I'm trying to read a single row from a Hive table and use its fields as constants in the rest of my Spark application.
object IniConstHive extends Serializable {
val techTbl: DataFrame = spark.table("my_db.my_conf_table").orderBy(col("my_date").desc)
val firstrow: Row = techTbl.head
val my_firstfield: Double = firstrow.getAs[java.math.BigDecimal](0).doubleValue
val my_secondfield: Double = firstrow.getAs[java.math.BigDecimal](1).doubleValue
}
Unfortunately, I get this exception when I call IniConstHive.my_firstfield:
Caused by: java.util.NoSuchElementException: None.get
at scala.None$.get(Option.scala:347)
at scala.None$.get(Option.scala:345)
at org.apache.spark.storage.BlockInfoManager.releaseAllLocksForTask(BlockInfoManager.scala:343)
at org.apache.spark.storage.BlockManager.releaseAllLocksForTask(BlockManager.scala:676)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:329)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Is this the right approach to do this? Is there a more efficient way to acheive the result?
It turned out the problem was not in the IniConstHive object but the place where I called it. In fact, I was trying to call IniConstHive.my_firstfield inside a custom UDAF, which runs inside executors. For this reason my application tried to instantiate multiple SparkContext outside the driver.
I solved by calling IniConstHive.my_firstfield in the driver and then passing the result as a parameter in the UDAF constructor.
Related
Below I provide my code. I iterate over the DataFrame prodRows and for each product_PK I find some matching sub-list of product_PKs from prodRows.
numRecProducts = 10
var listOfProducts: Map[Long,Array[(Long, Int)]] = Map()
prodRows.foreach{ row : Row =>
val product_PK = row.get(row.fieldIndex("product_PK")).toString.toLong
val gender = row.get(row.fieldIndex("gender_PK")).toString
val selection = prodRows.filter($"gender_PK" === gender || $"gender_PK" === "UNISEX").limit(numRecProducts).select($"product_PK")
var productList: Array[(Long, Int)] = Array()
if (!selection.rdd.isEmpty()) {
productList = selection.rdd.map(x => (x(0).toString.toLong,1)).collect()
}
listOfProducts = listOfProducts + (product_PK -> productList)
}
But when I execute it, it gives me the following error. It looks like selection is empty in some iterations. However, I do not understand how can I handle this error:
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1690)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1678)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1677)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1677)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:855)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:855)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:855)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1905)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1860)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1849)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:671)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1.apply(RDD.scala:918)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1.apply(RDD.scala:916)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.foreach(RDD.scala:916)
at org.apache.spark.sql.Dataset$$anonfun$foreach$1.apply$mcV$sp(Dataset.scala:2325)
at org.apache.spark.sql.Dataset$$anonfun$foreach$1.apply(Dataset.scala:2325)
at org.apache.spark.sql.Dataset$$anonfun$foreach$1.apply(Dataset.scala:2325)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2823)
at org.apache.spark.sql.Dataset.foreach(Dataset.scala:2324)
at org.test.ComputeNumSim.run(ComputeNumSim.scala:69)
at org.test.ComputeNumSimRunner$.main(ComputeNumSimRunner.scala:19)
at org.test.ComputeNumSimRunner.main(ComputeNumSimRunner.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:635)
Caused by: java.lang.NullPointerException
at org.apache.spark.sql.Dataset.<init>(Dataset.scala:170)
at org.apache.spark.sql.Dataset$.apply(Dataset.scala:61)
at org.apache.spark.sql.Dataset.withTypedPlan(Dataset.scala:2877)
at org.apache.spark.sql.Dataset.filter(Dataset.scala:1304)
at org.test.ComputeNumSim$$anonfun$run$1.apply(ComputeNumSim.scala:74)
at org.test.ComputeNumSim$$anonfun$run$1.apply(ComputeNumSim.scala:69)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:918)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:918)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
What does it mean and how can I handle it?
