Currently, when running tests with IO threads, we set the
`events-per-io-thread` config to 0. This activated IO threads 100% of
the time, regardless of the number of IO events.
This is causing issues with tests running multiple server instances, as
it drained machine CPU resources. As a result, tests could have very
long runtimes, especially on limited instances.
For example, in
https://github.com/valkey-io/valkey/actions/runs/10066315827/job/27827426986?pr=804,
the `Cluster consistency during live resharding` test ran for 1 hour and
41 minutes.
This PR addresses the issue by:
1. Deactivating IO threads when there are no IO events
2. Continuing to offload all IO events to IO threads
Tested on 16 cores instance, after implementing these changes, the
runtime for the `Cluster consistency during live resharding` test
dropped from 7 minutes an 14 seconds to 3 minutes and 28 seconds.
Signed-off-by: Uri Yagelnik <uriy@amazon.com>
### IO-Threads Work Offloading
This PR is the 2nd of 3 PRs intended to achieve the goal of 1M requests
per second.
(1st PR: https://github.com/valkey-io/valkey/pull/758)
This PR offloads additional work to the I/O threads, beyond the current
read-parse/write operations, to better utilize the I/O threads and
reduce the load on the main thread.
It contains the following 3 commits:
### Poll Offload
Currently, the main thread is responsible for executing the poll-wait
system call, while the IO threads wait for tasks from the main thread.
The poll-wait operation is expensive and can consume up to 30% of the
main thread's time. We could have let the IO threads do the poll-wait by
themselves, with each thread listening to some of the clients and
notifying the main thread when a client's command is ready to execute.
However, the current approach, where the main thread listens for events
from the network, has several benefits. The main thread remains in
charge, allowing it to know the state of each client
(idle/read/write/close) at any given time. Additionally, it makes the
threads flexible, enabling us to drain an IO thread's job queue and stop
a thread when the load is light without modifying the event loop and
moving its clients to a different IO thread. Furthermore, with this
approach, the IO threads don't need to wait for both messages from the
network and from the main thread instead, the threads wait only for
tasks from the main thread.
To enjoy the benefits of both the main thread remaining in charge and
the poll being offloaded, we propose offloading the poll-wait as a
single-time, non-blocking job to one of the IO threads. The IO thread
will perform a poll-wait non-blocking call while the main thread
processes the client commands. Later, in `aeProcessEvents`, instead of
sleeping on the poll, we check for the IO thread's poll-wait results.
The poll-wait will be offloaded in `beforeSleep` only when there are
ready events for the main thread to process. If no events are pending,
the main thread will revert to the current behavior and sleep on the
poll by itself.
**Implementation Details**
A new call back `custompoll` was added to the `aeEventLoop` when not set
to `NULL` the ae will call the `custompoll` callback instead of the
`aeApiPoll`.
When the poll is offloaded we will set the `custompoll` to
`getIOThreadPollResults` and send a poll-job to the thread. the thread
will take a mutex, call a non-blocking (with timeout 0) to `aePoll`
which will populate the fired events array. the IO thread will set the
`server.io_fired_events` to the number of the returning `numevents`,
later the main-thread in `custompoll` will return the
`server.io_fired_events` and will set the `customPoll` back to `NULL`.
To ensure thread safety when accessing server.el, all functions that
modify the eventloop events were wrapped with a mutex to ensure mutual
exclusion when modifying the events.
### Command Lookup Offload
As the IO thread parses the command from the client's Querybuf, it can
perform a command lookup in the commands dictionary, which can consume
up to ~5% of the main-thread runtime.
**Implementation details**
The IO thread will store the looked-up command in the client's new field
`io_parsed_cmd` field. We can't use `c->cmd` for that since we use
`c->cmd `to check if a command was reprocessed or not.
To ensure thread safety when accessing the command dictionary, we make
sure the main thread isn't changing the dictionary while IO threads are
accessing it. This is accomplished by introducing a new flag called
`no_incremental_rehash` for the `dictType` commands. When performing
`dictResize`, we will rehash the entire dictionary in place rather than
deferring the process.
### Free Offload
Since the command arguments are allocated by the I/O thread, it would be
beneficial if they were also freed by the same thread. If the main
thread frees objects allocated by the I/O thread, two issues arise:
1. During the freeing process, the main thread needs to access the SDS
pointed to by the object to get its length.
2. With Jemalloc, each thread manages thread local pool (`tcache`) of
buffers for quick reallocation without accessing the arena. If the main
thread constantly frees objects allocated by other threads, those
threads will have to frequently access the shared arena to obtain new
memory allocations
**Implementation Details**
When freeing the client's argv, we will send the argv array to the
thread that allocated it. The thread will be identified by the client
ID. When freeing an object during `dbOverwrite`, we will offload the
object free as well. We will extend this to offload the free during
`dbDelete` in a future PR, as its effects on defrag/memory evictions
need to be studied.
