Spark2.3（四十二）：Spark Streaming和Spark Structured Streaming更新broadcast总结（二）
本次此时是在SPARK2,3 structured streaming下测试，不过这种方案，在spark2.2 structured streaming下应该也可行（请自行测试）。以下是我测试结果：
成功测试结果：
准备工作：创建maven项目，并在pom.xml导入一下依赖配置：
<properties> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> <spark.version>2.3.0</spark.version> <scala.version>2.11</scala.version> </properties> <dependencies> <!--Spark --> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_${scala.version}</artifactId> <version>${spark.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_${scala.version}</artifactId> <version>${spark.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql-kafka-0-10_${scala.version}</artifactId> <version>${spark.version}</version> </dependency> </dependencies>
第一步：LoadResourceManager.java是一个对broadcast类进行管理的类，
包含了以下方法：
1）unpersist()方法：对LoadResourceManager对象的broadcast属性进行清理；
2）load(SparkSession spark, LongAccumulator loadCountAccumulator)方法：实现对broadcast对象属性broadcast的初始化赋值；
3）get()方法：返回LoadResourceManager对象的broadcast属性值。
注意：该LoadResourceManager 类必须实现Serializable接口，否则会抛出不可序列化异常。
class LoadResourceManager implements Serializable { private static final long serialVersionUID = 7896720904164793792L; private volatile Broadcast<Map<String, String>> broadcast = null; public Broadcast<Map<String, String>> get() { return broadcast; } public void unpersist() { broadcast.unpersist(true);
//broadcast.destory(); 不能调用该方法，否则会抛出异常：Attempted to use Broadcast(x) after it was destroyed } public void load(SparkSession spark, LongAccumulator loadCountAccumulator) { loadCountAccumulator.add(1); int val = new Random().nextInt(100); // 这里可以添加时间判断是否重新加载 Map<String, String> innerMap = new HashMap<String, String>(10); innerMap.put("1", "1," + val); innerMap.put("2", "2," + val); innerMap.put("3", "3," + val); innerMap.put("4", "4," + val); innerMap.put("5", "5," + val); innerMap.put("6", "6," + val); innerMap.put("7", "7," + val); innerMap.put("8", "8," + val); innerMap.put("9", "9," + val); innerMap.put("10", "10," + val); System.out.println("the value is :" + val); JavaSparkContext jsc = JavaSparkContext.fromSparkContext(spark.sparkContext()); broadcast = jsc.broadcast(innerMap); } }
第二步：自定义StreamingQueryListener类MyStreamingQueryListener.java
该类是spark.streams().addListener(new MyStreamingQueryListener(...))使用，在structured streaming每次trigger触发结束时打印进度信息，另外调用更新broadcast代码。其中更新broadcast的功能包含两个步骤：
1）清空旧的broadcast，也就是调用LoadResourceManager 对象的unpersist()方法；
2）给broadcast管理对象的broacast属性赋新值，也就是调用LoadResourceManager 对象的load(...)方法。
注意：这个自定义的监听类的方法是在Driver端执行，也只有在Driver端修改broadcast，才能真正修改executor中的broadcast值。具体原因，请查阅broadcast的原理。
class MyStreamingQueryListener extends StreamingQueryListener { private SparkSession spark = null; private LoadResourceManager loadResourceManager = null; private LongAccumulator triggerAccumulator = null; private LongAccumulator loadCountAccumulator = null; public MyStreamingQueryListener() { } public MyStreamingQueryListener(SparkSession spark, LoadResourceManager loadResourceManager, LongAccumulator triggerAccumulator, LongAccumulator loadCountAccumulator) { this.spark = spark; this.loadResourceManager = loadResourceManager; this.triggerAccumulator = triggerAccumulator; this.loadCountAccumulator = loadCountAccumulator; } @Override public void onQueryStarted(QueryStartedEvent queryStarted) { System.out.println("Query started: " + queryStarted.id()); } @Override public void onQueryTerminated(QueryTerminatedEvent queryTerminated) { System.out.println("Query terminated: " + queryTerminated.id()); } @Override public void onQueryProgress(QueryProgressEvent queryProgress) { System.out.println("Query made progress: " + queryProgress.progress()); // sparkSession.sql("select * from " + queryProgress.progress().name()).show(); triggerAccumulator.add(1);
this.loadResourceManager.unpersist(); this.loadResourceManager.load(spark, loadCountAccumulator); System.out.println("Trigger accumulator value: " + triggerAccumulator.value()); System.out.println("Load count accumulator value: " + loadCountAccumulator.value()); } }
第三步：写structured streaming测试类
该测试类分为以下步骤：
1）初始化SparkSession对象spark；
2）给SparkSession对象的streams()添加监控事件：spark.streams().addListener(new MyStreamingQueryListener(...))；
3）初始化broadcast管理类LoadResourceManager ，并初始化LoadResourceManager对象的broacast属性值；
4）使用Rate Source生成测试数据；
5）对测试数据进行map操作，并在MapFunction对象的call(...)方法中调用LoadResourceManager 对象的broacast属性值，使用该broacast属性值；
6）定义Sink：sourceDataset.writeStream().format("console").outputMode(OutputMode.Append()).trigger(Trigger.ProcessingTime(10, TimeUnit.SECONDS)).start();；
7）阻塞流，等待结束。
public class BroadcastTest { public static void main(String[] args) { LogManager.getLogger("org.apache.spark.sql.execution.streaming.RateSourceProvider").setLevel(Level.ERROR); LogManager.getLogger("org.apache.spark").setLevel(Level.ERROR); LogManager.getLogger("org.apache.spark.launcher.OutputRedirector").setLevel(Level.ERROR); // 确定是yarn方式运行。 