We all know that Kafka is very useful for your streaming and real time projects thanks to its reliability, low latency, escalation and so on. If not, read on this post: Do you know why Kafka is being a success? But, have you ever thought that Kafka may be just a commodity for your products not only your projects? It is well known that most successful companies are built on top of products, not projects. Apple started with a computer and now is triumphant with a phone: products.
Coca-Cola with a drink. Just the third and the fifth are more services oriented. The same could happen to Kafka: instead of just being used for inside projects in the last years we have seen a gradual approach to use Kafka as another component just like the database or a security service.
It can be used on premise or cloud. To simplify we will describe a BPM as a tool where you model your business process i.
This design is graphical, multilevel process and sub processesparallel and branched, some of them quite complex. They evolve in time and some parts can be even ad hoc processes.
Appian uses Kafka for dealing with its internal messaging among its components. This means that all actions in the system will be sent and distributed using Kafka. The obvious advantages are those from Kafka: reliability, speed, fault-tolerance, capacity. But this also eases the problem of communication between components, less ports, less protocols, K. In addition to this albeit Appian has not opened their internal message format for developers, it is quite obvious that the flow of messages is the perfect source of information for KPIs.
Hyperledger Fabric HF is one of the flavors of the famous Blockchain. Blockchain became famous in the last years for Bitcoin high prices and speculation and some news on stealing electricity to mine cryptocurrencies.Lesson 2 - Kafka vs. Standard Messaging
This is getting interesting: do you know the origin of Bitcoin and Blockchain? But Blockchain is not about the cryptocurrencies, those are the rewards for some work done for Blockchain networks. The real underlying benefit of Blockchain is the ability to exchange money, goods and information, to execute contracts and so on without the need of trusting a central authority; instead, the Blockchain network is a large amount of entities and the trust is what the majority says it is the truth, so the larger the network the more difficult it is to fool.
Note: this is true for a certain type of Blockchain network, other consensus algorithms work different. HF has one component: the Orderer service that uses Kafka to assure all the transactions are executed in order, because any change has to be taken into account for future transactions.
HF is using a single Kafka topic per channel this is a Hyperledger concept that relates to the all the transactions inside a group of actors and therefore those have to be in order using a single partition with at least four replicas. The replicas allow the system to have a Kafka or Orderer broker down and still work in sync.
CDC products have the ability to detect changes in a system typically a database and transport those changes to other systems other databases, HDFS, filesystems, etc. There are a vast number of products and solutions using Kafka, why not also yours?How do humans make decisions?
In everyday life, emotion is often the circuit-breaking factor in pulling the trigger on a complex or overwhelming decision. Today there are dozens of messaging technologies, countless ESBs, and nearly iPaaS vendors in market. Naturally, this leads to questions about how to choose the right messaging technology for your needs - particularly for those already invested in a particular choice.
Do we switch wholesale?
Benchmarking NATS Streaming and Apache Kafka
Just use the right tool for the right job? Have we correctly framed the job at hand for the business need? Given that, what is the right tool for me? Worse, an exhaustive market analysis might never finish, but due diligence is critical given the average lifespan of integration code. This post endeavors give the unconscious, expert mind some even handed treatment to consider, starting with the most modern, popular choices today: RabbitMQ and Apache Kafka.
Origins are revealing about the overall design intent for any piece of software, and make good starting point. It was one of the first open source message brokers to achieve a reasonable level of features, client libraries, dev tools, and quality documentation. RabbitMQ was originally developed to implement AMQPan open wire protocol for messaging with powerful routing features. With the advent of AMQP, cross-language flexibility became real for open source message brokers.
Apache Kafka is developed in Scala and started out at LinkedIn as a way to connect different internal systems. At the time, LinkedIn was moving to a more distributed architecture and needed to reimagine capabilities like data integration and realtime stream processing, breaking away from previously monolithic approaches to these problems.
