Unicode error while reading data from file/rdd - dataframe

Question

I am trying to create dataframe with proper schema after fetching data from text file. in RDD, all data types are strings however one of the field data type is interger, which i want to ensure that created as integer. So i created Structtype and created dataframe. but it throws an error as below.
Error Message:
--------------------------------------------------------------------------- Py4JJavaError Traceback (most recent call
last) in ()
----> 1 df.show()
/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/pyspark/sql/dataframe.pyc
in show(self, n, truncate, vertical)
376 """
377 if isinstance(truncate, bool) and truncate:
--> 378 print(self._jdf.showString(n, 20, vertical))
379 else:
380 print(self._jdf.showString(n, int(truncate), vertical))
/Applications/anaconda2/lib/python2.7/site-packages/py4j/java_gateway.pyc
in call(self, *args) 1284 answer =
self.gateway_client.send_command(command) 1285 return_value
= get_return_value(
-> 1286 answer, self.gateway_client, self.target_id, self.name) 1287 1288 for temp_arg in temp_args:
/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/pyspark/sql/utils.pyc
in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
/Applications/anaconda2/lib/python2.7/site-packages/py4j/protocol.pyc
in get_return_value(answer, gateway_client, target_id, name)
326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
--> 328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(
Py4JJavaError: An error occurred while calling o64.showString. :
org.apache.spark.SparkException: Job aborted due to stage failure:
Task 0 in stage 3.0 failed 1 times, most recent failure: Lost task 0.0
in stage 3.0 (TID 5, localhost, executor driver):
org.apache.spark.api.python.PythonException: Traceback (most recent
call last): File
"/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py",
line 377, in main
process() File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py",
line 372, in process
serializer.dump_stream(func(split_index, iterator), outfile) File
"/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/serializers.py",
line 393, in dump_stream
vs = list(itertools.islice(iterator, batch)) File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/util.py",
line 99, in wrapper
return f(*args, **kwargs) File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/pyspark/sql/session.py",
line 730, in prepare
verify_func(obj) File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/pyspark/sql/types.py",
line 1389, in verify
verify_value(obj) File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/pyspark/sql/types.py",
line 1370, in verify_struct
verifier(v) File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/pyspark/sql/types.py",
line 1389, in verify
verify_value(obj) File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/pyspark/sql/types.py",
line 1315, in verify_integer
verify_acceptable_types(obj) File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/pyspark/sql/types.py",
line 1278, in verify_acceptable_types
% (dataType, obj, type(obj)))) TypeError: field id: IntegerType can not accept object u'1' in type
at
org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452)
at
org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:588)
at
org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:571)
at
org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440) at
scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) at
scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) at
scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
Source) at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) at
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at
org.apache.spark.scheduler.Task.run(Task.scala:121) at
org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace: at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at scala.Option.foreach(Option.scala:257) at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at
org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061) at
org.apache.spark.SparkContext.runJob(SparkContext.scala:2082) at
org.apache.spark.SparkContext.runJob(SparkContext.scala:2101) at
org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)
at
org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at
org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3383)
at
org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2544)
at
org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2544)
at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3364)
at
org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
at
org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3363) at
org.apache.spark.sql.Dataset.head(Dataset.scala:2544) at
org.apache.spark.sql.Dataset.take(Dataset.scala:2758) at
org.apache.spark.sql.Dataset.getRows(Dataset.scala:254) at
org.apache.spark.sql.Dataset.showString(Dataset.scala:291) 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:
org.apache.spark.api.python.PythonException: Traceback (most recent
call last): File
"/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py",
line 377, in main
process() File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py",
line 372, in process
serializer.dump_stream(func(split_index, iterator), outfile) File
"/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/serializers.py",
line 393, in dump_stream
vs = list(itertools.islice(iterator, batch)) File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/util.py",
line 99, in wrapper
return f(*args, **kwargs) File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/pyspark/sql/session.py",
line 730, in prepare
verify_func(obj) File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/pyspark/sql/types.py",
line 1389, in verify
verify_value(obj) File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/pyspark/sql/types.py",
line 1370, in verify_struct
verifier(v) File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/pyspark/sql/types.py",
line 1389, in verify
verify_value(obj) File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/pyspark/sql/types.py",
line 1315, in verify_integer
verify_acceptable_types(obj) File "/Users/nagaraju.n/spark-2.4.3-bin-hadoop2.7/python/pyspark/sql/types.py",
line 1278, in verify_acceptable_types
% (dataType, obj, type(obj)))) TypeError: field id: IntegerType can not accept object u'1' in type
at
org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452)
at
org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:588)
at
org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:571)
at
org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440) at
scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) at
scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) at
scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
Source) at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) at
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at
org.apache.spark.scheduler.Task.run(Task.scala:121) at
org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
#!/usr/bin/env python
coding: utf-8
In[11]:
import os import sys from pyspark import SparkContext from pyspark.sql
import SparkSession from pyspark.sql.types import *
spark=SparkSession.builder.getOrCreate() sc =
SparkContext.getOrCreate()
In[12]:
Reads data from file and creates rdd rdd=sc.textFile('/Users/nagaraju.n/Downloads/sample_data.txt')
In[13]:
type(rdd)
In[14]:
rdd_data=rdd.map(lambda p: p.split(","))
In[15]:
rdd_data.collect()
In[16]:
print(rdd_data)
In[17]:
orig_header=rdd_data.first()
In[18]:
type(orig_header)
In[19]:
rdd_withoutheader=rdd_data.filter(lambda p:p != orig_header)
In[20]:
rdd_withoutheader.collect()
In[21]:
Create Schema header = StructType([StructField("id", IntegerType(), True),StructField("first_name", StringType(),
True),StructField("last_name", StringType(),
True),StructField("email", StringType(), True),StructField("phone",
StringType(), True),StructField("city", StringType(),
True),StructField("country", StringType(), True)])
In[22]:
header
In[23]:
df=spark.createDataFrame(rdd_withoutheader,header)
In[24]:
df.show()

