Hi Mukesh,
If my understanding is correct, each Stream only has a single Receiver. So, if you have each receiver consuming 9 partitions, you need 10 input DStreams to create 10 concurrent receivers: https://spark.apache.org/docs/latest/streaming-programming-guide.html#level-of-parallelism-in-data-receiving Would you mind sharing a bit more on how you achieve this ? — FG On Wed, Jan 7, 2015 at 3:00 PM, Mukesh Jha <me.mukesh....@gmail.com> wrote: > Hi Guys, > I have a kafka topic having 90 partitions and I running > SparkStreaming(1.2.0) to read from kafka via KafkaUtils to create 10 > kafka-receivers. > My streaming is running fine and there is no delay in processing, just that > some partitions data is never getting picked up. From the kafka console I > can see that each receiver is consuming data from 9 partitions but the lag > for some offsets keeps on increasing. > Below is my kafka-consumers parameters. > Any of you have face this kind of issue, if so then do you have any > pointers to fix it? > Map<String, String> kafkaConf = new HashMap<String, String>(); > kafkaConf.put("zookeeper.connect", kafkaZkQuorum); > kafkaConf.put("group.id", kafkaConsumerGroup); > kafkaConf.put("consumer.timeout.ms", "30000"); > kafkaConf.put("auto.offset.reset", "largest"); > kafkaConf.put("fetch.message.max.bytes", "20000000"); > kafkaConf.put("zookeeper.session.timeout.ms", "6000"); > kafkaConf.put("zookeeper.connection.timeout.ms", "6000"); > kafkaConf.put("zookeeper.sync.time.ms", "2000"); > kafkaConf.put("rebalance.backoff.ms", "10000"); > kafkaConf.put("rebalance.max.retries", "20"); > -- > Thanks & Regards, > *Mukesh Jha <me.mukesh....@gmail.com>*
