") > OK > 5.0. Packages > 5.1. com.databricks.spark.csv - read/write OK > (--packages com.databricks:spark-csv_2.11:1.2.0-s_2.11 didn’t work. > But > com.databricks:spark-csv_2.11:1.2.0 worked) > 6.0. DataFrames > 6.1. cast,dtypes OK > 6.2. groupBy,avg,crosstab,corr,isNull,na.drop OK > 6.3. All joins,sql,set operations,udf OK > > Two Problems: > > 1. The synthetic column names are lowercase ( i.e. now > ‘sum(OrderPrice)’; previously ‘SUM(OrderPrice)’, now ‘avg(Total)’; > previously 'AVG(Total)'). So programs that depend on the case of the > synthetic column names would fail. > 2. orders_3.groupBy("Year","Month").sum('Total').show() > fails with the error ‘java.io.IOException: Unable to acquire > 4194304 > bytes of memory’ > orders_3.groupBy("CustomerID","Year").sum('Total').show() - fails > with the same error > Is this a known bug ? > Cheers > > P.S: Sorry for the spam, forgot Reply All > > On Tue, Sep 1, 2015 at 1:41 PM, Reynold Xin > wrote: > > Please vote on releasing the following candidate as Apache Spark > version > 1.5.0. The vote is open until Friday, Sep 4, 2015 at 21:00 UTC and > passes if > a majority of at least 3 +1 PMC votes are cast. > > [ ] +1 Release this package as Apache Spark 1.5.0 > [ ] -1 Do not release this package because ... > > To learn more about Apache Spark, please see http://spark.apache.org/ > > > The tag to be voted on is v1.5.0-rc3: > > > https://github.com/apache/spark/commit/908e37bcc10132bb2aa7f80ae694a9df6e40f31a > > The release files, including signatures, digests, etc. can be found > at: > > http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-bin/ > > Release artifacts are signed with the following key: > https://people.apache.org/keys/committer/pwendell.asc > > The staging repository for this release (published as 1.5.0-rc3) can > be > found at: > >>
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
Thanks, Krishna, for the report. We should fix your problem using the Python UDFs in 1.6 too. I'm going to close this vote now. Thanks everybody for voting. This vote passes with 8 +1 votes (3 binding) and no 0 or -1 votes. +1: Reynold Xin* Tom Graves* Burak Yavuz Michael Armbrust* Davies Liu Forest Fang Krishna Sankar Denny Lee 0: -1: I will work on packaging this release in the next few days. On Fri, Sep 4, 2015 at 8:08 PM, Krishna Sankar wrote: > Excellent & Thanks Davies. Yep, now runs fine and takes 1/2 the time ! > This was exactly why I had put in the elapsed time calculations. > And thanks for the new pyspark.sql.functions. > > +1 from my side for 1.5.0 RC3. > Cheers > > > On Fri, Sep 4, 2015 at 9:57 PM, Davies Liu wrote: > >> Could you update the notebook to use builtin SQL function month and year, >> instead of Python UDF? (they are introduced in 1.5). >> >> Once remove those two udfs, it runs successfully, also much faster. >> >> On Fri, Sep 4, 2015 at 2:22 PM, Krishna Sankar >> wrote: >> > Yin, >> >It is the >> > https://github.com/xsankar/global-bd-conf/blob/master/004-Orders.ipynb. >> > Cheers >> > >> > >> > On Fri, Sep 4, 2015 at 9:58 AM, Yin Huai wrote: >> >> >> >> Hi Krishna, >> >> >> >> Can you share your code to reproduce the memory allocation issue? >> >> >> >> Thanks, >> >> >> >> Yin >> >> >> >> On Fri, Sep 4, 2015 at 8:00 AM, Krishna Sankar >> >> wrote: >> >>> >> >>> Thanks Tom. Interestingly it happened between RC2 and RC3. >> >>> Now my vote is +1/2 unless the memory error is known and has a >> >>> workaround. >> >>> >> >>> Cheers >> >>> >> >>> >> >>> >> >>> On Fri, Sep 4, 2015 at 7:30 AM, Tom Graves >> wrote: >> >> The upper/lower case thing is known. >> https://issues.apache.org/jira/browse/SPARK-9550 >> I assume it was decided to be ok and its going to be in the release >> notes but Reynold or Josh can probably speak to it more. >> >> Tom >> >> >> >> On Thursday, September 