Time series data management using InfluxDB

After a pretty positive experience with influxdb I wanted to create a super simple telemetry producer (this one in Node.js) to spotlight a few types of time series data query supported in influxdb. (Source code available on GitHub)

To get live data for this demo, I created a simple script that generates metric data for CPU Utilization and Free Memory on your local machine at 1 sec resolution.

Sample queries

Select of values based on arbitrary time window.

select sample_value 
from cpu_series 
where time > ‘2013–08–12 23:32:01.232’ 
and time < 2013–08–12 23:22:055.134

On the fly 90th percentile of value in 5 second intervals. No windowing or period tables required.

select percentile(sample_value, 90) 
from mem_series 
group by time(5s);

Standard deviation of value in 5 second intervals. Again, all ad-hoc, down-sampling with no priori declarations.

select stddev(sample_value) 
from cpu_series 
group by time(1m);

Why influxdb

Having done a few time series systems in Cassandra, HBase and yes, even Mongo, I was looking for something that would be already optimized for that specific data type. Furthermore, I wanted clean API as well as support for many of common telemetry aggregate functions:

count(), min(), max(), mean(), mode(), median(), distinct(), 
percentile(), histogram(), derivative(), sum(), stddev(), first(), last()

Additionally:

• Open source (MIT), hosted on GitHub
• No external dependancies (nope, no zookeeper)
• SQL-like query and built-in UI
• On the fly, downsample aggregate (no need to define windows, just record it and query by ad-hoc period: e.g. 1s, 4s, 2m etc.)
• Clustering support (there is currently a limit of 2M writes per second in 0.9 release, which suppose to be removed in 1.0)
• Pure Golang since 0.9 (plus for me, may not be for others)