AE_BARRIER was implemented like:
- Fire the readable event.
- Do not fire the writabel event if the readable fired.
However this may lead to the writable event to never be called if the
readable event is always fired. There is an alterantive, we can just
invert the sequence of the calls in case AE_BARRIER is set. This commit
does that.
AE_BARRIER was implemented like:
- Fire the readable event.
- Do not fire the writabel event if the readable fired.
However this may lead to the writable event to never be called if the
readable event is always fired. There is an alterantive, we can just
invert the sequence of the calls in case AE_BARRIER is set. This commit
does that.
In case the write handler is already installed, it could happen that we
serve the reply of a query in the same event loop cycle we received it,
preventing beforeSleep() from guaranteeing that we do the AOF fsync
before sending the reply to the client.
The AE_BARRIER mechanism, introduced in a previous commit, prevents this
problem. This commit makes actual use of this new feature to fix the
bug.
In case the write handler is already installed, it could happen that we
serve the reply of a query in the same event loop cycle we received it,
preventing beforeSleep() from guaranteeing that we do the AOF fsync
before sending the reply to the client.
The AE_BARRIER mechanism, introduced in a previous commit, prevents this
problem. This commit makes actual use of this new feature to fix the
bug.
Add AE_BARRIER to the writable event loop so that slaves requesting
votes can't be served before we re-enter the event loop in the next
iteration, so clusterBeforeSleep() will fsync to disk in time.
Also add the call to explicitly fsync, given that we modified the last
vote epoch variable.
Add AE_BARRIER to the writable event loop so that slaves requesting
votes can't be served before we re-enter the event loop in the next
iteration, so clusterBeforeSleep() will fsync to disk in time.
Also add the call to explicitly fsync, given that we modified the last
vote epoch variable.
AOF fsync=always, and certain Redis Cluster bus operations, require to
fsync data on disk before replying with an acknowledge.
In such case, in order to implement Group Commits, we want to be sure
that queries that are read in a given cycle of the event loop, are never
served to clients in the same event loop iteration. This way, by using
the event loop "before sleep" callback, we can fsync the information
just one time before returning into the event loop for the next cycle.
This is much more efficient compared to calling fsync() multiple times.
Unfortunately because of a bug, this was not always guaranteed: the
actual way the events are installed was the sole thing that could
control. Normally this problem is hard to trigger when AOF is enabled
with fsync=always, because we try to flush the output buffers to the
socekt directly in the beforeSleep() function of Redis. However if the
output buffers are full, we actually install a write event, and in such
a case, this bug could happen.
This change to ae.c modifies the event loop implementation to make this
concept explicit. Write events that are registered with:
AE_WRITABLE|AE_BARRIER
Are guaranteed to never fire after the readable event was fired for the
same file descriptor. In this way we are sure that data is persisted to
disk before the client performing the operation receives an
acknowledged.
However note that this semantics does not provide all the guarantees
that one may believe are automatically provided. Take the example of the
blocking list operations in Redis.
With AOF and fsync=always we could have:
Client A doing: BLPOP myqueue 0
Client B doing: RPUSH myqueue a b c
In this scenario, Client A will get the "a" elements immediately after
the Client B RPUSH will be executed, even before the operation is persisted.
However when Client B will get the acknowledge, it can be sure that
"b,c" are already safe on disk inside the list.
What to note here is that it cannot be assumed that Client A receiving
the element is a guaranteed that the operation succeeded from the point
of view of Client B.
This is due to the fact that the barrier exists within the same socket,
and not between different sockets. However in the case above, the
element "a" was not going to be persisted regardless, so it is a pretty
synthetic argument.
AOF fsync=always, and certain Redis Cluster bus operations, require to
fsync data on disk before replying with an acknowledge.
In such case, in order to implement Group Commits, we want to be sure
that queries that are read in a given cycle of the event loop, are never
served to clients in the same event loop iteration. This way, by using
the event loop "before sleep" callback, we can fsync the information
just one time before returning into the event loop for the next cycle.
This is much more efficient compared to calling fsync() multiple times.
Unfortunately because of a bug, this was not always guaranteed: the
actual way the events are installed was the sole thing that could
control. Normally this problem is hard to trigger when AOF is enabled
with fsync=always, because we try to flush the output buffers to the
socekt directly in the beforeSleep() function of Redis. However if the
output buffers are full, we actually install a write event, and in such
a case, this bug could happen.
This change to ae.c modifies the event loop implementation to make this
concept explicit. Write events that are registered with:
AE_WRITABLE|AE_BARRIER
Are guaranteed to never fire after the readable event was fired for the
same file descriptor. In this way we are sure that data is persisted to
disk before the client performing the operation receives an
acknowledged.
However note that this semantics does not provide all the guarantees
that one may believe are automatically provided. Take the example of the
blocking list operations in Redis.
With AOF and fsync=always we could have:
Client A doing: BLPOP myqueue 0
Client B doing: RPUSH myqueue a b c
In this scenario, Client A will get the "a" elements immediately after
the Client B RPUSH will be executed, even before the operation is persisted.
However when Client B will get the acknowledge, it can be sure that
"b,c" are already safe on disk inside the list.
What to note here is that it cannot be assumed that Client A receiving
the element is a guaranteed that the operation succeeded from the point
of view of Client B.
This is due to the fact that the barrier exists within the same socket,
and not between different sockets. However in the case above, the
element "a" was not going to be persisted regardless, so it is a pretty
synthetic argument.
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.
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.
