scala - Spark - 将 CSV 文件加载为 DataFrame？

Question

我想在 spark 中读取 CSV 并将其转换为 DataFrame 并使用 df.registerTempTable("table_name") 将其存储在 HDFS 中

我试过:

scala> val df = sqlContext.load("hdfs:///csv/file/dir/file.csv")

我得到的错误:

java.lang.RuntimeException: hdfs:///csv/file/dir/file.csv is not a Parquet file. expected magic number at tail [80, 65, 82, 49] but found [49, 59, 54, 10]
    at parquet.hadoop.ParquetFileReader.readFooter(ParquetFileReader.java:418)
    at org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache$$anonfun$refresh$6.apply(newParquet.scala:277)
    at org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache$$anonfun$refresh$6.apply(newParquet.scala:276)
    at scala.collection.parallel.mutable.ParArray$Map.leaf(ParArray.scala:658)
    at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply$mcV$sp(Tasks.scala:54)
    at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:53)
    at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:53)
    at scala.collection.parallel.Task$class.tryLeaf(Tasks.scala:56)
    at scala.collection.parallel.mutable.ParArray$Map.tryLeaf(ParArray.scala:650)
    at scala.collection.parallel.AdaptiveWorkStealingTasks$WrappedTask$class.compute(Tasks.scala:165)
    at scala.collection.parallel.AdaptiveWorkStealingForkJoinTasks$WrappedTask.compute(Tasks.scala:514)
    at scala.concurrent.forkjoin.RecursiveAction.exec(RecursiveAction.java:160)
    at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
    at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
    at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
    at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)

在 Apache Spark 中加载 CSV 文件作为 DataFrame 的正确命令是什么？

Answer 1

spark-csv 是核心 Spark 功能的一部分，不需要单独的库。所以你可以做例如

df = spark.read.format("csv").option("header", "true").load("csvfile.csv")

在 scala 中，(这适用于任何格式的定界符，对于 csv 提及“,”，对于 tsv 等提及“\t”)

val df = sqlContext.read.format("com.databricks.spark.csv")
.option("分隔符", ",")
.load("csvfile.csv")