I have a bolt that works in small batches of tuples. I essentially have a switch statement that listens for either a stream from a previous bolt, or a tick tuple. It looks something like this:
switch(component) {
bolt1:
do some work...
anchors.add(tuple)
tick:
do some work...
collector.emit(anchors, value)
collector.ack(tuple)
anchors.clear()
When I run this, Storm UI shows a very small number of tuples acked from this bolt. Is this the correct way to anchor them or do I need to call collector.ack(tuple) within the bolt switch statement as well? Even though Storm UI counts are weird, the topology is running without any of the tuples timing out.
You should not ack the tuples in the bolt1 case if you want to replay the pending anchors if the worker crashes. You never want to ack tuples more than once either.
It looks like you're acking the tick tuple and not the anchors in the tick case? You should ack the anchors as well, or the spout will be told they've failed once you hit the topology message timeout.
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.
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.
As part of the development of streamparse, we have a BatchingBolt that processes tuples in batches. It's intended for use with things like databases that are more performant when you send things in batches.
I've recently proposed switching our BatchingBolt implementation over from using a timer/thread approach to using tick tuples; however, one of my fellow devs pointed out that with our current approach the final batch will definitely get processed when a topology is shutdown (and it's in the inactive state), whereas that isn't explicitly documented anywhere about tick tuples.
Therefore, my question is this: does Storm continue sending tick tuples to bolts after a kill/deactivate has been issued, while it is in the waiting/inactive period? The topology lifecycle docs don't make it clear.
http://mail-archives.apache.org/mod_mbox/storm-user/201506.mbox/%3CCAF5108ijGpdMeax1LaKQ1MG6MSZQF=YM=vO8AacmN0RUiNfNkQ#mail.gmail.com%3E
AFAIK, "setup-tick!" is called from start of executor (which schedules tick timer for each executor), and tick tuples will be emitted unless worker is going to be shutdown.
In short, your fellow is correct.
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.
We have a fairly simple storm topology with one head ache.
One of our bolts can either find the data it is processing to be valid and every thing carries on down the stream as normal or it can find it to be invalid but fixable. In which case we need to send it for some additional processing.
We tried making this step part of the topology with a separate bolt and stream.
declarer.declareStream(NORMAL_STREAM, getStreamFields());
declarer.declareStream(ERROR_STREAM, getErrorStreamFields());
Followed by some thing like the following at the end of the execute method.
if(errorOutput != null) {
collector.emit(ERROR_STREAM, input, errorOutput);
}
else {
collector.emit(NORMAL_STREAM, input, output);
}
collector.ack(input);
This does work however it has a breaking effect of causing all of the tuples that do not go down this error path to fail and get re-sent by the spout endlessly.
I think this is because the error bolt can not send acks for messages it doesn't receive but the acker thing waits for all the bolts in a topology to ack before sending the ack back to the spout. At the very least taking out the error processing bolt causes every thing to get acked back to the spout correctly.
What is the best way to achieve some thing like this?
It's possible that the error bolt is slower than you suspect, causing a backup on error_stream which, in turn, causes a backup into your first bolt and finally causing tuples to start timing out. When a tuple times out, it gets resent by the spout.
Try:
Increasing the timeout config (topology.message.timeout.secs),
Limiting the number of inflight tuples from the spout (topology.max.spout.pending) and/or
Increasing the parallelism count for your bolts