rule-engine | 基于流程 , 事件驱动 , 可拓展 , 响应式 , 轻量级的规则引擎。 | Rule Engine library

There is no need to use a third party rule engine with Apache Kafka. As part of the project there is Kafka Streams and also to ease off a bit the need to write Java code to express rules there is ksqlDB that is based on a subset of ANSI SQL.

While these options are not necessarily a rule engine per-se; they share the same semantics which is: given an intermediate processing output the relevant result based on the computation. The difference will be in the how and not in the what. So I think they are decent replacements.

You can always integrate both as well. Several rule engines such as Drools from Red Hat are Java-based and thus; can be easily accessed from a Kafka Streams processor. As long the if-then-else rules run in the same JVM space of the Kafka Streams application you won't have any performance penalties other than a possibly bigger JVM heap.

The json-rules-engine module takes data in a different format. In your Repl.it you have not defined any facts.

Facts should be:

If this is about SHACL-AF inference rules (https://w3c.github.io/shacl/shacl-af/#rules) then the TopBraid SHACL API does have support, including a shaclinfer command line tool.

https://github.com/TopQuadrant/shacl

The term "rule" is overloaded and some people call validation constraints also rules, so I am not 100% sure what you are referring to.

I got the answer, if you want "deviceA trig deviceB", you should create a relation between deviceA and deviceB, then add a rule node "change the originator" to change the originator from deviceA to deviceB.

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Unfortunately I don't know a better approach but you can optimize this solution a little bit.

I would do the same:

• Store the segment conditions in a collection
• Once you find a matching user, store the segment id in the user's document (segments)

An important requirement here is that the segments get updated in realtime and not just periodically.

You have no choice, you need to run the segmentation query every times when a segment changes.

I would have to execute all queries for all segments every time a user's data changes

This is where I would change your solution, actually just optimise it a little bit:

• You don't need to run the segmentation queries on the whole collection. If you put your user id into the query with an $and, Mongodb will fetch the user first and after that will check the rest of the segmentation conditions. You need to make sure Mongodb uses the user's _id as an index, for this you can use .explain() to check it or .hint() to force it. Unfortunately you need to run N+1 queries if you have N segments (+1 is for the user update)

• I would fetch every segments and store them in a cache (redis). If someone changed the segment I would update the cache as well. (Or just invalidate the cache and the next query will handle the rest, depends on the implementation). The point is that I would have every segments without fetching the database and if a user updated a record I would go through every segments with Node.js and validate the user by the conditions and I could update the user's segments array in the original update query so it would not require any extra database operation. I know it could be a pain in the ass implementing something like this but it doesn't overload the database ...

Update

Let me give you some technical details about my second suggestion: (This is just a pseudo code!)

Segment cache