Hi, Recently I was working on a PR to use Tachyon as OFF_HEAP store for Spark Streaming and make sure Spark Streaming can recover from Driver failure and recover the blocks form Tachyon.
The The Motivation for this PR is : If Streaming application stores the blocks OFF_HEAP, it may not need any WAL like feature to recover from Driver failure. As long as the writing of blocks to Tachyon from Streaming receiver is durable, it should be recoverable from Tachyon directly on Driver failure. This can solve the issue of expensive WAL write and duplicating the blocks both in MEMORY and also WAL and also guarantee end to end No-Data-Loss channel using OFF_HEAP store. https://github.com/apache/spark/pull/8817 This PR still under review . But having done various fail over testing in my environment , I see this PR worked perfectly fine without any data loss . Let see what TD and other have to say on this PR . Below is the configuration I used to test this PR .. Spark : 1.6 from Master Tachyon : 0.7.1 SparkConfiguration Details : SparkConf conf = new SparkConf().setAppName("TestTachyon") .set("spark.streaming.unpersist", "true") .set("spark.local.dir", "/mnt1/spark/tincan") .set("tachyon.zookeeper.address","10.252.5.113:2182") .set("tachyon.usezookeeper","true") .set("spark.externalBlockStore.url", "tachyon-ft:// ip-10-252-5-113.asskickery.us:19998") .set("spark.externalBlockStore.baseDir", "/sparkstreaming") .set("spark.externalBlockStore.folderName","pearson") .set("spark.externalBlockStore.dirId", "subpub") .set("spark.externalBlockStore.keepExternalBlockStoreDirOnShutdown","true"); JavaStreamingContext jsc = new JavaStreamingContext(conf, new Duration( 10000)); String checkpointDirectory = "hdfs://10.252.5.113:9000/user/hadoop/spark/wal "; jsc.checkpoint(checkpointDirectory); //I am using the My Receiver Based Consumer ( https://github.com/dibbhatt/kafka-spark-consumer) . But KafkaUtil.CreateStream will also work JavaDStream<MessageAndMetadata> unionStreams = ReceiverLauncher.launch( jsc, props, numberOfReceivers, StorageLevel.OFF_HEAP()); Regards, Dibyendu On Sat, Sep 26, 2015 at 11:59 AM, N B <nb.nos...@gmail.com> wrote: > Hi Dibyendu, > > How does one go about configuring spark streaming to use tachyon as its > place for storing checkpoints? Also, can one do this with tachyon running > on a completely different node than where spark processes are running? > > Thanks > Nikunj > > > On Thu, May 21, 2015 at 8:35 PM, Dibyendu Bhattacharya < > dibyendu.bhattach...@gmail.com> wrote: > >> Hi Tathagata, >> >> Thanks for looking into this. Further investigating I found that the >> issue is with Tachyon does not support File Append. The streaming receiver >> which writes to WAL when failed, and again restarted, not able to append to >> same WAL file after restart. >> >> I raised this with Tachyon user group, and Haoyuan told that within 3 >> months time Tachyon file append will be ready. Will revisit this issue >> again then . >> >> Regards, >> Dibyendu >> >> >> On Fri, May 22, 2015 at 12:24 AM, Tathagata Das <t...@databricks.com> >> wrote: >> >>> Looks like somehow the file size reported by the FSInputDStream of >>> Tachyon's FileSystem interface, is returning zero. >>> >>> On Mon, May 11, 2015 at 4:38 AM, Dibyendu Bhattacharya < >>> dibyendu.bhattach...@gmail.com> wrote: >>> >>>> Just to follow up this thread further . >>>> >>>> I was doing some fault tolerant testing of Spark Streaming with Tachyon >>>> as OFF_HEAP block store. As I said in earlier email, I could able to solve >>>> the BlockNotFound exception when I used Hierarchical Storage of >>>> Tachyon , which is good. >>>> >>>> I continue doing some testing around storing the Spark Streaming WAL >>>> and CheckPoint files also in Tachyon . Here is few finding .. >>>> >>>> >>>> When I store the Spark Streaming Checkpoint location in Tachyon , the >>>> throughput is much higher . I tested the Driver and Receiver failure cases >>>> , and Spark Streaming is able to recover without any Data Loss on Driver >>>> failure. >>>> >>>> *But on Receiver failure , Spark Streaming looses data* as I see >>>> Exception while reading the WAL file from Tachyon "receivedData" location >>>> for the same Receiver id which just failed. >>>> >>>> If I change the Checkpoint location back to HDFS , Spark Streaming can >>>> recover from both Driver and Receiver failure . >>>> >>>> Here is the Log details when Spark Streaming receiver failed ...I >>>> raised a JIRA for the same issue : >>>> https://issues.apache.org/jira/browse/SPARK-7525 >>>> >>>> >>>> >>>> INFO : org.apache.spark.scheduler.DAGScheduler - *Executor lost: 2 >>>> (epoch 1)* >>>> INFO : org.apache.spark.storage.BlockManagerMasterEndpoint - Trying to >>>> remove executor 2 from BlockManagerMaster. >>>> INFO : org.apache.spark.storage.BlockManagerMasterEndpoint - Removing >>>> block manager BlockManagerId(2, 10.252.5.54, 45789) >>>> INFO : org.apache.spark.storage.BlockManagerMaster - Removed 2 >>>> successfully in removeExecutor >>>> INFO : org.apache.spark.streaming.scheduler.ReceiverTracker - *Registered >>>> receiver for stream 2 from 10.252.5.62*:47255 >>>> WARN : org.apache.spark.scheduler.TaskSetManager - Lost task 2.1 in >>>> stage 103.0 (TID 421, 10.252.5.62): org.apache.spark.SparkException: *Could >>>> not read data from write ahead log record >>>> FileBasedWriteAheadLogSegment(tachyon-ft://10.252.5.113:19998/tachyon/checkpoint/receivedData/2/log-1431341091711-1431341151711,645603894,10891919 >>>> <http://10.252.5.113:19998/tachyon/checkpoint/receivedData/2/log-1431341091711-1431341151711,645603894,10891919>)* >>>> at org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD.org >>>> $apache$spark$streaming$rdd$WriteAheadLogBackedBlockRDD$$getBlockFromWriteAheadLog$1(WriteAheadLogBackedBlockRDD.scala:144) >>>> at >>>> org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD$$anonfun$compute$1.apply(WriteAheadLogBackedBlockRDD.scala:168) >>>> at >>>> org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD$$anonfun$compute$1.apply(WriteAheadLogBackedBlockRDD.scala:168) >>>> at scala.Option.getOrElse(Option.scala:120) >>>> at >>>> org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD.compute(WriteAheadLogBackedBlockRDD.scala:168) >>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) >>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) >>>> at org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:87) >>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) >>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) >>>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63) >>>> at org.apache.spark.scheduler.Task.run(Task.scala:70) >>>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213) >>>> at >>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) >>>> at >>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) >>>> at java.lang.Thread.run(Thread.java:744) >>>> Caused by: java.lang.IllegalArgumentException:* Seek position is past >>>> EOF: 645603894, fileSize = 0* >>>> at tachyon.hadoop.HdfsFileInputStream.seek(HdfsFileInputStream.java:239) >>>> at >>>> org.apache.hadoop.fs.FSDataInputStream.seek(FSDataInputStream.java:37) >>>> at >>>> org.apache.spark.streaming.util.FileBasedWriteAheadLogRandomReader.read(FileBasedWriteAheadLogRandomReader.scala:37) >>>> at >>>> org.apache.spark.streaming.util.FileBasedWriteAheadLog.read(FileBasedWriteAheadLog.scala:104) >>>> at org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD.org >>>> $apache$spark$streaming$rdd$WriteAheadLogBackedBlockRDD$$getBlockFromWriteAheadLog$1(WriteAheadLogBackedBlockRDD.scala:141) >>>> ... 