## Introduction
Redis introduced IO Thread in 6.0, allowing IO threads to handle client
request reading, command parsing and reply writing, thereby improving
performance. The current IO thread implementation has a few drawbacks.
- The main thread is blocked during IO thread read/write operations and
must wait for all IO threads to complete their current tasks before it
can continue execution. In other words, the entire process is
synchronous. This prevents the efficient utilization of multi-core CPUs
for parallel processing.
- When the number of clients and requests increases moderately, it
causes all IO threads to reach full CPU utilization due to the busy wait
mechanism used by the IO threads. This makes it challenging for us to
determine which part of Redis has reached its bottleneck.
- When IO threads are enabled with TLS and io-threads-do-reads, a
disconnection of a connection with pending data may result in it being
assigned to multiple IO threads simultaneously. This can cause race
conditions and trigger assertion failures. Related issue:
redis#12540
Therefore, we designed an asynchronous IO threads solution. The IO
threads adopt an event-driven model, with the main thread dedicated to
command processing, meanwhile, the IO threads handle client read and
write operations in parallel.
## Implementation
### Overall
As before, we did not change the fact that all client commands must be
executed on the main thread, because Redis was originally designed to be
single-threaded, and processing commands in a multi-threaded manner
would inevitably introduce numerous race and synchronization issues. But
now each IO thread has independent event loop, therefore, IO threads can
use a multiplexing approach to handle client read and write operations,
eliminating the CPU overhead caused by busy-waiting.
the execution process can be briefly described as follows:
the main thread assigns clients to IO threads after accepting
connections, IO threads will notify the main thread when clients
finish reading and parsing queries, then the main thread processes
queries from IO threads and generates replies, IO threads handle
writing reply to clients after receiving clients list from main thread,
and then continue to handle client read and write events.
### Each IO thread has independent event loop
We now assign each IO thread its own event loop. This approach
eliminates the need for the main thread to perform the costly
`epoll_wait` operation for handling connections (except for specific
ones). Instead, the main thread processes requests from the IO threads
and hands them back once completed, fully offloading read and write
events to the IO threads.
Additionally, all TLS operations, including handling pending data, have
been moved entirely to the IO threads. This resolves the issue where
io-threads-do-reads could not be used with TLS.
### Event-notified client queue
To facilitate communication between the IO threads and the main thread,
we designed an event-notified client queue. Each IO thread and the main
thread have two such queues to store clients waiting to be processed.
These queues are also integrated with the event loop to enable handling.
We use pthread_mutex to ensure the safety of queue operations, as well
as data visibility and ordering, and race conditions are minimized, as
each IO thread and the main thread operate on independent queues,
avoiding thread suspension due to lock contention. And we implemented an
event notifier based on `eventfd` or `pipe` to support event-driven
handling.
### Thread safety
Since the main thread and IO threads can execute in parallel, we must
handle data race issues carefully.
**client->flags**
The primary tasks of IO threads are reading and writing, i.e.
`readQueryFromClient` and `writeToClient`. However, IO threads and the
main thread may concurrently modify or access `client->flags`, leading
to potential race conditions. To address this, we introduced an io-flags
variable to record operations performed by IO threads, thereby avoiding
race conditions on `client->flags`.
**Pause IO thread**
In the main thread, we may want to operate data of IO threads, maybe
uninstall event handler, access or operate query/output buffer or resize
event loop, we need a clean and safe context to do that. We pause IO
thread in `IOThreadBeforeSleep`, do some jobs and then resume it. To
avoid thread suspended, we use busy waiting to confirm the target
status. Besides we use atomic variable to make sure memory visibility
and ordering. We introduce these functions to pause/resume IO Threads as
below.
```
pauseIOThread, resumeIOThread
pauseAllIOThreads, resumeAllIOThreads
pauseIOThreadsRange, resumeIOThreadsRange
```
Testing has shown that `pauseIOThread` is highly efficient, allowing the
main thread to execute nearly 200,000 operations per second during
stress tests. Similarly, `pauseAllIOThreads` with 8 IO threads can
handle up to nearly 56,000 operations per second. But operations
performed between pausing and resuming IO threads must be quick;
otherwise, they could cause the IO threads to reach full CPU
utilization.
