throwed exception: ./scripts/guacamole: line 39 :exec: time: not found

[car2008]

Hi , my command is ./scripts/guacamole somatic-joint /home/zhipeng/soft/guacamole/src/test/resources/synth1.normal.100k-200k.withmd.bam /home/zhipeng/soft/guacamole/src/test/resources/synth1.tumor.100k-200k.withmd.bam --reference-fasta /home/zhipengcheng/file/ucsc.hg19.fasta --out /home/zhipengcheng/file/result/out.vcf
but it throwed exception :

./scripts/guacamole: line 39 :exec: time: not found

I don't know how to resole it?

[timodonnell]

Looks like your shell doesn't have the "time" command. Try just deleting
the word "time" from the last line of scripts/guacamole, i.e. instead of:

exec time java -Xmx4g -XX:MaxPermSize=512m "-Dspark.master=local[1]" -cp
"$GUACAMOLE_JAR:$GUACAMOLE_DEPS_JAR" org.hammerlab.guacamole.Main "$@"

replace with:

exec java -Xmx4g -XX:MaxPermSize=512m "-Dspark.master=local[1]" -cp
"$GUACAMOLE_JAR:$GUACAMOLE_DEPS_JAR" org.hammerlab.guacamole.Main "$@"

We should probably do this permanently so others don't hit this issue.

On Fri, Sep 9, 2016 at 5:00 AM, ZhipengCheng notifications@github.com
wrote:

Hi , my command is ./scripts/guacamole somatic-joint
/home/zhipeng/soft/guacamole/src/test/resources/synth1.normal.100k-200k.withmd.bam
/home/zhipeng/soft/guacamole/src/test/resources/synth1.tumor.100k-200k.withmd.bam
--reference-fasta /home/zhipengcheng/file/ucsc.hg19.fasta --out
/home/zhipengcheng/file/result/out.vcf
and it throwed exception :

./scripts/guacamole: line 39 :exec: time: not found

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[ryan-williams]

Interesting, I thought time was pretty standard in bash shells, which scripts/guacamole's shebang line specifies.

What shell are you using @car2008?

Seems like we probably implicitly make stronger assumptions elsewhere about the shell environment we're running in, so might be worth trying to understand what that space looks like as well.

[car2008]

Hi @timodonnell and @ryan-williams ,thank you very much , the shell informations i'm using are :

 zhipengcheng@192$ cat /etc/shells
/bin/sh
/bin/bash
/sbin/nologin
/usr/bin/sh
/usr/bin/bash
/usr/sbin/nologin
08:32:50 ~/soft1/guacamole
zhipengcheng@192$bash --version
GNU bash， version 4.2.46(1)-release (x86_64-redhat-linux-gnu)
Copyright (C) 2011 Free Software Foundation, Inc.
 licence GPLv3+: GNU GPL  licence version 3 or higher <http://gnu.org/licenses/gpl.html>

And I deleted
the word "time" from the last line of scripts/guacamole as @timodonnell mentioned above.
It can run ,but throws another exception :
the command is:
./scripts/guacamole somatic-joint /home/zhipengcheng/soft1/guacamole/src/test/resources/synth1.normal.100k-200k.withmd.bam /home/zhipengcheng/soft1/guacamole/src/test/resources/synth1.tumor.100k-200k.withmd.bam --reference-fasta /home/zhipengcheng/file/ucsc.hg19.fasta --out /home/zhipengcheng/file/result/out.vcf
the exception is (Is there something wrong with my spark1.6.1?):
Found guac jar: target/guacamole-0.0.1-SNAPSHOT.jar
--> [Mon Sep 12 08:01:15 CST 2016]: Guacamole starting.
2016-09-12 08:01:15 WARN NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
--> [3.00 sec. later]: Using samtools without BAM index (index unavailable) to read: /home/zhipengcheng/soft1/guacamole/src/test/resources/synth1.normal.100k-200k.withmd.bam
--> [1.00 sec. later]: Using samtools without BAM index (index unavailable) to read: /home/zhipengcheng/soft1/guacamole/src/test/resources/synth1.tumor.100k-200k.withmd.bam
--> [89.83 sec. later]: Partitioning loci
2016-09-12 08:02:53 WARN TaskSetManager:70 - Stage 0 contains a task of very large size (32697 KB). The maximum recommended task size is 100 KB.
--> [13.03 sec. later]: Partitioned loci: 2 partitions.
Partition-size stats:
num: 2, mean: 100166.5, stddev: 12070.5, mad: 12070.5
elems: 112237, 88096
sorted: 88096, 112237

Contigs-spanned-per-partition stats:
num: 2, mean: 1.5, stddev: 0.5, mad: 0.5
elems: 2, 1
sorted: 1, 2
--> [0.02 sec. later]: Partitioning reads into 2 partitions according to loci partitioning:
19:99899-107890=0,19:107942-121113=0,19:121116-146986=0,19:146989-186580=0,19:186582-188055=0,19:188055-200095=1,20:99902-200099=1
2016-09-12 08:03:02 WARN TaskSetManager:70 - Stage 5 contains a task of very large size (32697 KB). The maximum recommended task size is 100 KB.
2016-09-12 08:03:10 ERROR Executor:74 - Managed memory leak detected; size = 174498752 bytes, TID = 10
2016-09-12 08:03:10 ERROR Executor:95 - Exception in task 0.0 in stage 6.0 (TID 10)
org.hammerlab.guacamole.reference.ContigNotFound: Contig 19 does not exist in the current reference. Available contigs are chr11,chr1_gl000191_random,chr5,chr22,chr1_gl000192_random,chrUn_gl000221,chrUn_gl000234,chr18_gl000207_random,chrUn_gl000232,chrUn_gl000224,chr8,chrUn_gl000248,chrUn_gl000243,chrUn_gl000238,chrUn_gl000249,chrUn_gl000228,chr21_gl000210_random,chrUn_gl000217,chr19,chrY,chr9_gl000198_random,chr6_mann_hap4,chr1,chr4_gl000193_random,chr15,chrUn_gl000213,chr17_gl000204_random,chr19_gl000208_random,chrUn_gl000212,chr17_gl000206_random,chrUn_gl000219,chr12,chr18,chr20,chr8_gl000197_random,chr6_cox_hap2,chr2,chrUn_gl000223,chrUn_gl000237,chr9_gl000200_random,chr7_gl000195_random,chr13,chr6_apd_hap1,chrUn_gl000233,chrUn_gl000227,chrUn_gl000222,chrUn_gl000211,chr9_gl000199_random,chrUn_gl000245,chrUn_gl000242,chrUn_gl000216,chr7,chr19_gl000209_random,chrUn_gl000230,chr14,chrUn_gl000215,chrUn_gl000236,chr6_mcf_hap5,chr3,chrUn_gl000226,chr17_gl000203_random,chrM,chr17,chr6_dbb_hap3,chrUn_gl000246,chr4_gl000194_random,chr11_gl000202_random,chr17_ctg5_hap1,chrUn_gl000241,chr6_qbl_hap6,chr4,chrUn_gl000225,chrUn_gl000231,chrUn_gl000240,chr6,chr9,chr9_gl000201_random,chr4_ctg9_hap1,chrUn_gl000235,chrUn_gl000244,chrUn_gl000220,chrUn_gl000214,chr17_gl000205_random,chrX,chrUn_gl000239,chr6_ssto_hap7,chrUn_gl000218,chrUn_gl000229,chr10,chr21,chr16,chrUn_gl000247,chr8_gl000196_random
at org.hammerlab.guacamole.reference.ReferenceBroadcast.getContig(ReferenceBroadcast.scala:18)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1$$anonfun$7.apply(PileupFlatMapUtils.scala:123)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1$$anonfun$7.apply(PileupFlatMapUtils.scala:116)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1.apply(PileupFlatMapUtils.scala:116)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1.apply(PileupFlatMapUtils.scala:104)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$windowFlatMapWithState$1$$anonfun$apply$1.apply(WindowFlatMapUtils.scala:65)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$windowFlatMapWithState$1$$anonfun$apply$1.apply(WindowFlatMapUtils.scala:55)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$splitPartitionByContigAndMap$2.apply(WindowFlatMapUtils.scala:141)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$splitPartitionByContigAndMap$2.apply(WindowFlatMapUtils.scala:131)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:284)
at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171)
at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
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)
2016-09-12 08:03:10 WARN TaskSetManager:70 - Lost task 0.0 in stage 6.0 (TID 10, localhost): org.hammerlab.guacamole.reference.ContigNotFound: Contig 19 does not exist in the current reference. Available contigs are chr11,chr1_gl000191_random,chr5,chr22,chr1_gl000192_random,chrUn_gl000221,chrUn_gl000234,chr18_gl000207_random,chrUn_gl000232,chrUn_gl000224,chr8,chrUn_gl000248,chrUn_gl000243,chrUn_gl000238,chrUn_gl000249,chrUn_gl000228,chr21_gl000210_random,chrUn_gl000217,chr19,chrY,chr9_gl000198_random,chr6_mann_hap4,chr1,chr4_gl000193_random,chr15,chrUn_gl000213,chr17_gl000204_random,chr19_gl000208_random,chrUn_gl000212,chr17_gl000206_random,chrUn_gl000219,chr12,chr18,chr20,chr8_gl000197_random,chr6_cox_hap2,chr2,chrUn_gl000223,chrUn_gl000237,chr9_gl000200_random,chr7_gl000195_random,chr13,chr6_apd_hap1,chrUn_gl000233,chrUn_gl000227,chrUn_gl000222,chrUn_gl000211,chr9_gl000199_random,chrUn_gl000245,chrUn_gl000242,chrUn_gl000216,chr7,chr19_gl000209_random,chrUn_gl000230,chr14,chrUn_gl000215,chrUn_gl000236,chr6_mcf_hap5,chr3,chrUn_gl000226,chr17_gl000203_random,chrM,chr17,chr6_dbb_hap3,chrUn_gl000246,chr4_gl000194_random,chr11_gl000202_random,chr17_ctg5_hap1,chrUn_gl000241,chr6_qbl_hap6,chr4,chrUn_gl000225,chrUn_gl000231,chrUn_gl000240,chr6,chr9,chr9_gl000201_random,chr4_ctg9_hap1,chrUn_gl000235,chrUn_gl000244,chrUn_gl000220,chrUn_gl000214,chr17_gl000205_random,chrX,chrUn_gl000239,chr6_ssto_hap7,chrUn_gl000218,chrUn_gl000229,chr10,chr21,chr16,chrUn_gl000247,chr8_gl000196_random
at org.hammerlab.guacamole.reference.ReferenceBroadcast.getContig(ReferenceBroadcast.scala:18)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1$$anonfun$7.apply(PileupFlatMapUtils.scala:123)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1$$anonfun$7.apply(PileupFlatMapUtils.scala:116)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1.apply(PileupFlatMapUtils.scala:116)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1.apply(PileupFlatMapUtils.scala:104)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$windowFlatMapWithState$1$$anonfun$apply$1.apply(WindowFlatMapUtils.scala:65)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$windowFlatMapWithState$1$$anonfun$apply$1.apply(WindowFlatMapUtils.scala:55)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$splitPartitionByContigAndMap$2.apply(WindowFlatMapUtils.scala:141)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$splitPartitionByContigAndMap$2.apply(WindowFlatMapUtils.scala:131)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:284)
at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171)
at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
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)

