redis

Commit Graph

Author	SHA1	Message	Date
Yuan Wang	033abd6f57	Async IO threads (#13665 ) ## 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: https://github.com/redis/redis/issues/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>	2024-12-22 19:30:37 +08:00
Oran Agra	997fa41e99	Attempt to solve MacOS CI issues in GH Actions (#12013 ) The MacOS CI in github actions often hangs without any logs. GH argues that it's due to resource utilization, either running out of disk space, memory, or CPU starvation, and thus the runner is terminated. This PR contains multiple attempts to resolve this: 1. introducing pause_process instead of SIGSTOP, which waits for the process to stop before resuming the test, possibly resolving race conditions in some tests, this was a suspect since there was one test that could result in an infinite loop in that case, in practice this didn't help, but still a good idea to keep. 2. disable the `save` config in many tests that don't need it, specifically ones that use heavy writes and could create large files. 3. change the `populate` proc to use short pipeline rather than an infinite one. 4. use `--clients 1` in the macos CI so that we don't risk running multiple resource demanding tests in parallel. 5. enable `--verbose` to be repeated to elevate verbosity and print more info to stdout when a test or a server starts.	2023-04-12 09:19:21 +03:00
Viktor Söderqvist	45a155bd0f	Wait for replicas when shutting down (#9872 ) To avoid data loss, this commit adds a grace period for lagging replicas to catch up the replication offset. Done: * Wait for replicas when shutdown is triggered by SIGTERM and SIGINT. * Wait for replicas when shutdown is triggered by the SHUTDOWN command. A new blocked client type BLOCKED_SHUTDOWN is introduced, allowing multiple clients to call SHUTDOWN in parallel. Note that they don't expect a response unless an error happens and shutdown is aborted. * Log warning for each replica lagging behind when finishing shutdown. * CLIENT_PAUSE_WRITE while waiting for replicas. * Configurable grace period 'shutdown-timeout' in seconds (default 10). * New flags for the SHUTDOWN command: - NOW disables the grace period for lagging replicas. - FORCE ignores errors writing the RDB or AOF files which would normally prevent a shutdown. - ABORT cancels ongoing shutdown. Can't be combined with other flags. * New field in the output of the INFO command: 'shutdown_in_milliseconds'. The value is the remaining maximum time to wait for lagging replicas before finishing the shutdown. This field is present in the Server section only during shutdown. Not directly related: * When shutting down, if there is an AOF saving child, it is killed even if AOF is disabled. This can happen if BGREWRITEAOF is used when AOF is off. * Client pause now has end time and type (WRITE or ALL) per purpose. The different pause purposes are CLIENT PAUSE command, failover and shutdown. If clients are unpaused for one purpose, it doesn't affect client pause for other purposes. For example, the CLIENT UNPAUSE command doesn't affect client pause initiated by the failover or shutdown procedures. A completed failover or a failed shutdown doesn't unpause clients paused by the CLIENT PAUSE command. Notes: * DEBUG RESTART doesn't wait for replicas. * We already have a warning logged when a replica disconnects. This means that if any replica connection is lost during the shutdown, it is either logged as disconnected or as lagging at the time of exit. Co-authored-by: Oran Agra <oran@redislabs.com>	2022-01-02 09:50:15 +02:00

Author

SHA1

Message

Date

Yuan Wang

033abd6f57

Async IO threads (#13665 )

## 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:
https://github.com/redis/redis/issues/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>

2024-12-22 19:30:37 +08:00

Oran Agra

997fa41e99

Attempt to solve MacOS CI issues in GH Actions (#12013 )

The MacOS CI in github actions often hangs without any logs. GH argues that
it's due to resource utilization, either running out of disk space, memory, or CPU
starvation, and thus the runner is terminated.

This PR contains multiple attempts to resolve this:
1. introducing pause_process instead of SIGSTOP, which waits for the process
  to stop before resuming the test, possibly resolving race conditions in some tests,
  this was a suspect since there was one test that could result in an infinite loop in that
 case, in practice this didn't help, but still a good idea to keep.
2. disable the `save` config in many tests that don't need it, specifically ones that use
  heavy writes and could create large files.
3. change the `populate` proc to use short pipeline rather than an infinite one.
4. use `--clients 1` in the macos CI so that we don't risk running multiple resource
  demanding tests in parallel.
5. enable `--verbose` to be repeated to elevate verbosity and print more info to stdout
  when a test or a server starts.

2023-04-12 09:19:21 +03:00

Viktor Söderqvist

45a155bd0f

Wait for replicas when shutting down (#9872 )

To avoid data loss, this commit adds a grace period for lagging replicas to
catch up the replication offset.

Done:

* Wait for replicas when shutdown is triggered by SIGTERM and SIGINT.

* Wait for replicas when shutdown is triggered by the SHUTDOWN command. A new
  blocked client type BLOCKED_SHUTDOWN is introduced, allowing multiple clients
  to call SHUTDOWN in parallel.
  Note that they don't expect a response unless an error happens and shutdown is aborted.

* Log warning for each replica lagging behind when finishing shutdown.

* CLIENT_PAUSE_WRITE while waiting for replicas.

* Configurable grace period 'shutdown-timeout' in seconds (default 10).

* New flags for the SHUTDOWN command:

    - NOW disables the grace period for lagging replicas.

    - FORCE ignores errors writing the RDB or AOF files which would normally
      prevent a shutdown.

    - ABORT cancels ongoing shutdown. Can't be combined with other flags.

* New field in the output of the INFO command: 'shutdown_in_milliseconds'. The
  value is the remaining maximum time to wait for lagging replicas before
  finishing the shutdown. This field is present in the Server section **only**
  during shutdown.

Not directly related:

* When shutting down, if there is an AOF saving child, it is killed **even** if AOF
  is disabled. This can happen if BGREWRITEAOF is used when AOF is off.

* Client pause now has end time and type (WRITE or ALL) per purpose. The
  different pause purposes are *CLIENT PAUSE command*, *failover* and
  *shutdown*. If clients are unpaused for one purpose, it doesn't affect client
  pause for other purposes. For example, the CLIENT UNPAUSE command doesn't
  affect client pause initiated by the failover or shutdown procedures. A completed
  failover or a failed shutdown doesn't unpause clients paused by the CLIENT
  PAUSE command.

Notes:

* DEBUG RESTART doesn't wait for replicas.

* We already have a warning logged when a replica disconnects. This means that
  if any replica connection is lost during the shutdown, it is either logged as
  disconnected or as lagging at the time of exit.

Co-authored-by: Oran Agra <oran@redislabs.com>

2022-01-02 09:50:15 +02:00

3 Commits