Memory Requirements

Costa Tsaousis edited this page Nov 13, 2016 · 10 revisions


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Netdata Memory Requirements

Although netdata does all its calculations using long double (128 bit) arithmetics, it stores all values using a custom-made 32-bit number.

This custom-made number can store in 29 bits values from -167772150000000.0 to 167772150000000.0 with a precision of 0.00001 (yes, it is a floating point number, meaning that higher integer values have less decimal precision) and 3 bits for flags.

This provides an extremely optimized memory footprint with just 0.0001% max accuracy loss.

Sizing memory

So, for each dimension of a chart, netdata will need: 4 bytes for the value * the entries of its history. It will not store any other data for each value in the time series database. Since all its values are stored in a time series with fixed step, the time each value corresponds can be calculated at run time, using the position of a value in the round robin database.

The default history is 3.600 entries, thus it will need 14.4KB for each chart dimension. If you need 1.000 dimensions, they will occupy just 14.4MB.

Of course, 3.600 entries is a very short history, especially if data collection frequency is set to 1 second. You will have just one hour of data.

For a day of data and 1.000 dimensions, you will need: 86.400 seconds * 4 bytes * 1.000 dimensions = 345MB of RAM.

Currently the only option you have to lower this number is to use Memory Deduplication - Kernel Same Page Merging - KSM.

Memory modes

Currently netdata supports 3 memory modes:

  1. ram where the chart data are purely in memory. Data are never saved on disk.
  2. save (the default) where the data are only in RAM while netdata runs and are saved to / loaded from disk on netdata restart.
  3. map where the data are in memory mapped files. This works like the swap. Keep in mind though, this will have a constant write on your disk. When netdata writes data on its memory, the Linux kernel marks the related memory pages as dirty and automatically starts updating them on disk. Unfortunately we cannot control how frequently this works. The Linux kernel uses exactly the same algorithm it uses for its swap memory.

You can select the memory mode by editing netdata.conf and setting:

    # ram, save (the default, save on exit, load on start), map (swap like)
    memory mode = save

    # the directory where data are saved
    cache directory = /var/cache/netdata

The future

I investigate several alternatives to lower this number. The best so far is to split the in-memory round robin database in a small realtime database (e.g. an hour long) and a larger compressed archive database to store longer durations. So (for example) every hour netdata will compress the last hour of data using LZ4 (which is very fast: 350MB/s compression, 1850MB/s decompression) and append these compressed data to an archive round robin database. This archive database will be saved to disk and loaded back to memory on demand, when a chart is zoomed or panned to the compressed timeframe.

This is future though. For the moment, if you need a long history, you will need a lot of RAM.

Running netdata in embedded devices

Embedded devices usually have very limited RAM resources available.

There are 2 settings for you to tweak:

  1. update every, which controls the data collection frequency
  2. history, which controls the size of the database in RAM

By default update every = 1 and history = 3600. This gives you an hour of data with per second updates.

If you set update every = 2 and history = 1800, you will still have an hour of data, but collected once every 2 seconds. This will cut in half both CPU and RAM resources consumed by netdata. Of course experiment a bit. On very weak devices you might have to use update every = 5 and history = 720 (still 1 hour of data, but 1/5 of the CPU and RAM resources).

You can also disable plugins you don't need. Disabling the plugins will also free both CPU and RAM resources.