SparkSession spark = SparkSession.builder().master("yarn").appName("test_broadcast_app").getOrCreate(); LongAccumulator triggerAccumulator = spark.sparkContext().longAccumulator("triggerAccumulator"); LongAccumulator loadCountAccumulator = spark.sparkContext().longAccumulator("loadCountAccumulator"); LoadResourceManager loadResourceManager = new LoadResourceManager(); loadResourceManager.load(spark, loadCountAccumulator); spark.streams().addListener( new MyStreamingQueryListener(spark, loadResourceManager, triggerAccumulator, loadCountAccumulator)); Dataset<Row> sourceDataset = spark.readStream().format("rate").option("rowsPerSecond", 100).load(); UDF1<Long, Long> long_fomat_func = new UDF1<Long, Long>() { private static final long serialVersionUID = 1L; public Long call(final Long value) throws Exception { return value % 15; } }; spark.udf().register("long_fomat_func", long_fomat_func, DataTypes.LongType); sourceDataset = sourceDataset.withColumn("int_id", functions.callUDF("long_fomat_func", functions.col("value"))); sourceDataset.printSchema(); StructType resulStructType = new StructType(); resulStructType = resulStructType.add("int_id", DataTypes.StringType, false); resulStructType = resulStructType.add("job_result", DataTypes.StringType, true); ExpressionEncoder<Row> resultEncoder = RowEncoder.apply(resulStructType); sourceDataset = sourceDataset.map(new MapFunction<Row, Row>() { private static final long serialVersionUID = 1L; @Override public Row call(Row row) throws Exception { int int_id_idx = row.schema().fieldIndex("int_id"); Object int_idObject = row.get(int_id_idx); String int_id = int_idObject.toString(); Map<String, String> resources = loadResourceManager.get().getValue(); // 可能会涉及到当跨executor的情况下，依然会出现innerMap.size()返回的值为0的情况. if (resources.size() == 0) { throw new RuntimeException("the resources size is zero"); } String job_result = resources.get(int_id); Object[] values = new Object[2]; values[0] = int_id; values[1] = job_result; return RowFactory.create(values); } }, resultEncoder); sourceDataset.printSchema(); sourceDataset.writeStream().format("console").outputMode(OutputMode.Append()) .trigger(Trigger.ProcessingTime(10, TimeUnit.SECONDS)).start(); try { spark.streams().awaitAnyTermination(); } catch (StreamingQueryException e) { e.printStackTrace(); } } }
第四步：测试。
测试脚本submit_test.sh：
#/bin/sh jarspath='' for file in `ls /home/dx/tommy_duan/sparkjars/*.jar` do jarspath=${file},$jarspath done jarspath=${jarspath%?} echo $jarspath /home1/opt/cloudera/parcels/SPARK2-2.3.0.cloudera3-1.cdh5.13.3.p0.458809/lib/spark2/bin/spark-submit \ --master yarn \ --deploy-mode client \ --class com.dx.test.BroadcastTest \ --properties-file ./conf/spark-properties.conf \ --jars $jarspath \ --num-executors 10 \ --executor-memory 3G \ --executor-cores 1 \ --driver-memory 2G \ --driver-java-options "-XX:+TraceClassPaths" \ ./test.jar $1 $2 $3 $4
执行打印结果：
。。。。。。
19/03/27 20:01:18 INFO impl.YarnClientImpl: Submitted application application_1548381669007_0775 19/03/27 20:01:47 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@3404e5c4{/metrics/json,null,AVAILABLE,@Spark} the value is :80 19/03/27 20:01:48 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@66b0e207{/SQL,null,AVAILABLE,@Spark} 19/03/27 20:01:48 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@5d601832{/SQL/json,null,AVAILABLE,@Spark} 19/03/27 20:01:48 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@3e52ed5d{/SQL/execution,null,AVAILABLE,@Spark} 19/03/27 20:01:48 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@3d40498a{/SQL/execution/json,null,AVAILABLE,@Spark} 19/03/27 20:01:48 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@1de5cc88{/static/sql,null,AVAILABLE,@Spark} 19/03/27 20:01:49 INFO util.Version: Elasticsearch Hadoop v6.4.2 [54a631a014] root |-- timestamp: timestamp (nullable = true) |-- value: long (nullable = true) |-- int_id: long (nullable = true) root |-- int_id: string (nullable = false) |-- job_result: string (nullable = true) Query started: d4c76196-c874-4a09-ae2d-832c03a70ffb ------------------------------------------- Batch: 0 ------------------------------------------- +------+----------+ |int_id|job_result| +------+----------+ +------+----------+ Query made progress: { "id" : "d4c76196-c874-4a09-ae2d-832c03a70ffb", "runId" : "0317a974-930f-47ae-99d8-3fa582383a65", "name" : null, "timestamp" : "2019-03-27T12:01:51.686Z", "batchId" : 0, "numInputRows" : 0, "processedRowsPerSecond" : 0.0, "durationMs" : { "addBatch" : 2312, "getBatch" : 38, "getOffset" : 0, "queryPlanning" : 495, "triggerExecution" : 3030, "walCommit" : 131 }, "stateOperators" : [ ], "sources" : [ { "description" : "RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]", "startOffset" : null, "endOffset" : 0, "numInputRows" : 0, "processedRowsPerSecond" : 0.0 } ], "sink" : { "description" : "org.apache.spark.sql.execution.streaming.ConsoleSinkProvider@3b5afcc" } } the value is :26 Trigger accumulator value: 1 Load count accumulator value: 2 ------------------------------------------- Batch: 1 ------------------------------------------- +------+----------+ |int_id|job_result| +------+----------+ | 0| null| | 1| 1,26| | 2| 2,26| | 3| 3,26| | 4| 4,26| | 5| 5,26| | 6| 6,26| | 7| 7,26| | 8| 8,26| | 9| 9,26| | 10| 10,26| | 11| null| | 12| null| | 13| null| | 14| null| | 0| null| | 1| 1,26| | 2| 2,26| | 3| 3,26| | 4| 4,26| +------+----------+ only showing top 20 rows Query made progress: { "id" : "d4c76196-c874-4a09-ae2d-832c03a70ffb", "runId" : "0317a974-930f-47ae-99d8-3fa582383a65", "name" : null, "timestamp" : "2019-03-27T12:02:00.001Z", "batchId" : 1, "numInputRows" : 800, "inputRowsPerSecond" : 96.21166566446182, "processedRowsPerSecond" : 14.361625736033318, "durationMs" : { "addBatch" : 55166, "getBatch" : 70, "getOffset" : 0, "queryPlanning" : 59, "triggerExecution" : 55704, "walCommit" : 402 }, "stateOperators" : [ ], "sources" : [ { "description" : "RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]", "startOffset" : 0, "endOffset" : 8, "numInputRows" : 800, "inputRowsPerSecond" : 96.21166566446182, "processedRowsPerSecond" : 14.361625736033318 } ], "sink" : { "description" : "org.apache.spark.sql.execution.streaming.ConsoleSinkProvider@3b5afcc" } } the value is :7 Trigger accumulator value: 2 Load count accumulator value: 3 ------------------------------------------- Batch: 2 ------------------------------------------- +------+----------+ |int_id|job_result| +------+----------+ | 5| 5,7| | 6| 6,7| | 7| 7,7| | 8| 8,7| | 9| 9,7| | 10| 10,7| | 11| null| | 12| null| | 13| null| | 14| null| | 0| null| | 1| 1,7| | 2| 2,7| | 3| 