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From Raymond Xie <xie3208...@gmail.com>
Subject Re: Error when loading json to spark
Date Sun, 01 Jan 2017 22:32:52 GMT
Thank you very much Marco, is your code in Scala? do you have a python
example? Can anyone give me a python example to handle json data on Spark?


*------------------------------------------------*
*Sincerely yours,*


*Raymond*

On Sun, Jan 1, 2017 at 12:29 PM, Marco Mistroni <mmistroni@gmail.com> wrote:

> Hi
>    you will need to pass the schema, like in the snippet below (even
> though the code might have been superseeded in spark 2.0)
>
>     import sqlContext.implicits._
>     val jsonRdd = sc.textFile("file:///c:/tmp/1973-01-11.json")
>     val schema = (new StructType).add("hour", StringType).add("month",
> StringType)
>                   .add("second", StringType).add("year", StringType)
>                   .add("timezone", StringType).add("day", StringType)
>                   .add("minute", StringType)
>     val jsonContentWithSchema = sqlContext.jsonRDD(jsonRdd, schema)
>
> But somehow i seem to remember that there was a way , in Spark 2.0, so
> that Spark will infer the schema  for you..
>
> hth
> marco
>
>
>
>
>
> On Sun, Jan 1, 2017 at 12:40 PM, Raymond Xie <xie3208080@gmail.com> wrote:
>
>> I found the cause:
>>
>> I need to "put" the json file onto hdfs first before it can be used, here
>> is what I did:
>>
>> hdfs dfs -put  /root/Downloads/data/json/world_bank.json
>> hdfs://localhost:9000/json
>> df = sqlContext.read.json("/json/")
>> df.show(10)
>>
>> .....
>>
>> However, there is a new problem here, the json data needs to be sort of
>> treaked before it can be really used, simply using df =
>> sqlContext.read.json("/json/") just makes the df messy, I need the df know
>> the fields in the json file.
>>
>> How?
>>
>> Thank you.
>>
>>
>>
>>
>> *------------------------------------------------*
>> *Sincerely yours,*
>>
>>
>> *Raymond*
>>
>> On Sat, Dec 31, 2016 at 11:52 PM, Miguel Morales <therevoltingx@gmail.com
>> > wrote:
>>
>>> Looks like it's trying to treat that path as a folder, try omitting
>>> the file name and just use the folder path.
>>>
>>> On Sat, Dec 31, 2016 at 7:58 PM, Raymond Xie <xie3208080@gmail.com>
>>> wrote:
>>> > Happy new year!!!
>>> >
>>> > I am trying to load a json file into spark, the json file is attached
>>> here.
>>> >
>>> > I received the following error, can anyone help me to fix it? Thank
>>> you very
>>> > much. I am using Spark 1.6.2 and python 2.7.5
>>> >
>>> >>>> from pyspark.sql import SQLContext
>>> >>>> sqlContext = SQLContext(sc)
>>> >>>> df = sqlContext.read.json("/root/Downloads/data/json/world_bank.j
>>> son")
>>> > 16/12/31 22:54:53 INFO json.JSONRelation: Listing
>>> > hdfs://localhost:9000/root/Downloads/data/json/world_bank.json on
>>> driver
>>> > 16/12/31 22:54:54 INFO storage.MemoryStore: Block broadcast_0 stored as
>>> > values in memory (estimated size 212.4 KB, free 212.4 KB)
>>> > 16/12/31 22:54:54 INFO storage.MemoryStore: Block broadcast_0_piece0
>>> stored
>>> > as bytes in memory (estimated size 19.6 KB, free 232.0 KB)
>>> > 16/12/31 22:54:54 INFO storage.BlockManagerInfo: Added
>>> broadcast_0_piece0 in
>>> > memory on localhost:39844 (size: 19.6 KB, free: 511.1 MB)
>>> > 16/12/31 22:54:54 INFO spark.SparkContext: Created broadcast 0 from
>>> json at
>>> > NativeMethodAccessorImpl.java:-2
>>> > Traceback (most recent call last):
>>> >   File "<stdin>", line 1, in <module>
>>> >   File "/opt/spark/python/pyspark/sql/readwriter.py", line 176, in
>>> json
>>> >     return self._df(self._jreader.json(path))
>>> >   File "/opt/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py",
>>> line
>>> > 813, in __call__
>>> >   File "/opt/spark/python/pyspark/sql/utils.py", line 45, in deco
>>> >     return f(*a, **kw)
>>> >   File "/opt/spark/python/lib/py4j-0.9-src.zip/py4j/protocol.py",
>>> line 308,
>>> > in get_return_value
