Release Notes
NATS Concepts
Using NATS
Running a NATS service
Reference
Subject Mapping and Traffic Shaping
Subject mapping is a very powerful feature of the NATS server, useful for canary deployments, A/B testing, chaos testing, and migrating to a new subject namespace.
There are two places where you can apply subject mappings: each account has its own set of subject mappings, which will apply to any message published by client applications, and you can also use subject mappings as part of the imports and exports between accounts.
When not using operator JWT security, you can define the subject mappings in server configuration files, and you simply need to send a signal for the nats-server process to reload the configuration whenever you change a mapping for the change to take effect.
When using operator JWT security with the built-in resolver you define the mappings and the import/exports in the account JWT so after modifying them they will take effect as soon as you push the updated account JWT to the servers.
Simple Mapping
The example of
foo:bar is straightforward. All messages the server receives on subject foo are remapped and can be received by clients subscribed to bar.Subject Token Reordering
Wildcard tokens may be referenced by position number in the destination mapping using (only for versions 2.8.0 and above of
nats-server) {{wildcard(position)}}. E.g. {{wildcard(1)}} references the first wildcard token, {{wildcard(2)}} references the second wildcard token, etc...You can also (for all versions of
nats-server) use the legacy notation of $position. E.g. $1 references the first wild card token, $2 the second wildcard token, etc...Example: with this mapping
"bar.*.*" : "baz.{{wildcard(2)}}.{{wildcard(1)}}", messages that were originally published to bar.a.b are remapped in the server to baz.b.a. Messages arriving at the server on bar.one.two would be mapped to baz.two.one, and so forth.Deterministic Subject token Partitioning
Deterministic token partitioning allows you to use subject based addressing to deterministically divide (partition) a flow of messages where one or more of the subject tokens make up the key upon which the partitioning will be based, into a number of smaller message flows.
For example: new customer orders are published on
neworders.<customer id>, you can partition those messages over 3 partition numbers (buckets), using the partition(number of partitions, wildcard token positions...) function which returns a partition number (between 0 and number of partitions-1) by using the following mapping "neworders.*" : "neworders.{{wildcard(1)}}.{{partition(3,1)}}".This particular mapping means that any message published on
neworders.<customer id> will be mapped to neworders.<customer id>.<a partition number 0, 1, or 2>. i.e.:Published on
Mapped to
neworders.customerid1
neworders.customerid1.0
neworders.customerid2
neworders.customerid2.2
neworders.customerid3
neworders.customerid3.1
neworders.customerid4
neworders.customerid4.2
neworders.customerid5
neworders.customerid5.1
neworders.customerid6
neworders.customerid6.0
The mapping is deterministic because (as long as the number of partitions is 3) 'customerid1' will always map to the same partition number. The mapping is hash based, it's distribution is random but tending towards 'perfectly balanced' distribution (i.e. the more keys you map the more the number of keys for each partition will tend to converge to the same number).
You can partition on more than one subject wildcard token at a time, e.g.:
{{partition(10,1,2)}} distributes the union of token wildcards 1 and 2 over 10 partitions.Published on
Mapped to
foo.1.a
foo.1.a.1
foo.1.b
foo.1.b.0
foo.2.b
foo.2.b.9
foo.2.a
foo.2.a.2
What this deterministic partition mapping enables is the distribution of the messages that are subscribed to using a single subscriber (on
neworders.*) into three separate subscribers (respectively on neworders.*.0, neworders.*.1 and neworders.*.2) that can operate in parallel.When is deterministic partitioning needed
The core NATS queue-groups and JetStream durable consumer mechanisms to distribute messages amongst a number of subscribers are partition-less and non-deterministic, meaning that there is no guarantee that two sequential messages published on the same subject are going to be distributed to the same subscriber. While in most use cases a completely dynamic, demand-driven distribution is what you need, it does come at the cost of guaranteed ordering because if two subsequent messages can be sent to two different subscribers which would then both process those messages at the same time at different speeds (or the message has to be re-transmitted, or the network is slow, etc...) and that could result in potential 'out of order' message delivery.
This means that if the application requires strictly ordered message processing, you need to limit distribution of messages to 'one at a time' (per consumer/queue-group, i.e. using the 'max acks pending' setting), which in turns hurts scalability because it means no matter how many workers you have subscribed only one at a time is doing any processing work.
Being able to evenly split (i.e. partition) subjects in a deterministic manner (meaning that all the messages on a particular subject are always mapped to the same partition) allows you to distribute and scale the processing of messages in a subject stream while still maintaining strict ordering per subject.
Another reason to need deterministic mapping is in the extreme message rates scenarios where you are reaching the limits of the throughput of incoming messages into a stream capturing messages using a wildcard subject. This limit can be ultimately reached at very high message rates due to the fact that a single nats-server process is acting as the RAFT leader (coordinator) for any given stream and can therefore become a limiting factor. In that case, distributing (i.e. partitioning) that stream into a number of smaller streams (each one with their own RAFT leader and therefore all these RAFT leaders are spread over all of the JetStream-enabled nats-servers in the cluster rather than a single one) in order to scale.
Yet another use case where deterministic partitioning can help is if you want to leverage local data caching of data (context or potentially heavy historical data for example) that the subscribing process need to access as part of the processing of the messages.
Weighted Mappings for A/B Testing or Canary Releases
Traffic can be split by percentage from one subject to multiple subjects. Here's an example for canary deployments, starting with version 1 of your service.
Applications would make requests of a service at
myservice.requests. The responders doing the work of the server would subscribe to myservice.requests.v1. Your configuration would look like this:1
myservice.requests: [
2
{ destination: myservice.requests.v1, weight: 100% }
3
]
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All requests to
myservice.requests will go to version 1 of your service.When version 2 comes along, you'll want to test it with a canary deployment. Version 2 would subscribe to
myservice.requests.v2. Launch instances of your service.Update the configuration file to redirect some portion of the requests made to
myservice.requests to version 2 of your service.For example the configuration below means 98% of the requests will be sent to version 1 and 2% to version 2.
1
myservice.requests: [
2
{ destination: myservice.requests.v1, weight: 98% },
3
{ destination: myservice.requests.v2, weight: 2% }
4
]
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Once you've determined Version 2 is stable you can switch 100% of the traffic over to it and you can then shutdown the version 1 instance of your service.
Traffic Shaping in Testing
Traffic shaping is also useful in testing. You might have a service that runs in QA that simulates failure scenarios which could receive 20% of the traffic to test the service requestor.
myservice.requests.*: [{ destination: myservice.requests.$1, weight: 80% }, { destination: myservice.requests.fail.$1, weight: 20% }Artificial Loss
Alternatively, introduce loss into your system for chaos testing by mapping a percentage of traffic to the same subject. In this drastic example, 50% of the traffic published to
foo.loss.a would be artificially dropped by the server.foo.loss.>: [ { destination: foo.loss.>, weight: 50% } ]You can both split and introduce loss for testing. Here, 90% of requests would go to your service, 8% would go to a service simulating failure conditions, and the unaccounted for 2% would simulate message loss.
myservice.requests: [{ destination: myservice.requests.v3, weight: 90% }, { destination: myservice.requests.v3.fail, weight: 8% }] the remaining 2% is "lost"Last modified 11d ago