InfluxDB vs. kdb+ vs. Riak

InfluxDB is very good at storing monitoring metrics (e.g. performance data). InfluxDB is not the right choice if you need to store other data types (like plain text, data relations etc.).

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kdb is well suited for real time tick data and time series analytics.

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Riak is well suited to applications such as:

Transaction logging e.g. financial transactions and/or exchange rates.

Storing time series data, especially IoT.

Storing massive amounts of data e.g. corporation wide backups, data lakes etc.

A fully s3 compatible replacement for Amazon s3 ensuring data privacy.

Riak is not as well suited to:

Traditional RDBMS functions, especially those that join the outputs of one or more queries together to produce the desired result.

• Perfect handling telemetry data.
• Low latency, near real time.
• SQL-like language makes it easier to query.

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• Time series analysis. The built-in vector operations are extremely fast. Also with the q language you can code up any customized analytical ideas quickly.
• The database are all file based, very easy to maintain.
• Very solid and fast interface to websocket, so you can interface with javascript easily.

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• Highly available: If nodes go offline for any reason, the system still operates.
• Highly scalable: There is a minimum of 5 nodes, which can handle a lot by themselves. When scaling is required, it can be done easily, with minimal to no downtime on large scales.
• Very fast searching: Riak has SOLR indexing built-into the core product, which makes querying for data very fast.

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• GUI based administrator console

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• Run time error message readability, particularly for new users.
• Backwards compatibility between versions.

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• Deletes!!! We've seen on numerous occasions where Riak has "resurrected" deleted data. We've worked with Basho numerous times and tried multiple changes to the way we interact with Riak to prevent the problem but it still remains. The deletes seem to reappear weeks, even months, after the delete was issued. We've had to work around this issue by providing a "deleted" flag for all data objects stored in Riak. Thus, we do no delete but simply flip the flag. Excess baggage we would really like to not have to worry about.
• Search. Currently there's no way to tell what data you have in Riak without already knowing a particular bucket/key. There is a way to list the keys for a given bucket but due to performance implications, this is not a viable method to lookup data. Especially when you have a large amount of keys in the bucket.

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InfluxDB is a near perfect product for time series database engines. The relatively small list of cons are heavily outweighed by it's ability to just work and be a very flexible and powerful database engine. The community and support provided by the corporation are the only areas I have little experience.

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switching costs

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Right now, I'm on a project where we need databases that can run on embedded systems. Riak isn't necessarily the best fit for that scenario. But when we need a clustered database, that's where we'd start considering Riak.

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its ok

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We have worked with the InfluxDB support team a few times so far and it has been positive. Issues submitted are worked on promptly and we have good feedback.

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We don't use it.

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Despite Basho going bankrupt and the project becoming fully open-source, community support is reasonably good, albeit a little slow at times. Paid enterprise-grade support is also available from former Basho engineers but the same company also contributes to the community support for free for basic questions or specific knowledge areas.

To be honest, I didn't look at alternatives since InfluxDB performs very well if you can oversee the lack of security and HA features. But for all challenges, there is an easy solution which brings you forward (e.g. read load balancing can be achieved by using a common HTTPS load balancer).

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Python is very commonly used for large data analysis and in general is much easier to pickup than kdb+. The biggest drawback of kdb+ is the learning curve.

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Because of the RESTful HTTP interface, the consistency model, and because of the catalog-driven data model, Riak was an easy win over Redis and Memcached.

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• Provided us a time series DB.
• With the SQL-like language, it is very easy to learn.
• Empowered us to keep track of our events.

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• It perfectly solves most of our real time tick data needs.
• Finding good kdb resources is slightly difficult. Also new people trying to learn kdb experience a relatively longer learning curve.

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• Riak has been a key part of our company's build process for our client's search backend. It is valuable for is in that it provides a reliable way to view the current search index.

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