The Database Engine works like a traditional database. It dedicates a certain amount of RAM to data caching and indexing, while the rest of the data resides compressed on disk. Unlike other memory modes, the amount of historical metrics stored is based on the amount of disk space you allocate and the effective compression ratio, not a fixed number of metrics collected.
By using both RAM and disk space, the database engine allows for long-term storage of per-second metrics inside of the Agent itself.
In addition, the database engine is the only memory mode that supports changing the data collection update frequency
update_every) without losing the metrics your Agent already gathered and stored.
To use the database engine, open
netdata.conf and set
memory mode to
To configure the database engine, look for the
page cache size and
dbengine disk space settings in the
section of your
netdata.conf. The Agent ignores the
history setting when using the database engine.
The above values are the default values for Page Cache size and DB engine disk space quota. Both numbers are in MiB.
page cache size option determines the amount of RAM in MiB dedicated to caching Netdata metric values. The
actual page cache size will be slightly larger than this figure—see the memory requirements
section for details.
dbengine multihost disk space option determines the amount of disk space in MiB that is dedicated to storing
Netdata metric values and all related metadata describing them.
dbengine disk space option determines the amount of disk space in MiB that is dedicated to storing
Netdata metric values per legacy database engine instance (see below).
Use the database engine calculator to correctly set
dbengine disk space(deprecated) 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.
Streaming metrics to the database engine
When streaming metrics, the Agent on the parent node used to create one instance (legacy, version <= 1.23.2) of the
database engine for itself, and another instance for every child node it receives metrics from. If you had four
streaming nodes, you would have five instances in total (
1 parent + 4 child nodes = 5 instances).
The Agent allocated resources for each instance separately using the
dbengine disk space(deprecated) setting. If
dbengine disk space(deprecated) is set to the default
256, each instance is given 256 MiB in disk space, which
means the total disk space required to store all instances is, roughly,
256 MiB * 1 parent * 4 child nodes = 1280 MiB.
See the database engine calculator to help you correctly
dbengine disk space(deprecated) and understand the total disk space required based on your streaming setup.
Multi host DB mode
In the newer agent versions the parent and child nodes all share
page cache size and
dbengine multihost disk space
in a single dbengine multi-host instance.
All existing metrics belonging to child nodes are automatically converted to legacy dbengine instances and the localhost metrics are transferred to the multi-host dbengine instance.
All new child nodes are automatically transferred to the mult-host dbengine instance and share its page cache and disk
space. If you want to migrate a child node from its legacy dbengine instance to the multi-host dbengine instance you
must delete the instance's directory located in
/var/cache/netdata/MACHINE_GUID/dbengine after stopping the netdata
For more information about setting
memory mode on your nodes, in addition to other streaming configurations, see
Using memory mode
dbengine we can overcome most memory restrictions and store a dataset that is much larger than the
There are explicit memory requirements per DB engine instance:
The total page cache memory footprint will be an additional
#dimensions-being-collected x 4096 x 2bytes over what the user configured with
page cache size.
#pages-on-disk x 4096 x 0.03bytes of RAM are allocated for metadata.
roughly speaking this is 3% of the uncompressed disk space taken by the DB files.
for very highly compressible data (compression ratio > 90%) this RAM overhead is comparable to the disk space footprint.
An important observation is that RAM usage depends on both the
page cache size and the
dbengine multihost disk space
You can use our database engine calculator to validate the memory requirements for your particular system(s) and configuration (out-of-date).
File descriptor requirements
The Database Engine may keep a significant amount of files open per instance (e.g. per streaming child or
parent server). When configuring your system you should make sure there are at least 50 file descriptors available per
Netdata allocates 25% of the available file descriptors to its Database Engine instances. This means that only 25% of
the file descriptors that are available to the Netdata service are accessible by dbengine instances. You should take
that into account when configuring your service or system-wide file descriptor limits. You can roughly estimate that the
Netdata service needs 2048 file descriptors for every 10 streaming child hosts when streaming is configured to use
memory mode = dbengine.
If for example one wants to allocate 65536 file descriptors to the Netdata service on a systemd system one needs to
override the Netdata service by running
sudo systemctl edit netdata and creating a file with contents:
For other types of services one can add the line:
at the beginning of the service file. Alternatively you can change the system-wide limits of the kernel by changing
/etc/sysctl.conf. For linux that would be:
In FreeBSD and OS X you change the lines like this:
You can apply the settings by running
sysctl -p or by rebooting.
