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[SPARK-5484][GraphX] Periodically do checkpoint in Pregel #15125

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[SPARK-5484][GraphX] Periodically do checkpoint in Pregel #15125

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3834981
test
Sep 16, 2016
166fd6d
period do checkpoint in pregel
Sep 16, 2016
b119e4a
remove unused code
Sep 16, 2016
d183a7c
fix mima
Sep 16, 2016
352dcb2
fix mima
Sep 16, 2016
ad82e45
fix scala style fail
Sep 16, 2016
e786838
rebase upstream
Jan 17, 2017
a25d00c
code rebase
Feb 11, 2017
38e6238
Update a couple of test suite package names, and enhance the
Feb 14, 2017
f2efef6
Merge pull request #1 from VideoAmp/spark-5484-periodic_pregel_checkp…
dding3 Feb 16, 2017
194dc27
update checkpoint interval and some comments
Feb 17, 2017
9d7e796
add config value to controll checkpoint interval in pergel
Feb 17, 2017
dae94aa
document spark.graphx.pregel.checkpointInterval into graphx-programmi…
Feb 19, 2017
dd6c366
remove details of checkpointer in pregel implementation sketch
Feb 19, 2017
2639eb1
remove unused variable in pregel sketch implementation
Feb 19, 2017
11bc349
small changes for documentation
Feb 23, 2017
9a6fd1f
mv PeriodicRDDCheckpointer to rdd.util and explain why use cache in P…
Apr 19, 2017
5015b44
fix scalastyle error
Apr 20, 2017
24d4ad6
turn off checkpoint graph and messages in pregel by default
Apr 22, 2017
ec62659
recommend a good value in checkpoint interval in pregel
Apr 23, 2017
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14 changes: 12 additions & 2 deletions ...ark/mllib/impl/PeriodicCheckpointer.scala → ...che/spark/util/PeriodicCheckpointer.scala
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Expand Up @@ -15,7 +15,7 @@
* limitations under the License.
*/

package org.apache.spark.mllib.impl
package org.apache.spark.util

import scala.collection.mutable

Expand Down Expand Up @@ -58,7 +58,7 @@ import org.apache.spark.storage.StorageLevel
* @param sc SparkContext for the Datasets given to this checkpointer
* @tparam T Dataset type, such as RDD[Double]
*/
private[mllib] abstract class PeriodicCheckpointer[T](
private[spark] abstract class PeriodicCheckpointer[T](
val checkpointInterval: Int,
val sc: SparkContext) extends Logging {

Expand Down Expand Up @@ -127,6 +127,16 @@ private[mllib] abstract class PeriodicCheckpointer[T](
/** Get list of checkpoint files for this given Dataset */
protected def getCheckpointFiles(data: T): Iterable[String]

/**
* Call this to unpersist the Dataset.
*/
def unpersistDataSet(): Unit = {
while (persistedQueue.nonEmpty) {
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@felixcheung felixcheung Apr 19, 2017

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I don't know if it's the goal but this isn't thread-safe?

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@mallman mallman Apr 19, 2017

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I would agree with you, that this is not thread safe. Is that a concern?

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@felixcheung felixcheung Apr 19, 2017

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with the limited internal only use, it should be ok

val dataToUnpersist = persistedQueue.dequeue()
unpersist(dataToUnpersist)
}
}

/**
* Call this at the end to delete any remaining checkpoint files.
*/
Expand Down
2 changes: 1 addition & 1 deletion .../mllib/impl/PeriodicRDDCheckpointer.scala → .../spark/util/PeriodicRDDCheckpointer.scala
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Expand Up @@ -15,7 +15,7 @@
* limitations under the License.
*/

package org.apache.spark.mllib.impl
package org.apache.spark.util
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@felixcheung felixcheung Apr 19, 2017 •
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it's PeriodicRDDCheckpointer, shouldn't this be in the org.apache.spark.rdd.util namespace?


