Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
That's correct for the 1.5 branch, right? this doesn't mean that the next RC would have this value. You choose the release version during the release process. On Tue, Sep 1, 2015 at 2:40 AM, Chester Chenwrote: > Seems that Github branch-1.5 already changing the version to 1.5.1-SNAPSHOT, > > I am a bit confused are we still on 1.5.0 RC3 or we are in 1.5.1 ? > > Chester > > On Mon, Aug 31, 2015 at 3:52 PM, Reynold Xin wrote: >> >> I'm going to -1 the release myself since the issue @yhuai identified is >> pretty serious. It basically OOMs the driver for reading any files with a >> large number of partitions. Looks like the patch for that has already been >> merged. >> >> I'm going to cut rc3 momentarily. >> >> >> On Sun, Aug 30, 2015 at 11:30 AM, Sandy Ryza >> wrote: >>> >>> +1 (non-binding) >>> built from source and ran some jobs against YARN >>> >>> -Sandy >>> >>> On Sat, Aug 29, 2015 at 5:50 AM, vaquar khan >>> wrote: +1 (1.5.0 RC2)Compiled on Windows with YARN. Regards, Vaquar khan +1 (non-binding, of course) 1. Compiled OSX 10.10 (Yosemite) OK Total time: 42:36 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. joins,sql,set operations,udf OK Cheers On Tue, Aug 25, 2015 at 9:28 PM, Reynold Xin wrote: > > Please vote on releasing the following candidate as Apache Spark > version 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: > > https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a > > The release files, including signatures, digests, etc. can be found at: > http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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-rc2) can be > found at: > https://repository.apache.org/content/repositories/orgapachespark-1141/ > > The staging repository for this release (published as 1.5.0) can be > found at: > https://repository.apache.org/content/repositories/orgapachespark-1140/ > > The documentation corresponding to this release can be found at: > http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
Sorry, I am still not follow. I assume the release would build from 1.5.0 before moving to 1.5.1. Are you saying the 1.5.0 rc3 could build from 1.5.1 snapshot during release ? Or 1.5.0 rc3 would build from the last commit of 1.5.0 (before changing to 1.5.1 snapshot) ? Sent from my iPad > On Sep 1, 2015, at 1:52 AM, Sean Owenwrote: > > That's correct for the 1.5 branch, right? this doesn't mean that the > next RC would have this value. You choose the release version during > the release process. > >> On Tue, Sep 1, 2015 at 2:40 AM, Chester Chen wrote: >> Seems that Github branch-1.5 already changing the version to 1.5.1-SNAPSHOT, >> >> I am a bit confused are we still on 1.5.0 RC3 or we are in 1.5.1 ? >> >> Chester >> >>> On Mon, Aug 31, 2015 at 3:52 PM, Reynold Xin wrote: >>> >>> I'm going to -1 the release myself since the issue @yhuai identified is >>> pretty serious. It basically OOMs the driver for reading any files with a >>> large number of partitions. Looks like the patch for that has already been >>> merged. >>> >>> I'm going to cut rc3 momentarily. >>> >>> >>> On Sun, Aug 30, 2015 at 11:30 AM, Sandy Ryza >>> wrote: +1 (non-binding) built from source and ran some jobs against YARN -Sandy On Sat, Aug 29, 2015 at 5:50 AM, vaquar khan wrote: > > > +1 (1.5.0 RC2)Compiled on Windows with YARN. > > Regards, > Vaquar khan > > +1 (non-binding, of course) > > 1. Compiled OSX 10.10 (Yosemite) OK Total time: 42:36 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. joins,sql,set operations,udf OK > > Cheers > > > On Tue, Aug 25, 2015 at 9:28 PM, Reynold Xin > wrote: >> >> Please vote on releasing the following candidate as Apache Spark >> version 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: >> >> https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a >> >> The release files, including signatures, digests, etc. can be found at: >> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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-rc2) can be >> found at: >> https://repository.apache.org/content/repositories/orgapachespark-1141/ >> >> The staging repository for this release (published as 1.5.0) can be >> found at: >> https://repository.apache.org/content/repositories/orgapachespark-1140/ >> >> The documentation corresponding to this release can be found at: >> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-docs/ >> >> >> === >> How can I help test this release? >> === >> If you are a Spark user, you can help us test this release
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
The head of branch 1.5 will always be a "1.5.x-SNAPSHOT" version. Yeah technically you would expect it to be 1.5.0-SNAPSHOT until 1.5.0 is released. In practice I think it's simpler to follow the defaults of the Maven release plugin, which will set this to 1.5.1-SNAPSHOT after any 1.5.0-rc is released. It doesn't affect later RCs. This has nothing to do with what commits go into 1.5.0; it's an ignorable detail of the version in POMs in the source tree, which don't mean much anyway as the source tree itself is not a released version. On Tue, Sep 1, 2015 at 2:48 PM,wrote: > Sorry, I am still not follow. I assume the release would build from 1.5.0 > before moving to 1.5.1. Are you saying the 1.5.0 rc3 could build from 1.5.1 > snapshot during release ? Or 1.5.0 rc3 would build from the last commit of > 1.5.0 (before changing to 1.5.1 snapshot) ? > > > > Sent from my iPad > >> On Sep 1, 2015, at 1:52 AM, Sean Owen wrote: >> >> That's correct for the 1.5 branch, right? this doesn't mean that the >> next RC would have this value. You choose the release version during >> the release process. >> >>> On