foo.bar and foo.*.
Kafka scales and performs exceptionally well by shifting the complexity to other parts of the system, namely the client and ZooKeeper.
But also keep in mind that consumers might not have access to fast stable storage, such as with an IoT device or ephemeral container.
Each connector is a one-way bridge. These nuances often get confounded by first–time users as it’s not always clear that NATS and NATS Streaming are completely separate systems. For example, in an e-commerce application, we might have two topics, purchases and inventory, each with two partitions. Hopefully you found it useful or, at the very least, interesting. Immutability, at least in theory, should make it “easy” to scale to a large number of consumers because we don’t have to read from the leader to get correct results (ignoring log compaction and other “mutable” operations), so long as we’re okay with strong eventual consistency with respect to tailing the log. Right now, we pushing .wav files through NiFi, Kafka, and running FFT’s in Spark — as our demonstration use-case. Bench is an attempt to get back to basics. For improved performance, the client library should only periodically checkpoint this offset. Flotilla also attempted to capture a better view of latency by looking at the latency distribution, though it only went up to the 99th percentile, which can sweep a lot of really bad things under the rug as we’ll see later. Similarly, chaos and fault-injection testing such as Kyle Kingsbury’s Jepsen help too. Unlike NATS, it’s a more traditional message queue in the sense that it supports binding queues and transactional-delivery semantics. So performance is at odds with fault-tolerance and scalability, but another factor is what I call simplicity of mechanism.
But: Tencent actually uses Kafka more …
By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy. In part four of this series, we will discuss some of the key trade-offs involved with implementing a distributed log and some lessons learned while building NATS Streaming.Follow @tyler_treat, Introducing Liftbridge: Lightweight, Fault-Tolerant Message Streams, Building a Distributed Log from Scratch, Part 5: Sketching a New System, we can always build stronger guarantees on top, but we can’t always remove them from below, multi-user authentication and subject-level permissions, Building a Distributed Log from Scratch, Part 4: Trade-Offs and Lessons Learned, Building a Distributed Log from Scratch, Part 3: Scaling Message Delivery, additive increase/multiplicative decrease, Everything You Know About Latency Is Wrong, Getting big wins with small teams on tight deadlines, Building a Distributed Log from Scratch, Part 1: Storage Mechanics, Using Google-Managed Certificates and Identity-Aware Proxy With GKE, Zero-Trust Security on GCP With Context-Aware Access, Designed to be high-throughput (more on this to come).
foo.> matches foo.bar, foo.bar.baz, etc.). Finally, how about real-time communication, can gRPC replace websocket/socket.io/signalR completely? Second, lean on existing work. The existing technology and use case at Tinder can be different than that of facebook. For example, we might structure it as consumer-topic-partition. This project implements a multi-connector bridge between NATS or NATS streaming and Kafka topics. As discussed above, messages in Jetstream are simply NATS messages. At the same time, simplicity of “UX” makes performance harder.
There is much less complexity with this. The resemblance becomes more apparent when we overlay the two distributions for the 1KB and 5KB runs. To be clear, it’s still a separate server, but it merely acts as a write-ahead log for NATS subjects. Do flavors other than the standard Gnome Ubuntu 20.10 support Raspberry Pi on the desktop? In effect, this is the daisy chaining of streams mentioned earlier but done implicitly by the system. This is sometimes a factor when choosing a solution. Note that, in the case of NATS Streaming, this isn’t quite as simple as it sounds due to the delivery mechanism used, which we’ll describe next. The difference is even more pronounced in the 1MB case, which has roughly a 90% improvement up to the 90th percentile. Third, running “pedal to the metal” and looking at the resulting latency isn’t a very useful benchmark because it’s not representative of a production environment (as Gil Tene likes to say, this is like driving your car as fast as possible, crashing it into a pole, and looking at the shape of the bumper afterwards—it’s always going to look bad). Once again, the 1KB, 20,000 requests/sec run is distributed across 25 concurrent connections.
Messages coming from Kafka will have their key ignored. Message values in Kafka are mapped to message bodies in NATS. 61,0.023782
If I write data to the system and the system acknowledges that, that data should not be lost in the event of a failure. There are API implications with this decision too, particularly from an ergonomics and complexity perspective. This is demonstrated through many of the design and implementation decisions. It also means we rely on timeouts even in cases where the server could send a response immediately, such as when there is no leader elected for the cluster.
The other important takeaway with respect to benchmarking is to look at the complete latency distribution.
As with 0.8, Kafka 0.9 does an impressive job dealing with 1MB messages in comparison to NATS, especially when looking at the 92nd percentile and beyond. About a year and a half ago, I published Dissecting Message Queues, which broke down a few different messaging systems and did some performance benchmarking. This design is graphical, multilevel (process and sub processes), parallel and branched, some of them quite complex. The problem with this is we lose out on ordering, which is an important characteristic of the log. We can make these types of systems fault-tolerant by introducing a standby server and allowing clients to failover, but there are a couple issues worth mentioning with this. This is a trade-off we make for enabling subject fan-out and wildcards while remaining scalable and fast. However, benchmarking Kafka 0.9.0.0 (blue and red) shows an astounding difference in tail latencies compared to 0.8.2.2 (orange and green).
In this case it is Kafka who beats Nats.
Once this replication completes, the stream has been created and its leader begins processing messages. This means that all actions in the system will be sent and distributed using Kafka. Its good to know the maximum throughput but it’s also good to compare kafka and rabbit where persistence is required. Sep 2, 2020 | Golang Bridge. The graph below shows the same benchmark with coordinated omission both uncorrected (red) and corrected (blue): HDR Histogram attempts to correct coordinated omission by filling in additional samples when a request falls outside of its expected interval. In this case, it retains only the last message for each key (if no key is present, the message is always retained). This plays out at multiple levels. The system under test and benchmarking client are on two different m4.xlarge EC2 instances (2.4 GHz Intel Xeon Haswell, 16GB RAM) with enhanced networking enabled.
Another issue is data is not replicated unless done so out-of-band by the storage layer. 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. Cypress: Install and Write your first e2e test in less than 5 min, Using Docker Overlay Networks: Configuration Guide. 36,0.015225 This makes push even more complicated. The alternative is to punt the problem to the consumer. Thus, if we have five consumers reading from the same shard, we’ve already hit our fan-out limit. The key factor largely comes down to flow control. Fourth, without extending its protocol, NATS Streaming’s authorization is intrinsically limited to the authorization provided by NATS since all communication goes through it. NATS performance looks comparable to Redis. The key features that differentiate Liftbridge are the shared message namespace, wildcards, log compaction, and horizontal scalability.
But this also eases the problem of communication between components, less ports, less protocols, K.I.S.S. I also like to build violins as a hobby. Now, the question, to what extent are my assumptions true? With pull, the consumer fetches all available messages after its current position in the log, which basically removes the guesswork around tuning batching and latency. This has the advantage that we don’t need to worry about partitioning so long as NATS is able to withstand the load (there is also an assumption that we can ensure reasonable balance of stream leaders across the cluster).