import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
Expand Down
8 changes: 3 additions & 5 deletions ...b/impl/PeriodicRDDCheckpointerSuite.scala → ...k/util/PeriodicRDDCheckpointerSuite.scala
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Expand Up @@ -15,18 +15,16 @@
* limitations under the License.
*/

package org.apache.spark.mllib.impl
package org.apache.spark.util

import org.apache.hadoop.fs.Path

import org.apache.spark.{SparkContext, SparkFunSuite}
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.{SharedSparkContext, SparkContext, SparkFunSuite}
import org.apache.spark.rdd.RDD
import org.apache.spark.storage.StorageLevel
import org.apache.spark.util.Utils


class PeriodicRDDCheckpointerSuite extends SparkFunSuite with MLlibTestSparkContext {
class PeriodicRDDCheckpointerSuite extends SparkFunSuite with SharedSparkContext {

import PeriodicRDDCheckpointerSuite._

Expand Down
14 changes: 14 additions & 0 deletions docs/configuration.md
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Expand Up @@ -2115,6 +2115,20 @@ showDF(properties, numRows = 200, truncate = FALSE)

</table>

### GraphX

<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>spark.graphx.pregel.checkpointInterval</code></td>
<td>10</td>
<td>
Checkpoint interval for graph and message in Pregel. It used to avoid stackOverflowError due to long lineage chains
after lots of iterations. The checkpoint can be disabled by set as -1.
</td>
</tr>
</table>

### Deploy

<table class="table">
Expand Down
15 changes: 9 additions & 6 deletions docs/graphx-programming-guide.md
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Expand Up @@ -708,7 +708,9 @@ messages remaining.
> messaging function. These constraints allow additional optimization within GraphX.

The following is the type signature of the [Pregel operator][GraphOps.pregel] as well as a *sketch*
of its implementation (note calls to graph.cache have been removed):
of its implementation (note: to avoid stackOverflowError due to long lineage chains, graph and
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@mallman mallman Feb 20, 2017

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I think @viirya is suggesting we remove all references to checkpointing from this implementation sketch. @viirya, correct me if I'm wrong. I suggest reverting these changes to graphx-programming-guide.md. Just add a brief note that GraphX periodically checkpoints the graph and message lineages, and this checkpoint interval can be configured with the spark.graphx.pregel.checkpointInterval Spark configuration property.

@viirya @felixcheung Should we document this config property in the Spark configuration document as well?

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@mallman mallman Feb 20, 2017

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Also, we should document the default value and that checkpointing can be disabled by setting this config property to -1.

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@dding3 dding3 Feb 20, 2017

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OK. I am thinking in the original graphx-programming-guide, only cache is called. After we add do checkpointing and give notes we do that in the sketch, do we need to show the information in the sketch. If there is an agreement on remove all references to checkpoint, I will revert the changes.

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@felixcheung felixcheung Feb 21, 2017

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I guess it would help reviewers to understand if you could explain those changes in the impl sketch is required to use checkpoint

I'd agree we should probably document the config property

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@mallman mallman Feb 21, 2017

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I guess it would help reviewers to understand if you could explain those changes in the impl sketch is required to use checkpoint

Sorry @felixcheung, I don't understand what you mean here. Can you elaborate?

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@dding3 dding3 Feb 21, 2017

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About document this config property in the Spark configuration document. Is it OK if I add a new Graphx section to include the config, or just add the config under existing section, say "execution behavor"?

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@felixcheung felixcheung Feb 24, 2017

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I mean if there is a reason to have graphCheckpointer in the example, if you could explain it, it would make it easier for us to review those.

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@mallman mallman Feb 28, 2017

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Personally, I don't think we need to include the checkpointer in the implementation sketch. I think it's more an implementation detail than an essential part of the algorithm. I think it's enough to simply document that GraphX's implementation of pregel includes checkpointing to avoid unbounded RDD lineages.