Tue, Sep 1, 2015 at 2:40 AM, Chester Chen wrote: >>> Seems that Github branch-1.5 already changing the version to 1.5.1-SNAPSHOT, >>> >>> I am a bit confused are we still on 1.5.0 RC3 or we are in 1.5.1 ? >>> >>> Chester >>> On Mon, Aug 31, 2015 at 3:52 PM, Reynold Xin wrote: I'm going to -1 the release myself since the issue @yhuai identified is pretty serious. It basically OOMs the driver for reading any files with a large number of partitions. Looks like the patch for that has already been merged. I'm going to cut rc3 momentarily. On Sun, Aug 30, 2015 at 11:30 AM, Sandy Ryza wrote: > > +1 (non-binding) > built from source and ran some jobs against YARN > > -Sandy > > On Sat, Aug 29, 2015 at 5:50 AM, vaquar khan > wrote: >> >> >> +1 (1.5.0 RC2)Compiled on Windows with YARN. >> >> Regards, >> Vaquar khan >> >> +1 (non-binding, of course) >> >> 1. Compiled OSX 10.10 (Yosemite) OK Total time: 42:36 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. joins,sql,set operations,udf OK >> >> Cheers >> >> >> On Tue, Aug 25, 2015 at 9:28 PM, Reynold Xin >> wrote: >>> >>> Please vote on releasing the following candidate as Apache Spark >>> version 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: >>> >>> https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a >>> >>> The release files, including signatures, digests, etc. can be found at: >>> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-bin/ >>> >>> Release artifacts are signed with the following key: >>> https://people.apache.org/keys/committer/pwendell.asc >>> >>> The staging repository
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
Thanks for the explanation. Since 1.5.0 rc3 is not yet released, I assume it would cut from 1.5 branch, doesn't that bring 1.5.1 snapshot code ? The reason I am asking these questions is that I would like to know If I want build 1.5.0 myself, which commit should I use ? Sent from my iPad > On Sep 1, 2015, at 6:57 AM, Sean Owenwrote: > > The head of branch 1.5 will always be a "1.5.x-SNAPSHOT" version. Yeah > technically you would expect it to be 1.5.0-SNAPSHOT until 1.5.0 is > released. In practice I think it's simpler to follow the defaults of > the Maven release plugin, which will set this to 1.5.1-SNAPSHOT after > any 1.5.0-rc is released. It doesn't affect later RCs. This has > nothing to do with what commits go into 1.5.0; it's an ignorable > detail of the version in POMs in the source tree, which don't mean > much anyway as the source tree itself is not a released version. > >> On Tue, Sep 1, 2015 at 2:48 PM, wrote: >> Sorry, I am still not follow. I assume the release would build from 1.5.0 >> before moving to 1.5.1. Are you saying the 1.5.0 rc3 could build from 1.5.1 >> snapshot during release ? Or 1.5.0 rc3 would build from the last commit of >> 1.5.0 (before changing to 1.5.1 snapshot) ? >> >> >> >> Sent from my iPad >> >>> On Sep 1, 2015, at 1:52 AM, Sean Owen wrote: >>> >>> That's correct for the 1.5 branch, right? this doesn't mean that the >>> next RC would have this value. You choose the release version during >>> the release process. >>> On Tue, Sep 1, 2015 at 2:40 AM, Chester Chen wrote: Seems that Github branch-1.5 already changing the version to 1.5.1-SNAPSHOT, I am a bit confused are we still on 1.5.0 RC3 or we are in 1.5.1 ? Chester > On Mon, Aug 31, 2015 at 3:52 PM, Reynold Xin wrote: > > I'm going to -1 the release myself since the issue @yhuai identified is > pretty serious. It basically OOMs the driver for reading any files with a > large number of partitions. Looks like the patch for that has already been > merged. > > I'm going to cut rc3 momentarily. > > > On Sun, Aug 30, 2015 at 11:30 AM, Sandy Ryza > wrote: >> >> +1 (non-binding) >> built from source and ran some jobs against YARN >> >> -Sandy >> >> On Sat, Aug 29, 2015 at 5:50 AM, vaquar khan >> wrote: >>> >>> >>> +1 (1.5.0 RC2)Compiled on Windows with YARN. >>> >>> Regards, >>> Vaquar khan >>> >>> +1 (non-binding, of course) >>> >>> 1. Compiled OSX 10.10 (Yosemite) OK Total time: 42:36 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. joins,sql,set operations,udf OK >>> >>> Cheers >>> >>> >>> On Tue, Aug 25, 2015 at 9:28 PM, Reynold Xin >>> wrote: Please vote on releasing the following candidate as Apache Spark version 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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,
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
Any 1.5 RC comes from the latest state of the 1.5 branch at some point in time. The next RC will be cut from whatever the latest commit is. You can see the tags in git for the specific commits for each RC. There's no such thing as "1.5.1 SNAPSHOT" commits, just commits to branch 1.5. I would ignore the "SNAPSHOT" version for your purpose. You can always build from the exact commit that an RC did by looking at tags. There is no 1.5.0 yet so you can't build that, but once it's released, you would be able to find its tag as well. You can always build the latest 1.5.x branch by building from HEAD of that branch. On Tue, Sep 1, 2015 at 3:13 PM,wrote: > Thanks for the explanation. Since 1.5.0 rc3 is not yet released, I assume it > would cut from 1.5 branch, doesn't that bring 1.5.1 snapshot code ? > > The reason I am asking these questions is that I would like to know If I want > build 1.5.0 myself, which commit should I use ? > > Sent from my iPad > >> On Sep 1, 2015, at 6:57 AM, Sean Owen wrote: >> >> The head of branch 1.5 will always be a "1.5.x-SNAPSHOT" version. Yeah >> technically you would expect it to be 1.5.0-SNAPSHOT until 1.5.0 is >> released. In practice I think it's simpler to follow the defaults of >> the Maven release plugin, which will set this