我正在努力完成这项Spark SQL查询，该查询使用了一些具有一些逻辑条件的简单连接。
我可以使用相对较小的数据集获得输出，但是，情况随着更大的数据集而变化。我想让这个连接适用于A 超过 1400万行，B 100万行的数据集。
我正在使用10个 r4.4xlarge 实例的EMR群集。
这些是我传递给作业的以下配置参数：
spark.driver.memory 100g
spark.executor.cores 5
spark.executor.memory 39g
用于创建SparkSession的参数是：
sq = SparkSession.builder.config('spark.rpc.message.maxSize', '1536')\
.config("spark.sql.shuffle.partitions", 490)\
.config("spark.sql.broadcastTimeout", 2000)\
.config("spark.sql.autoBroadcastJoinThreshold", 1024*1024*900)\
.getOrCreate()
数据集A和B是通过工作流获得的，但只有在此查询中才是流程终止的位置。
sql_1 = """
SELECT
A.userid,
A.eventtime,
A.latitude as userid_latitude,
A.longitude as userid_longitude,
A.events,
B.unique_reference_number,
B.name as poi_name,
B.pointx_classification_name as poi_classification_name,
B.brand,
B.lat as poi_latitude,
B.long as poi_longitude,
acos(sin(pi()*A.latitude/180.0)*sin(pi()*B.lat/180.0)+cos(pi()*A.latitude/180.0)*cos(pi()*B.lat/180.0)*cos(pi()*B.long/180.0-pi()*A.longitude/180.0))*6371 as distance,
B.poi_radious_meters/1000 as poi_radious_km,
CASE WHEN (acos(sin(pi()*A.latitude/180.0)*sin(pi()*B.lat/180.0)+cos(pi()*A.latitude/180.0)*cos(pi()*B.lat/180.0)*cos(pi()*B.long/180.0-pi()*A.longitude/180.0))*6371) <= B.poi_radious_meters/1000 THEN 1 ELSE 0 END as is_within_radius
FROM A
LEFT JOIN B ON array_contains(B.grid_array, A.grid_id)
WHERE (acos(sin(pi()*A.latitude/180.0)*sin(pi()*B.lat/180.0)+cos(pi()*A.latitude/180.0)*cos(pi()*B.lat/180.0)*cos(pi()*B.long/180.0-pi()*A.longitude/180.0))*6371) <= 0.6
"""
interim = sq.sql(sql_1)
# Aggregate the events
output = interim.groupBy("userid", "eventtime", "unique_reference_number").agg((F.sum('events')).alias("events"))
这是我得到的错误：
: org.apache.spark.SparkException: Exception thrown in awaitResult:
at org.apache.spark.util.ThreadUtils$.awaitResultInForkJoinSafely(ThreadUtils.scala:215)
at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec.doExecuteBroadcast(BroadcastExchangeExec.scala:131)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeBroadcast$1.apply(SparkPlan.scala:124)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeBroadcast$1.apply(SparkPlan.scala:124)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:135)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan.executeBroadcast(SparkPlan.scala:123)
at org.apache.spark.sql.execution.joins.BroadcastNestedLoopJoinExec.doExecute(BroadcastNestedLoopJoinExec.scala:343)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:135)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:113)
at org.apache.spark.sql.execution.InputAdapter.inputRDDs(WholeStageCodegenExec.scala:235)
at org.apache.spark.sql.execution.FilterExec.inputRDDs(basicPhysicalOperators.scala:124)
at org.apache.spark.sql.execution.ProjectExec.inputRDDs(basicPhysicalOperators.scala:42)
at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:368)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:135)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:113)
at org.apache.spark.sql.execution.columnar.InMemoryRelation.buildBuffers(InMemoryRelation.scala:96)
at org.apache.spark.sql.execution.columnar.InMemoryRelation.<init>(InMemoryRelation.scala:85)
at org.apache.spark.sql.execution.columnar.InMemoryRelation$.apply(InMemoryRelation.scala:41)
at org.apache.spark.sql.execution.CacheManager$$anonfun$cacheQuery$1.apply(CacheManager.scala:98)
at org.apache.spark.sql.execution.CacheManager.writeLock(CacheManager.scala:65)
at org.apache.spark.sql.execution.CacheManager.cacheQuery(CacheManager.scala:89)
at org.apache.spark.sql.Dataset.persist(Dataset.scala:2479)
at org.apache.spark.sql.Dataset.cache(Dataset.scala:2489)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.util.concurrent.TimeoutException: Futures timed out after [2000 seconds]
在Spark Session构建器中不使用任何广播配置时出现此错误：
org.apache.spark.SparkException: Exception thrown in awaitResult:
