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https://issues.apache.org/jira/browse/SPARK-10357?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14729563#comment-14729563
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Randy Gelhausen commented on SPARK-10357:
-----------------------------------------
Regardless of what spark-csv package does, I'm using the DataFrame API itself
to drop columns. SparkSQL's attempt to access those columns seems like a
problem with SparkSQL's toDF method. Am I reading the API wrong? Can the docs
maybe be updated to indicate that their function depends on the underlying read
format?
val raw = sqlContext.read.format("com.databricks.spark.csv").option("header",
"true").option("DROPMALFORMED", "true").load(input)
val columns = raw.columns.map(x => x.replaceAll(" ", "_"))
raw.toDF(columns:_*).registerTempTable(table)
val clean = sqlContext.sql("select " + columns.filter(x => x.length() > 0 && x
!= " ").mkString(", ") + " from " + table)
> DataFrames unable to drop unwanted columns
> ------------------------------------------
>
> Key: SPARK-10357
> URL: https://issues.apache.org/jira/browse/SPARK-10357
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.4.1
> Reporter: Randy Gelhausen
>
> spark-csv seems to be exposing an issue with DataFrame's inability to drop
> unwanted columns.
> Related GitHub issue: https://github.com/databricks/spark-csv/issues/61
> My data (with header) looks like:
> MI_PRINX,offense_id,rpt_date,occur_date,occur_time,poss_date,poss_time,beat,apt_office_prefix,apt_office_num,location,MinOfucr,MinOfibr_code,dispo_code,MaxOfnum_victims,Shift,Avg
> Day,loc_type,UC2 Literal,neighborhood,npu,x,y,,,
> 934782,90360664,2/5/2009,2/3/2009,13:50:00,2/3/2009,15:00:00,305,NULL,NULL,55
> MCDONOUGH BLVD SW,670,2308,NULL,1,Day,Tue,35,LARCENY-NON VEHICLE,South
> Atlanta,Y,-84.38654,33.72024,,,
> 934783,90370891,2/6/2009,2/6/2009,8:50:00,2/6/2009,10:45:00,502,NULL,NULL,464
> ANSLEY WALK TER NW,640,2305,NULL,1,Day,Fri,18,LARCENY-FROM VEHICLE,Ansley
> Park,E,-84.37276,33.79685,,,
> Despite using sqlContext (also tried with the programmatic raw.select, same
> result) to remove columns from the dataframe, attempts to operate on it cause
> failures.
> Snippet:
> // Read CSV file, clean field names
> val raw =
> sqlContext.read.format("com.databricks.spark.csv").option("header",
> "true").option("DROPMALFORMED", "true").load(input)
> val columns = raw.columns.map(x => x.replaceAll(" ", "_"))
> raw.toDF(columns:_*).registerTempTable(table)
> val clean = sqlContext.sql("select " + columns.filter(x => x.length() > 0
> && x != " ").mkString(", ") + " from " + table)
> System.err.println(clean.schema)
> System.err.println(clean.columns.mkString(","))
> System.err.println(clean.take(1).mkString("|"))
> StackTrace:
> {code}
> 15/08/30 18:23:13 INFO scheduler.TaskSetManager: Starting task 0.0 in stage
> 0.0 (TID 0, docker.dev, NODE_LOCAL, 1482 bytes)
> 15/08/30 18:23:14 INFO storage.BlockManagerInfo: Added broadcast_1_piece0 in
> memory on docker.dev:58272 (size: 1811.0 B, free: 530.0 MB)
> 15/08/30 18:23:14 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in
> memory on docker.dev:58272 (size: 21.9 KB, free: 530.0 MB)
> 15/08/30 18:23:15 INFO scheduler.TaskSetManager: Finished task 0.0 in stage
> 0.0 (TID 0) in 1350 ms on docker.dev (1/1)
> 15/08/30 18:23:15 INFO scheduler.DAGScheduler: ResultStage 0 (take at
> CsvRelation.scala:174) finished in 1.354 s
> 15/08/30 18:23:15 INFO cluster.YarnScheduler: Removed TaskSet 0.0, whose
> tasks have all completed, from pool
> 15/08/30 18:23:15 INFO scheduler.DAGScheduler: Job 0 finished: take at
> CsvRelation.scala:174, took 1.413674 s
> StructType(StructField(MI_PRINX,StringType,true),
> StructField(offense_id,StringType,true),
> StructField(rpt_date,StringType,true),
> StructField(occur_date,StringType,true),
> StructField(occur_time,StringType,true),
> StructField(poss_date,StringType,true),
> StructField(poss_time,StringType,true), StructField(beat,StringType,true),
> StructField(apt_office_prefix,StringType,true),
