distributed lock redis distributed lock redis

Introduction to Reliable and Secure Distributed Programming, The Proposal The core ideas were to: Remove /.*hazelcast. The unique random value it uses does not provide the required monotonicity. 2023 Redis. Because of this, these classes are maximally efficient when using TryAcquire semantics with a timeout of zero. generating fencing tokens. address that is not yet loaded into memory, so it gets a page fault and is paused until the page is On the other hand, the Redlock algorithm, with its 5 replicas and majority voting, looks at first I wont go into other aspects of Redis, some of which have already been critiqued Join the DZone community and get the full member experience. All the other keys will expire later, so we are sure that the keys will be simultaneously set for at least this time. correctness, most of the time is not enough you need it to always be correct. */ig; Distributed locks are used to let many separate systems agree on some shared state at any given time, often for the purposes of master election or coordinating access to a resource. Keep reminding yourself of the GitHub incident with the increases (e.g. If you found this post useful, please Please consider thoroughly reviewing the Analysis of Redlock section at the end of this page. https://redislabs.com/ebook/part-2-core-concepts/chapter-6-application-components-in-redis/6-2-distributed-locking/, Any thread in the case multi-threaded environment (see Java/JVM), Any other manual query/command from terminal, Deadlock free locking as we are using ttl, which will automatically release the lock after some time. to a shared storage system, to perform some computation, to call some external API, or suchlike. That work might be to write some data In that case we will be having multiple keys for the multiple resources. rejects the request with token 33. This can be handled by specifying a ttl for a key. Clients 1 and 2 now both believe they hold the lock. For simplicity, assume we have two clients and only one Redis instance. book, now available in Early Release from OReilly. Generally, the setnx (set if not exists) instruction can be used to simply implement locking. If waiting to acquire a lock or other primitive that is not available, the implementation will periodically sleep and retry until the lease can be taken or the acquire timeout elapses. says that the time it returns is subject to discontinuous jumps in system time above, these are very reasonable assumptions. Update 9 Feb 2016: Salvatore, the original author of Redlock, has I think the Redlock algorithm is a poor choice because it is neither fish nor fowl: it is distributed systems. A simpler solution is to use a UNIX timestamp with microsecond precision, concatenating the timestamp with a client ID. The algorithm does not produce any number that is guaranteed to increase Safety property: Mutual exclusion. He makes some good points, but Many developers use a standard database locking, and so are we. Features of Distributed Locks A distributed lock service should satisfy the following properties: Mutual. Using redis to realize distributed lock. network delay is small compared to the expiry duration; and that process pauses are much shorter So this was all it on locking using redis. Lets leave the particulars of Redlock aside for a moment, and discuss how a distributed lock is With distributed locking, we have the same sort of acquire, operate, release operations, but instead of having a lock thats only known by threads within the same process, or processes on the same machine, we use a lock that different Redis clients on different machines can acquire and release. assumes that delays, pauses and drift are all small relative to the time-to-live of a lock; if the Dont bother with setting up a cluster of five Redis nodes. In plain English, timing issues become as large as the time-to-live, the algorithm fails. enough? Thats hard: its so tempting to assume networks, processes and clocks are more This assumption closely resembles a real-world computer: every computer has a local clock and we can usually rely on different computers to have a clock drift which is small. If one service preempts the distributed lock and other services fail to acquire the lock, no subsequent operations will be carried out. The purpose of a lock is to ensure that among several nodes that might try to do the same piece of We already described how to acquire and release the lock safely in a single instance. assumptions[12]. In this article, I am going to show you how we can leverage Redis for locking mechanism, specifically in distributed system. accidentally sent SIGSTOP to the process. Distributed Locking with Redis and Ruby. At RedLock(Redis Distributed Lock) redis TTL timeout cd 90-second packet delay. period, and the client doesnt realise that it has expired, it may go ahead and make some unsafe The following picture illustrates this situation: As a solution, there is a WAIT command that waits for specified numbers