**freeClient and freeClientAsync**
The main thread may need to terminate a client currently running on an
IO thread, for example, due to ACL rule changes, reaching the output
buffer limit, or evicting a client. In such cases, we need to pause the
IO thread to safely operate on the client.
**maxclients and maxmemory-clients updating**
When adjusting `maxclients`, we need to resize the event loop for all IO
threads. Similarly, when modifying `maxmemory-clients`, we need to
traverse all clients to calculate their memory usage. To ensure safe
operations, we pause all IO threads during these adjustments.
**Client info reading**
The main thread may need to read a client’s fields to generate a
descriptive string, such as for the `CLIENT LIST` command or logging
purposes. In such cases, we need to pause the IO thread handling that
client. If information for all clients needs to be displayed, all IO
threads must be paused.
**Tracking redirect**
Redis supports the tracking feature and can even send invalidation
messages to a connection with a specified ID. But the target client may
be running on IO thread, directly manipulating the client’s output
buffer is not thread-safe, and the IO thread may not be aware that the
client requires a response. In such cases, we pause the IO thread
handling the client, modify the output buffer, and install a write event
handler to ensure proper handling.
**clientsCron**
In the `clientsCron` function, the main thread needs to traverse all
clients to perform operations such as timeout checks, verifying whether
they have reached the soft output buffer limit, resizing the
output/query buffer, or updating memory usage. To safely operate on a
client, the IO thread handling that client must be paused.
If we were to pause the IO thread for each client individually, the
efficiency would be very low. Conversely, pausing all IO threads
simultaneously would be costly, especially when there are many IO
threads, as clientsCron is invoked relatively frequently.
To address this, we adopted a batched approach for pausing IO threads.
At most, 8 IO threads are paused at a time. The operations mentioned
above are only performed on clients running in the paused IO threads,
significantly reducing overhead while maintaining safety.
### Observability
In the current design, the main thread always assigns clients to the IO
thread with the least clients. To clearly observe the number of clients
handled by each IO thread, we added the new section in INFO output. The
`INFO THREADS` section can show the client count for each IO thread.
```
# Threads
io_thread_0:clients=0
io_thread_1:clients=2
io_thread_2:clients=2
```
Additionally, in the `CLIENT LIST` output, we also added a field to
indicate the thread to which each client is assigned.
`id=244 addr=127.0.0.1:41870 laddr=127.0.0.1:6379 ... resp=2 lib-name=
lib-ver= io-thread=1`
## Trade-off
### Special Clients
For certain special types of clients, keeping them running on IO threads
would result in severe race issues that are difficult to resolve.
Therefore, we chose not to offload these clients to the IO threads.
For replica, monitor, subscribe, and tracking clients, main thread may
directly write them a reply when conditions are met. Race issues are
difficult to resolve, so we have them processed in the main thread. This
includes the Lua debug clients as well, since we may operate connection
directly.
For blocking client, after the IO thread reads and parses a command and
hands it over to the main thread, if the client is identified as a
blocking type, it will be remained in the main thread. Once the blocking
operation completes and the reply is generated, the client is
transferred back to the IO thread to send the reply and wait for event
triggers.
### Clients Eviction
To support client eviction, it is necessary to update each client’s
memory usage promptly during operations such as read, write, or command
execution. However, when a client operates on an IO thread, it is not
feasible to update the memory usage immediately due to the risk of data
races. As a result, memory usage can only be updated either in the main
thread while processing commands or in the `ClientsCron` periodically.
The downside of this approach is that updates might experience a delay
of up to one second, which could impact the precision of memory
management for eviction.
To avoid incorrectly evicting clients. We adopted a best-effort
compensation solution, when we decide to eviction a client, we update
its memory usage again before evicting, if the memory used by the client
does not decrease or memory usage bucket is not changed, then we will
evict it, otherwise, not evict it.
However, we have not completely solved this problem. Due to the delay in
memory usage updates, it may lead us to make incorrect decisions about
the need to evict clients.
### Defragment
In the majority of cases we do NOT use the data from argv directly in
the db.
1. key names
We store a copy that we allocate in the main thread, see `sdsdup()` in
`dbAdd()`.
2. hash key and value
We store key as hfield and store value as sds, see `hfieldNew()` and
`sdsdup()` in `hashTypeSet()`.
3. other datatypes
They don't even use SDS, so there is no reference issues.