2016-09-12 08:03:10 ERROR TaskSetManager:74 - Task 0 in stage 6.0 failed 1 times; aborting job
2016-09-12 08:03:10 WARN TaskSetManager:70 - Lost task 1.0 in stage 6.0 (TID 11, localhost): TaskKilled (killed intentionally)
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 6.0 failed 1 times, most recent failure: Lost task 0.0 in stage 6.0 (TID 10, localhost): org.hammerlab.guacamole.reference.ContigNotFound: Contig 19 does not exist in the current reference. Available contigs are chr11,chr1_gl000191_random,chr5,chr22,chr1_gl000192_random,chrUn_gl000221,chrUn_gl000234,chr18_gl000207_random,chrUn_gl000232,chrUn_gl000224,chr8,chrUn_gl000248,chrUn_gl000243,chrUn_gl000238,chrUn_gl000249,chrUn_gl000228,chr21_gl000210_random,chrUn_gl000217,chr19,chrY,chr9_gl000198_random,chr6_mann_hap4,chr1,chr4_gl000193_random,chr15,chrUn_gl000213,chr17_gl000204_random,chr19_gl000208_random,chrUn_gl000212,chr17_gl000206_random,chrUn_gl000219,chr12,chr18,chr20,chr8_gl000197_random,chr6_cox_hap2,chr2,chrUn_gl000223,chrUn_gl000237,chr9_gl000200_random,chr7_gl000195_random,chr13,chr6_apd_hap1,chrUn_gl000233,chrUn_gl000227,chrUn_gl000222,chrUn_gl000211,chr9_gl000199_random,chrUn_gl000245,chrUn_gl000242,chrUn_gl000216,chr7,chr19_gl000209_random,chrUn_gl000230,chr14,chrUn_gl000215,chrUn_gl000236,chr6_mcf_hap5,chr3,chrUn_gl000226,chr17_gl000203_random,chrM,chr17,chr6_dbb_hap3,chrUn_gl000246,chr4_gl000194_random,chr11_gl000202_random,chr17_ctg5_hap1,chrUn_gl000241,chr6_qbl_hap6,chr4,chrUn_gl000225,chrUn_gl000231,chrUn_gl000240,chr6,chr9,chr9_gl000201_random,chr4_ctg9_hap1,chrUn_gl000235,chrUn_gl000244,chrUn_gl000220,chrUn_gl000214,chr17_gl000205_random,chrX,chrUn_gl000239,chr6_ssto_hap7,chrUn_gl000218,chrUn_gl000229,chr10,chr21,chr16,chrUn_gl000247,chr8_gl000196_random
at org.hammerlab.guacamole.reference.ReferenceBroadcast.getContig(ReferenceBroadcast.scala:18)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1$$anonfun$7.apply(PileupFlatMapUtils.scala:123)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1$$anonfun$7.apply(PileupFlatMapUtils.scala:116)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1.apply(PileupFlatMapUtils.scala:116)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1.apply(PileupFlatMapUtils.scala:104)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$windowFlatMapWithState$1$$anonfun$apply$1.apply(WindowFlatMapUtils.scala:65)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$windowFlatMapWithState$1$$anonfun$apply$1.apply(WindowFlatMapUtils.scala:55)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$splitPartitionByContigAndMap$2.apply(WindowFlatMapUtils.scala:141)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$splitPartitionByContigAndMap$2.apply(WindowFlatMapUtils.scala:131)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:284)
at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171)
at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
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)

Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
at org.apache.spark.rdd.RDD.count(RDD.scala:1157)
at org.hammerlab.guacamole.commands.SomaticJoint$Caller$.run(SomaticJointCaller.scala:108)
at org.hammerlab.guacamole.commands.SomaticJoint$Caller$.run(SomaticJointCaller.scala:58)
at org.hammerlab.guacamole.commands.SparkCommand.run(SparkCommand.scala:12)
at org.hammerlab.guacamole.commands.Command.run(Command.scala:27)
at org.hammerlab.guacamole.Main$.main(Main.scala:49)
at org.hammerlab.guacamole.Main.main(Main.scala)
Caused by: org.hammerlab.guacamole.reference.ContigNotFound: Contig 19 does not exist in the current reference. Available contigs are chr11,chr1_gl000191_random,chr5,chr22,chr1_gl000192_random,chrUn_gl000221,chrUn_gl000234,chr18_gl000207_random,chrUn_gl000232,chrUn_gl000224,chr8,chrUn_gl000248,chrUn_gl000243,chrUn_gl000238,chrUn_gl000249,chrUn_gl000228,chr21_gl000210_random,chrUn_gl000217,chr19,chrY,chr9_gl000198_random,chr6_mann_hap4,chr1,chr4_gl000193_random,chr15,chrUn_gl000213,chr17_gl000204_random,chr19_gl000208_random,chrUn_gl000212,chr17_gl000206_random,chrUn_gl000219,chr12,chr18,chr20,chr8_gl000197_random,chr6_cox_hap2,chr2,chrUn_gl000223,chrUn_gl000237,chr9_gl000200_random,chr7_gl000195_random,chr13,chr6_apd_hap1,chrUn_gl000233,chrUn_gl000227,chrUn_gl000222,chrUn_gl000211,chr9_gl000199_random,chrUn_gl000245,chrUn_gl000242,chrUn_gl000216,chr7,chr19_gl000209_random,chrUn_gl000230,chr14,chrUn_gl000215,chrUn_gl000236,chr6_mcf_hap5,chr3,chrUn_gl000226,chr17_gl000203_random,chrM,chr17,chr6_dbb_hap3,chrUn_gl000246,chr4_gl000194_random,chr11_gl000202_random,chr17_ctg5_hap1,chrUn_gl000241,chr6_qbl_hap6,chr4,chrUn_gl000225,chrUn_gl000231,chrUn_gl000240,chr6,chr9,chr9_gl000201_random,chr4_ctg9_hap1,chrUn_gl000235,chrUn_gl000244,chrUn_gl000220,chrUn_gl000214,chr17_gl000205_random,chrX,chrUn_gl000239,chr6_ssto_hap7,chrUn_gl000218,chrUn_gl000229,chr10,chr21,chr16,chrUn_gl000247,chr8_gl000196_random
at org.hammerlab.guacamole.reference.ReferenceBroadcast.getContig(ReferenceBroadcast.scala:18)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1$$anonfun$7.apply(PileupFlatMapUtils.scala:123)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1$$anonfun$7.apply(PileupFlatMapUtils.scala:116)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1.apply(PileupFlatMapUtils.scala:116)
at org.hammerlab.guacamole.distributed.PileupFlatMapUtils$$anonfun$pileupFlatMapMultipleSamples$1.apply(PileupFlatMapUtils.scala:104)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$windowFlatMapWithState$1$$anonfun$apply$1.apply(WindowFlatMapUtils.scala:65)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$windowFlatMapWithState$1$$anonfun$apply$1.apply(WindowFlatMapUtils.scala:55)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$splitPartitionByContigAndMap$2.apply(WindowFlatMapUtils.scala:141)
at org.hammerlab.guacamole.distributed.WindowFlatMapUtils$$anonfun$splitPartitionByContigAndMap$2.apply(WindowFlatMapUtils.scala:131)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:284)
at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171)
at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
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)

[arahuja]

Hi @car2008

The error is actually "ContigNotFound: Contig 19". I believe the files you are using are aligned to b37 where the chromosomes don't have the chr prefix. The file you provided, uscs.hg19.fasta has the chromosomes labeled at chr1, chr2, ... chr19 etc. If you have a b37 fasta file available or can download the Broad version available here: ftp://ftp.ncbi.nlm.nih.gov/sra/reports/Assembly/GRCh37-HG19_Broad_variant/Homo_sapiens_assembly19.fasta

[car2008]

Thank you @arahuja ,it can run well when i using the b37 fasta file .

[arahuja]

That is actually not something we have tested, we typically run on Yarn or
locally, but interested to hear if there are issues.

In that stacktrace at least, it is recommending different parameters for
the number of workers

SPARK_WORKER_INSTANCES was detected (set to '4').
This is deprecated in Spark 1.0+.

Please instead use:

   - ./spark-submit with --num-executors to specify the number of executors
   - Or set SPARK_EXECUTOR_INSTANCES
   - spark.executor.instances to configure the number of instances in the
   spark config.

Does one of those solutions resolve the issue?

On Mon, Sep 12, 2016 at 4:15 AM, ZhipengCheng notifications@github.com
wrote:

Hi , i have another question: Can the guacamole work on the spark
standalone mode?
Just now i ran the command spark-submit --master spark://12.122.23.2:7070
--class org.hammerlab.guacamole.Main --jars target/guacamole-deps-only-0.0.1-SNAPSHOT.jar
--verbose target/guacamole-0.0.1-SNAPSHOT.jar somatic-joint
/home/zhipengcheng/file/tmp/dedup.bam --reference-fasta
/home/zhipengcheng/file/ucsc.hg19.fasta --out /home/zhipengcheng/file/
result/out.vcf,but it throws exception:
Using properties file: /home/zhipengcheng/soft/spark-
1.6.1-bin-hadoop2.6/conf/spark-defaults.conf
Adding default property: spark.shuffle.spill.compress=false
Adding default property: spark.shuffle.manager=org.
apache.spark.shuffle.sort.SortShuffleManager
Adding default property: spark.shuffle.sort.bypassMergeThreshold=100
Adding default property: spark.shuffle.compress=false
Adding default property: spark.shuffle.io.maxRetries=6
Adding default property: spark.storage.memoryFraction=0.5
Adding default property: spark.shuffle.io.retryWait=15s
Adding default property: spark.shuffle.memoryFraction=0.1
Parsed arguments:
master spark://12.122.23.2:7070
deployMode null
executorMemory 25g
executorCores null
totalExecutorCores null
propertiesFile /home/zhipengcheng/soft/spark-1.6.1-bin-hadoop2.6/conf/
spark-defaults.conf
driverMemory null
driverCores null
driverExtraClassPath null
driverExtraLibraryPath null
driverExtraJavaOptions null
supervise false
queue null
numExecutors null
files null
pyFiles null
archives null
mainClass org.hammerlab.guacamole.Main
primaryResource file:/home/zhipengcheng/soft1/
guacamole/target/guacamole-0.0.1-SNAPSHOT.jar
name org.hammerlab.guacamole.Main
childArgs [somatic-joint /home/zhipengcheng/file/tmp/dedup.bam
--reference-fasta /home/zhipengcheng/file/ucsc.hg19.fasta --out
/home/zhipengcheng/file/result/out.vcf]
jars file:/home/zhipengcheng/soft1/guacamole/target/guacamole-
deps-only-0.0.1-SNAPSHOT.jar
packages null
packagesExclusions null
repositories null
verbose true

Spark properties used, including those specified through
--conf and those from the properties file /home/zhipengcheng/soft/spark-
1.6.1-bin-hadoop2.6/conf/spark-defaults.conf:
spark.shuffle.spill.compress -> false
spark.shuffle.manager -> org.apache.spark.shuffle.sort.SortShuffleManager
spark.shuffle.sort.bypassMergeThreshold -> 100
spark.shuffle.compress -> false
spark.shuffle.io.retryWait -> 15s
spark.shuffle.io.maxRetries -> 6
spark.storage.memoryFraction -> 0.5
spark.shuffle.memoryFraction -> 0.1

Main class:
org.hammerlab.guacamole.Main
Arguments:
somatic-joint
/home/zhipengcheng/file/tmp/dedup.bam
--reference-fasta
/home/zhipengcheng/file/ucsc.hg19.fasta
--out
/home/zhipengcheng/file/result/out.vcf
System properties:
spark.shuffle.spill.compress -> false
spark.shuffle.manager -> org.apache.spark.shuffle.sort.SortShuffleManager
spark.shuffle.sort.bypassMergeThreshold -> 100
spark.executor.memory -> 25g
spark.shuffle.compress -> false
spark.shuffle.io.retryWait -> 15s
SPARK_SUBMIT -> true
spark.app.name -> org.hammerlab.guacamole.Main
spark.shuffle.io.maxRetries -> 6
spark.jars -> file:/home/zhipengcheng/soft1/guacamole/target/guacamole-
deps-only-0.0.1-SNAPSHOT.jar,file:/home/zhipengcheng/soft1/
guacamole/target/guacamole-0.0.1-SNAPSHOT.jar
spark.submit.deployMode -> client
spark.master -> spark://12.122.23.2:7070
spark.shuffle.memoryFraction -> 0.1
spark.storage.memoryFraction -> 0.5
Classpath elements:
file:/home/zhipengcheng/soft1/guacamole/target/guacamole-0.
0.1-SNAPSHOT.jar
file:/home/zhipengcheng/soft1/guacamole/target/guacamole-
deps-only-0.0.1-SNAPSHOT.jar

--> [Mon Sep 12 15:41:08 CST 2016]: Guacamole starting.
2016-09-12 15:41:08 WARN NativeCodeLoader:62 - Unable to load
native-hadoop library for your platform... using builtin-java classes where
applicable
2016-09-12 15:41:08 WARN SparkConf:70 - Detected deprecated memory
fraction settings: [spark.shuffle.memoryFraction,
spark.storage.memoryFraction]. As of Spark 1.6, execution and storage
memory management are unified. All memory fractions used in the old model
are now deprecated and no longer read. If you wish to use the old memory
management, you may explicitly enable spark.memory.useLegacyMode (not
recommended).
2016-09-12 15:41:08 WARN SparkConf:70 -
SPARK_WORKER_INSTANCES was detected (set to '4').
This is deprecated in Spark 1.0+.