to 1.5.1-SNAPSHOT after >> any 1.5.0-rc is released. It doesn't affect later RCs. This has >> nothing to do with what commits go into 1.5.0; it's an ignorable >> detail of the version in POMs in the source tree, which don't mean >> much anyway as the source tree itself is not a released version. >> >>> On Tue, Sep 1, 2015 at 2:48 PM, wrote: >>> Sorry, I am still not follow. I assume the release would build from 1.5.0 >>> before moving to 1.5.1. Are you saying the 1.5.0 rc3 could build from 1.5.1 >>> snapshot during release ? Or 1.5.0 rc3 would build from the last commit of >>> 1.5.0 (before changing to 1.5.1 snapshot) ? >>> >>> >>> >>> Sent from my iPad >>> On Sep 1, 2015, at 1:52 AM, Sean Owen wrote: That's correct for the 1.5 branch, right? this doesn't mean that the next RC would have this value. You choose the release version during the release process. > On Tue, Sep 1, 2015 at 2:40 AM, Chester Chen > wrote: > Seems that Github branch-1.5 already changing the version to > 1.5.1-SNAPSHOT, > > I am a bit confused are we still on 1.5.0 RC3 or we are in 1.5.1 ? > > Chester > >> On Mon, Aug 31, 2015 at 3:52 PM, Reynold Xin wrote: >> >> I'm going to -1 the release myself since the issue @yhuai identified is >> pretty serious. It basically OOMs the driver for reading any files with a >> large number of partitions. Looks like the patch for that has already >> been >> merged. >> >> I'm going to cut rc3 momentarily. >> >> >> On Sun, Aug 30, 2015 at 11:30 AM, Sandy Ryza >> wrote: >>> >>> +1 (non-binding) >>> built from source and ran some jobs against YARN >>> >>> -Sandy >>> >>> On Sat, Aug 29, 2015 at 5:50 AM, vaquar khan >>> wrote: +1 (1.5.0 RC2)Compiled on Windows with YARN. Regards, Vaquar khan +1 (non-binding, of course) 1. Compiled OSX 10.10 (Yosemite) OK Total time: 42:36 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
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
Thanks Sean, that make it clear. On Tue, Sep 1, 2015 at 7:17 AM, Sean Owenwrote: > Any 1.5 RC comes from the latest state of the 1.5 branch at some point > in time. The next RC will be cut from whatever the latest commit is. > You can see the tags in git for the specific commits for each RC. > There's no such thing as "1.5.1 SNAPSHOT" commits, just commits to > branch 1.5. I would ignore the "SNAPSHOT" version for your purpose. > > You can always build from the exact commit that an RC did by looking > at tags. There is no 1.5.0 yet so you can't build that, but once it's > released, you would be able to find its tag as well. You can always > build the latest 1.5.x branch by building from HEAD of that branch. > > On Tue, Sep 1, 2015 at 3:13 PM, wrote: > > Thanks for the explanation. Since 1.5.0 rc3 is not yet released, I > assume it would cut from 1.5 branch, doesn't that bring 1.5.1 snapshot code > ? > > > > The reason I am asking these questions is that I would like to know If I > want build 1.5.0 myself, which commit should I use ? > > > > Sent from my iPad > > > >> On Sep 1, 2015, at 6:57 AM, Sean Owen wrote: > >> > >> The head of branch 1.5 will always be a "1.5.x-SNAPSHOT" version. Yeah > >> technically you would expect it to be 1.5.0-SNAPSHOT until 1.5.0 is > >> released. In practice I think it's simpler to follow the defaults of > >> the Maven release plugin, which will set this to 1.5.1-SNAPSHOT after > >> any 1.5.0-rc is released. It doesn't affect later RCs. This has > >> nothing to do with what commits go into 1.5.0; it's an ignorable > >> detail of the version in POMs in the source tree, which don't mean > >> much anyway as the source tree itself is not a released version. > >> > >>> On Tue, Sep 1, 2015 at 2:48 PM, wrote: > >>> Sorry, I am still not follow. I assume the release would build from > 1.5.0 before moving to 1.5.1. Are you saying the 1.5.0 rc3 could build from > 1.5.1 snapshot during release ? Or 1.5.0 rc3 would build from the last > commit of 1.5.0 (before changing to 1.5.1 snapshot) ? > >>> > >>> > >>> > >>> Sent from my iPad > >>> > On Sep 1, 2015, at 1:52 AM, Sean Owen wrote: > > That's correct for the 1.5 branch, right? this doesn't mean that the > next RC would have this value. You choose the release version during > the release process. > > > On Tue, Sep 1, 2015 at 2:40 AM, Chester Chen > wrote: > > Seems that Github branch-1.5 already changing the version to > 1.5.1-SNAPSHOT, > > > > I am a bit confused are we still on 1.5.0 RC3 or we are in 1.5.1 ? > > > > Chester > > > >> On Mon, Aug 31, 2015 at 3:52 PM, Reynold Xin > wrote: > >> > >> I'm going to -1 the release myself since the issue @yhuai > identified is > >> pretty serious. It basically OOMs the driver for reading any files > with a > >> large number of partitions. Looks like the patch for that has > already been > >> merged. > >> > >> I'm going to cut rc3 momentarily. > >> > >> > >> On Sun, Aug 30, 2015 at 11:30 AM, Sandy Ryza < > sandy.r...@cloudera.com> > >> wrote: > >>> > >>> +1 (non-binding) > >>> built from source and ran some jobs against YARN > >>> > >>> -Sandy > >>> > >>> On Sat, Aug 29, 2015 at 5:50 AM, vaquar khan < > vaquar.k...@gmail.com> > >>> wrote: > > > +1 (1.5.0 RC2)Compiled on Windows with YARN. > > Regards, > Vaquar khan > > +1 (non-binding, of course) > > 1. Compiled OSX 10.10 (Yosemite) OK Total time: 42:36 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 =
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
