The PR introduces a new shared secret that is shared over all the nodes
on the Redis cluster. The main idea is to leverage the cluster bus to
share a secret between all the nodes such that later the nodes will be
able to authenticate using this secret and send internal commands to
each other (see #13740 for more information about internal commands).
The way the shared secret is chosen is the following:
1. Each node, when start, randomly generate its own internal secret.
2. Each node share its internal secret over the cluster ping messages.
3. If a node gets a ping message with secret smaller then his current
secret, it embrace it.
4. Eventually all nodes should embrace the minimal secret
The converges of the secret is as good as the topology converges.
To extend the ping messages to contain the secret, we leverage the
extension mechanism. Nodes that runs an older Redis version will just
ignore those extensions.
Specific tests were added to verify that eventually all nodes see the
secrets. In addition, a verification was added to the test infra to
verify the secret on `cluster_config_consistent` and to
`assert_cluster_state`.
This PR adds a flag to the `RM_GetContextFlags` module-API function that
depicts whether the context may execute debug commands, according to
redis's standards.
This PR addresses an issue where if a module does not provide a
defragmentation callback, we cannot defragment the fragmentation it
generates. However, the defragmentation process still considers a large
amount of fragmentation to be present, leading to more aggressive
defragmentation efforts that ultimately have no effect.
To mitigate this, the PR introduces a mechanism to gradually reduce the
CPU consumption for defragmentation when the defragmentation
effectiveness is poor. This occurs when the fragmentation rate drops
below 2% and the hit ratio is less than 1%, or when the fragmentation
rate increases by no more than 2%. The CPU consumption will be gradually
decreased until it reaches the minimum threshold defined by
`active-defrag-cycle-min`.
---------
Co-authored-by: oranagra <oran@redislabs.com>
This PR is based on:
https://github.com/redis/redis/pull/12109https://github.com/valkey-io/valkey/pull/60
Closes: https://github.com/redis/redis/issues/11678
**Motivation**
During a full sync, when master is delivering RDB to the replica,
incoming write commands are kept in a replication buffer in order to be
sent to the replica once RDB delivery is completed. If RDB delivery
takes a long time, it might create memory pressure on master. Also, once
a replica connection accumulates replication data which is larger than
output buffer limits, master will kill replica connection. This may
cause a replication failure.
The main benefit of the rdb channel replication is streaming incoming
commands in parallel to the RDB delivery. This approach shifts
replication stream buffering to the replica and reduces load on master.
We do this by opening another connection for RDB delivery. The main
channel on replica will be receiving replication stream while rdb
channel is receiving the RDB.
This feature also helps to reduce master's main process CPU load. By
opening a dedicated connection for the RDB transfer, the bgsave process
has access to the new connection and it will stream RDB directly to the
replicas. Before this change, due to TLS connection restriction, the
bgsave process was writing RDB bytes to a pipe and the main process was
forwarding
it to the replica. This is no longer necessary, the main process can
avoid these expensive socket read/write syscalls. It also means RDB
delivery to replica will be faster as it avoids this step.
In summary, replication will be faster and master's performance during
full syncs will improve.
**Implementation steps**
1. When replica connects to the master, it sends 'rdb-channel-repl' as
part of capability exchange to let master to know replica supports rdb
channel.
2. When replica lacks sufficient data for PSYNC, master sends
+RDBCHANNELSYNC reply with replica's client id. As the next step, the
replica opens a new connection (rdb-channel) and configures it against
the master with the appropriate capabilities and requirements. It also
sends given client id back to master over rdbchannel, so that master can
associate these channels. (initial replica connection will be referred
as main-channel) Then, replica requests fullsync using the RDB channel.
3. Prior to forking, master attaches the replica's main channel to the
replication backlog to deliver replication stream starting at the
snapshot end offset.
4. The master main process sends replication stream via the main
channel, while the bgsave process sends the RDB directly to the replica
via the rdb-channel. Replica accumulates replication stream in a local
buffer, while the RDB is being loaded into the memory.
5. Once the replica completes loading the rdb, it drops the rdb channel
and streams the accumulated replication stream into the db. Sync is
completed.
**Some details**
- Currently, rdbchannel replication is supported only if
`repl-diskless-sync` is enabled on master. Otherwise, replication will
happen over a single connection as in before.
