Exception in thread "broadcast-exchange-0" java.lang.OutOfMemoryError: Not enough memory to build and broadcast the table to all worker nodes

Question

I am running a spark application on below configuration :

1 Master , 2 Worker nodes.

• Each worker has 88 Cores , hence total no. of cores 176

• Each worker has 502 GB memory , so total memory available is 1004 GB

I am getting below exception while running the application :

Exception in thread "broadcast-exchange-0" java.lang.OutOfMemoryError: Not enough memory to build and broadcast the table to all worker nodes. As a workaround, you can either disable broadcast by setting spark.sql.autoBroadcastJoinThreshold to -1 or increase the spark driver memory by setting spark.driver.memory to a higher value
        at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1$$anonfun$apply$1.apply(BroadcastExchangeExec.scala:115)
        at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1$$anonfun$apply$1.apply(BroadcastExchangeExec.scala:73)
        at org.apache.spark.sql.execution.SQLExecution$.withExecutionId(SQLExecution.scala:97)
        at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1.apply(BroadcastExchangeExec.scala:72)
        at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1.apply(BroadcastExchangeExec.scala:72)
        at scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)
        at scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        at java.lang.Thread.run(Thread.java:745)

There are two solutions mentioned in this error itself :

1. As a workaround, you can either disable broadcast by setting spark.sql.autoBroadcastJoinThreshold to -1.

   OR

2. Increase the spark driver memory by setting spark.driver.memory to a higher value.

I am trying to run with setting more driver memory, however i want to understand the root cause of this issue. Can anyone please explain.

I have used Java in my code.

EDIT 1

I am using broadcast variables in my code.

EDIT 2

Adding code that contains broadcast variables.

//1.
        Dataset<Row> currencySet1 = sparkSession.read().format("jdbc").option("url",connection ).option("dbtable", CI_CURRENCY_CD).load();
        currencySetCache = currencySet1.select(CURRENCY_CD, DECIMAL_POSITIONS).persist(StorageLevel.MEMORY_ONLY());
        Dataset<Row> currencyCodes = currencySetCache.select(CURRENCY_CD);
        currencySet = currencyCodes.as(Encoders.STRING()).collectAsList();

        //2.
        Dataset<Row>  divisionSet = sparkSession.read().format("jdbc").option("url",connection ).option("dbtable", CI_CIS_DIVISION).load();
        divisionSetCache = divisionSet.select(CIS_DIVISION).persist(StorageLevel.MEMORY_ONLY());
        divisionList = divisionSetCache.as(Encoders.STRING()).collectAsList();

        //3.
        Dataset<Row> userIdSet =  sparkSession.read().format("jdbc").option("url",connection ).option("dbtable", SC_USER).load();
        userIdSetCache = userIdSet.select(USER_ID).persist(StorageLevel.MEMORY_ONLY());
        userIdList = userIdSetCache.as(Encoders.STRING()).collectAsList();

ClassTag<List<String>> evidenceForDivision = scala.reflect.ClassTag$.MODULE$.apply(List.class);
        Broadcast<List<String>> broadcastVarForDiv = context.broadcast(divisionList, evidenceForDivision);

        ClassTag<List<String>> evidenceForCurrency = scala.reflect.ClassTag$.MODULE$.apply(List.class);
        Broadcast<List<String>> broadcastVarForCurrency = context.broadcast(currencySet, evidenceForCurrency);

        ClassTag<List<String>> evidenceForUserID = scala.reflect.ClassTag$.MODULE$.apply(List.class);
        Broadcast<List<String>> broadcastVarForUserID = context.broadcast(userIdList, evidenceForUserID);


        //Validation -- Start
        Encoder<RuleParamsBean> encoder = Encoders.bean(RuleParamsBean.class);
        Dataset<RuleParamsBean> ds = new Dataset<RuleParamsBean>(sparkSession, finalJoined.logicalPlan(), encoder);


        Dataset<RuleParamsBean> validateDataset = ds.map(ruleParamsBean -> validateTransaction(ruleParamsBean,broadcastVarForDiv.value(),broadcastVarForCurrency.value(),
                broadcastVarForUserID.value()),encoder);
        validateDataset.persist(StorageLevel.MEMORY_ONLY());

Answer 1

Possible root cause: Default value of "spark.driver.memory" 1 Gb only (depends on distributive), it is very small number. If you are reading significant amounts of data on driver, OutOfMemory can occurs easily, advice from exception is correct.

Solution: Increase "spark.driver.memory" and "spark.executor.memory" at least to 16Gb.