Answer 1

/// Part of your code:
header = StructType([StructField("stockticker", StringType(), True),StructField("tradedate", IntegerType(), True),StructField("openprice", FloatType(), True),StructField("highprice", FloatType(), True),StructField("lowprice", FloatType(), True),StructField("closeprice", FloatType(), True),StructField("volume", IntegerType(), True)])
df=spark.createDataFrame(rdd_data,header)
///
My answer:
Schema is used most to avoid a full table scan to infer types and doesn't perform any type casting. Hence above method best works for Json/avro/parquet input files not for text files. For textfiles following are the best methods:
Method 1 based on your code, convert rdd to dataframe and define schema as below:
rdd=sc.textFile('/Users/nagaraju.n/Downloads/sample_data.txt')
df_noType=data.map(lambda p: p.split(",")).toDF(["id", "first_name", "last_name", "email", "phone", "city", "country"])
Now you can type cast either of these ways:
Way1:
df_typecast=df_noType.select(df_noType.id.cast('int'), df_noType.first_name, df_noType.last_name, df_noType.email, df_noType.phone, df_noType.city, df_noType.country)
Note: in above line no need to type cast other fields to string as they are bydefault string
Note: if decimals are there then you can use df_noType.id.cast('float')
(or)
way2:
from pyspark.sql.types import *
df_typecast=df_noType.select(df_noType.id.cast(IntegerType()), df_noType.first_name.cast(StringType()), df_noType.last_name.cast(StringType()), df_noType.email.cast(StringType()), df_noType.phone.cast(StringType()), df_noType.city.cast(StringType()), df_noType.country.cast(StringType()))
Method 2: I usually use this always which I feel best and easy
rdd=sc.textFile('/Users/nagaraju.n/Downloads/sample_data.txt')
from pyspark.sql import Row
df=rdd.map(lambda p: Row(id= int(p.split(",")[0]), first_name= p.split(",")[1], last_name= p.split(",")[2], email= p.split(",")[3], phone= p.split(",")[4], city= p.split(",")[5], country=p.split(",")[6])).toDF()
df.printSchema()
Note: if decimals are there then you can use float(p.split(",")[0])