Kafka is well adopted today within the Apache Software Foundation ecosystem of products and is particularly useful in event-driven architecture. It is mature, performs well when configured correctly, is well supported client libraries Java. NET, node. Figure 1 - Simplified overall RabbitMQ architecture. Communication in RabbitMQ can be either synchronous or asynchronous as needed. Publishers send messages to exchanges, and consumers retrieve messages from queues.
Decoupling producers from queues via exchanges ensures that producers aren't burdened with hardcoded routing decisions. RabbitMQ also offers a number of distributed deployment scenarios and does require all nodes be able to resolve hostnames. It can be setup for multi-node clusters to cluster federation and does not have dependencies on external services but some cluster formation plugins can use AWS APIs, DNS, Consul, etcd.
Apache Kafka is designed for high volume publish-subscribe messages and streams, meant to be durable, fast, and scalable. At its essence, Kafka provides a durable message store, similar to a log, run in a server cluster, that stores streams of records in categories called topics.Comment 0.
The purpose of this benchmark is to test drive the newly released NATS Streaming system, which was made generally available just in the last few months. Unlike conventional message queues, commit logs are an append-only data structure. This results in several nice properties like total ordering of messages, at-least-once delivery, and message replay semantics. Kafka, which originated at LinkedIn, is by far the most popular and most mature implementation of the commit log AWS offers their own flavor of it called Kinesis, and imitation is the sincerest form of flattery.
Fundamental to the notion of a log is a way to globally order events. Neither NATS Streaming nor Kafka are actually a single log but many logs, each totally ordered using a sequence number or offset, respectively. In Kafka, topics are partitioned into multiple logs which are then replicated across a number of servers for fault tolerance, making it a distributed commit log. Each partition has a server that acts as the leader. Cluster membership and leader election is managed by ZooKeeper.
Unlike Kafka, NATS Streaming does not support replication or partitioning of channels, though my understanding is clustering support is slated for Q1 Its message store is pluggable, so it can provide durability using a file-backed implementation, like Kafka, or simply an in-memory store. NATS Streaming is closer to a hybrid of traditional message queues and the commit log. Like Kafka, it allows replaying the log from a specific offset, the beginning of time, or the newest offset, but it also exposes an API for reading from the log at a specific physical time offset, i.
Kafka, on the other hand, only has a notion of logical offsets Correction: Kafka added support for offset lookup by timestamp in 0.
Generally, relying on physical time is an anti-pattern in distributed systems due to clock drift and the fact that clocks are not always monotonic. For example, imagine a situation where a NATS Streaming server is restarted and the clock is changed. Messages are still ordered by their sequence numbers but their timestamps might not reflect that. Developers would need to be aware of this while implementing their business logic. NATS Streaming allows clients to either track their sequence number or use a durable subscription, which causes the server to track the last acknowledged message for a client.
If the client restarts, the server will resume delivery starting at the earliest unacknowledged message. This is closer to what you would expect from a traditional message-oriented middleware like RabbitMQ. This works by configuring the maximum number of in-flight unacknowledged messages either from the publisher to the server or from the server to the subscriber. Kafka was designed to avoid tracking any client state on the server for performance and scalability reasons. Throughput and storage capacity scale linearly with the number of nodes.
NATS Streaming provides some additional features over Kafka at the cost of some added state on the server. Message contents are randomized to account for compression. We then plot the latency distribution by percentile on a logarithmic scale from the 0th percentile to the The benchmark code used is open source. Unsurprisingly, the 1MB configuration has much higher latencies than the other configurations, but everything falls within single-digit-millisecond latencies.
This provides the same durability guarantees as Kafka running with a replication factor of one. As expected, latencies increase by about an order of magnitude once we start going to disk.While the conference is oriented towards Microsoft technology, I mixed it up by covering a set of messaging technologies in the open source space; you can view my presentation here.
