Kafka vs SQLite: What are the differences?
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; SQLite: A software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine. SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.
Kafka and SQLite are primarily classified as "Message Queue" and "Databases" tools respectively.
"High-throughput", "Distributed" and "Scalable" are the key factors why developers consider Kafka; whereas "Lightweight", "Portable" and "Simple" are the primary reasons why SQLite is favored.
Kafka is an open source tool with 12.7K GitHub stars and 6.81K GitHub forks. Here's a link to Kafka's open source repository on GitHub.
According to the StackShare community, Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to SQLite, which is listed in 314 company stacks and 477 developer stacks.
What is Kafka?
What is SQLite?
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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.
SQLite is a tricky beast. It's great if you're working single-threaded, but a Terrible Idea if you've got more than one concurrent connection. You use it because it's easy to setup, light, and portable (it's just a file).
In Paperless, we've built a self-hosted web application, so it makes sense to standardise on something small & light, and as we don't have to worry about multiple connections (it's just you using the app), it's a perfect fit.
For users wanting to scale Paperless up to a multi-user environment though, we do provide the hooks to switch to PostgreSQL .
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.
Used during the "build process" of Coolfront Mobile's Flat rate search engine database. Flat rate data that resides in Salesforce is transformed using SQLite into a format that is usable for our mobile Flat rate search engine (AKA: Charlie).
RDBTools is a self-hosted application, and it is important that the installation process is simple. With SQLite, we create a new database file for every analysis. Once the analysis is done, the SQLite file can be thrown away easily.
All the dynamic data (i.e.: jobs) is stored in a simple SQLite database.
Все динамические данные (вакансии) хранятся в простой SQLite БД.
Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.
There's really no call for something heavier for this site. SQLite is simple, easy to use and quite reliable given its age.