15 more >>>> >>>> INFO : org.apache.spark.scheduler.TaskSetManager - Starting task 2.2 in >>>> stage 103.0 (TID 422, 10.252.5.61, ANY, 1909 bytes) >>>> INFO : org.apache.spark.scheduler.TaskSetManager - Lost task 2.2 in >>>> stage 103.0 (TID 422) on executor 10.252.5.61: >>>> org.apache.spark.SparkException (Could not read data from write ahead log >>>> record FileBasedWriteAheadLogSegment(tachyon-ft:// >>>> 10.252.5.113:19998/tachyon/checkpoint/receivedData/2/log-1431341091711-1431341151711,645603894,10891919)) >>>> [duplicate 1] >>>> INFO : org.apache.spark.scheduler.TaskSetManager - Starting task 2.3 in >>>> stage 103.0 (TID 423, 10.252.5.62, ANY, 1909 bytes) >>>> INFO : org.apache.spark.deploy.client.AppClient$ClientActor - Executor >>>> updated: app-20150511104442-0048/2 is now LOST (worker lost) >>>> INFO : org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend - >>>> Executor app-20150511104442-0048/2 removed: worker lost >>>> ERROR: org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend - >>>> Asked to remove non-existent executor 2 >>>> INFO : org.apache.spark.scheduler.TaskSetManager - Lost task 2.3 in >>>> stage 103.0 (TID 423) on executor 10.252.5.62: >>>> org.apache.spark.SparkException (Could not read data from write ahead log >>>> record FileBasedWriteAheadLogSegment(tachyon-ft:// >>>> 10.252.5.113:19998/tachyon/checkpoint/receivedData/2/log-1431341091711-1431341151711,645603894,10891919)) >>>> [duplicate 2] >>>> ERROR: org.apache.spark.scheduler.TaskSetManager - Task 2 in stage >>>> 103.0 failed 4 times; aborting job >>>> INFO : org.apache.spark.scheduler.TaskSchedulerImpl - Removed TaskSet >>>> 103.0, whose tasks have all completed, from pool >>>> INFO : org.apache.spark.scheduler.TaskSchedulerImpl - Cancelling stage >>>> 103 >>>> INFO : org.apache.spark.scheduler.DAGScheduler - ResultStage 103 >>>> (foreachRDD at Consumer.java:92) failed in 0.943 s >>>> INFO : org.apache.spark.scheduler.DAGScheduler - Job 120 failed: >>>> foreachRDD at Consumer.java:92, took 0.953482 s >>>> ERROR: org.apache.spark.streaming.scheduler.JobScheduler - Error >>>> running job streaming job 1431341145000 ms.0 >>>> org.apache.spark.SparkException: Job aborted due to stage failure: Task >>>> 2 in stage 103.0 failed 4 times, most recent failure: Lost task 2.3 in >>>> stage 103.0 (TID 423, 10.252.5.62): org.apache.spark.SparkException: Could >>>> not read data from write ahead log record >>>> FileBasedWriteAheadLogSegment(tachyon-ft:// >>>> 10.252.5.113:19998/tachyon/checkpoint/receivedData/2/log-1431341091711-1431341151711,645603894,10891919 >>>> ) >>>> at org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD.org >>>> $apache$spark$streaming$rdd$WriteAheadLogBackedBlockRDD$$getBlockFromWriteAheadLog$1(WriteAheadLogBackedBlockRDD.scala:144) >>>> at >>>> org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD$$anonfun$compute$1.apply(WriteAheadLogBackedBlockRDD.scala:168) >>>> at >>>> org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD$$anonfun$compute$1.apply(WriteAheadLogBackedBlockRDD.scala:168) >>>> at scala.Option.getOrElse(Option.scala:120) >>>> at >>>> org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD.compute(WriteAheadLogBackedBlockRDD.scala:168) >>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) >>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) >>>> at org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:87) >>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) >>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) >>>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63) >>>> at org.apache.spark.scheduler.Task.run(Task.scala:70) >>>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213) >>>> at >>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) >>>> at >>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) >>>> at java.lang.Thread.run(Thread.java:744) >>>> Caused by: java.lang.IllegalArgumentException: Seek position is past >>>> EOF: 645603894, fileSize = 0 >>>> at tachyon.hadoop.HdfsFileInputStream.seek(HdfsFileInputStream.java:239) >>>> at >>>> org.apache.hadoop.fs.FSDataInputStream.seek(FSDataInputStream.java:37) >>>> at >>>> org.apache.spark.streaming.util.FileBasedWriteAheadLogRandomReader.read(FileBasedWriteAheadLogRandomReader.scala:37) >>>> at >>>> org.apache.spark.streaming.util.FileBasedWriteAheadLog.read(FileBasedWriteAheadLog.scala:104) >>>> at org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD.org >>>> $apache$spark$streaming$rdd$WriteAheadLogBackedBlockRDD$$getBlockFromWriteAheadLog$1(WriteAheadLogBackedBlockRDD.scala:141) >>>> ... 