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@dding3 dding3 Feb 28, 2017

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OK. Have removed references to checkpoint in the sketch and documented the config property in the spark configuration document in the latest update.

messages are periodically checkpoint and the checkpoint interval is set by
"spark.graphx.pregel.checkpointInterval", it can be disable by set as -1):

{% highlight scala %}
class GraphOps[VD, ED] {
Expand All @@ -722,8 +724,9 @@ class GraphOps[VD, ED] {
: Graph[VD, ED] = {
// Receive the initial message at each vertex
var g = mapVertices( (vid, vdata) => vprog(vid, vdata, initialMsg) ).cache()

// compute the messages
var messages = g.mapReduceTriplets(sendMsg, mergeMsg)
var messages = g.mapReduceTriplets(sendMsg, mergeMsg)
var activeMessages = messages.count()
// Loop until no messages remain or maxIterations is achieved
var i = 0
Expand All @@ -732,10 +735,10 @@ class GraphOps[VD, ED] {
g = g.joinVertices(messages)(vprog).cache()
val oldMessages = messages
// Send new messages, skipping edges where neither side received a message. We must cache
// messages so it can be materialized on the next line, allowing us to uncache the previous
// iteration.
messages = g.mapReduceTriplets(
sendMsg, mergeMsg, Some((oldMessages, activeDirection))).cache()
// and periodic checkpoint messages so it can be materialized on the next line, and avoid
// to have a long lineage chain.
messages = GraphXUtils.mapReduceTriplets(
g, sendMsg, mergeMsg, Some((oldMessages, activeDirection))).cache()
activeMessages = messages.count()
i += 1
}
Expand Down
29 changes: 23 additions & 6 deletions graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala
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Expand Up @@ -19,7 +19,10 @@ package org.apache.spark.graphx

import scala.reflect.ClassTag

import org.apache.spark.graphx.util.PeriodicGraphCheckpointer
import org.apache.spark.internal.Logging
import org.apache.spark.rdd.RDD
import org.apache.spark.util.PeriodicRDDCheckpointer

/**
* Implements a Pregel-like bulk-synchronous message-passing API.
Expand Down Expand Up @@ -122,27 +125,39 @@ object Pregel extends Logging {
require(maxIterations > 0, s"Maximum number of iterations must be greater than 0," +
s" but got ${maxIterations}")

var g = graph.mapVertices((vid, vdata) => vprog(vid, vdata, initialMsg)).cache()
val checkpointInterval = graph.vertices.sparkContext.getConf
.getInt("spark.graphx.pregel.checkpointInterval", 10)
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@viirya viirya Feb 18, 2017 •
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nit: Do we need to document this config into GraphX related document?

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@dding3 dding3 Feb 20, 2017

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I think so. Currently I add the document in graphx-programming-guide.md. But I am not sure if it's the right place, please let me know if there is a better place to add the document.

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@mallman mallman Feb 21, 2017

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I would also suggest incorporating this change into the Spark 2.2 release notes under a section for GraphX, but I don't see where these notes are maintained. The release notes for 2.1 are published at http://spark.apache.org/releases/spark-release-2-1-0.html, but I can't find them in the repo. Anybody know how these are generated or how to contribute to them? Is there another repo for this documentation?

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@felixcheung felixcheung Apr 19, 2017

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@mallman it's at https://github.com/apache/spark-website/tree/asf-site/releases/_posts

var g = graph.mapVertices((vid, vdata) => vprog(vid, vdata, initialMsg))
val graphCheckpointer = new PeriodicGraphCheckpointer[VD, ED](
checkpointInterval, graph.vertices.sparkContext)
graphCheckpointer.update(g)

// compute the messages
var messages = GraphXUtils.mapReduceTriplets(g, sendMsg, mergeMsg)
val messageCheckpointer = new PeriodicRDDCheckpointer[(VertexId, A)](
checkpointInterval, graph.vertices.sparkContext)
messageCheckpointer.update(messages.asInstanceOf[RDD[(VertexId, A)]])
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@viirya viirya Feb 10, 2017

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Do we need to update it before the loop? I think it should be enough to do the update in the loop.

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@mallman mallman Feb 10, 2017

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I agree. What do you think, @dding3?

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@mallman mallman Feb 10, 2017

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Actually, for the sake of simplicity and consistency, I'm going to suggest we keep the checkpointer update calls but remove all .cache() calls. The update calls persist the underlying data, making the calls to .cache() unnecessary.

