I am reading up on Apache Storm to evaluate if it is suited for our real time processing needs.
One thing that I couldn't figure out until now is — Where does it store the tuples during the time when next node is not available for processing it. For e.g. Let's say spout A is producing at the speed of 1000 tuples per second, but the next level of bolts(that process spout A output) can only collectively consume at a rate of 500 tuples per second. What happens to the other tuples ? Does it have a disk-based buffer(or something else) to account for this ?
Storm used internal in-memory message queues. Thus, if a bolt cannot keep up processing, the messages are buffered there.
Before Storm 1.0.0 those queues may grow out-of-bound (ie, you get an out-of-memory exception and your worker dies). To protect from data loss, you need to make sure that the spout can re-read the data (see https://storm.apache.org/releases/1.0.0/Guaranteeing-message-processing.html)
You could use "max.spout.pending" parameter, to limit the tuples in-flight to tackle this problem though.
As of Storm 1.0.0, backpressure is supported (see https://storm.apache.org/2016/04/12/storm100-released.html). This allows bolt to notify its upstream producers to "slow down" if a queues grows too large (and speed up again in a queues get empty). In your spout-bolt-example, the spout would slow down to emit messaged in this case.
Typically, Storm spouts read off of some persistent store and track that completion of tuples to determine when it's safe to remove or ack a message in that store. Vanilla Storm itself does not persist tuples. Tuples are replayed from the source in the event of a failure.
I have to agree with others that you should check out Heron. Stream processing frameworks have advanced significantly since the inception of Storm.
Related
Let's say I have an Apache Storm topology processing some tuples. I ack most of them but sometimes, due to an error, they are not processed and therefore not acked.
What happens to these 'lost' tuples? Does Storm fail them automatically, or should I do that explicitly every time?
From Storm's docs:
http://storm.apache.org/releases/1.2.2/Guaranteeing-message-processing.html
By failing the tuple explicitly, the spout tuple can be replayed faster than if you waited for the tuple to time-out. (=30 seconds by default)
Every tuple you process must be acked or failed. Storm uses memory to track each tuple, so if you don't ack/fail every tuple, the task will eventually run out of memory.
What happens to these 'lost' tuples? Does Storm fail them
automatically
Yes, storm failed them automatically after the tuple timeout. But it's better for you to do that explicitly .
New to Storm and just understanding the concept of Spouts and how to achieve parallelism in them.
I have defined a Spout A and have set 3 tasks and 3 executors and 1 Bolt(Lets not worry about Bolt). Lets assume each of the spout task
is assigned a dedicated worker. That means there are 3 spouts ready to receive a Stream. A message or stream (say X) enters the topology. How is this handled in the Spout?
a. Will all the spouts receive the stream A ? If yes, then all the 3 spouts will process it and the same message is processed multiple times right?
b. Who will decide in above case which spout should receive this stream?
c. Is it possible to balance the load across the spouts?
d. Is it that there should be only one spout in the topology ?
P.S: Consider this is general spout, not to confuse with the Kafka spouts.
Storm is just a frame, your questions are basically determined by implementation of spout code. So,sadly, there is no way to consider "general spout". We have to discuss some specific spout.
Let's take kafka spout for example. Basically, it has no difference with normal kafka consumer. Kafka spout has a logic to distribute partitions to different spout tasks, and load balance is also handled at this period, one partition will be consumed by only one spout task,so there will be no multiple data.
Recently I got a really strange problem. The storm cluster have 3 machines. The topology structure is like this, Kafka Spout A -> Bolt B -> Bolt C. I have acked all tuples in every bolt, even though there possibly throw exceptions inner bolt (in bolt execute method I try and catch all exceptions, and finally ack the tuple).
But here the strange thing happens. I print the log of the spout, on one machine all the tuples acked by the spout, but on other 2 machines, almost all tuples failed. And after 60 seconds the tuple replayed once again and again and again. 'Almost' means at the begin time, all tuples failed on the other 2 machines. After a time, there's a small amount of tuples acked on the 2 machines.
Absolutely the tuples are failed because of timeout. But I really don't know why they timed out. According to the logs I've printed, I'm really sure all tuples acked at the end of the execute method in every bolt. So I want to know why some of the tuples failed on the 2 machines.