Please instead use:

• ./spark-submit with --num-executors to specify the number of
  executors
• Or set SPARK_EXECUTOR_INSTANCES
• spark.executor.instances to configure the number of instances in the
  spark config.

2016-09-12 15:41:11 WARN AppClient$ClientEndpoint:91 - Failed to connect
to master 192.168.2.24:7070
java.io.IOException: Failed to connect to /12.122.23.2:7070
at org.apache.spark.network.client.TransportClientFactory.createClient(
TransportClientFactory.java:216)
at org.apache.spark.network.client.TransportClientFactory.createClient(
TransportClientFactory.java:167)
at org.apache.spark.rpc.netty.NettyRpcEnv.createClient(
NettyRpcEnv.scala:200)
at org.apache.spark.rpc.netty.Outbox$$anon$1.call(Outbox.scala:187)
at org.apache.spark.rpc.netty.Outbox$$anon$1.call(Outbox.scala:183)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(
ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(
ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.net.ConnectException: 拒绝连接: /12.122.23.2:7070
at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:739)
at io.netty.channel.socket.nio.NioSocketChannel.doFinishConnect(
NioSocketChannel.java:224)
at io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.
finishConnect(AbstractNioChannel.java:289)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(
NioEventLoop.java:528)
at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(
NioEventLoop.java:468)
at io.netty.channel.nio.NioEventLoop.processSelectedKeys(
NioEventLoop.java:382)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.
run(SingleThreadEventExecutor.java:111)
... 1 more
2016-09-12 15:41:30 WARN AppClient$ClientEndpoint:91 - Failed to connect
to master 192.168.2.24:7070
java.io.IOException: Failed to connect to /12.122.23.2:7070
at org.apache.spark.network.client.TransportClientFactory.createClient(
TransportClientFactory.java:216)
at org.apache.spark.network.client.TransportClientFactory.createClient(
TransportClientFactory.java:167)
at org.apache.spark.rpc.netty.NettyRpcEnv.createClient(
NettyRpcEnv.scala:200)
at org.apache.spark.rpc.netty.Outbox$$anon$1.call(Outbox.scala:187)
at org.apache.spark.rpc.netty.Outbox$$anon$1.call(Outbox.scala:183)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(
ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(
ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.net.ConnectException: 拒绝连接: /12.122.23.2:7070
at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:739)
at io.netty.channel.socket.nio.NioSocketChannel.doFinishConnect(
NioSocketChannel.java:224)
at io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.
finishConnect(AbstractNioChannel.java:289)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(
NioEventLoop.java:528)
at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(
NioEventLoop.java:468)
at io.netty.channel.nio.NioEventLoop.processSelectedKeys(
NioEventLoop.java:382)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.
run(SingleThreadEventExecutor.java:111)
... 1 more
2016-09-12 15:41:50 WARN AppClient$ClientEndpoint:91 - Failed to connect
to master 192.168.2.24:7070
java.io.IOException: Failed to connect to /12.122.23.2:7070
at org.apache.spark.network.client.TransportClientFactory.createClient(
TransportClientFactory.java:216)
at org.apache.spark.network.client.TransportClientFactory.createClient(
TransportClientFactory.java:167)
at org.apache.spark.rpc.netty.NettyRpcEnv.createClient(
NettyRpcEnv.scala:200)
at org.apache.spark.rpc.netty.Outbox$$anon$1.call(Outbox.scala:187)
at org.apache.spark.rpc.netty.Outbox$$anon$1.call(Outbox.scala:183)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(
ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(
ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.net.ConnectException: 拒绝连接: /12.122.23.2:7070
at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:739)
at io.netty.channel.socket.nio.NioSocketChannel.doFinishConnect(
NioSocketChannel.java:224)
at io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.
finishConnect(AbstractNioChannel.java:289)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(
NioEventLoop.java:528)
at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(
NioEventLoop.java:468)
at io.netty.channel.nio.NioEventLoop.processSelectedKeys(
NioEventLoop.java:382)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.
run(SingleThreadEventExecutor.java:111)
... 1 more
2016-09-12 15:42:10 ERROR SparkDeploySchedulerBackend:74 - Application has
been killed. Reason: All masters are unresponsive! Giving up.
2016-09-12 15:42:10 WARN SparkDeploySchedulerBackend:70 - Application ID
is not initialized yet.
2016-09-12 15:42:10 WARN AppClient$ClientEndpoint:91 - Failed to connect
to master 192.168.2.24:7070
java.io.IOException: Failed to connect to /12.122.23.2:7070
at org.apache.spark.network.client.TransportClientFactory.createClient(
TransportClientFactory.java:216)
at org.apache.spark.network.client.TransportClientFactory.createClient(
TransportClientFactory.java:167)
at org.apache.spark.rpc.netty.NettyRpcEnv.createClient(
NettyRpcEnv.scala:200)
at org.apache.spark.rpc.netty.Outbox$$anon$1.call(Outbox.scala:187)
at org.apache.spark.rpc.netty.Outbox$$anon$1.call(Outbox.scala:183)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(
ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(
ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.net.ConnectException: 拒绝连接: /12.122.23.2:7070
at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:739)
at io.netty.channel.socket.nio.NioSocketChannel.doFinishConnect(
NioSocketChannel.java:224)
at io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.
finishConnect(AbstractNioChannel.java:289)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(
NioEventLoop.java:528)
at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(
NioEventLoop.java:468)
at io.netty.channel.nio.NioEventLoop.processSelectedKeys(
NioEventLoop.java:382)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.
run(SingleThreadEventExecutor.java:111)
... 1 more
2016-09-12 15:42:11 ERROR SparkContext:95 - Error initializing
SparkContext.
java.lang.IllegalStateException: Cannot call methods on a stopped
SparkContext.
This stopped SparkContext was created at:

org.apache.spark.SparkContext.(SparkContext.scala:82)
org.hammerlab.guacamole.commands.SparkCommand.createSparkContext(
SparkCommand.scala:65)
org.hammerlab.guacamole.commands.SparkCommand.run(SparkCommand.scala:10)
org.hammerlab.guacamole.commands.Command.run(Command.scala:27)
org.hammerlab.guacamole.Main$.main(Main.scala:49)
org.hammerlab.guacamole.Main.main(Main.scala)
sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:
57)
sun.reflect.DelegatingMethodAccessorImpl.invoke(
DelegatingMethodAccessorImpl.java:43)
java.lang.reflect.Method.invoke(Method.java:606)
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$
deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

The currently active SparkContext was created at:

(No active SparkContext.)

at org.apache.spark.SparkContext.org$apache$spark$SparkContext$$assertNotStopped(SparkContext.scala:106)
at org.apache.spark.SparkContext.getSchedulingMode(SparkContext.scala:1578)
at org.apache.spark.SparkContext.postEnvironmentUpdate(SparkContext.scala:2179)
at org.apache.spark.SparkContext.(SparkContext.scala:579)
at org.hammerlab.guacamole.commands.SparkCommand.createSparkContext(SparkCommand.scala:65)
at org.hammerlab.guacamole.commands.SparkCommand.run(SparkCommand.scala:10)
at org.hammerlab.guacamole.commands.Command.run(Command.scala:27)
at org.hammerlab.guacamole.Main$.main(Main.scala:49)
at org.hammerlab.guacamole.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

Exception in thread "main" java.lang.IllegalStateException: Cannot call
methods on a stopped SparkContext.
This stopped SparkContext was created at:

org.apache.spark.SparkContext.(SparkContext.scala:82)
org.hammerlab.guacamole.commands.SparkCommand.createSparkContext(
SparkCommand.scala:65)
org.hammerlab.guacamole.commands.SparkCommand.run(SparkCommand.scala:10)
org.hammerlab.guacamole.commands.Command.run(Command.scala:27)
org.hammerlab.guacamole.Main$.main(Main.scala:49)
org.hammerlab.guacamole.Main.main(Main.scala)
sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:
57)
sun.reflect.DelegatingMethodAccessorImpl.invoke(
DelegatingMethodAccessorImpl.java:43)
java.lang.reflect.Method.invoke(Method.java:606)
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$
deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

The currently active SparkContext was created at:

