I have three tables with the following schema:
case class *date_d*(WID: Int, CALENDAR_DATE: java.sql.Timestamp,
DATE_STRING: String, DAY_OF_WEEK: String, DAY_OF_MONTH: Int, DAY_OF_YEAR:
Int, END_OF_MONTH_FLAG: String, YEARWEEK: Int, CALENDAR_MONTH: String,
MONTH_NUM: Int, YEARMONTH: Int, QUARTER: Int, YEAR: Int)
case class *interval_f*(ORG_ID: Int, CHANNEL_WID: Int, SDP_WID: Int,
MEAS_WID: Int, DATE_WID: Int, TIME_WID: Int, VALIDATION_STATUS_CD: Int,
VAL_FAIL_CD:Int, INTERVAL_FLAG_CD: Int, CHANGE_METHOD_WID:Int,
SOURCE_LAST_UPD_TIME: java.sql.Timestamp, INTERVAL_END_TIME:
java.sql.Timestamp, LOCKED: String, EXT_VERSION_TIME: java.sql.Timestamp,
INTERVAL_VALUE: Double, INSERT_TIME: java.sql.Timestamp, LAST_UPD_TIME:
java.sql.Timestamp)
class * sdp_d*( WID :Option[Int], BATCH_ID :Option[Int], SRC_ID
:Option[String], ORG_ID :Option[Int], CLASS_WID :Option[Int], DESC_TEXT
:Option[String], PREMISE_WID :Option[Int], FEED_LOC :Option[String], GPS_LAT
:Option[Double], GPS_LONG :Option[Double], PULSE_OUTPUT_BLOCK
:Option[String], UDC_ID :Option[String], UNIVERSAL_ID :Option[String],
IS_VIRTUAL_FLG :Option[String], SEAL_INFO :Option[String], ACCESS_INFO
:Option[String], ALT_ACCESS_INFO :Option[String], LOC_INFO :Option[String],
ALT_LOC_INFO :Option[String], TYPE :Option[String], SUB_TYPE
:Option[String], TIMEZONE_ID :Option[Int], GIS_ID :Option[String],
BILLED_UPTO_TIME :Option[java.sql.Timestamp], POWER_STATUS :Option[String],
LOAD_STATUS :Option[String], BILLING_HOLD_STATUS :Option[String],
INSERT_TIME :Option[java.sql.Timestamp], LAST_UPD_TIME
:Option[java.sql.Timestamp]) extends Product{
@throws(classOf[IndexOutOfBoundsException])
override def productElement(n: Int) = n match
{
case 0 => WID; case 1 => BATCH_ID; case 2 => SRC_ID; case 3 =>
ORG_ID; case 4 => CLASS_WID; case 5 => DESC_TEXT; case 6 => PREMISE_WID;
case 7 => FEED_LOC; case 8 => GPS_LAT; case 9 => GPS_LONG; case 10 =>
PULSE_OUTPUT_BLOCK; case 11 => UDC_ID; case 12 => UNIVERSAL_ID; case 13 =>
IS_VIRTUAL_FLG; case 14 => SEAL_INFO; case 15 => ACCESS_INFO; case 16 =>
ALT_ACCESS_INFO; case 17 => LOC_INFO; case 18 => ALT_LOC_INFO; case 19 =>
TYPE; case 20 => SUB_TYPE; case 21 => TIMEZONE_ID; case 22 => GIS_ID; case
23 => BILLED_UPTO_TIME; case 24 => POWER_STATUS; case 25 => LOAD_STATUS;
case 26 => BILLING_HOLD_STATUS; case 27 => INSERT_TIME; case 28 =>
LAST_UPD_TIME; case _ => throw new IndexOutOfBoundsException(n.toString())
}
override def productArity: Int = 29; override def canEqual(that: Any):
Boolean = that.isInstanceOf[sdp_d]
}
Non-join queries work fine:
*val q1 = sqlContext.sql("""SELECT YEAR, DAY_OF_YEAR, MAX(WID), MIN(WID),
COUNT(*) FROM date_d GROUP BY YEAR, DAY_OF_YEAR ORDER BY YEAR,
DAY_OF_YEAR""")*
res4: Array[org.apache.spark.sql.Row] =
Array([2014,305,20141101,20141101,1], [2014,306,20141102,20141102,1],
[2014,307,20141103,20141103,1], [2014,308,20141104,20141104,1],
[2014,309,20141105,20141105,1], [2014,310,20141106,20141106,1],
[2014,311,20141107,20141107,1], [2014,312,20141108,20141108,1],
[2014,313,20141109,20141109,1], [2014,314,20141110,20141110,1],
[2014,315,20141111,20141111,1], [2014,316,20141112,20141112,1],
[2014,317,20141113,20141113,1], [2014,318,20141114,20141114,1],
[2014,319,20141115,20141115,1], [2014,320,20141116,20141116,1],
[2014,321,20141117,20141117,1], [2014,322,20141118,20141118,1],
[2014,323,20141119,20141119,1], [2014,324,20141120,20141120,1],
[2014,325,20141121,20141121,1], [2014,326,20141122,20141122,1],
[2014,327,20141123,20141123,1], [2014,328,20141...
