Kafka vs Neo4j: What are the differences?
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 Neo4j? The world’s leading Graph Database. Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
Kafka can be classified as a tool in the "Message Queue" category, while Neo4j is grouped under "Graph Databases".
Some of the features offered by Kafka are:
- Written at LinkedIn in Scala
- Used by LinkedIn to offload processing of all page and other views
- Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled)
On the other hand, Neo4j provides the following key features:
- intuitive, using a graph model for data representation
- reliable, with full ACID transactions
- durable and fast, using a custom disk-based, native storage engine
"High-throughput" is the top reason why over 95 developers like Kafka, while over 55 developers mention "Cypher – graph query language" as the leading cause for choosing Neo4j.
Kafka and Neo4j are both open source tools. It seems that Kafka with 12.7K GitHub stars and 6.81K forks on GitHub has more adoption than Neo4j with 6.61K GitHub stars and 1.63K GitHub forks.
Uber Technologies, Spotify, and Slack are some of the popular companies that use Kafka, whereas Neo4j is used by Medium, Movielala, and Hinge. Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to Neo4j, which is listed in 114 company stacks and 47 developer stacks.
What is Kafka?
What is Neo4j?
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Beamery runs a #microservices architecture in the backend on top of Google Cloud with Kubernetes There are a 100+ different microservice split between Node.js and Go . Data flows between the microservices over REST and gRPC and passes through Kafka RabbitMQ as a message bus. Beamery stores data in MongoDB with near-realtime replication to Elasticsearch . In addition, Beamery uses Redis for various memory-optimized tasks.
Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :
Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:
(Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )
I use Kafka because it has almost infinite scaleability in terms of processing events (could be scaled to process hundreds of thousands of events), great 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". 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.
I would recommend the RabbitMQ message broker, which not only implements the AMQP standard 0.9.1 (it can support 1.x or other protocols as well) but has also several very useful extensions built in. 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 500 client. What I found was that it was orders of magnitude more complex ...and powerful than my daily Beanstalkd , and far more flexible, resilient, and manageable than RabbitMQ.
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.
Neo4j is a great graph database, but it's also a great tool for any application in general. The data model is easy to figure out and is flexible to use as your application changes in the early stages. Further, there are constraints you can add to get data consistency once you have a firm data model. The built in admin tool makes it easy to review the data, see how your application is being used, and has a great query plan visualizer for when you want to optimize for performance.
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.
To be evaluated
- + Leading Graph DB provider, large community
- + Rich querying language
- + Tools to visualise and interact visually with results
Possible alternative to triple store.
- does it support full text search?
- does it support some sort of inference or derived relationships (e.g. transitivity, symmetry)?
- does it support faceted search?
Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.