During the refactoring of blocking operations, commit
83e363d3e67c27865d7679c27f466c5e12b3d4ee, a bug was introduced where
a milliseconds time is compared to a seconds time, so all the clients
always appear to timeout if timeout is set to non-zero value.
Thanks to Jonathan Leibiusky for finding the bug and helping verifying
the cause and fix.
Some are just to know if the master is down, and in this case the runid
in the request is set to "*", others are actually in order to seek for a
vote and get elected. In the latter case the runid is set to the runid
of the instance seeking for the vote.
All the internal state of cluster involving time is now using mstime_t
and mstime() in order to use milliseconds resolution.
Also the clusterCron() function is called with a 10 hz frequency instead
of 1 hz.
The cluster node_timeout must be also configured in milliseconds by the
user in redis.conf.
Example:
db0:keys=221913,expires=221913,avg_ttl=655
The algorithm uses a running average with only two samples (current and
previous). Keys found to be expired are considered at TTL zero even if
the actual TTL can be negative.
The TTL is reported in milliseconds.
We don't want to repeat a fast cycle too soon, the previous code was
broken, we need to wait two times the period *since* the start of the
previous cycle in order to avoid there is an even space between cycles:
.-> start .-> second start
| |
+-------------+-------------+--------------+
| first cycle | pause | second cycle |
+-------------+-------------+--------------+
The second and first start must be PERIOD*2 useconds apart hence the *2
in the new code.
This commit makes the fast collection cycle time configurable, at
the same time it does not allow to run a new fast collection cycle
for the same amount of time as the max duration of the fast
collection cycle.
The main idea here is that when we are no longer to expire keys at the
rate the are created, we can't block more in the normal expire cycle as
this would result in too big latency spikes.
For this reason the commit introduces a "fast" expire cycle that does
not run for more than 1 millisecond but is called in the beforeSleep()
hook of the event loop, so much more often, and with a frequency bound
to the frequency of executed commnads.
The fast expire cycle is only called when the standard expiration
algorithm runs out of time, that is, consumed more than
REDIS_EXPIRELOOKUPS_TIME_PERC of CPU in a given cycle without being able
to take the number of already expired keys that are yet not collected
to a number smaller than 25% of the number of keys.
You can test this commit with different loads, but a simple way is to
use the following:
Extreme load with pipelining:
redis-benchmark -r 100000000 -n 100000000 \
-P 32 set ele:rand:000000000000 foo ex 2
Remove the -P32 in order to avoid the pipelining for a more real-world
load.
In another terminal tab you can monitor the Redis behavior with:
redis-cli -i 0.1 -r -1 info keyspace
and
redis-cli --latency-history
Note: this commit will make Redis printing a lot of debug messages, it
is not a good idea to use it in production.
Previously two string encodings were used for string objects:
1) REDIS_ENCODING_RAW: a string object with obj->ptr pointing to an sds
stirng.
2) REDIS_ENCODING_INT: a string object where the obj->ptr void pointer
is casted to a long.
This commit introduces a experimental new encoding called
REDIS_ENCODING_EMBSTR that implements an object represented by an sds
string that is not modifiable but allocated in the same memory chunk as
the robj structure itself.
The chunk looks like the following:
+--------------+-----------+------------+--------+----+
| robj data... | robj->ptr | sds header | string | \0 |
+--------------+-----+-----+------------+--------+----+
| ^
+-----------------------+
The robj->ptr points to the contiguous sds string data, so the object
can be manipulated with the same functions used to manipulate plan
string objects, however we need just on malloc and one free in order to
allocate or release this kind of objects. Moreover it has better cache
locality.
This new allocation strategy should benefit both the memory usage and
the performances. A performance gain between 60 and 70% was observed
during micro-benchmarks, however there is more work to do to evaluate
the performance impact and the memory usage behavior.