But in some cases client the data from argv may be retain by the main
thread.
As a result, during fragmentation cleanup, we need to move allocations
from the IO thread’s arena to the main thread’s arena. We always
allocate new memory in the main thread’s arena, but the memory released
by IO threads may not yet have been reclaimed. This ultimately causes
the fragmentation rate to be higher compared to creating and allocating
entirely within a single thread.
The following cases below will lead to memory allocated by the IO thread
being kept by the main thread.
1. string related command: `append`, `getset`, `mset` and `set`.
If `tryObjectEncoding()` does not change argv, we will keep it directly
in the main thread, see the code in `tryObjectEncoding()`(specifically
`trimStringObjectIfNeeded()`)
2. block related command.
the key names will be kept in `c->db->blocking_keys`.
3. watch command
the key names will be kept in `c->db->watched_keys`.
4. [s]subscribe command
channel name will be kept in `serverPubSubChannels`.
5. script load command
script will be kept in `server.lua_scripts`.
7. some module API: `RM_RetainString`, `RM_HoldString`
Those issues will be handled in other PRs.
## Testing
### Functional Testing
The commit with enabling IO Threads has passed all TCL tests, but we did
some changes:
**Client query buffer**: In the original code, when using a reusable
query buffer, ownership of the query buffer would be released after the
command was processed. However, with IO threads enabled, the client
transitions from an IO thread to the main thread for processing. This
causes the ownership release to occur earlier than the command
execution. As a result, when IO threads are enabled, the client's
information will never indicate that a shared query buffer is in use.
Therefore, we skip the corresponding query buffer tests in this case.
**Defragment**: Add a new defragmentation test to verify the effect of
io threads on defragmentation.
**Command delay**: For deferred clients in TCL tests, due to clients
being assigned to different threads for execution, delays may occur. To
address this, we introduced conditional waiting: the process proceeds to
the next step only when the `client list` contains the corresponding
commands.
### Sanitizer Testing
The commit passed all TCL tests and reported no errors when compiled
with the `fsanitizer=thread` and `fsanitizer=address` options enabled.
But we made the following modifications: we suppressed the sanitizer
warnings for clients with watched keys when updating `client->flags`, we
think IO threads read `client->flags`, but never modify it or read the
`CLIENT_DIRTY_CAS` bit, main thread just only modifies this bit, so
there is no actual data race.
## Others
### IO thread number
In the new multi-threaded design, the main thread is primarily focused
on command processing to improve performance. Typically, the main thread
does not handle regular client I/O operations but is responsible for
clients such as replication and tracking clients. To avoid breaking
changes, we still consider the main thread as the first IO thread.
When the io-threads configuration is set to a low value (e.g., 2),
performance does not show a significant improvement compared to a
single-threaded setup for simple commands (such as SET or GET), as the
main thread does not consume much CPU for these simple operations. This
results in underutilized multi-core capacity. However, for more complex
commands, having a low number of IO threads may still be beneficial.
Therefore, it’s important to adjust the `io-threads` based on your own
performance tests.
Additionally, you can clearly monitor the CPU utilization of the main
thread and IO threads using `top -H -p $redis_pid`. This allows you to
easily identify where the bottleneck is. If the IO thread is the
bottleneck, increasing the `io-threads` will improve performance. If the
main thread is the bottleneck, the overall performance can only be
scaled by increasing the number of shards or replicas.
---------
Co-authored-by: debing.sun <debing.sun@redis.com>
Co-authored-by: oranagra <oran@redislabs.com>
Users who abuse lua error_reply will generate a new error object on each
error call, which can make server.errors get bigger and bigger. This
will
cause the server to block when calling INFO (we also return errorstats
by
default).
To prevent the damage it can cause, when a misuse is detected, we will
print a warning log and disable the errorstats to avoid adding more new
errors. It can be re-enabled via CONFIG RESETSTAT.
Because server.errors may be very large (it may be better now since we
have the limit), config resetstat may block for a while. So in
resetErrorTableStats, we will try to lazyfree server.errors.
See the related discussion at the end of #8217.
Sometimes we need to make fast judgement about why Redis is suddenly
taking more memory. One of the reasons is main DB's dicts doing
rehashing.