of acknowledgments from replicas and returns the number of replicas that acknowledged the write commands sent before the WAIT command, both in the case where the specified number of replicas is reached or when the timeout is reached. use. However we want to also make sure that multiple clients trying to acquire the lock at the same time cant simultaneously succeed. We need to free the lock over the key such that other clients can also perform operations on the resource. If Redisson instance which acquired MultiLock crashes then such MultiLock could hang forever in acquired state. algorithm just to generate the fencing tokens. Distributed Locks Manager (C# and Redis) | by Majid Qafouri | Towards Dev 500 Apologies, but something went wrong on our end. out, that doesnt mean that the other node is definitely down it could just as well be that there Eventually it is always possible to acquire a lock, even if the client that locked a resource crashes or gets partitioned. Arguably, distributed locking is one of those areas. For example: var connection = await ConnectionMultiplexer. of the time this is known as a partially synchronous system[12]. correctly configured NTP to only ever slew the clock. Implements Redis based Transaction, Redis based Spring Cache, Redis based Hibernate Cache and Tomcat Redis based Session Manager. After synching with the new master, all replicas and the new master do not have the key that was in the old master! As long as the majority of Redis nodes are up, clients are able to acquire and release locks. The key is set to a value my_random_value. And its not obvious to me how one would change the Redlock algorithm to start generating fencing We are going to use Redis for this case. some transient, approximate, fast-changing data between servers, and where its not a big deal if the algorithm safety is retained as long as when an instance restarts after a Martin Kleppman's article and antirez's answer to it are very relevant. that a lock in a distributed system is not like a mutex in a multi-threaded application. clock is manually adjusted by an administrator). 2023 Redis. Context I am developing a REST API application that connects to a database. Deadlock free: Every request for a lock must be eventually granted; even clients that hold the lock crash or encounter an exception. Lets examine it in some more So the resource will be locked for at most 10 seconds. App1, use the Redis lock component to take a lock on a shared resource. To protect against failure where our clients may crash and leave a lock in the acquired state, well eventually add a timeout, which causes the lock to be released automatically if the process that has the lock doesnt finish within the given time. As for optimistic lock, database access libraries, like Hibernate usually provide facilities, but in a distributed scenario we would use more specific solutions that use to implement more. To make all slaves and the master fully consistent, we should enable AOF with fsync=always for all Redis instances before getting the lock. manner while working on the shared resource. this article we will assume that your locks are important for correctness, and that it is a serious Other processes that want the lock dont know what process had the lock, so cant detect that the process failed, and waste time waiting for the lock to be released. different processes must operate with shared resources in a mutually Normally, To distinguish these cases, you can ask what There are two ways to use the distributed locking API: ABP's IAbpDistributedLock abstraction and DistributedLock library's API. Also the faster a client tries to acquire the lock in the majority of Redis instances, the smaller the window for a split brain condition (and the need for a retry), so ideally the client should try to send the SET commands to the N instances at the same time using multiplexing. It is not as safe, but probably sufficient for most environments. you occasionally lose that data for whatever reason. is designed for. The idea of distributed lock is to provide a global and unique "thing" to obtain the lock in the whole system, and then each system asks this "thing" to get a lock when it needs to be locked, so that different systems can be regarded as the same lock. The master crashes before the write to the key is transmitted to the replica. You simply cannot make any assumptions (The diagrams above are taken from my Salvatore Sanfilippo for reviewing a draft of this article. [6] Martin Thompson: Java Garbage Collection Distilled, assumptions. could easily happen that the expiry of a key in Redis is much faster or much slower than expected. It can happen: sometimes you need to severely curtail access to a resource. As soon as those timing assumptions are broken, Redlock may violate its safety properties, doi:10.1145/2639988.2639988. This page describes a more canonical algorithm to implement Getting locks is not fair; for example, a client may wait a long time to get the lock, and at the same time, another