---------
Signed-off-by: Uri Yagelnik <uriy@amazon.com>
This PR is 1 of 3 PRs intended to achieve the goal of 1 million requests
per second, as detailed by [dan touitou](https://github.com/touitou-dan)
in https://github.com/valkey-io/valkey/issues/22. This PR modifies the
IO threads to be fully asynchronous, which is a first and necessary step
to allow more work offloading and better utilization of the IO threads.
### Current IO threads state:
Valkey IO threads were introduced in Redis 6.0 to allow better
utilization of multi-core machines. Before this, Redis was
single-threaded and could only use one CPU core for network and command
processing. The introduction of IO threads helps in offloading the IO
operations to multiple threads.
**Current IO Threads flow:**
1. Initialization: When Redis starts, it initializes a specified number
of IO threads. These threads are in addition to the main thread, each
thread starts with an empty list, the main thread will populate that
list in each event-loop with pending-read-clients or
pending-write-clients.
2. Read Phase: The main thread accepts incoming connections and reads
requests from clients. The reading of requests are offloaded to IO
threads. The main thread puts the clients ready-to-read in a list and
set the global io_threads_op to IO_THREADS_OP_READ, the IO threads pick
the clients up, perform the read operation and parse the first incoming
command.
3. Command Processing: After reading the requests, command processing is
still single-threaded and handled by the main thread.
4. Write Phase: Similar to the read phase, the write phase is also be
offloaded to IO threads. The main thread prepares the response in the
clients’ output buffer then the main thread puts the client in the list,
and sets the global io_threads_op to the IO_THREADS_OP_WRITE. The IO
threads then pick the clients up and perform the write operation to send
the responses back to clients.
5. Synchronization: The main-thread communicate with the threads on how
many jobs left per each thread with atomic counter. The main-thread
doesn’t access the clients while being handled by the IO threads.
**Issues with current implementation:**
* Underutilized Cores: The current implementation of IO-threads leads to
the underutilization of CPU cores.
* The main thread remains responsible for a significant portion of
IO-related tasks that could be offloaded to IO-threads.
* When the main-thread is processing client’s commands, the IO threads
are idle for a considerable amount of time.
* Notably, the main thread's performance during the IO-related tasks is
constrained by the speed of the slowest IO-thread.
* Limited Offloading: Currently, Since the Main-threads waits
synchronously for the IO threads, the Threads perform only read-parse,
and write operations, with parsing done only for the first command. If
the threads can do work asynchronously we may offload more work to the
threads reducing the load from the main-thread.
* TLS: Currently, we don't support IO threads with TLS (where offloading
IO would be more beneficial) since TLS read/write operations are not
thread-safe with the current implementation.
### Suggested change
Non-blocking main thread - The main thread and IO threads will operate
in parallel to maximize efficiency. The main thread will not be blocked
by IO operations. It will continue to process commands independently of
the IO thread's activities.
**Implementation details**
**Inter-thread communication.**
* We use a static, lock-free ring buffer of fixed size (2048 jobs) for
the main thread to send jobs and for the IO to receive them. If the ring
buffer fills up, the main thread will handle the task itself, acting as
back pressure (in case IO operations are more expensive than command
processing). A static ring buffer is a better candidate than a dynamic
job queue as it eliminates the need for allocation/freeing per job.
* An IO job will be in the format: ` [void* function-call-back | void
*data] `where data is either a client to read/write from and the
function-ptr is the function to be called with the data for example
readQueryFromClient using this format we can use it later to offload
other types of works to the IO threads.
* The Ring buffer is one way from the main-thread to the IO thread, Upon
read/write event the main thread will send a read/write job then in
before sleep it will iterate over the pending read/write clients to
checking for each client if the IO threads has already finished handling
it. The IO thread signals it has finished handling a client read/write
by toggling an atomic flag read_state / write_state on the client
struct.
**Thread Safety**
As suggested in this solution, the IO threads are reading from and
writing to the clients' buffers while the main thread may access those
clients.
We must ensure no race conditions or unsafe access occurs while keeping
the Valkey code simple and lock free.
Minimal Action in the IO Threads
The main change is to limit the IO thread operations to the bare
minimum. The IO thread will access only the client's struct and only the
necessary fields in this struct.
The IO threads will be responsible for the following:
* Read Operation: The IO thread will only read and parse a single
command. It will not update the server stats, handle read errors, or
parsing errors. These tasks will be taken care of by the main thread.
* Write Operation: The IO thread will only write the available data. It
will not free the client's replies, handle write errors, or update the
server statistics.
To achieve this without code duplication, the read/write code has been
refactored into smaller, independent components:
* Functions that perform only the read/parse/write calls.
* Functions that handle the read/parse/write results.
This refactor accounts for the majority of the modifications in this PR.
**Client Struct Safe Access**
As we ensure that the IO threads access memory only within the client
struct, we need to ensure thread safety only for the client's struct's
shared fields.