*But the join queries throw this error:
java.lang.ArrayIndexOutOfBoundsException*
*scala> val q = sqlContext.sql("""select * from date_d dd join interval_f
intf on intf.DATE_WID = dd.WID Where intf.DATE_WID >= 20141101 AND
intf.DATE_WID <= 20141110""")*
q: org.apache.spark.sql.SchemaRDD =
SchemaRDD[38] at RDD at SchemaRDD.scala:103
== Query Plan ==
== Physical Plan ==
Project
[WID#0,CALENDAR_DATE#1,DATE_STRING#2,DAY_OF_WEEK#3,DAY_OF_MONTH#4,DAY_OF_YEAR#5,END_OF_MONTH_FLAG#6,YEARWEEK#7,CALENDAR_MONTH#8,MONTH_NUM#9,YEARMONTH#10,QUARTER#11,YEAR#12,ORG_ID#13,CHANNEL_WID#14,SDP_WID#15,MEAS_WID#16,DATE_WID#17,TIME_WID#18,VALIDATION_STATUS_CD#19,VAL_FAIL_CD#20,INTERVAL_FLAG_CD#21,CHANGE_METHOD_WID#22,SOURCE_LAST_UPD_TIME#23,INTERVAL_END_TIME#24,LOCKED#25,EXT_VERSION_TIME#26,INTERVAL_VALUE#27,INSERT_TIME#28,LAST_UPD_TIME#29]
ShuffledHashJoin [WID#0], [DATE_WID#17], BuildRight
Exchange (HashPartitioning [WID#0], 200)
InMemoryColumnarTableScan
[WID#0,CALENDAR_DATE#1,DATE_STRING#2,DAY_OF_WEEK#3,DAY_OF_MONTH#4,DAY_OF_YEAR#5,END_OF_MONTH_FLA...
*scala> q.take(5).foreach(println)*
15/02/27 15:50:26 INFO SparkContext: Starting job: runJob at
basicOperators.scala:136
15/02/27 15:50:26 INFO DAGScheduler: Registering RDD 46 (mapPartitions at
Exchange.scala:48)
15/02/27 15:50:26 INFO FileInputFormat: Total input paths to process : 1
15/02/27 15:50:26 INFO DAGScheduler: Registering RDD 42 (mapPartitions at
Exchange.scala:48)
15/02/27 15:50:26 INFO DAGScheduler: Got job 2 (runJob at
basicOperators.scala:136) with 1 output partitions (allowLocal=false)
15/02/27 15:50:26 INFO DAGScheduler: Final stage: Stage 5(runJob at
basicOperators.scala:136)
15/02/27 15:50:26 INFO DAGScheduler: Parents of final stage: List(Stage 6,
Stage 7)
15/02/27 15:50:26 INFO DAGScheduler: Missing parents: List(Stage 6, Stage 7)
15/02/27 15:50:26 INFO DAGScheduler: Submitting Stage 6
(MapPartitionsRDD[46] at mapPartitions at Exchange.scala:48), which has no
missing parents
15/02/27 15:50:26 INFO MemoryStore: ensureFreeSpace(48744) called with
curMem=1042656, maxMem=278302556
15/02/27 15:50:26 INFO MemoryStore: Block broadcast_7 stored as values in
memory (estimated size 47.6 KB, free 264.4 MB)
15/02/27 15:50:26 INFO DAGScheduler: Submitting 2 missing tasks from Stage 6
(MapPartitionsRDD[46] at mapPartitions at Exchange.scala:48)
15/02/27 15:50:26 INFO TaskSchedulerImpl: Adding task set 6.0 with 2 tasks
15/02/27 15:50:26 INFO TaskSetManager: Starting task 0.0 in stage 6.0 (TID
594, localhost, ANY, 1197 bytes)
15/02/27 15:50:26 INFO TaskSetManager: Starting task 1.0 in stage 6.0 (TID
595, localhost, ANY, 1197 bytes)
15/02/27 15:50:26 INFO Executor: Running task 0.0 in stage 6.0 (TID 594)
15/02/27 15:50:26 INFO Executor: Running task 1.0 in stage 6.0 (TID 595)
15/02/27 15:50:26 INFO DAGScheduler: Submitting Stage 7
(MapPartitionsRDD[42] at mapPartitions at Exchange.scala:48), which has no
missing parents
15/02/27 15:50:26 INFO MemoryStore: ensureFreeSpace(50184) called with
curMem=1091400, maxMem=278302556
15/02/27 15:50:26 INFO MemoryStore: Block broadcast_8 stored as values in
memory (estimated size 49.0 KB, free 264.3 MB)
15/02/27 15:50:26 INFO DAGScheduler: Submitting 2 missing tasks from Stage 7
(MapPartitionsRDD[42] at mapPartitions at Exchange.scala:48)
15/02/27 15:50:26 INFO TaskSchedulerImpl: Adding task set 7.0 with 2 tasks
15/02/27 15:50:26 INFO TaskSetManager: Starting task 0.0 in stage 7.0 (TID
596, localhost, ANY, 1201 bytes)
15/02/27 15:50:26 INFO TaskSetManager: Starting task 1.0 in stage 7.0 (TID
597, localhost, ANY, 1201 bytes)
15/02/27 15:50:26 INFO Executor: Running task 0.0 in stage 7.0 (TID 596)
15/02/27 15:50:26 INFO Executor: Running task 1.0 in stage 7.0 (TID 597)
15/02/27 15:50:26 INFO BlockManager: Found block rdd_19_0 locally
15/02/27 15:50:26 INFO BlockManager: Found block rdd_19_1 locally
15/02/27 15:50:26 INFO CacheManager: Partition rdd_21_0 not found, computing
it
15/02/27 15:50:26 INFO CacheManager: Partition rdd_21_1 not found, computing
it
15/02/27 15:50:26 INFO HadoopRDD: Input split:
hdfs://CDH-Master-1.cdhcluster/user/spark/Interval_f.csv:0+279831
15/02/27 15:50:26 INFO HadoopRDD: Input split:
hdfs://CDH-Master-1.cdhcluster/user/spark/Interval_f.csv:279831+279831
15/02/27 15:50:26 INFO Executor: Finished task 1.0 in stage 6.0 (TID 595).
2134 bytes result sent to driver
15/02/27 15:50:26 INFO Executor: Finished task 0.0 in stage 6.0 (TID 594).
2134 bytes result sent to driver
15/02/27 15:50:26 INFO TaskSetManager: Finished task 1.0 in stage 6.0 (TID
595) in 66 ms on localhost (1/2)
