... of a program to just test the hashing functions collisions on a 24
bit output with strings that are very likely Redis key names, and names
of a kind that are particularly prone to collisions.
We have 24 total bits of space in each object in order to implement
an LFU (Least Frequently Used) eviction policy.
We split the 24 bits into two fields:
8 bits 16 bits
+--------+----------------+
| LOG_C | Last decr time |
+--------+----------------+
LOG_C is a logarithmic counter that provides an indication of the access
frequency. However this field must also be deceremented otherwise what used
to be a frequently accessed key in the past, will remain ranked like that
forever, while we want the algorithm to adapt to access pattern changes.
So the remaining 16 bits are used in order to store the "decrement time",
a reduced-precision unix time (we take 16 bits of the time converted
in minutes since we don't care about wrapping around) where the LOG_C
counter is halved if it has an high value, or just decremented if it
has a low value.
New keys don't start at zero, in order to have the ability to collect
some accesses before being trashed away, so they start at COUNTER_INIT_VAL.
The logaritmic increment performed on LOG_C takes care of COUNTER_INIT_VAL
when incrementing the key, so that keys starting at COUNTER_INIT_VAL
(or having a smaller value) have a very high chance of being incremented
on access.
The simulation starts with a power-law access pattern, and later converts
into a flat access pattern in order to see how the algorithm adapts.
Currenty the decrement operation period is 1 minute, however note that
it is not guaranteed that each key will be scanned 1 time every minute,
so the actual frequency can be lower. However under high load, we access
3/5 keys every newly inserted key (because of how Redis eviction works).
This is a work in progress at this point to evaluate if this works well.
1. Scan keys with pause to account for actual LRU precision.
2. Test cross-DB with 100 keys allocated in DB1.
3. Output results that don't fluctuate depending on number of keys.
4. Output results in percentage to make more sense.
5. Save file instead of outputting to STDOUT.
6. Support running multiple times with average of outputs.
7. Label each square (DIV) with its ID as HTML title.
This PR adds the ability to execute the installation script non-interactively, useful for automated provisioning scripts such as Chef, Puppet, Ansible, Salt, etc.
Simply feed the environment variables into the install script to skip the prompts.
For debug and verification purposes, the script will still output the selected config variables.
The plus side is that the environment variables also support command substitution (see REDIS_EXECUTABLE).
```
sudo REDIS_PORT=1234 REDIS_CONFIG_FILE=/etc/redis/1234.conf REDIS_LOG_FILE=/var/log/redis_1234.log REDIS_DATA_DIR=/var/lib/redis/1234 REDIS_EXECUTABLE=`command -v redis-server` ./utils/install_server.sh
Welcome to the redis service installer
This script will help you easily set up a running redis server
Selected config:
Port : 1234
Config file : /etc/redis/1234.conf
Log file : /var/log/redis_1234.log
Data dir : /var/lib/redis/1234
Executable : /usr/local/bin/redis-server
Cli Executable : /usr/local/bin/redis-cli
Copied /tmp/1234.conf => /etc/init.d/redis_1234
Installing service...
Successfully added to chkconfig!
Successfully added to runlevels 345!
Starting Redis server...
Installation successful!
```
Sometimes Redis patch releases are released in a matter of weeks or days
one after the other. In order to have less release friction the idea is
to stop writing changelogs by hand, in order to also cover everything
interesting there is to say. Useless things can be deleted manually by
the changelog. Also this gives more credits to contributors since often
in the commit message involved people are cited even when they are not
the authors of the commit.