(No active SparkContext.)

at org.apache.spark.SparkContext.org$apache$spark$SparkContext$$assertNotStopped(SparkContext.scala:106)
at org.apache.spark.SparkContext.getSchedulingMode(SparkContext.scala:1578)
at org.apache.spark.SparkContext.postEnvironmentUpdate(SparkContext.scala:2179)
at org.apache.spark.SparkContext.(SparkContext.scala:579)
at org.hammerlab.guacamole.commands.SparkCommand.createSparkContext(SparkCommand.scala:65)
at org.hammerlab.guacamole.commands.SparkCommand.run(SparkCommand.scala:10)
at org.hammerlab.guacamole.commands.Command.run(Command.scala:27)
at org.hammerlab.guacamole.Main$.main(Main.scala:49)
at org.hammerlab.guacamole.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

2016-09-12 15:42:11 WARN AppClient$ClientEndpoint:70 - Drop
UnregisterApplication(null) because has not yet connected to master
2016-09-12 15:42:11 ERROR MapOutputTrackerMaster:95 - Error communicating
with MapOutputTracker
java.lang.InterruptedException
at java.util.concurrent.locks.AbstractQueuedSynchronizer.
tryAcquireSharedNanos(AbstractQueuedSynchronizer.java:1325)
at scala.concurrent.impl.Promise$DefaultPromise.tryAwait(
Promise.scala:208)
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:218)
at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(
BlockContext.scala:53)
at scala.concurrent.Await$.result(package.scala:107)
at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(
RpcEndpointRef.scala:101)
at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(
RpcEndpointRef.scala:77)
at org.apache.spark.MapOutputTracker.askTracker(
MapOutputTracker.scala:110)
at org.apache.spark.MapOutputTracker.sendTracker(
MapOutputTracker.scala:120)
at org.apache.spark.MapOutputTrackerMaster.stop(
MapOutputTracker.scala:462)
at org.apache.spark.SparkEnv.stop(SparkEnv.scala:93)
at org.apache.spark.SparkContext$$anonfun$stop$12.apply$mcV$sp(
SparkContext.scala:1756)
at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1229)
at org.apache.spark.SparkContext.stop(SparkContext.scala:1755)
at org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.dead(
SparkDeploySchedulerBackend.scala:127)
at org.apache.spark.deploy.client.AppClient$ClientEndpoint.markDead(
AppClient.scala:264)
at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2$$
anonfun$run$1.apply$mcV$sp(AppClient.scala:134)
at org.apache.spark.util.Utils$.tryOrExit(Utils.scala:1163)
at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2.run(
AppClient.scala:129)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:304)
at java.util.concurrent.ScheduledThreadPoolExecutor$
ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:178)
at java.util.concurrent.ScheduledThreadPoolExecutor$
ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
at java.util.concurrent.ThreadPoolExecutor.runWorker(
ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(
ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
2016-09-12 15:42:11 ERROR Utils:95 - Uncaught exception in thread
appclient-registration-retry-thread
org.apache.spark.SparkException: Error communicating with MapOutputTracker
at org.apache.spark.MapOutputTracker.askTracker(
MapOutputTracker.scala:114)
at org.apache.spark.MapOutputTracker.sendTracker(
MapOutputTracker.scala:120)
at org.apache.spark.MapOutputTrackerMaster.stop(
MapOutputTracker.scala:462)
at org.apache.spark.SparkEnv.stop(SparkEnv.scala:93)
at org.apache.spark.SparkContext$$anonfun$stop$12.apply$mcV$sp(
SparkContext.scala:1756)
at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1229)
at org.apache.spark.SparkContext.stop(SparkContext.scala:1755)
at org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.dead(
SparkDeploySchedulerBackend.scala:127)
at org.apache.spark.deploy.client.AppClient$ClientEndpoint.markDead(
AppClient.scala:264)
at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2$$
anonfun$run$1.apply$mcV$sp(AppClient.scala:134)
at org.apache.spark.util.Utils$.tryOrExit(Utils.scala:1163)
at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2.run(
AppClient.scala:129)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:304)
at java.util.concurrent.ScheduledThreadPoolExecutor$
ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:178)
at java.util.concurrent.ScheduledThreadPoolExecutor$
ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
at java.util.concurrent.ThreadPoolExecutor.runWorker(
ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(
ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.InterruptedException
at java.util.concurrent.locks.AbstractQueuedSynchronizer.
tryAcquireSharedNanos(AbstractQueuedSynchronizer.java:1325)
at scala.concurrent.impl.Promise$DefaultPromise.tryAwait(
Promise.scala:208)
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:218)
at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(
BlockContext.scala:53)
at scala.concurrent.Await$.result(package.scala:107)
at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(
RpcEndpointRef.scala:101)
at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(
RpcEndpointRef.scala:77)
at org.apache.spark.MapOutputTracker.askTracker(
MapOutputTracker.scala:110)
... 18 more
2016-09-12 15:42:11 ERROR SparkUncaughtExceptionHandler:95 - [Container
in shutdown] Uncaught exception in thread Thread[appclient-registration-
retry-thread,5,main]
org.apache.spark.SparkException: Exiting due to error from cluster
scheduler: All masters are unresponsive! Giving up.
at org.apache.spark.scheduler.TaskSchedulerImpl.error(
TaskSchedulerImpl.scala:438)
at org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.dead(
SparkDeploySchedulerBackend.scala:124)
at org.apache.spark.deploy.client.AppClient$ClientEndpoint.markDead(
AppClient.scala:264)
at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2$$
anonfun$run$1.apply$mcV$sp(AppClient.scala:134)
at org.apache.spark.util.Utils$.tryOrExit(Utils.scala:1163)
at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2.run(
AppClient.scala:129)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:304)
at java.util.concurrent.ScheduledThreadPoolExecutor$
ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:178)
at java.util.concurrent.ScheduledThreadPoolExecutor$
ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
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)

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[car2008]