client gets the lock immediately. redis command. Arguably, distributed locking is one of those areas. The simplest way to use Redis to lock a resource is to create a key in an instance. Basically if there are infinite continuous network partitions, the system may become not available for an infinite amount of time. at 12th ACM Symposium on Operating Systems Principles (SOSP), December 1989. A tag already exists with the provided branch name. (HYTRADBOI), 05 Apr 2022 at 9th Workshop on Principles and Practice of Consistency for Distributed Data (PaPoC), 07 Dec 2021 at 2nd International Workshop on Distributed Infrastructure for Common Good (DICG), Creative Commons ConnectAsync ( connectionString ); // uses StackExchange.Redis var @lock = new RedisDistributedLock ( "MyLockName", connection. of lock reacquisition attempts should be limited, otherwise one of the liveness DistributedLock.Redis Download the NuGet package The DistributedLock.Redis package offers distributed synchronization primitives based on Redis. Therefore, two locks with the same name targeting the same underlying Redis instance but with different prefixes will not see each other. Solutions are needed to grant mutual exclusive access by processes. In the context of Redis, weve been using WATCH as a replacement for a lock, and we call it optimistic locking, because rather than actually preventing others from modifying the data, were notified if someone else changes the data before we do it ourselves. Co-Creator of Deno-Redlock: a highly-available, Redis-based distributed systems lock manager for Deno with great safety and liveness guarantees. A lot of work has been put in recent versions (1.7+) to introduce Named Locks with implementations that will allow us to use distributed locking facilities like Redis with Redisson or Hazelcast. If youre depending on your lock for If Redis restarted (crashed, powered down, I mean without a graceful shutdown) at this duration, we lose data in memory so other clients can get the same lock: To solve this issue, we must enable AOF with the fsync=always option before setting the key in Redis. I would recommend sticking with the straightforward single-node locking algorithm for relies on a reasonably accurate measurement of time, and would fail if the clock jumps. (If they could, distributed algorithms would do Redis distributed lock Redis is a single process and single thread mode. HDFS or S3). Horizontal scaling seems to be the answer of providing scalability and. maximally inconvenient for you (between the last check and the write operation). Clients want to have exclusive access to data stored on Redis, so clients need to have access to a lock defined in a scope that all clients can seeRedis. Now once our operation is performed we need to release the key if not expired. In that case, lets look at an example of how Let's examine it in some more detail. However, Redis has been gradually making inroads into areas of data management where there are stronger consistency and durability expectations - which worries me, because this is not what Redis is designed for. the modified file back, and finally releases the lock. Opinions expressed by DZone contributors are their own. The auto release of the lock (since keys expire): eventually keys are available again to be locked. In the distributed version of the algorithm we assume we have N Redis masters. In this case for the argument already expressed above, for MIN_VALIDITY no client should be able to re-acquire the lock. The value value of the lock must be unique; 3. doi:10.1007/978-3-642-15260-3. a DLM (Distributed Lock Manager) with Redis, but every library uses a different This exclusiveness of access is called mutual exclusion between processes. When we actually start building the lock, we wont handle all of the failures right away. detector. We are going to model our design with just three properties that, from our point of view, are the minimum guarantees needed to use distributed locks in an effective way. If and only if the client was able to acquire the lock in the majority of the instances (at least 3), and the total time elapsed to acquire the lock is less than lock validity time, the lock is considered to be acquired. replication to a secondary instance in case the primary crashes. [1] Cary G Gray and David R Cheriton: several nodes would mean they would go out of sync. The Chubby lock service for loosely-coupled distributed systems, An important project maintenance signal to consider for safe_redis_lock is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which . Suppose you are working on a web application which serves millions of requests per day, you will probably need multiple instances of your application (also of course, a load balancer), to serve your customers requests efficiently and in a faster way. for at least a bit more than the max TTL we use. Multi-lock: In some cases, you may want to manage several distributed locks as a single "multi-lock" entity. At least if youre relying on a single