Before this doc update, the comments in valkey.conf said that DEL is a
blocking command, and even refered to other synchronous freeing as "in a
blocking way, like if DEL was called". This has now become confusing and
incorrect, since DEL is now non-blocking by default.
The comments also mentioned too much about the "old default" and only
later explain that the "new default" is non-blocking.
This doc update focuses on the current default and expresses it like
"Starting from Valkey 8.0, lazy freeing is enabled by default", rather
than using words like old and new.
This is a follow-up to #913.
---------
Signed-off-by: Viktor Söderqvist <viktor.soderqvist@est.tech>
Signed-off-by: Ping Xie <pingxie@google.com>
Implement data masking for user data in server logs and diagnostic output. This change prevents potential exposure of confidential information, such as PII, and enhances privacy protection. It masks all command arguments, client names, and client usernames.
Added a new hide-user-data-from-log configuration item, default yes.
---------
Signed-off-by: Amit Nagler <anagler123@gmail.com>
## Set replica-lazy-flush and lazyfree-lazy-user-flush to yes by
default.
There are many problems with running flush synchronously. Even in
single CPU environments, the thread managers should balance between
the freeing and serving incoming requests.
## Set lazy eviction, expire, server-del, user-del to yes by default
We now have a del and a lazyfree del, we also have these configuration
items to control: lazyfree-lazy-eviction, lazyfree-lazy-expire,
lazyfree-lazy-server-del, lazyfree-lazy-user-del. In most cases lazyfree
is better since it reduces the risk of blocking the main thread, and
because we have lazyfreeGetFreeEffort, on those with high effor
(currently
64) will use lazyfree.
Part of #653.
---------
Signed-off-by: Binbin <binloveplay1314@qq.com>
Currently, the `dual-channel-replication` feature flag is immutable if
`enable-protected-configs` is enabled, which is the default behavior.
This PR proposes to make the `dual-channel-replication` flag mutable,
allowing it to be changed dynamically without restarting the cluster.
**Motivation:**
The ability to change the `dual-channel-replication` flag dynamically is
essential for testing and validating the feature on real clusters
running in production environments. By making the flag mutable, we can
enable or disable the feature without disrupting the cluster's
operations, facilitating easier testing and experimentation.
Additionally, this change would provide more flexibility for users to
enable or disable the feature based on their specific requirements or
operational needs without requiring a cluster restart.
---------
Signed-off-by: naglera <anagler123@gmail.com>
This PR utilizes the IO threads to execute commands in batches, allowing
us to prefetch the dictionary data in advance.
After making the IO threads asynchronous and offloading more work to
them in the first 2 PRs, the `lookupKey` function becomes a main
bottle-neck and it takes about 50% of the main-thread time (Tested with
SET command). This is because the Valkey dictionary is a straightforward
but inefficient chained hash implementation. While traversing the hash
linked lists, every access to either a dictEntry structure, pointer to
key, or a value object requires, with high probability, an expensive
external memory access.
### Memory Access Amortization
Memory Access Amortization (MAA) is a technique designed to optimize the
performance of dynamic data structures by reducing the impact of memory
access latency. It is applicable when multiple operations need to be
executed concurrently. The principle behind it is that for certain
dynamic data structures, executing operations in a batch is more
efficient than executing each one separately.
Rather than executing operations sequentially, this approach interleaves
the execution of all operations. This is done in such a way that
whenever a memory access is required during an operation, the program
prefetches the necessary memory and transitions to another operation.
This ensures that when one operation is blocked awaiting memory access,
other memory accesses are executed in parallel, thereby reducing the
average access latency.
We applied this method in the development of `dictPrefetch`, which takes
as parameters a vector of keys and dictionaries. It ensures that all
memory addresses required to execute dictionary operations for these
keys are loaded into the L1-L3 caches when executing commands.
Essentially, `dictPrefetch` is an interleaved execution of dictFind for
all the keys.
**Implementation details**
When the main thread iterates over the `clients-pending-io-read`, for
clients with ready-to-execute commands (i.e., clients for which the IO
thread has parsed the commands), a batch of up to 16 commands is
created. Initially, the command's argv, which were allocated by the IO
thread, is prefetched to the main thread's L1 cache. Subsequently, all
the dict entries and values required for the commands are prefetched
from the dictionary before the command execution. Only then will the
commands be executed.
---------
Signed-off-by: Uri Yagelnik <uriy@amazon.com>
Add new optional, immutable string config called `unixsocketgroup`.
Change the group of the unix socket to `unixsocketgroup` after `bind()`
if specified.
Adds tests to validate the behavior.
Fixes#873.
Signed-off-by: Ayush Sharma <mrayushs933@gmail.com>
The repl-backlog-size 1mb is too small in most cases, now network
transmission and bandwidth performance have improved rapidly in more
than ten years.