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@dding3 dding3 Feb 12, 2017

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I think we need cache graph/messages here so they don't need to be computed again in the loop. I agree with you and I will keep the checkpointer update calls and remove all .cache calls.

var activeMessages = messages.count()

// Loop
var prevG: Graph[VD, ED] = null
var i = 0
while (activeMessages > 0 && i < maxIterations) {
// Receive the messages and update the vertices.
prevG = g
g = g.joinVertices(messages)(vprog).cache()
g = g.joinVertices(messages)(vprog)
graphCheckpointer.update(g)

val oldMessages = messages
// Send new messages, skipping edges where neither side received a message. We must cache
// messages so it can be materialized on the next line, allowing us to uncache the previous
// iteration.
// and periodic checkpoint messages so it can be materialized on the next line, and avoid
// to have a long lineage chain.
messages = GraphXUtils.mapReduceTriplets(
g, sendMsg, mergeMsg, Some((oldMessages, activeDirection))).cache()
g, sendMsg, mergeMsg, Some((oldMessages, activeDirection)))
// The call to count() materializes `messages` and the vertices of `g`. This hides oldMessages
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@felixcheung felixcheung Feb 17, 2017

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should the comment here be updated?

// (depended on by the vertices of g) and the vertices of prevG (depended on by oldMessages
// and the vertices of g).
messageCheckpointer.update(messages.asInstanceOf[RDD[(VertexId, A)]])
activeMessages = messages.count()

logInfo("Pregel finished iteration " + i)
Expand All @@ -154,7 +169,9 @@ object Pregel extends Logging {
// count the iteration
i += 1
}
messages.unpersist(blocking = false)
messageCheckpointer.unpersistDataSet()
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@mallman mallman Feb 20, 2017

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Sorry, I don't understand this change. Why do we replace

messages.unpersist(blocking = false)

with

messageCheckpointer.unpersistDataSet()

Especially because this adds a new public method to PeriodicCheckpointer that no other code has needed before.

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@dding3 dding3 Feb 20, 2017

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I think the thing is we use messageCheckpointer.update to do the cache, to make a pair, we can use it to unpersist data. Please correct me if I understand wrong.
I think it's fine to add this new method as there is already a public method to cache data in PersistQueue, we should provide a public method to clean the queue.

graphCheckpointer.deleteAllCheckpoints()
messageCheckpointer.deleteAllCheckpoints()
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@felixcheung felixcheung Feb 17, 2017

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shouldn't this be inside a finally clause to make sure checkpoint data is cleaned up even in error cases?

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@dding3 dding3 Feb 17, 2017

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I think when there is an exception during training, if we keep the checkpoints, there is a chance for user to recover from it. I checked in RandomForest/GBT in spark, looks like they only delete the checkpoints when the training successful finished.

g
} // end of apply

Expand Down
10 changes: 5 additions & 5 deletions ...llib/impl/PeriodicGraphCheckpointer.scala → ...aphx/util/PeriodicGraphCheckpointer.scala
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Expand Up @@ -15,11 +15,12 @@
* limitations under the License.
*/

package org.apache.spark.mllib.impl
package org.apache.spark.graphx.util

import org.apache.spark.SparkContext
import org.apache.spark.graphx.Graph
import org.apache.spark.storage.StorageLevel
import org.apache.spark.util.PeriodicCheckpointer


/**
Expand Down Expand Up @@ -74,9 +75,8 @@ import org.apache.spark.storage.StorageLevel
* @tparam VD Vertex descriptor type
* @tparam ED Edge descriptor type
*
* TODO: Move this out of MLlib?
*/
private[mllib] class PeriodicGraphCheckpointer[VD, ED](
private[spark] class PeriodicGraphCheckpointer[VD, ED](
checkpointInterval: Int,
sc: SparkContext)
extends PeriodicCheckpointer[Graph[VD, ED]](checkpointInterval, sc) {
Expand All @@ -87,10 +87,10 @@ private[mllib] class PeriodicGraphCheckpointer[VD, ED](

override protected def persist(data: Graph[VD, ED]): Unit = {
if (data.vertices.getStorageLevel == StorageLevel.NONE) {
data.vertices.persist()
data.vertices.cache()
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@felixcheung felixcheung Feb 17, 2017

