我使用Spark 1.6.1并写入HDFS。在某些情况下，似乎所有的工作都是由一个线程完成的。为什么呢?
我还需要parquet.enable。summary-将parquet文件注册到Impala的元数据
Df.write().partitionBy("COLUMN").parquet(outputFileLocation);
似乎所有这些都发生在执行器的一个cpu上。
16/11/03 14:59:20 INFO datasources.DynamicPartitionWriterContainer: Using user defined output committer class org.apache.parquet.hadoop.ParquetOutputCommitter
16/11/03 14:59:20 INFO mapred.SparkHadoopMapRedUtil: No need to commit output of task because needsTaskCommit=false: attempt_201611031459_0154_m_000029_0
16/11/03 15:17:56 INFO sort.UnsafeExternalSorter: Thread 545 spilling sort data of 41.9 GB to disk (3 times so far)
16/11/03 15:21:05 INFO storage.ShuffleBlockFetcherIterator: Getting 0 non-empty blocks out of 0 blocks
16/11/03 15:21:05 INFO storage.ShuffleBlockFetcherIterator: Started 0 remote fetches in 1 ms
16/11/03 15:21:05 INFO datasources.DynamicPartitionWriterContainer: Using user defined output committer class org.apache.parquet.hadoop.ParquetOutputCommitter
16/11/03 15:21:05 INFO codec.CodecConfig: Compression: GZIP
16/11/03 15:21:05 INFO hadoop.ParquetOutputFormat: Parquet block size to 134217728
16/11/03 15:21:05 INFO hadoop.ParquetOutputFormat: Parquet page size to 1048576
16/11/03 15:21:05 INFO hadoop.ParquetOutputFormat: Parquet dictionary page size to 1048576
16/11/03 15:21:05 INFO hadoop.ParquetOutputFormat: Dictionary is on
16/11/03 15:21:05 INFO hadoop.ParquetOutputFormat: Validation is off
16/11/03 15:21:05 INFO hadoop.ParquetOutputFormat: Writer version is: PARQUET_1_0
16/11/03 15:21:05 INFO parquet.CatalystWriteSupport: Initialized Parquet WriteSupport with Catalyst schema:
16/11/03 15:21:05 INFO compress.CodecPool: Got brand-new compressor [.gz]
16/11/03 15:21:05 INFO datasources.DynamicPartitionWriterContainer: Maximum partitions reached, falling back on sorting.
16/11/03 15:32:37 INFO sort.UnsafeExternalSorter: Thread 545 spilling sort data of 31.8 GB to disk (0 time so far)
16/11/03 15:45:47 INFO sort.UnsafeExternalSorter: Thread 545 spilling sort data of 31.8 GB to disk (1 time so far)
16/11/03 15:48:44 INFO datasources.DynamicPartitionWriterContainer: Sorting complete. Writing out partition files one at a time.
16/11/03 15:48:44 INFO codec.CodecConfig: Compression: GZIP
16/11/03 15:48:44 INFO hadoop.ParquetOutputFormat: Parquet block size to 134217728
16/11/03 15:48:44 INFO hadoop.ParquetOutputFormat: Parquet page size to 1048576
16/11/03 15:48:44 INFO hadoop.ParquetOutputFormat: Parquet dictionary page size to 1048576
16/11/03 15:48:44 INFO hadoop.ParquetOutputFormat: Dictionary is on
16/11/03 15:48:44 INFO hadoop.ParquetOutputFormat: Validation is off
16/11/03 15:48:44 INFO hadoop.ParquetOutputFormat: Writer version is: PARQUET_1_0
16/11/03 15:48:44 INFO parquet.CatalystWriteSupport: Initialized Parquet WriteSupport with Catalyst schema:
Schema
下面的200行，一遍又一遍地重复20次左右。
16/11/03 15:48:44 INFO compress.CodecPool: Got brand-new compressor [.gz]
16/11/03 15:49:50 INFO hadoop.InternalParquetRecordWriter: mem size 135,903,551 > 134,217,728: flushing 1,040,100 records to disk.
16/11/03 15:49:50 INFO hadoop.InternalParquetRecordWriter: Flushing mem columnStore to file. allocated memory: 89,688,651
以下约200行
16/11/03 15:49:51 INFO hadoop.ColumnChunkPageWriteStore: written 413,231B for [a17bbfb1_2808_11e6_a4e6_77b5e8f92a4f] BINARY: 1,040,100 values, 1,138,534B raw, 412,919B comp, 8 pages, encodings: [RLE, BIT_PACKED, PLAIN_DICTIONARY], dic { 356 entries, 2,848B raw, 356B comp}
最后:-
16/11/03 16:15:41 INFO output.FileOutputCommitter: Saved output of task 'attempt_201611031521_0154_m_000040_0' to hdfs://PATH/_temporary/0/task_201611031521_0154_m_000040
16/11/03 16:15:41 INFO mapred.SparkHadoopMapRedUtil: attempt_201611031521_0154_m_000040_0: Committed
16/11/03 16:15:41 INFO executor.Executor: Finished task 40.0 in stage 154.0 (TID 8545). 3757 bytes result sent to driver
更新:parquet.enable。摘要-元数据设置为false。
将分区减少到21个。
Df.write().mode(SaveMode.Append).partitionBy("COL").parquet(outputFileLocation);
它确实提高了速度，但仍然需要一个小时才能完成。
更新:-大多数问题的原因是多个左外连接，在写入之前实现了非常小的数据。发生泄漏是因为追加模式使文件保持打开状态。在此模式下，默认限制为5个打开文件。您可以使用属性"spark.sql.sources.maxConcurrentWrites"来增加这个值
最后，在到达写部分之前对代码进行了一些优化，我们得到了更好的写时间。在此之前，我们无法进行重分区，因为shuffle超过4-5 Gb。在之前的更改之后，我将代码从coalesce更改为repartition，通过给每个执行器中的CPU提供大约相同数量的数据来将数据分布到所有执行器中。因此，如果您看到作业创建的parquet文件大小不同，那么在写入之前尝试重新分区您的Dataframe。
同样，这也可以帮助提高写性能:-
sc.hadoopConfiguration.set("parquet.enable.dictionary", "false")