InfluxDB vs Kafka: What are the differences?
InfluxDB: An open-source distributed time series database with no external dependencies. InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.; 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.
InfluxDB can be classified as a tool in the "Databases" category, while Kafka is grouped under "Message Queue".
Some of the features offered by InfluxDB are:
- Time-Centric Functions
- Scalable Metrics
On the other hand, Kafka provides the following key features:
- 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)
"Time-series data analysis" is the primary reason why developers consider InfluxDB over the competitors, whereas "High-throughput" was stated as the key factor in picking Kafka.
InfluxDB and Kafka are both open source tools. It seems that InfluxDB with 16.7K GitHub stars and 2.39K forks on GitHub has more adoption than Kafka with 12.7K GitHub stars and 6.81K GitHub forks.
Uber Technologies, Spotify, and Slack are some of the popular companies that use Kafka, whereas InfluxDB is used by trivago, Redox Engine, and Thumbtack. Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to InfluxDB, which is listed in 119 company stacks and 39 developer stacks.
What is InfluxDB?
What is Kafka?
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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.
We use InfluxDB as a store for our data that gets fed into Grafana. It's ideal for this as it's a lightweight storage engine that can be modified on the fly by scripts without having to log into the server itself and manage tables. The HTTP API also makes it ideal for integrating with frontend services.
Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.
To track time-series of course, utilizing few retention rules and continuous queries to keep time-series data fast and maintanable
InfluxDB ingests information from various sources (mostly Telegraf instances) into one place for monitoring purposes.