org.apache.spark.SparkException：无法初始化 class com.google.cloud.spark.bigquery.SparkBigQueryConnectorUserAgentProvider

Question

Below is the code i was using to import a bigquery table to my PySpark cluster(dataproc) and then run fp-growth algorithm on it. But, today when i ran the same code it was throwing an error. It returns the schema of the imported df with.printSchema() but when i try to run.show() or.fit(), it throws the below error.

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, split
from pyspark.sql.types import ArrayType, IntegerType
from pyspark.ml.fpm import FPGrowth
from pyspark.sql.functions import concat_ws
spark = SparkSession.builder.appName('Jupyter BigQuery Storage').config('spark.jars','gs://spark-lib/bigquery/spark-bigquery-latest.jar').getOrCreate()

table = "project_name.dataset_name.test_table"
df = spark.read.format("bigquery").option("table",table).load()
df.printSchema()


df = df.withColumn(
    "item",
    split(col("item"), ",").cast(ArrayType(IntegerType())).alias("item")
    )

df.printSchema()

df.show(2)

fpGrowth = FPGrowth(itemsCol="item", minSupport=0.01, minConfidence=0.01)
model = fpGrowth.fit(df)

Below is the error i'm getting:

Py4JJavaError                             Traceback (most recent call last)
<ipython-input-10-74ec76b0ec20> in <module>
     14     df.printSchema()
     15 
---> 16     df.show(2)
     17 
     18     fpGrowth = FPGrowth(itemsCol="item", minSupport=0.01, minConfidence=0.01)

/usr/lib/spark/python/pyspark/sql/dataframe.py in show(self, n, truncate, vertical)
    378         """
    379         if isinstance(truncate, bool) and truncate:
--> 380             print(self._jdf.showString(n, 20, vertical))
    381         else:
    382             print(self._jdf.showString(n, int(truncate), vertical))

/opt/conda/anaconda/lib/python3.6/site-packages/py4j/java_gateway.py in __call__(self, *args)
   1255         answer = self.gateway_client.send_command(command)
   1256         return_value = get_return_value(
-> 1257             answer, self.gateway_client, self.target_id, self.name)
   1258 
   1259         for temp_arg in temp_args:

/usr/lib/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
     61     def deco(*a, **kw):
     62         try:
---> 63             return f(*a, **kw)
     64         except py4j.protocol.Py4JJavaError as e:
     65             s = e.java_exception.toString()

/opt/conda/anaconda/lib/python3.6/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling o377.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 3.0 failed 4 times, most recent failure: Lost task 0.3 in stage 3.0 (TID 18, cluster-we8z-x-0.c.project_name.dataset_name, executor 1): java.lang.NoClassDefFoundError: Could not initialize class com.google.cloud.spark.bigquery.SparkBigQueryConnectorUserAgentProvider
    at com.google.cloud.spark.bigquery.direct.DirectBigQueryRelation$.headerProvider(DirectBigQueryRelation.scala:356)
    at com.google.cloud.spark.bigquery.direct.DirectBigQueryRelation$.createReadClient(DirectBigQueryRelation.scala:333)
    at com.google.cloud.spark.bigquery.direct.DirectBigQueryRelation$$anonfun$$lessinit$greater$default$3$1.apply(DirectBigQueryRelation.scala:42)
    at com.google.cloud.spark.bigquery.direct.DirectBigQueryRelation$$anonfun$$lessinit$greater$default$3$1.apply(DirectBigQueryRelation.scala:42)
    at com.google.cloud.spark.bigquery.direct.BigQueryRDD.compute(BigQueryRDD.scala:46)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1892)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1880)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1879)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1879)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:927)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:927)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:927)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2113)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2062)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2051)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:738)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)
    at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
    at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3389)
    at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
    at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
    at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370)
    at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:80)
    at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)
    at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3369)
    at org.apache.spark.sql.Dataset.head(Dataset.scala:2550)
    at org.apache.spark.sql.Dataset.take(Dataset.scala:2764)
    at org.apache.spark.sql.Dataset.getRows(Dataset.scala:254)
    at org.apache.spark.sql.Dataset.showString(Dataset.scala:291)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.NoClassDefFoundError: Could not initialize class com.google.cloud.spark.bigquery.SparkBigQueryConnectorUserAgentProvider
    at com.google.cloud.spark.bigquery.direct.DirectBigQueryRelation$.headerProvider(DirectBigQueryRelation.scala:356)
    at com.google.cloud.spark.bigquery.direct.DirectBigQueryRelation$.createReadClient(DirectBigQueryRelation.scala:333)
    at com.google.cloud.spark.bigquery.direct.DirectBigQueryRelation$$anonfun$$lessinit$greater$default$3$1.apply(DirectBigQueryRelation.scala:42)
    at com.google.cloud.spark.bigquery.direct.DirectBigQueryRelation$$anonfun$$lessinit$greater$default$3$1.apply(DirectBigQueryRelation.scala:42)
    at com.google.cloud.spark.bigquery.direct.BigQueryRDD.compute(BigQueryRDD.scala:46)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    ... 1 more

Answer 1

I have also experienced this issue this morning. I was using the gs://spark-lib/bigquery/spark-bigquery-latest.jar when creating the DataProc cluster.

--properties spark:spark.jars=gs://spark-lib/bigquery/spark-bigquery-latest.jar

This connector was update from 2.11 to 2.12 yesterday.

I had to down-graded down to the spark-bigquery-latest_2.11.jar connector to fix my scripts.

--properties spark:spark.jars=gs://spark-lib/bigquery/spark-bigquery-latest_2.11.jar

The issue with the new 2.12 driver has been created on Github project: https://github.com/GoogleCloudDataproc/spark-bigquery-connector/issues/187

Answer 2

Please use version 0.16.1 and above of the spark-bigquery connector, available in gs://spark-lib/bigquery/spark-bigquery-with-dependencies_2.11-0.16.1.jar and gs://spark-lib/bigquery/spark-bigquery-with-dependencies_2.12-0.16.1.jar (based on Spark's Scala version). It is also available from the maven central repository.