ibzib commented on a change in pull request #13743:
URL: https://github.com/apache/beam/pull/13743#discussion_r567279548
##########
File path:
runners/spark/src/main/java/org/apache/beam/runners/spark/SparkPipelineRunner.java
##########
@@ -123,10 +134,33 @@ public PortablePipelineResult run(RunnerApi.Pipeline
pipeline, JobInfo jobInfo)
"Will stage {} files. (Enable logging at DEBUG level to see which
files will be staged.)",
pipelineOptions.getFilesToStage().size());
LOG.debug("Staging files: {}", pipelineOptions.getFilesToStage());
-
PortablePipelineResult result;
final JavaSparkContext jsc =
SparkContextFactory.getSparkContext(pipelineOptions);
+ EventLoggingListener eventLoggingListener = null;
+ if (pipelineOptions.getEventLogEnabled()) {
+ eventLoggingListener =
+ new EventLoggingListener(
+ jobInfo.jobId(),
+ scala.Option.apply(jobInfo.jobName()),
+ new URI(pipelineOptions.getSparkHistoryDir()),
+ jsc.getConf(),
+ jsc.hadoopConfiguration());
+ eventLoggingListener.initializeLogIfNecessary(false, false);
+ eventLoggingListener.start();
+ scala.collection.immutable.Map<String, String> logUrlMap =
+ new scala.collection.immutable.HashMap<String, String>();
+ Tuple2<String, String>[] sparkMasters =
jsc.getConf().getAllWithPrefix("spark.master");
+ Tuple2<String, String>[] sparkExecutors =
jsc.getConf().getAllWithPrefix("spark.executor.id");
+ for (int i = 0; i < sparkMasters.length; i++) {
+ eventLoggingListener.onExecutorAdded(
+ new SparkListenerExecutorAdded(
+ Instant.now().getMillis(),
+ sparkExecutors[i]._2(),
Review comment:
This assumes the number of spark.master is the same as the number of
spark.executor.id. Which I don't think is a safe assumption.
In the Spark execution model, usually there is exactly one spark.master and
possibly many executors. I'm not sure if it's ever possible for there to be
multiple masters.
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