- On replica, there is a limit to replication stream buffering. Replica
uses a new config `replica-full-sync-buffer-limit` to limit number of
bytes to accumulate. If it is not set, replica inherits
`client-output-buffer-limit <replica>` hard limit config. If we reach
this limit, replica stops accumulating. This is not a failure scenario
though. Further accumulation will happen on master side. Depending on
the configured limits on master, master may kill the replica connection.
**API changes in INFO output:**
1. New replica state: `send_bulk_and_stream`. Indicates full sync is
still in progress for this replica. It is receiving replication stream
and rdb in parallel.
```
slave0:ip=127.0.0.1,port=5002,state=send_bulk_and_stream,offset=0,lag=0
```
Replica state changes in steps:
- First, replica sends psync and receives +RDBCHANNELSYNC
:`state=wait_bgsave`
- After replica connects with rdbchannel and delivery starts:
`state=send_bulk_and_stream`
- After full sync: `state=online`
2. On replica side, replication stream buffering metrics:
- replica_full_sync_buffer_size: Currently accumulated replication
stream data in bytes.
- replica_full_sync_buffer_peak: Peak number of bytes that this instance
accumulated in the lifetime of the process.
```
replica_full_sync_buffer_size:20485
replica_full_sync_buffer_peak:1048560
```
**API changes in CLIENT LIST**
In `client list` output, rdbchannel clients will have 'C' flag in
addition to 'S' replica flag:
```
id=11 addr=127.0.0.1:39108 laddr=127.0.0.1:5001 fd=14 name= age=5 idle=5 flags=SC db=0 sub=0 psub=0 ssub=0 multi=-1 watch=0 qbuf=0 qbuf-free=0 argv-mem=0 multi-mem=0 rbs=1024 rbp=0 obl=0 oll=0 omem=0 tot-mem=1920 events=r cmd=psync user=default redir=-1 resp=2 lib-name= lib-ver= io-thread=0
```
**Config changes:**
- `replica-full-sync-buffer-limit`: Controls how much replication data
replica can accumulate during rdbchannel replication. If it is not set,
a value of 0 means replica will inherit `client-output-buffer-limit
<replica>` hard limit config to limit accumulated data.
- `repl-rdb-channel` config is added as a hidden config. This is mostly
for testing as we need to support both rdbchannel replication and the
older single connection replication (to keep compatibility with older
versions and rdbchannel replication will not be enabled if
repl-diskless-sync is not enabled). it affects both the master (not to
respond to rdb channel requests), and the replica (not to declare
capability)
**Internal API changes:**
Changes that were introduced to Redis replication:
- New replication capability is added to replconf command: `capa
rdb-channel-repl`. Indicates replica is capable of rdb channel
replication. Replica sends it when it connects to master along with
other capabilities.
- If replica needs fullsync, master replies `+RDBCHANNELSYNC
<client-id>` to the replica's PSYNC request.
- When replica opens rdbchannel connection, as part of replconf command,
it sends `rdb-channel 1` to let master know this is rdb channel. Also,
it sends `main-ch-client-id <client-id>` as part of replconf command so
master can associate channels.
**Testing:**
As rdbchannel replication is enabled by default, we run whole test suite
with it. Though, as we need to support both rdbchannel and single
connection replication, we'll be running some tests twice with
`repl-rdb-channel yes/no` config.