The bigger the replication backlog, the longer the replica can endure
the disconnect and later be able to perform a partial resynchronization.
Part of #653.
---------
Signed-off-by: Binbin <binloveplay1314@qq.com>
I think it is a good idea to mention this.
The Cluster config file is written relative this directory, if the
'cluster-config-file' configuration directive is a relative path.
Signed-off-by: Binbin <binloveplay1314@qq.com>
Co-authored-by: Viktor Söderqvist <viktor.soderqvist@est.tech>
The metric tracks cpu time in micro-seconds, sharing the same value as
`INFO COMMANDSTATS`, aggregated under per-slot context.
---------
Signed-off-by: Kyle Kim <kimkyle@amazon.com>
Signed-off-by: Madelyn Olson <madelyneolson@gmail.com>
Co-authored-by: Madelyn Olson <madelyneolson@gmail.com>
In this PR we introduce the main benefit of dual channel replication by
continuously steaming the COB (client output buffers) in parallel to the
RDB and thus keeping the primary's side COB small AND accelerating the
overall sync process. By streaming the replication data to the replica
during the full sync, we reduce
1. Memory load from the primary's node.
2. CPU load from the primary's main process. [Latest performance
tests](#data)
## Motivation
* Reduce primary memory load. We do that by moving the COB tracking to
the replica side. This also decrease the chance for COB overruns. Note
that primary's input buffer limits at the replica side are less
restricted then primary's COB as the replica plays less critical part in
the replication group. While increasing the primary’s COB may end up
with primary reaching swap and clients suffering, at replica side we’re
more at ease with it. Larger COB means better chance to sync
successfully.
* Reduce primary main process CPU load. By opening a new, dedicated
connection for the RDB transfer, child processes can have direct access
to the new connection. Due to TLS connection restrictions, this was not
possible using one main connection. We eliminate the need for the child
process to use the primary's child-proc -> main-proc pipeline, thus
freeing up the main process to process clients queries.
## Dual Channel Replication high level interface design
- Dual channel replication begins when the replica sends a `REPLCONF
CAPA DUALCHANNEL` to the primary during initial
handshake. This is used to state that the replica is capable of dual
channel sync and that this is the replica's main channel, which is not
used for snapshot transfer.
- When replica lacks sufficient data for PSYNC, the primary will send
`-FULLSYNCNEEDED` response instead
of RDB data. As a next step, the replica creates a new connection
(rdb-channel) and configures it against
the primary with the appropriate capabilities and requirements. The
replica then requests a sync
using the RDB channel.
- Prior to forking, the primary sends the replica the snapshot's end
repl-offset, and attaches the replica
to the replication backlog to keep repl data until the replica requests
psync. The replica uses the main
channel to request a PSYNC starting at the snapshot end offset.
- The primary main threads sends incremental changes via the main
channel, while the bgsave process
sends the RDB directly to the replica via the rdb-channel. As for the
replica, the incremental
changes are stored on a local buffer, while the RDB is loaded into
memory.
- Once the replica completes loading the rdb, it drops the
rdb-connection and streams the accumulated incremental
changes into memory. Repl steady state continues normally.
## New replica state machine
## Data <a name="data"></a>
## Explanation
These graphs demonstrate performance improvements during full sync
sessions using rdb-channel + streaming rdb directly from the background
process to the replica.
First graph- with at most 50 clients and light weight commands, we saw
5%-7.5% improvement in write latency during sync session.
Two graphs below- full sync was tested during heavy read commands from
the primary (such as sdiff, sunion on large sets). In that case, the
child process writes to the replica without sharing CPU with the loaded
main process. As a result, this not only improves client response time,
but may also shorten sync time by about 50%. The shorter sync time
results in less memory being used to store replication diffs (>60% in
some of the tested cases).
## Test setup
Both primary and replica in the performance tests ran on the same
machine. RDB size in all tests is 3.7gb. I generated write load using
valkey-benchmark ` ./valkey-benchmark -r 100000 -n 6000000 lpush my_list
__rand_int__`.
---------
Signed-off-by: naglera <anagler123@gmail.com>
Signed-off-by: naglera <58042354+naglera@users.noreply.github.com>
Co-authored-by: Viktor Söderqvist <viktor.soderqvist@est.tech>
Co-authored-by: Ping Xie <pingxie@outlook.com>
Co-authored-by: Madelyn Olson <madelyneolson@gmail.com>
Allows cluster admins to configure the blacklist TTL as needed to allow
sufficient time for `CLUSTER FORGET` to be executed on every node in the
cluster.
Config name `cluster-blacklist-ttl`; unit seconds; deault 60.
---------
Signed-off-by: Brennan Cathcart <brennancathcart@gmail.com>
New configs:
* `cluster-announce-client-ipv4`
* `cluster-announce-client-ipv6`
New module API function:
* `ValkeyModule_GetClusterNodeInfoForClient`, takes a client id and is
otherwise just like its non-ForClient cousin.