3, 2015 10:21 PM, Krishna Sankar >> wrote: >> >> >> +? >> >> 1. Compiled OSX 10.10 (Yosemite) OK Total time: 26:09 min >> mvn clean package -Pyarn -Phadoop-2.6 -DskipTests >> 2. Tested pyspark, mllib >> 2.1. statistics (min,max,mean,Pearson,Spearman) OK >> 2.2. Linear/Ridge/Laso Regression OK >> 2.3. Decision Tree, Naive Bayes OK >> 2.4. KMeans OK >> Center And Scale OK >> 2.5. RDD operations OK >> State of the Union Texts - MapReduce, Filter,sortByKey (word >> count) >> 2.6. Recommendation (Movielens medium dataset ~1 M ratings) OK >> Model evaluation/optimization (rank, numIter, lambda) with >> itertools OK >> 3. Scala - MLlib >> 3.1. statistics (min,max,mean,Pearson,Spearman) OK >> 3.2. LinearRegressionWithSGD OK >> 3.3. Decision Tree OK >> 3.4. KMeans OK >> 3.5. Recommendation (Movielens medium dataset ~1 M ratings) OK >> 3.6. saveAsParquetFile OK >> 3.7. Read and verify the 4.3 save(above) - sqlContext.parquetFile, >> registerTempTable, sql OK >> 3.8. result = sqlContext.sql("SELECT >> OrderDetails.OrderID,ShipCountry,UnitPrice,Qty,Discount FROM Orders >> INNER >> JOIN OrderDetails ON Orders.OrderID = OrderDetails.OrderID") OK >> 4.0. Spark SQL from Python OK >> 4.1. result = sqlContext.sql("SELECT * from people WHERE State = >> 'WA'") >> OK >> 5.0. Packages >> 5.1. com.databricks.spark.csv - read/write OK >> (--packages com.databricks:spark-csv_2.11:1.2.0-s_2.11 didn’t work. >> But >> com.databricks:spark-csv_2.11:1.2.0 worked) >> 6.0. DataFrames >> 6.1. cast,dtypes OK >> 6.2. groupBy,avg,crosstab,corr,isNull,na.drop OK >> 6.3. All joins,sql,set operations,udf OK >> >> Two Problems: >> >> 1. The synthetic column names are lowercase ( i.e. now >> ‘sum(OrderPrice)’; previously ‘SUM(OrderPrice)’, now ‘avg(Total)’; >> previously 'AVG(Total)'). So programs that depend on the case of the >> synthetic column names would fail. >> 2. orders_3.groupBy("Year","Month").sum('Total').show() >> fails with the error ‘java.io.IOException: Unable to acquire >> 4194304 >> bytes of memory’ >> orders_3.groupBy("CustomerID","Year").sum('Total').show() - fails >> with the same error >> Is this a known bug ? >> Cheers >> >> P.S: Sorry for the spam, forgot Reply All >> >> On Tue, Sep 1, 2015 at 1:41 PM, Reynold Xin >> wrote: >> >> Please vote on releasing the following candidate as Apache Spark >> version >> 1.5.0. The vote is open until Friday, Sep 4, 2015 at 21:00 UTC and >> passes if >> a majority of at least 3 +1 PMC votes are cast. >> >> [ ] +1 Release this package as Apache Spark 1.5.0 >> [ ] -1 Do not release this package because ... >> >> To learn more about Apache
RE: [VOTE] Release Apache Spark 1.5.0 (RC3)
Not sure if it’s too late, but we found a critical bug at https://issues.apache.org/jira/browse/SPARK-10466 UnsafeRow ser/de will cause assert error, particularly for sort-based shuffle with data spill, this is not acceptable as it’s very common in a large table joins. From: Reynold Xin [mailto:r...@databricks.com] Sent: Saturday, September 5, 2015 3:30 PM To: Krishna Sankar Cc: Davies Liu; Yin Huai; Tom Graves; dev@spark.apache.org Subject: Re: [VOTE] Release Apache Spark 1.5.0 (RC3) Thanks, Krishna, for the report. We should fix your problem using the Python UDFs in 1.6 too. I'm going to close this vote now. Thanks everybody for voting. This vote passes with 8 +1 votes (3 binding) and no 0 or -1 votes. +1: Reynold Xin* Tom Graves* Burak Yavuz Michael Armbrust* Davies Liu Forest Fang Krishna Sankar Denny Lee 0: -1: I will work on packaging this release in the next few days. On Fri, Sep 4, 2015 at 8:08 PM, Krishna Sankar mailto:ksanka...@gmail.com>> wrote: Excellent & Thanks Davies. Yep, now runs fine and takes 1/2 the time ! This was exactly why I had put in the elapsed time calculations. And thanks for the new pyspark.sql.functions. +1 from my side for 1.5.0 RC3. Cheers On Fri, Sep 4, 2015 at 9:57 PM, Davies Liu mailto:dav...