3,7| | 4| 4,7| | 5| 5,7| | 6| 6,7| | 7| 7,7| | 8| 8,7| | 9| 9,7| +------+----------+ only showing top 20 rows Query made progress: { "id" : "d4c76196-c874-4a09-ae2d-832c03a70ffb", "runId" : "0317a974-930f-47ae-99d8-3fa582383a65", "name" : null, "timestamp" : "2019-03-27T12:02:55.822Z", "batchId" : 2, "numInputRows" : 5600, "inputRowsPerSecond" : 100.3206678490174, "processedRowsPerSecond" : 378.45509224842874, "durationMs" : { "addBatch" : 14602, "getBatch" : 24, "getOffset" : 0, "queryPlanning" : 61, "triggerExecution" : 14797, "walCommit" : 108 }, "stateOperators" : [ ], "sources" : [ { "description" : "RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]", "startOffset" : 8, "endOffset" : 64, "numInputRows" : 5600, "inputRowsPerSecond" : 100.3206678490174, "processedRowsPerSecond" : 378.45509224842874 } ], "sink" : { "description" : "org.apache.spark.sql.execution.streaming.ConsoleSinkProvider@3b5afcc" } } the value is :7 Trigger accumulator value: 3 Load count accumulator value: 4 ------------------------------------------- Batch: 3 ------------------------------------------- +------+----------+ |int_id|job_result| +------+----------+ | 10| 10,7| | 11| null| | 12| null| | 13| null| | 14| null| | 0| null| | 1| 1,7| | 2| 2,7| | 3| 3,7| | 4| 4,7| | 5| 5,7| | 6| 6,7| | 7| 7,7| | 8| 8,7| | 9| 9,7| | 10| 10,7| | 11| null| | 12| null| | 13| null| | 14| null| +------+----------+ only showing top 20 rows Query made progress: { "id" : "d4c76196-c874-4a09-ae2d-832c03a70ffb", "runId" : "0317a974-930f-47ae-99d8-3fa582383a65", "name" : null, "timestamp" : "2019-03-27T12:03:10.698Z", "batchId" : 3, "numInputRows" : 1500, "inputRowsPerSecond" : 100.83355740790536, "processedRowsPerSecond" : 437.4453193350831, "durationMs" : { "addBatch" : 3253, "getBatch" : 18, "getOffset" : 0, "queryPlanning" : 42, "triggerExecution" : 3429, "walCommit" : 113 }, "stateOperators" : [ ], "sources" : [ { "description" : "RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]", "startOffset" : 64, "endOffset" : 79, "numInputRows" : 1500, "inputRowsPerSecond" : 100.83355740790536, "processedRowsPerSecond" : 437.4453193350831 } ], "sink" : { "description" : "org.apache.spark.sql.execution.streaming.ConsoleSinkProvider@3b5afcc" } } the value is :21 Trigger accumulator value: 4 Load count accumulator value: 5 ------------------------------------------- Batch: 4 ------------------------------------------- +------+----------+ |int_id|job_result| +------+----------+ | 10| 10,21| | 11| null| | 12| null| | 13| null| | 14| null| | 0| null| | 1| 1,21| | 2| 2,21| | 3| 3,21| | 4| 4,21| | 5| 5,21| | 6| 6,21| | 7| 7,21| | 8| 8,21| | 9| 9,21| | 10| 10,21| | 11| null| | 12| null| | 13| null| | 14| null| +------+----------+ only showing top 20 rows Query made progress: { "id" : "d4c76196-c874-4a09-ae2d-832c03a70ffb", "runId" : "0317a974-930f-47ae-99d8-3fa582383a65", "name" : null, "timestamp" : "2019-03-27T12:03:20.000Z", "batchId" : 4, "numInputRows" : 900, "inputRowsPerSecond" : 96.7533863685229, "processedRowsPerSecond" : 664.2066420664207, "durationMs" : { "addBatch" : 599, "getBatch" : 23, "getOffset" : 0, "queryPlanning" : 36, "triggerExecution" : 1355, "walCommit" : 692 }, "stateOperators" : [ ], "sources" : [ { "description" : "RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]", "startOffset" : 79, "endOffset" : 88, "numInputRows" : 900, "inputRowsPerSecond" : 96.7533863685229, "processedRowsPerSecond" : 664.2066420664207 } ], "sink" : { "description" : "org.apache.spark.sql.execution.streaming.ConsoleSinkProvider@3b5afcc" } } the value is :76 Trigger accumulator value: 5 Load count accumulator value: 6 ------------------------------------------- Batch: 5 ------------------------------------------- +------+----------+ |int_id|job_result| +------+----------+ | 10| 10,76| | 11| null| | 12| null| | 13| null| | 14| null| | 0| null| | 1| 1,76| | 2| 2,76| | 3| 3,76| | 4| 4,76| | 5| 5,76| | 6| 6,76| | 7| 7,76| | 8| 8,76| | 9| 9,76| | 10| 10,76| | 11| null| | 12| null| | 13| null| | 14| null| +------+----------+ only showing top 20 rows Query made progress: { "id" : "d4c76196-c874-4a09-ae2d-832c03a70ffb", "runId" : "0317a974-930f-47ae-99d8-3fa582383a65", "name" : null, "timestamp" : "2019-03-27T12:03:30.000Z", "batchId" : 5, "numInputRows" : 1000, "inputRowsPerSecond" : 100.0, "processedRowsPerSecond" : 1189.0606420927468, "durationMs" : { "addBatch" : 613, "getBatch" : 21, "getOffset" : 0, "queryPlanning" : 40, "triggerExecution" : 841, "walCommit" : 164 }, "stateOperators" : [ ], "sources" : [ { "description" : "RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]", "startOffset" : 88, "endOffset" : 98, "numInputRows" : 1000, "inputRowsPerSecond" : 100.0, "processedRowsPerSecond" : 1189.0606420927468 } ], "sink" : { "description" : "org.apache.spark.sql.execution.streaming.ConsoleSinkProvider@3b5afcc" } } the value is :1 Trigger accumulator value: 6 Load count accumulator value: 7 ------------------------------------------- Batch: 6 ------------------------------------------- +------+----------+ |int_id|job_result| +------+----------+ | 5| 5,1| | 6| 6,1| | 7| 7,1| | 8| 8,1| | 9| 9,1| | 10| 10,1| | 11| null| | 12| null| | 13| null| | 14| null| | 0| null| | 1| 1,1| | 2| 2,1| | 3| 3,1| | 4| 4,1| | 5| 5,1| | 6| 6,1| | 7| 7,1| | 8| 8,1| | 9| 9,1| +------+----------+ only showing top 20 rows Query made progress: { "id" : "d4c76196-c874-4a09-ae2d-832c03a70ffb", "runId" : "0317a974-930f-47ae-99d8-3fa582383a65", "name" : null, "timestamp" : "2019-03-27T12:03:40.000Z", "batchId" : 6, "numInputRows" : 1000, "inputRowsPerSecond" : 100.0, "processedRowsPerSecond" : 1116.0714285714284, "durationMs" : { "addBatch" : 522, "getBatch" : 18, "getOffset" : 0, "queryPlanning" : 27, "triggerExecution" : 896, "walCommit" : 325 }, "stateOperators" : [ ], "sources" : [ { "description" : "RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]", "startOffset" : 98, "endOffset" : 108, "numInputRows" : 1000, "inputRowsPerSecond" : 100.0, "processedRowsPerSecond" : 1116.0714285714284 } ], "sink" : { "description" : "org.apache.spark.sql.execution.streaming.ConsoleSinkProvider@3b5afcc" } } the value is :5 Trigger accumulator value: 7 Load count accumulator value: 8 ------------------------------------------- Batch: 7 ------------------------------------------- +------+----------+ |int_id|job_result| +------+----------+ | 0| null| | 1| 1,5| | 2| 2,5| | 3| 3,5| | 4| 4,5| | 5| 5,5| | 6| 6,5| | 7| 7,5| | 8| 8,5| | 9| 9,5| | 10| 10,5| | 11| null| | 12| null| | 13| null| | 14| null| | 0| null| | 1| 1,5| | 2| 2,5| | 3| 3,5| | 4| 4,5| +------+----------+ only showing top 20 rows Query made progress: { "id" : "d4c76196-c874-4a09-ae2d-832c03a70ffb", "runId" : "0317a974-930f-47ae-99d8-3fa582383a65", "name" : null, "timestamp" : "2019-03-27T12:03:50.000Z", "batchId" : 7, "numInputRows" : 1000, "inputRowsPerSecond" : 100.0, "processedRowsPerSecond" : 1605.1364365971108, "durationMs" : { "addBatch" : 454, "getBatch" : 23, "getOffset" : 0, "queryPlanning" : 41, "triggerExecution" : 623, "walCommit" : 102 }, "stateOperators" : [ ], "sources" : [ { "description" : "RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]", "startOffset" : 108, "endOffset" : 118, "numInputRows" : 1000, "inputRowsPerSecond" : 100.0, "processedRowsPerSecond" : 1605.1364365971108 } ], "sink" : { "description" : "org.apache.spark.sql.execution.streaming.ConsoleSinkProvider@3b5afcc" } } the value is :49 Trigger accumulator value: 8 Load count accumulator value: 9 ------------------------------------------- Batch: 8 ------------------------------------------- +------+----------+ |int_id|job_result| +------+----------+ | 10| 10,49| | 11| null| | 12| null| | 13| null| | 14| null| | 0| null| | 1| 1,49| | 2| 2,49| | 3| 3,49| | 4| 4,49| | 5| 5,49| | 6| 6,49| | 7| 7,49| | 8| 8,49| | 9| 9,49| | 10| 10,49| | 11| null| | 12| null| | 13| null| | 14| null| +------+----------+ only showing top 20 rows Query made progress: { "id" : "d4c76196-c874-4a09-ae2d-832c03a70ffb", "runId" : "0317a974-930f-47ae-99d8-3fa582383a65", "name" : null, "timestamp" : "2019-03-27T12:04:00.000Z", "batchId" : 8, "numInputRows" : 1000, "inputRowsPerSecond" : 100.0, "processedRowsPerSecond" : 1436.7816091954023, "durationMs" : { "addBatch" : 543, "getBatch" : 23, "getOffset" : 0, "queryPlanning" : 34, "triggerExecution" : 696, "walCommit" : 93 }, "stateOperators" : [ ], "sources" : [ { "description" : "RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]", "startOffset" : 118, "endOffset" : 128, "numInputRows" : 1000, "inputRowsPerSecond" : 100.0, "processedRowsPerSecond" : 1436.7816091954023 } ], "sink" : { "description" : "org.apache.spark.sql.execution.streaming.ConsoleSinkProvider@3b5afcc" } } the value is :62 Trigger accumulator value: 9 Load count accumulator value: 10 ------------------------------------------- Batch: 9 ------------------------------------------- +------+----------+ |int_id|job_result| +------+----------+ | 5| 5,62| | 6| 6,62| | 7| 7,62| | 8| 8,62| | 9| 9,62| | 10| 10,62| | 11| null| | 12| null| | 13| null| | 14| null| | 0| null| | 1| 1,62| | 2| 2,62| | 3| 3,62| | 4| 4,62| | 5| 5,62| | 6| 6,62| | 7| 7,62| | 8| 8,62| | 9| 9,62| +------+----------+ only showing top 20 rows Query made progress: { "id" : "d4c76196-c874-4a09-ae2d-832c03a70ffb", "runId" : "0317a974-930f-47ae-99d8-3fa582383a65", "name" : null, "timestamp" : "2019-03-27T12:04:10.001Z", "batchId" : 9, "numInputRows" : 1000, "inputRowsPerSecond" : 99.99000099990002, "processedRowsPerSecond" : 1519.756838905775, "durationMs" : { "addBatch" : 459, "getBatch" : 21, "getOffset" : 0, "queryPlanning" : 45, "triggerExecution" : 658, "walCommit" : 130 }, "stateOperators" : [ ], "sources" : [ { "description" : "RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]", "startOffset" : 128, "endOffset" : 138, "numInputRows" : 1000, "inputRowsPerSecond" : 99.99000099990002, "processedRowsPerSecond" : 1519.756838905775 } ], "sink" : { "description" : "org.apache.spark.sql.execution.streaming.ConsoleSinkProvider@3b5afcc" } } the value is :58 Trigger accumulator value: 10 Load count accumulator value: 11 ------------------------------------------- Batch: 10 ------------------------------------------- +------+----------+ |int_id|job_result| +------+----------+ | 0| null| | 1| 1,58| | 2| 2,58| | 3| 3,58| | 4| 4,58| | 5| 5,58| | 6| 6,58| | 7| 7,58| | 8| 8,58| | 9| 9,58| | 10| 10,58| | 11| null| | 12| null| | 13| null| | 14| null| | 0| null| | 1| 1,58| | 2| 2,58| | 3| 3,58| | 4| 4,58| +------+----------+ only showing top 20 rows Query made progress: { "id" : "d4c76196-c874-4a09-ae2d-832c03a70ffb", "runId" : "0317a974-930f-47ae-99d8-3fa582383a65", "name" : null, "timestamp" : "2019-03-27T12:04:20.000Z", "batchId" : 10, "numInputRows" : 1000, "inputRowsPerSecond" : 100.0100010001, "processedRowsPerSecond" : 1577.2870662460568, "durationMs" : { "addBatch" : 481, "getBatch" : 21, "getOffset" : 0, "queryPlanning" : 32, "triggerExecution" : 633, "walCommit" : 98 }, "stateOperators" : [ ], "sources" : [ { "description" : "RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]", "startOffset" : 138, "endOffset" : 148, "numInputRows" : 1000, "inputRowsPerSecond" : 100.0100010001, "processedRowsPerSecond" : 1577.2870662460568 } ], "sink" : { "description" : "org.apache.spark.sql.execution.streaming.ConsoleSinkProvider@3b5afcc" } } the value is :42 Trigger accumulator value: 11 Load count accumulator value: 12 ------------------------------------------- Batch: 11 ------------------------------------------- +------+----------+ |int_id|job_result| +------+----------+ | 10| 10,42| | 11| null| | 12| null| | 13| null| | 14| null| | 0| null| | 1| 1,42| | 2| 2,42| | 3| 3,42| | 4| 4,42| | 5| 5,42| | 6| 6,42| | 7| 7,42| | 8| 8,42| | 9| 9,42| | 10| 10,42| | 11| null| | 12| null| | 13| null| | 14| null| +------+----------+ only showing top 20 rows Query made progress: { "id" : "d4c76196-c874-4a09-ae2d-832c03a70ffb", "runId" : "0317a974-930f-47ae-99d8-3fa582383a65", "name" : null, "timestamp" : "2019-03-27T12:04:30.000Z", "batchId" : 11, "numInputRows" : 1000, "inputRowsPerSecond" : 100.0, "processedRowsPerSecond" : 1694.9152542372883, "durationMs" : { "addBatch" : 415, "getBatch" : 18, "getOffset" : 0, "queryPlanning" : 38, "triggerExecution" : 590, "walCommit" : 118 }, "stateOperators" : [ ], "sources" : [ { "description" : "RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]", "startOffset" : 148, "endOffset" : 158, "numInputRows" : 1000, "inputRowsPerSecond" : 100.0, "processedRowsPerSecond" : 1694.9152542372883 } ], "sink" : { "description" : "org.apache.spark.sql.execution.streaming.ConsoleSinkProvider@3b5afcc" } } the value is :29 Trigger accumulator value: 12 Load count accumulator value: 13 ------------------------------------------- Batch: 12 ------------------------------------------- +------+----------+ |int_id|job_result| +------+----------+ | 5| 5,29| | 6| 6,29| | 7| 7,29| | 8| 8,29| | 9| 9,29| | 10| 10,29| | 11| null| | 12| null| | 13| null| | 14| null| | 0| null| | 1| 1,29| | 2| 2,29| | 3| 3,29| | 4| 4,29| | 5| 5,29| | 6| 6,29| | 7| 7,29| | 8| 8,29| | 9| 9,29| +------+----------+ only showing top 20 rows Query made progress: { "id" : "d4c76196-c874-4a09-ae2d-832c03a70ffb", "runId" : "0317a974-930f-47ae-99d8-3fa582383a65", "name" : null, "timestamp" : "2019-03-27T12:04:40.000Z", "batchId" : 12, "numInputRows" : 1000, "inputRowsPerSecond" : 100.0, "processedRowsPerSecond" : 1406.4697609001407, "durationMs" : { "addBatch" : 479, "getBatch" : 25, "getOffset" : 0, "queryPlanning" : 34, "triggerExecution" : 711, "walCommit" : 171 }, "stateOperators" : [ ], "sources" : [ { "description" : "RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]", "startOffset" : 158, "endOffset" : 168, "numInputRows" : 1000, "inputRowsPerSecond" : 100.0, "processedRowsPerSecond" : 1406.4697609001407 } ], "sink" : { "description" : "org.apache.spark.sql.execution.streaming.ConsoleSinkProvider@3b5afcc" } } the value is :11 Trigger accumulator value: 13 Load count accumulator value: 14
失败测试结果：
所有类如下：
package com.dx.test; import java.io.Serializable; import java.util.HashMap; import java.util.Map; import java.util.Random; import java.util.concurrent.TimeUnit; import org.apache.log4j.Level; import org.apache.log4j.LogManager; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.api.java.function.MapFunction; import org.apache.spark.broadcast.Broadcast; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.RowFactory; import org.apache.spark.sql.SparkSession; import org.apache.spark.sql.functions; import org.apache.spark.sql.api.java.UDF1; import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder; import org.apache.spark.sql.catalyst.encoders.RowEncoder; import org.apache.spark.sql.streaming.OutputMode; import org.apache.spark.sql.streaming.StreamingQueryException; import org.apache.spark.sql.streaming.StreamingQueryListener; import org.apache.spark.sql.streaming.Trigger; import org.apache.spark.sql.types.DataTypes; import org.apache.spark.sql.types.StructType; import org.apache.spark.util.LongAccumulator; public class BroadcastTest2 { public static void main(String[] args) { LogManager.getLogger("org.apache.spark.sql.execution.streaming.RateSourceProvider").setLevel(Level.ERROR); LogManager.getLogger("org.apache.spark").setLevel(Level.ERROR); LogManager.getLogger("org.apache.spark.launcher.OutputRedirector").setLevel(Level.ERROR); // 确定是yarn方式运行。 SparkSession spark = SparkSession.builder().master("yarn").appName("test_broadcast_app").getOrCreate(); LongAccumulator triggerAccumulator = spark.sparkContext().longAccumulator("triggerAccumulator"); LongAccumulator loadCountAccumulator = spark.sparkContext().longAccumulator("loadCountAccumulator"); LoadResourceManager.load(spark, loadCountAccumulator); spark.streams().addListener( new MyStreamingQueryListener(spark, triggerAccumulator, loadCountAccumulator)); Dataset<Row> sourceDataset = spark.readStream().format("rate").option("rowsPerSecond", 100).load(); UDF1<Long, Long> long_fomat_func = new UDF1<Long, Long>() { private static final long serialVersionUID = 1L; public Long call(final Long value) throws Exception { return value % 15; } }; spark.udf().register("long_fomat_func", long_fomat_func, DataTypes.LongType); sourceDataset = sourceDataset .withColumn("int_id", functions.callUDF("long_fomat_func", functions.col("value"))); sourceDataset.printSchema(); StructType resulStructType = new StructType(); resulStructType = resulStructType.add("int_id", DataTypes.StringType, false); resulStructType = resulStructType.add("job_result", DataTypes.StringType, true); ExpressionEncoder<Row> resultEncoder = RowEncoder.apply(resulStructType); sourceDataset = sourceDataset.map(new MapFunction<Row, Row>() { private static final long serialVersionUID = 1L; @Override public Row call(Row row) throws Exception { int int_id_idx = row.schema().fieldIndex("int_id"); Object int_idObject = row.get(int_id_idx); String int_id = int_idObject.toString(); Map<String, String> resources = LoadResourceManager.get().getValue(); // 可能会涉及到当跨executor的情况下，依然会出现innerMap.size()返回的值为0的情况. if (resources.size() == 0) { throw new RuntimeException("the resources size is zero"); } String job_result = resources.get(int_id); Object[] values = new Object[2]; values[0] = int_id; values[1] = job_result; return RowFactory.create(values); } }, resultEncoder); sourceDataset.printSchema(); sourceDataset.writeStream().format("console").outputMode(OutputMode.Append()) .trigger(Trigger.ProcessingTime(10, TimeUnit.SECONDS)).start(); try { spark.streams().awaitAnyTermination(); } catch (StreamingQueryException e) { e.printStackTrace(); } } } class LoadResourceManager implements Serializable{ private static final long serialVersionUID = 7896720904164793792L; private static volatile Broadcast<Map<String, String>> broadcast = null; public static Broadcast<Map<String, String>> get() { return broadcast; } public static void unpersist() { broadcast.unpersist(true);