>>> > py4j.protocol.Py4JJavaError: An error occurred while calling o19.json.
>>> > : java.io.IOException: No input paths specified in job
>>> > at
>>> > org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInpu
>>> tFormat.java:201)
>>> > at
>>> > org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInput
>>> Format.java:313)
>>> > at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:199)
>>> > at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
>>> > at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
>>> > at scala.Option.getOrElse(Option.scala:120)
>>> > at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
>>> > at
>>> > org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapParti
>>> tionsRDD.scala:35)
>>> > at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
>>> > at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
>>> > at scala.Option.getOrElse(Option.scala:120)
>>> > at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
>>> > at
>>> > org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapParti
>>> tionsRDD.scala:35)
>>> > at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
>>> > at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
>>> > at scala.Option.getOrElse(Option.scala:120)
>>> > at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
>>> > at org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1.apply(RDD.
>>> scala:1129)
>>> > at
>>> > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperati
>>> onScope.scala:150)
>>> > at
>>> > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperati
>>> onScope.scala:111)
>>> > at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
>>> > at org.apache.spark.rdd.RDD.treeAggregate(RDD.scala:1127)
>>> > at
>>> > org.apache.spark.sql.execution.datasources.json.InferSchema$
>>> .infer(InferSchema.scala:65)
>>> > at
>>> > org.apache.spark.sql.execution.datasources.json.JSONRelation
>>> $$anonfun$4.apply(JSONRelation.scala:114)
>>> > at
>>> > org.apache.spark.sql.execution.datasources.json.JSONRelation
>>> $$anonfun$4.apply(JSONRelation.scala:109)
>>> > at scala.Option.getOrElse(Option.scala:120)
>>> > at
>>> > org.apache.spark.sql.execution.datasources.json.JSONRelation
>>> .dataSchema$lzycompute(JSONRelation.scala:109)
>>> > at
>>> > org.apache.spark.sql.execution.datasources.json.JSONRelation
>>> .dataSchema(JSONRelation.scala:108)
>>> > at
>>> > org.apache.spark.sql.sources.HadoopFsRelation.schema$lzycomp
>>> ute(interfaces.scala:636)
>>> > at
>>> > org.apache.spark.sql.sources.HadoopFsRelation.schema(interfa
>>> ces.scala:635)
>>> > at
>>> > org.apache.spark.sql.execution.datasources.LogicalRelation.<
>>> init>(LogicalRelation.scala:37)
>>> > at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.sc
>>> ala:125)
>>> > at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.sc
>>> ala:109)
>>> > at org.apache.spark.sql.DataFrameReader.json(DataFrameReader.sc
>>> ala:244)
>>> > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>>> > at
>>> > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAcce
>>> ssorImpl.java:62)
>>> > at
>>> > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMe
>>> thodAccessorImpl.java:43)
>>> > at java.lang.reflect.Method.invoke(Method.java:497)
>>> > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
>>> > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
>>> > at py4j.Gateway.invoke(Gateway.java:259)
>>> > at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.j
>>> ava:133)
>>> > at py4j.commands.CallCommand.execute(CallCommand.java:79)
>>> > at py4j.GatewayConnection.run(GatewayConnection.java:209)
>>> > at java.lang.Thread.run(Thread.java:745)
>>> >
>>> >
>>> > ------------------------------------------------
>>> > Sincerely yours,
>>> >
>>> >
>>> > Raymond
>>> >
>>> >
>>> > ---------------------------------------------------------------------
>>> > To unsubscribe e-mail: user-unsubscribe@spark.apache.org
>>>
>>
>>
>

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