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https://issues.apache.org/jira/browse/SPARK-37321?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Izek Greenfield updated SPARK-37321:
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Summary: Wrong size estimation leads to "Cannot broadcast the table that is
larger than 8GB: 8 GB" (was: Wrong size estimation that leads to "Cannot
broadcast the table that is larger than 8GB: 8 GB")
> Wrong size estimation leads to "Cannot broadcast the table that is larger
> than 8GB: 8 GB"
> -----------------------------------------------------------------------------------------
>
> Key: SPARK-37321
> URL: https://issues.apache.org/jira/browse/SPARK-37321
> Project: Spark
> Issue Type: Bug
> Components: Optimizer
> Affects Versions: 3.1.1, 3.2.0
> Reporter: Izek Greenfield
> Priority: Major
>
> When CBO is enabled then a situation occurs where spark tries to broadcast
> very large DataFrame due to wrong output size estimation.
>
> In `EstimationUtils.getSizePerRow`, if there is no statistics then spark will
> use `DataType.defaultSize`.
> In the case where the output contains `functions.concat_ws`, the
> `getSizePerRow` function will estimate the size to be 20 bytes, while in our
> case the actual size can be a lot larger.
> As a result, we in some cases end up with an estimated size of < 300K while
> the actual size can be > 8GB, thus leading to exceptions as spark thinks the
> tables may be broadcast but later realizes the data size is too large.
>
> Code sample to reproduce:
> {code:scala}
> import spark.implicits._
> (1 to 100000).toDF("index").withColumn("index",
> col("index").cast("string")).write.parquet("/tmp/a")
> (1 to 1000).toDF("index_b").withColumn("index_b",
> col("index_b").cast("string")).write.parquet("/tmp/b")
> val a = spark.read
> .parquet("/tmp/a")
> .withColumn("b", col("index"))
> .withColumn("l1", functions.concat_ws("/", col("index"),
> functions.current_date(), functions.current_date(), functions.current_date(),
> functions.current_date()))
> .withColumn("l2", functions.concat_ws("/", col("index"),
> functions.current_date(), functions.current_date(), functions.current_date(),
> functions.current_date()))
> .withColumn("l3", functions.concat_ws("/", col("index"),
> functions.current_date(), functions.current_date(), functions.current_date(),
> functions.current_date()))
> .withColumn("l4", functions.concat_ws("/", col("index"),
> functions.current_date(), functions.current_date(), functions.current_date(),
> functions.current_date()))
> .withColumn("l5", functions.concat_ws("/", col("index"),
> functions.current_date(), functions.current_date(), functions.current_date(),
> functions.current_date()))
> val r = Random.alphanumeric
> val l = 220
> val i = 2800
> val b = spark.read
> .parquet("/tmp/b")
> .withColumn("l1", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l2", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l3", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l4", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l5", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l6", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l7", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
>
> a.join(b, col("index") === col("index_b")).show(2000)
> {code}
>
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