Netdata helps you collect thousands of system and application metrics every second, but what about storing them for the long term?
Many people think Netdata can only store about an hour's worth of real-time metrics, but that's simply not true any more. With the right settings, Netdata is quite capable of efficiently storing hours or days worth of historical, per-second metrics without having to rely on an exporting engine.
This guide gives two options for configuring Netdata to store more metrics. We recommend the default database engine, but you can stick with or switch to the round-robin database if you prefer.
Let's get started.
Using the database engine
The database engine uses RAM to store recent metrics while also using a "spill to disk" feature that takes advantage of available disk space for long-term metrics storage. This feature of the database engine allows you to store a much larger dataset than your system's available RAM.
The database engine is currently the default method of storing metrics, but if you're not sure which database you're
using, check out your
netdata.conf file and look for the
memory mode setting:
memory mode = dbengine
memory mode is set to anything but
dbengine, change it and restart Netdata using the standard command for
restarting services on your system. You're now using the database engine!
What makes the database engine efficient? While it's structured like a traditional database, the database engine splits data between RAM and disk. The database engine caches and indexes data on RAM to keep memory usage low, and then compresses older metrics onto disk for long-term storage.
When the Netdata dashboard queries for historical metrics, the database engine will use its cache, stored in RAM, to return relevant metrics for visualization in charts.
Now, given that the database engine uses both RAM and disk, there are two other settings to consider:
dbengine multihost disk space.
page cache size = 32
dbengine multihost disk space = 256
page cache size sets the maximum amount of RAM (in MiB) the database engine will use for caching and indexing.
dbengine multihost disk space sets the maximum disk space (again, in MiB) the database engine will use for storing
compressed metrics. The default settings retain about two day's worth of metrics on a system collecting 2,000 metrics
See our database engine
to help you correctly set
dbengine multihost disk space based on your needs. The calculator gives an accurate estimate
based on how many child nodes you have, how many metrics your Agent collects, and more.
With the database engine active, you can back up your
/var/cache/netdata/dbengine/ folder to another location for
Now that you know how to switch to the database engine, let's cover the default round-robin database for those who aren't ready to make the move.
Using the round-robin database
In previous versions, Netdata used a round-robin database to store 1 hour of per-second metrics.
To see if you're still using this database, or if you would like to switch to it, open your
netdata.conf file and see
memory mode option is set to
memory mode = save
memory mode is set to
save, then you're using the round-robin database. If so, the
history option is set to
3600, which is the equivalent to 3,600 seconds, or one hour.
To increase your historical metrics, you can increase
history to the number of seconds you'd like to store:
# 2 hours = 2 * 60 * 60 = 7200 seconds
history = 7200
# 4 hours = 4 * 60 * 60 = 14440 seconds
history = 14440
# 24 hours = 24 * 60 * 60 = 86400 seconds
history = 86400
And so on.
Next, check to see how many metrics Netdata collects on your system, and how much RAM that uses. Visit the Netdata dashboard and look at the bottom-right corner of the interface. You'll find a sentence similar to the following:
Every second, Netdata collects 1,938 metrics, presents them in 299 charts and monitors them with 81 alarms. Netdata is using 25 MB of memory on netdata-linux for 1 hour, 6 minutes and 36 seconds of real-time history.
On this desktop system, using a Ryzen 5 1600 and 16GB of RAM, the round-robin databases uses 25 MB of RAM to store just over an hour's worth of data for nearly 2,000 metrics.
To increase the
history option, you need to edit your
netdata.conf file and increase the
history setting. In most
installations, you'll find it at
/etc/netdata/netdata.conf, but some operating systems place it at
/etc/netdata/edit-config netdata.conf, or your favorite text editor, to replace
3600 with the number of seconds
you'd like to store.
You should base this number on two things: How much history you need for your use case, and how much RAM you're willing to dedicate to Netdata.
Take care when you change the
historyoption on production systems. Netdata is configured to stop its process if your system starts running out of RAM, but you can never be too careful. Out of memory situations are very bad.
How much RAM will a longer history use? Let's use a little math.
The round-robin database needs 4 bytes for every value Netdata collects. If Netdata collects metrics every second, that's 4 bytes, per second, per metric.
4 bytes * X seconds * Y metrics = RAM usage in bytes
Let's assume your system collects 1,000 metrics per second.
4 bytes * 3600 seconds * 1,000 metrics = 14400000 bytes = 14.4 MB RAM
With that formula, you can calculate the RAM usage for much larger history settings.
# 2 hours at 1,000 metrics per second
4 bytes * 7200 seconds * 1,000 metrics = 28800000 bytes = 28.8 MB RAM
# 2 hours at 2,000 metrics per second
4 bytes * 7200 seconds * 2,000 metrics = 57600000 bytes = 57.6 MB RAM
# 4 hours at 2,000 metrics per second
4 bytes * 14440 seconds * 2,000 metrics = 115520000 bytes = 115.52 MB RAM
# 24 hours at 1,000 metrics per second
4 bytes * 86400 seconds * 1,000 metrics = 345600000 bytes = 345.6 MB RAM
Now that you have either configured database engine or round-robin database engine to store more metrics, you'll probably want to see it in action!
For more information about how to pan charts to view historical metrics, see our documentation on using charts.
And if you'd now like to reduce Netdata's resource usage, view our performance guide for our best practices on optimization.