You cannot access any of Spark's "driver-side" abstractions (RDDs, DataFrames, Datasets, SparkSession...) from within a function passed on to one of Spark's DataFrame/RDD transformations. You also cannot update driver-side mutable objects from within these functions.
In your case - you're trying to use prodRows and selection (both are DataFrames) within a function passed to DataFrame.foreach. You're also trying to update listOfProducts (a local driver-side variable) from within that same function.
Why?
DataFrames, RDDs, and SparkSession only exist on your Driver application. They serve as a "handle" to access data distributed over the cluster of worker machines.
Functions passed to RDD/DataFrame transformations get serialized and sent to that cluster, to be executed on the data partitions on each of the worker machines. When the serialized DataFrames/RDDs get deserialized on those machines - they are useless, they can't still represent the data on the cluster as they are just hollow copies of the ones created on the driver application, which actually maintains a connection to the cluster machines
For the same reason, attempting to update driver-side variables will fail: the variables (starting out as empty, in most cases) will be serialized, deserialized on each of the workers, get updated locally on the workers, and stay there... the original driver-side variable will remain unchanged
How can you solve this?
When working with Spark, especially with DataFrames, you should try to avoid "iteration" over the data, and use DataFrame's declarative operations instead. In most cases, when you want to reference data of another DataFrame for each record in your DataFrame, you'd want to use join to create a new DataFrame with records combining data from the two DataFrames.
In this specific case, here's a roughly equivalent solution that does what you're trying to do, if I managed to conclude it correctly. Try to use this and read the DataFrame documentation to figure out the details:
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
import spark.implicits._
val numRecProducts = 10
val result = prodRows.as("left")
// self-join by gender:
.join(prodRows.as("right"), $"left.gender_PK" === $"right.gender_PK" || $"right.gender_PK" === "UNISEX")
// limit to 10 results per record:
.withColumn("rn", row_number().over(Window.partitionBy($"left.product_PK").orderBy($"right.product_PK")))
.filter($"rn" <= numRecProducts).drop($"rn")
// group and collect_list to create products column:
.groupBy($"left.product_PK" as "product_PK")
.agg(collect_list(struct($"right.product_PK", lit(1))) as "products")
The problem is that you try to access prodRows from within prodRows.foreach. You cannot use a dataframe within a transformation, dataframes only exist on the driver.
Below I provide my code. I iterate over the DataFrame prodRows and for each product_PK I find some matching sub-list of product_PKs from prodRows.
numRecProducts = 10
var listOfProducts: Map[Long,Array[(Long, Int)]] = Map()
prodRows.foreach{ row : Row =>
val product_PK = row.get(row.fieldIndex("product_PK")).toString.toLong
val gender = row.get(row.fieldIndex("gender_PK")).toString
val selection = prodRows.filter($"gender_PK" === gender || $"gender_PK" === "UNISEX").limit(numRecProducts).select($"product_PK")
var productList: Array[(Long, Int)] = Array()
if (!selection.rdd.isEmpty()) {
productList = selection.rdd.map(x => (x(0).toString.toLong,1)).collect()
}
listOfProducts = listOfProducts + (product_PK -> productList)
}
But when I execute it, it gives me the following error. It looks like selection is empty in some iterations. However, I do not understand how can I handle this error:
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1690)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1678)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1677)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1677)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:855)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:855)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:855)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1905)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1860)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1849)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:671)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1.apply(RDD.scala:918)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1.apply(RDD.scala:916)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.foreach(RDD.scala:916)
at org.apache.spark.sql.Dataset$$anonfun$foreach$1.apply$mcV$sp(Dataset.scala:2325)
at org.apache.spark.sql.Dataset$$anonfun$foreach$1.apply(Dataset.scala:2325)
at org.apache.spark.sql.Dataset$$anonfun$foreach$1.apply(Dataset.scala:2325)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2823)
at org.apache.spark.sql.Dataset.foreach(Dataset.scala:2324)
at org.test.ComputeNumSim.run(ComputeNumSim.scala:69)
at org.test.ComputeNumSimRunner$.main(ComputeNumSimRunner.scala:19)
at org.test.ComputeNumSimRunner.main(ComputeNumSimRunner.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:635)
Caused by: java.lang.NullPointerException
at org.apache.spark.sql.Dataset.<init>(Dataset.scala:170)
at org.apache.spark.sql.Dataset$.apply(Dataset.scala:61)
at org.apache.spark.sql.Dataset.withTypedPlan(Dataset.scala:2877)
at org.apache.spark.sql.Dataset.filter(Dataset.scala:1304)
at org.test.ComputeNumSim$$anonfun$run$1.apply(ComputeNumSim.scala:74)
at org.test.ComputeNumSim$$anonfun$run$1.apply(ComputeNumSim.scala:69)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:918)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:918)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
What does it mean and how can I handle it?