* Query Buffer
* Command parsing - The main thread will not try to parse a command from
the query buffer when a client is offloaded to the IO thread.
* Client's memory checks in client-cron - The main thread will not
access the client query buffer if it is offloaded and will handle the
querybuf grow/shrink when the client is back.
* CLIENT LIST command - The main thread will busy-wait for the IO thread
to finish handling the client, falling back to the current behavior
where the main thread waits for the IO thread to finish their
processing.
* Output Buffer
* The IO thread will not change the client's bufpos and won't free the
client's reply lists. These actions will be done by the main thread on
the client's return from the IO thread.
* bufpos / block→used: As the main thread may change the bufpos, the
reply-block→used, or add/delete blocks to the reply list while the IO
thread writes, we add two fields to the client struct: io_last_bufpos
and io_last_reply_block. The IO thread will write until the
io_last_bufpos, which was set by the main-thread before sending the
client to the IO thread. If more data has been added to the cob in
between, it will be written in the next write-job. In addition, the main
thread will not trim or merge reply blocks while the client is
offloaded.
* Parsing Fields
* Client's cmd, argc, argv, reqtype, etc., are set during parsing.
* The main thread will indicate to the IO thread not to parse a cmd if
the client is not reset. In this case, the IO thread will only read from
the network and won't attempt to parse a new command.
* The main thread won't access the c→cmd/c→argv in the CLIENT LIST
command as stated before it will busy wait for the IO threads.
* Client Flags
* c→flags, which may be changed by the main thread in multiple places,
won't be accessed by the IO thread. Instead, the main thread will set
the c→io_flags with the information necessary for the IO thread to know
the client's state.
* Client Close
* On freeClient, the main thread will busy wait for the IO thread to
finish processing the client's read/write before proceeding to free the
client.
* Client's Memory Limits
* The IO thread won't handle the qb/cob limits. In case a client crosses
the qb limit, the IO thread will stop reading for it, letting the main
thread know that the client crossed the limit.
**TLS**
TLS is currently not supported with IO threads for the following
reasons:
1. Pending reads - If SSL has pending data that has already been read
from the socket, there is a risk of not calling the read handler again.
To handle this, a list is used to hold the pending clients. With IO
threads, multiple threads can access the list concurrently.
2. Event loop modification - Currently, the TLS code
registers/unregisters the file descriptor from the event loop depending
on the read/write results. With IO threads, multiple threads can modify
the event loop struct simultaneously.
3. The same client can be sent to 2 different threads concurrently
(https://github.com/redis/redis/issues/12540).
Those issues were handled in the current PR:
1. The IO thread only performs the read operation. The main thread will
check for pending reads after the client returns from the IO thread and
will be the only one to access the pending list.
2. The registering/unregistering of events will be similarly postponed
and handled by the main thread only.
3. Each client is being sent to the same dedicated thread (c→id %
num_of_threads).
**Sending Replies Immediately with IO threads.**
Currently, after processing a command, we add the client to the
pending_writes_list. Only after processing all the clients do we send
all the replies. Since the IO threads are now working asynchronously, we
can send the reply immediately after processing the client’s requests,
reducing the command latency. However, if we are using AOF=always, we
must wait for the AOF buffer to be written, in which case we revert to
the current behavior.
**IO threads dynamic adjustment**
Currently, we use an all-or-nothing approach when activating the IO
threads. The current logic is as follows: if the number of pending write
clients is greater than twice the number of threads (including the main
thread), we enable all threads; otherwise, we enable none. For example,
if 8 IO threads are defined, we enable all 8 threads if there are 16
pending clients; else, we enable none.
It makes more sense to enable partial activation of the IO threads. If
we have 10 pending clients, we will enable 5 threads, and so on. This
approach allows for a more granular and efficient allocation of
resources based on the current workload.
In addition, the user will now be able to change the number of I/O
threads at runtime. For example, when decreasing the number of threads
from 4 to 2, threads 3 and 4 will be closed after flushing their job
queues.
**Tests**
Currently, we run the io-threads tests with 4 IO threads
(443d80f168/.github/workflows/daily.yml (L353)).
This means that we will not activate the IO threads unless there are 8
(threads * 2) pending write clients per single loop, which is unlikely
to happened in most of tests, meaning the IO threads are not currently
being tested.
To enforce the main thread to always offload work to the IO threads,
regardless of the number of pending events, we add an
events-per-io-thread configuration with a default value of 2. When set
to 0, this configuration will force the main thread to always offload
work to the IO threads.
When we offload every single read/write operation to the IO threads, the
IO-threads are running with 100% CPU when running multiple tests
concurrently some tests fail as a result of larger than expected command
latencies. To address this issue, we have to add some after or wait_for
calls to some of the tests to ensure they pass with IO threads as well.
Signed-off-by: Uri Yagelnik <uriy@amazon.com>
uriyage