Right now Storm Spouts have an open method to configure them and Bolts have a prepare method. Is there any way to make all the Spout instances wait for all the prepare methods on the Bolts listening to them to finish?
I have a case where I would like to pass some config info to the bolts on the fly (since this config info changes all the time). I've read in some places that we should use Zookeeper or an in-memory key-value storage like redis to do this. My worry though is, what happens if the Bolts aren't ready to process data from Spouts yet, and the Spouts start emitting tuples? Is there a way to make the Spouts wait for an update from the Bolts saying they're ready?
I found a slightly more elegant solution for this (I think). The problem was that certain bolts needed config info in order to process incoming tuples. I figured out Storm's capability to replay tuples, so now my bolts listen for updates from one spout, and tuples from the other. As long as I dont receive updates, I keep failing the tuples and having the spout replay them after a configurable amount of time.
Yes, you can use Redis to store your configuration then read it from the prepare method.
The prepare method is invoked by the worker process which start processing tuples after finishing. Actually, I think that no tuple is emitted until all components of a worker process are ready. http://nathanmarz.github.io/storm/doc-0.8.1/index.html
Finally, you can have an additional spout which look up for configuration changes. Then, if a newer configuration is available it is send to your bolts via named streams.
You don't have to worry about this. Storm framework loads Bolt before Spout. Storm loads the bolts in reverse order. Bolts towards the end of the topology are loaded before the bolts in the middle of the topology and in the end, Spout gets loaded.
Related
Consider following configuration for a topology:
#spouts: 2
#bolt: 8
#workers: 3
With this configuration one of the workers won't have spout and If we apply localorshuffle grouping between spout and bolt, does worker-3 bolts receive any tuple ?
No, not as far as I know. When you use localOrShuffle grouping, you're saying that you want to send to any bolts that are within the same JVM if possible, and only send outside the JVM if there is no appropriate bolt task inside the VM.
I have a bolt to process the data in filedGrouping way from a spout in storm . And one day one machine was down, the bolt was transfered to another machine automatically. In filedGrouping way , some tuples from the spout will not be processed by any bolt . How can I do with it?
If you use acking, the tuples that are lost should be retried by the spout.
I am reading the storm applied book. I found the following code snippet in the book
LocalCluster lc = new LocalCluster()
lc.submitTopology("GitHub-commit-count-topology"), config, topology);
Utils.sleep(TEN_MINUTES)
lc.killTopology("GitHub-commit-count-topology")
lc.shutdown()
So this code will submit the topology for execution wait for fixed 10 minutes and then kill the topology. But this is odd. How can I say. submitTopology wait for it to complete and completed. kill and shutdown.
Like in Akka Streams we get Future[Done] and we just wait on that future to complete. (rather than fixed 10 minutes).
You can do this with https://github.com/apache/storm/blob/master/storm-server/src/main/java/org/apache/storm/Testing.java#L376.
The reason this isn't used in some cases is that it requires every spout in the topology to implement the CompletableSpout interface https://github.com/apache/storm/blob/4137328b75c06771f84414c3c2113e2d1c757c08/storm-client/src/jvm/org/apache/storm/testing/CompletableSpout.java.
Most Storm spouts never reach a point where they're "done" (since it's a stream processing framework, not a batch processing framework), so there's no way to tell when the topology is finished. For example, if you're consuming messages from a Kafka topic, the producers may at any point add more messages to the topic, so how will the consumer determine it is finished consuming?
CompletableSpout exists mostly to ease testing, because it's then possible for a spout to say whether it is done. The completeTopology method I linked can then use this extra feature to tell whether all spouts in the topology are "done", and can stop the topology after that.
If the spout you're using in a test doesn't implement CompletableSpout (which most spouts don't), there's no way to tell when the topology is finished in general. In many cases you can still do better than the example you linked, e.g. if my topology is supposed to write 10 messages to a queue in the test, I can make the test end once 10 messages have been written to the queue.
To relate to Akka streams, I'm not really familiar with them, but looking at the introductory documentation, you could consider CompletableSpouts to be similar to bounded Sources (eg. a Source(1 to 100)), while "normal" spouts are unbounded Sources (e.g. a Source.repeat(1)).
We have an use case where we do not want to run storm topology continuously. Instead, there are set of inputs( 10K+) that should be processed at the specified time, Spout continuously emits these inputs and get processed by rest of the bolts in the topology. Once all the inputs are processed, there is nothing to emit from nextTuple in my spout.
At this time we wanted our topology to go to sleep and restart the process everyday night 12:00 am.
Is there any property to set in the storm config to run the topology once a day and sleep after processing is done and start at the specified time?
I'm not aware of a feature like what you're asking for. Storm isn't a batch processing system, it's meant to be running continuously. Consider if Storm is a great fit for this use case.
That said, you should be able to implement what you want. You could put in an "I'm done" message at the end of your spout input. When the spout hits that message and all other pending messages are acked, it could use the Nimbus client to kill or deactivate the topology (depending on whether you want to kill or deactivate), see https://stackoverflow.com/a/37134473/8845188. Then the final step would be using your favorite scheduling software to resubmit/reactivate the topology every day at midnight.
Say I deploy a topology with 2 workers, the topo has 1 spout and 1 bolt with 2 tasks. Then my understanding is, 1 worker will run spout executor and 1 bolt executor, the other worker will run 1 bolt executor.
Is my understanding correct?
If my understanding is correct, then my question comes. Say the bolt is implemented by Python. Since storm transfers data between multi-lang bolts via stdout/stdin, if the 2 workers run on different hosts, how spout can send data to bolt that locates on the other host?
Little more clarification to your question. Storm uses various types of queue for data/tuple transfer between various components of topology
Example :
1) Intra-worker communication in Storm (inter-thread on the same Storm node): LMAX Disruptor
2) Inter-worker communication (node-to-node across the network): ZeroMQ or Netty
3) Inter-topology communication: nothing built into Storm, you must take care of this yourself with e.g. a messaging system such as Kafka/RabbitMQ, a database, etc.
For further reference :
http://www.michael-noll.com/blog/2013/06/21/understanding-storm-internal-message-buffers/
To give a more detailed answer:
Storm will sent the data to both bolt executors. For the spout-local bolt, this happens in-memory; for the other bolt via network. Afterwards, each bolt-instance will deliver the input to an local-running python process. Thus, your describe stdout/stdin delivery happens locally on each machine. The data is transfer to each bolt before the data delivery from Java to Python happens.
Thus, stdout/stdin bridge is used within each bolt, and not from spout to bolt.
I have done a test by myself. Storm can properly deliver spout emitted data to bolts on different hosts.