**Replica state diagram**
```
* * Replica state machine *
*
* Main channel state
* ┌───────────────────┐
* │RECEIVE_PING_REPLY │
* └────────┬──────────┘
* │ +PONG
* ┌────────▼──────────┐
* │SEND_HANDSHAKE │ RDB channel state
* └────────┬──────────┘ ┌───────────────────────────────┐
* │+OK ┌───► RDB_CH_SEND_HANDSHAKE │
* ┌────────▼──────────┐ │ └──────────────┬────────────────┘
* │RECEIVE_AUTH_REPLY │ │ REPLCONF main-ch-client-id <clientid>
* └────────┬──────────┘ │ ┌──────────────▼────────────────┐
* │+OK │ │ RDB_CH_RECEIVE_AUTH_REPLY │
* ┌────────▼──────────┐ │ └──────────────┬────────────────┘
* │RECEIVE_PORT_REPLY │ │ │ +OK
* └────────┬──────────┘ │ ┌──────────────▼────────────────┐
* │+OK │ │ RDB_CH_RECEIVE_REPLCONF_REPLY│
* ┌────────▼──────────┐ │ └──────────────┬────────────────┘
* │RECEIVE_IP_REPLY │ │ │ +OK
* └────────┬──────────┘ │ ┌──────────────▼────────────────┐
* │+OK │ │ RDB_CH_RECEIVE_FULLRESYNC │
* ┌────────▼──────────┐ │ └──────────────┬────────────────┘
* │RECEIVE_CAPA_REPLY │ │ │+FULLRESYNC
* └────────┬──────────┘ │ │Rdb delivery
* │ │ ┌──────────────▼────────────────┐
* ┌────────▼──────────┐ │ │ RDB_CH_RDB_LOADING │
* │SEND_PSYNC │ │ └──────────────┬────────────────┘
* └─┬─────────────────┘ │ │ Done loading
* │PSYNC (use cached-master) │ │
* ┌─▼─────────────────┐ │ │
* │RECEIVE_PSYNC_REPLY│ │ ┌────────────►│ Replica streams replication
* └─┬─────────────────┘ │ │ │ buffer into memory
* │ │ │ │
* │+RDBCHANNELSYNC client-id │ │ │
* ├──────┬───────────────────┘ │ │
* │ │ Main channel │ │
* │ │ accumulates repl data │ │
* │ ┌──▼────────────────┐ │ ┌───────▼───────────┐
* │ │ REPL_TRANSFER ├───────┘ │ CONNECTED │
* │ └───────────────────┘ └────▲───▲──────────┘
* │ │ │
* │ │ │
* │ +FULLRESYNC ┌───────────────────┐ │ │
* ├────────────────► REPL_TRANSFER ├────┘ │
* │ └───────────────────┘ │
* │ +CONTINUE │
* └──────────────────────────────────────────────┘
*/
```
-----
This PR also contains changes and ideas from:
https://github.com/valkey-io/valkey/pull/837https://github.com/valkey-io/valkey/pull/1173https://github.com/valkey-io/valkey/pull/804https://github.com/valkey-io/valkey/pull/945https://github.com/valkey-io/valkey/pull/989
---------
Co-authored-by: Yuan Wang <wangyuancode@163.com>
Co-authored-by: debing.sun <debing.sun@redis.com>
Co-authored-by: Moti Cohen <moticless@gmail.com>
Co-authored-by: naglera <anagler123@gmail.com>
Co-authored-by: Amit Nagler <58042354+naglera@users.noreply.github.com>
Co-authored-by: Madelyn Olson <madelyneolson@gmail.com>
Co-authored-by: Binbin <binloveplay1314@qq.com>
Co-authored-by: Viktor Söderqvist <viktor.soderqvist@est.tech>
Co-authored-by: Ping Xie <pingxie@outlook.com>
Co-authored-by: Ran Shidlansik <ranshid@amazon.com>
Co-authored-by: ranshid <88133677+ranshid@users.noreply.github.com>
Co-authored-by: xbasel <103044017+xbasel@users.noreply.github.com>
Found by @ShooterIT
## Describe
If a client first creates a command with a very large number of
parameters, such as 10,000 parameters, the argv will be expanded to
accommodate 10,000. If the subsequent commands have fewer than 10,000
parameters, this argv will continue to be reused and will never be
shrunk.
## Solution
When determining whether it is necessary to rebuild argv, if the length
of the previous argv has already exceeded 1024, we will progressively
create argv regardless.
## Free argv in cron
Add a new condition to determine whether argv needs to be resized in
cron. When the number of parameters exceeds 128, we will resize it
regardless to avoid a single client consuming too much memory. It will
now occupy a maximum of (128 * 8 bytes).
---------
Co-authored-by: Yuan Wang <wangyuancode@163.com>
Introduced by https://github.com/redis/redis/issues/13521
If the client argv was released due to a timeout before sending the
complete command, `argv_len` will be reset to 0.
When argv is parsed again and resized, requesting a length of 0 may
result in argv being NULL, then leading to a crash.
And fix a bug that `argv_len` is not updated correctly in
`replaceClientCommandVector()`.
---------
Co-authored-by: ShooterIT <wangyuancode@163.com>
Co-authored-by: meiravgri <109056284+meiravgri@users.noreply.github.com>
close#13709
Fix the index error of CRLF character for integer-encoded strings
in addReplyBulk function
---------
Co-authored-by: debing.sun <debing.sun@redis.com>
## 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>
The bug was introduced in #13558 .
When merging dense hll structures, `hllDenseCompress` writes to wrong
location and the result will be zero. The unit tests didn't cover this
case.
