This sounds like a stupid question, but it does solve certain problems if it's possible.
Say my topology has only 1 spout and 1 bolt. Of course, spout is upstream of bolt. Is it possible for the bolt to define a stream and the data emit to this stream will be received by other instance of the bolt?
I am not sure what you mean be "other instance of the bolt". However, it seems you want to define a cyclic topology, and yes, this is possible in Storm. Of course, you need to be careful not to spin tuples through the cycle forever...
There is nothing special to do it. Just connect to the output stream as to any other one:
builder.setSpout("spout", new MySpout());
builder.setBolt("bolt", new MyBolt())
.shuffleGrouping("spout")
.shuffleGrouping("bolt");
Related
I tried to create a small example in Trident. The goal was to see how tuples are replayed in Case of failures. Below is the topology definition
Random rand = new Random();
Config config = new Config();
config.setDebug(true);
config.setNumWorkers(1);
TridentTopology topology = new TridentTopology();
topology.newStream("spout", new RandomIntegerSpout())
.map((MapFunction) tridentTuple -> {
if ((tridentTuple.getLongByField("msgid") % 50 == 0) &&
(rand.nextInt(2) == 1)) {
System.out.println(String.format("Failed to process tuple %d", tridentTuple.getLongByField("msgid")));
throw new ReportedFailedException("Divisible by 50");
}
return new Values(tridentTuple.toArray());
})
.peek((Consumer) tridentTuple -> System.out.println(tridentTuple.getValues()));
I use the RandomIntegerSpout from storm-starter which extends BaseRichSpout and just generates random numbers. I then apply a MapFunction that just draws a random number every 50 tuples and randomly fails the tuple.
The Problem is, I do not get any acks or fails.
I played around with the spout and ran it in debug mode, tried same sample output, tried it with standard storm bolts. The anchoring is working fine, it just does not get called by trident.
I reproduced this problem with LocalCluster and StormSubmitter, in v1.2.3 and v2.0.0.
Below is a screenshot of the Storm UI:
The bolts corresponding to the map ack and fail the tuple as expected, but this is are never propagated back to the spout.
I thought the trident mastercoord might expect some kind of persistence in a state to realize the topology is done, but replacing peek by some persistentAggregate did not help. I also ruled out a bug in map by doing the same with each.
Seeing the code is almost trivial by inspection I probably misunderstand something fundamental about Trident / Storm. Am I wrong to expect trident to call the spout's and ack method if a batch is done? I realized there is no fail method in IBatchSpout. how does Trident handle replaying of batches??
Trident spouts don't ack or fail tuples at the individual tuple level. Instead, tuples are acked as a batch.
Trident spouts will often look something like this interface.
M emitPartitionBatch(TransactionAttempt tx, TridentCollector collector, PartitionT partition, M lastPartitionMeta);
The idea is that Trident will manage keeping track of acks/fails of the batch tuples, and then if the batch fails, it will ask the spout for to repeat the batch, and if not, it simply won't.
Note how this is different from a standard Storm spout. With a normal spout, the framework basically tells the spout "Hey, emit something. Up to you what you emit.", and then the ack and fail methods are used to tell the spout whether it should emit a particular tuple again.
With Trident, the spout is instead told "Hey, (re)emit batch number x", and it is then up to the spout to know which tuples were in that batch. With this model there's no need for a fail method. Some Trident spouts will have an ack/succeed method though, to allow the spout to drop any state it may have related to a particular in-progress batch.
For wrapped IRichSpouts, there's some bridging code that wraps them into the Trident API. Basically, the wrapper calls nextTuple until it has a full batch, then it stores the ids in a cache. If the wrapper is asked to reemit a batch, it calls fail on the spout. Otherwise, it calls ack once the batch has succeeded.
I think the reason you're not seeing anything in Storm UI related to this, is that the IRichBolt isn't actually represented there. Instead it's wrapped, so the ack/fail calls are happening "under the hood" inside the spout-spout component. If you want to know for sure whether ack/fail is being called, try adding some logging to the ack/fail methods of your IRichSpout.
I have a Storm topology where I have to send output to kafka as well as update a value in redis. For this I have a Kafkabolt as well as a RedisBolt.