I'm going to -1 the release myself since the issue @yhuai identified is pretty serious. It basically OOMs the driver for reading any files with a large number of partitions. Looks like the patch for that has already been merged. I'm going to cut rc3 momentarily. On Sun, Aug 30, 2015 at 11:30 AM, Sandy Ryzawrote: > +1 (non-binding) > built from source and ran some jobs against YARN > > -Sandy > > On Sat, Aug 29, 2015 at 5:50 AM, vaquar khan > wrote: > >> >> +1 (1.5.0 RC2)Compiled on Windows with YARN. >> >> Regards, >> Vaquar khan >> +1 (non-binding, of course) >> >> 1. Compiled OSX 10.10 (Yosemite) OK Total time: 42:36 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. joins,sql,set operations,udf OK >> >> Cheers >> >> >> On Tue, Aug 25, 2015 at 9:28 PM, Reynold Xin wrote: >> >>> Please vote on releasing the following candidate as Apache Spark version >>> 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: >>> >>> https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a >>> >>> The release files, including signatures, digests, etc. can be found at: >>> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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-rc2) can be >>> found at: >>> https://repository.apache.org/content/repositories/orgapachespark-1141/ >>> >>> The staging repository for this release (published as 1.5.0) can be >>> found at: >>> https://repository.apache.org/content/repositories/orgapachespark-1140/ >>> >>> The documentation corresponding to this release can be found at: >>> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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,
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
Seems that Github branch-1.5 already changing the version to 1.5.1-SNAPSHOT, I am a bit confused are we still on 1.5.0 RC3 or we are in 1.5.1 ? Chester On Mon, Aug 31, 2015 at 3:52 PM, Reynold Xinwrote: > I'm going to -1 the release myself since the issue @yhuai identified is > pretty serious. It basically OOMs the driver for reading any files with a > large number of partitions. Looks like the patch for that has already been > merged. > > I'm going to cut rc3 momentarily. > > > On Sun, Aug 30, 2015 at 11:30 AM, Sandy Ryza > wrote: > >> +1 (non-binding) >> built from source and ran some jobs against YARN >> >> -Sandy >> >> On Sat, Aug 29, 2015 at 5:50 AM, vaquar khan >> wrote: >> >>> >>> +1 (1.5.0 RC2)Compiled on Windows with YARN. >>> >>> Regards, >>> Vaquar khan >>> +1 (non-binding, of course) >>> >>> 1. Compiled OSX 10.10 (Yosemite) OK Total time: 42:36 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. joins,sql,set operations,udf OK >>> >>> Cheers >>> >>> >>> On Tue, Aug 25, 2015 at 9:28 PM, Reynold Xin >>> wrote: >>> Please vote on releasing the following candidate as Apache Spark version 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a The release files, including signatures, digests, etc. can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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-rc2) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1141/ The staging repository for this release (published as 1.5.0) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1140/ The documentation corresponding to this release can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
+1 (non-binding) built from source and ran some jobs against YARN -Sandy On Sat, Aug 29, 2015 at 5:50 AM, vaquar khan vaquar.k...@gmail.com wrote: +1 (1.5.0 RC2)Compiled on Windows with YARN. Regards, Vaquar khan +1 (non-binding, of course) 1. Compiled OSX 10.10 (Yosemite) OK Total time: 42:36 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. joins,sql,set operations,udf OK Cheers k/ On Tue, Aug 25, 2015 at 9:28 PM, Reynold Xin r...@databricks.com wrote: Please vote on releasing the following candidate as Apache Spark version 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a The release files, including signatures, digests, etc. can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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-rc2) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1141/ The staging repository for this release (published as 1.5.0) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1140/ The documentation corresponding to this release can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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,
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
+1 (1.5.0 RC2)Compiled on Windows with YARN. Regards, Vaquar khan +1 (non-binding, of course) 1. Compiled OSX 10.10 (Yosemite) OK Total time: 42:36 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. joins,sql,set operations,udf OK Cheers k/ On Tue, Aug 25, 2015 at 9:28 PM, Reynold Xin r...@databricks.com wrote: Please vote on releasing the following candidate as Apache Spark version 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a The release files, including signatures, digests, etc. can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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-rc2) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1141/ The staging repository for this release (published as 1.5.0) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1140/ The documentation corresponding to this release can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