This PR
+ fixes the bug
+ adds `PFDEBUG SIMD (ON|OFF)` for unit tests
+ adds a new TCL test to cover the cases
Synchronized from https://github.com/valkey-io/valkey/pull/1293
---------
Signed-off-by: Xuyang Wang <xuyangwang@link.cuhk.edu.cn>
Co-authored-by: debing.sun <debing.sun@redis.com>
- Add empty string test for the new API
`RedisModule_ACLCheckKeyPrefixPermissions`.
- Fix order of checks: `(pattern[patternLen - 1] != '*' || patternLen ==
0)`
---------
Co-authored-by: debing.sun <debing.sun@redis.com>
This PR introduces API to query Expiration time of hash fields.
# New `RedisModule_HashFieldMinExpire()`
For a given hash, retrieves the minimum expiration time across all
fields. If no fields have expiration or if the key is not a hash then
return `REDISMODULE_NO_EXPIRE` (-1).
```
mstime_t RM_HashFieldMinExpire(RedisModuleKey *hash);
```
# Extension to `RedisModule_HashGet()`
Adds a new flag, `REDISMODULE_HASH_EXPIRE_TIME`, to retrieve the
expiration time of a specific hash field. If the field does not exist or
has no expiration, returns `REDISMODULE_NO_EXPIRE`. It is fully
backward-compatible (RM_HashGet retains its original behavior unless the
new flag is used).
Example:
```
mstime_t expiry1, expiry2;
RedisModule_HashGet(mykey, REDISMODULE_HASH_EXPIRE_TIME, "field1", &expiry1, NULL);
RedisModule_HashGet(mykey, REDISMODULE_HASH_EXPIRE_TIME, "field1", &expiry1, "field2", &expiry2, NULL);
```
This PR introduces a new API function to the Redis Module API:
```
int RedisModule_ACLCheckKeyPrefixPermissions(RedisModuleUser *user, RedisModuleString *prefix, int flags);
```
Purpose:
The function checks if a given user has access permissions to any key
that match a specific prefix. This validation is based on the user’s ACL
permissions and the specified flags.
Note, this prefix-based approach API may fail to detect prefixes that
are individually uncovered but collectively covered by the patterns. For
example the prefix `ID-*` is not fully included in pattern `ID-[0]*` and
is not fully included in pattern `ID-[^0]*` but it is fully included in
the set of patterns `{ID-[0]*, ID-[^0]*}`
Starting from https://github.com/redis/redis/pull/13133, we allocate a
jemalloc thread cache and use it for lua vm.
On certain cases, like `script flush` or `function flush` command, we
free the existing thread cache and create a new one.
Though, for `function flush`, we were not actually destroying the
existing thread cache itself. Each call creates a new thread cache on
jemalloc and we leak the previous thread cache instances. Jemalloc
allows maximum 4096 thread cache instances. If we reach this limit,
Redis prints "Failed creating the lua jemalloc tcache" log and abort.
There are other cases that can cause this memory leak, including
replication scenarios when emptyData() is called.
The implication is that it looks like redis `used_memory` is low, but
`allocator_allocated` and RSS remain high.
Co-authored-by: debing.sun <debing.sun@redis.com>
PR #10285 introduced support for modules to register four types of
configurations — Bool, Numeric, String, and Enum. Accessible through the
Redis config file and the CONFIG command.
With this PR, it will be possible to register configuration parameters
without automatically prefixing the parameter names. This provides
greater flexibility in configuration naming, enabling, for instance,
both `bf-initial-size` or `initial-size` to be defined in the module
without automatically prefixing with `<MODULE-NAME>.`. In addition it
will also be possible to create a single additional alias via the same
API. This brings us another step closer to integrate modules into redis
core.
**Example:** Register a configuration parameter `bf-initial-size` with
an alias `initial-size` without the automatic module name prefix, set
with new `REDISMODULE_CONFIG_UNPREFIXED` flag:
```
RedisModule_RegisterBoolConfig(ctx, "bf-initial-size|initial-size", default_val, optflags | REDISMODULE_CONFIG_UNPREFIXED, getfn, setfn, applyfn, privdata);
```
# API changes
Related functions that now support unprefixed configuration flag
(`REDISMODULE_CONFIG_UNPREFIXED`) along with optional alias:
```
RedisModule_RegisterBoolConfig
RedisModule_RegisterEnumConfig
RedisModule_RegisterNumericConfig
RedisModule_RegisterStringConfig
```
# Implementation Details:
`config.c`: On load server configuration, at function
`loadServerConfigFromString()`, it collects all unknown configurations
into `module_configs_queue` dictionary. These may include valid module
configurations or invalid ones. They will be validated later by
`loadModuleConfigs()` against the configurations declared by the loaded
module(s).