We may use `MEMORY STATS` to monitor the overhead memory of each DB, but
there still lacks a total sum to show an overall trend. So this PR adds
the total overhead of all DBs to `INFO MEMORY` section, together with
the total count of rehashing DB dicts, providing some intuitive metrics
about main dicts rehashing.
This PR adds the following metrics to INFO MEMORY
* `mem_overhead_db_hashtable_rehashing` - only size of ht[0] in
dictionaries we're rehashing (i.e. the memory that's gonna get released
soon)
and a similar ones to MEMORY STATS:
* `overhead.db.hashtable.lut` (complements the existing
`overhead.hashtable.main` and `overhead.hashtable.expires` which also
counts the `dictEntry` structs too)
* `overhead.db.hashtable.rehashing` - temporary rehashing overhead.
* `db.dict.rehashing.count` - number of top level dictionaries being
rehashed.
---------
Co-authored-by: zhaozhao.zz <zhaozhao.zz@alibaba-inc.com>
Co-authored-by: Oran Agra <oran@redislabs.com>
There is a timing issue in the test, close may arrive late, or in
freeClientAsync we will free the client in async way, which will
lead to errors in watching_clients statistics, since we will only
unwatch all keys when we truly freeClient.
Add a wait here to avoid this problem. Also fixed some outdated
comments i saw. The test was introduced in #12966.
Redis has some special commands that mark the client's state, such as
`subscribe` and `blpop`, which mark the client as `CLIENT_PUBSUB` or
`CLIENT_BLOCKED`, and we have metrics for the special use cases.
However, there are also other special commands, like `WATCH`, which
although do not have a specific flags, and should also be considered
stateful client types. For stateful clients, in many scenarios, the
connections cannot be shared in "connection pool", meaning connection
pool cannot be used. For example, whenever the `WATCH` command is
executed, a new connection is required to put the client into the "watch
state" because the watched keys are stored in the client.
If different business logic requires watching different keys, separate
connections must be used; otherwise, there will be contamination. This
also means that if a user's business heavily relies on the `WATCH`
command, a large number of connections will be required.
Recently we have encountered this situation in our platform, where some
users consume a significant number of connections when using Redis
because of `WATCH`.
I hope we can have a way to observe these special use cases and special
client connections. Here I add a few monitoring metrics:
1. `watching_clients` in `INFO` reply: The number of clients currently
in the "watching" state.
2. `total_watched_keys` in `INFO` reply: The total number of keys being
watched.
3. `watch` in `CLIENT LIST` reply: The number of keys each client is
currently watching.
In INFO CLIENTS section, we already have blocked_clients and
tracking_clients. We should add a new metric showing the number of
pubsub connections, which helps performance monitoring and trouble
shooting.
This change overcomes many stability issues experienced with the
vmactions action.
We need to limit VMs to 8GB for better stability, as the 13GB default
seems to hang them occasionally.
Shell code has been simplified since this action seem to use `bash -e`
which will abort on non-zero exit codes anyway.
The new test added in #12476 causes reply-schemas-validator to fail.
When doing `catch {r get key}`, the req-res output is:
```
3
get
3
key
12
__argv_end__
$100000
aaaaaaaaaaaaaaaaaaaa...4
info
5
stats
12
__argv_end__
=1670
txt:# Stats
...
```
And we can see the link after `$100000`, there is a 4 in the last,
it break the req-res-log-validator script since the format is wrong.
The reason i guess is after the client reconnection (after the output
buf limit), we will not add newlines, but append args directly.
Since obuf-limits.tcl is doing the same thing, and it had the logreqres:skip
flag, so this PR is following it.
Add these INFO metrics:
* client_query_buffer_limit_disconnections
* client_output_buffer_limit_disconnections
Sometimes it is useful to monitor whether clients reaches size limit of
query buffer and output buffer, to decide whether we need to adjust the
buffer size limit or reduce client query payload.