Marcelo, Thanks for replying -- after looking at my test again, I misinterpreted another issue I'm seeing which is unrelated (note I'm not using a pre-built binary, rather had to build my own with Yarn/Hive support, as I want to use it on an older cluster (CDH5.1.0)). I can start up a pyspark app on YARN, so I don't want to block this. +1 Best, Jonathan On Fri, Aug 28, 2015 at 2:34 PM, Marcelo Vanzin van...@cloudera.com wrote: Hi Jonathan, Can you be more specific about what problem you're running into? SPARK-6869 fixed the issue of pyspark vs. assembly jar by shipping the pyspark archives separately to YARN. With that fix in place, pyspark doesn't need to get anything from the Spark assembly, so it has no problems running on YARN. I just downloaded spark-1.5.0-bin-hadoop2.6.tgz and tried that out, and pyspark works fine on YARN for me. On Fri, Aug 28, 2015 at 2:22 PM, Jonathan Bender jonathan.ben...@gmail.com wrote: -1 for regression on PySpark + YARN support It seems like this JIRA https://issues.apache.org/jira/browse/SPARK-7733 added a requirement for Java 7 in the build process. Due to some quirks with the Java archive format changes between Java 6 and 7, using PySpark with a YARN uberjar seems to break when compiled with anything after Java 6 (see https://issues.apache.org/jira/browse/SPARK-1920 for reference). -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC2-tp13826p13890.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 -- Marcelo
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
-1 Found a problem on reading partitioned table. Right now, we may create a SQL project/filter operator for every partition. When we have thousands of partitions, there will be a huge number of SQLMetrics (accumulators), which causes high memory pressure to the driver and then takes down the cluster (long GC time causes different kinds of timeouts). https://issues.apache.org/jira/browse/SPARK-10339 Will have a fix soon. On Fri, Aug 28, 2015 at 3:18 PM, Jon Bender jonathan.ben...@gmail.com wrote: Marcelo, Thanks for replying -- after looking at my test again, I misinterpreted another issue I'm seeing which is unrelated (note I'm not using a pre-built binary, rather had to build my own with Yarn/Hive support, as I want to use it on an older cluster (CDH5.1.0)). I can start up a pyspark app on YARN, so I don't want to block this. +1 Best, Jonathan On Fri, Aug 28, 2015 at 2:34 PM, Marcelo Vanzin van...@cloudera.com wrote: Hi Jonathan, Can you be more specific about what problem you're running into? SPARK-6869 fixed the issue of pyspark vs. assembly jar by shipping the pyspark archives separately to YARN. With that fix in place, pyspark doesn't need to get anything from the Spark assembly, so it has no problems running on YARN. I just downloaded spark-1.5.0-bin-hadoop2.6.tgz and tried that out, and pyspark works fine on YARN for me. On Fri, Aug 28, 2015 at 2:22 PM, Jonathan Bender jonathan.ben...@gmail.com wrote: -1 for regression on PySpark + YARN support It seems like this JIRA https://issues.apache.org/jira/browse/SPARK-7733 added a requirement for Java 7 in the build process. Due to some quirks with the Java archive format changes between Java 6 and 7, using PySpark with a YARN uberjar seems to break when compiled with anything after Java 6 (see https://issues.apache.org/jira/browse/SPARK-1920 for reference). -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC2-tp13826p13890.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 -- Marcelo
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
Hi Jonathan, Can you be more specific about what problem you're running into? SPARK-6869 fixed the issue of pyspark vs. assembly jar by shipping the pyspark archives separately to YARN. With that fix in place, pyspark doesn't need to get anything from the Spark assembly, so it has no problems running on YARN. I just downloaded spark-1.5.0-bin-hadoop2.6.tgz and tried that out, and pyspark works fine on YARN for me. On Fri, Aug 28, 2015 at 2:22 PM, Jonathan Bender jonathan.ben...@gmail.com wrote: -1 for regression on PySpark + YARN support It seems like this JIRA https://issues.apache.org/jira/browse/SPARK-7733 added a requirement for Java 7 in the build process. Due to some quirks with the Java archive format changes between Java 6 and 7, using PySpark with a YARN uberjar seems to break when compiled with anything after Java 6 (see https://issues.apache.org/jira/browse/SPARK-1920 for reference). -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC2-tp13826p13890.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 -- Marcelo - 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 (RC2)
The binary archives seems to be having some issues, which seems consistent on few of the different ones (different versions of hadoop) that I tried. tar -xvf spark-1.5.0-bin-hadoop2.6.tgz x spark-1.5.0-bin-hadoop2.6/lib/spark-examples-1.5.0-hadoop2.6.0.jar x spark-1.5.0-bin-hadoop2.6/lib/spark-assembly-1.5.0-hadoop2.6.0.jar x spark-1.5.0-bin-hadoop2.6/lib/spark-1.5.0-yarn-shuffle.jar x spark-1.5.0-bin-hadoop2.6/README.md tar: copyfile unpack (spark-1.5.0-bin-hadoop2.6/python/test_support/sql/orc_partitioned/SUCCESS.crc) failed: No such file or directory tar tzf spark-1.5.0-bin-hadoop2.3.tgz | grep SUCCESS.crc spark-1.5.0-bin-hadoop2.3/python/test_support/sql/orc_partitioned/._SUCCESS.crc This seems similar to a problem Avro release was having recently. On Tue, Aug 25, 2015 at 9:28 PM, Reynold Xin r...@databricks.com wrote: Please vote on releasing the following candidate as Apache Spark version 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a The release files, including signatures, digests, etc. can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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-rc2) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1141/ The staging repository for this release (published as 1.5.0) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1140/ The documentation corresponding to this release can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
-1 for regression on PySpark + YARN support It seems like this JIRA https://issues.apache.org/jira/browse/SPARK-7733 added a requirement for Java 7 in the build process. Due to some quirks with the Java archive format changes between Java 6 and 7, using PySpark with a YARN uberjar seems to break when compiled with anything after Java 6 (see https://issues.apache.org/jira/browse/SPARK-1920 for reference). -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC2-tp13826p13890.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 (RC2)
