So I just read about redlock. What I understood is that it needs 3 independent machines to work. By independent they mean that all the machines are masters and there is no replication amongst them, which means they are serving different types of data. So why would I need to lock a key present in three independent redis instances acting as masters ? What are the use cases where I would need to use redlock ?
So why would I need to lock a key present in three independent redis instances acting as masters?
It's not that you're locking a key within Redis. Rather, the key is the lock, and used to control access to some other resource. That other resource could be anything, and generally is something outside of Redis since Redis has its own mechanisms for allowing atomic access to its data structures.
What are the use cases where I would need to use redlock?
You would use a distributed lock when you want only one member at a time of a distributed system to do something.
To take a random example from the internet, here's Coinbase talking about their use of a distributed lock to "ensure that multiple processes do not concurrently generate and broadcast separate transactions to the network".
Related
we have a small project, and we want to start using a non-clustered version of either keydb or redis. I've read a lot of reviews. I would like to hear more. Which system will be easier to turn into a cluster in the future, and maybe transfer to kubernetes?
Regarding scaling/simplicity, I would point out both Redis and KeyDB are able to turn into sharded clusters, or add replica nodes, KeyDB also offers active replication (some limits, but avoids sentinel). Both are also compatible with RESP protocol so can use any Redis client.
A few points relevant to both KeyDB and Redis when trying to simplify scaling in the future (ie. moving to a sharded data set):
Ensure you use a client that is compatible with cluster-mode enabled as not all are
Be careful of how you use transactions. If you rely heavily on transactions that hit multiple keys, you may need to rethink this when spreading data across multiple shards.
The point above also applies to certain commands that can hit multiple shards such as SCAN, KEYS, batch requests (ie. MGET), SUNION, etc. Planning how you structure your data may make this easier when you decide to scale up.
Two issues
Do lua scripts really solve all cases for redis transactions?
What are best practices for asynchronous transactions from one client?
Let me explain, first issue
Redis transactions are limited, with an inability to unwatch specific keys, and all keys being unwatched upon exec; we are limited to a single ongoing transaction on a given client.
I've seen threads where many redis users claim that lua scripts are all they need. Even the redis official docs state they may remove transactions in favour of lua scripts. However, there are cases where this is insufficient, such as the most standard case: using redis as a cache.
Let's say we want to cache some data from a persistent data store, in redis. Here's a quick process:
Check cache -> miss
Load data from database
Store in redis
However, what if, between step 2 (loading data), and step 3 (storing in redis) the data is updated by another client?
The data stored in redis would be stale. So... we use a redis transaction right? We watch the key before loading from db, and if the key is updated somewhere else before storage, storage would fail. Great! However, within an atomic lua script, we cannot load data from an external database, so lua cannot be used here. Hopefully I'm simply missing something, or there is something wrong with our process.
Moving on to the 2nd issue (asynchronous transactions)
Let's say we have a socket.io cluster which processes various messages, and requests for a game, for high speed communication between server and client. This cluster is written in node.js with appropriate use of promises and asynchronous concepts.
Say two requests hit a server in our cluster, which require data to be loaded and cached in redis. Using our transaction from above, multiple keys could be watched, and multiple multi->exec transactions would run in overlapping order on one redis connection. Once the first exec is run, all watched keys will be unwatched, even if the other transaction is still running. This may allow the second transaction to succeed when it should have failed.
These overlaps could happen in totally separate requests happening on the same server, or even sometimes in the same request if multiple data types need to load at the same time.
What is best practice here? Do we need to create a separate redis connection for every individual transaction? Seems like we would lose a lot of speed, and we would see many connections created just from one server if this is case.
As an alternative we could use redlock / mutex locking instead of redis transactions, but this is slow by comparison.
Any help appreciated!
I have received the following, after my query was escalated to redis engineers:
Hi Jeremy,
Your method using multiple backend connections would be the expected way to handle the problem. We do not see anything wrong with multiple backend connections, each using an optimistic Redis transaction (WATCH/MULTI/EXEC) - there is no chance that the “second transaction will succeed where it should have failed”.
Using LUA is not a good fit for this problem.
Best Regards,
The Redis Labs Team
I've found different zookeeper definitions across multiple resources. Maybe some of them are taken out of context, but look at them pls:
A canonical example of Zookeeper usage is distributed-memory computation...
ZooKeeper is an open source Apache™ project that provides a centralized infrastructure and services that enable synchronization across a cluster.
Apache ZooKeeper is an open source file application program interface (API) that allows distributed processes in large systems to synchronize with each other so that all clients making requests receive consistent data.
I've worked with Redis and Hazelcast, that would be easier for me to understand Zookeeper by comparing it with them.
Could you please compare Zookeeper with in-memory-data-grids and Redis?
If distributed-memory computation, how does zookeeper differ from in-memory-data-grids?
If synchronization across cluster, than how does it differs from all other in-memory storages? The same in-memory-data-grids also provide cluster-wide locks. Redis also has some kind of transactions.
If it's only about in-memory consistent data, than there are other alternatives. Imdg allow you to achieve the same, don't they?
https://zookeeper.apache.org/doc/current/zookeeperOver.html
By default, Zookeeper replicates all your data to every node and lets clients watch the data for changes. Changes are sent very quickly (within a bounded amount of time) to clients. You can also create "ephemeral nodes", which are deleted within a specified time if a client disconnects. ZooKeeper is highly optimized for reads, while writes are very slow (since they generally are sent to every client as soon as the write takes place). Finally, the maximum size of a "file" (znode) in Zookeeper is 1MB, but typically they'll be single strings.
