【原创】大叔问题定位分享（12）Spark保存文本类型文件（text、csv、json等）到hdfs时为什么是压缩格式的

问题重现

rdd.repartition(1).write.csv(outPath)

写文件之后发现文件是压缩过的

write时首先会获取hadoopConf，然后从中获取是否压缩以及压缩格式

org.apache.spark.sql.execution.datasources.DataSource

  def write(

org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand

    val hadoopConf = sparkSession.sessionState.newHadoopConfWithOptions(options)

org.apache.hadoop.mapreduce.lib.output.TextOutputFormat

    public RecordWriter<K, V> getRecordWriter(TaskAttemptContext job) throws IOException, InterruptedException {

        Configuration conf = job.getConfiguration();

        boolean isCompressed = getCompressOutput(job);

        String keyValueSeparator = conf.get(SEPERATOR, "\t");

        CompressionCodec codec = null;

        String extension = "";

        if (isCompressed) {

            Class<? extends CompressionCodec> codecClass = getOutputCompressorClass(job, GzipCodec.class);

            codec = (CompressionCodec)ReflectionUtils.newInstance(codecClass, conf);

            extension = codec.getDefaultExtension();

        }

isCompressed取的是mapreduce.output.fileoutputformat.compress，codecClass取的是mapreduce.output.fileoutputformat.compress.codec

hadoopConf初始化过程为

org.apache.spark.sql.internal.SessionState

  def newHadoopConf(): Configuration = {

    val hadoopConf = new Configuration(sparkSession.sparkContext.hadoopConfiguration)

org.apache.spark.SparkContext

  _hadoopConfiguration = SparkHadoopUtil.get.newConfiguration(_conf)

  def newConfiguration(conf: SparkConf): Configuration = {

    val hadoopConf = new Configuration()

    appendS3AndSparkHadoopConfigurations(conf, hadoopConf)

    hadoopConf

  }

  def appendS3AndSparkHadoopConfigurations(conf: SparkConf, hadoopConf: Configuration): Unit = {

  ...

      conf.getAll.foreach { case (key, value) =>

        if (key.startsWith("spark.hadoop.")) {

          hadoopConf.set(key.substring("spark.hadoop.".length), value)

        }

      }

hadoopConf默认会从classpath中加载所有的hadoop相关配置文件，可以通过spark-shell来简单测试：

scala> val hc = spark.sparkContext.hadoopConfiguration

hc: org.apache.hadoop.conf.Configuration = Configuration: core-default.xml, core-site.xml, mapred-default.xml, mapred-site.xml, yarn-default.xml, yarn-site.xml, hdfs-default.xml, hdfs-site.xml

scala> println(hc.get("mapreduce.output.fileoutputformat.compress"))

true

scala> println(hc.get("mapreduce.output.fileoutputformat.compress.codec"))

org.apache.hadoop.io.compress.DefaultCodec

综上，只需要在创建SparkConf的时候设置spark.hadoop.mapreduce.output.fileoutputformat.compress=false即可不压缩，

val sparkConf = new SparkConf().set("spark.hadoop.mapreduce.output.fileoutputformat.compress", "false")

另外还可以通过option来控制

rdd.repartition(1).write.option("compression", "none").csv(outPath)