Hi @arahuja ,thank you, i have tried to run the command on spark local model firstly./scripts/guacamole somatic-joint /home/zhipengcheng/file/tmp/dedup.bam --reference-fasta /home/zhipengcheng/file/ucsc.hg19.fasta --out /home/zhipengcheng/file/result/out.vcf, the memory i'm using is 80g , the cores are 64, the file dedup.bam is 7g, but it throwed exception:
Found guac jar: target/guacamole-0.0.1-SNAPSHOT.jar
--> [Tue Sep 13 15:59:21 CST 2016]: Guacamole starting.
2016-09-13 15:59:22 WARN NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
--> [3.15 sec. later]: Using samtools without BAM index (index unavailable) to read: /home/zhipengcheng/file/tmp/dedup.bam
--> [73.95 sec. later]: Partitioning loci
Exception in thread "qtp420034849-106" java.lang.OutOfMemoryError: GC overhead limit exceeded
at org.spark-project.jetty.io.nio.SelectorManager$SelectSet.doSelect(SelectorManager.java:708)
at org.spark-project.jetty.io.nio.SelectorManager$1.run(SelectorManager.java:290)
at org.spark-project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:608)
at org.spark-project.jetty.util.thread.QueuedThreadPool$3.run(QueuedThreadPool.java:543)
at java.lang.Thread.run(Thread.java:745)
2016-09-13 16:03:10 WARN AbstractConnector:955 -
java.lang.OutOfMemoryError: GC overhead limit exceeded
at java.io.FileDescriptor.(FileDescriptor.java:62)
at sun.nio.ch.ServerSocketChannelImpl.accept(ServerSocketChannelImpl.java:232)
at org.spark-project.jetty.server.nio.SelectChannelConnector.accept(SelectChannelConnector.java:109)
at org.spark-project.jetty.server.AbstractConnector$Acceptor.run(AbstractConnector.java:938)
at org.spark-project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:608)
at org.spark-project.jetty.util.thread.QueuedThreadPool$3.run(QueuedThreadPool.java:543)
at java.lang.Thread.run(Thread.java:745)
2016-09-13 16:03:10 WARN NettyRpcEndpointRef:91 - Error sending message [message = Heartbeat(driver,[Lscala.Tuple2;@76e55122,BlockManagerId(driver, localhost, 36128))] in 1 attempts
org.apache.spark.rpc.RpcTimeoutException: Futures timed out after [10 seconds]. This timeout is controlled by spark.executor.heartbeatInterval
at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:76)
at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:101)
at org.apache.spark.executor.Executor.org$apache$spark$executor$Executor$$reportHeartBeat(Executor.scala:449)
at org.apache.spark.executor.Executor$$anon$1$$anonfun$run$1.apply$mcV$sp(Executor.scala:470)
at org.apache.spark.executor.Executor$$anon$1$$anonfun$run$1.apply(Executor.scala:470)
at org.apache.spark.executor.Executor$$anon$1$$anonfun$run$1.apply(Executor.scala:470)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1765)
at org.apache.spark.executor.Executor$$anon$1.run(Executor.scala:470)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:304)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:178)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.util.concurrent.TimeoutException: Futures timed out after [10 seconds]
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
at scala.concurrent.Await$.result(package.scala:107)
at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
... 14 more
2016-09-13 16:03:10 WARN NettyRpcEnv:70 - Ignored message: HeartbeatResponse(false)
2016-09-13 16:03:16 ERROR ActorSystemImpl:66 - Uncaught fatal error from thread [sparkDriverActorSystem-akka.remote.default-remote-dispatcher-34] shutting down ActorSystem [sparkDriverActorSystem]
java.lang.OutOfMemoryError: GC overhead limit exceeded
at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.growArray(ForkJoinPool.java:1090)
at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1978)
at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
2016-09-13 16:03:16 WARN nio:498 -
java.lang.InterruptedException
at java.lang.Object.wait(Native Method)
at org.spark-project.jetty.io.nio.SelectChannelEndPoint.blockWritable(SelectChannelEndPoint.java:494)
at org.spark-project.jetty.http.AbstractGenerator.blockForOutput(AbstractGenerator.java:517)
at org.spark-project.jetty.server.HttpOutput.write(HttpOutput.java:170)
at org.spark-project.jetty.server.HttpOutput.write(HttpOutput.java:107)
at java.util.zip.GZIPOutputStream.finish(GZIPOutputStream.java:165)
at java.util.zip.DeflaterOutputStream.close(DeflaterOutputStream.java:238)
at org.spark-project.jetty.http.gzip.AbstractCompressedStream.finish(AbstractCompressedStream.java:191)
at org.spark-project.jetty.http.gzip.CompressedResponseWrapper.finish(CompressedResponseWrapper.java:347)
at org.spark-project.jetty.server.handler.GzipHandler.handle(GzipHandler.java:296)
at org.spark-project.jetty.server.handler.ContextHandlerCollection.handle(ContextHandlerCollection.java:255)
at org.spark-project.jetty.server.handler.HandlerWrapper.handle(HandlerWrapper.java:116)
at org.spark-project.jetty.server.Server.handle(Server.java:370)
at org.spark-project.jetty.server.AbstractHttpConnection.handleRequest(AbstractHttpConnection.java:494)
at org.spark-project.jetty.server.AbstractHttpConnection.headerComplete(AbstractHttpConnection.java:971)
at org.spark-project.jetty.server.AbstractHttpConnection$RequestHandler.headerComplete(AbstractHttpConnection.java:1033)
at org.spark-project.jetty.http.HttpParser.parseNext(HttpParser.java:644)
at org.spark-project.jetty.http.HttpParser.parseAvailable(HttpParser.java:235)
at org.spark-project.jetty.server.AsyncHttpConnection.handle(AsyncHttpConnection.java:82)
at org.spark-project.jetty.io.nio.SelectChannelEndPoint.handle(SelectChannelEndPoint.java:667)
at org.spark-project.jetty.io.nio.SelectChannelEndPoint$1.run(SelectChannelEndPoint.java:52)
at org.spark-project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:608)
at org.spark-project.jetty.util.thread.QueuedThreadPool$3.run(QueuedThreadPool.java:543)
at java.lang.Thread.run(Thread.java:745)
2016-09-13 16:03:16 WARN nio:498 -
java.lang.InterruptedException
at java.lang.Object.wait(Native Method)
at org.spark-project.jetty.io.nio.SelectChannelEndPoint.blockWritable(SelectChannelEndPoint.java:494)
at org.spark-project.jetty.http.AbstractGenerator.blockForOutput(AbstractGenerator.java:517)
at org.spark-project.jetty.server.HttpOutput.write(HttpOutput.java:147)
at org.spark-project.jetty.server.HttpOutput.write(HttpOutput.java:107)
at java.util.zip.GZIPOutputStream.finish(GZIPOutputStream.java:165)
at java.util.zip.DeflaterOutputStream.close(DeflaterOutputStream.java:238)
at org.spark-project.jetty.http.gzip.AbstractCompressedStream.finish(AbstractCompressedStream.java:191)
at org.spark-project.jetty.http.gzip.CompressedResponseWrapper.finish(CompressedResponseWrapper.java:347)
at org.spark-project.jetty.server.handler.GzipHandler.handle(GzipHandler.java:296)
at org.spark-project.jetty.server.handler.ContextHandlerCollection.handle(ContextHandlerCollection.java:255)
at org.spark-project.jetty.server.handler.HandlerWrapper.handle(HandlerWrapper.java:116)
at org.spark-project.jetty.server.Server.handle(Server.java:370)
at org.spark-project.jetty.server.AbstractHttpConnection.handleRequest(AbstractHttpConnection.java:494)
at org.spark-project.jetty.server.AbstractHttpConnection.headerComplete(AbstractHttpConnection.java:971)
at org.spark-project.jetty.server.AbstractHttpConnection$RequestHandler.headerComplete(AbstractHttpConnection.java:1033)
at org.spark-project.jetty.http.HttpParser.parseNext(HttpParser.java:644)
at org.spark-project.jetty.http.HttpParser.parseAvailable(HttpParser.java:235)
at org.spark-project.jetty.server.AsyncHttpConnection.handle(AsyncHttpConnection.java:82)
at org.spark-project.jetty.io.nio.SelectChannelEndPoint.handle(SelectChannelEndPoint.java:667)
at org.spark-project.jetty.io.nio.SelectChannelEndPoint$1.run(SelectChannelEndPoint.java:52)
at org.spark-project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:608)
at org.spark-project.jetty.util.thread.QueuedThreadPool$3.run(QueuedThreadPool.java:543)
at java.lang.Thread.run(Thread.java:745)
2016-09-13 16:03:16 WARN QueuedThreadPool:145 - 10 threads could not be stopped
Exception in thread "main" java.lang.OutOfMemoryError: GC overhead limit exceeded
at htsjdk.samtools.BinaryTagCodec.readTags(BinaryTagCodec.java:282)
at htsjdk.samtools.BAMRecord.decodeAttributes(BAMRecord.java:308)
at htsjdk.samtools.BAMRecord.getAttribute(BAMRecord.java:288)
at htsjdk.samtools.SAMRecord.getReadGroup(SAMRecord.java:691)
at org.hammerlab.guacamole.reads.Read$.apply(Read.scala:77)
at org.hammerlab.guacamole.readsets.ReadSets$$anonfun$8.apply(ReadSets.scala:276)
at org.hammerlab.guacamole.readsets.ReadSets$$anonfun$8.apply(ReadSets.scala:266)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at scala.collection.Iterator$class.toStream(Iterator.scala:1143)
at scala.collection.AbstractIterator.toStream(Iterator.scala:1157)
at scala.collection.Iterator$$anonfun$toStream$1.apply(Iterator.scala:1143)
at scala.collection.Iterator$$anonfun$toStream$1.apply(Iterator.scala:1143)
at scala.collection.immutable.Stream$Cons.tail(Stream.scala:1085)
at scala.collection.immutable.Stream$Cons.tail(Stream.scala:1077)
at scala.collection.immutable.Stream.length(Stream.scala:284)
at scala.collection.SeqLike$class.size(SeqLike.scala:106)
at scala.collection.AbstractSeq.size(Seq.scala:40)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:248)
at scala.collection.AbstractTraversable.toArray(Traversable.scala:105)
at org.apache.spark.rdd.ParallelCollectionRDD$.slice(ParallelCollectionRDD.scala:154)
at org.apache.spark.rdd.ParallelCollectionRDD.getPartitions(ParallelCollectionRDD.scala:97)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)

[ryan-williams]

Are you able to share that BAM with us, @car2008?