Taken together, this means that zookeeper is not meant to store for much data, and definitely not a cache. Instead, it's for managing heartbeats/knowing what servers are online, storing/updating configuration, and possibly message passing (though if you have large #s of messages or high throughput demands, something like RabbitMQ will be much better for this task).
Basically, ZooKeeper (and Curator, which is built on it) helps in handling the mechanics of clustering -- heartbeats, distributing updates/configuration, distributed locks, etc.
It's not really comparable to Redis, but for the specific questions...
It doesn't support any computation and for most data sets, won't be able to store the data with any performance.
It's replicated to all nodes in the cluster (there's nothing like Redis clustering where the data can be distributed). All messages are processed atomically in full and are sequenced, so there's no real transactions. It can be USED to implement cluster-wide locks for your services (it's very good at that in fact), and tehre are a lot of locking primitives on the znodes themselves to control which nodes access them.
Sure, but ZooKeeper fills a niche. It's a tool for making a distributed applications play nice with multiple instances, not for storing/sharing large amounts of data. Compared to using an IMDG for this purpose, Zookeeper will be faster, manages heartbeats and synchronization in a predictable way (with a lot of APIs for making this part easy), and has a "push" paradigm instead of "pull" so nodes are notified very quickly of changes.
The quotation from the linked question...
A canonical example of Zookeeper usage is distributed-memory computation
... is, IMO, a bit misleading. You would use it to orchestrate the computation, not provide the data. For example, let's say you had to process rows 1-100 of a table. You might put 10 ZK nodes up, with names like "1-10", "11-20", "21-30", etc. Client applications would be notified of this change automatically by ZK, and the first one would grab "1-10" and set an ephemeral node clients/192.168.77.66/processing/rows_1_10
The next application would see this and go for the next group to process. The actual data to compute would be stored elsewhere (ie Redis, SQL database, etc). If the node failed partway through the computation, another node could see this (after 30-60 seconds) and pick up the job again.
I'd say the canonical example of ZooKeeper is leader election, though. Let's say you have 3 nodes -- one is master and the other 2 are slaves. If the master goes down, a slave node must become the new leader. This type of thing is perfect for ZK.
Consistency Guarantees
ZooKeeper is a high performance, scalable service. Both reads and write operations are designed to be fast, though reads are faster than writes. The reason for this is that in the case of reads, ZooKeeper can serve older data, which in turn is due to ZooKeeper's consistency guarantees:
Sequential Consistency
Updates from a client will be applied in the order that they were sent.
Atomicity
Updates either succeed or fail -- there are no partial results.
Single System Image
A client will see the same view of the service regardless of the server that it connects to.
Reliability
Once an update has been applied, it will persist from that time forward until a client overwrites the update. This guarantee has two corollaries:
If a client gets a successful return code, the update will have been applied. On some failures (communication errors, timeouts, etc) the client will not know if the update has applied or not. We take steps to minimize the failures, but the only guarantee is only present with successful return codes. (This is called the monotonicity condition in Paxos.)
Any updates that are seen by the client, through a read request or successful update, will never be rolled back when recovering from server failures.
Timeliness
The clients view of the system is guaranteed to be up-to-date within a certain time bound. (On the order of tens of seconds.) Either system changes will be seen by a client within this bound, or the client will detect a service outage.
I'm using Redis as a session store in my app. Can I use the same instance (and db) of Redis for my job queue? If it's of any significance, it's hosted with redistogo.
It is perfectly fine to use the same redis for multiple operations.
We had a similar use case where we used Redis as a key value store as well as a job queue.
However you may want to consider other aspects like the performance requirements for your application. Redis can ideally handle around 70k operations per second and if at some time in future you think you may hit these benchmarks it's much better to split your operations to multiple redis instances based on the kind of operations you perform. This will allow you to make decisions about availability and replication at a more finer level depending on the requirements. As a simple use case once your key size grows you may be able to flush your session app redis or shard your keys using redis cluster without affecting job queing infrastructure.
We have 75 (and growing) servers that need to share data via Redis. All 75 servers would ideally want to write to two fields in Redis with INCRBYFLOAT operations. We anticipate eventually having potentially millions of daily write operations and billions of daily reads on these two fields. This data must be persistent.
We're concerned that Redis locking might cause write operations to be repeatedly retried with many simultaneous attempts to increment the same field.
Questions:
Is multiple, simultaneous INCRBYFLOAT on a single field a bad idea under a very heavy load?
Should we have an external process "summarize" separate fields and write the two fields instead? (this introduces another failure point)
Will reads on those two fields block while writing?
Redis does not lock. Also, it is single threaded; so there are no race conditions. Reads or Writes do not block.
You can run millions of INCRBYFLOAT on the same key without any problems. No need for external processes. Reading those fields does not pose any problems.
That said, "Millions of updates to two keys" sounds strange. If you can explain your use case, perhaps there might be a better way to handle it within Redis.
Since Redis is single threaded, you will probably want to use master-slave replication to separate writes from reads, since yes, writes will generally block reads.
Alternatively you can consider using Apache Zookeeper for this, it provides reliable cluster coordination without single points of failure (like single Redis instance).