Spark：加载或选择ORC格式的Hive表

Question

我正在尝试使用Spark SQL加载以ORC格式创建的托管Hive表。

SparkConf conf = new SparkConf().setAppName(ConnectionTest.class.getName()).setMaster(master);
JavaSparkContext context = new JavaSparkContext(conf);

SQLContext sqlContext = new HiveContext(context);

sqlContext.sql("SELECT * FROM schema.tableName").show(20);

但我遇到了这个错误：

Exception in thread "main" java.lang.RuntimeException: serious problem
    at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.generateSplitsInfo(OrcInputFormat.java:1021)
    at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.getSplits(OrcInputFormat.java:1048)
    at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:199)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:242)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:240)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:240)
    at org.apache.spark.rdd.HadoopRDD$HadoopMapPartitionsWithSplitRDD.getPartitions(HadoopRDD.scala:381)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:242)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:240)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:240)
    at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:242)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:240)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:240)
    at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:242)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:240)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:240)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:190)
    at org.apache.spark.sql.execution.Limit.executeCollect(basicOperators.scala:165)
    at org.apache.spark.sql.execution.SparkPlan.executeCollectPublic(SparkPlan.scala:174)
    at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1499)
    at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1499)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)
    at org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:2086)
    at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$execute$1(DataFrame.scala:1498)
    at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$collect(DataFrame.scala:1505)
    at org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1375)
    at org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1374)
    at org.apache.spark.sql.DataFrame.withCallback(DataFrame.scala:2099)
    at org.apache.spark.sql.DataFrame.head(DataFrame.scala:1374)
    at org.apache.spark.sql.DataFrame.take(DataFrame.scala:1456)
    at org.apache.spark.sql.DataFrame.showString(DataFrame.scala:170)
    at org.apache.spark.sql.DataFrame.show(DataFrame.scala:350)
    at org.apache.spark.sql.DataFrame.show(DataFrame.scala:311)
    at com.daimler.dbdp.spark.ConnectionTest.run(ConnectionTest.java:45)
    at com.daimler.dbdp.spark.ConnectionTest.main(ConnectionTest.java:26)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.NullPointerException
        at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$BISplitStrategy.getSplits(OrcInputFormat.java:560)
        at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.generateSplitsInfo(OrcInputFormat.java:1010)
        ... 49 more

似乎与ORC格式有关。 在使用ORC格式时，访问Hive表的最佳方法是什么？

谢谢!!!

Spark 1.6.2。 Java 8 Hortonworks分布式。

- josele

---

你尝试过 sqlContext.table("schema.tableName").show() 吗？ - Thiago Baldim

刚刚完成了。但是很不幸。无论如何还是谢谢。 - josele

我遇到了完全相同的问题。该表格被设计为transactional=true属性。我将其改为false，这个错误就消失了。但是，我更喜欢使用true。 - Bala

请告诉我们解决此问题的解决方法。我也遇到了同样的问题。这个问题主要出现在ORC表中，且transactional=true。任何解决方案都将是有帮助的。谢谢。 另外，请告诉我还有哪些允许支持Hive表上的事务或ACID操作的其他允许格式的表。 - Sam

Answer 1

您可以尝试在Spark中设置以下参数：

scala> sql("set spark.sql.hive.convertMetastoreOrc=true") 
// output = res3: org.apache.spark.sql.DataFrame = [key: string, value: string] 

然后在Spark中对ORC表执行查询。

如果设置了上述参数仍然遇到问题，您可以尝试设置以下参数。

scala> sql("set spark.sql.orc.impl=native")
// output = res4: org.apache.spark.sql.DataFrame = [key: string, value: string]

- axnet