With the DB engine memory mode the metric data are stored in database files. These files are organized in pairs, the datafiles and their corresponding journalfiles, e.g.:
They are located under their host's cache directory in the directory
./dbengine (e.g. for localhost the default
/var/cache/netdata/dbengine/*). The higher numbered filenames contain more recent metric data. The user
can safely delete some pairs of files when Netdata is stopped to manually free up some space.
Users should back up their
./dbengine folders if they consider this data to be important. You can also set up
one or more exporting connectors to send your Netdata metrics to other databases for long-term
storage at lower granularity.
The DB engine stores chart metric values in 4096-byte pages in memory. Each chart dimension gets its own page to store consecutive values generated from the data collectors. Those pages comprise the Page Cache.
When those pages fill up they are slowly compressed and flushed to disk. It can take
4096 / 4 = 1024 seconds = 17
minutes, for a chart dimension that is being collected every 1 second, to fill a page. Pages can be cut short when we
stop Netdata or the DB engine instance so as to not lose the data. When we query the DB engine for data we trigger disk
read I/O requests that fill the Page Cache with the requested pages and potentially evict cold (not recently used)
When the disk quota is exceeded the oldest values are removed from the DB engine at real time, by automatically deleting the oldest datafile and journalfile pair. Any corresponding pages residing in the Page Cache will also be invalidated and removed. The DB engine logic will try to maintain between 10 and 20 file pairs at any point in time.
The Database Engine uses direct I/O to avoid polluting the OS filesystem caches and does not generate excessive I/O traffic so as to create the minimum possible interference with other applications.
We have evaluated the performance of the
dbengine API that the netdata daemon uses internally. This is not the web
API of netdata. Our benchmarks ran on a single
dbengine instance, multiple of which can be running in a Netdata
parent node. We used a server with an AMD Ryzen Threadripper 2950X 16-Core Processor and 2 disk drives, a Seagate
Constellation ES.3 2TB magnetic HDD and a SAMSUNG MZQLB960HAJR-00007 960GB NAND Flash SSD.
For our workload, we defined 32 charts with 128 metrics each, giving us a total of 4096 metrics. We defined 1 worker thread per chart (32 threads) that generates new data points with a data generation interval of 1 second. The time axis of the time-series is emulated and accelerated so that the worker threads can generate as many data points as possible without delays.
We also defined 32 worker threads that perform queries on random metrics with semi-random time ranges. The starting time of the query is randomly selected between the beginning of the time-series and the time of the latest data point. The ending time is randomly selected between 1 second and 1 hour after the starting time. The pseudo-random numbers are generated with a uniform distribution.
The data are written to the database at the same time as they are read from it. This is a concurrent read/write mixed
workload with a duration of 60 seconds. The faster
dbengine runs, the bigger the dataset size becomes since more
data points will be generated. We set a page cache size of 64MiB for the two disk-bound scenarios. This way, the dataset
size of the metric data is much bigger than the RAM that is being used for caching so as to trigger I/O requests most
of the time. In our final scenario, we set the page cache size to 16 GiB. That way, the dataset fits in the page cache
so as to avoid all disk bottlenecks.
The reported numbers are the following:
|HDD||64 MiB||4.1 GiB||813K||18.0M|
|SSD||64 MiB||9.8 GiB||1.7M||43.0M|
|N/A||16 GiB||6.8 GiB||118.2M||30.2M|
where "reads/sec" is the number of metric data points being read from the database via its API per second and "writes/sec" is the number of metric data points being written to the database per second.
Notice that the HDD numbers are pretty high and not much slower than the SSD numbers. This is thanks to the database engine design being optimized for rotating media. In the database engine disk I/O requests are:
- asynchronous to mask the high I/O latency of HDDs.
- mostly large to reduce the amount of HDD seeking time.
- mostly sequential to reduce the amount of HDD seeking time.
- compressed to reduce the amount of required throughput.
As a result, the HDD is not thousands of times slower than the SSD, which is typical for other workloads.
An interesting observation to make is that the CPU-bound run (16 GiB page cache) generates fewer data than the SSD run (6.8 GiB vs 9.8 GiB). The reason is that the 32 reader threads in the SSD scenario are more frequently blocked by I/O, and generate a read load of 1.7M/sec, whereas in the CPU-bound scenario the read load is 70 times higher at 118M/sec. Consequently, there is a significant degree of interference by the reader threads, that slow down the writer threads. This is also possible because the interference effects are greater than the SSD impact on data generation throughput.