15/02/27 15:50:26 INFO TaskSetManager: Finished task 0.0 in stage 6.0 (TID
594) in 68 ms on localhost (2/2)
15/02/27 15:50:26 INFO TaskSchedulerImpl: Removed TaskSet 6.0, whose tasks
have all completed, from pool
15/02/27 15:50:26 INFO DAGScheduler: Stage 6 (mapPartitions at
Exchange.scala:48) finished in 0.068 s
15/02/27 15:50:26 INFO DAGScheduler: looking for newly runnable stages
15/02/27 15:50:26 INFO DAGScheduler: running: Set(Stage 7)
15/02/27 15:50:26 INFO DAGScheduler: waiting: Set(Stage 5)
15/02/27 15:50:26 INFO DAGScheduler: failed: Set()
15/02/27 15:50:26 INFO DAGScheduler: Missing parents for Stage 5: List(Stage
7)
15/02/27 15:50:26 INFO BlockManager: Removing broadcast 7
15/02/27 15:50:26 INFO BlockManager: Removing block broadcast_7
15/02/27 15:50:26 INFO MemoryStore: Block broadcast_7 of size 48744 dropped
from memory (free 277209716)
15/02/27 15:50:26 INFO ContextCleaner: Cleaned broadcast 7
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [ORG_ID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@409ea4d1,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [CHANNEL_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@6f56b67b,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [SDP_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@25e67e58,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [MEAS_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@3f70d6d8,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [DATE_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@9291f72,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [TIME_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@6acf7a10,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for
[VALIDATION_STATUS_CD]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@5b56e738,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [VAL_FAIL_CD]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@26663c61,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [INTERVAL_FLAG_CD]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@20617f9,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [CHANGE_METHOD_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@5e0fdd78,
ratio: 1.0
15/02/27 15:50:26 INFO TimestampColumnBuilder: Compressor for
[SOURCE_LAST_UPD_TIME]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@63952186,
ratio: 1.0
15/02/27 15:50:26 INFO TimestampColumnBuilder: Compressor for
[INTERVAL_END_TIME]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@53cc177d,
ratio: 1.0
15/02/27 15:50:26 INFO StringColumnBuilder: Compressor for [LOCKED]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@7c1a3a85,
ratio: 1.0
15/02/27 15:50:26 INFO TimestampColumnBuilder: Compressor for
[EXT_VERSION_TIME]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@1df8316a,
ratio: 1.0
15/02/27 15:50:26 INFO DoubleColumnBuilder: Compressor for [INTERVAL_VALUE]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@11743585,
ratio: 1.0
15/02/27 15:50:26 INFO TimestampColumnBuilder: Compressor for [INSERT_TIME]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@6465b7b6,
ratio: 1.0
15/02/27 15:50:26 INFO TimestampColumnBuilder: Compressor for
[LAST_UPD_TIME]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@52004138,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [ORG_ID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@6bcc5ad1,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [CHANNEL_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@355e86a1,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [SDP_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@50d8cf66,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [MEAS_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@4a185b01,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [DATE_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@4fec4a8,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [TIME_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@7220f427,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for
[VALIDATION_STATUS_CD]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@517d66fa,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [VAL_FAIL_CD]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@7ff3d0e1,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [INTERVAL_FLAG_CD]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@6016a567,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [CHANGE_METHOD_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@6ec53e79,
ratio: 1.0
15/02/27 15:50:26 INFO TimestampColumnBuilder: Compressor for
