如何将 gzip 压缩的 json 行文件读入 PySpark 数据帧？

Question

我有一个 JSON 行的文件，我希望将其读入 PySpark 数据框。该文件是 gzipped 压缩的。

文件名如下所示：file.jl.gz

我知道如何将此文件读入 pandas 数据框：

df= pd.read_json('file.jl.gz', lines=True, compression='gzip)

我是 pyspark 的新手，我想学习与此等效的 pyspark。有没有办法将此文件读入 pyspark 数据帧？

编辑 2

%pyspark
df=spark.read.option('multiline','true').json("s3n:AccessKey:secretkey@bucketname/ds_dump_00000.jl.gz")

我执行了上面的命令并得到了这个错误。

Fail to execute line 1: 
Traceback (most recent call last):
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 63, in deco
    return f(*a, **kw)
  File "/usr/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
    format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling o130.json.
: java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: 
    at org.apache.hadoop.fs.Path.initialize(Path.java:259)
    at org.apache.hadoop.fs.Path.<init>(Path.java:217)
    at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary.apply(DataSource.scala:560)
    at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary.apply(DataSource.scala:559)
    at scala.collection.TraversableLike$$anonfun$flatMap.apply(TraversableLike.scala:241)
    at scala.collection.TraversableLike$$anonfun$flatMap.apply(TraversableLike.scala:241)
    at scala.collection.immutable.List.foreach(List.scala:392)
    at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
    at scala.collection.immutable.List.flatMap(List.scala:355)
    at org.apache.spark.sql.execution.datasources.DataSource.org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary(DataSource.scala:559)
    at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:373)
    at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:242)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:230)
    at org.apache.spark.sql.DataFrameReader.json(DataFrameReader.scala:411)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Thread.java:748)
Caused by: java.net.URISyntaxException: Relative path in absolute URI: 
    at java.net.URI.checkPath(URI.java:1823)
    at java.net.URI.<init>(URI.java:745)
    at org.apache.hadoop.fs.Path.initialize(Path.java:256)
    ... 24 more


During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/tmp/zeppelin_pyspark-8814422034403105951.py", line 380, in <module>
    exec(code, _zcUserQueryNameSpace)
  File "<stdin>", line 1, in <module>
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/readwriter.py", line 274, in json
    return self._df(self._jreader.json(self._spark._sc._jvm.PythonUtils.toSeq(path)))
  File "/usr/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
    answer, self.gateway_client, self.target_id, self.name)
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 79, in deco
    raise IllegalArgumentException(s.split(': ', 1)[1], stackTrace)
pyspark.sql.utils.IllegalArgumentException: 'java.net.URISyntaxException: Relative path in absolute URI:'

Answer 1

df=spark.read.option('multiline','true').json('file.jl.gz')

在阅读复杂内容时最好提供一个架构 json

Answer 2

#This command just considered all the 90 Million rows to one row

df = spark.read.option('multiline', 'true').json('file.jl.gz')


#This command below worked fine for me:

df = spark.read.json('file.jl.gz')

如何将 gzip 压缩的 json 行文件读入 PySpark 数据帧？

How to read a gzip compressed json lines file into PySpark dataframe?

python

apache-spark

apache-spark-sql

pyspark

pyspark-dataframes