`Module.c:` The `ModuleConfig` structure has been modified to store now:
(1) Full configuration name (2) Alias (3) Unprefixed flag status -
ensuring that configurations retain their original registration format
when triggered in notifications.
Added error printout:
This change introduces an error printout for unresolved configurations,
detailing each unresolved parameter detected during startup. The last
line in the output existed prior to this change and has been retained to
systems relies on it:
```
595011:M 18 Nov 2024 08:26:23.616 # Unresolved Configuration(s) Detected:
595011:M 18 Nov 2024 08:26:23.616 # >>> 'bf-initiel-size 8'
595011:M 18 Nov 2024 08:26:23.616 # >>> 'search-sizex 32'
595011:M 18 Nov 2024 08:26:23.616 # Module Configuration detected without loadmodule directive or no ApplyConfig call: aborting
```
# Backward Compatibility:
Existing modules will function without modification, as the new
functionality only applies if REDISMODULE_CONFIG_UNPREFIXED is
explicitly set.
# Module vs. Core API Conflict Behavior
The new API allows to modules loading duplication of same configuration
name or same configuration alias, just like redis core configuration
allows (i.e. the users sets two configs with a different value, but
these two configs are actually the same one). Unlike redis core, given a
name and its alias, it doesn't allow have both configuration on load. To
implement it, it is required to modify DS `module_configs_queue` to
reflect the order of their loading and later on, during
`loadModuleConfigs()`, resolve pairs of names and aliases and which one
is the last one to apply. "Relaxing" this limitation can be deferred to
a future update if necessary, but for now, we error in this case.
Fix to https://github.com/redis/redis/issues/13650
providing an invalid config to a module with datatype crashes when redis
tries to unload the module due to the invalid config
---------
Co-authored-by: debing.sun <debing.sun@redis.com>
If `hide-user-data-from-log` config is enabled, we don't print client
argv in the crashlog to avoid leaking user info.
Though, debugging a crash becomes harder as we don't see the command
arguments causing the crash.
With this PR, we'll be printing command tokens to the log. As we have
command tokens defined in json schema for each command, using this data,
we can find tokens in the client argv.
e.g.
`SET key value GET EX 10` ---> we'll print `SET * * GET EX *` in the
log.
Modules should introduce their command structure via
`RM_SetCommandInfo()`.
Then, on a crash we'll able to know module command tokens.
This PR adds a new section to the `INFO` command output, called
`keysizes`. This section provides detailed statistics on the
distribution of key sizes for each data type (strings, lists, sets,
hashes and zsets) within the dataset. The distribution is tracked using
a base-2 logarithmic histogram.
# Motivation
Currently, Redis lacks a built-in feature to track key sizes and item
sizes per data type at a granular level. Understanding the distribution
of key sizes is critical for monitoring memory usage and optimizing
performance, particularly in large datasets. This enhancement will allow
users to inspect the size distribution of keys directly from the `INFO`
command, assisting with performance analysis and capacity planning.
# Changes
New Section in `INFO` Command: A new section called `keysizes` has been
added to the `INFO` command output. This section reports a per-database,
per-type histogram of key sizes. It provides insights into how many keys
fall into specific size ranges (represented in powers of 2).
**Example output:**
```
127.0.0.1:6379> INFO keysizes
# Keysizes
db0_distrib_strings_sizes:1=19,2=655,512=100899,1K=31,2K=29,4K=23,8K=16,16K=3,32K=2
db0_distrib_lists_items:1=5784492,32=3558,64=1047,128=676,256=533,512=218,4K=1,8K=42
db0_distrib_sets_items:1=735564=50612,8=21462,64=1365,128=974,2K=292,4K=154,8K=89,
db0_distrib_hashes_items:2=1,4=544,32=141169,64=207329,128=4349,256=136226,1K=1
```
## Future Use Cases:
The key size distribution is collected per slot as well, laying the
groundwork for future enhancements related to Redis Cluster.
From 7.4, Redis allows `GET` options in cluster mode when the pattern maps to
the same slot as the key, but GET # pattern that represents key itself is missed.
This commit resolves it, bug report #13607.
