Published: Oct 10, 2019 by K. E. Claytor

InfluxDays

I’ve been using the InfluxDB time series NoSQL database lately. I applied and was accecpted for a talk at InfluxDays, their 2019 user conference.

Here’s the recording:

I think it’s a decent match for research systems. You can get up to speed fairly quickly without a background in database systems, and the time-series first aspect of it is good for a wide range of datasets.

Pros:

• Easy to get started with many clients across most languages and a HTTP API.
• Telegraf also records system metrics
• Chronograf integration gives you visualization with low overhead.
• NoSQL means you can change your schema as the experiment evolves (and you don’t even need a schema to get started).

Cons:

• Memory issues on IoT platforms (I’m using a snickerdoodle board).
  • Runs out of memory on start (supposedly fixed v1.7.8?)
  • Runs out of memory on compactions (fixed by moving to smaller shards)
  • Takes a long time to boot up and read lots of shards.
• Python client doesn’t read into Pandas tables very well (it can write from them well). I usually export from chronograf into a .csv file and then pivot_table() a bunch.
• Will drop points if the type doesn’t match (explicitly cast python variables).