Re: How to restrict foreach on a streaming RDD only once upon receiver completion
Thanks. I’ll look into it. But the JSON string I push via receiver goes through
a series of transformations, before it ends up in the final RDD. I need to take
care to ensure that this magic value propagates all the way down to the last
one that I’m iterating on.
Currently, I’m calling “stop from the receiver once its done fetching all the
records and have a StreamingListener to act on it via the “onReceiverStopped”
hook through which I’m stopping the streamingContext and it seems to be working
except that I see this message 2015-04-06 16:41:48,002 WARN
[StreamingListenerBus] org.apache.spark.Logging$class.logWarning - All of the
receivers have not deregistered, Map(0 -
ReceiverInfo(0,ElasticSearchResponseReceiver-0,null,false,localhost,XYZ,)):
Is this not advised? BTW I’m running in local mode.
On Apr 7, 2015, at 1:43 AM, Michael Malak michaelma...@yahoo.com
mailto:michaelma...@yahoo.com wrote:
You could have your receiver send a magic value when it is done. I discuss
this Spark Streaming pattern in my presentation Spark Gotchas and
Anti-Patterns. In the PDF version, it's slides 34-36.
http://www.datascienceassn.org/content/2014-11-05-spark-gotchas-and-anti-patterns-julia-language
http://www.datascienceassn.org/content/2014-11-05-spark-gotchas-and-anti-patterns-julia-language
YouTube version cued to that place:
http://www.youtube.com/watch?v=W5Uece_JmNst=23m18s
http://www.youtube.com/watch?v=W5Uece_JmNst=23m18s
From: Hari Polisetty hpoli...@icloud.com mailto:hpoli...@icloud.com
To: Tathagata Das t...@databricks.com mailto:t...@databricks.com
Cc: user user@spark.apache.org mailto:user@spark.apache.org
Sent: Monday, April 6, 2015 2:02 PM
Subject: Re: How to restrict foreach on a streaming RDD only once upon
receiver completion
Yes, I’m using updateStateByKey and it works. But then I need to perform
further computation on this Stateful RDD (see code snippet below). I perform
forEach on the final RDD and get the top 10 records. I just don’t want the
foreach to be performed every time a new batch is received. Only when the
receiver is done fetching all the records.
My requirements are to programmatically invoke the E.S query (it varies by
usecase) , get all the records and apply certain transformations and get the
top 10 results based on certain criteria back into the driver program for
further processing. I’m able to apply the transformations on the batches of
records fetched from E.S using streaming. So, I don’t need to wait for all
the records to be fetched. The RDD transformations are happening all the time
and the top k results are getting updated constantly until all the records
are fetched by the receiver. Is there any drawback with this approach?
Can you give more pointers on what you mean by creating a custom RDD that
reads from ElasticSearch?
Here is the relevant portion of my Spark streaming code:
//Create a custom streaming receiver to query for relevant data
from E.S
JavaReceiverInputDStreamString jsonStrings =
ssc.receiverStream(
new ElasticSearchResponseReceiver(query…….));
//Apply JSON Paths to extract specific value(s) from each record
JavaDStreamString fieldVariations = jsonStrings.flatMap(new
FlatMapFunctionString, String() {
private static final long serialVersionUID =
465237345751948L;
@Override
public IterableString call(String jsonString) {
ListString r = JsonPath.read(jsonString,
attributeDetail.getJsonPath());
return r;
}
});
//Perform a stateful map reduce on each variation
JavaPairDStreamString, Integer fieldVariationCounts =
fieldVariations.mapToPair(
new PairFunctionString, String, Integer() {
private static final long
serialVersionUID = -1241276515559408238L;
@Override public Tuple2String,
Integer call(String s) {
return new Tuple2String,
Integer(s, 1);
}
}).updateStateByKey(new Function2ListInteger,
OptionalInteger,
OptionalInteger () {
private static final long
serialVersionUID = 7598681835161199865L;
public OptionalInteger
call(ListInteger nums, OptionalInteger current) {
Integer sum = current.or((int)
0L);
return (OptionalInteger)
Optional.of(sum + nums.size
Re: How to restrict foreach on a streaming RDD only once upon receiver completion
You could have your receiver send a magic value when it is done. I discuss
this Spark Streaming pattern in my presentation Spark Gotchas and
Anti-Patterns. In the PDF version, it's slides
34-36.http://www.datascienceassn.org/content/2014-11-05-spark-gotchas-and-anti-patterns-julia-language
YouTube version cued to that place:
http://www.youtube.com/watch?v=W5Uece_JmNst=23m18s
From: Hari Polisetty hpoli...@icloud.com
To: Tathagata Das t...@databricks.com
Cc: user user@spark.apache.org
Sent: Monday, April 6, 2015 2:02 PM
Subject: Re: How to restrict foreach on a streaming RDD only once upon
receiver completion
Yes, I’m using updateStateByKey and it works. But then I need to perform
further computation on this Stateful RDD (see code snippet below). I perform
forEach on the final RDD and get the top 10 records. I just don’t want the
foreach to be performed every time a new batch is received. Only when the
receiver is done fetching all the records.
