pyspark .show（）有效，但.collect（）无效

Question

我有一个非常简单的pyspark程序，该程序使用dataframe从一组ORC文件中查询数据。我在Windows中使用anaconda python并在其上安装了pyspark。

该程序是这样的：

from pyspark.sql import SparkSession

spark_session = SparkSession.builder.appName("test").getOrCreate()
df_orc = spark_session .read.orc("./raw_data/")
df_orc.createOrReplaceTempView("orc")

这很好用：

spark.sql("select count(*) from orc").show()

但这会产生错误：

spark.sql("select count(*) from orc").collect()

错误消息是：

WARN Utils: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.debug.maxToStringFields' in SparkEnv.conf.

Py4JJavaError: An error occurred while calling o81.collectToPython.
: java.lang.IllegalArgumentException
        at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
        at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
        at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
        at org.apache.spark.util.ClosureCleaner$.getClassReader(ClosureCleaner.scala:46)
        at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.sca
        at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.sca
        at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733)
        at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103)
        at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103)
        at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230)
        at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
        at scala.collection.mutable.HashMap$$anon$1.foreach(HashMap.scala:103)
        at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)
        at org.apache.spark.util.FieldAccessFinder$$anon$3.visitMethodInsn(ClosureCleaner.scala:426)
        at org.apache.xbean.asm5.ClassReader.a(Unknown Source)
        at org.apache.xbean.asm5.ClassReader.b(Unknown Source)
        at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
        at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
        at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(
        at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(
        at scala.collection.immutable.List.foreach(List.scala:381)
        at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.sc
        at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:156)
        at org.apache.spark.SparkContext.clean(SparkContext.scala:2294)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2068)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2094)
        at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
        at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
        at org.apache.spark.rdd.RDD.collect(RDD.scala:935)
        at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:278)
        at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply$mcI$sp(Dataset.scala:2808)
        at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2805)
        at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2805)
        at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
        at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2828)
        at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:2805)
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
        at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
        at java.base/java.lang.reflect.Method.invoke(Unknown Source)
        at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
        at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
        at py4j.Gateway.invoke(Gateway.java:280)
        at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
        at py4j.commands.CallCommand.execute(CallCommand.java:79)
        at py4j.GatewayConnection.run(GatewayConnection.java:214)
        at java.base/java.lang.Thread.run(Unknown Source)

Answer 1

这工作正常的原因是：

spark.sql("select count(*) from orc").show()

是因为.show()仅适用于数据的前 5 行

但是当你运行时：

spark.sql("select count(*) from orc").collect()

.collect()将适用于您的所有数据

从你的错误消息：

WARN Utils: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.debug.maxToStringFields' in SparkEnv.conf.

根据火花文档：

为宽模式创建和记录字符串的性能开销可能很大。为了限制影响，我们限制了默认包含的字段数量。这可以通过在 SparkEnv 中设置“spark.debug.maxToStringFields”conf 来覆盖。

但是，它可能会影响您的工作表现，因此您需要以下内容：

spark = SparkSession 
.builder 
.master('local[*]') 
.appName('Notebook') 
.config('spark.sql.debug.maxToStringFields', 200) 
.getOrCreate()

这是一个新的 SQL 配置spark.sql.debug.maxToStringFields，用于控制truncatedString剪切输入序列的最大字段数。

默认值为：DEFAULT_MAX_TO_STRING_FIELDS = 25

您还可以添加spark.sql.debug.maxToStringFields=100到spark-defaults.conf

使用以前版本的 Sparkspark.debug.maxToStringFields代替spark.sql.debug.maxToStringFields