diff options
Diffstat (limited to 'database/engine/README.md')
| -rw-r--r-- | database/engine/README.md | 186 |
1 files changed, 115 insertions, 71 deletions
diff --git a/database/engine/README.md b/database/engine/README.md index 7791a549f..78f3b15ec 100644 --- a/database/engine/README.md +++ b/database/engine/README.md @@ -1,18 +1,17 @@ # Database engine -The Database Engine works like a traditional -database. There is some amount of RAM dedicated to data caching and indexing and the rest of -the data reside compressed on disk. The number of history entries is not fixed in this case, -but depends on the configured disk space and the effective compression ratio of the data stored. -This is the **only mode** that supports changing the data collection update frequency -(`update_every`) **without losing** the previously stored metrics. +The Database Engine works like a traditional database. There is some amount of RAM dedicated to data caching and +indexing and the rest of the data reside compressed on disk. The number of history entries is not fixed in this case, +but depends on the configured disk space and the effective compression ratio of the data stored. This is the **only +mode** that supports changing the data collection update frequency (`update_every`) **without losing** the previously +stored metrics. ## 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.: +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.: -``` +```sh datafile-1-0000000001.ndf journalfile-1-0000000001.njf datafile-1-0000000002.ndf @@ -22,21 +21,19 @@ 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. +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._ ## Configuration -There is one DB engine instance per Netdata host/node. That is, there is one `./dbengine` folder -per node, and all charts of `dbengine` memory mode in such a host share the same storage space -and DB engine instance memory state. You can select the memory mode for localhost by editing -netdata.conf and setting: +There is one DB engine instance per Netdata host/node. That is, there is one `./dbengine` folder per node, and all +charts of `dbengine` memory mode in such a host share the same storage space and DB engine instance memory state. You +can select the memory mode for localhost by editing netdata.conf and setting: -``` +```conf [global] memory mode = dbengine ``` @@ -44,110 +41,157 @@ netdata.conf and setting: For setting the memory mode for the rest of the nodes you should look at [streaming](../../streaming/). -The `history` configuration option is meaningless for `memory mode = dbengine` and is ignored -for any metrics being stored in the DB engine. +The `history` configuration option is meaningless for `memory mode = dbengine` and is ignored for any metrics being +stored in the DB engine. -All DB engine instances, for localhost and all other streaming recipient nodes inherit their -configuration from `netdata.conf`: +All DB engine instances, for localhost and all other streaming recipient nodes inherit their configuration from +`netdata.conf`: -``` +```conf [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**. All DB engine instances will allocate the configured resources -separately. +The above values are the default and minimum values for Page Cache size and DB engine disk space quota. Both numbers are +in **MiB**. All DB engine instances will allocate the configured resources separately. -The `page cache size` option determines the amount of RAM in **MiB** that is dedicated to caching -Netdata metric values themselves. +The `page cache size` option determines the amount of RAM in **MiB** that is dedicated to caching Netdata metric values +themselves as far as queries are concerned. The total page cache size will be greater since data collection itself will +consume additional memory as is described in the [Memory requirements](#memory-requirements) section. -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. +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. ## 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**. +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 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. +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. +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. ## Memory requirements -Using memory mode `dbengine` we can overcome most memory restrictions and store a dataset that -is much larger than the available memory. +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): +There are explicit memory requirements **per** DB engine **instance**, meaning **per** Netdata **node** (e.g. localhost +and streaming recipient nodes): -- `page cache size` must be at least `#dimensions-being-collected x 4096 x 2` bytes. +- 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. + - 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. +An important observation is that RAM usage depends on both the `page cache size` and the `dbengine disk space` options. ## File descriptor requirements -The Database Engine may keep a **significant** amount of files open per instance (e.g. per streaming -slave or master server). When configuring your system you should make sure there are at least 50 -file descriptors available per `dbengine` instance. +The Database Engine may keep a **significant** amount of files open per instance (e.g. per streaming slave or master +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 slave hosts when streaming is configured to use `memory mode = dbengine`. +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 slave 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: +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: -``` +```sh [Service] LimitNOFILE=65536 ``` For other types of services one can add the line: -``` +```sh 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: +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: -``` +```conf fs.file-max = 65536 ``` In FreeBSD and OS X you change the lines like this: -``` +```conf kern.maxfilesperproc=65536 kern.maxfiles=65536 ``` You can apply the settings by running `sysctl -p` or by rebooting. +## 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 master server. 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: + +| device | page cache | dataset | reads/sec | writes/sec | +| :---: | :---: | ---: | ---: | ---: | +| 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. + [](<>) |