You cannot access any of Spark's "driver-side" abstractions (RDDs, DataFrames, Datasets, SparkSession...) from within a function passed on to one of Spark's DataFrame/RDD transformations. You also cannot update driver-side mutable objects from within these functions.
In your case - you're trying to use prodRows and selection (both are DataFrames) within a function passed to DataFrame.foreach. You're also trying to update listOfProducts (a local driver-side variable) from within that same function.
Why?
DataFrames, RDDs, and SparkSession only exist on your Driver application. They serve as a "handle" to access data distributed over the cluster of worker machines.
Functions passed to RDD/DataFrame transformations get serialized and sent to that cluster, to be executed on the data partitions on each of the worker machines. When the serialized DataFrames/RDDs get deserialized on those machines - they are useless, they can't still represent the data on the cluster as they are just hollow copies of the ones created on the driver application, which actually maintains a connection to the cluster machines
For the same reason, attempting to update driver-side variables will fail: the variables (starting out as empty, in most cases) will be serialized, deserialized on each of the workers, get updated locally on the workers, and stay there... the original driver-side variable will remain unchanged
How can you solve this?
When working with Spark, especially with DataFrames, you should try to avoid "iteration" over the data, and use DataFrame's declarative operations instead. In most cases, when you want to reference data of another DataFrame for each record in your DataFrame, you'd want to use join to create a new DataFrame with records combining data from the two DataFrames.
In this specific case, here's a roughly equivalent solution that does what you're trying to do, if I managed to conclude it correctly. Try to use this and read the DataFrame documentation to figure out the details:
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
import spark.implicits._
val numRecProducts = 10
val result = prodRows.as("left")
// self-join by gender:
.join(prodRows.as("right"), $"left.gender_PK" === $"right.gender_PK" || $"right.gender_PK" === "UNISEX")
// limit to 10 results per record:
.withColumn("rn", row_number().over(Window.partitionBy($"left.product_PK").orderBy($"right.product_PK")))
.filter($"rn" <= numRecProducts).drop($"rn")
// group and collect_list to create products column:
.groupBy($"left.product_PK" as "product_PK")
.agg(collect_list(struct($"right.product_PK", lit(1))) as "products")
The problem is that you try to access prodRows from within prodRows.foreach. You cannot use a dataframe within a transformation, dataframes only exist on the driver.
I am unable to parallelize a list in scala, getting java.lang.NullPointerException
messages.foreachRDD( rdd => {
for(avroLine <- rdd){
val record = Injection.injection.invert(avroLine.getBytes).get
val field1Value = record.get("username")
val jsonStrings=Seq(record.toString())
val newRow = sqlContext.sparkContext.parallelize(Seq(record.toString()))
}
})
output
jsonStrings...List({"username": "user_118", "tweet": "tweet_218", "timestamp": 18})
Exception
Caused by: java.lang.NullPointerException
at com.capitalone.AvroConsumer$$anonfun$main$1$$anonfun$apply$1.apply(AvroConsumer.scala:83)
at com.capitalone.AvroConsumer$$anonfun$main$1$$anonfun$apply$1.apply(AvroConsumer.scala:74)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at org.apache.spark.util.CompletionIterator.foreach(CompletionIterator.scala:26)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:917)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:917)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1944)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1944)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
Thanks in Advance!!
