org.apache.spark.sparkexception:无法初始化类com.google.cloud.spark.bigquery.sparkbigqueryconnectoruseragentprovider

6tdlim6h  于 2021-05-29  发布在  Spark
关注(0)|答案(2)|浏览(439)

下面是我用来将bigquery表导入到pyspark集群（dataproc）并在其上运行fp growth算法的代码。但是，今天当我运行相同的代码时，它抛出了一个错误。它用.printschema（）返回导入的df的模式，但是当我尝试运行.show（）或.fit（）时，它抛出以下错误。

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)

下面是我得到的错误：

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
apache-sparkpysparkgoogle-bigquerygoogle-cloud-platformgoogle-cloud-dataproc

2条答案

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vuv7lop3

vuv7lop31#

请使用0.16.1及更高版本的spark bigquery连接器，在 gs://spark-lib/bigquery/spark-bigquery-with-dependencies_2.11-0.16.1.jar 以及 gs://spark-lib/bigquery/spark-bigquery-with-dependencies_2.12-0.16.1.jar （基于spark的scala版本）。它也可以从maven中央存储库获得。

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lsmepo6l

lsmepo6l2#

今天上午我也遇到过这个问题。在创建dataproc集群时，我使用了gs://spark lib/bigquery/spark-bigquery-latest.jar。
--属性spark:spark.jars=gs：//spark lib/bigquery/spark-bigquery-latest.jar
这个连接器昨天从2.11更新到了2.12。
我不得不降级到spark-bigquery-latest_2.11.jar连接器来修复我的脚本。
--属性spark:spark.jars=gs：//spark lib/bigquery/spark-bigquery-latest_2.11.jar
新2.12驱动程序的问题已在github项目上创建：https://github.com/googleclouddataproc/spark-bigquery-connector/issues/187

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