@databricks.com>> wrote: Could you update the notebook to use builtin SQL function month and year, instead of Python UDF? (they are introduced in 1.5). Once remove those two udfs, it runs successfully, also much faster. On Fri, Sep 4, 2015 at 2:22 PM, Krishna Sankar mailto:ksanka...@gmail.com>> wrote: > Yin, >It is the > https://github.com/xsankar/global-bd-conf/blob/master/004-Orders.ipynb. > Cheers > > > On Fri, Sep 4, 2015 at 9:58 AM, Yin Huai > mailto:yh...@databricks.com>> wrote: >> >> Hi Krishna, >> >> Can you share your code to reproduce the memory allocation issue? >> >> Thanks, >> >> Yin >> >> On Fri, Sep 4, 2015 at 8:00 AM, Krishna Sankar >> mailto:ksanka...@gmail.com>> >> wrote: >>> >>> Thanks Tom. Interestingly it happened between RC2 and RC3. >>> Now my vote is +1/2 unless the memory error is known and has a >>> workaround. >>> >>> Cheers >>> >>> >>> >>> On Fri, Sep 4, 2015 at 7:30 AM, Tom Graves >>> mailto:tgraves...@yahoo.com>> wrote: >>>> >>>> The upper/lower case thing is known. >>>> https://issues.apache.org/jira/browse/SPARK-9550 >>>> I assume it was decided to be ok and its going to be in the release >>>> notes but Reynold or Josh can probably speak to it more. >>>> >>>> Tom >>>> >>>> >>>> >>>> On Thursday, September 3, 2015 10:21 PM, Krishna Sankar >>>> mailto:ksanka...@gmail.com>> wrote: >>>> >>>> >>>> +? >>>> >>>> 1. Compiled OSX 10.10 (Yosemite) OK Total time: 26:09 min >>>> mvn clean package -Pyarn -Phadoop-2.6 -DskipTests >>>> 2. Tested pyspark, mllib >>>> 2.1. statistics (min,max,mean,Pearson,Spearman) OK >>>> 2.2. Linear/Ridge/Laso Regression OK >>>> 2.3. Decision Tree, Naive Bayes OK >>>> 2.4. KMeans OK >>>>Center And Scale OK >>>> 2.5. RDD operations OK >>>> State of the Union Texts - MapReduce, Filter,sortByKey (word >>>> count) >>>> 2.6. Recommendation (Movielens medium dataset ~1 M ratings) OK >>>>Model evaluation/optimization (rank, numIter, lambda) with >>>> itertools OK >>>> 3. Scala - MLlib >>>> 3.1. statistics (min,max,mean,Pearson,Spearman) OK >>>> 3.2. LinearRegressionWithSGD OK >>>> 3.3. Decision Tree OK >>>> 3.4. KMeans OK >>>> 3.5. Recommendation (Movielens medium dataset ~1 M ratings) OK >>>> 3.6. saveAsParquetFile OK >>>> 3.7. Read and verify the 4.3 save(above) - sqlContext.parquetFile, >>>> registerTempTable, sql OK >>>> 3.8. result = sqlContext.sql("SELECT >>>> OrderDetails.OrderID,ShipCountry,UnitPrice,Qty,Discount FROM Orders INNER >>>> JOIN OrderDetails ON Orders.OrderID = OrderDetails.OrderID") OK >>>> 4.0. Spark SQL from Python OK >>>> 4.1. result = sqlContext.sql("SELECT * from people WHERE State = 'WA'") >>>> OK >>>> 5.0. Packages >>>> 5.1. com.databricks.spark.csv - read/write OK >>>> (--packages com.databricks:spark-csv_2.11:1.2.0-s_2.11 didn’t work. But >>>> com.databricks:spark-csv_2.11:1.2.0 worked) >>
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
I saw a new "spark.shuffle.manager=tungsten-sort" implemented in https://issues.apache.org/jira/browse/SPARK-7081, but it can't be found its corresponding description in http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-docs/configuration.html(Currenlty there are only 'sort' and 'hash' two options). -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC3-tp13928p13984.html Sent from the Apache Spark Developers List mailing list archive at Nabble.com. - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
add a critical bug https://issues.apache.org/jira/browse/SPARK-10474 (Aggregation failed with unable to acquire memory) -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC3-tp13928p13987.html Sent from the Apache Spark Developers List mailing list archive at Nabble.com. - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org