Redis instance, it is A distributed lock service should satisfy the following properties: Mutual exclusion: Only one client can hold a lock at a given moment. without clocks entirely, but then consensus becomes impossible[10]. But in the messy reality of distributed systems, you have to be very doi:10.1145/114005.102808, [12] Cynthia Dwork, Nancy Lynch, and Larry Stockmeyer: careful with your assumptions. We already described how to acquire and release the lock safely in a single instance. server remembers that it has already processed a write with a higher token number (34), and so it The solution. Even so-called And if youre feeling smug because your programming language runtime doesnt have long GC pauses, How to do distributed locking. Both RedLock and the semaphore algorithm mentioned above claim locks for only a specified period of time. Let's examine what happens in different scenarios. Using delayed restarts it is basically possible to achieve safety even Short story about distributed locking and implementation of distributed locks with Redis enhanced by monitoring with Grafana. In this way a DLM provides software applications which are distributed across a cluster on multiple machines with a means to synchronize their accesses to shared resources . The lock that is not added by yourself cannot be released. writes on which the token has gone backwards. concurrent garbage collectors like the HotSpot JVMs CMS cannot fully run in parallel with the Ethernet and IP may delay packets arbitrarily, and they do[7]: in a famous Single Redis instance implements distributed locks. A client acquires the lock in 3 of 5 instances. At the t1 time point, the key of the distributed lock is resource_1 for application 1, and the validity period for the resource_1 key is set to 3 seconds. Here, we will implement distributed locks based on redis. incident at GitHub, packets were delayed in the network for approximately 90 Attribution 3.0 Unported License. non-critical purposes. "Redis": { "Configuration": "127.0.0.1" } Usage. // This is important in order to avoid removing a lock, // Remove the key 'lockName' if it have value 'lockValue', // wait until we get acknowledge from other replicas or throws exception otherwise, // THIS IS BECAUSE THE CLIENT THAT HOLDS THE. Basically to see the problem here, lets assume we configure Redis without persistence at all. I spent a bit of time thinking about it and writing up these notes. However, Redlock is not like this. Replication, Zab and Paxos all fall in this category. Note that RedisDistributedSemaphore does not support multiple databases, because the RedLock algorithm does not work with semaphores.1 When calling CreateSemaphore() on a RedisDistributedSynchronizationProvider that has been constructed with multiple databases, the first database in the list will be used. ISBN: 978-1-4493-6130-3. The purpose of distributed lock mechanism is to solve such problems and ensure mutually exclusive access to shared resources among multiple services. When we building distributed systems, we will face that multiple processes handle a shared resource together, it will cause some unexpected problems due to the fact that only one of them can utilize the shared resource at a time! The clock on node C jumps forward, causing the lock to expire. The DistributedLock.Redis package offers distributed synchronization primitives based on Redis. If a client locked the majority of instances using a time near, or greater, than the lock maximum validity time (the TTL we use for SET basically), it will consider the lock invalid and will unlock the instances, so we only need to consider the case where a client was able to lock the majority of instances in a time which is less than the validity time. Refresh the page, check Medium 's site status, or find something interesting to read. for all the keys about the locks that existed when the instance crashed to A similar issue could happen if C crashes before persisting the lock to disk, and immediately support me on Patreon of five-star reviews. This is the time needed Maybe there are many other processes Arguably, distributed locking is one of those areas. academic peer review (unlike either of our blog posts). But sadly, many implementations of locks in Redis are only mostly correct. Block lock. It tries to acquire the lock in all the N instances sequentially, using the same key name and random value in all the instances. The lock is only considered aquired if it is successfully acquired on more than half of the databases. complex or alternative designs. For example if the auto-release time is 10 seconds, the timeout could be in the ~ 5-50 milliseconds range. translate into an availability penalty. Terms of use & privacy policy. To set the expiration time, it should be noted that the setnx command can not set the timeout . I will argue in the following sections that it is not suitable for that purpose. email notification, Even though the problem can be mitigated by preventing admins from manually setting the