Ted, Is there anything in the works or are there tasks already to do the back-porting?
Just curious. Thanks, Ben > On Mar 13, 2016, at 3:46 PM, Ted Yu <yuzhih...@gmail.com> wrote: > > class HFileWriterImpl (in standalone file) is only present in master branch. > It is not in branch-1. > > compressionByName() resides in class with @InterfaceAudience.Private which > got moved in master branch. > > So looks like there is some work to be done for backporting to branch-1 :-) > > On Sun, Mar 13, 2016 at 1:35 PM, Benjamin Kim <bbuil...@gmail.com > <mailto:bbuil...@gmail.com>> wrote: > Ted, > > I did as you said, but it looks like that HBaseContext relies on some > differences in HBase itself. > > [ERROR] > /home/bkim/hbase-rel-1.0.2/hbase-spark/src/main/scala/org/apache/hadoop/hbase/spark/HBaseContext.scala:30: > error: object HFileWriterImpl is not a member of package > org.apache.hadoop.hbase.io.hfile > [ERROR] import org.apache.hadoop.hbase.io.hfile.{CacheConfig, > HFileContextBuilder, HFileWriterImpl} > [ERROR] ^ > [ERROR] > /home/bkim/hbase-rel-1.0.2/hbase-spark/src/main/scala/org/apache/hadoop/hbase/spark/HBaseContext.scala:627: > error: not found: value HFileWriterImpl > [ERROR] val hfileCompression = HFileWriterImpl > [ERROR] ^ > [ERROR] > /home/bkim/hbase-rel-1.0.2/hbase-spark/src/main/scala/org/apache/hadoop/hbase/spark/HBaseContext.scala:750: > error: not found: value HFileWriterImpl > [ERROR] val defaultCompression = HFileWriterImpl > [ERROR] ^ > [ERROR] > /home/bkim/hbase-rel-1.0.2/hbase-spark/src/main/scala/org/apache/hadoop/hbase/spark/HBaseContext.scala:898: > error: value COMPARATOR is not a member of object > org.apache.hadoop.hbase.CellComparator > [ERROR] > .withComparator(CellComparator.COMPARATOR).withFileContext(hFileContext) > > So… back to my original question… do you know when these incompatibilities > were introduced? If so, I can pulled that version at time and try again. > > Thanks, > Ben > >> On Mar 13, 2016, at 12:42 PM, Ted Yu <yuzhih...@gmail.com >> <mailto:yuzhih...@gmail.com>> wrote: >> >> Benjamin: >> Since hbase-spark is in its own module, you can pull the whole hbase-spark >> subtree into hbase 1.0 root dir and add the following to root pom.xml: >> <module>hbase-spark</module> >> >> Then you would be able to build the module yourself. >> >> hbase-spark module uses APIs which are compatible with hbase 1.0 >> >> Cheers >> >> On Sun, Mar 13, 2016 at 11:39 AM, Benjamin Kim <bbuil...@gmail.com >> <mailto:bbuil...@gmail.com>> wrote: >> Hi Ted, >> >> I see that you’re working on the hbase-spark module for hbase. I recently >> packaged the SparkOnHBase project and gave it a test run. It works like a >> charm on CDH 5.4 and 5.5. All I had to do was add >> /opt/cloudera/parcels/CDH/jars/htrace-core-3.1.0-incubating.jar to the >> classpath.txt file in /etc/spark/conf. Then, I ran spark-shell with “—jars >> /path/to/spark-hbase-0.0.2-clabs.jar” as an argument and used the >> easy-to-use HBaseContext for HBase operations. Now, I want to use the latest >> in Dataframes. Since the new functionality is only in the hbase-spark >> module, I want to know how to get it and package it for CDH 5.5, which still >> uses HBase 1.0.0. Can you tell me what version of hbase master is still >> backwards compatible? >> >> By the way, we are using Spark 1.6 if it matters. >> >> Thanks, >> Ben >> >>> On Feb 10, 2016, at 2:34 AM, Ted Yu <yuzhih...@gmail.com >>> <mailto:yuzhih...@gmail.com>> wrote: >>> >>> Have you tried adding hbase client jars to spark.executor.extraClassPath ? >>> >>> Cheers >>> >>> On Wed, Feb 10, 2016 at 12:17 AM, Prabhu Joseph <prabhujose.ga...@gmail.com >>> <mailto:prabhujose.ga...@gmail.com>> wrote: >>> + Spark-Dev >>> >>> For a Spark job on YARN accessing hbase table, added all hbase client jars >>> into spark.yarn.dist.files, NodeManager when launching container i.e >>> executor, does localization and brings all hbase-client jars into executor >>> CWD, but still the executor tasks fail with ClassNotFoundException of hbase >>> client jars, when i checked launch container.sh , Classpath does not have >>> $PWD/* and hence all the hbase client jars are ignored. >>> >>> Is spark.yarn.dist.files not for adding jars into the executor classpath. >>> >>> Thanks, >>> Prabhu Joseph >>> >>> On Tue, Feb 9, 2016 at 1:42 PM, Prabhu Joseph <prabhujose.ga...@gmail.com >>> <mailto:prabhujose.ga...@gmail.com>> wrote: >>> Hi All, >>> >>> When i do count on a Hbase table from Spark Shell which runs as >>> yarn-client mode, the job fails at count(). >>> >>> MASTER=yarn-client ./spark-shell >>> >>> import org.apache.hadoop.hbase.{HBaseConfiguration, HTableDescriptor, >>> TableName} >>> import org.apache.hadoop.hbase.client.HBaseAdmin >>> import org.apache.hadoop.hbase.mapreduce.TableInputFormat >>> >>> val conf = HBaseConfiguration.create() >>> conf.set(TableInputFormat.INPUT_TABLE,"spark") >>> >>> val hBaseRDD = sc.newAPIHadoopRDD(conf, >>> classOf[TableInputFormat],classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable],classOf[org.apache.hadoop.hbase.client.Result]) >>> hBaseRDD.count() >>> >>> >>> Tasks throw below exception, the actual exception is swallowed, a bug >>> JDK-7172206. After installing hbase client on all NodeManager machines, the >>> Spark job ran fine. So I confirmed that the issue is with executor >>> classpath. >>> >>> But i am searching for some other way of including hbase jars in spark >>> executor classpath instead of installing hbase client on all NM machines. >>> Tried adding all hbase jars in spark.yarn.dist.files , NM logs shows that >>> it localized all hbase jars, still the job fails. Tried >>> spark.executor.extraClasspath, still the job fails. >>> >>> Is there any way we can access hbase from Executor without installing >>> hbase-client on all machines. >>> >>> >>> 16/02/09 02:34:57 WARN TaskSetManager: Lost task 0.0 in stage 0.0 (TID 0, >>> prabhuFS1): java.lang.IllegalStateException: unread block data >>> at >>> java.io.ObjectInputStream$BlockDataInputStream.setBlockDataMode(ObjectInputStream.java:2428) >>> at >>> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1382) >>> at >>> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1997) >>> at >>> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1921) >>> at >>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798) >>> at >>> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) >>> at java.io.ObjectInputStream.readObject(ObjectInputStream.java:370) >>> at >>> org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:68) >>> at >>> org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:94) >>> at >>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:185) >>> at >>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) >>> at >>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) >>> at java.lang.Thread.run(Thread.java:745) >>> >>> >>> >>> Thanks, >>> Prabhu Joseph >>> >>> >> >> > >