The test fails on freebsd CI:
```
*** [err]: stats: eventloop metrics in tests/unit/info.tcl
Expected '31777' to be less than '16183' (context: type eval line 17 cmd
{assert_lessthan $el_sum2 [expr $el_sum1+10000] } proc ::test)
```
The test added in #11963, fails on freebsd CI which is slow,
increase tollerance and also add some verbose logs, now we can
see these logs in verbose mode (for better views):
```
eventloop metrics cycle1: 12, cycle2: 15
eventloop metrics el_sum1: 315, el_sum2: 411
eventloop metrics cmd_sum1: 126, cmd_sum2: 137
[ok]: stats: eventloop metrics (111 ms)
instantaneous metrics instantaneous_eventloop_cycles_per_sec: 8
instantaneous metrics instantaneous_eventloop_duration_usec: 55
[ok]: stats: instantaneous metrics (1603 ms)
[ok]: stats: debug metrics (112 ms)
```
In #11963, some new tests about eventloop duration were added, which includes time measurement in TCL scripts. This has caused some unexpected CI failures, such as #12169 and #12177, due to slow test servers or some performance jittering.
The measured latency(duration) includes the list below, which can be shown by `INFO STATS`.
```
eventloop_cycles // ever increasing counter
eventloop_duration_sum // cumulative duration of eventloop in microseconds
eventloop_duration_cmd_sum // cumulative duration of executing commands in microseconds
instantaneous_eventloop_cycles_per_sec // average eventloop count per second in recent 1.6s
instantaneous_eventloop_duration_usec // average single eventloop duration in recent 1.6s
```
Also added some experimental metrics, which are shown only when `INFO DEBUG` is called.
This section isn't included in the default INFO, or even in `INFO ALL` and the fields in this section
can change in the future without considering backwards compatibility.
```
eventloop_duration_aof_sum // cumulative duration of writing AOF
eventloop_duration_cron_sum // cumulative duration cron jobs (serverCron, beforeSleep excluding IO and AOF)
eventloop_cmd_per_cycle_max // max number of commands executed in one eventloop
eventloop_duration_max // max duration of one eventloop
```
All of these are being reset by CONFIG RESETSTAT
This is a followup work for #10278, and a discussion about #10279
The changes:
- fix failed_calls in command stats for blocked clients that got error.
including CLIENT UNBLOCK, and module replying an error from a thread.
- fix latency stats for XREADGROUP that filed with -NOGROUP
Theory behind which errors should be counted:
- error stats represents errors returned to the user, so an error handled by a
module should not be counted.
- total error counter should be the same.
- command stats represents execution of commands (even with RM_Call, and if
they fail or get rejected it counts these calls in commandstats, so it should
also count failed_calls)
Some thoughts about Scripts:
for scripts it could be different since they're part of user code, not the infra (not an extension to redis)
we certainly want commandstats to contain all calls and errors
a simple script is like mult-exec transaction so an error inside it should be counted in error stats
a script that replies with an error to the user (using redis.error_reply) should also be counted in error stats
but then the problem is that a plain `return redis.call("SET")` should not be counted twice (once for the SET
and once for EVAL)
so that's something left to be resolved in #10279
Summary of changes:
1. Rename `redisCommand->name` to `redisCommand->declared_name`, it is a
const char * for native commands and SDS for module commands.
2. Store the [sub]command fullname in `redisCommand->fullname` (sds).
3. List subcommands in `ACL CAT`
4. List subcommands in `COMMAND LIST`
5. `moduleUnregisterCommands` now will also free the module subcommands.
6. RM_GetCurrentCommandName returns full command name
Other changes:
1. Add `addReplyErrorArity` and `addReplyErrorExpireTime`
2. Remove `getFullCommandName` function that now is useless.
3. Some cleanups about `fullname` since now it is SDS.
4. Delete `populateSingleCommand` function from server.h that is useless.
5. Added tests to cover this change.
6. Add some module unload tests and fix the leaks
7. Make error messages uniform, make sure they always contain the full command
name and that it's quoted.
7. Fixes some typos
see the history in #9504, fixes#10124
Co-authored-by: Oran Agra <oran@redislabs.com>
Co-authored-by: guybe7 <guy.benoish@redislabs.com>
since `info commandstats` already shows sub-commands, we should do the same in `info latencystats`.
similarly, the LATENCY HISTOGRAM command now shows sub-commands (with their full name) when:
* asking for all commands
* asking for a specific container command
* asking for a specific sub-command)
Co-authored-by: Oran Agra <oran@redislabs.com>
# Short description
The Redis extended latency stats track per command latencies and enables:
- exporting the per-command percentile distribution via the `INFO LATENCYSTATS` command.
**( percentile distribution is not mergeable between cluster nodes ).**
- exporting the per-command cumulative latency distributions via the `LATENCY HISTOGRAM` command.