[SOURCE_LAST_UPD_TIME]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@5788b2bf,
ratio: 1.0
15/02/27 15:50:26 INFO TimestampColumnBuilder: Compressor for
[INTERVAL_END_TIME]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@4f8f467e,
ratio: 1.0
15/02/27 15:50:26 INFO StringColumnBuilder: Compressor for [LOCKED]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@475d2300,
ratio: 1.0
15/02/27 15:50:26 INFO TimestampColumnBuilder: Compressor for
[EXT_VERSION_TIME]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@33a4f99a,
ratio: 1.0
15/02/27 15:50:26 INFO DoubleColumnBuilder: Compressor for [INTERVAL_VALUE]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@13ff07f3,
ratio: 1.0
15/02/27 15:50:26 INFO TimestampColumnBuilder: Compressor for [INSERT_TIME]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@72a6e257,
ratio: 1.0
15/02/27 15:50:26 INFO TimestampColumnBuilder: Compressor for
[LAST_UPD_TIME]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@2a55f88f,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [ORG_ID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@5541f4b4,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [CHANNEL_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@5d288126,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [SDP_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@e371592,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [MEAS_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@42692b88,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [DATE_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@6a90fc8,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [TIME_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@454b16e2,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for
[VALIDATION_STATUS_CD]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@44cb72f8,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [VAL_FAIL_CD]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@8e91b11,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [INTERVAL_FLAG_CD]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@7feffda8,
ratio: 1.0
15/02/27 15:50:26 INFO IntColumnBuilder: Compressor for [CHANGE_METHOD_WID]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@64f66236,
ratio: 1.0
15/02/27 15:50:26 INFO TimestampColumnBuilder: Compressor for
[SOURCE_LAST_UPD_TIME]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@6ba9fb02,
ratio: 1.0
15/02/27 15:50:26 INFO TimestampColumnBuilder: Compressor for
[INTERVAL_END_TIME]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@649e7786,
ratio: 1.0
15/02/27 15:50:26 INFO StringColumnBuilder: Compressor for [LOCKED]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@5fb93205,
ratio: 1.0
15/02/27 15:50:26 INFO TimestampColumnBuilder: Compressor for
[EXT_VERSION_TIME]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@7783175b,
ratio: 1.0
15/02/27 15:50:26 INFO DoubleColumnBuilder: Compressor for [INTERVAL_VALUE]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@3f7294a9,
ratio: 1.0
15/02/27 15:50:26 INFO TimestampColumnBuilder: Compressor for [INSERT_TIME]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@7b7e03c9,
ratio: 1.0
15/02/27 15:50:26 INFO TimestampColumnBuilder: Compressor for
[LAST_UPD_TIME]:
org.apache.spark.sql.columnar.compression.PassThrough$Encoder@e2ac076,
ratio: 1.0
15/02/27 15:50:26 INFO MemoryStore: ensureFreeSpace(180264) called with
curMem=1092840, maxMem=278302556
15/02/27 15:50:26 INFO MemoryStore: Block rdd_21_0 stored as values in
memory (estimated size 176.0 KB, free 264.2 MB)
15/02/27 15:50:26 INFO BlockManagerInfo: Added rdd_21_0 in memory on
CDH-Master-1.cdhcluster:45530 (size: 176.0 KB, free: 265.2 MB)
15/02/27 15:50:26 INFO BlockManagerMaster: Updated info of block rdd_21_0
15/02/27 15:50:26 ERROR Executor: Exception in task 1.0 in stage 7.0 (TID
597)
*java.lang.ArrayIndexOutOfBoundsException: 10*
at
$line17.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:30)
at
$line17.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:30)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$1.next(Iterator.scala:853)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at
org.apache.spark.sql.columnar.InMemoryRelation$$anonfun$1$$anon$1.next(InMemoryColumnarTableScan.scala:62)
at
org.apache.spark.sql.columnar.InMemoryRelation$$anonfun$1$$anon$1.next(InMemoryColumnarTableScan.scala:50)
at
org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:236)
at
org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:163)
