SparkSQL真的不支持存储NullType类型数据到Parquet吗?

大数据学习与分享 2020-10-29

911

最近后台有小伙伴提了一些实际工作中使用Spark遇到的问题，笔者挑选了几个相对常见的问题，分别从场景模拟/问题现象、问题分析、解决方案三个层面，来深入分析这些问题，并且提供一个解决类似问题的思路。

>> 问题1

使用SparkSQL（2.4版本）往存储格式为parquet的Hive分区表中存储NullType类型的数据时报错：

org.apache.spark.sql.AnalysisException: Parquet data source does not support null data type.

虽然在Stack OverFlow上找到了类似的问题，但没有具体阐明到底是什么原因导致了这种问题以及如何解决？

1. 场景模拟

1）创建temp view：test_view

sparkSession.sql(
      """
        |select 1 as id, null as name
      """.stripMargin
      ).createOrReplaceTempView("test_view")

2）打印test_view的schema信息

-- id为integer类型，name对应到Spark SQL内部字段数据类型即位NullType
root
 |-- id: integer (nullable = false)
 |-- name: null (nullable = true)

3）将test_tab中数据存入Hive分区表test_partition_tab的分区partitionCol=20201009中

df.write.mode(SaveMode.Overwrite).format("parquet").save("/bigdatalearnshare/test_partition_tab/partitionCol=20201009")

4）报错信息

2. 问题分析

根据报错信息，提示Parquet数据源不支持null type类型的数据。既然是保存数据，我们很容易联想到FileFormatWriter，再结合错误信息：

org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:100

对应的源码为：

DataSourceUtils.verifyWriteSchema(fileFormat, dataSchema)

debug进去，看看这个方法究竟干了什么？

根据源码分析可知，上述程序中SparkSQL在保存数据时会对数据的schema进行校验，并且不同的存储格式（parquet、csv、json等）支持的数据类型会有所不同，以parquet为例，查看源码：

3. 解决方案

-- 使用insert sql进行数据的保存
insert overwrite table test_partition_tab partition(partitionCol=20201009) select * from test_view;

>> 问题2

1. 问题现象

在利用Spark和Kafka处理数据时，同时在maven pom中引入Spark和Kafka的相关依赖。但是当利用SparkSQL处理数据生成的DataSet/DataFrame进行collect或者show等操作时，抛出以下异常信息：

in stage 3.0 (TID 403, localhost, executor driver): java.lang.NoSuchMethodError: net.jpountz.lz4.LZ4BlockInputStream.<init>(Ljava/io/InputStream;Z)V
  at org.apache.spark.io.LZ4CompressionCodec.compressedInputStream(CompressionCodec.scala:122)
  at org.apache.spark.serializer.SerializerManager.wrapForCompression(SerializerManager.scala:163)
  at org.apache.spark.serializer.SerializerManager.wrapStream(SerializerManager.scala:124)
  at org.apache.spark.shuffle.BlockStoreShuffleReader$$anonfun$3.apply(BlockStoreShuffleReader.scala:50)
  at org.apache.spark.shuffle.BlockStoreShuffleReader$$anonfun$3.apply(BlockStoreShuffleReader.scala:50)
  at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:453)
  at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:64)
  at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
  at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
  at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
  at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
  at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
  at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
  at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.agg_doAggregateWithKeys_0$(Unknown Source)
  at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
  at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
  at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)

2. 问题分析

错误信息提示找不到方法：

net.jpountz.lz4.LZ4BlockInputStream.<init>(Ljava/io/InputStream;Z)V

根据经验，找不到某个方法，一般主要有两个原因造成：

没有相应的jar包依赖
jar包依赖冲突

经过排查发现导致本问题发生的原因是：Spark内部使用的包net.jpountz.lz4和Kafka中包产生冲突

3. 解决方案

排除Kafka中net.jpountz.lz4的依赖包：

  <dependency>
      <groupId>org.apache.kafka</groupId>
      <artifactId>kafka-clients</artifactId>
      <version>0.10.0.0</version>
      <exclusions>
          <exclusion>
             <groupId>net.jpountz.lz4</groupId>
             <artifactId>lz4</artifactId>
          </exclusion>
      </exclusions>
  </dependency>

>> 问题3

通过SparkSQL，对两个存在map类型字段的Hive表进行union操作，报如下错误：

org.apache.spark.sql.AnalysisException: Cannot have map type columns in DataFrame which calls set operations(intersect, except, etc.), but the type of column map is map<string,string>;

1. 场景模拟

1）通过函数str_to_map/map生成map类型的字段，然后进行union操作

select 1 id, str_to_map("k1:v1,k2:v2") map


union


select 2 id, map("k1","v1","k2","v2") map

2）报错信息

org.apache.spark.sql.AnalysisException: Cannot have map type columns in DataFrame which calls set operations(intersect, except, etc.), but the type of column map is map<string,string>;;
Distinct
+- Union
   :- Project [1 AS id#116, str_to_map(k1:v1,k2:v2, ,, :) AS map#117]
   :  +- OneRowRelation
   +- Project [2 AS id#118, map(k1, v1, k2, v2) AS map#119]
      +- OneRowRelation


  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:42)
  at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:95)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:364)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:85)
  at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:85)