// broadcast.destory();不能调用该方法，否则会抛出：Attempted to use Broadcast(x) after it was destroyed
} public static void load(SparkSession spark, LongAccumulator loadCountAccumulator) { loadCountAccumulator.add(1); int val = new Random().nextInt(100); // 这里可以添加时间判断是否重新加载 Map<String, String> innerMap = new HashMap<String, String>(10); innerMap.put("1", "1," + val); innerMap.put("2", "2," + val); innerMap.put("3", "3," + val); innerMap.put("4", "4," + val); innerMap.put("5", "5," + val); innerMap.put("6", "6," + val); innerMap.put("7", "7," + val); innerMap.put("8", "8," + val); innerMap.put("9", "9," + val); innerMap.put("10", "10," + val); System.out.println("the value is :" + val); JavaSparkContext jsc = JavaSparkContext.fromSparkContext(spark.sparkContext()); broadcast = jsc.broadcast(innerMap); } } class MyStreamingQueryListener extends StreamingQueryListener { private SparkSession spark = null; private LongAccumulator triggerAccumulator = null; private LongAccumulator loadCountAccumulator = null; public MyStreamingQueryListener() { } public MyStreamingQueryListener(SparkSession spark, LongAccumulator triggerAccumulator, LongAccumulator loadCountAccumulator) { this.spark = spark; this.triggerAccumulator = triggerAccumulator; this.loadCountAccumulator = loadCountAccumulator; } @Override public void onQueryStarted(QueryStartedEvent queryStarted) { System.out.println("Query started: " + queryStarted.id()); } @Override public void onQueryTerminated(QueryTerminatedEvent queryTerminated) { System.out.println("Query terminated: " + queryTerminated.id()); } @Override public void onQueryProgress(QueryProgressEvent queryProgress) { System.out.println("Query made progress: " + queryProgress.progress()); // sparkSession.sql("select * from " + // queryProgress.progress().name()).show(); triggerAccumulator.add(1); LoadResourceManager.unpersist(); LoadResourceManager.load(spark, loadCountAccumulator); System.out.println("Trigger accumulator value: " + triggerAccumulator.value()); System.out.println("Load count accumulator value: " + loadCountAccumulator.value()); } }
测试打印如下：
[Opened /usr/java/jdk1.8.0_152/jre/lib/charsets.jar] 19/03/27 20:25:41 INFO impl.YarnClientImpl: Submitted application application_1548381669007_0777 19/03/27 20:26:19 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@42d9e8d2{/metrics/json,null,AVAILABLE,@Spark} the value is :1 19/03/27 20:26:38 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@7308c820{/SQL,null,AVAILABLE,@Spark} 19/03/27 20:26:38 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@640c216b{/SQL/json,null,AVAILABLE,@Spark} 19/03/27 20:26:38 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@28e94c2{/SQL/execution,null,AVAILABLE,@Spark} 19/03/27 20:26:38 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@482a58c7{/SQL/execution/json,null,AVAILABLE,@Spark} 19/03/27 20:26:38 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@2d4fb0d8{/static/sql,null,AVAILABLE,@Spark} 19/03/27 20:26:39 INFO util.Version: Elasticsearch Hadoop v6.4.2 [54a631a014] root |-- timestamp: timestamp (nullable = true) |-- value: long (nullable = true) |-- int_id: long (nullable = true) root |-- int_id: string (nullable = false) |-- job_result: string (nullable = true) Query started: 53290edb-bfca-4de3-9686-719ffece7fc0 ------------------------------------------- Batch: 0 ------------------------------------------- +------+----------+ |int_id|job_result| +------+----------+ +------+----------+ Query made progress: { "id" : "53290edb-bfca-4de3-9686-719ffece7fc0", "runId" : "cf449470-f377-4270-b5ea-25230c49add2", "name" : null, "timestamp" : "2019-03-27T12:26:41.980Z", "batchId" : 0, "numInputRows" : 0, "processedRowsPerSecond" : 0.0, "durationMs" : { "addBatch" : 2383, "getBatch" : 38, "getOffset" : 0, "queryPlanning" : 554, "triggerExecution" : 3170, "walCommit" : 140 }, "stateOperators" : [ ], "sources" : [ { "description" : "RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]", "startOffset" : null, "endOffset" : 0, "numInputRows" : 0, "processedRowsPerSecond" : 0.0 } ], "sink" : { "description" : "org.apache.spark.sql.execution.streaming.ConsoleSinkProvider@511c2d85" } } the value is :43 Trigger accumulator value: 1 Load count accumulator value: 2 19/03/27 20:27:00 ERROR scheduler.TaskSetManager: Task 0 in stage 0.0 failed 4 times; aborting job 19/03/27 20:27:01 ERROR v2.WriteToDataSourceV2Exec: Data source writer org.apache.spark.sql.execution.streaming.sources.MicroBatchWriter@e074fee is aborting. 19/03/27 20:27:01 ERROR v2.WriteToDataSourceV2Exec: Data source writer org.apache.spark.sql.execution.streaming.sources.MicroBatchWriter@e074fee aborted. 