My requirements are to programmatically invoke the E.S query (it varies by
usecase) , get all the records and apply certain transformations and get the
top 10 results based on certain criteria back into the driver program for
further processing. I’m able to apply the transformations on the batches of
records fetched from E.S using streaming. So, I don’t need to wait for all the
records to be fetched. The RDD transformations are happening all the time and
the top k results are getting updated constantly until all the records are
fetched by the receiver. Is there any drawback with this approach?
Can you give more pointers on what you mean by creating a custom RDD that reads
from ElasticSearch?
Here is the relevant portion of my Spark streaming code:
//Create a custom streaming receiver to query for relevant data from E.S
JavaReceiverInputDStreamString jsonStrings = ssc.receiverStream( new
ElasticSearchResponseReceiver(query…….));
//Apply JSON Paths to extract specific value(s) from each record
JavaDStreamString fieldVariations = jsonStrings.flatMap(new
FlatMapFunctionString, String() { private static final long serialVersionUID
= 465237345751948L;
@Override public IterableString call(String jsonString) { ListString r =
JsonPath.read(jsonString, attributeDetail.getJsonPath()); return r; }
});
//Perform a stateful map reduce on each variation JavaPairDStreamString,
Integer fieldVariationCounts = fieldVariations.mapToPair( new
PairFunctionString, String, Integer() { private static final long
serialVersionUID = -1241276515559408238L;
@Override public Tuple2String, Integer call(String s) { return new
Tuple2String, Integer(s, 1); } }).updateStateByKey(new
Function2ListInteger, OptionalInteger, OptionalInteger () { private
static final long serialVersionUID = 7598681835161199865L;
public OptionalInteger call(ListInteger nums, OptionalInteger current) {
Integer sum = current.or((int) 0L); return (OptionalInteger) Optional.of(sum
+ nums.size()); } }).reduceByKey(new Function2Integer, Integer, Integer() {
private static final long serialVersionUID = -5906059838295609562L;
@Override public Integer call(Integer i1, Integer i2) { return i1 + i2; } });
//Swap the Map from Enum String,Int to Int,Enum String. This is so that we can
sort on frequencies JavaPairDStreamInteger, String swappedPair =
fieldVariationCounts.mapToPair(new PairFunctionTuple2String, Integer,
Integer, String() { private static final long serialVersionUID =
-5889774695187619957L;
@Override public Tuple2Integer, String call(Tuple2String, Integer item)
throws Exception { return item.swap(); }
});
//Sort based on Key i.e, frequency JavaPairDStreamInteger, String
sortedCounts = swappedPair.transformToPair( new FunctionJavaPairRDDInteger,
String, JavaPairRDDInteger, String() { private static final long
serialVersionUID = -4172702039963232779L;
public JavaPairRDDInteger, String call(JavaPairRDDInteger, String in)
throws Exception { //False to denote sort in descending order return
in.sortByKey(false); } });
//Iterate through the RDD and get the top 20 values in the sorted pair and
write to results list sortedCounts.foreach( new FunctionJavaPairRDDInteger,
String, Void () { private static final long serialVersionUID =
2186144129973051920L;
public Void call(JavaPairRDDInteger, String rdd) { resultList.clear(); for
(Tuple2Integer, String t: rdd.take(MainDriver.NUMBER_OF_TOP_VARIATIONS)) {
resultList.add(new Tuple3String,Integer, Double(t._2(), t._1(), (double)
(100*t._1())/totalProcessed.value())); } return null; } } );
On Apr 7, 2015, at 1:14 AM, Tathagata Das t...@databricks.com wrote:
So you want to sort based on the total count of the all the records received
through receiver? In that case, you have to combine all the counts using
updateStateByKey
(https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala)
But stepping back, if you want
Re: How to restrict foreach on a streaming RDD only once upon receiver completion
Yes, I’m using updateStateByKey and it works. But then I need to perform
further computation on this Stateful RDD (see code snippet below). I perform
forEach on the final RDD and get the top 10 records. I just don’t want the
foreach to be performed every time a new batch is received. Only when the
receiver is done fetching all the records.
