[
https://issues.apache.org/jira/browse/SPARK-44354?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17741925#comment-17741925
]
Kai-Michael Roesner commented on SPARK-44354:
---------------------------------------------
PS: I tried to work around the exception by using `StringType()` in the schema
and then doing
{code}
df.withColumn('STR', col('STR').cast(CharType(3)))
{code}
That got me a
{code}
WARN CharVarcharUtils: The Spark cast operator does not support char/varchar
type and simply treats them as string type.
{code}
So now I'm wondering whether `CharType()` is supported as column data type at
all...
> Cannot create dataframe with CharType/VarcharType column
> --------------------------------------------------------
>
> Key: SPARK-44354
> URL: https://issues.apache.org/jira/browse/SPARK-44354
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Affects Versions: 3.4.0
> Reporter: Kai-Michael Roesner
> Priority: Major
>
> When trying to create a dataframe with a CharType or VarcharType column like
> so:
> {code}
> from datetime import date
> from decimal import Decimal
> from pyspark.sql import SparkSession
> from pyspark.sql.types import *
> data = [
> (1, 'abc', Decimal(3.142), date(2023, 1, 1)),
> (2, 'bcd', Decimal(1.414), date(2023, 1, 2)),
> (3, 'cde', Decimal(2.718), date(2023, 1, 3))]
> schema = StructType([
> StructField('INT', IntegerType()),
> StructField('STR', CharType(3)),
> StructField('DEC', DecimalType(4, 3)),
> StructField('DAT', DateType())])
> spark = SparkSession.builder.appName('data-types').getOrCreate()
> df = spark.createDataFrame(data, schema)
> df.show()
> {code}
> a {{java.lang.IllegalStateException}} is thrown
> [here|https://github.com/apache/spark/blob/85e252e8503534009f4fb5ea005d44c9eda31447/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala#L168].
> I'm expecting this to work...
> PS: Excerpt from the logs:
> {code}
> py4j.protocol.Py4JJavaError: An error occurred while calling
> o24.applySchemaToPythonRDD.
> : java.lang.IllegalStateException: [BUG] logical plan should not have output
> of char/varchar type: LogicalRDD [INT#0, STR#1, DEC#2, DAT#3], false
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis.$anonfun$checkAnalysis0$1(CheckAnalysis.scala:168)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis.$anonfun$checkAnalysis0$1$adapted(CheckAnalysis.scala:163)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:295)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis.checkAnalysis0(CheckAnalysis.scala:163)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis.checkAnalysis0$(CheckAnalysis.scala:160)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis0(Analyzer.scala:188)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis.checkAnalysis(CheckAnalysis.scala:156)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis.checkAnalysis$(CheckAnalysis.scala:146)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:188)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:211)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:330)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:208)
> at
> org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:76)
> at
> org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
> at
> org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$2(QueryExecution.scala:202)
> at
> org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:526)
> at
> org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:202)
> at
> org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:827)
> at
> org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:201)
> at
> org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:76)
> at
> org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:74)
> at
> org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:66)
> at org.apache.spark.sql.Dataset$.$anonfun$ofRows$1(Dataset.scala:90)
> at
> org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:827)
> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:88)
> at
> org.apache.spark.sql.SparkSession.internalCreateDataFrame(SparkSession.scala:571)
> at
> org.apache.spark.sql.SparkSession.applySchemaToPythonRDD(SparkSession.scala:804)
> at
> org.apache.spark.sql.SparkSession.applySchemaToPythonRDD(SparkSession.scala:789)
> at
> java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.base/java.lang.reflect.Method.invoke(Method.java:566)
> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374)
> 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.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
> at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
> at java.base/java.lang.Thread.run(Thread.java:829)
> {code}
--
This message was sent by Atlassian Jira
(v8.20.10#820010)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
