diff options
Diffstat (limited to 'database/engine/README.md')
| -rw-r--r-- | database/engine/README.md | 302 |
1 files changed, 302 insertions, 0 deletions
diff --git a/database/engine/README.md b/database/engine/README.md new file mode 100644 index 0000000..c67e400 --- /dev/null +++ b/database/engine/README.md @@ -0,0 +1,302 @@ +<!-- +title: "Database engine" +description: "Netdata's highly-efficient database engine use both RAM and disk for distributed, long-term storage of per-second metrics." +custom_edit_url: https://github.com/netdata/netdata/edit/master/database/engine/README.md +--> + +# Database engine + +The Database Engine works like a traditional time series database. Unlike other [database modes](/database/README.md), +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. + +## Tiering + +Tiering is a mechanism of providing multiple tiers of data with +different [granularity on metrics](/docs/store/distributed-data-architecture.md#granularity-of-metrics). + +For Netdata Agents with version `netdata-1.35.0.138.nightly` and greater, `dbengine` supports Tiering, allowing almost +unlimited retention of data. + + +### Metric size + +Every Tier down samples the exact lower tier (lower tiers have greater resolution). You can have up to 5 +Tiers **[0. . 4]** of data (including the Tier 0, which has the highest resolution) + +Tier 0 is the default that was always available in `dbengine` mode. Tier 1 is the first level of aggregation, Tier 2 is +the second, and so on. + +Metrics on all tiers except of the _Tier 0_ also store the following five additional values for every point for accurate +representation: + +1. The `sum` of the points aggregated +2. The `min` of the points aggregated +3. The `max` of the points aggregated +4. The `count` of the points aggregated (could be constant, but it may not be due to gaps in data collection) +5. The `anomaly_count` of the points aggregated (how many of the aggregated points found anomalous) + +Among `min`, `max` and `sum`, the correct value is chosen based on the user query. `average` is calculated on the fly at +query time. + +### Tiering in a nutshell + +The `dbengine` is capable of retaining metrics for years. To further understand the `dbengine` tiering mechanism let's +explore the following configuration. + +``` +[db] + mode = dbengine + + # per second data collection + update every = 1 + + # enables Tier 1 and Tier 2, Tier 0 is always enabled in dbengine mode + storage tiers = 3 + + # Tier 0, per second data for a week + dbengine multihost disk space MB = 1100 + + # Tier 1, per minute data for a month + dbengine tier 1 multihost disk space MB = 330 + + # Tier 2, per hour data for a year + dbengine tier 2 multihost disk space MB = 67 +``` + +For 2000 metrics, collected every second and retained for a week, Tier 0 needs: 1 byte x 2000 metrics x 3600 secs per +hour x 24 hours per day x 7 days per week = 1100MB. + +By setting `dbengine multihost disk space MB` to `1100`, this node will start maintaining about a week of data. But pay +attention to the number of metrics. If you have more than 2000 metrics on a node, or you need more that a week of high +resolution metrics, you may need to adjust this setting accordingly. + +Tier 1 is by default sampling the data every **60 points of Tier 0**. In our case, Tier 0 is per second, if we want to +transform this information in terms of time then the Tier 1 "resolution" is per minute. + +Tier 1 needs four times more storage per point compared to Tier 0. So, for 2000 metrics, with per minute resolution, +retained for a month, Tier 1 needs: 4 bytes x 2000 metrics x 60 minutes per hour x 24 hours per day x 30 days per month += 330MB. + +Tier 2 is by default sampling data every 3600 points of Tier 0 (60 of Tier 1, which is the previous exact Tier). Again +in term of "time" (Tier 0 is per second), then Tier 2 is per hour. + +The storage requirements are the same to Tier 1. + +For 2000 metrics, with per hour resolution, retained for a year, Tier 2 needs: 4 bytes x 2000 metrics x 24 hours per day +x 365 days per year = 67MB. + +## Legacy configuration + +### v1.35.1 and prior + +These versions of the Agent do not support [Tiering](#Tiering). You could change the metric retention for the parent and +all of its children only with the `dbengine multihost disk space MB` setting. This setting accounts the space allocation +for the parent node and all of its children. + +To configure the database engine, look for the `page cache size MB` and `dbengine multihost disk space MB` settings in +the `[db]` section of your `netdata.conf`. + +```conf +[db] + dbengine page cache size MB = 32 + dbengine multihost disk space MB = 256 +``` + +### v1.23.2 and prior + +_For Netdata Agents earlier than v1.23.2_, the Agent on the parent node uses one dbengine instance 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 allocates resources for each instance separately using the `dbengine disk space MB` (**deprecated**) setting. +If +`dbengine disk space MB`(**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`. + +#### Backward compatibility + +All existing metrics belonging to child nodes are automatically converted to legacy dbengine instances and the localhost +metrics are transferred to the multihost dbengine instance. + +All new child nodes are automatically transferred to the multihost 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 multihost dbengine instance, you +must delete the instance's directory, which is located in `/var/cache/netdata/MACHINE_GUID/dbengine`, after stopping the +Agent. + +##### Information + +For more information about setting `[db].mode` on your nodes, in addition to other streaming configurations, see +[streaming](/streaming/README.md). + +## Requirements & limitations + +### Memory + +Using database 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**: + +- The total page cache memory footprint will be an additional `#dimensions-being-collected x 4096 x 2` bytes over what + the user configured with `dbengine page cache size MB`. + + +- 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 multihost disk space` +options. + +You can use +our [database engine calculator](/docs/store/change-metrics-storage.md#calculate-the-system-resources-ram-disk-space-needed-to-store-metrics) +to validate the memory requirements for your particular system(s) and configuration (**out-of-date**). + +### Disk space + +There are explicit disk space requirements **per** DB engine **instance**: + +- The total disk space footprint will be the maximum between `#dimensions-being-collected x 4096 x 2` bytes or what the + user configured with `dbengine multihost disk space` or `dbengine disk space`. + +### File descriptor + +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 +`[db].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: + +```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: + +```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. + +## Files + +With the DB engine 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 +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](/exporting/README.md) 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: + +| 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. + + |