Spark 3.5.0读取PostgreSQL bpchar列执行show()触发OutOfMemoryError问题

问题现象 #

在Spark 3.5.0的PySpark环境下，读取PostgreSQL中bpchar类型的列并执行show()等操作时，会抛出OutOfMemoryError，该问题在Spark 3.4.1及更低版本中不会出现。

复现步骤 #

1. PostgreSQL建表语句：

CREATE TABLE test.test_table (id bpchar);

2. PySpark读取并执行show()代码：

df = spark.read.format("jdbc").option("url", f"jdbc:postgresql://{host_db}:{db_port}/{dbname}").option("driver", "org.postgresql.Driver").option("query", 'select id from test.test_table').option("user", n).option("password", s).load()
df.show()

触发的错误信息 #

ERROR Executor: 阶段1.0中任务0.0（TID 1）出现异常
java.lang.OutOfMemoryError: Requested array size exceeds VM limit
    at org.apache.spark.unsafe.types.UTF8String.rpad(UTF8String.java:880)
    at org.apache.spark.sql.catalyst.util.CharVarcharCodegenUtils.readSidePadding(CharVarcharCodegenUtils.java:62)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenEvaluatorFactory$WholeStageCodegenPartitionEvaluator$$anon$1.hasNext(WholeStageCodegenEvaluatorFactory.scala:43)
    at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:388)
    at org.apache.spark.sql.execution.SparkPlan$$Lambda$2449/0x0000000801093040.apply(Unknown Source)
    at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:890)
    at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:890)
    at org.apache.spark.rdd.RDD$$Lambda$2450/0x0000000801094040.apply(Unknown Source)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:328)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:93)
    at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
    at org.apache.spark.scheduler.Task.run(Task.scala:141)
    at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$4(Executor.scala:620)
    at org.apache.spark.executor.Executor$TaskRunner$$Lambda$2410/0x000000080105b840.apply(Unknown Source)
    at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:64)
    at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(SparkErrorUtils.scala:61)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:94)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:623)
    at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
    at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
    at java.base/java.lang.Thread.run(Thread.java:829)

问题原因 #

Spark 3.5.0对char/bpchar类型的处理逻辑进行了变更，当读取未指定长度的bpchar列时，错误地尝试将其填充到极大的长度，导致申请超出JVM限制的数组，最终触发OutOfMemoryError。该问题已在Spark社区被反馈。

解决方案 #

1. 显式指定bpchar列长度：建表时明确指定bpchar的长度，例如：

CREATE TABLE test.test_table (id bpchar(10));

2. 读取时转换列类型：在JDBC查询中将bpchar列显式转换为varchar类型，避免Spark的错误填充逻辑：

df = spark.read.format("jdbc").option("url", f"jdbc:postgresql://{host_db}:{db_port}/{dbname}").option("driver", "org.postgresql.Driver").option("query", 'select cast(id as varchar) from test.test_table').option("user", n).option("password", s).load()

3. 降级Spark版本：暂时回退到Spark 3.4.1及以下版本，避开该问题。
4. 等待官方修复：关注Spark后续版本更新，该bug预计会在后续版本中修复。

内容的提问来源于stack exchange，提问作者yjsa