Thanks for sharing the code.

The 1st problem in the first code is the map is allocated in Driver, but it’s trying to put data in Executors, then retrieve it in driver to send to Kafka.

You are using this map as accumulator’s feature, but it doesn’t work in this way.

 

The 2nd problem is both codes are trying to collect rdd level data to generate a single Json string then send to Kafka, which could cause very long json string if your TPS is very high.

If possible you can send smaller json strings at task level, such as:

    .foreachRDD(stringLongJavaPairRDD -> {

      stringLongJavaPairRDD.foreachPartition{partition ->{

          Map<String, Long> map = new HashMap<>(); //Defined in a task

          partition.foreach(stringLongTuple2 -> {

            map.put(stringLongTuple2._1(), stringLongTuple2._2())

          });

          producer.send(new ProducerRecord<>("topicA", gson.toJson(map))); // send smaller json in a task

        }

      }

    });

When you do it, make sure kafka producer (seek kafka sink for it) and gson’s environment setup correctly in executors.

 

If after this, there is still OOM, let’s discuss further.

 

Best Regards

Richard

 

 

From: kant kodali <kanth909@gmail.com>
Date: Thursday, December 7, 2017 at 2:30 AM
To: Gerard Maas <gerard.maas@gmail.com>
Cc: "Qiao, Richard" <Richard.Qiao@capitalone.com>, "user @spark" <user@spark.apache.org>
Subject: Re: Do I need to do .collect inside forEachRDD

 

@Richard I had pasted the two versions of the code below and I still couldn't figure out why it wouldn't work without .collect ?  Any help would be great

 

 

The code below doesn't work and sometime I also run into OutOfMemory error.

jsonMessagesDStream
    .window(
new Duration(60000), new Duration(1000))
    .mapToPair(val -> {
      JsonParser parser =
new JsonParser();
      JsonObject jsonObj = parser.parse(val).getAsJsonObject();
     
if (jsonObj.has("key4")) {
       
return new Tuple2<>("", 0L);
      }
      String symbol = jsonObj.get(
"key1").getAsString();
     
long uuantity = jsonObj.get("key2").getAsLong();
     
return new Tuple2<>(symbol, quantity);
    })
    .reduceByKey((v1, v2) -> v1 + v2)
    .foreachRDD(stringLongJavaPairRDD -> {

       
Map<String, Long> map = new HashMap<>();
        stringLongJavaPairRDD.foreach(stringLongTuple2 -> {
            System.out.println(stringLongTuple2._1()); // Works I can see values getting printed out
            System.out.println(stringLongTuple2._2()); // Works I can see values getting printed out
            map.put(stringLongTuple2._1(), stringLongTuple2._2())
        });
        System.out.println(gson.toJson(map)); // Prints empty json doc string "{}" always. But why? especially
        // when the map is getting filled values as confirmed by the print statements above    
        producer.send(new ProducerRecord<>("topicA", gson.toJson(map)));
    });
    jssc.start();
    jssc.awaitTermination();
 
                          VS
The below code works but it is slow because .collectAsMap
 
jsonMessagesDStream
    .window(
new Duration(60000), new Duration(1000))
    .mapToPair(val -> {
      JsonParser parser =
new JsonParser();
      JsonObject jsonObj = parser.parse(val).getAsJsonObject();
     
if (jsonObj.has("key4")) {
       
return new Tuple2<>("", 0L);
      }
      String symbol = jsonObj.get(
"key1").getAsString();
     
long uuantity = jsonObj.get("key2").getAsLong();
     
return new Tuple2<>(symbol, quantity);
    })
    .reduceByKey((v1, v2) -> v1 + v2)
    .foreachRDD(stringLongJavaPairRDD -> {
        LinkedHashMap<String, Long> map = new LinkedHashMap<>(stringLongJavaPairRDD.collectAsMap());
        producer.send(new ProducerRecord<>("topicA", gson.toJson(map)));
    });
    jssc.start();
    jssc.awaitTermination();
 
 

 

On Wed, Dec 6, 2017 at 1:43 AM, Gerard Maas <gerard.maas@gmail.com> wrote:

Hi Kant,

 

 but would your answer on .collect() change depending on running the spark app in client vs cluster mode? 

 

No, it should make no difference. 

 

-kr, Gerard.

 

On Tue, Dec 5, 2017 at 11:34 PM, kant kodali <kanth909@gmail.com> wrote:

@Richard I don't see any error in the executor log but let me run again to make sure.

 

@Gerard Thanks much!  but would your answer on .collect() change depending on running the spark app in client vs cluster mode? 

 

Thanks!

 

On Tue, Dec 5, 2017 at 1:54 PM, Gerard Maas <gerard.maas@gmail.com> wrote:

The general answer to your initial question is that "it depends". If the operation in the rdd.foreach() closure can be parallelized, then you don't need to collect first. If it needs some local context (e.g. a socket connection), then you need to do rdd.collect first to bring the data locally, which has a perf penalty and also is restricted to the memory size to the driver process.

 

Given the further clarification:

>Reads from Kafka and outputs to Kafka. so I check the output from Kafka.

 

If it's writing to Kafka, that operation can be done in a distributed form. 

 

 

Or, if you can upgrade to Spark 2.2 version, you can pave your way to migrate to structured streaming by already adopting the 'structured' APIs within Spark Streaming:

 

case class KV(key: String, value: String)

 

dstream.map().reduce().forEachRdd{rdd -> 

    import spark.implicits._

    val kv = rdd.map{e => KV(extractKey(e), extractValue(e))} // needs to be in a (key,value) shape

    val dataFrame = rdd.toDF()

    dataFrame.write

                     .format("kafka")

                     .option("kafka.bootstrap.servers", "host1:port1,host2:port2")

                     .option("topic", "topic1")

                     .save()

}

 

-kr, Gerard.

 

 

 

On Tue, Dec 5, 2017 at 10:38 PM, kant kodali <kanth909@gmail.com> wrote:

Reads from Kafka and outputs to Kafka. so I check the output from Kafka.

 

On Tue, Dec 5, 2017 at 1:26 PM, Qiao, Richard <Richard.Qiao@capitalone.com> wrote:

Where do you check the output result for both case?

Sent from my iPhone

 


> On Dec 5, 2017, at 15:36, kant kodali <kanth909@gmail.com> wrote:
>
> Hi All,
>
> I have a simple stateless transformation using Dstreams (stuck with the old API for one of the Application). The pseudo code is rough like this
>
> dstream.map().reduce().forEachRdd(rdd -> {
>      rdd.collect(),forEach(); // Is this necessary ? Does execute fine but a bit slow
> })
>
> I understand collect collects the results back to the driver but is that necessary? can I just do something like below? I believe I tried both and somehow the below code didn't output any results (It can be issues with my env. I am not entirely sure) but I just would like some clarification on .collect() since it seems to slow things down for me.
>
> dstream.map().reduce().forEachRdd(rdd -> {
>      rdd.forEach(() -> {} ); //
> })
>
> Thanks!
>
>

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