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 <aassud...@impetus.com<mailto:aassud...@impetus.com>> Date: Friday, June 26, 2015 at 11:21 AM To: Cody Koeninger <c...@koeninger.org<mailto:c...@koeninger.org>> Cc: "user@spark.apache.org<mailto:user@spark.apache.org>" <user@spark.apache.org<mailto:user@spark.apache.org>>, Tathagata Das <t...@databricks.com<mailto:t...@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 <c...@koeninger.org<mailto:c...@koeninger.org>> Date: Friday, June 26, 2015 at 11:16 AM To: amit assudani <aassud...@impetus.com<mailto:aassud...@impetus.com>> Cc: "user@spark.apache.org<mailto:user@spark.apache.org>" <user@spark.apache.org<mailto:user@spark.apache.org>>, Tathagata Das <t...@databricks.com<mailto:t...@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 <aassud...@impetus.com<mailto:aassud...@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. ________________________________ NOTE: This message may contain information that is confidential, proprietary, privileged or otherwise protected by law. 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