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Posted to issues@spark.apache.org by "Sameer Agarwal (JIRA)" <ji...@apache.org> on 2018/01/08 20:10:00 UTC
[jira] [Updated] (SPARK-21076) R dapply doesn't return array or raw
columns when array have different length
[ https://issues.apache.org/jira/browse/SPARK-21076?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sameer Agarwal updated SPARK-21076:
-----------------------------------
Target Version/s: 2.4.0 (was: 2.3.0)
> R dapply doesn't return array or raw columns when array have different length
> -----------------------------------------------------------------------------
>
> Key: SPARK-21076
> URL: https://issues.apache.org/jira/browse/SPARK-21076
> Project: Spark
> Issue Type: Bug
> Components: SparkR
> Affects Versions: 2.1.0
> Reporter: Xu Yang
>
> Calling SparkR::dapplyCollect with R functions that return dataframes produces an error. This comes up when returning columns of binary data- ie. serialized fitted models. Also happens when functions return columns containing vectors. [SPARK-16785|https://issues.apache.org/jira/browse/SPARK-16785]
> still have this issue when input data is an array column not having the same length on each vector, like:
> {code}
> head(test1)
> key value
> 1 4dda7d68a202e9e3 1595297780
> 2 4e08f349deb7392 641991337
> 3 4e105531747ee00b 374773009
> 4 4f1d5ef7fdb4620a 2570136926
> 5 4f63a71e6dde04cd 2117602722
> 6 4fa2f96b689624fc 3489692062, 1344510747, 1095592237, 424510360, 3211239587
> sparkR.stop()
> sc <- sparkR.init()
> sqlContext <- sparkRSQL.init(sc)
> spark_df = createDataFrame(sqlContext, test1)
> # Fails
> dapplyCollect(spark_df, function(x) x)
> Caused by: org.apache.spark.SparkException: R computation failed with
> Error in (function (..., deparse.level = 1, make.row.names = TRUE, stringsAsFactors = default.stringsAsFactors()) :
> invalid list argument: all variables should have the same length
> at org.apache.spark.api.r.RRunner.compute(RRunner.scala:108)
> at org.apache.spark.sql.execution.r.MapPartitionsRWrapper.apply(MapPartitionsRWrapper.scala:59)
> at org.apache.spark.sql.execution.r.MapPartitionsRWrapper.apply(MapPartitionsRWrapper.scala:29)
> at org.apache.spark.sql.execution.MapPartitionsExec$$anonfun$6.apply(objects.scala:186)
> at org.apache.spark.sql.execution.MapPartitionsExec$$anonfun$6.apply(objects.scala:183)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> at org.apache.spark.scheduler.Task.run(Task.scala:99)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> ... 1 more
> # Works fine
> spark_df <- selectExpr(spark_df, "key", "explode(value) value")
> dapplyCollect(spark_df, function(x) x)
> key value
> 1 4dda7d68a202e9e3 1595297780
> 2 4e08f349deb7392 641991337
> 3 4e105531747ee00b 374773009
> 4 4f1d5ef7fdb4620a 2570136926
> 5 4f63a71e6dde04cd 2117602722
> 6 4fa2f96b689624fc 3489692062
> 7 4fa2f96b689624fc 1344510747
> 8 4fa2f96b689624fc 1095592237
> 9 4fa2f96b689624fc 424510360
> 10 4fa2f96b689624fc 3211239587
> {code}
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