19/03/27 20:27:01 ERROR streaming.MicroBatchExecution: Query [id = 53290edb-bfca-4de3-9686-719ffece7fc0, runId = cf449470-f377-4270-b5ea-25230c49add2] terminated with error org.apache.spark.SparkException: Writing job aborted. at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.doExecute(WriteToDataSourceV2.scala:112) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) at org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:247) at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:294) at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3273) at org.apache.spark.sql.Dataset$$anonfun$collect$1.apply(Dataset.scala:2722) at org.apache.spark.sql.Dataset$$anonfun$collect$1.apply(Dataset.scala:2722) at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3254) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3253) at org.apache.spark.sql.Dataset.collect(Dataset.scala:2722) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3$$anonfun$apply$16.apply(MicroBatchExecution.scala:478) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3.apply(MicroBatchExecution.scala:473) at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271) at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58) at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:472) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:133) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121) at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271) at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:121) at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56) at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:117) at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279) at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189) Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 12, vm10.60.0.8.com.cn, executor 6): java.lang.NullPointerException at com.dx.test.BroadcastTest$2.call(BroadcastTest.java:74) at com.dx.test.BroadcastTest$2.call(BroadcastTest.java:1) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.mapelements_doConsume_0$(Unknown Source) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.deserializetoobject_doConsume_0$(Unknown Source) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614) at scala.collection.Iterator$class.foreach(Iterator.scala:893) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.foreach(WholeStageCodegenExec.scala:612) at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$$anonfun$run$3.apply(WriteToDataSourceV2.scala:133) at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$$anonfun$run$3.apply(WriteToDataSourceV2.scala:132) at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1415) at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.run(WriteToDataSourceV2.scala:138) at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec$$anonfun$2.apply(WriteToDataSourceV2.scala:79) at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec$$anonfun$2.apply(WriteToDataSourceV2.scala:78) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at org.apache.spark.scheduler.Task.run(Task.scala:109) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1609) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1597) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1596) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1596) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831) at scala.Option.foreach(Option.scala:257) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1830) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1779) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1768) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034) at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.doExecute(WriteToDataSourceV2.scala:82) ... 31 more Caused by: java.lang.NullPointerException at com.dx.test.BroadcastTest$2.call(BroadcastTest.java:74) at com.dx.test.BroadcastTest$2.call(BroadcastTest.java:1) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.mapelements_doConsume_0$(Unknown Source) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.deserializetoobject_doConsume_0$(Unknown Source) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614) at scala.collection.Iterator$class.foreach(Iterator.scala:893) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.foreach(WholeStageCodegenExec.scala:612) at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$$anonfun$run$3.apply(WriteToDataSourceV2.scala:133) at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$$anonfun$run$3.apply(WriteToDataSourceV2.scala:132) at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1415) at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.run(WriteToDataSourceV2.scala:138) at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec$$anonfun$2.apply(WriteToDataSourceV2.scala:79) at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec$$anonfun$2.apply(WriteToDataSourceV2.scala:78) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at org.apache.spark.scheduler.Task.run(Task.scala:109) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) org.apache.spark.sql.streaming.StreamingQueryException: Writing job aborted. === Streaming Query === Identifier: [id = 53290edb-bfca-4de3-9686-719ffece7fc0, runId = cf449470-f377-4270-b5ea-25230c49add2] Current Committed Offsets: {RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]: 0} Current Available Offsets: {RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64]: 8} Current State: ACTIVE Thread State: RUNNABLE Logical Plan: SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 0, int_id), StringType), true, false) AS int_id#14, if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 1, job_result), StringType), true, false) AS job_result#15] +- MapElements com.dx.test.BroadcastTest$2@3a907688, interface org.apache.spark.sql.Row, [StructField(timestamp,TimestampType,true), StructField(value,LongType,true), StructField(int_id,LongType,true)], obj#13: org.apache.spark.sql.Row +- DeserializeToObject createexternalrow(staticinvoke(class org.apache.spark.sql.catalyst.util.DateTimeUtils$, ObjectType(class java.sql.Timestamp), toJavaTimestamp, timestamp#2, true, false), value#3L, int_id#6L, StructField(timestamp,TimestampType,true), StructField(value,LongType,true), StructField(int_id,LongType,true)), obj#12: org.apache.spark.sql.Row +- Project [timestamp#2, value#3L, UDF:long_fomat_func(value#3L) AS int_id#6L] +- StreamingExecutionRelation RateSource[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=64], [timestamp#2, value#3L] at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:295) at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189) Caused by: org.apache.spark.SparkException: Writing job aborted. at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.doExecute(WriteToDataSourceV2.scala:112) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) at org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:247) at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:294) at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3273) at org.apache.spark.sql.Dataset$$anonfun$collect$1.apply(Dataset.scala:2722) at org.apache.spark.sql.Dataset$$anonfun$collect$1.apply(Dataset.scala:2722) at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3254) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3253) at org.apache.spark.sql.Dataset.collect(Dataset.scala:2722) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3$$anonfun$apply$16.apply(MicroBatchExecution.scala:478) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3.apply(MicroBatchExecution.scala:473) at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271) at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58) at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:472) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:133) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121) at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271) at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:121) at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56) at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:117) at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279) ... 1 more Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 12, vm10.60.0.8.com.cn, executor 6): java.lang.NullPointerException at com.dx.test.BroadcastTest$2.call(BroadcastTest.java:74) at com.dx.test.BroadcastTest$2.call(BroadcastTest.java:1) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.mapelements_doConsume_0$(Unknown Source) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.deserializetoobject_doConsume_0$(Unknown Source) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614) at scala.collection.Iterator$class.foreach(Iterator.scala:893) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.foreach(WholeStageCodegenExec.scala:612) at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$$anonfun$run$3.apply(WriteToDataSourceV2.scala:133) at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$$anonfun$run$3.apply(WriteToDataSourceV2.scala:132) at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1415) at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.run(WriteToDataSourceV2.scala:138) at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec$$anonfun$2.apply(WriteToDataSourceV2.scala:79) at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec$$anonfun$2.apply(WriteToDataSourceV2.scala:78) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at org.apache.spark.scheduler.Task.run(Task.scala:109) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1609) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1597) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1596) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1596) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831) at scala.Option.foreach(Option.scala:257) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1830) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1779) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1768) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034) at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.doExecute(WriteToDataSourceV2.scala:82) ... 31 more Caused by: java.lang.NullPointerException at com.dx.test.BroadcastTest$2.call(BroadcastTest.java:74) at com.dx.test.BroadcastTest$2.call(BroadcastTest.java:1) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.mapelements_doConsume_0$(Unknown Source) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.deserializetoobject_doConsume_0$(Unknown Source) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614) at scala.collection.Iterator$class.foreach(Iterator.scala:893) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.foreach(WholeStageCodegenExec.scala:612) at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$$anonfun$run$3.apply(WriteToDataSourceV2.scala:133) at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$$anonfun$run$3.apply(WriteToDataSourceV2.scala:132) at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1415) at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.run(WriteToDataSourceV2.scala:138) at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec$$anonfun$2.apply(WriteToDataSourceV2.scala:79) at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec$$anonfun$2.apply(WriteToDataSourceV2.scala:78) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at org.apache.spark.scheduler.Task.run(Task.scala:109) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) Query terminated: 53290edb-bfca-4de3-9686-719ffece7fc0 19/03/27 20:27:01 INFO server.AbstractConnector: Stopped Spark@af78c87{HTTP/1.1,[http/1.1]}{0.0.0.0:4040} bash-4.1$
两个测试主要不同之处对比：
关于这两个类测试结果不同，希望读者可以发表自己的看法：为什么使用静态方法就测试失败呢？
基础才是编程人员应该深入研究的问题，比如:
1）List/Set/Map内部组成原理|区别
2）mysql索引存储结构&如何调优/b-tree特点、计算复杂度及影响复杂度的因素。。。
3）JVM运行组成与原理及调优
4）Java类加载器运行原理
5）Java中GC过程原理|使用的回收算法原理
6）Redis中hash一致性实现及与hash其他区别
7）Java多线程、线程池开发、管理Lock与Synchroined区别
8）Spring IOC/AOP 原理；加载过程的。。。
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