My requirements are to programmatically invoke the E.S query (it varies by
usecase) , get all the records and apply certain transformations and get the
top 10 results based on certain criteria back into the driver program for
further processing. I’m able to apply the transformations on the batches of
records fetched from E.S using streaming. So, I don’t need to wait for all the
records to be fetched. The RDD transformations are happening all the time and
the top k results are getting updated constantly until all the records are
fetched by the receiver. Is there any drawback with this approach?
Can you give more pointers on what you mean by creating a custom RDD that reads
from ElasticSearch?
Here is the relevant portion of my Spark streaming code:
//Create a custom streaming receiver to query for relevant data
from E.S
JavaReceiverInputDStreamString jsonStrings =
ssc.receiverStream(
new ElasticSearchResponseReceiver(query…….));
//Apply JSON Paths to extract specific value(s) from each record
JavaDStreamString fieldVariations = jsonStrings.flatMap(new
FlatMapFunctionString, String() {
private static final long serialVersionUID =
465237345751948L;
@Override
public IterableString call(String jsonString) {
ListString r = JsonPath.read(jsonString,
attributeDetail.getJsonPath());
return r;
}
});
//Perform a stateful map reduce on each variation
JavaPairDStreamString, Integer fieldVariationCounts =
fieldVariations.mapToPair(
new PairFunctionString, String, Integer() {
private static final long
serialVersionUID = -1241276515559408238L;
@Override public Tuple2String,
Integer call(String s) {
return new Tuple2String,
Integer(s, 1);
}
}).updateStateByKey(new Function2ListInteger,
OptionalInteger,
OptionalInteger () {
private static final long
serialVersionUID = 7598681835161199865L;
public OptionalInteger
call(ListInteger nums, OptionalInteger current) {
Integer sum = current.or((int)
0L);
return (OptionalInteger)
Optional.of(sum + nums.size());
}
}).reduceByKey(new Function2Integer, Integer,
Integer() {
private static final long
serialVersionUID = -5906059838295609562L;
@Override
public Integer call(Integer i1, Integer
i2) {
return i1 + i2;
}
});
//Swap the Map from Enum String,Int to Int,Enum String. This is
so that we can sort on frequencies
JavaPairDStreamInteger, String swappedPair =
fieldVariationCounts.mapToPair(new PairFunctionTuple2String, Integer,
Integer, String() {
private static final long serialVersionUID =
-5889774695187619957L;
@Override
public Tuple2Integer, String call(Tuple2String,
Integer item) throws Exception {
return item.swap();
}
});
//Sort based on Key i.e, frequency
JavaPairDStreamInteger, String sortedCounts =
swappedPair.transformToPair(
new FunctionJavaPairRDDInteger, String,
JavaPairRDDInteger, String() {
private static final long
serialVersionUID = -4172702039963232779L;
public JavaPairRDDInteger, String
call(JavaPairRDDInteger, String in) throws Exception {
//False to denote sort in
descending order
return in.sortByKey(false);
}
Re: How to restrict foreach on a streaming RDD only once upon receiver completion
So you want to sort based on the total count of the all the records received through receiver? In that case, you have to combine all the counts using updateStateByKey ( https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala ) But stepping back, if you want to get the final results at the end of the receiving all the data (as opposed to continuously), why are you even using streaming? You could create a custom RDD that reads from ElasticSearch and then use it in a Spark program. I think that's more natural as your application is more batch-like than streaming-like as you are using the results in real-time. TD On Mon, Apr 6, 2015 at 12:31 PM, Hari Polisetty hpoli...@icloud.com wrote: I have created a Custom Receiver to fetch records pertaining to a specific query from Elastic Search and have implemented Streaming RDD transformations to process the data generated by the receiver. The final RDD is a sorted list of name value pairs and I want to read the top 20 results programmatically rather than write to an external file. I use foreach on the RDD and take the top 20 values into a list. I see that forEach is processed every time there is a new microbatch from the receiver. However, I want the foreach computation to be done only once when the receiver has finished fetching all the records from Elastic Search and before the streaming context is killed so that I can populate the results into a list and process it in my driver program. Appreciate any guidance in this regard. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