If configured, one of these IP addresses are reported to each client in
CLUSTER SLOTS, CLUSTER SHARDS, CLUSTER NODES and redirects, replacing
the IP (`custer-announce-ip` or the auto-detected IP) of each node.
Which one is reported to the client depends on whether the client is
connected over IPv4 or IPv6.
Benefits:
* This allows clients using IPv4 to get the IPv4 addresses of all
cluster nodes and IPv6 clients to get the IPv6 clients.
* This allows the IPs visible to clients to be different to the IPs used
between the cluster nodes due to NAT'ing.
The information is propagated in the cluster bus using new Ping
extensions. (Old nodes without this feature ignore unknown Ping
extensions.)
This adds another dimension to CLUSTER SLOTS reply. It now depends on
the client's use of TLS, the IP address family and RESP version.
Refactoring: The cached connection type definition is moved from
connection.h (it actually has nothing to do with the connection
abstraction) to server.h and is changed to a bitmap, with one bit for
each of TLS, IPv6 and RESP3.
Fixes#337
---------
Signed-off-by: Viktor Söderqvist <viktor.soderqvist@est.tech>
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>
When Redis/Valkey/KeyDB is run in a cloud environment across multiple
AZ's it is preferable to keep traffic local to an AZ both for cost
reasons and for latency. This is typically done when you are enabling
reads on replicas with the READONLY command.
For this change we are creating a setting that is echo'd back in the
info command. We do not want to add the cloud SDKs as dependencies and
this is the easiest way around that. It is fairly trivial to grab the AZ
from the cloud and push that into your setting file.
Currently at Snapchat we have a custom client that after connecting
reads this from the server and will preferentially use that server if
the AZ string matches its internally configured AZ.
In the future it would be ideal if we used this information when
performing failover or even exposed it in cluster nodes.
Signed-off-by: John Sully <john@csquare.ca>
Make the one backwards compatible config change we are allowed to
replace for removing master from our API.
`masterauth` and `masteruser` are still used as an alias, but aren't
explicitly referenced. As an addendum to
https://github.com/valkey-io/valkey/pull/591, it would be good to have
this in 8. Given the related PR for updated other references for master,
I just updated the ones around this specific change.
Signed-off-by: Madelyn Olson <madelyneolson@gmail.com>
Changes the default value for the `pidfile` config.
The template config file `valkey.conf` already contains `pidfile
/var/run/valkey_6379.pid`. This is not a default. The default is what
you get when you start valkey without config.
Tests suites config pidfile changed to valkey accordingly.
Signed-off-by: Shivshankar-Reddy <shiva.sheri.github@gmail.com>
Default value for the "syslog-ident" config changed from "redis" to
"valkey".
Fixes#301.
---------
Signed-off-by: Karthick Ariyaratnam <karthyuom@gmail.com>
New config 'extended-redis-compatibility' (yes/no) default no
* When yes:
* Use "Redis" in the following error replies:
- `-LOADING Redis is loading the dataset in memory`
- `-BUSY Redis is busy`...
- `-MISCONF Redis is configured to`...
* Use `=== REDIS BUG REPORT` in the crash log delimiters (START and
END).
* The HELLO command returns `"server" => "redis"` and `"version" =>
"7.2.4"` (our Redis OSS compatibility version).
* The INFO field for mode is called `"redis_mode"`.
* When no:
* Use "Valkey" instead of "Redis" in the mentioned errors and crash log
delimiters.
* The HELLO command returns `"server" => "valkey"` and the Valkey
version for `"version"`.
* The INFO field for mode is called `"server_mode"`.
* Documentation added in valkey.conf:
> Valkey is largely compatible with Redis OSS, apart from a few cases
where
> Redis OSS compatibility mode makes Valkey pretend to be Redis. Enable
this
> only if you have problems with tools or clients. This is a temporary
> configuration added in Valkey 8.0 and is scheduled to have no effect
in Valkey
> 9.0 and be completely removed in Valkey 10.0.
* A test case for the config is added. It is designed to fail if the
config is not deprecated (has no effect) in Valkey 9 and deleted in
Valkey 10.
* Other test cases are adjusted to work regardless of this config.
Fixes#274Fixes#61
---------
Signed-off-by: Viktor Söderqvist <viktor.soderqvist@est.tech>
The default pid file is created at /var/run/redis.pid based on the code
at
da831c0d22/src/server.h (L132).
Until we update it, we should reflect that in the conf file.
Signed-off-by: Madelyn Olson <madelyneolson@gmail.com>
Remove trademarked wording on configuration layer.
Following changes for release notes:
1. Rename redis.conf to valkey.conf
2. Pre-filled config in the template config file: Changing pidfile to `/var/run/valkey_6379.pid`
Signed-off-by: Harkrishn Patro <harkrisp@amazon.com>
