python - 如何从 S3 读取 Parquet 数据以激发 Python 数据框？

Question

我是 Spark 的新手，我找不到这个...我有很多 Parquet 文件上传到 s3 的位置:

s3://a-dps/d-l/sco/alpha/20160930/parquet/

此文件夹的总大小为 20+ Gb。如何分 block 并将其读入数据帧如何将所有这些文件加载​​到数据框中？

分配给 spark 集群的内存为 6 GB。

    from pyspark import SparkContext
    from pyspark.sql import SQLContext
    from pyspark import SparkConf
    from pyspark.sql import SparkSession
    import pandas
    # SparkConf().set("spark.jars.packages","org.apache.hadoop:hadoop-aws:3.0.0-alpha3")
    sc = SparkContext.getOrCreate()

    sc._jsc.hadoopConfiguration().set("fs.s3.awsAccessKeyId", 'A')
    sc._jsc.hadoopConfiguration().set("fs.s3.awsSecretAccessKey", 's')

    sqlContext = SQLContext(sc)
    df2 = sqlContext.read.parquet("s3://sm/data/scor/alpha/2016/parquet/*")

错误:

    Py4JJavaError: An error occurred while calling o33.parquet.    : java.io.IOException: No FileSystem for scheme: s3        at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2660)        at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2667)        at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:94)        at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2703)        at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2685)        at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:373)        at org.apache.hadoop.fs.Path.getFileSystem(Path.java:295)        at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:372)        at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:370)        at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)        at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)        at scala.collection.immutable.List.foreach(List.scala:381)        at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)        at scala.collection.immutable.List.flatMap(List.scala:344) 

Answer 1

从 Spark 2.0 开始，您必须使用 SparkSession 而不是 sqlContext

spark = SparkSession.builder
                        .master("local")             
                        .appName("app name")             
                        .config("spark.some.config.option", true).getOrCreate()

df = spark.read.parquet("s3://path/to/parquet/file.parquet")