Is there any thing I can do to find out what's wrong with the topology or the storm cluster? Really thanks and hoping for your reply.
Your problem is related to the handling of backpressure by KafkaSpout in the StormTopology.
You can handle the back pressure of the KafkaSpout by setting the maxSpoutPending value in the topology configuration,
Config config = new Config();
config.setMaxSpoutPending(200);
config.setMessageTimeoutSecs(100);
StormSubmitter.submitTopology("testtopology", config, builder.createTopology());
maxSpoutPending is the number of tuples that can be pending acknowledgement in your topology at a given time. Setting this property, will intimate the KafkaSpout not to consume any more data from Kafka unless the unacknowledged tuple count is less than maxSpoutPending value.
Also, make sure you can fine tune your Bolts to be lightweight as possible so that the tuples get acknowledged before they timeout.
I have a Topology with Spout S, and 3 bolts - A, B, C.
Bolt A reads from Spout S. Bolt A then splits the data into Bolts B and C (based on some filter). Bolts B and C have their respective data sinks.
If I use Storm's anchoring and anchor the tuple at Bolt A, and then later on Bolt B ack's successfully, but Bolt C does a fail(). Will replaying by Storm at Spout S cause duplication of events at Bolt B and so into the data sink at B?
If so, what is the way to avoid that while still using storm's reliability feature of anchoring?
Storm's anchoring feature only support at-least-once processing and there is no support to handle duplicates in case of failure. Depending on you application semantics, this might be an issues or not.
For example if you do idempotent operation later on, duplicate are not issues (an example of an idempotent operation is updating a key-value store -- if you to two put operation because of a duplicate, the state of the key-value store still be the same).
If you have non idempotent operations and duplicates are an issue, you could try to take care if this by you own -- but this is pretty difficult to get right.
As an alternative you could use Trident API instead of low-level API which does provide exactly-once guarantees.
Or, as last resort, use a different system, that does provide exactly-once semantics out-of-the box.
I have already read related materials about storm parallel but still keep something unclear. Suppose we take Tweets processing as an example. Generally what we are doing is retrieving tweets streaming, counting numbers of words of each tweets and write the numbers into a local file.
My question is how to understand the value of the parallelism of spouts as well as bolts. Within the function of builder.setSpout and builder.setBolt we can assign the parallel value. But in the case of word counting of tweets is it correct that only one spout should be set? More than one spouts are regarded as copies of the first same spout by which identical tweets flow into several spouts. If that is the case what is the value of setting more than one spouts?
Another unclear point is how to assign works to bolts? Is the parallel mechanism achieve in the way of Storm will find currently available bolts to process a next emitting spout? I revise the basic tweets counting code so the final counting results will be written into a specific directory, however, all results are actually combined in one file on nimbus. Therefore after processing data on supervisors all results will be sent back to nimbus. If this is true what is the communication mechanism between nimbus and supervisors?
I really want to figure out those problems!!! Do appreciate for the help!!
Setting the parallelism for spouts larger than one, required that the user code does different things for different instances. Otherwise (as you mentioned already), data is just sent through the topology twice. For example, you can have a list of ports you want to listen to (or a list of different Kafka topics). Thus, you need to ensure, that different instanced listen to different ports or topics... This can be achieved in open(...) method by looking at topology metadata like own task ID, and dop. As each instance has a unique ID, you can partition your ports/topics such that each instance picks different ports/topics from the overall list.
About parallelism: this depends on the connection pattern you are using when pluging your topology together. For example, using shuffleGrouping results in a round robin distribution of your emitted tuples to the consuming bolt instances. For this case, Storm does not "look" if any bolt instance is available for processing. Tuples are simply transfered and buffered at the receiver if necessary.
Furthermore, Nimbus and Supervisor only exchange meta data. There is not dataflow (ie, flow of tuples) between them.
In some cases like "Kafka's Consumer Group" you have queue behaviour - which means that if one consumer read from the queue, other consumer will read different message from the queue.
This will distribute read load from the queue across all workers.
In those cases you can have multiple spout reading from the queue