You're trying to create an RDD in the spark worker context. While foreachRDD operates in the driver, the foreach operation you perform on each RDD is distributed to the workers. It seems unlikely that you actually want to create a new RDD for each line of the input stream.
Update after comments:
It's hard to have this discussion in a comment thread where there is no formatting for code. My basic question is why aren't you doing something like this:
val messages: ReceiverInputDStream[String] = RabbitMQUtils.createStream(ssc, rabbitParams)
def toJsonString(message: String): String = SparkUtils.getRecordInjection(QUEUE_NAME).invert(message.getBytes()).get
val jsonStrings: DStream[String] = messages map toJsonString
I haven't bothered to figure out and track down all the libraries you're using (please, next time, submit a MCVE), so I haven't tried to compile that. But it looks like all you want is to map each input message to a JSON string. Maybe you want to do something fancy with the resulting DStream of Strings but that might be a different question.
def toJsonString(message: String): String = {val record =
SparkUtils.getRecordInjection(QUEUE_NAME).invert(message.getBytes()).get }
dStreams.foreachRDD( rdd => {
val jsonStrings = rdd.map (stream =>toJsonString(stream))
val df = sqlContext.read.json(jsonStrings)
df.write.mode("Append").csv("/Users/Documents/kafka-poc/consumer-out/def/")}
I have Spark 1.5.0 running on cluster. I want to use Hive UDF from ESRI's API. I can use these API in Spark Application but due to some issues in my cluster, I am not able to use HiveContext. I want to use Existing Hive UDF in Spark-SQL application.
// val sqlContext = new SQLContext(sc)
// import sqlContext.implicits._
// val hc = new HiveContext(sc)
// hc.sql("create temporary function ST_Point as 'com.esri.hadoop.hive.ST_Point'")
// hc.sql("create temporary function ST_Within as 'com.esri.hadoop.hive.ST_Within'")
// hc.sql("create temporary function ST_Polygon as 'com.esri.hadoop.hive.ST_Polygon'")
// val resultDF = hc.sql("select ST_Within(ST_Point(2, 3), ST_Polygon(1,1, 1,4, 4,4, 4,1))")
The above code is for HiveContext but I want to use similar thing in SparkContext so wrote something as per this-
sqlContext.sql("""create function ST_Point as 'com.esri.hadoopcom.esri.hadoop.hive.ST_Point'""")
But seems like same error I am getting. (See below)
Exception in thread "main" java.lang.RuntimeException: [1.1] failure: ``with'' expected but identifier create found
create function ST_Point as 'com.esri.hadoopcom.esri.hadoop.hive.ST_Point'
^
at scala.sys.package$.error(package.scala:27)
I tried to make functions with existing UDFs but seems like need to make scala wrapper to call java classes. I tried as below-
def ST_Point_Spark = new ST_Point()
sqlContext.udf.register("ST_Point_Spark", ST_Point_Spark _)
def ST_Within_Spark = new ST_Within()
sqlContext.udf.register("ST_Within_Spark", ST_Within_Spark _)
def ST_Polygon_Spark = new ST_Polygon()
sqlContext.udf.register("ST_Polygon_Spark", ST_Polygon_Spark _)
sqlContext.sql("select ST_Within_Spark(ST_Point_Spark(2, 3), ST_Polygon_Spark(1,1, 1,4, 4,4, 4,1))")
but in this case getting error-
Exception in thread "main" scala.reflect.internal.Symbols$CyclicReference: illegal cyclic reference involving object InterfaceAudience
at scala.reflect.internal.Symbols$Symbol$$anonfun$info$3.apply(Symbols.scala:1220)
at scala.reflect.internal.Symbols$Symbol$$anonfun$info$3.apply(Symbols.scala:1218)
at scala.Function0$class.apply$mcV$sp(Function0.scala:40)
at scala.runtime.AbstractFunction0.apply$mcV$sp(AbstractFunction0.scala:12)
I am just wondering, Is there any way to call Hive/Java UDF without using HiveContext, directly using SqlContext.
