- Almost all Cluster Manager related code moved to
the same section.
- Many macroes converted to functions
- Added various comments
- Little code restyling
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 is possible to do BGREWRITEAOF even if appendonly=no. This is by design.
stopAppendonly() didn't turn off aof_rewrite_scheduled (it can be turned on
again by BGREWRITEAOF even while appendonly is off anyway).
After configuring `appendonly yes` it will see that the state is AOF_OFF,
there's no RDB fork, so it will do rewriteAppendOnlyFileBackground() which
will fail since the aof_child_pid is set (was scheduled and started by cron).
Solution:
stopAppendonly() will turn off the schedule flag (regardless of who asked for it).
startAppendonly() will terminate any existing fork and start a new one (so it is the most recent).
When feeding the master with a high rate traffic the the slave's feed is much slower.
This causes the replication buffer to grow (indefinitely) which leads to slave disconnection.
The problem is that writeToClient() decides to stop writing after NET_MAX_WRITES_PER_EVENT
writes (In order to be fair to clients).
We should ignore this when the client is a slave.
It's better if clients wait longer, the alternative is that the slave has no chance to stay in
sync in this situation.
See #3462 and related PRs.
We use a simple algorithm to calculate the level of affinity violation,
and then an optimizer that performs random swaps until things improve.