15 more >>>> >>>> >>>> >>>> >>>> >>>> >>>> On Fri, May 8, 2015 at 11:03 PM, Haoyuan Li <haoyuan...@gmail.com> >>>> wrote: >>>> >>>>> Thanks for the updates! >>>>> >>>>> Best, >>>>> >>>>> Haoyuan >>>>> >>>>> On Fri, May 8, 2015 at 8:40 AM, Dibyendu Bhattacharya < >>>>> dibyendu.bhattach...@gmail.com> wrote: >>>>> >>>>>> Just a followup on this Thread . >>>>>> >>>>>> I tried Hierarchical Storage on Tachyon ( >>>>>> http://tachyon-project.org/Hierarchy-Storage-on-Tachyon.html ) , and >>>>>> that >>>>>> seems to have worked and I did not see any any Spark Job failed due to >>>>>> BlockNotFoundException. >>>>>> below is my Hierarchical Storage settings.. >>>>>> >>>>>> -Dtachyon.worker.hierarchystore.level.max=2 >>>>>> -Dtachyon.worker.hierarchystore.level0.alias=MEM >>>>>> -Dtachyon.worker.hierarchystore.level0.dirs.path=$TACHYON_RAM_FOLDER >>>>>> >>>>>> >>>>>> -Dtachyon.worker.hierarchystore.level0.dirs.quota=$TACHYON_WORKER_MEMORY_SIZE >>>>>> -Dtachyon.worker.hierarchystore.level1.alias=HDD >>>>>> -Dtachyon.worker.hierarchystore.level1.dirs.path=/mnt/tachyon >>>>>> -Dtachyon.worker.hierarchystore.level1.dirs.quota=50GB >>>>>> -Dtachyon.worker.allocate.strategy=MAX_FREE >>>>>> -Dtachyon.worker.evict.strategy=LRU >>>>>> >>>>>> Regards, >>>>>> Dibyendu >>>>>> >>>>>> On Thu, May 7, 2015 at 1:46 PM, Dibyendu Bhattacharya < >>>>>> dibyendu.bhattach...@gmail.com> wrote: >>>>>> >>>>>> > Dear All , >>>>>> > >>>>>> > I have been playing with Spark Streaming on Tachyon as the OFF_HEAP >>>>>> block >>>>>> > store . Primary reason for evaluating Tachyon is to find if >>>>>> Tachyon can >>>>>> > solve the Spark BlockNotFoundException . >>>>>> > >>>>>> > In traditional MEMORY_ONLY StorageLevel, when blocks are evicted , >>>>>> jobs >>>>>> > failed due to block not found exception and storing blocks in >>>>>> > MEMORY_AND_DISK is not a good option either as it impact the >>>>>> throughput a >>>>>> > lot . >>>>>> > >>>>>> > >>>>>> > To test how Tachyon behave , I took the latest spark 1.4 from >>>>>> master , and >>>>>> > used Tachyon 0.6.4 and configured Tachyon in Fault Tolerant Mode . >>>>>> Tachyon >>>>>> > is running in 3 Node AWS x-large cluster and Spark is running in 3 >>>>>> node AWS >>>>>> > x-large cluster. >>>>>> > >>>>>> > I have used the low level Receiver based Kafka consumer ( >>>>>> > https://github.com/dibbhatt/kafka-spark-consumer) which I have >>>>>> written >>>>>> > to pull from Kafka and write Blocks to Tachyon >>>>>> > >>>>>> > >>>>>> > I found there is similar improvement in throughput (as MEMORY_ONLY >>>>>> case ) >>>>>> > but very good overall memory utilization (as it is off heap store) . >>>>>> > >>>>>> > >>>>>> > But I found one issue on which I need to clarification . >>>>>> > >>>>>> > >>>>>> > In Tachyon case also , I find BlockNotFoundException , but due to >>>>>> a >>>>>> > different reason . What I see TachyonBlockManager.scala put the >>>>>> blocks in >>>>>> > WriteType.TRY_CACHE configuration . And because of this Blocks ate >>>>>> evicted >>>>>> > from Tachyon Cache and when Spark try to find the block it throws >>>>>> > BlockNotFoundException . >>>>>> > >>>>>> > I see a pull request which discuss the same .. >>>>>> > >>>>>> > https://github.com/apache/spark/pull/158#discussion_r11195271 >>>>>> > >>>>>> > >>>>>> > When I modified the WriteType to CACHE_THROUGH , BlockDropException >>>>>> is >>>>>> > gone , but it again impact the throughput .. >>>>>> > >>>>>> > >>>>>> > Just curious to know , if Tachyon has any settings which can solve >>>>>> the >>>>>> > Block Eviction from Cache to Disk, other than explicitly setting >>>>>> > CACHE_THROUGH ? >>>>>> > >>>>>> > Regards, >>>>>> > Dibyendu >>>>>> > >>>>>> > >>>>>> > >>>>>> >>>>> >>>>> >>>>> >>>>> -- >>>>> Haoyuan Li >>>>> CEO, Tachyon Nexus <http://www.tachyonnexus.com/> >>>>> AMPLab, EECS, UC Berkeley http://www.cs.berkeley.edu/~haoyuan/ >>>>> >>>> >>>> >>> >> >