One thing I tried to make clear in my presentation was how these open source tools differ from classic ESB software. With Kafka you can do both real-time and batch processing.
Ingest tons of data, route via publish-subscribe or queuing. The broker barely knows anything about the consumer.
The architecture is fairly unique; topics are arranged in partitions for parallelismand partitions are replicated across nodes for high availability. For this set of demos, I used Vagrant to stand up an Ubuntu box, and then installed both Zookeeper and Kafka. I also installed Kafka Managerbuilt by the engineering team at Yahoo!. I showed the conference audience this UI, and then added a new topic to hold server telemetry data.
For the Kafka demos, I used the kafka-node module. For my second Kafka demo, I showed off the replay capability. Data is removed from Kafka based on an expiration policy, but assuming the data is still there, consumers can go back in time and replay from any available point.
In the code below, I go back to offset position 40 and read everything from that point on. Super useful if apps fail to process data and you need to try again, or if you have batch processing needs. It follows a standard store-and-forward pattern where you have the option to store the data in RAM, on disk, or both. It supports a variety of message routing paradigms. RabbitMQ can be deployed in a clustered fashion for performance, and mirrored fashion for high availability.
For these demos I only had time to do two of them at the conferenceI used Vagrant to build another Ubuntu box and installed RabbitMQ and the management console. The management console is straightforward and easy to use. For the first demo, I did a publish-subscribe example. First, I added a pair of queues: notes1 and notes2. In order to send the inbound message to ALL subscribers, I used a fanout routing type.
Other options include direct specific routing keytopic depends on matching a routing key patternor headers route on message headers. I have an option to bind this exchange to another exchange or a queue.
Here, see that I bound it to the new queues I created. Any message into this exchange goes to both bound queues.What is Kafka? Distributed, fault tolerant, high throughput pub-sub messaging system. Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. What is NATS? Unlike traditional enterprise messaging systems, NATS has an always-on dial tone that does whatever it takes to remain available.
This forms a great base for building modern, reliable, and scalable cloud and distributed systems. Kafka is an open source tool with Here's a link to Kafka's open source repository on GitHub. I use Kafka because it has almost infinite scaleability in terms of processing events could be scaled to process hundreds of thousands of eventsgreat monitoring all sorts of metrics are exposed via JMX.
Downsides of using Kafka are: - you have to deal with Zookeeper - you have to implement advanced routing yourself compared to RabbitMQ it has no advanced routing. The question for which Message Queue to use mentioned "availability, distributed, scalability, and monitoring".
Comparing Apache Kafka, Amazon Kinesis, Microsoft Event Hubs and Google Pub/Sub
I don't think that this excludes many options already. I does not sound like you would take advantage of Kafka 's strengths replayability, based on an even sourcing architecture. You could pick one of the AMQP options.
It ticks the boxes you mentioned and on top you will get a very flexible system, that allows you to build the architecture, pick the options and trade-offs that suite your case best. For more information about RabbitMQ, please have a look at the linked markdown I assembled. The second half explains many configuration options. It also contains links to managed hosting and to libraries though it is missing Python's - which should be Puka, I assume. I used Kafka originally because it was mandated as part of the top-level IT requirements at a Fortune client.
What I found was that it was orders of magnitude more complex So for any case where utmost flexibility and resilience are part of the deal, I would use Kafka again.
But due to the complexities involved, for any time where this level of scalability is not required, I would probably just use Beanstalkd for its simplicity. I tend to find RabbitMQ to be in an uncomfortable middle place between these two extremities. Front-end messages are logged to Kafka by our API and application servers. We have batch processing on the middle-left and real-time processing on the middle-right pipelines to process the experiment data.
For batch processing, after daily raw log get to s3, we start our nightly experiment workflow to figure out experiment users groups and experiment metrics. We use our in-house workflow management system Pinball to manage the dependencies of all these MapReduce jobs. Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.My primary involvement with NATS Streaming was building out the early data replication and clustering solution for high availability, which has continued to evolve since I left the project.
