remote pyspark shell and spark-submit error java.lang.NoSuchFieldError: METASTORE_CLIENT_SOCKET_LIFETIME

Question

我们正在从远程气流 docker 不受 CDH CM 节点管理的容器执行 pyspark 和 spark-submit 到 kerberized CDH 5.15v，例如气流容器不在 CDH 环境中。 hive、spark 和 java 的版本与 CDH 上的相同。在执行 spark-submit 或 pyspark 之前有一个有效的 kerberos 票证。

Python 脚本：

from pyspark.sql import SparkSession, functions as F
spark = SparkSession.builder.enableHiveSupport().appName('appName').getOrCreate()
sa_df=spark.sql("SELECT * FROM lnz_ch.lnz_cfg_codebook")

错误是：

To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
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   /__ / .__/\_,_/_/ /_/\_\   version 2.3.0
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Using Python version 3.6.12 (default, Oct 13 2020 21:45:01)
SparkSession available as 'spark'.
>>> from pyspark.sql import SparkSession, functions as F
>>> spark = SparkSession.builder.enableHiveSupport().appName('appName').getOrCreate()
>>> sa_df=spark.sql("SELECT * FROM lnz_ch.lnz_cfg_codebook")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/var/lib/airflow/spark/spark-2.3.0-bin-without-hadoop/python/pyspark/sql/session.py", line 708, in sql
    return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped)
  File "/var/lib/airflow/spark/spark-2.3.0-bin-without-hadoop/python/lib/py4j-0.10.6-src.zip/py4j/java_gateway.py", line 1160, in __call__
  File "/var/lib/airflow/spark/spark-2.3.0-bin-without-hadoop/python/pyspark/sql/utils.py", line 63, in deco
    return f(*a, **kw)
  File "/var/lib/airflow/spark/spark-2.3.0-bin-without-hadoop/python/lib/py4j-0.10.6-src.zip/py4j/protocol.py", line 320, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o24.sql.
: java.lang.NoSuchFieldError: METASTORE_CLIENT_SOCKET_LIFETIME
        at org.apache.spark.sql.hive.HiveUtils$.formatTimeVarsForHiveClient(HiveUtils.scala:195)
        at org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:286)
        at org.apache.spark.sql.hive.HiveExternalCatalog.client$lzycompute(HiveExternalCatalog.scala:66)
        at org.apache.spark.sql.hive.HiveExternalCatalog.client(HiveExternalCatalog.scala:65)
        at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$databaseExists.apply$mcZ$sp(HiveExternalCatalog.scala:195)
        at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$databaseExists.apply(HiveExternalCatalog.scala:195)
        at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$databaseExists.apply(HiveExternalCatalog.scala:195)
        at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:97)
        at org.apache.spark.sql.hive.HiveExternalCatalog.databaseExists(HiveExternalCatalog.scala:194)
        at org.apache.spark.sql.internal.SharedState.externalCatalog$lzycompute(SharedState.scala:114)
        at org.apache.spark.sql.internal.SharedState.externalCatalog(SharedState.scala:102)
        at org.apache.spark.sql.hive.HiveSessionStateBuilder.externalCatalog(HiveSessionStateBuilder.scala:39)
        at org.apache.spark.sql.hive.HiveSessionStateBuilder.catalog$lzycompute(HiveSessionStateBuilder.scala:54)
        at org.apache.spark.sql.hive.HiveSessionStateBuilder.catalog(HiveSessionStateBuilder.scala:52)
        at org.apache.spark.sql.hive.HiveSessionStateBuilder$$anon.<init>(HiveSessionStateBuilder.scala:69)
        at org.apache.spark.sql.hive.HiveSessionStateBuilder.analyzer(HiveSessionStateBuilder.scala:69)
        at org.apache.spark.sql.internal.BaseSessionStateBuilder$$anonfun$build.apply(BaseSessionStateBuilder.scala:293)
        at org.apache.spark.sql.internal.BaseSessionStateBuilder$$anonfun$build.apply(BaseSessionStateBuilder.scala:293)
        at org.apache.spark.sql.internal.SessionState.analyzer$lzycompute(SessionState.scala:79)
        at org.apache.spark.sql.internal.SessionState.analyzer(SessionState.scala:79)
        at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:57)
        at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:55)
        at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:47)
        at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:74)
        at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:638)
        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:214)
        at java.lang.Thread.run(Thread.java:748)

执行 spark-submit 时从 yarn 返回相同的错误。

详情：

• beeline 从容器开始工作

Answer 1

我们的解决方案的详细信息可以在这里找到： https://community.cloudera.com/t5/Support-Questions/remote-pyspark-shell-and-spark-submit-error-java-lang/td-p/309553