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isn't persist better? this could potentially support different storage level later

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@mallman mallman Feb 17, 2017

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We need to use cache because persist does not honor the default storage level requested when constructing the graph. Only cache does that. It's confusing, but true. To verify this for yourself, change these values to persist and run the PeriodicGraphCheckpointerSuite tests.

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@felixcheung felixcheung Apr 19, 2017

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@mallman @dding3 it will be good to add a comment on that here

}
if (data.edges.getStorageLevel == StorageLevel.NONE) {
data.edges.persist()
data.edges.cache()
}
}

Expand Down
105 changes: 55 additions & 50 deletions ...impl/PeriodicGraphCheckpointerSuite.scala → ...util/PeriodicGraphCheckpointerSuite.scala
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Expand Up @@ -15,77 +15,81 @@
* limitations under the License.
*/

package org.apache.spark.mllib.impl
package org.apache.spark.graphx.util

import org.apache.hadoop.fs.Path

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@mallman mallman Feb 10, 2017

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Is there a reason this test suite isn't moved into the GraphX codebase?

import org.apache.spark.{SparkContext, SparkFunSuite}
import org.apache.spark.graphx.{Edge, Graph}
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.graphx.{Edge, Graph, LocalSparkContext}
import org.apache.spark.storage.StorageLevel
import org.apache.spark.util.Utils


class PeriodicGraphCheckpointerSuite extends SparkFunSuite with MLlibTestSparkContext {
class PeriodicGraphCheckpointerSuite extends SparkFunSuite with LocalSparkContext {

import PeriodicGraphCheckpointerSuite._

test("Persisting") {
var graphsToCheck = Seq.empty[GraphToCheck]

val graph1 = createGraph(sc)
val checkpointer =
new PeriodicGraphCheckpointer[Double, Double](10, graph1.vertices.sparkContext)
checkpointer.update(graph1)
graphsToCheck = graphsToCheck :+ GraphToCheck(graph1, 1)
checkPersistence(graphsToCheck, 1)

var iteration = 2
while (iteration < 9) {
val graph = createGraph(sc)
checkpointer.update(graph)
graphsToCheck = graphsToCheck :+ GraphToCheck(graph, iteration)
checkPersistence(graphsToCheck, iteration)
iteration += 1
withSpark { sc =>
val graph1 = createGraph(sc)
val checkpointer =
new PeriodicGraphCheckpointer[Double, Double](10, graph1.vertices.sparkContext)
checkpointer.update(graph1)
graphsToCheck = graphsToCheck :+ GraphToCheck(graph1, 1)
checkPersistence(graphsToCheck, 1)

var iteration = 2
while (iteration < 9) {
val graph = createGraph(sc)
checkpointer.update(graph)
graphsToCheck = graphsToCheck :+ GraphToCheck(graph, iteration)
checkPersistence(graphsToCheck, iteration)
iteration += 1
}
}
}

test("Checkpointing") {
val tempDir = Utils.createTempDir()
val path = tempDir.toURI.toString
val checkpointInterval = 2
var graphsToCheck = Seq.empty[GraphToCheck]
sc.setCheckpointDir(path)
val graph1 = createGraph(sc)
val checkpointer = new PeriodicGraphCheckpointer[Double, Double](
checkpointInterval, graph1.vertices.sparkContext)
checkpointer.update(graph1)
graph1.edges.count()
graph1.vertices.count()
graphsToCheck = graphsToCheck :+ GraphToCheck(graph1, 1)
checkCheckpoint(graphsToCheck, 1, checkpointInterval)

