Merging upstream version 1.18.0.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

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.
+
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