The stream then records messages on that subject to a replicated write-ahead log. Multiple consumers can read back from the same stream, and multiple streams can be attached to the same subject. This meant not relying on external coordination services like ZooKeeper, not using the JVM, keeping the API as simple and small as possible, and keeping client libraries thin. It relies on the Raft consensus algorithm to do coordination.
The goal is to keep Liftbridge very lightweight— in terms of runtime, operations, and complexity. 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. This means it can be added to an existing NATS deployment to provide message durability with no code changes.
However, it is an entirely separate protocol built on top of NATS. NATS Streaming provides a broader set of features such as durable subscriptions, queue groups, pluggable storage backends, and multiple fault-tolerance modes.
Liftbridge aims to have a relatively small API surface area.
The key features that differentiate Liftbridge are the shared message namespace, wildcards, log compaction, and horizontal scalability. NATS Streaming replicates channels to the entire cluster through a single Raft group, so adding servers does not help with scalability and actually creates a head-of-line bottleneck since everything is replicated through a single consensus group n. NATS Streaming does have a partitioning mechanism, but it cannot be used in conjunction with clustering.
Liftbridge allows replicating to a subset of the cluster, and each stream is replicated independently in parallel. This allows the cluster to scale horizontally and partition workloads more easily within a single, multi-tenant cluster.
Over the coming weeks and months, I will be going into more detail on Liftbridge, including the internals of it—such as its replication protocol—and providing benchmarks for the system. There are many interesting problems that still need solved, so consider this my appeal to contributors.
It was built as a separate layer on top of NATS. NATS Streaming is the voicemail—leave a message after the beep and someone will get to it later. The architecture is shown below in a diagram borrowed from the NATS website.This item in japanese. Aug 20, 11 min read. Richard Seroter. Research firm Gartner claims that enterprises are turning their attention to event-driven IT.
Byit'll be a top 3 CIO priorityaccording to their research. The growth of Apache Kafka —the widely-used event streaming platform—seems to bolster that claim. All three major public cloud providers offer first-party services for event stream processing, and a number of industry leaders have ralied around the CloudEvents spec.
In a blog post announcing the open-sourcing of the projectTreat explained that the project's goal is to "bridge the gap between sophisticated log-based messaging systems like Apacha Kafka and Apache Pulsar and simpler, cloud-native systems. Tyler Treat : Fundamentally, Liftbridge is solving the same types of problems that systems like Apache Kafka solve.
At its core, it's a messaging system that allows you to decouple data producers from consumers. But like Kafka, Liftbridge is a message log. Unlike a traditional message broker like RabbitMQ, messages in Liftbridge are appended to an immutable commit log. With a traditional broker, messages are typically pulled off of a queue by consumers.
With Liftbridge, messages remain in the log when they are consumed, and there can be many consumers reading from the same log. It's almost like the write-ahead log of a database ripped out and exposed as a service. This lets us replay messages sent through the system. This approach lends itself to a lot of interesting use cases around event sourcing and stream processing. For example, with log aggregation we can write all of our application and system logs to Liftbridge, which acts as a buffer, and pipe them to various backends like Splunk, Datadog, cold storage, or whatever else cares about log data.
At one company I worked at, this approach allowed us to evaluate multiple logging providers simultaneously by providing a way to tee our data. Systems like Liftbridge and Kafka provide a very powerful data pipeline. However, Liftbridge wasn't built just as an alternative to Kafka. The bigger goal was to provide Kafka-like semantics to NATS, which is a high-performance, but fire-and-forgetmessaging system. NATS is often described as a "dial tone" for distributed systems because it's designed to be always on and dead simple.
The drawback to this is that it's limited in its capabilities. If we continue our analogy, Liftbridge is the voicemail to NATS—if a message is published to NATS and an interested consumer is offline, Liftbridge ensures the message is still delivered by recording it to the log.