[car2008]

Hi @ryan-williams , i'm sorry i can't share the BAM because it belongs to one of my customers , and i can tell you someting about the BAM: it was generated after comparing with hg19.fasta , and you can tell me other necessary informations you want to know .May the BAM have some problems ?
Is there other public BAM file i can have a test on spark local model?

[arahuja]

@car2008 Is the BAM very large? In local mode, by default, we use samtools to read in the BAM which loads them all into memory. This is useful if you are providing the --loci argument to only look at a few positions. Otherwise, if you are not using --loci or the BAM is very large, I would suggest adding the flag ----bam-reader-api hadoopbam to use Hadoop-BAM to read in the BAM instead which will be more efficient.

This is issue is being addressed in #569 as well to simplify this.

[ryan-williams]

He mentioned above that it's a 7GB BAM; since that's compressed you could get arbitrary blow-up when reading the whole thing in, so not terribly surprising that it would blow up in local mode, I guess!

Aside: I'll give #569 a review today @arahuja, sorry for the delay. Does it let us stream over a local BAM in spark-local-mode? Do we not have a way to do that pre-#569?

[car2008]

Hi @arahuja and @ryan-williams , thank you ,now i want to run the command in local mode : ./scripts/guacamole somatic-joint /mnt/hgfs/D/shiyan/tmp/dedup.bam --bam-reader-api hadoopbam --reference /mnt/hgfs/D/shiyan/ucsc.hg19.fasta --out /mnt/hgfs/D/shiyan/result/out.vcf using the latest release,but it throwed exception:

Using default {local,kryo} Spark config files
spark.serializer          org.apache.spark.serializer.KryoSerializer
spark.kryo.registrator    org.hammerlab.guacamole.kryo.Registrar
spark.master  local[1]
spark.driver.memory 4g
--> [Mon Sep 19 09:40:31 CST 2016]: Guacamole starting.
"--bam-reader-api" is not a valid option
 VAL                                                    : FILE1 FILE2 FILE3
 --analytes STRING[]                                    : [dna|rna] ... [dna|rna]
 --any-allele-min-supporting-percent N                  : min percent of reads to call any allele (somatic or germline) (default: 2.0)
 --any-allele-min-supporting-reads N                    : min reads to call any allele (somatic or germline) (default: 2)
 --compress                                             : Whether to compress the output partitioned reads (default: false). (default: false)
 --debug                                                : If set, prints a higher level of debug output. (default: false)
 --filter-somatic-normal-depth N                        : filter somatic calls with total pooled normal dna depth lower than this value (default: 30)
 --filter-somatic-normal-nonreference-percent N         : filter somatic calls with pooled normal dna percent of reads not matching reference lower
                                                          than this value (default: 3.0)
 --filter-strand-bias-phred N                           : filter calls with strand bias p-value exceeding this phred-scaled value (default: 60.0)
 --force-call-loci VAL                                  : Always call the given sites (default: )
 --force-call-loci-file VAL                             : Always call the given sites (default: )
 --germline-negative-log10-heterozygous-prior N         : prior on a germline het call, higher number means fewer calls (default: 4.0)
 --germline-negative-log10-homozygous-alternate-prior N : prior on a germline hom-alt call (default: 5.0)
 --header-metadata STRING[]                             : Extra header metadata for VCF output in format KEY=VALUE KEY=VALUE ...
 --include-filtered                                     : Include filtered calls (default: false)
 --loci VAL                                             : Loci at which to call variants. Either 'all' or contig:start-end,contig:start-end,...
                                                          (default: )
 --loci-file VAL                                        : Path to file giving loci at which to call variants. (default: )
 --loci-partitioner VAL                                 : Loci partitioner to use: 'exact', 'approximate', or 'uniform' (default: 'exact'). (default:
                                                          exact)
 --loci-partitioning path                               : Load a LociPartitioning from this path if it exists; else write a computed partitioning to
                                                          this path. (default: None)
 --max-alleles-per-site N                               : maximum number of alt alleles to consider at a site (default: 4)
 --max-calls-per-site N                                 : maximum number of calls to make at a site. 0 indicates unlimited (default: 1)
 --max-reads-per-partition N                            : Maximum number of reads to allow any one partition to have. Loci that have more depth than
                                                          this will be dropped. (default: 100000)
 --no-sequence-dictionary                               : If set, get contigs and lengths directly from reads instead of from sequence dictionary.
                                                          (default: false)
 --only-somatic                                         : Output only somatic calls, no germline calls (default: false)
 --out VAL                                              : Output path for all variants in VCF. Default: no output (default: )
 --out-dir VAL                                          : Output dir for all variants, split into separate files for somatic/germline (default: )
 --parallelism N                                        : Number of variant calling partitions. Set to 0 (default) to use the number of Spark
                                                          partitions. (default: 0)
 --partial-reference                                    : Treat the reference fasta as a "partial FASTA", comprised of segments (possibly in the
                                                          interior) of contigs. (default: false)
 --partition-accuracy N                                 : Number of micro-partitions to use, per Spark partition, when partitioning loci (default:
                                                          250). Set to 0 to partition loci uniformly (default: 250)
 --partitioned-reads path                               : Directory from which to read an existing partition-reads RDD, if it exists; otherwise, save
                                                          it here. (default: None)
 --partitioning-dir path                                : Directory from which to read an existing partition-reads RDD (and accompanying
                                                          LociPartitioning), if the directory exists; otherwise, save them here. If set, precludes
                                                          use of --partitioned-reads and --loci-partitioning (default: None)
 --partitioning-stats                                   : Compute additional statistics about the partitioned loci and reads; causes additional Spark
                                                          jobs to be run, so should be disabled in performance-critical environments. Default: false.
                                                          (default: false)
 --reference VAL                                        : Local path to a reference FASTA file
 --sample-names STRING[]                                : name1 ... nameN
 --somatic-genotype-policy VAL                          : how to genotype (e.g. 0/0 vs. 0/1) somatic calls. Valid options: 'presence' (default),
                                                          'trigger'. 'presence' will genotype as a het (0/1) if there is any evidence for that call
                                                          in the sample (num variant reads > 0 and num variant reads > any other non-reference
                                                          allele). 'trigger' will genotype a call as a het only if that sample actually triggered the
                                                          call. (default: presence)
 --somatic-max-germline-error-rate-percent N            : max germline error rate percent to have a somatic call (default: 4.0)
 --somatic-negative-log10-variant-prior N               : prior on somatic call, higher number means fewer calls (default: 6.0)
 --somatic-vaf-floor N                                  : min VAF to use in the likelihood calculation (default: 0.05)
 --tissue-types STRING[]                                : [normal|tumor] ... [normal|tumor]
 -h (-help, --help, -?)                                 : Print help (default: false)
 -print_metrics                                         : Print metrics to the log on completion (default: false)

real    0m0.936s
user    0m0.537s
sys 0m0.245s

Is there not the flag --bam-reader-api hadoopbam in the latest release?
How can i use Hadoop-BAM to read in the BAM?

[ryan-williams]

You got it, --bam-reader-api is now gone, as of #569. Just leave that flag out altogether and you will be using Hadoop-BAM!

[car2008]

Ok @ryan-williams , i know it , and i have tried the command ./scripts/guacamole somatic-joint /mnt/hgfs/D/shiyan/tmp/dedup.bam --reference /mnt/hgfs/D/shiyan/reference-human/ucsc.hg19.fasta --out /mnt/hgfs/D/shiyan/result/out.vcf in local mode. It runs well when the BAM file is 97K or 97M ,but throws exception when the BAM increased to 7G . The memory i'm using is 10g , the cpu cores are 2 .Was it because the memory too small or other reason like network? :

