While the kafka brokers are up and running, spark process running in cluster mode is able to read the messages from the kafka topic. But when the brokers were shutdown intentionally, the spark consumer is still in RUNNING status.
Is there any parameter to control the handshake interval between spark consumer and the zookeeper process, so that the spark process can fail if the brokers are not reachable. Or is there any alternate way to fail the consumer. Please suggest.
No there is not.
KAFKA & Spark Structured Steaming (SSS) are loosely coupled, and given high availability scenarios, failure etc. SSS just waits and will process rebalanced topics when KAFKA rebalances the load.
The whole premise is that KAFKA will do something to alleviate the situation - if a Broker goes down. Even if there are zero Brokers after a while, SSS will wait as you have noted already. It is of course knows nothing, but just waits.
As long as the topics still exist and the "fail on data loss" is not set to true if a topic is deleted, the SSS Apps will go on.
Related
I have a Spark streaming (Scala) application running in CDH 5.13 consuming messages from Kafka using client 0.10.0. My Kafka cluster contains 3 brokers. Kafka topic is divided into 12 partitions evenly distributed between these 3 brokers. My Spark streaming consumer has 12 executors with 1 core each.
Spark streaming starts reading millions of messages from Kafka in each batch, but reduces the number to thousands due to the fact that Spark is not capable to cope with the load and queue of unprocessed batches is created. That is fine and my expectation though is that Spark processes the small batches very quickly and returns to normal, however I see that from time to time one of the executors that processes only few hundreds of messages gets 'request timeout' error just after reading the last offset from Kafka:
DEBUG org.apache.clients.NetworkClient Disconnecting from node 12345 due to request timeout
After this error, executor sends several RPC requests driver that take ~40 seconds and after this time executor reconnects to the same broker from which it disconnected.
My question is how can I prevent this request timeout and what is the best way to find the root cause for it?
Thank you
The root cause for disconnection was the fact that response for data request arrived from Kafka too late. i.e. after request.timeout.ms parameter which was set to default 40000 ms. The disconnection problem was fixed when I increased this value.
i am building a spark streamming application, read input message from kafka topic, transformation message and output the result message into another kafka topic. Now i am confused how to prevent data loss when application restart, including kafka read and output. Setting the spark configuration "spark.streaming.stopGracefullyOnShutdow" true can help?
You can configure Spark to do checkpoint to HDFS and store the Kafka offsets in Zookeeper (or Hbase, or configure elsewhere for fast, fault tolerant lookups)
Though, if you process some records and write the results before you're able to commit offsets, then you'll end up reprocessing those records on restart. It's claimed that Spark can do exactly once with Kafka, but that is a only with proper offset management, as far as I know, for example, Set enable.auto.commit to false in the Kafka priorities, then only commit after the you've processed and written the data to its destination
If you're just moving data between Kafka topics, Kafka Streams is the included Kafka library to do that, which doesn't require YARN or a cluster scheduler
I have a requirement from my client to process the application(Tomcat) server log file for a back end REST Based App server which is deployed on a cluster. Clint wants to generate "access" and "frequency" report from those data with different parameter.
My initial plan is that get those data from App server log --> push to Spark Streaming using kafka and process the data --> store those data to HIVE --> use zeppelin to get back those processed and centralized log data and generate reports as per client requirement.
But as per my knowledge Kafka does not any feature which can read data from log file and post them in Kafka broker by its own , in that case we have write a scheduler job process which will read the log time to time and send them in Kafka broker , which I do not prefer to do, as in that case it will not be a real time and there can be synchronization issue which we have to bother about as we have 4 instances of application server.
Another option, I think we have in this case is Apache Flume.
Can any one suggest me which one would be better approach in this case or if in Kafka, we have any process to read data from log file by its own and what are the advantage or disadvantages we can have in feature in both the cases?
I guess another option is Flume + kakfa together , but I can not speculate much what will happen as I have almost no knowledge about flume.
Any help will be highly appreciated...... :-)
Thanks a lot ....
You can use Kafka Connect (file source connector) to read/consume Tomcat logs files & push them to Kafka. Spark Streaming can then consume from Kafka topics and churn the data
tomcat -> logs ---> kafka connect -> kafka -> spark -> Hive
I am streaming loads of data through kafka. And then I have spark streaming which is consuming these messages. Basically down the line, spark streaming throws this error:
kafka.common.OffsetOutOfRangeException
Now I am aware what this error means. So I changed the retention policy to 5 days. However I still encountered the same issue. Then I listed all the messages for a topic using --from-beginning in kafka. Surely enough, ton of messages from the beginning of the kafka streaming part were not present and since spark streaming is a little behind the kafka streaming part, spark streaming tries to consume messages that have been deleted by kafka. However I thought changing the retention policy would take care of this:
--add-config retention.ms=....
What I suspect is happening that kafka is deleting messages from the topic to free up space (because we are streaming tons of data) for the new messages. Is there a property which I can configure that specifies how much bytes of data kafka can store before deleting the prior messages?
You can set the maximum size of the topic when u create the topic using the topic configuration property retention.bytes via console like:
bin/kafka-topics.sh --zookeeper localhost:2181 --create --topic my-topic --partitions 1 --replication-factor 1 --config retention.bytes=10485760 --config
or u can use global broker configuration property log.retention.bytes to set the maximum size for all topics.
what is important to know is that log.retention.bytes doesn't enforce a hard limit on a topic size, but it just signal to Kafka when to start deleting the oldest messages
Another way to solve this problem is to specify in the configuration the spark parameter :
spark.streaming.kafka.maxRatePerPartition
I'm curious if it is an absolute must that a Spark streaming application is brought down gracefully or it runs the risk of causing duplicate data via the write-ahead log. In the below scenario I outline sequence of steps where a queue receiver interacts with a queue requires acknowledgements for messages.
Spark queue receiver pulls a batch of messages from the queue.
Spark queue receiver stores the batch of messages into the write-ahead log.
Spark application is terminated before an ack is sent to the queue.
Spark application starts up again.
The messages in the write-ahead log are processed through the streaming application.
Spark queue receiver pulls a batch of messages from the queue which have already been seen in step 1 because they were not acknowledged as received.
...
Is my understanding correct on how custom receivers should be implemented, the problems of duplication that come with it, and is it normal to require a graceful shutdown?
Bottom line: It depends on your output operation.
Using the Direct API approach, which was introduced on V1.3, eliminates inconsistencies between Spark Streaming and Kafka, and so each record is received by Spark Streaming effectively exactly once despite failures because offsets are tracked by Spark Streaming within its checkpoints.
In order to achieve exactly-once semantics for output of your results, your output operation that saves the data to an external data store must be either idempotent, or an atomic transaction that saves results and offsets.
For further information on the Direct API and how to use it, check out this blog post by Databricks.