Using the cumulative distribution of latencies we can merge several stats from different cluster nodes
to calculate aggregate metrics .
By default, the extended latency monitoring is enabled since the overhead of keeping track of the
command latency is very small.
If you don't want to track extended latency metrics, you can easily disable it at runtime using the command:
- `CONFIG SET latency-tracking no`
By default, the exported latency percentiles are the p50, p99, and p999.
You can alter them at runtime using the command:
- `CONFIG SET latency-tracking-info-percentiles "0.0 50.0 100.0"`
## Some details:
- The total size per histogram should sit around 40 KiB. We only allocate those 40KiB when a command
was called for the first time.
- With regards to the WRITE overhead As seen below, there is no measurable overhead on the achievable
ops/sec or full latency spectrum on the client. Including also the measured redis-benchmark for unstable
vs this branch.
- We track from 1 nanosecond to 1 second ( everything above 1 second is considered +Inf )
## `INFO LATENCYSTATS` exposition format
- Format: `latency_percentiles_usec_<CMDNAME>:p0=XX,p50....`
## `LATENCY HISTOGRAM [command ...]` exposition format
Return a cumulative distribution of latencies in the format of a histogram for the specified command names.
The histogram is composed of a map of time buckets:
- Each representing a latency range, between 1 nanosecond and roughly 1 second.
- Each bucket covers twice the previous bucket's range.
- Empty buckets are not printed.
- Everything above 1 sec is considered +Inf.
- At max there will be log2(1000000000)=30 buckets
We reply a map for each command in the format:
`<command name> : { `calls`: <total command calls> , `histogram` : { <bucket 1> : latency , < bucket 2> : latency, ... } }`
Co-authored-by: Oran Agra <oran@redislabs.com>
Some people complain that QUIT is missing from help/command table.
Not appearing in COMMAND command, command stats, ACL, etc.
and instead, there's a hack in processCommand with a comment that looks outdated.
Note that it is [documented](https://redis.io/commands/quit)
At the same time, HOST: and POST are there in the command table although these are not real commands.
They would appear in the COMMAND command, and even in commandstats.
Other changes:
1. Initialize the static logged_time static var in securityWarningCommand
2. add `no-auth` flag to RESET so it can always be executed.
## Intro
The purpose is to allow having different flags/ACL categories for
subcommands (Example: CONFIG GET is ok-loading but CONFIG SET isn't)
We create a small command table for every command that has subcommands
and each subcommand has its own flags, etc. (same as a "regular" command)
This commit also unites the Redis and the Sentinel command tables
## Affected commands
CONFIG
Used to have "admin ok-loading ok-stale no-script"
Changes:
1. Dropped "ok-loading" in all except GET (this doesn't change behavior since
there were checks in the code doing that)
XINFO
Used to have "read-only random"
Changes:
1. Dropped "random" in all except CONSUMERS
XGROUP
Used to have "write use-memory"
Changes:
1. Dropped "use-memory" in all except CREATE and CREATECONSUMER
COMMAND
No changes.
MEMORY
Used to have "random read-only"
Changes:
1. Dropped "random" in PURGE and USAGE
ACL
Used to have "admin no-script ok-loading ok-stale"
Changes:
1. Dropped "admin" in WHOAMI, GENPASS, and CAT
LATENCY
No changes.
MODULE
No changes.
SLOWLOG
Used to have "admin random ok-loading ok-stale"
Changes:
1. Dropped "random" in RESET
OBJECT
Used to have "read-only random"
Changes:
1. Dropped "random" in ENCODING and REFCOUNT
SCRIPT
Used to have "may-replicate no-script"
Changes:
1. Dropped "may-replicate" in all except FLUSH and LOAD
CLIENT
Used to have "admin no-script random ok-loading ok-stale"
Changes:
1. Dropped "random" in all except INFO and LIST
2. Dropped "admin" in ID, TRACKING, CACHING, GETREDIR, INFO, SETNAME, GETNAME, and REPLY
STRALGO
No changes.
PUBSUB
No changes.
CLUSTER
Changes:
1. Dropped "admin in countkeysinslots, getkeysinslot, info, nodes, keyslot, myid, and slots
SENTINEL
No changes.
