Database engine

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.

Configuration

To use the database engine, open netdata.conf and set memory mode to dbengine.

[global]
memory mode = dbengine

To configure the database engine, look for the page cache size and dbengine disk space settings in the [global] section of your netdata.conf. The Agent ignores the history setting when using the database engine.

[global]
page cache size = 32
dbengine disk space = 256

The above values are the default and minimum values for Page Cache size and DB engine disk space quota. Both numbers are in MiB.

The 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.

The dbengine 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.

Use the database engine calculator to correctly set dbengine 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.

Streaming metrics to the database engine

When streaming metrics, the Agent on the parent node creates one instance of the database engine for itself, and another instance for every child node it receives metrics from. If you have four streaming nodes, you will have five instances in total (1 parent + 4 child nodes = 5 instances).

The Agent allocates resources for each instance separately using the dbengine disk space setting. If dbengine disk space 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 set dbengine disk space and undertand the toal disk space required based on your streaming setup.

For more information about setting memory mode on your nodes, in addition to other streaming configurations, see streaming.

Memory requirements

Using memory mode dbengine we can overcome most memory restrictions and store a dataset that is much larger than the available memory.

There are explicit memory requirements per DB engine instance, meaning per Netdata node (e.g. localhost and streaming recipient nodes):

  • The total page cache memory footprint will be an additional #dimensions-being-collected x 4096 x 2 bytes over what the user configured with page cache size.

  • an additional #pages-on-disk x 4096 x 0.03 bytes 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 disk space options.

You can use our database engine calculator to validate the memory requirements for your particular system(s) and configuration.

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 dbengine instance.

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:

[Service]
LimitNOFILE=65536

For other types of services one can add the line:

ulimit -n 65536

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:

fs.file-max = 65536

In FreeBSD and OS X you change the lines like this:

kern.maxfilesperproc=65536
kern.maxfiles=65536

You can apply the settings by running sysctl -p or by rebooting.

Files

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.:

datafile-1-0000000001.ndf
journalfile-1-0000000001.njf
datafile-1-0000000002.ndf
journalfile-1-0000000002.njf
datafile-1-0000000003.ndf
journalfile-1-0000000003.njf
...

They are located under their host's cache directory in the directory ./dbengine (e.g. for localhost the default location is /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.

Operation

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) pages.

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.

Evaluation

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:

devicepage cachedatasetreads/secwrites/sec
HDD64 MiB4.1 GiB813K18.0M
SSD64 MiB9.8 GiB1.7M43.0M
N/A16 GiB6.8 GiB118.2M30.2M

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.

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