var iteration = 2
while (iteration < 9) {
val graph = createGraph(sc)
checkpointer.update(graph)
graph.vertices.count()
graph.edges.count()
graphsToCheck = graphsToCheck :+ GraphToCheck(graph, iteration)
checkCheckpoint(graphsToCheck, iteration, checkpointInterval)
iteration += 1
}
withSpark { sc =>
val tempDir = Utils.createTempDir()
val path = tempDir.toURI.toString
val checkpointInterval = 2
var graphsToCheck = Seq.empty[GraphToCheck]
sc.setCheckpointDir(path)
val graph1 = createGraph(sc)
val checkpointer = new PeriodicGraphCheckpointer[Double, Double](
checkpointInterval, graph1.vertices.sparkContext)
checkpointer.update(graph1)
graph1.edges.count()
graph1.vertices.count()
graphsToCheck = graphsToCheck :+ GraphToCheck(graph1, 1)
checkCheckpoint(graphsToCheck, 1, checkpointInterval)

var iteration = 2
while (iteration < 9) {
val graph = createGraph(sc)
checkpointer.update(graph)
graph.vertices.count()
graph.edges.count()
graphsToCheck = graphsToCheck :+ GraphToCheck(graph, iteration)
checkCheckpoint(graphsToCheck, iteration, checkpointInterval)
iteration += 1
}

checkpointer.deleteAllCheckpoints()
graphsToCheck.foreach { graph =>
confirmCheckpointRemoved(graph.graph)
}
checkpointer.deleteAllCheckpoints()
graphsToCheck.foreach { graph =>
confirmCheckpointRemoved(graph.graph)
}

Utils.deleteRecursively(tempDir)
Utils.deleteRecursively(tempDir)
}
}
}

private object PeriodicGraphCheckpointerSuite {
private val defaultStorageLevel = StorageLevel.MEMORY_ONLY_SER

case class GraphToCheck(graph: Graph[Double, Double], gIndex: Int)

Expand All @@ -96,7 +100,8 @@ private object PeriodicGraphCheckpointerSuite {
Edge[Double](3, 4, 0))

def createGraph(sc: SparkContext): Graph[Double, Double] = {
Graph.fromEdges[Double, Double](sc.parallelize(edges), 0)
Graph.fromEdges[Double, Double](
sc.parallelize(edges), 0, defaultStorageLevel, defaultStorageLevel)
}

def checkPersistence(graphs: Seq[GraphToCheck], iteration: Int): Unit = {
Expand All @@ -116,8 +121,8 @@ private object PeriodicGraphCheckpointerSuite {
assert(graph.vertices.getStorageLevel == StorageLevel.NONE)
assert(graph.edges.getStorageLevel == StorageLevel.NONE)
} else {
assert(graph.vertices.getStorageLevel != StorageLevel.NONE)
assert(graph.edges.getStorageLevel != StorageLevel.NONE)
assert(graph.vertices.getStorageLevel == defaultStorageLevel)
assert(graph.edges.getStorageLevel == defaultStorageLevel)
}
} catch {
case _: AssertionError =>
Expand Down
3 changes: 1 addition & 2 deletions mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
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Expand Up @@ -34,7 +34,6 @@ import org.apache.spark.mllib.clustering.{DistributedLDAModel => OldDistributedL
EMLDAOptimizer => OldEMLDAOptimizer, LDA => OldLDA, LDAModel => OldLDAModel,
LDAOptimizer => OldLDAOptimizer, LocalLDAModel => OldLocalLDAModel,
OnlineLDAOptimizer => OldOnlineLDAOptimizer}
import org.apache.spark.mllib.impl.PeriodicCheckpointer
import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
import org.apache.spark.mllib.linalg.MatrixImplicits._
import org.apache.spark.mllib.linalg.VectorImplicits._
Expand All @@ -43,9 +42,9 @@ import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
import org.apache.spark.sql.functions.{col, monotonically_increasing_id, udf}
import org.apache.spark.sql.types.StructType
import org.apache.spark.util.PeriodicCheckpointer
import org.apache.spark.util.VersionUtils


private[clustering] trait LDAParams extends Params with HasFeaturesCol with HasMaxIter
with HasSeed with HasCheckpointInterval {

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