Amazon uses the following impl https://gist.github.com/apivovarov/bb215f08318318570567 But for some reason Spark show error at the end of the job
16/02/26 08:16:54 INFO scheduler.DAGScheduler: ResultStage 0 (saveAsTextFile at <console>:28) finished in 14.305 s 16/02/26 08:16:54 INFO cluster.YarnScheduler: Removed TaskSet 0.0, whose tasks have all completed, from pool 16/02/26 08:16:54 INFO scheduler.DAGScheduler: Job 0 finished: saveAsTextFile at <console>:28, took 14.467271 s java.io.FileNotFoundException: File s3n://my-backup/test/test1/_temporary/0 does not exist. at org.apache.hadoop.fs.s3native.NativeS3FileSystem.listStatus(NativeS3FileSystem.java:564) at org.apache.hadoop.fs.FileSystem.listStatus(FileSystem.java:1485) at org.apache.hadoop.fs.FileSystem.listStatus(FileSystem.java:1525) at org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter.getAllCommittedTaskPaths(FileOutputCommitter.java:269) at org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter.commitJob(FileOutputCommitter.java:309) at org.apache.hadoop.mapred.FileOutputCommitter.commitJob(FileOutputCommitter.java:136) at org.apache.spark.SparkHadoopWriter.commitJob(SparkHadoopWriter.scala:112) at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1.apply$mcV$sp(PairRDDFunctions.scala:1214) at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1.apply(PairRDDFunctions.scala:1156) at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1.apply(PairRDDFunctions.scala:1156) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111) at org.apache.spark.rdd.RDD.withScope(RDD.scala:316) Another implementation works fine https://gist.github.com/aarondav/c513916e72101bbe14ec On Thu, Feb 25, 2016 at 10:24 PM, Takeshi Yamamuro <linguin....@gmail.com> wrote: > Hi, > > Great work! > What is the concrete performance gain of the committer on s3? > I'd like to know. > > I think there is no direct committer for files because these kinds of > committer has risks > to loss data (See: SPARK-10063). > Until this resolved, ISTM files cannot support direct commits. > > thanks, > > > > On Fri, Feb 26, 2016 at 8:39 AM, Teng Qiu <teng...@gmail.com> wrote: > >> yes, should be this one >> https://gist.github.com/aarondav/c513916e72101bbe14ec >> >> then need to set it in spark-defaults.conf : >> https://github.com/zalando/spark/commit/3473f3f1ef27830813c1e0b3686e96a55f49269c#diff-f7a46208be9e80252614369be6617d65R13 >> >> Am Freitag, 26. Februar 2016 schrieb Yin Yang : >> > The header of DirectOutputCommitter.scala says Databricks. >> > Did you get it from Databricks ? >> > On Thu, Feb 25, 2016 at 3:01 PM, Teng Qiu <teng...@gmail.com> wrote: >> >> >> >> interesting in this topic as well, why the DirectFileOutputCommitter >> not included? >> >> we added it in our fork, >> under core/src/main/scala/org/apache/spark/mapred/DirectOutputCommitter.scala >> >> moreover, this DirectFileOutputCommitter is not working for the insert >> operations in HiveContext, since the Committer is called by hive (means >> uses dependencies in hive package) >> >> we made some hack to fix this, you can take a look: >> >> >> https://github.com/apache/spark/compare/branch-1.6...zalando:branch-1.6-zalando >> >> >> >> may bring some ideas to other spark contributors to find a better way >> to use s3. >> >> >> >> 2016-02-22 23:18 GMT+01:00 igor.berman <igor.ber...@gmail.com>: >> >>> >> >>> Hi, >> >>> Wanted to understand if anybody uses DirectFileOutputCommitter or >> alikes >> >>> especially when working with s3? >> >>> I know that there is one impl in spark distro for parquet format, but >> not >> >>> for files - why? >> >>> >> >>> Imho, it can bring huge performance boost. >> >>> Using default FileOutputCommiter with s3 has big overhead at commit >> stage >> >>> when all parts are copied one-by-one to destination dir from >> _temporary, >> >>> which is bottleneck when number of partitions is high. >> >>> >> >>> Also, wanted to know if there are some problems when using >> >>> DirectFileOutputCommitter? >> >>> If writing one partition directly will fail in the middle is spark >> will >> >>> notice this and will fail job(say after all retries)? >> >>> >> >>> thanks in advance >> >>> >> >>> >> >>> >> >>> >> >>> -- >> >>> View this message in context: >> http://apache-spark-user-list.1001560.n3.nabble.com/DirectFileOutputCommiter-tp26296.html >> >>> Sent from the Apache Spark User List mailing list archive at >> Nabble.com. >> >>> >> >>> --------------------------------------------------------------------- >> >>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >> >>> For additional commands, e-mail: user-h...@spark.apache.org >> >>> >> >> >> > >> > >> > > > > -- > --- > Takeshi Yamamuro >