---------
Co-authored-by: Yuan Wang <yuan.wang@redis.com>
- Add a new 'EXPERIMENTAL' command flag, which causes the command
generator to skip over it and make the command to be unavailable for
execution
- Skip experimental tests by default
- Move the SFLUSH tests from the old framework to the new one
---------
Co-authored-by: YaacovHazan <yaacov.hazan@redislabs.com>
Test 1 - give more time for expiration
Test 2 - Evaluate expiration time boundaries [+1,+2] before setting expiration [+1]
Test 3 - Avoid race on test HFEs propagated to replica
The PR extends `RedisModule_OpenKey`'s flags to include
`REDISMODULE_OPEN_KEY_ACCESS_EXPIRED`, which allows to access expired
keys.
It also allows to access expired subkeys. Currently relevant only for
hash fields
and has its impact on `RM_HashGet` and `RM_Scan`.
If the hash previously had HFEs (hash-fields with expiration) but later no longer
does, the key ref in the hash might become outdated after a MOVE, COPY,
RENAME or RESTORE operation. These commands maintain the key ref only
if HFEs are present. That is, we can only be sure that key ref is valid as long as the
hash has HFEs.
When a client in no-touch mode issues a TOUCH command on a key, the
key’s access time should be updated, but in scripts, and module's
RM_Call, it isn’t updated.
Command proc should be matched to the executing client, not the current
client.
Co-authored-by: Udi Ron <udi@speedb.io>
#13495 introduced a change to reply -LOADING while flushing existing db on a replica. Some of our tests are
sensitive to this change and do no expect -LOADING reply.
Fixing a couple of tests that fail time to time.
This PR is based on the commits from PR
https://github.com/valkey-io/valkey/pull/258,
https://github.com/valkey-io/valkey/pull/593,
https://github.com/valkey-io/valkey/pull/639
This PR optimizes client query buffer handling in Redis by introducing
a reusable query buffer that is used by default for client reads. This
reduces memory usage by ~20KB per client by avoiding allocations for
most clients using short (<16KB) complete commands. For larger or
partial commands, the client still gets its own private buffer.
The primary changes are:
* Adding a reusable query buffer `thread_shared_qb` that clients use by
default.
* Modifying client querybuf initialization and reset logic.
* Freeing idle client query buffers when empty to allow reuse of the
reusable query buffer.
* Master client query buffers are kept private as their contents need to
be preserved for replication stream.
* When nested commands is executed, only the first user uses the reuse
buffer, and subsequent users will still use the private buffer.
In addition to the memory savings, this change shows a 3% improvement in
latency and throughput when running with 1000 active clients.
The memory reduction may also help reduce the need to evict clients when
reaching max memory limit, as the query buffer is the main memory
consumer per client.
This PR is different from https://github.com/valkey-io/valkey/pull/258
1. When a client is in the mid of requiring a reused buffer and
returning it, regardless of whether the query buffer has changed
(expanded), we do not update the reused query buffer in the middle, but
return the reused query buffer (expanded or with data remaining) or
reset it at the end.
2. Adding a new thread variable `thread_shared_qb_used` to avoid
multiple clients requiring the reusable query buffer at the same time.
---------
Signed-off-by: Uri Yagelnik <uriy@amazon.com>
Signed-off-by: Madelyn Olson <matolson@amazon.com>
Co-authored-by: Uri Yagelnik <uriy@amazon.com>
Co-authored-by: Madelyn Olson <madelyneolson@gmail.com>
Co-authored-by: oranagra <oran@redislabs.com>
RM_RdbLoad() disables AOF temporarily while loading RDB.
Later, it does not enable it back as it checks AOF state (disabled by then)
rather than AOF config parameter.
Added a change to restart AOF according to config parameter.
All the defrag allocations API expects to get a value and replace it, leaving the old value untouchable.
In some cases a value might be shared between multiple keys, in such cases we can not simply replace
it when the defrag callback is called.
To allow support such use cases, the PR adds two new API's to the defrag API:
1. `RM_DefragAllocRaw` - allocate memory base on a given size.
2. `RM_DefragFreeRaw` - Free the given pointer.
Those API's avoid using tcache so they operate just like `RM_DefragAlloc` but allows the user to split
the allocation and the memory free operations into two stages and control when those happen.
In addition the PR adds new API to allow the module to receive notifications when defrag start and end: `RM_RegisterDefragCallbacks`
Those callbacks are the same as `RM_RegisterDefragFunc` but promised to be called and the start
and the end of the defrag process.
Fixed the issue about GETRANGE and SUBSTR command
return unexpected result caused by the `start` and `end` out of
definition range of string.