Are you just submitting from Windows or are you also running YARN on Windows? If the former, I think the only fix that would be needed is this line (from that same patch): https://github.com/apache/spark/blob/master/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala#L434 I don't believe YARN running on Windows worked at all before that patch (regardless of that individual issue). I'll leave it to Reynold whether Windows support is critical enough to warrant a new rc. On Thu, Aug 27, 2015 at 8:50 AM, saurfang forest.f...@outlook.com wrote: Nevermind. It looks like this has been fixed in https://github.com/apache/spark/pull/8053 but didn't make the cut? Even though the associated JIRA is targeted for 1.6, I was able to submit to YARN from Windows without a problem with 1.4. I'm wondering if this fix will be merged to 1.5 branch. Let me know if someone thinks I'm just not doing the compile and/or spark-submit right. -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC2-tp13826p13872.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 -- Marcelo - 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 (RC2)
Nevermind. It looks like this has been fixed in https://github.com/apache/spark/pull/8053 but didn't make the cut? Even though the associated JIRA is targeted for 1.6, I was able to submit to YARN from Windows without a problem with 1.4. I'm wondering if this fix will be merged to 1.5 branch. Let me know if someone thinks I'm just not doing the compile and/or spark-submit right. -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC2-tp13826p13872.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 (RC2)
Agree on the line fix. I'm submitting from Windows to YARN running on Linux. I imagine that this isn't that uncommon especially for developers working in corporate setting. On Thu, Aug 27, 2015 at 12:52 PM Marcelo Vanzin van...@cloudera.com wrote: Are you just submitting from Windows or are you also running YARN on Windows? If the former, I think the only fix that would be needed is this line (from that same patch): https://github.com/apache/spark/blob/master/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala#L434 I don't believe YARN running on Windows worked at all before that patch (regardless of that individual issue). I'll leave it to Reynold whether Windows support is critical enough to warrant a new rc. On Thu, Aug 27, 2015 at 8:50 AM, saurfang forest.f...@outlook.com wrote: Nevermind. It looks like this has been fixed in https://github.com/apache/spark/pull/8053 but didn't make the cut? Even though the associated JIRA is targeted for 1.6, I was able to submit to YARN from Windows without a problem with 1.4. I'm wondering if this fix will be merged to 1.5 branch. Let me know if someone thinks I'm just not doing the compile and/or spark-submit right. -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC2-tp13826p13872.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 -- Marcelo - 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 (RC2)
Compiled on Windows with YARN and HIVE. However I got exception when submitting application to YARN due to: java.net.URISyntaxException: Illegal character in opaque part at index 2: D:\TEMP\spark-b32c5b5b-a9fa-4cfd-a233-3977588d4092\__spark_conf__1960856096319316224.zip at java.net.URI$Parser.fail(URI.java:2829) at java.net.URI$Parser.checkChars(URI.java:3002) at java.net.URI$Parser.parse(URI.java:3039) at java.net.URI.init(URI.java:595) at org.apache.spark.deploy.yarn.Client.org$apache$spark$deploy$yarn$Client$$distribute$1(Client.scala:321) at org.apache.spark.deploy.yarn.Client.prepareLocalResources(Client.scala:417) It looks like either we can do `new File(path).toURI` at here: https://github.com/apache/spark/blob/v1.5.0-rc2/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala#L321 Or make sure the file path use '/' separator here: https://github.com/apache/spark/blob/v1.5.0-rc2/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala#L417 -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC2-tp13826p13871.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 (RC2)
+1. I tested the without hadoop binary package and ran our internal tests on it with dynamic allocation both on and off. The Windows issue Sen raised could be considered a regression / blocker, though, and it's a one line fix. If we feel that's important, let me know and I'll put up a PR against branch-1.5. On Tue, Aug 25, 2015 at 9:28 PM, Reynold Xin r...@databricks.com wrote: Please vote on releasing the following candidate as Apache Spark version 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a The release files, including signatures, digests, etc. can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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-rc2) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1141/ The staging repository for this release (published as 1.5.0) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1140/ The documentation corresponding to this release can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
