RabbitMQ, ZeroMQ, and NATS are probably your best bets out of the 10 options considered. This means it can be added to an existing NATS deployment to provide message durability with no code changes.
This design is graphical, multilevel (process and sub processes), parallel and branched, some of them quite complex. Does NATS scale horizontally ( throughput ). With Kafka 0.8.2.2, the difference is over 126x in the same direction. An optional HTTP/HTTPS endpoint can be used for monitoring. Aside from the usual issues with distributed locks, this design requires a shared storage layer. For example, creating two streams for the subject foo.bar named foo and bar, respectively, will create two streams which will independently sequence all of the messages on the NATS subject foo.bar, but attempting to create two streams for the same subject both named foo will result in creating just a single stream (creation is idempotent). From a consumer’s perspective, a replica is either up-to-date with the leader or in the process of catching up, but in either case, if we read all of the records, we should end up in the same state. Most benchmarks don’t capture this time spent waiting in line, yet it can have a dramatic effect on the results. Before that, clients had to manage offsets themselves or store them in ZooKeeper. This particular architecture, however, introduces a few challenges (disclosure: while I’m still a fan, I’m no longer involved with the NATS project and the NATS team is aware of these problems and no doubt working to address many of them). We end up just pushing the complexity around based on our design decisions, but one can argue that the smart client and dumb server is a more scalable approach. 66,0.026652 If I write data to the system and the system acknowledges that, that data should not be lost in the event of a failure. I tend to describe NATS as a dial tone—ubiquitous and always on—perfect for “online” communications. There are a number of advantages to this approach. In effect, this is the daisy chaining of streams mentioned earlier but done implicitly by the system.
The existing technology and use case at Tinder can be different than that of facebook. Committed messages are always preserved during a leadership change, but uncommitted messages could be lost. This way, all purchase operations by the same account are ordered, as are all inventory operations pertaining to the same SKU. The workaround is to partition among multiple channels at the application level. When a message is received on a stream, it’s assigned an offset by the leader and then replicated. Formal methods and property-based testing can help a lot here. I don’t claim to have gotten it right, but over the past year and a half I’ve learned a lot, tried to build some better tools, and improve my methodology. It is a workaround which I strongly not recommend. HTTP atop TCP.
It’s a side project of mine I hope to get to at some point. It’s also the product of scratching an itch I’ve had since these are the kinds of problems I enjoy working on, and I needed something to code.
41,0.01643 Once the replica has caught up, it’s added back into the ISR and the system resumes its fully replicated state. At version 2.2, Kafka is 8 years old. It would be great to see CPU and Memory consumption of the different choices. In part one, we introduced the log abstraction and talked about the storage mechanics behind it. As in Kafka, we also support a fourth kind: key compaction.
The preceding parts of this series described at various levels how we can accomplish these three goals, but astute readers likely noticed that some of these things conflict with one another. ZeroMQ - Fast, lightweight messaging library that allows you to design complex communication system without much effort Replicas acknowledge they’ve replicated the message. * matches foo.bar, foo.baz, etc.)
But by storing offsets in the log, they are treated just like any other write to a Kafka topic, which scales quite well (offsets are stored in an internal Kafka topic called __consumer_offsets partitioned by consumer group; there is also a special read cache for speeding up the read path). The key point here is that NATS and NATS Streaming are distinct systems with distinct protocols, distinct APIs, and distinct client libraries. Whatever the case may be, both RabbitMQ and Kafka appear to handle large messages extremely well in contrast to Redis and NATS. The trade-off here is the server needs to do some bookkeeping, which we’ll get to in a bit. This is a decision pertaining to latency versus throughput. download the GitHub extension for Visual Studio, configured with a NATS server-like format, Support for bridging from/to Kafka topics, Arbitrary subjects in NATS, wildcards for incoming messages, Optional durable subscriber names for streaming, A single configuration file, with support for reload, Optional SSL to/from Kafka, NATS and NATS streaming, HTTP/HTTPS-based monitoring endpoints for health or statistics. The problem with push is we also have to deal with redeliveries, whereas with pull they are implicitly handled by the client. In Kafka, the log is actually the partition. We repeat this for several configurations of request rate and request size. With a single node, as NATS Streaming was initially designed, this isn’t much of a problem. r/NATS_io Articles/news of interest to developers using or considering using NATS for IoT, Big Data, or Microservices messaging.
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