(note that DEBUG also fits, but we decided not to convert it since it's for
debugging and anyway undocumented)
## New sub-command
This commit adds another element to the per-command output of COMMAND,
describing the list of subcommands, if any (in the same structure as "regular" commands)
Also, it adds a new subcommand:
```
COMMAND LIST [FILTERBY (MODULE <module-name>|ACLCAT <cat>|PATTERN <pattern>)]
```
which returns a set of all commands (unless filters), but excluding subcommands.
## Module API
A new module API, RM_CreateSubcommand, was added, in order to allow
module writer to define subcommands
## ACL changes:
1. Now, that each subcommand is actually a command, each has its own ACL id.
2. The old mechanism of allowed_subcommands is redundant
(blocking/allowing a subcommand is the same as blocking/allowing a regular command),
but we had to keep it, to support the widespread usage of allowed_subcommands
to block commands with certain args, that aren't subcommands (e.g. "-select +select|0").
3. I have renamed allowed_subcommands to allowed_firstargs to emphasize the difference.
4. Because subcommands are commands in ACL too, you can now use "-" to block subcommands
(e.g. "+client -client|kill"), which wasn't possible in the past.
5. It is also possible to use the allowed_firstargs mechanism with subcommand.
For example: `+config -config|set +config|set|loglevel` will block all CONFIG SET except
for setting the log level.
6. All of the ACL changes above required some amount of refactoring.
## Misc
1. There are two approaches: Either each subcommand has its own function or all
subcommands use the same function, determining what to do according to argv[0].
For now, I took the former approaches only with CONFIG and COMMAND,
while other commands use the latter approach (for smaller blamelog diff).
2. Deleted memoryGetKeys: It is no longer needed because MEMORY USAGE now uses the "range" key spec.
4. Bugfix: GETNAME was missing from CLIENT's help message.
5. Sentinel and Redis now use the same table, with the same function pointer.
Some commands have a different implementation in Sentinel, so we redirect
them (these are ROLE, PUBLISH, and INFO).
6. Command stats now show the stats per subcommand (e.g. instead of stats just
for "config" you will have stats for "config|set", "config|get", etc.)
7. It is now possible to use COMMAND directly on subcommands:
COMMAND INFO CONFIG|GET (The pipeline syntax was inspired from ACL, and
can be used in functions lookupCommandBySds and lookupCommandByCString)
8. STRALGO is now a container command (has "help")
## Breaking changes:
1. Command stats now show the stats per subcommand (see (5) above)
This commit revives the improves the ability to run the test suite against
external servers, instead of launching and managing `redis-server` processes as
part of the test fixture.
This capability existed in the past, using the `--host` and `--port` options.
However, it was quite limited and mostly useful when running a specific tests.
Attempting to run larger chunks of the test suite experienced many issues:
* Many tests depend on being able to start and control `redis-server` themselves,
and there's no clear distinction between external server compatible and other
tests.
* Cluster mode is not supported (resulting with `CROSSSLOT` errors).
This PR cleans up many things and makes it possible to run the entire test suite
against an external server. It also provides more fine grained controls to
handle cases where the external server supports a subset of the Redis commands,
limited number of databases, cluster mode, etc.
The tests directory now contains a `README.md` file that describes how this
works.
This commit also includes additional cleanups and fixes:
* Tests can now be tagged.
* Tag-based selection is now unified across `start_server`, `tags` and `test`.
* More information is provided about skipped or ignored tests.
* Repeated patterns in tests have been extracted to common procedures, both at a
global level and on a per-test file basis.
* Cleaned up some cases where test setup was based on a previous test executing
(a major anti-pattern that repeats itself in many places).
* Cleaned up some cases where test teardown was not part of a test (in the
future we should have dedicated teardown code that executes even when tests
fail).
* Fixed some tests that were flaky running on external servers.
This Commit pushes forward the observability on overall error statistics and command statistics within redis-server:
It extends INFO COMMANDSTATS to have
- failed_calls in - so we can keep track of errors that happen from the command itself, broken by command.
- rejected_calls - so we can keep track of errors that were triggered outside the commmand processing per se
Adds a new section to INFO, named ERRORSTATS that enables keeping track of the different errors that
occur within redis ( within processCommand and call ) based on the reply Error Prefix ( The first word
after the "-", up to the first space ).
This commit also fixes RM_ReplyWithError so that it can be correctly identified as an error reply.