Below is what my topology looks like -
tp.setSpout("kafkaSpout", kafkaSpout, 3);
tp.setBolt("EvaluatorBolt", evaluatorBolt, 6).shuffleGrouping("kafkaStream");
tp.setBolt("ResultToRedisBolt",ResultsToRedisBolt,3).shuffleGrouping("EvaluatorBolt","ResultStream");
tp.setBolt("ResultToKafkaBolt", ResultsToKafkaBolt, 3).shuffleGrouping("EvaluatorBolt","ResultStream");
The problem is that both of the end bolts (Redis and Kafka) are listening to the same stream from the preceding bolt (ResultStream), hence both can fail independently. What I really need is that if the result is successfully published in Kafka, then only I update the value in Redis. Is there a way to have an output stream from a kafkaBolt where I can get the messages published successfully to Kafka? I can then probably listen to that stream in my RedisBolt and act accordingly.
It is not currently possible, unless you modify the bolt code. You would likely be better off changing your design slightly, since doing extra processing after the tuple is written to Kafka has some drawbacks. If you write the tuple to Kafka and you fail to write to Redis, you will get duplicates in Kafka, since the processing will start over at the spout.
It might be better, depending on your use case, to write the result to Kafka, and then have another topology read the result from Kafka and write to Redis.
If you still need to be able to emit new tuples from the bolt, it should be pretty easy to implement. The bolt recently got the ability to add a custom Producer callback, so we could extend that mechanism.
See the discussion at https://github.com/apache/storm/pull/2790#issuecomment-411709331 for context.
My topology looks like this :
Data_Enrichment_Persistence_Topology
So basically the problem I am trying to solve here is that every time any issue comes in the Stop or Load service bolts, and a tuple fails , it replays and the spout re emits it. This makes the Cassandra bolt re process the tuple and rewrite data.
I can not make the tuples in the load and stop bolts unanchored as i need them to be replayed in case of any failure. However I only want to get the upper workflow replayed.
I am using a KafkaSpout to emit data ( it is emitting it on the " default" stream). Not sure how to duplicate the streams at the Kafka Spout's emit level.
If I can duplicate the streams the replay on any of of the two will only re emit the message on a particular stream right at the spout level leaving the other stream untouched right?
TIA!
You need to use two output streams in your Spout -- one for each downstream pass. Furthermore, you emit each tuple to both streams (using different message-id).
Thus, if one fails, you can reply this tuple to just this stream.
I've 1 spout and 3 bolts in a topology sharing a single stream declared originally using declarer.declareStream(s1,...) in the declareOutputFields() method of the spout.
The spout emits to the stream s1, and all downstream bolts also emit Values to the same stream s1. The bolts also declare the same stream s1 in their declareOutputFields().
Is there any problem with that? What is the correct way to do it? Please provide sufficient references.
I don't see any problem with your design, except it is unncessary unless you have a specific reason. According to Storm documentation:
Saying declarer.shuffleGrouping("1") subscribes to the default stream
on component "1" and is equivalent to declarer.shuffleGrouping("1",
DEFAULT_STREAM_ID).
Thus if your bolts and spouts do not need to emit more than one stream, there is really no need to specify the steam ID yourself. You can just use the default stream ID.
As I understand things, ZooKeeper will persist tuples emitted by bolts so if a bolt crashes (or a computer with the bolt crashes, or the entire cluster crashes), the tuple emitted by the bolt will not be lost. Once everything is restarted, the tuples will be fetched from ZooKeeper, and everything will continue on as if nothing bad ever happened.
What I don't yet understand is if the same thing is true for spouts. If a spout emits a tuple (i.e., the emit() function within a spout is executed), and the computer the spout is running on crashes shortly thereafter, will that tuple be resurrected by ZooKeeper? Or do we need Kafka in order to guarantee this?
P.S. I understand that the tuple emitted by the spout must be assigned a unique ID in the call to emit().
P.P.S. I see sample code in books that uses something like ConcurrentHashMap<UUID, Values> to track which spouted tuples have not yet been acked. Is this somehow automatically persisted with ZooKeeper? If not, then I shouldn't really be doing that, should I? What should I being doing instead? Using Kafka?
Florian Hussonnois answered my question thoroughly and clearly in this storm-user thread. This was his answer:
Actually, the tuples aren't persisted into "zookeeper". If your
"spout" emits a tuple with a unique id, it will be automatically
follow internally by storm (i.e ackers) . Thus, in case the emitted
tuple comes to fail because of a bolt failure, Storm invokes the
method 'fail' on the origin spout task with the unique id as argument.
It's then up to you to re-emit the failed tuple.
In sample codes, spouts use a Map to track which tuples are fully
processed by your entire topology in order to be able to re-emit in
case of a bolt failure.
However, if the failure doesn't come from a bolt but from your spout,
the in memory Map will be lost and your topology will not be able to
remit failed tuples.
For a such scenario you can rely on Kafka. In fact, the Kafka Spout
store its read offset into zookeeper. In that way, if a spout task
goes down it will be able to read its offset from zookeeper after
restarting.