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Storm does not allow arbitrary state in operators (what Nathan Marz calls "bolts"). Maybe I misunderstand your point, but Storm does allow state in Bolts - a B

Storm does not allow arbitrary state in operators (what Nathan Marz calls "bolts"). Maybe I misunderstand your point, but Storm does allow state in Bolts - a Bolt is just a Java object, so it can have member variables. That's how aggregation (e.g. counting events per user) is done. Of course, if you want a Bolt that scales horizontally, you need to account for the state being split across several instances of the Bolt class; and if you need the state to survive a restart, you need to keep it in an external database instead of in the object's memory.

scott_s 15y ago
Then I stand corrected. Is there any means to declare partitioned state that the runtime then handles for the user, or does the user always have to manage it themselves? This is one of those things that is, I think, easier to do declaratively one level up in abstraction. (You can see examples of this by looking at invocations of our Aggregate operator in the above documents.) I had assumed there was no arbitrary state because of the replay semantics. Let's say bolt A sends tuples to bolt B. B has internal state. A sends tuples t1, t2, t3 and t4. A receives acknowledgements that t1, t3 and t4 were processed. So t2 needs to be replayed. But the semantics of what that means is undefined - B has internal state that already incorporates, for certain, t3 and t4, and maybe t2. (While it's unlikely, you never know where a tuple got lost.) So replaying t2 is problematic - do you just blindly replay it, and allow potentially broken semantics? The alternative is to do rollback, which is quite hairy.
- samstokes 15y ago
  partitioned state that the runtime then handles for the user Yes, when "wiring up" a bolt to a spout or another bolt, you can say that it should receive tuples grouped by one or more of the tuple fields. (e.g. tuples [user, num_events] grouped by "user".) Then Storm takes care of the consistent hashing needed to ensure that each instance of the bolt receives all of the tuples in a given group (e.g. instance 1 gets all events for user 42, instance 2 gets all events for user 53). I had assumed there was no arbitrary state because of the replay semantics. Storm itself doesn't implement replay; it relies on the ability of an external event source (e.g. a message queue) to replay tuples if needed. It just provides the ability to notify the source of whether replay is needed (by tracking the tuples as they flow through the topology). So whether or not you replay tuples is an application choice. But yes, if you have stateful bolts and replay then you do need to make sure that processing a given tuple is idempotent.

scott_s 15y ago

Then I stand corrected. Is there any means to declare partitioned state that the runtime then handles for the user, or does the user always have to manage it themselves? This is one of those things that is, I think, easier to do declaratively one level up in abstraction. (You can see examples of this by looking at invocations of our Aggregate operator in the above documents.) I had assumed there was no arbitrary state because of the replay semantics. Let's say bolt A sends tuples to bolt B. B has internal state. A sends tuples t1, t2, t3 and t4. A receives acknowledgements that t1, t3 and t4 were processed. So t2 needs to be replayed. But the semantics of what that means is undefined - B has internal state that already incorporates, for certain, t3 and t4, and maybe t2. (While it's unlikely, you never know where a tuple got lost.) So replaying t2 is problematic - do you just blindly replay it, and allow potentially broken semantics? The alternative is to do rollback, which is quite hairy.

samstokes 15y ago
partitioned state that the runtime then handles for the user Yes, when "wiring up" a bolt to a spout or another bolt, you can say that it should receive tuples grouped by one or more of the tuple fields. (e.g. tuples [user, num_events] grouped by "user".) Then Storm takes care of the consistent hashing needed to ensure that each instance of the bolt receives all of the tuples in a given group (e.g. instance 1 gets all events for user 42, instance 2 gets all events for user 53). I had assumed there was no arbitrary state because of the replay semantics. Storm itself doesn't implement replay; it relies on the ability of an external event source (e.g. a message queue) to replay tuples if needed. It just provides the ability to notify the source of whether replay is needed (by tracking the tuples as they flow through the topology). So whether or not you replay tuples is an application choice. But yes, if you have stateful bolts and replay then you do need to make sure that processing a given tuple is idempotent.