Note: This was a helpful post but not as per my requirement.
I am trying to define a udf in spark(2.0) from a string containing scala function definition.Here is the snippet:
val universe: scala.reflect.runtime.universe.type = scala.reflect.runtime.universe
import universe._
import scala.reflect.runtime.currentMirror
import scala.tools.reflect.ToolBox
val toolbox = currentMirror.mkToolBox()
val f = udf(toolbox.eval(toolbox.parse("(s:String) => 5")).asInstanceOf[String => Int])
sc.parallelize(Seq("1","5")).toDF.select(f(col("value"))).show
This gives me an error :
Caused by: java.lang.ClassCastException: cannot assign instance of scala.collection.immutable.List$SerializationProxy to field org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$dependencies_ of type scala.collection.Seq in instance of org.apache.spark.rdd.MapPartitionsRDD
at java.io.ObjectStreamClass$FieldReflector.setObjFieldValues(ObjectStreamClass.java:2133)
at java.io.ObjectStreamClass.setObjFieldValues(ObjectStreamClass.java:1305)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2024)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1942)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1808)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1353)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2018)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1942)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1808)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1353)
at java.io.ObjectInputStream.readObject(ObjectInputStream.java:373)
at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:75)
at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:114)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:85)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
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)
However when I define the udf as :
val f = udf((s:String) => 5)
it works just fine. What is the issue here?The end objective is to take a string which has the defn of a scala function and use it as a udf.
As Giovanny observed, the problem lies in the class loaders being different (you can investigate this more by calling .getClass.getClassLoader on whatever object). Then, when the workers try to deserialize your reflected function, all hell breaks loose.
Here is a solution that does not involve any class loader hackery. The idea is to move the reflection step to the workers. We'll end up having to redo the reflection step, but only once per worker. I think this is pretty optimal - even if you did the reflection only once on the master node, you would have to do a fair bit of work per worker to get them to recognize the function.
val f = udf {
new Function1[String,Int] with Serializable {
import scala.reflect.runtime.universe._
import scala.reflect.runtime.currentMirror
import scala.tools.reflect.ToolBox
lazy val toolbox = currentMirror.mkToolBox()
lazy val func = {
println("reflected function") // triggered at every worker
toolbox.eval(toolbox.parse("(s:String) => 5")).asInstanceOf[String => Int]
}
def apply(s: String): Int = func(s)
}
}
Then, calling sc.parallelize(Seq("1","5")).toDF.select(f(col("value"))).show works just fine.
Feel free to comment out the println - it is just an easy way of counting how many times the reflection happened. In spark-shell --master 'local' that's only once, but in spark-shell --master 'local[2]' it's twice.
How it works
The UDF gets evaluated immediately, but it never gets used until it reaches the worker nodes, so the lazy values toolbox and func only get evaluated on the workers. Furthermore, since they are lazy, they only ever get evaluated once per worker.
I had the same error, and it doesn't show the ClassNotFoundException because the JavaDeserializationStream class is catching the exception, depending on your environment it is failing because it coudn't find the class you're trying to execute from your RDD/DataSet but it doesn't show the ClassNotFoundError . To fix this issue I had to generate a jar with all the classes on my project (including the function and dependencies ) and include the jar inside the spark environment
This for an standalone cluster
conf.setJars ( Array ("/fullpath/yourgeneratedjar.jar", "/fullpath/otherdependencies.jar") )
and this for a yarn cluster
conf.set("spark.yarn.jars", "/fullpath/yourgeneratedjar.jar,/fullpath/otherdependencies.jar")