CODEWITHSUNDEEP
apache-sparkrddcheckpointing
Jatin Ganhotra
Jatin Ganhotra

Reputation: 7035

Apache Spark - accessing internal data on RDDs?

I started doing the amp-camp 5 exercises. I tried the following 2 scenarios:

Scenario #1 

val pagecounts = sc.textFile("data/pagecounts")
pagecounts.checkpoint
pagecounts.count

Scenario #2 

val pagecounts = sc.textFile("data/pagecounts")
pagecounts.count

The total time show in the Spark shell Application UI was different for both scenarios.
Scenario #1 took 0.5 seconds, while scenario #2 took only 0.2 s

In scenario #1, checkpoint command does nothing, it's neither a transformation nor an action. It's saying that once the RDD materializes after an action, go ahead and save to disk. Am I missing something here?

Questions:

  1. I understand that scenario #1 is taking more time, because the RDD is check-pointed (written to disk). Is there a way I can know the time taken for checkpoint, from the total time?
    The Spark shell Application UI shows the following - Scheduler delay, Task Deserialization time, GC time, Result serialization time, getting result time. But, doesn't show the breakdown for checkpointing.

  2. Is there a way to access the above metrics e.g. scheduler delay, GC time and save them programmatically? I want to log some of the above metrics for every action invoked on an RDD.

  3. How can I programmatically access the following information:

    • Size of an RDD, when persisted to disk on checkpointing?
    • How much percentage of an RDD is in memory currently?
    • Overall time taken for computing an RDD?

Please let me know if you need more information.

Upvotes: 3

Views: 331

Answers (1)

mehmetminanc
mehmetminanc

Reputation: 1379

Spark REST API provides almost all you are asking for.

Some examples;

How much percentage of an RDD is in memory currently?

GET /api/v1/applications/[app-id]/storage/rdd/0

will be responded with:

{
  "id" : 0,
  "name" : "ParallelCollectionRDD",
  "numPartitions" : 2,
  "numCachedPartitions" : 2,
  "storageLevel" : "Memory Deserialized 1x Replicated",
  "memoryUsed" : 28000032,
  "diskUsed" : 0,
  "dataDistribution" : [ {
    "address" : "localhost:54984",
    "memoryUsed" : 28000032,
    "memoryRemaining" : 527755733,
    "diskUsed" : 0
  } ],
  "partitions" : [ {
    "blockName" : "rdd_0_0",
    "storageLevel" : "Memory Deserialized 1x Replicated",
    "memoryUsed" : 14000016,
    "diskUsed" : 0,
    "executors" : [ "localhost:54984" ]
  }, {
    "blockName" : "rdd_0_1",
    "storageLevel" : "Memory Deserialized 1x Replicated",
    "memoryUsed" : 14000016,
    "diskUsed" : 0,
    "executors" : [ "localhost:54984" ]
  } ]
}

Overall time taken for computing an RDD?

To compute an RDD is also called either Job, stage, or attempt. GET /applications/[app-id]/stages/[stage-id]/[stage-attempt-id]/taskSummary

will be responded with:

{
  "quantiles" : [ 0.05, 0.25, 0.5, 0.75, 0.95 ],
  "executorDeserializeTime" : [ 2.0, 2.0, 2.0, 2.0, 2.0 ],
  "executorRunTime" : [ 3.0, 3.0, 4.0, 4.0, 4.0 ],
  "resultSize" : [ 1457.0, 1457.0, 1457.0, 1457.0, 1457.0 ],
  "jvmGcTime" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
  "resultSerializationTime" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
  "memoryBytesSpilled" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
  "diskBytesSpilled" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
  "shuffleReadMetrics" : {
    "readBytes" : [ 340.0, 340.0, 342.0, 342.0, 342.0 ],
    "readRecords" : [ 10.0, 10.0, 10.0, 10.0, 10.0 ],
    "remoteBlocksFetched" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
    "localBlocksFetched" : [ 2.0, 2.0, 2.0, 2.0, 2.0 ],
    "fetchWaitTime" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
    "remoteBytesRead" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
    "totalBlocksFetched" : [ 2.0, 2.0, 2.0, 2.0, 2.0 ]
  }
}

Your question is too broad, hence I will not respond to all. I believe everything spark has to reflect is reflected with the REST API.

Upvotes: 2

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