/disclaimer/ I work for Basho
My guess though is that Basho would opt to write their own backend off of influxdb's notes..
On a side note I find influxdb's use of nanosecond time precision spot on and Riak TS's millisecond resolution disappointing.
For example Prometheus (which I work on) is great at reliable monitoring and powerful processing of metrics at high volumes, but it'd be unwise to use it for event logging or customer billing.
If you're doing IoT or event logging then InfluxDB might be a good choice for you, though if you're doing more text-based logging then Elasticsearch is nearer to what you're looking for.
https://docs.google.com/spreadsheets/d/1sMQe9oOKhMhIVw9WmuCE... is one comparison of the various open source options.
We initially used Influx, but it could not perform well at the time (0.8). Our events are also heavily label-based. Basically, we do ETL at the time of write, collecting multiple documents into one mega-event, which is a complex, nested JSON document. It may have perhaps 150-200 fields. A single event may be something like "clicked button X". By storing the original document, we can aggregate based on any field value, including text and scalar fields, without having to think about a schema or about planning ahead of time what fields should be indexed or not. ES handles the rest pretty well.
To do the same thing with Influx or Prometheus I suspect we'd have to reverse this and store the document as the labels, along with a single count (1) as the "metric". I don't know how well Influx etc. scale with number of unique label values, though I'd love to find out. The last time I read about this, I think they recommended not going overboard with them.
What's different with business analytics is that the end product is typically multidimensional rollup reports over large time windows (number of page views per customer per web property per month, comparing by 2015 vs 2016, for example), and it's almost all "group by count", sometimes "count distinct" or averages. Whereas "rate per second"-type metrics aren't used anywhere in our app, for example.
Apologies for the email gateway for the video, but you can also see my slides here: https://www.elastic.co/elasticon/conf/2016/sf/web-content-an...
We found that as we scaled it up, we couldn't really keep the data in raw form, so we had to build rollup documents that cover 5-minute and 1-day buckets. Do you use the same trick, or is the number of pageview events for you manageable enough that you just keep it all raw?
My ideal solution would be one that rotated the dataset into historical rollups on a daily basis, so that we only stored the raw data for today, and gradually merged earlier entries at lower granularities. However, I haven't thought much about how to do that with Elasticsearch. I can see a way of doing it by embedding the value in the field label, and using the field value as a count, but Elasticsearch really doesn't like lots of unique fields; you shouldn't be using more than a few hundred at most in a single installation (across all indexes).
Less impressed with the Influx org and their SaaS, but I definitely want to find a usecase for getting stuck into Influx for time series collection
We're also working on something with Apache Phoenix over HBase to store TS so that we can query the data in more flexible ways.
(I work for this company so I'm a bit biased)
The thing is, yes we have a time series database, but a lot of value comes from giving people the tools to analyze, distribute, and act on the data stored (why store data if you can't do anything useful with it?)