【发布时间】：2022-03-21 04:32:42
【问题描述】：
我正在使用 Apache Hudi 将非分区表写入 AWS S3 并将其同步到配置单元。这是正在使用的DataSourceWriteOptions。
val hudiOptions: Map[String, String] = Map[String, String](
DataSourceWriteOptions.TABLE_TYPE_OPT_KEY -> "MERGE_ON_READ",
DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY -> "PERSON_ID",
DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY -> "",
DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY -> "UPDATED_DATE",
DataSourceWriteOptions.HIVE_PARTITION_FIELDS_OPT_KEY -> "",
DataSourceWriteOptions.HIVE_PARTITION_EXTRACTOR_CLASS_OPT_KEY -> classOf[NonPartitionedExtractor].getName,
DataSourceWriteOptions.HIVE_STYLE_PARTITIONING_OPT_KEY -> "true",
DataSourceWriteOptions.KEYGENERATOR_CLASS_OPT_KEY -> "org.apache.hudi.keygen.NonpartitionedKeyGenerator"
)
如果分区表写入成功，但如果我尝试写入非分区表则会出错。这是错误输出sn-p
Caused by: java.lang.NullPointerException
at org.apache.hudi.hadoop.utils.HoodieInputFormatUtils.getTableMetaClientForBasePath(HoodieInputFormatUtils.java:283)
at org.apache.hudi.hadoop.InputPathHandler.parseInputPaths(InputPathHandler.java:100)
at org.apache.hudi.hadoop.InputPathHandler.<init>(InputPathHandler.java:60)
at org.apache.hudi.hadoop.HoodieParquetInputFormat.listStatus(HoodieParquetInputFormat.java:81)
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:288)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:204)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
at org.apache.spark.rdd.RDD.getNumPartitions(RDD.scala:289)
at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.mapOutputStatisticsFuture$lzycompute(ShuffleExchangeExec.scala:83)
at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.mapOutputStatisticsFuture(ShuffleExchangeExec.scala:82)
at org.apache.spark.sql.execution.adaptive.ShuffleQueryStageExec.cancel(QueryStageExec.scala:152)
at org.apache.spark.sql.execution.adaptive.MaterializeExecutable.cancel(AdaptiveExecutable.scala:357)
at org.apache.spark.sql.execution.adaptive.AdaptiveExecutorRuntime.fail(AdaptiveExecutor.scala:280)
... 41 more
这是HoodieInputFormatUtils.getTableMetaClientForBasePath()的代码
/**
* Extract HoodieTableMetaClient from a partition path(not base path).
* @param fs
* @param dataPath
* @return
* @throws IOException
*/
public static HoodieTableMetaClient getTableMetaClientForBasePath(FileSystem fs, Path dataPath) throws IOException {
int levels = HoodieHiveUtils.DEFAULT_LEVELS_TO_BASEPATH;
if (HoodiePartitionMetadata.hasPartitionMetadata(fs, dataPath)) {
HoodiePartitionMetadata metadata = new HoodiePartitionMetadata(fs, dataPath);
metadata.readFromFS();
levels = metadata.getPartitionDepth();
}
Path baseDir = HoodieHiveUtils.getNthParent(dataPath, levels);
LOG.info("Reading hoodie metadata from path " + baseDir.toString());
return new HoodieTableMetaClient(fs.getConf(), baseDir.toString());
}
第 283 行是 LOG.info()，这会导致 NullPointerException。所以看起来为分区提供的配置值已经搞砸了。此代码正在 AWS EMR 上运行。
Release label:emr-5.30.1
Hadoop distribution:Amazon 2.8.5
Applications:Hive 2.3.6, Spark 2.4.5
【问题讨论】：
-
上周我对此表示赞同，因为我遇到了同样的问题。我现在可以使用它，我认为唯一的区别是我没有指定
HIVE_PARTITION_FIELDS_OPT_KEY、HIVE_PARTITION_EXTRACTOR_CLASS_OPT_KEY或HIVE_STYLE_PARTITIONING_OPT_KEY。删除这些可以解决您的问题吗？
标签： apache-spark hadoop hive apache-hudi