+1 (non-binding, of course) 1. Compiled OSX 10.10 (Yosemite) OK Total time: 42:36 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. joins,sql,set operations,udf OK Cheers k/ On Tue, Aug 25, 2015 at 9:28 PM, Reynold Xin r...@databricks.com wrote: Please vote on releasing the following candidate as Apache Spark version 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a The release files, including signatures, digests, etc. can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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-rc2) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1141/ The staging repository for this release (published as 1.5.0) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1140/ The documentation corresponding to this release can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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 -
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
Marcelo - please submit a patch anyway. If we don't include it in this release, it will go into 1.5.1. On Thu, Aug 27, 2015 at 4:56 PM, Marcelo Vanzin van...@cloudera.com wrote: On Thu, Aug 27, 2015 at 4:42 PM, Marcelo Vanzin van...@cloudera.com wrote: The Windows issue Sen raised could be considered a regression / blocker, though, and it's a one line fix. If we feel that's important, let me know and I'll put up a PR against branch-1.5. Looks like Josh just found a blocker, so maybe we can squeeze this in?
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
rxin wrote The release files, including signatures, digests, etc. can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-bin/ Release artifacts are signed with the following key: https://people.apache.org/keys/committer/pwendell.asc I was looking for a version of this release based on Hadoop 2.2. Is there some reason there isn't one, especially since 2.2.0 is described as the default Hadoop version? -- Randy Kerber -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-5-0-RC2-tp13826p13828.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 (RC2)
- tested the backpressure/rate controlling in streaming. It works as expected. - there is a problem with the Scala 2.11 sbt build: https://issues.apache.org/jira/browse/SPARK-10227 Luc Bourlier Luc Bourlier *Spark Team - Typesafe, Inc.* luc.bourl...@typesafe.com http://www.typesafe.com On Wed, Aug 26, 2015 at 6:28 AM, Reynold Xin r...@databricks.com wrote: Please vote on releasing the following candidate as Apache Spark version 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a The release files, including signatures, digests, etc. can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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-rc2) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1141/ The staging repository for this release (published as 1.5.0) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1140/ The documentation corresponding to this release can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
My quick take: no blockers at this point, except for one potential issue. Still some 'critical' bugs worth a look. The release seems to pass tests but i get a lot of spurious failures; it took about 16 hours of running tests to get everything to pass at least once. Current score: 56 issues targeted at 1.5.0, of which 14 bugs, of which no blockers and 8 critical. This one might be a blocker as it seems to mean that SBT + Scala 2.11 does not compile: https://issues.apache.org/jira/browse/SPARK-10227 pretty simple issue, but weigh in on the PR: https://github.com/apache/spark/pull/8433 For reference here are the Critical ones: Key Component Summary Assignee 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-10287 SQL After processing a query using JSON data, Spark SQL continuously refreshes metadata of the table Total: 8 issues I'm seeing the following tests fail intermittently, with -Phive -Phive-thriftserver -Phadoop-2.6 on Ubuntu 15 / Java 7: - security mismatch password *** FAILED *** Expected exception java.io.IOException to be thrown, but java.nio.channels.CancelledKeyException was thrown. (ConnectionManagerSuite.scala:123) DAGSchedulerSuite: ... - misbehaved resultHandler should not crash DAGScheduler and SparkContext *** FAILED *** java.lang.UnsupportedOperationException: taskSucceeded() called on a finished JobWaiter was not instance of org.apache.spark.scheduler.DAGSchedulerSuiteDummyException (DAGSchedulerSuite.scala:861) HeartbeatReceiverSuite: ... - normal heartbeat *** FAILED *** 3 did not equal 2 (HeartbeatReceiverSuite.scala:104) - Unpersisting HttpBroadcast on executors only in distributed mode *** FAILED *** ... - Unpersisting HttpBroadcast on executors and driver in distributed mode *** FAILED *** ... - Unpersisting TorrentBroadcast on executors only in distributed mode *** FAILED *** ... - Unpersisting TorrentBroadcast on executors and driver in distributed mode *** FAILED *** StreamingContextSuite: ... - stop gracefully *** FAILED *** 1749735 did not equal 1190429 Received records = 1749735, processed records = 1190428 (StreamingContextSuite.scala:279) DirectKafkaStreamSuite: - offset recovery *** FAILED *** The code passed to eventually never returned normally. Attempted 193 times over 10.010808486 seconds. Last failure message: strings.forall({ ((elem: Any) = DirectKafkaStreamSuite.collectedData.contains(elem)) }) was false. (DirectKafkaStreamSuite.scala:249) On Wed, Aug 26, 2015 at 5:28 AM, Reynold Xin r...@databricks.com wrote: Please vote on releasing the following candidate as Apache Spark version 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a The release files, including signatures, digests, etc. can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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-rc2) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1141/ The staging repository for this release (published as 1.5.0) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1140/ The documentation corresponding to this release can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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.
