I am a beginner of storm. Storm's creator created a very impressive method to check every Bolts in topology, which is using XOR.
But I start wondering why he just not use a counter. When a Bolts is successfully executed, the counter will minus one. So when the counter equal with 0, means the whole task is completetly.
Thanks
I believe one can reason why counters are not only inefficient but an incorrect acker tracker mechanism in an always running topology.
Storm tuple topology in itself can be a complex DAG. When a bolt receives ack from multiple downstream sources, what is it to do with the counters? Should it increment them, should it always decrement them? In what order?
Storm tuples have random message Ids. Counters will be finite. A topology runs forever emitting billions of tuples. How will you map the 673686557th tuple to a counter id? With XOR, you only have a single state to maintain and broadcast.
XOR operations are hardware instructions that execute extremely efficiently. Counters are longs which require huge amounts of storage. They have overflow problems and defeat the original requirement of a solution with a low space overhead.
Related
I am new to Apache storm and wondering how parallelism hint works.
For e.g. We have one stream containing two tuples <4>,<6>, one spout with only one task per executor and we have one bolt to perform some operation on the tuples and having parallelism hint as 2, so we have two executor of this bolt namely A and B, regarding this, I have 3 questions.
Considering above scenario is this possible that our tuple which contain value 4 is processed by A and another tuple which contain value 6 is processed by B.
If processing done in this manner i.e. mentioned in question (1), then won't it impact on operation in which sequence matter.
If processing not done in this manner, means both tuples going to same executor then what is the benefit of parallelism.
Considering above scenario is this possible that our tuple which contain value 4 is processed by A and another tuple which contain value 6 is processed by B.
Yes.
If processing done in this manner i.e. mentioned in question (1), then won't it impact on operation in which sequence matter.
It depends. You most likely have control over the sequence of the tuples in your spout. If sequence matters, it is advisable to either reduce parallelism or use fields grouping, to make sure tuples which depend on each other go to the same executor. If sequence does not matter use shuffleGrouping or localOrShuffleGrouping to get benefits from parallel processing.
If processing not done in this manner, means both tuples going to same executor then what is the benefit of parallelism.
If both tuples go to the same executor, there is no benefit, obviously.
Now there is a problem that puzzles me. How should I count the execution times of bolt and spout in Storm? I have tried to use ConcurrentHashmap (considering multithreading), but it can't be done on multiple machines. Can you help me solve this problem?
Considering your question i think you are trying to keep a track of number of tuple got executed and not the amount of time bolt or spout takes to execute one tuple.
You can use metices with graphite for visualisation. It gives a time series data.
Database can also be used for the same purpose.
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.
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
As per the given link the capacity of a bolt is the percentage of time spent in executing. Therefore this value should always be smaller than 1. But in my topology I have observed that it is coming over 1 in some cases. How is it possible and what does it mean ?
http://i.stack.imgur.com/rwuRP.png
It means that your bolt is running over capacity and your topology will fall behind in processing if the bolt is unable to catch up.
When you see a bolt that is running over (or close to over) capacity, that is your clue that you need to start tuning performance and tweaking parallelism.
Some things you can do:
Increase the parallelism of the bolt by increasing the number of executors & tasks.
Do some simple profiling within your slow bolts to see if you have a performance problem.
You can get more detail about what's happens in your bolts using Storm Metrics
https://storm.apache.org/documentation/Metrics.html