Using default {local,kryo} Spark config files
spark.serializer          org.apache.spark.serializer.KryoSerializer
spark.kryo.registrator    org.hammerlab.guacamole.kryo.Registrar
spark.master  local[1]
spark.driver.memory 4g
--> [Mon Sep 19 12:46:38 CST 2016]: Guacamole starting.
2016-09-19 12:46:39 WARN  NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
--> [167.16 sec. later]: Partitioning loci
2016-09-19 14:05:52 ERROR Executor:74 - Managed memory leak detected; size = 412345478 bytes, TID = 362
2016-09-19 14:05:52 ERROR Executor:95 - Exception in task 138.0 in stage 1.0 (TID 362)
com.esotericsoftware.kryo.KryoException: Encountered unregistered class ID: 4267
    at com.esotericsoftware.kryo.util.DefaultClassResolver.readClass(DefaultClassResolver.java:119)
    at com.esotericsoftware.kryo.Kryo.readClass(Kryo.java:610)
    at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:721)
    at org.apache.spark.serializer.KryoDeserializationStream.readObject(KryoSerializer.scala:228)
    at org.apache.spark.serializer.DeserializationStream.readValue(Serializer.scala:171)
    at org.apache.spark.serializer.DeserializationStream$$anon$2.getNext(Serializer.scala:201)
    at org.apache.spark.serializer.DeserializationStream$$anon$2.getNext(Serializer.scala:198)
    at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
    at org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:152)
    at org.apache.spark.Aggregator.combineCombinersByKey(Aggregator.scala:58)
    at org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:83)
    at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:98)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
2016-09-19 14:05:52 WARN  TaskSetManager:70 - Lost task 138.0 in stage 1.0 (TID 362, localhost): com.esotericsoftware.kryo.KryoException: Encountered unregistered class ID: 4267
    at com.esotericsoftware.kryo.util.DefaultClassResolver.readClass(DefaultClassResolver.java:119)
    at com.esotericsoftware.kryo.Kryo.readClass(Kryo.java:610)
    at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:721)
    at org.apache.spark.serializer.KryoDeserializationStream.readObject(KryoSerializer.scala:228)
    at org.apache.spark.serializer.DeserializationStream.readValue(Serializer.scala:171)
    at org.apache.spark.serializer.DeserializationStream$$anon$2.getNext(Serializer.scala:201)
    at org.apache.spark.serializer.DeserializationStream$$anon$2.getNext(Serializer.scala:198)
    at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
    at org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:152)
    at org.apache.spark.Aggregator.combineCombinersByKey(Aggregator.scala:58)
    at org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:83)
    at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:98)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)

2016-09-19 14:05:53 ERROR TaskSetManager:74 - Task 138 in stage 1.0 failed 1 times; aborting job
2016-09-19 14:05:53 WARN  TaskSetManager:70 - Lost task 139.0 in stage 1.0 (TID 363, localhost): TaskKilled (killed intentionally)
2016-09-19 14:05:54 ERROR DiskBlockManager:95 - Exception while deleting local spark dir: /mnt/hgfs/C/spark05-tmp/blockmgr-eedd40c6-6e43-49c3-8458-fd96c459cf45
java.io.IOException: Failed to delete: /mnt/hgfs/C/spark05-tmp/blockmgr-eedd40c6-6e43-49c3-8458-fd96c459cf45
    at org.apache.spark.util.Utils$.deleteRecursively(Utils.scala:928)
    at org.apache.spark.storage.DiskBlockManager$$anonfun$org$apache$spark$storage$DiskBlockManager$$doStop$1.apply(DiskBlockManager.scala:174)
    at org.apache.spark.storage.DiskBlockManager$$anonfun$org$apache$spark$storage$DiskBlockManager$$doStop$1.apply(DiskBlockManager.scala:170)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
    at org.apache.spark.storage.DiskBlockManager.org$apache$spark$storage$DiskBlockManager$$doStop(DiskBlockManager.scala:170)
    at org.apache.spark.storage.DiskBlockManager.stop(DiskBlockManager.scala:162)
    at org.apache.spark.storage.BlockManager.stop(BlockManager.scala:1233)
    at org.apache.spark.SparkEnv.stop(SparkEnv.scala:96)
    at org.apache.spark.SparkContext$$anonfun$stop$12.apply$mcV$sp(SparkContext.scala:1756)
    at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1229)
    at org.apache.spark.SparkContext.stop(SparkContext.scala:1755)
    at org.hammerlab.guacamole.commands.SparkCommand.run(SparkCommand.scala:14)
    at org.hammerlab.guacamole.commands.Command.run(Command.scala:27)
    at org.hammerlab.guacamole.Main$.main(Main.scala:49)
    at org.hammerlab.guacamole.Main.main(Main.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 138 in stage 1.0 failed 1 times, most recent failure: Lost task 138.0 in stage 1.0 (TID 362, localhost): com.esotericsoftware.kryo.KryoException: Encountered unregistered class ID: 4267
    at com.esotericsoftware.kryo.util.DefaultClassResolver.readClass(DefaultClassResolver.java:119)
    at com.esotericsoftware.kryo.Kryo.readClass(Kryo.java:610)
    at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:721)
    at org.apache.spark.serializer.KryoDeserializationStream.readObject(KryoSerializer.scala:228)
    at org.apache.spark.serializer.DeserializationStream.readValue(Serializer.scala:171)
    at org.apache.spark.serializer.DeserializationStream$$anon$2.getNext(Serializer.scala:201)
    at org.apache.spark.serializer.DeserializationStream$$anon$2.getNext(Serializer.scala:198)
    at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
    at org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:152)
    at org.apache.spark.Aggregator.combineCombinersByKey(Aggregator.scala:58)
    at org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:83)
    at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:98)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:927)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:926)
    at org.apache.spark.RangePartitioner$.sketch(Partitioner.scala:264)
    at org.apache.spark.RangePartitioner.<init>(Partitioner.scala:126)
    at org.apache.spark.rdd.OrderedRDDFunctions$$anonfun$sortByKey$1.apply(OrderedRDDFunctions.scala:62)
    at org.apache.spark.rdd.OrderedRDDFunctions$$anonfun$sortByKey$1.apply(OrderedRDDFunctions.scala:61)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
    at org.apache.spark.rdd.OrderedRDDFunctions.sortByKey(OrderedRDDFunctions.scala:61)
    at org.hammerlab.guacamole.readsets.rdd.CoverageRDD$$anonfun$coverage$1.apply(CoverageRDD.scala:54)
    at org.hammerlab.guacamole.readsets.rdd.CoverageRDD$$anonfun$coverage$1.apply(CoverageRDD.scala:54)
    at scala.collection.mutable.MapLike$class.getOrElseUpdate(MapLike.scala:189)
    at scala.collection.mutable.AbstractMap.getOrElseUpdate(Map.scala:91)
    at org.hammerlab.guacamole.readsets.rdd.CoverageRDD.coverage(CoverageRDD.scala:32)
    at org.hammerlab.guacamole.readsets.rdd.CoverageRDD.makeCappedLociSets(CoverageRDD.scala:67)
    at org.hammerlab.guacamole.loci.partitioning.CappedRegionsPartitioner.partition(CappedRegionsPartitioner.scala:109)
    at org.hammerlab.guacamole.loci.partitioning.LociPartitioning$.apply(LociPartitioning.scala:120)
    at org.hammerlab.guacamole.readsets.rdd.PartitionedRegions$.apply(PartitionedRegions.scala:135)
    at org.hammerlab.guacamole.commands.SomaticJoint$.makeCalls(SomaticJointCaller.scala:166)
    at org.hammerlab.guacamole.commands.SomaticJoint$Caller$.run(SomaticJointCaller.scala:91)
    at org.hammerlab.guacamole.commands.SomaticJoint$Caller$.run(SomaticJointCaller.scala:56)
    at org.hammerlab.guacamole.commands.SparkCommand.run(SparkCommand.scala:12)
    at org.hammerlab.guacamole.commands.Command.run(Command.scala:27)
    at org.hammerlab.guacamole.Main$.main(Main.scala:49)
    at org.hammerlab.guacamole.Main.main(Main.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: com.esotericsoftware.kryo.KryoException: Encountered unregistered class ID: 4267
    at com.esotericsoftware.kryo.util.DefaultClassResolver.readClass(DefaultClassResolver.java:119)
    at com.esotericsoftware.kryo.Kryo.readClass(Kryo.java:610)
    at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:721)
    at org.apache.spark.serializer.KryoDeserializationStream.readObject(KryoSerializer.scala:228)
    at org.apache.spark.serializer.DeserializationStream.readValue(Serializer.scala:171)
    at org.apache.spark.serializer.DeserializationStream$$anon$2.getNext(Serializer.scala:201)
    at org.apache.spark.serializer.DeserializationStream$$anon$2.getNext(Serializer.scala:198)
    at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
    at org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:152)
    at org.apache.spark.Aggregator.combineCombinersByKey(Aggregator.scala:58)
    at org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:83)
    at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:98)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)

real    79m18.773s
user    78m51.337s
sys 8m46.174s