> StructField(apt_office_num,StringType,true),
> StructField(location,StringType,true), StructField(MinOfucr,StringType,true),
> StructField(MinOfibr_code,StringType,true),
> StructField(dispo_code,StringType,true),
> StructField(MaxOfnum_victims,StringType,true),
> StructField(Shift,StringType,true), StructField(Avg_Day,StringType,true),
> StructField(loc_type,StringType,true),
> StructField(UC2_Literal,StringType,true),
> StructField(neighborhood,StringType,true), StructField(npu,StringType,true),
> StructField(x,StringType,true), StructField(y,StringType,true))
> MI_PRINX,offense_id,rpt_date,occur_date,occur_time,poss_date,poss_time,beat,apt_office_prefix,apt_office_num,location,MinOfucr,MinOfibr_code,dispo_code,MaxOfnum_victims,Shift,Avg_Day,loc_type,UC2_Literal,neighborhood,npu,x,y
> 15/08/30 18:23:16 INFO storage.MemoryStore: ensureFreeSpace(232400) called
> with curMem=259660, maxMem=278019440
> 15/08/30 18:23:16 INFO storage.MemoryStore: Block broadcast_2 stored as
> values in memory (estimated size 227.0 KB, free 264.7 MB)
> 15/08/30 18:23:16 INFO storage.MemoryStore: ensureFreeSpace(22377) called
> with curMem=492060, maxMem=278019440
> 15/08/30 18:23:16 INFO storage.MemoryStore: Block broadcast_2_piece0 stored
> as bytes in memory (estimated size 21.9 KB, free 264.6 MB)
> 15/08/30 18:23:16 INFO storage.BlockManagerInfo: Added broadcast_2_piece0 in
> memory on 172.17.0.19:41088 (size: 21.9 KB, free: 265.1 MB)
> 15/08/30 18:23:16 INFO spark.SparkContext: Created broadcast 2 from textFile
> at TextFile.scala:30
> Exception in thread "main" java.lang.IllegalArgumentException: The header
> contains a duplicate entry: '' in [MI_PRINX, offense_id, rpt_date,
> occur_date, occur_time, poss_date, poss_time, beat, apt_office_prefix,
> apt_office_num, location, MinOfucr, MinOfibr_code, dispo_code,
> MaxOfnum_victims, Shift, Avg Day, loc_type, UC2 Literal, neighborhood, npu,
> x, y, , , ]
> at org.apache.commons.csv.CSVFormat.validate(CSVFormat.java:770)
> at org.apache.commons.csv.CSVFormat.<init>(CSVFormat.java:364)
> at org.apache.commons.csv.CSVFormat.withHeader(CSVFormat.java:882)
> at com.databricks.spark.csv.CsvRelation.tokenRdd(CsvRelation.scala:84)
> at com.databricks.spark.csv.CsvRelation.buildScan(CsvRelation.scala:105)
> at
> org.apache.spark.sql.sources.DataSourceStrategy$.apply(DataSourceStrategy.scala:101)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:59)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner.planLater(QueryPlanner.scala:54)
> at
> org.apache.spark.sql.execution.SparkStrategies$BasicOperators$.apply(SparkStrategies.scala:300)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:59)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner.planLater(QueryPlanner.scala:54)
> at
> org.apache.spark.sql.execution.SparkStrategies$BasicOperators$.apply(SparkStrategies.scala:314)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:59)
> at
> org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan$lzycompute(SQLContext.scala:943)
> at
> org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan(SQLContext.scala:941)
> at
> org.apache.spark.sql.SQLContext$QueryExecution.executedPlan$lzycompute(SQLContext.scala:947)
> at
> org.apache.spark.sql.SQLContext$QueryExecution.executedPlan(SQLContext.scala:947)
> at org.apache.spark.sql.DataFrame.collect(DataFrame.scala:1269)
> at org.apache.spark.sql.DataFrame.head(DataFrame.scala:1203)
> at org.apache.spark.sql.DataFrame.take(DataFrame.scala:1262)
> at com.github.randerzander.CSVLoad$.main(CSVLoad.scala:29)
> at com.github.randerzander.CSVLoad.main(CSVLoad.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:497)
> at
> org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:665)
> at
> org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:170)
> at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:193)
> at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:112)
> at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
> {code}
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