InfluxData Blog

It isn't near as scalable as Riak, as the so-called clustering tops out at 3 nodes, but Influx is very very nice if you don't put gobs of data at it: https://influxdb.com Riak is one of those systems, where it really does scale out linearly.

Out of curiosity why weren't products like Druid http://druid.io/ or influxdb https://influxdb.com/ or possibly opentsdb taken into consideration ?

Hello -- rqlite author here. It was definitely interesting to code this. It was 50% to meet a need, 50% to better understand Raft consensus in general, and Ben Johnson's go-raft implementation in particular (https://github.com/goraft/raft). As it happens Ben and I now work together, on InfluxDB (http://influxdb.com/), which has a new Raft implementation at its core. Definitely fun.

This post it's quite old and graphite ecosystem improve, some examples: Grafana from my POV is the best dashboard at the moment: http://grafana.org/ http://grafana.org/blog/2014/05/25/monitorama-video-and-update.html http://play.grafana.org/ About alert system I'm using cabot: http://cabotapp.com/ About system metrics, at the moment I'm using Diamond. https://github.com/BrightcoveOS/Diamond In the other hand, Influxdb is growing, maybe I'll switch to InfluxDB next year, some features are better than graphite, and it's statsd compatible ;-) http://influxdb.com/ Regards ;-)

I have used http://influxdb.com/, I have used it with a few million records. Getting the data out is a bit slow because it goes over HTTP. Also make your InfluxDB library of choice can deal with HTTP chunking. I found that if you request a lot of data from InfluxDB and the system does not have enough memory, the process will silently die. If you have mega huge data http://opentsdb.net/ seems pretty decent, however I have not tried it out.

They've gone through a few engines. They have a post about the newest one which isn't quite out yet: https://influxdb.com/blog/2015/10/07/the_new_influxdb_storage_engine_a… We use them for storing "permanent" data. They slice up the data into weekly files, so if you don't access it often, it's no biggie to store a lot of data. If you constantly access data across time, you may run into performance degradation from a caching point of view (having to pull in the files from past, blowing caches of new stuff constantly).

Things are actually about to get dramatically better for InfluxDB: https://influxdb.com/blog/2015/10/07/the_new_influxdb_storage_engine_a…

Based on the changes in 0.9.1, there's no way that 0.9 was production ready and I'd be very cautious about using it. https://influxdb.com/blog/2015/07/02/InfluxDB-0_9_1-and-Telegraf-0_1_2…

Hi and thanks for the new release! With the new distinction between tags and fields, the documentation isn't clear about how to use them. I assume tags are indexed, but that you can still query by field, and that there are no limitations? We're basically just throwing documents (containing nested documents) at InfluxDB, with no predefined schema, so we don't know beforehand what users will be performing ad-hoc queries on; it's completely dynamic in the UI. Someone, perhaps it was you, mentioned in another thread that InfluxDB only supported 255 tag names per series, which we'd probably exceed with our schema. At the same time we'd like to benefit from the performance we'd presumably get from using tags. PS. This page is giving me an error "NoSuchKey": http://influxdb.com/docs/v0.9/concepts/aggregate_functions.html

Minor typo in http://influxdb.com/support/ "Purcahses" for InfluxDB development support provide...

Blog post with more useful information: http://influxdb.com/blog/2015/06/11/InfluxDB-v0_9_0-released-with-deve… The lack of an upgrade path from 0.8.x makes me feel pretty stupid for trying out that version. Then again, they don't have upgraded client libraries for Python either, so we wouldn't be ready to upgrade anyways...

Looks interesting. This answers a lot of questions about primary data, but I'm interested to know how continuous queries [1] are handled. If they're stored like any other data, the "inserts are far more likely than updates" assumption doesn't really make sense. For example, what if you have a continuous query grouped by year? PS. "challenges" is mis-spelled in the last sentence. 1. http://influxdb.com/docs/v0.8/api/continuous_queries.html

After reading http://influxdb.com/blog/2014/12/08/clustering_tags_and_enhancements_i… I'm not completely clear on what "tags" mean. Are they single-word names, or are they conceptually key=value pairs like labels in Prometheus or tags in OpenTSDB? EDIT: found https://github.com/influxdb/influxdb/pull/1059. Ok, does seem like tags mean key=value pairs.

Looks like I'm only waiting on custom functions[1] now. I used to also be waiting on continuous queries[2] but looks like that feature is done now. [1]: https://github.com/influxdb/influxdb/issues/68 [2]: http://influxdb.com/docs/v0.8/api/continuous_queries.html

Why would you choose a LSM Tree based storage mechanism for a message queue? The only reason I can come up with would be because it's a read-to-use library you can just plug in which gives OK performance and some handy features because you can use the KV store for other things. But it doesn't scale well and backups with LevelDB are not really easy either (close DB, copy all files). Message queues when they are ordered (at least on the local node/queue level) usually just need some kind of append-only log file. You don't do random reads or writes into the middle of the queue, you only modify the head and tail. InfluxDB, albeit being a time series db has similar write patterns to a message queue, learned it the hard way when they first tried to use a LSM Tree database (LevelDB), then switched to a B+Tree (BoltDB/LMDB) but that also doesn't scale once the DB gets big and the tree has quite some depth. They kindly did a nice writeup of their journey: https://influxdb.com/docs/v0.9/concepts/storage_engine.html Why not do it simple and use append-only files without complex structure and management? Check out Kafka for a better storage format for message queues of this kind. PS: every message queue should first clearly explain what guarantees it provides.