Note: this is true for a certain type of Blockchain network, other consensus algorithms work different. Show that three numbers form an arithmetic progression. After scanning the 0.9 release notes, I was unable to determine specifically what those fixes might be. At its core, Liftbridge is a server that implements a durable, replicated message log for the NATS messaging system. Again, we go back to the laws of systems here: “A complex system that works is invariably found to have evolved from a simple system that works.”. For improved performance, the client library should only periodically checkpoint this offset. In this case it is Kafka who beats Nats. Note that this might be an area for future improvement to increase usability, such as storing streams in a registry. Search for: Recent Posts. First, if the publisher doesn’t care about ensuring its message is stored, it need not set a reply subject. Simple is usually better—and faster. If a failed replica catches back up, it rejoins the ISR. 6,0.01052 A lot of great features have landed since the project was first conceived in 2016 and started in 2017—replication, log compaction and retention rules, stream partitioning, activity events, and stream pausing to name a few. But: Tencent actually uses Kafka more … However, the bigger goal of Liftbridge is to extend NATS with a durable, at-least-once delivery mechanism that upholds the NATS tenets of simplicity, performance, and scalability. If nothing happens, download the GitHub extension for Visual Studio and try again. More generally, clustering and data replication was more of an afterthought in NATS Streaming. The 1KB, 20,000 requests/sec run uses 25 concurrent connections. Because Jetstream is a separate server which is merely a consumer of NATS, it can provide ACLs or other authorization mechanisms on streams. This is a common technique for relational databases where we basically just run multiple databases and divide up the data by some key. The nice thing about reusing the log to track offsets is it greatly reduces the amount of code and complexity needed. The linear scale in the graph below hides this fact, but at the 90th percentile, for example, the pre-optimization latency is 10 ms and the optimized latency is 3.8 ms. Clearly, the large tail is mostly unaffected, however. The latencies of 20,000 requests/sec at 1KB per request are not terribly different than the latencies of 3,000 requests/sec at 1KB per request, both peaking around 250 ms. What’s particularly interesting is the behavior of 1MB messages vs. the rest. Both offer very lightweight, non-transactional messaging with no persistence options (discounting Redis’ RDB and AOF persistence, which don’t apply to pub/sub), and both support some level of topic pattern matching.
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