Databases at the intersection of storing data and processing streams, for, e.g. time series forecasting and realtime analytics. Do you need a specialist database for this? Sometimes, irritatingly, it seems to be useful to have a tool which can handle rapidly changing data.
Log files
Write to a file; This is usually OK for my purposes, but sometimes is not feasible due to concurrency problems.
RRD
RRD is not flashy or new but remarkably capable; designed for network and computer monitoring but basically any real time data with a ring buffer (i.e. fixed maximum history size of interest) can be stored and processed. I like the utilitarian aesthetic.
What data can be put into an RRD?
You name it, it will probably fit as long as it is some sort of time-series data. This means you have to be able to measure some value at several points in time and provide this information to RRDtool. If you can do this, RRDtool will be able to store it. The values must be numerical but don’t have to be integers…
This is good for debugging something that crashes and you want to know what happened just before.
Redis
Redis is adept at heavy write-transactions, so time series stuff works.
You can just run it without setting up your special dedicated server.
Convenient for things that are just big enough to fit in your
memory but you need to process the crap out of them fast.
Easy set up, built-in lua interpreter, fashionable enough for wide support.
These last two have met all my needs. Inevitably, when trying to find a solution to a problem I got recommendations for many more which I list below in the hope that someone else might need them. I do not.
InfluxDB
Influxdb is a database designed to query time-series live,
by current time, relative age and so on.
The sort of thing designed to run the kind of elaborate real time situation
visualisation that evil overlords have in holographic displays in their lairs.
Comes with free count aggregation and lite visualisations.
Haven’t used it, just noting it here, will return if I need a dashboard of
malevolence for my headquarters.
Haven’t used it.
Druid
druid, as used by airbnb, is “a high-performance, column-oriented,
distributed data store” that claims to be good at events also.
Prometheus
Prometheus is an open-source systems monitoring and alerting toolkit originally built at SoundCloud. […] Prometheus’s main features are:
- a multi-dimensional data model with time series data identified by metric name and key/value pairs
- PromQL, a flexible query language to leverage this dimensionality
- no reliance on distributed storage; single server nodes are autonomous
- time series collection happens via a pull model over HTTP
- pushing time series is supported via an intermediary gateway
- targets are discovered via service discovery or static configuration
- multiple modes of graphing and dashboarding support
Haven’t used it.
Heroic
Heroic, by Spotify
Heroic is our in-house time series database. We built it to address the challenges we were facing with near real-time data collection and presentation at scale. At the core are two key pieces of technology are Cassandra, and Elasticsearch. Cassandra acts as the primary means of storage with Elasticsearch being used to index all data. We currently operate over 200 Cassandra nodes in several clusters across the world serving over 50 million distinct time series.
Good onya. I have not used it.
RethinkDB
rethinkdb is a database which does push instead
of being polled. Recently
open-sourced, eminent pedigree, haven’t used it.
Materialize
Meterialize by materialize.io:
Materialize is a streaming database for real-time applications.
Materialize lets you ask questions of your live data, which it answers and then maintains for you as your data continue to change. The moment you need a refreshed answer, you can get it in milliseconds. Materialize is designed to help you interactively explore your streaming data, perform data warehousing analytics against live relational data, or just increase the freshness and reduce the load of your dashboard and monitoring tasks.
Materialize focuses on providing correct and consistent answers with minimal latency. It does not ask you to accept either approximate answers or eventual consistency. Whenever Materialize answers a query, that answer is the correct result on some specific (and recent) version of your data. Materialize does all of this by recasting your SQL92 queries as dataflows, which can react efficiently to changes in your data as they happen. Materialize is powered by timely dataflow, which connects the times at which your inputs change with the times of answers reported back to you.
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