---
## break change
Before this PR, when negative `end` was out of range (i.e., end <
-strlen), we would fix it to 0 to get the substring, which also resulted
in the first character still being returned for this kind of out of
range.
After this PR, we ensure that `GETRANGE` returns an empty bulk when the
negative end index is out of range.
Closes#11738
---------
Co-authored-by: debing.sun <debing.sun@redis.com>
Move the TYPE filtering to the scan callback so that avoided the
`lookupKey` operation. This is the follow-up to #12209 . In this thread
we introduced two breaking changes：
1. we will not attempt to do lazy expire (delete) a key that was
filtered by not matching the TYPE (like we already do for MATCH
pattern).
2. when the specified key TYPE filter is an unknown type, server will
reply a error immediately instead of doing a full scan that comes back
empty handed.
## Describe
When using the `XTRIM` command to trim a stream, it does not update the
maximal tombstone (`max_deleted_entry_id`). This leads to an issue where
the lag calculation incorrectly assumes that there are no tombstones
after the consumer group's last_id, resulting in an inaccurate lag.
The reason XTRIM doesn't need to update the maximal tombstone is that it
always trims from the beginning of the stream. This means that it
consistently changes the position of the first entry, leading to the
following scenarios:
1) First entry trimmed after maximal tombstone:
If the first entry is trimmed to a position after the maximal tombstone,
all tombstones will be before the first entry, so they won't affect the
consumer group's lag.
2) First entry trimmed before maximal tombstone:
If the first entry is trimmed to a position before the maximal
tombstone, the maximal tombstone will not be updated.
## Solution
Therefore, this PR optimizes the lag calculation by ensuring that when
both the consumer group's last_id and the maximal tombstone are behind
the first entry, the consumer group's lag is always equal to the number
of remaining elements in the stream.
Supplement to PR https://github.com/redis/redis/pull/13338
Hash field expiration is optimized to avoid frequent update global HFE DS for
each field deletion. Eventually active-expiration will run and update or remove
the hash from global HFE DS gracefully. Nevertheless, statistic "subexpiry"
might reflect wrong number of hashes with HFE to the user if HDEL deletes
the last field with expiration in hash (yet there are more fields without expiration).
Following this change, if HDEL the last field with expiration in the hash then
take care to remove the hash from global HFE DS as well.
This PR is based on the commits from PR
https://github.com/valkey-io/valkey/pull/52.
Ref: https://github.com/redis/redis/pull/12760
Close https://github.com/redis/redis/issues/13401
This PR will replace https://github.com/redis/redis/pull/13449
Fixes compatibilty of Redis cluster (7.2 - extensions enabled by
default) with older Redis cluster (< 7.0 - extensions not handled) .
With some of the extensions enabled by default in 7.2 version, new nodes
running 7.2 and above start sending out larger clusterbus message
payload including the ping extensions. This caused an incompatibility
with node running engine versions < 7.0. Old nodes (< 7.0) would receive
the payload from new nodes (> 7.2) would observe a payload length
(totlen) > (estlen) and would perform an early exit and won't process
the message.
This fix does the following things:
1. Always set `CLUSTERMSG_FLAG0_EXT_DATA`, because during the meet
phase, we do not know whether the connected node supports ext data, we
need to make sure that it knows and send back its ext data if it has.
2. If another node does not support ext data, we will not send it ext
data to avoid the handshake failure due to the incorrect payload length.
Note: A successful `PING`/`PONG` is required as a sender for a given
node to be marked as `CLUSTERMSG_FLAG0_EXT_DATA` and then extensions
message
will be sent to it. This could cause a slight delay in receiving the
extensions message(s).
---------
Signed-off-by: Harkrishn Patro <harkrisp@amazon.com>
Co-authored-by: Harkrishn Patro <harkrisp@amazon.com>
---------
Signed-off-by: Harkrishn Patro <harkrisp@amazon.com>
Co-authored-by: Harkrishn Patro <harkrisp@amazon.com>
1. Fix fuzzer test failure when the key was deleted due to expiration
before sending random traffic for the key.
After HFE, when all fields in a hash are expired, the hash might be
deleted due to expiration.
If the key was expired in the mid of `RESTORE` command and sending rand
trafic, `fuzzer` test will fail in the following code because the 'TYPE
key' will return `none` and then throw an exception because it cannot be
found in `$commands`
94b9072e44/tests/support/util.tcl (L712-L713)
This PR adds a `None` check for the reply of `KEY TYPE` command and adds
a print of `err` to avoid false positives in the future.
failed CI:
https://github.com/redis/redis/actions/runs/10127334121/job/28004985388
2. Fix the issue where key was deleted due to expiration before the
`scan.scan_key` command could execute, caused by premature enabling of
`set-active-expire`.
failed CI:
https://github.com/redis/redis/actions/runs/10153722715/job/28077610552
---------
Co-authored-by: oranagra <oran@redislabs.com>
Fix#13337
Ths PR fixes fixed two bugs that caused lag calculation errors.