at org.apache.spark.util.ThreadUtils$.awaitResultInForkJoinSafely(ThreadUtils.scala:215)
at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec.doExecuteBroadcast(BroadcastExchangeExec.scala:131)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeBroadcast$1.apply(SparkPlan.scala:124)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeBroadcast$1.apply(SparkPlan.scala:124)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:135)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan.executeBroadcast(SparkPlan.scala:123)
at org.apache.spark.sql.execution.joins.BroadcastNestedLoopJoinExec.doExecute(BroadcastNestedLoopJoinExec.scala:343)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:135)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:113)
at org.apache.spark.sql.execution.InputAdapter.inputRDDs(WholeStageCodegenExec.scala:235)
at org.apache.spark.sql.execution.FilterExec.inputRDDs(basicPhysicalOperators.scala:124)
at org.apache.spark.sql.execution.ProjectExec.inputRDDs(basicPhysicalOperators.scala:42)
at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:368)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:135)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:113)
at org.apache.spark.sql.execution.columnar.InMemoryRelation.buildBuffers(InMemoryRelation.scala:96)
at org.apache.spark.sql.execution.columnar.InMemoryRelation.<init>(InMemoryRelation.scala:85)
at org.apache.spark.sql.execution.columnar.InMemoryRelation$.apply(InMemoryRelation.scala:41)
at org.apache.spark.sql.execution.CacheManager$$anonfun$cacheQuery$1.apply(CacheManager.scala:98)
at org.apache.spark.sql.execution.CacheManager.writeLock(CacheManager.scala:65)
at org.apache.spark.sql.execution.CacheManager.cacheQuery(CacheManager.scala:89)
at org.apache.spark.sql.Dataset.persist(Dataset.scala:2479)
at org.apache.spark.sql.Dataset.cache(Dataset.scala:2489)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
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.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.SparkException: Job 14 cancelled because SparkContext was shut down
at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:809)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:807)
at scala.collection.mutable.HashSet.foreach(HashSet.scala:78)
at org.apache.spark.scheduler.DAGScheduler.cleanUpAfterSchedulerStop(DAGScheduler.scala:807)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onStop(DAGScheduler.scala:1738)
at org.apache.spark.util.EventLoop.stop(EventLoop.scala:83)
at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1657)
at org.apache.spark.SparkContext$$anonfun$stop$8.apply$mcV$sp(SparkContext.scala:1826)
at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1283)
at org.apache.spark.SparkContext.stop(SparkContext.scala:1825)
at org.apache.spark.SparkContext$$anon$3.run(SparkContext.scala:1770)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:629)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1944)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1958)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:935)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.collect(RDD.scala:934)
at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:275)
at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1$$anonfun$apply$1.apply(BroadcastExchangeExec.scala:78)
at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1$$anonfun$apply$1.apply(BroadcastExchangeExec.scala:75)
at org.apache.spark.sql.execution.SQLExecution$.withExecutionId(SQLExecution.scala:94)
at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1.apply(BroadcastExchangeExec.scala:74)
at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1.apply(BroadcastExchangeExec.scala:74)
at scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)
at scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
您知道调整spark的最佳方法是什么，并使此查询适用于大型数据集吗？