server's time and setting up NTP properly, there's still a chance of this issue occurring in real life and compromising consistency. leases[1]) on top of Redis, and the page asks for feedback from people who are into Append-only File (AOF): logs every write operation received by the server, that will be played again at server startup, reconstructing the original dataset. and security protocols at TU Munich. Rodrigues textbook, Leases: An Efficient Fault-Tolerant Mechanism for Distributed File Cache Consistency, The Chubby lock service for loosely-coupled distributed systems, HBase and HDFS: Understanding filesystem usage in HBase, Avoiding Full GCs in Apache HBase with MemStore-Local Allocation Buffers: Part 1, Unreliable Failure Detectors for Reliable Distributed Systems, Impossibility of Distributed Consensus with One Faulty Process, Consensus in the Presence of Partial Synchrony, Verifying distributed systems with Isabelle/HOL, Building the future of computing, with your help, 29 Apr 2022 at Have You Tried Rubbing A Database On It? Many users using Redis as a lock server need high performance in terms of both latency to acquire and release a lock, and number of acquire / release operations that it is possible to perform per second. use it in situations where correctness depends on the lock. diminishes the usefulness of Redis for its intended purposes. That means that a wall-clock shift may result in a lock being acquired by more than one process. What are you using that lock for? If we didnt had the check of value==client then the lock which was acquired by new client would have been released by the old client, allowing other clients to lock the resource and process simultaneously along with second client, causing race conditions or data corruption, which is undesired. [3] Flavio P Junqueira and Benjamin Reed: like a compare-and-set operation, which requires consensus[11].). None of the above In this story, I'll be. The purpose of a lock is to ensure that among several nodes that might try to do the same piece of work, only one actually does it (at least only one at a time). Note that Redis uses gettimeofday, not a monotonic clock, to 6.2 Distributed locking 6.2.1 Why locks are important 6.2.2 Simple locks 6.2.3 Building a lock in Redis 6.2.4 Fine-grained locking 6.2.5 Locks with timeouts 6.3 Counting semaphores 6.3.1 Building a basic counting semaphore 6.3.2 Fair semaphores 6.3.4 Preventing race conditions 6.5 Pull messaging 6.5.1 Single-recipient publish/subscribe replacement Remember that GC can pause a running thread at any point, including the point that is restarts. Expected output: stronger consistency and durability expectations which worries me, because this is not what Redis use smaller lock validity times by default, and extend the algorithm implementing There is also a proposed distributed lock by Redis creator named RedLock. If a client takes too long to process, during which the key expires, other clients can acquire lock and process simultaneously causing race conditions. doi:10.1145/74850.74870. set of currently active locks when the instance restarts were all obtained Instead, please use Note this requires the storage server to take an active role in checking tokens, and rejecting any Twitter, When used as a failure detector, Raft, Viewstamped Refresh the page, check Medium 's site status, or find something interesting to read. On the other hand, if you need locks for correctness, please dont use Redlock. 1. It is unlikely that Redlock would survive a Jepsen test. In this context, a fencing token is simply a number that Maybe your process tried to read an Overview of the distributed lock API building block. But this is not particularly hard, once you know the Safety property: Mutual exclusion. Here we will directly introduce the three commands that need to be used: SETNX, expire and delete. if the that is, it might suddenly jump forwards by a few minutes, or even jump back in time (e.g. In todays world, it is rare to see applications operating on a single instance or a single machine or dont have any shared resources among different application environments. For example, you can use a lock to: . Using the IAbpDistributedLock Service. properties is violated. I will argue that if you are using locks merely for efficiency purposes, it is unnecessary to incur IAbpDistributedLock is a simple service provided by the ABP framework for simple usage of distributed locking. When and whether to use locks or WATCH will depend on a given application; some applications dont need locks to operate correctly, some only require locks for parts, and some require locks at every step. The effect of SET key value EX second is equivalent to that of set key second value. You can use the monotonic fencing tokens provided by FencedLock to achieve mutual exclusion across multiple threads that live . Theme borrowed from ), and to . Distributed locks are a very useful primitive in many environments where Impossibility of Distributed Consensus with One Faulty Process,