This commit, in some parts derived from PR #3041 which is no longer
possible to merge (because the user deleted the original branch),
implements the ability of slaves to have a special configuration
preventing that they try to start a failover when the master is failing.
There are multiple reasons for wanting this, and the feautre was
requested in issue #3021 time ago.
The differences between this patch and the original PR are the
following:
1. The flag is saved/loaded on the nodes configuration.
2. The 'myself' node is now flag-aware, the flag is updated as needed
when the configuration is changed via CONFIG SET.
3. The flag name uses NOFAILOVER instead of NO_FAILOVER to be consistent
with existing NOADDR.
4. The redis.conf documentation was rewritten.
Thanks to @deep011 for the original patch.
This commit, in some parts derived from PR #3041 which is no longer
possible to merge (because the user deleted the original branch),
implements the ability of slaves to have a special configuration
preventing that they try to start a failover when the master is failing.
There are multiple reasons for wanting this, and the feautre was
requested in issue #3021 time ago.
The differences between this patch and the original PR are the
following:
1. The flag is saved/loaded on the nodes configuration.
2. The 'myself' node is now flag-aware, the flag is updated as needed
when the configuration is changed via CONFIG SET.
3. The flag name uses NOFAILOVER instead of NO_FAILOVER to be consistent
with existing NOADDR.
4. The redis.conf documentation was rewritten.
Thanks to @deep011 for the original patch.
other fixes / improvements:
- LUA script memory isn't taken from zmalloc (taken from libc malloc)
so it can cause high fragmentation ratio to be displayed (which is false)
- there was a problem with "fragmentation" info being calculated from
RSS and used_memory sampled at different times (now sampling them together)
other details:
- adding a few more allocator info fields to INFO and MEMORY commands
- improve defrag test to measure defrag latency of big keys
- increasing the accuracy of the defrag test (by looking at real grag info)
this way we can use an even lower threshold and still avoid false positives
- keep the old (total) "fragmentation" field unchanged, but add new ones for spcific things
- add these the MEMORY DOCTOR command
- deduct LUA memory from the rss in case of non jemalloc allocator (one for which we don't "allocator active/used")
- reduce sampling rate of the rss and allocator info
other fixes / improvements:
- LUA script memory isn't taken from zmalloc (taken from libc malloc)
so it can cause high fragmentation ratio to be displayed (which is false)
- there was a problem with "fragmentation" info being calculated from
RSS and used_memory sampled at different times (now sampling them together)
other details:
- adding a few more allocator info fields to INFO and MEMORY commands
- improve defrag test to measure defrag latency of big keys
- increasing the accuracy of the defrag test (by looking at real grag info)
this way we can use an even lower threshold and still avoid false positives
- keep the old (total) "fragmentation" field unchanged, but add new ones for spcific things
- add these the MEMORY DOCTOR command
- deduct LUA memory from the rss in case of non jemalloc allocator (one for which we don't "allocator active/used")
- reduce sampling rate of the rss and allocator info
- big keys are not defragged in one go from within the dict scan
instead they are scanned in parts after the main dict hash bucket is done.
- add latency monitor sample for defrag
- change default active-defrag-cycle-min to induce lower latency
- make active defrag start a new scan right away if needed, so it's easier
(for the test suite) to detect when it's done
- make active defrag quick the current cycle after each db / big key
- defrag some non key long term global allocations
- some refactoring for smaller functions and more reusable code
- during dict rehashing, one scan iteration of the dict, can end up scanning
one bucket in the smaller dict and many many buckets in the larger dict.
so waiting for 16 scan iterations before checking the time, may be much too long.
- big keys are not defragged in one go from within the dict scan
instead they are scanned in parts after the main dict hash bucket is done.
- add latency monitor sample for defrag
- change default active-defrag-cycle-min to induce lower latency
- make active defrag start a new scan right away if needed, so it's easier
(for the test suite) to detect when it's done
- make active defrag quick the current cycle after each db / big key
- defrag some non key long term global allocations
- some refactoring for smaller functions and more reusable code
- during dict rehashing, one scan iteration of the dict, can end up scanning
one bucket in the smaller dict and many many buckets in the larger dict.
so waiting for 16 scan iterations before checking the time, may be much too long.
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.
The main change introduced by this commit is pretending that help
arrays are more text than code, thus indenting them at level 0. This
improves readability, and is an old practice when defining arrays of
C strings describing text.
Additionally a few useless return statements are removed, and the HELP
subcommand capitalized when printed to the user.
