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https://issues.apache.org/jira/browse/SPARK-37442?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17580369#comment-17580369
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Dongjoon Hyun commented on SPARK-37442:
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Hi, [~irelandbird]. Apache Spark 2.4 and 3.0 are End-Of-Life release . Please
try to use the latest Apache Spark version like 3.3.0.
> In AQE, wrong InMemoryRelation size estimation causes "Cannot broadcast the
> table that is larger than 8GB: 8 GB" failure
> ------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-37442
> URL: https://issues.apache.org/jira/browse/SPARK-37442
> Project: Spark
> Issue Type: Sub-task
> Components: Optimizer, SQL
> Affects Versions: 3.1.1, 3.2.0
> Reporter: Michael Chen
> Assignee: Michael Chen
> Priority: Major
> Fix For: 3.2.1, 3.3.0
>
>
> There is a period in time where an InMemoryRelation will have the cached
> buffers loaded, but the statistics will be inaccurate (anywhere between 0 ->
> size in bytes reported by accumulators). When AQE is enabled, it is possible
> that join planning strategies will happen in this window. In this scenario,
> join children sizes including InMemoryRelation are greatly underestimated and
> a broadcast join can be planned when it shouldn't be. We have seen scenarios
> where a broadcast join is planned with the builder size greater than 8GB
> because at planning time, the optimizer believes the InMemoryRelation is 0
> bytes.
> Here is an example test case where the broadcast threshold is being ignored.
> It can mimic the 8GB error by increasing the size of the tables.
> {code:java}
> withSQLConf(
> SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> "true",
> SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "1048584") {
> // Spark estimates a string column as 20 bytes so with 60k rows, these
> relations should be
> // estimated at ~120m bytes which is greater than the broadcast join
> threshold
> Seq.fill(60000)("a").toDF("key")
> .createOrReplaceTempView("temp")
> Seq.fill(60000)("b").toDF("key")
> .createOrReplaceTempView("temp2")
> Seq("a").toDF("key").createOrReplaceTempView("smallTemp")
> spark.sql("SELECT key as newKey FROM temp").persist()
> val query =
> s"""
> |SELECT t3.newKey
> |FROM
> | (SELECT t1.newKey
> | FROM (SELECT key as newKey FROM temp) as t1
> | JOIN
> | (SELECT key FROM smallTemp) as t2
> | ON t1.newKey = t2.key
> | ) as t3
> | JOIN
> | (SELECT key FROM temp2) as t4
> | ON t3.newKey = t4.key
> |UNION
> |SELECT t1.newKey
> |FROM
> | (SELECT key as newKey FROM temp) as t1
> | JOIN
> | (SELECT key FROM temp2) as t2
> | ON t1.newKey = t2.key
> |""".stripMargin
> val df = spark.sql(query)
> df.collect()
> val adaptivePlan = df.queryExecution.executedPlan
> val bhj = findTopLevelBroadcastHashJoin(adaptivePlan)
> assert(bhj.length == 1) {code}
>
>
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