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
+1, tested that 1.5.0-RC2 works with Tachyon 0.7.1 as external block store.
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
One small update -- the vote should close Saturday Aug 29. Not Friday Aug 29. On Tue, Aug 25, 2015 at 9:28 PM, Reynold Xin r...@databricks.com wrote: Please vote on releasing the following candidate as Apache Spark version 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a The release files, including signatures, digests, etc. can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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-rc2) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1141/ The staging repository for this release (published as 1.5.0) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1140/ The documentation corresponding to this release can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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 details UI - Better load
Re: [VOTE] Release Apache Spark 1.5.0 (RC2)
The Scala 2.11 issue should be fixed, but doesn't need to be a blocker, since Maven builds fine. The sbt build is more aggressive to make sure we catch warnings. On Wed, Aug 26, 2015 at 10:01 AM, Sean Owen so...@cloudera.com wrote: My quick take: no blockers at this point, except for one potential issue. Still some 'critical' bugs worth a look. The release seems to pass tests but i get a lot of spurious failures; it took about 16 hours of running tests to get everything to pass at least once. Current score: 56 issues targeted at 1.5.0, of which 14 bugs, of which no blockers and 8 critical. This one might be a blocker as it seems to mean that SBT + Scala 2.11 does not compile: https://issues.apache.org/jira/browse/SPARK-10227 pretty simple issue, but weigh in on the PR: https://github.com/apache/spark/pull/8433 For reference here are the Critical ones: Key Component Summary Assignee 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-10287 SQL After processing a query using JSON data, Spark SQL continuously refreshes metadata of the table Total: 8 issues I'm seeing the following tests fail intermittently, with -Phive -Phive-thriftserver -Phadoop-2.6 on Ubuntu 15 / Java 7: - security mismatch password *** FAILED *** Expected exception java.io.IOException to be thrown, but java.nio.channels.CancelledKeyException was thrown. (ConnectionManagerSuite.scala:123) DAGSchedulerSuite: ... - misbehaved resultHandler should not crash DAGScheduler and SparkContext *** FAILED *** java.lang.UnsupportedOperationException: taskSucceeded() called on a finished JobWaiter was not instance of org.apache.spark.scheduler.DAGSchedulerSuiteDummyException (DAGSchedulerSuite.scala:861) HeartbeatReceiverSuite: ... - normal heartbeat *** FAILED *** 3 did not equal 2 (HeartbeatReceiverSuite.scala:104) - Unpersisting HttpBroadcast on executors only in distributed mode *** FAILED *** ... - Unpersisting HttpBroadcast on executors and driver in distributed mode *** FAILED *** ... - Unpersisting TorrentBroadcast on executors only in distributed mode *** FAILED *** ... - Unpersisting TorrentBroadcast on executors and driver in distributed mode *** FAILED *** StreamingContextSuite: ... - stop gracefully *** FAILED *** 1749735 did not equal 1190429 Received records = 1749735, processed records = 1190428 (StreamingContextSuite.scala:279) DirectKafkaStreamSuite: - offset recovery *** FAILED *** The code passed to eventually never returned normally. Attempted 193 times over 10.010808486 seconds. Last failure message: strings.forall({ ((elem: Any) = DirectKafkaStreamSuite.collectedData.contains(elem)) }) was false. (DirectKafkaStreamSuite.scala:249) On Wed, Aug 26, 2015 at 5:28 AM, Reynold Xin r...@databricks.com wrote: Please vote on releasing the following candidate as Apache Spark version 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a The release files, including signatures, digests, etc. can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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-rc2) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1141/ The staging repository for this release (published as 1.5.0) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1140/ The documentation corresponding to this release can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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.
[VOTE] Release Apache Spark 1.5.0 (RC2)
Please vote on releasing the following candidate as Apache Spark version 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a The release files, including signatures, digests, etc. can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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-rc2) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1141/ The staging repository for this release (published as 1.5.0) can be found at: https://repository.apache.org/content/repositories/orgapachespark-1140/ The documentation corresponding to this release can be found at: http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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 details UI - Better load balancing and scheduling of receivers across cluster - Include streaming storage in web UI Machine Learning and Advanced Analytics - Feature transformers: CountVectorizer, Discrete Cosine transformation, MinMaxScaler, NGram, PCA, RFormula,