Otherwise what happens is that the tracking table will never get garbage
collected if there are no longer clients with tracking enabled.
Now the invalidation function immediately checks if there is any table
allocated, otherwise it returns ASAP, so the overhead when the feature
is not used should be near zero.
This commit adds two new fields in the INFO output, stats section:
expired_stale_perc:0.34
expired_time_cap_reached_count:58
The first field is an estimate of the number of keys that are yet in
memory but are already logically expired. They reason why those keys are
yet not reclaimed is because the active expire cycle can't spend more
time on the process of reclaiming the keys, and at the same time nobody
is accessing such keys. However as the active expire cycle runs, while
it will eventually have to return to the caller, because of time limit
or because there are less than 25% of keys logically expired in each
given database, it collects the stats in order to populate this INFO
field.
Note that expired_stale_perc is a running average, where the current
sample accounts for 5% and the history for 95%, so you'll see it
changing smoothly over time.
The other field, expired_time_cap_reached_count, counts the number
of times the expire cycle had to stop, even if still it was finding a
sizeable number of keys yet to expire, because of the time limit.
This allows people handling operations to understand if the Redis
server, during mass-expiration events, is able to collect keys fast
enough usually. It is normal for this field to increment during mass
expires, but normally it should very rarely increment. When instead it
constantly increments, it means that the current workloads is using
a very important percentage of CPU time to expire keys.
This feature was created thanks to the hints of Rashmi Ramesh and
Bart Robinson from Twitter. In private email exchanges, they noted how
it was important to improve the observability of this parameter in the
Redis server. Actually in big deployments, the amount of keys that are
yet to expire in each server, even if they are logically expired, may
account for a very big amount of wasted memory.
It looks safer to return C_OK from freeMemoryIfNeeded() when clients are
paused because returning C_ERR may prevent success of writes. It is
possible that there is no difference in practice since clients cannot
execute writes while clients are paused, but it looks more correct this
way, at least conceptually.
Related to PR #4028.
BACKGROUND AND USE CASEj
Redis slaves are normally write only, however the supprot a "writable"
mode which is very handy when scaling reads on slaves, that actually
need write operations in order to access data. For instance imagine
having slaves replicating certain Sets keys from the master. When
accessing the data on the slave, we want to peform intersections between
such Sets values. However we don't want to intersect each time: to cache
the intersection for some time often is a good idea.
To do so, it is possible to setup a slave as a writable slave, and
perform the intersection on the slave side, perhaps setting a TTL on the
resulting key so that it will expire after some time.
THE BUG
Problem: in order to have a consistent replication, expiring of keys in
Redis replication is up to the master, that synthesize DEL operations to
send in the replication stream. However slaves logically expire keys
by hiding them from read attempts from clients so that if the master did
not promptly sent a DEL, the client still see logically expired keys
as non existing.
Because slaves don't actively expire keys by actually evicting them but
just masking from the POV of read operations, if a key is created in a
writable slave, and an expire is set, the key will be leaked forever:
1. No DEL will be received from the master, which does not know about
such a key at all.
2. No eviction will be performed by the slave, since it needs to disable
eviction because it's up to masters, otherwise consistency of data is
lost.
THE FIX
In order to fix the problem, the slave should be able to tag keys that
were created in the slave side and have an expire set in some way.
My solution involved using an unique additional dictionary created by
the writable slave only if needed. The dictionary is obviously keyed by
the key name that we need to track: all the keys that are set with an
expire directly by a client writing to the slave are tracked.
The value in the dictionary is a bitmap of all the DBs where such a key
name need to be tracked, so that we can use a single dictionary to track
keys in all the DBs used by the slave (actually this limits the solution
to the first 64 DBs, but the default with Redis is to use 16 DBs).
This solution allows to pay both a small complexity and CPU penalty,
which is zero when the feature is not used, actually. The slave-side
eviction is encapsulated in code which is not coupled with the rest of
the Redis core, if not for the hook to track the keys.
TODO
I'm doing the first smoke tests to see if the feature works as expected:
so far so good. Unit tests should be added before merging into the
4.0 branch.