1. When the latest tombstone is before the first entry, the tombstone
may stil be after the last id of consume group.
2. When a tombstone is after the last id of consume group, the group's
counter will be invalid, we should caculate the entries_read by using
estimates.
[exception]: Executing test client: ERR FAILOVER target replica is not
online.. ERR FAILOVER target replica is not online.
while executing
"$node_0 failover to $node_1_host $node_1_port"
("uplevel" body line 16)
invoked from within
"uplevel 1 $code"
(procedure "test" line 58)
invoked from within
"test {failover command to specific replica works} {
[err]: client evicted due to percentage of maxmemory in
tests/unit/client-eviction.tcl
Expected 33622 >= 220200 && 33622 < 440401 (context: type eval line 17
cmd {assert {$tot_mem >= $n && $tot_mem < $maxmemory_clients_actual}}
proc ::test)
When the tests are run against an external server in this order:
`--single unit/introspection --single unit/moduleapi/blockonbackground
--single integration/redis-cli`
the test would hang when the "ASK redirect test" test attempts to create
a listening socket (it fails, and then redis-cli itself hangs waiting
for a non-responsive socket created by the introspection test).
the reasons are:
1. the blockedbackground test includes util.tcl and resets the
`::last_port_attempted` variable
2. the test in introspection didn't close the listening server, so it's
still alive.
3. find_available_port doesn't properly detect the busy port, and it
thinks that the port is free even though it's busy.
fixing all 3 of these problems, even though fixing just one would be
enough to let the test pass.
Nowdays we do not trigger LUA GC after loading lua script. This means
that when a large number of scripts are loaded, such as when functions
are propagating from the master to the replica, if the LUA scripts are
never touched on the replica, the garbage might remain there
indefinitely.
Before this PR, we would share a gc_count between scripts and functions.
This means that, under certain circumstances, the GC trigger for scripts
and functions was not fair.
For example, loading a large number of scripts followed by a small
number of functions could result in the functions triggering GC.
In this PR, we assign a unique `gc_count` to each of them, so the GC
triggers between them will no longer affect each other.
on the other hand, this PR will to bring regession for script loading
commands(`FUNCTION LOAD` and `SCRIPT LOAD`), but they are not hot path,
we can ignore it, and it will be replaced
https://github.com/redis/redis/pull/13375 in the future.
---------
Co-authored-by: Oran Agra <oran@redislabs.com>
If `config resetstat` is executed and a defrag is started after it, the
`total_active_defrag_time` will not be 0.
When we start the defrag again, we will skip the following steps:
1. waiting for the defrag to start. (s total_active_defrag_time is equal
0)
2. waiting for the test to complete. (active_defrag_running is euqal 0)
which result in the test failed.
---------
Co-authored-by: oranagra <oran@redislabs.com>
### Issue
The current implementation of `FUNCTION FLUSH` command uses
`lua_unref()` to unreference script closures in Lua vm. However,
invoking `lua_unref()` during lazy free (`ASYNC` argument) is risky
since it is not thread-safe.
Another issue is that using `lua_unref()` to unreference references does
not trigger GC, This can result in the Lua VM leaves a significant
amount of garbage, which may never be cleaned up if not properly GC.
### Solution
The proposed solution is to completely rebuild the engines, resulting in
a brand new Lua VM.
---------
Co-authored-by: meir <meir@redis.com>
* INFO command : rename `hashes_with_expiry_fields` to `subexpiry`
* INFO command : rename `expired_hash_fields` to `expired_subkeys`
* Fix statistic of `expired_subkeys` to count also lazy expired
* Remove TODOs comments leftover in TCL
* Fix potential flaky test of rdb load of hash-field-expiration
If we run FLUSHALL when the 'save' config is set, and there's a fork
child ding BGSAVE, there's a chance the child is already finished, and
the parent process is unaware of it. in that case the child will not get
the kill signal and will finish successfully, but the parent process
thinks it killed it and will reset the dirty counter to 0, then the
backgroundSaveDoneHandlerDisk method can set the dirty counter to a
negative value.