at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:227)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:54)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:180)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
15/02/27 15:50:26 WARN TaskSetManager: Lost task 1.0 in stage 7.0 (TID 597,
localhost): java.lang.ArrayIndexOutOfBoundsException: 10
$line17.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:30)
$line17.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:30)
scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
scala.collection.Iterator$$anon$1.next(Iterator.scala:853)
scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
org.apache.spark.sql.columnar.InMemoryRelation$$anonfun$1$$anon$1.next(InMemoryColumnarTableScan.scala:62)
org.apache.spark.sql.columnar.InMemoryRelation$$anonfun$1$$anon$1.next(InMemoryColumnarTableScan.scala:50)
org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:236)
org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:163)
org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70)
org.apache.spark.rdd.RDD.iterator(RDD.scala:227)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
org.apache.spark.scheduler.Task.run(Task.scala:54)
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:180)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
java.lang.Thread.run(Thread.java:745)
15/02/27 15:50:26 ERROR TaskSetManager: Task 1 in stage 7.0 failed 1 times;
aborting job
15/02/27 15:50:26 INFO TaskSchedulerImpl: Cancelling stage 7
15/02/27 15:50:26 INFO Executor: Executor is trying to kill task 0.0 in
stage 7.0 (TID 596)
15/02/27 15:50:26 INFO TaskSchedulerImpl: Stage 7 was cancelled
15/02/27 15:50:26 INFO DAGScheduler: Failed to run runJob at
basicOperators.scala:136
15/02/27 15:50:26 INFO Executor: Executor killed task 0.0 in stage 7.0 (TID
596)
15/02/27 15:50:26 WARN TaskSetManager: Lost task 0.0 in stage 7.0 (TID 596,
localhost): TaskKilled (killed intentionally)
15/02/27 15:50:26 INFO TaskSchedulerImpl: Removed TaskSet 7.0, whose tasks
have all completed, from pool
org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in
stage 7.0 failed 1 times, most recent failure: Lost task 1.0 in stage 7.0
(TID 597, localhost): java.lang.ArrayIndexOutOfBoundsException: 10
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:30)
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:30)
scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
scala.collection.Iterator$$anon$1.next(Iterator.scala:853)
scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
org.apache.spark.sql.columnar.InMemoryRelation$$anonfun$1$$anon$1.next(InMemoryColumnarTableScan.scala:62)
org.apache.spark.sql.columnar.InMemoryRelation$$anonfun$1$$anon$1.next(InMemoryColumnarTableScan.scala:50)
org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:236)
org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:163)
org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70)
org.apache.spark.rdd.RDD.iterator(RDD.scala:227)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
org.apache.spark.scheduler.Task.run(Task.scala:54)
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:180)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1185)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1174)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1173)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1173)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:688)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:688)
at scala.Option.foreach(Option.scala:236)
at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:688)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1391)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
at akka.actor.ActorCell.invoke(ActorCell.scala:456)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
at akka.dispatch.Mailbox.run(Mailbox.scala:219)
at
akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
at
scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
at
scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
at
scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
*I am unable to comprehend the error log on my own. Please help resolve the
error.*
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/Facing-error-java-lang-ArrayIndexOutOfBoundsException-while-executing-SparkSQL-join-query-tp21846.html
Sent from the Apache Spark User List mailing list archive at Nabble.com.
---------------------------------------------------------------------
To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
For additional commands, e-mail: user-h...@spark.apache.org
