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From Amit Assudani <aassud...@impetus.com>
Subject Re: How to recover in case user errors in streaming
Date Fri, 26 Jun 2015 15:28:35 GMT
Also, what I understand is, max failures doesn’t stop the entire stream, it fails the job
created for the specific batch, but the subsequent batches still proceed, isn’t it right
? And question still remains, how to keep track of those failed batches ?

From: amit assudani <aassudani@impetus.com<mailto:aassudani@impetus.com>>
Date: Friday, June 26, 2015 at 11:21 AM
To: Cody Koeninger <cody@koeninger.org<mailto:cody@koeninger.org>>
Cc: "user@spark.apache.org<mailto:user@spark.apache.org>" <user@spark.apache.org<mailto:user@spark.apache.org>>,
Tathagata Das <tdas@databricks.com<mailto:tdas@databricks.com>>
Subject: Re: How to recover in case user errors in streaming

Thanks for quick response,

My question here is how do I know that the max retries are done ( because in my code I never
know whether it is failure of first try or the last try ) and I need to handle this message,
is there any callback ?

Also, I know the limitation of checkpoint in upgrading the code, but my main focus here to
mitigate the connectivity issues to persistent store which gets resolved in a while, but how
do I know which all messages failed and need rework ?

Regards,
Amit

From: Cody Koeninger <cody@koeninger.org<mailto:cody@koeninger.org>>
Date: Friday, June 26, 2015 at 11:16 AM
To: amit assudani <aassudani@impetus.com<mailto:aassudani@impetus.com>>
Cc: "user@spark.apache.org<mailto:user@spark.apache.org>" <user@spark.apache.org<mailto:user@spark.apache.org>>,
Tathagata Das <tdas@databricks.com<mailto:tdas@databricks.com>>
Subject: Re: How to recover in case user errors in streaming

If you're consistently throwing exceptions and thus failing tasks, once you reach max failures
the whole stream will stop.

It's up to you to either catch those exceptions, or restart your stream appropriately once
it stops.

Keep in mind that if you're relying on checkpoints, and fixing the error requires changing
your code, you may not be able to recover the checkpoint.

On Fri, Jun 26, 2015 at 9:05 AM, Amit Assudani <aassudani@impetus.com<mailto:aassudani@impetus.com>>
wrote:
Problem: how do we recover from user errors (connectivity issues / storage service down /
etc.)?
Environment: Spark streaming using Kafka Direct Streams
Code Snippet:

HashSet<String> topicsSet = new HashSet<String>(Arrays.asList("kafkaTopic1"));
HashMap<String, String> kafkaParams = new HashMap<String, String>();
kafkaParams.put("metadata.broker.list", "localhost:9092");
kafkaParams.put("auto.offset.reset", "smallest");


JavaPairInputDStream<String, String> messages = KafkaUtils
.createDirectStream(jssc, String.class, String.class, StringDecoder.class, StringDecoder.class,
kafkaParams, topicsSet);

JavaDStream<String> inputStream = messages
       .map(newFunction<Tuple2<String, String>, String>() {
       @Override
       public String call(Tuple2<String, String> tuple2) {
              returntuple2._2();
       }});

inputStream.foreachRDD(newFunction<JavaRDD<String>, Void>() {

       @Override
       public Void call(JavaRDD<String> rdd)throws Exception {
              if(!rdd.isEmpty())
              {
rdd.foreach(newVoidFunction<String>(){
@Override
                      publicvoid call(String arg0)throws Exception {
System.out.println("------------------------rdd----------"+arg0);
Thread.sleep(1000);

thrownew Exception(" :::::::::::::::user and/or service exception::::::::::::::"+arg0);

                      }});

              }
              returnnull;
       }
});

Detailed Description: Using spark streaming I read the text messages from kafka using direct
API. For sake of simplicity, all I do in processing is printing each message on console and
sleep of 1 sec. as a placeholder for actual processing. Assuming we get a user error may be
due to bad record, format error or the service connectivity issues or let’s say the persistent
store downtime. I’ve represented that with throwing an Exception from foreach block. I understand
spark retries this configurable number of times and  proceeds ahead. The question is what
happens to those failed messages, does ( if yes when ) spark re-tries those ? If not, does
it have any callback method so as user can log / dump it in error queue and provision it for
further analysis and / or retrials manually. Also, fyi, checkpoints are enabled and above
code is in create context method to recover from spark driver / worker failures.

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or interference.

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NOTE: This message may contain information that is confidential, proprietary, privileged or
otherwise protected by law. The message is intended solely for the named addressee. If received
in error, please destroy and notify the sender. Any use of this email is prohibited when received
in error. Impetus does not represent, warrant and/or guarantee, that the integrity of this
communication has been maintained nor that the communication is free of errors, virus, interception
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