I understand that functionally Multi/Exec and Pipelining are designed to serve different purpose and features.
However, considering only performance for block writes, which would perform better. My understanding is that the Multi/Exec would create a single request, and Pipe lining will create individual requests, but will avoid RTT.
Multi/Exec is slower.
Multi/Exec also create individual requests which stored at server side that would be executed one by one when server receiving 'EXEC', and two more requests Multi & Exec.
Every request executed in transaction would check if the watched keys changed, which pipelining would never do.
Related
I will be storing key-value pairs in Redis but the number of keys will be just 4. Since there will be multiple processes updating the values parallelly, I plan to use Redis transactions using WATCH, MULTI and EXEC commands.
My algorithm is something like this:
GET key
WATCH key
MULTI
SET key new_val
EXEC
My main concern is that, since WATCH uses optimistic locking, when I will have multiple processes (much more than the number of keys, which are only 4) trying to update values, the transaction failure rate will be very high.
Is this correct? Is there a way to prevent this?
It is unclear why you'd need a transaction here unless you're doing something in your application with the reply for GET key. I'll therefore assume that you are using the value for something meaningful, otherwise, you can drop the transaction semantics and just call SET key new_val.
Optimistic locking is mainly intended to be used in cases where there's low contention for the resources. Since the use case that you're describing is clearly the opposite, it would probably result in high failure rates. This isn't saying that Redis and your application will not work, but it does mean there a potential for a lot of wasted effort.
I would advise that you consider switching if possible to using Redis' server-side Lua scripts. These are blocking, atomic, and let you read and meaningfully manipulate the data in Redis programmatically. See the EVAL command for details.
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 am currently implementing a server system which has both an SQL database and a Redis datastore. The data written to Redis heavily depends on the SQL data (cache, objects representing logic entities defined by a number of SQL models and their relationships).
While looking for an error handling methodology to wrap client requests, something similar to SQL's transaction commit & rollback (Redis doesn't support rollbacks), I thought of a mechanism which can serve this purpose and I'd appreciate input regarding it.
Basically, I intend to wrap (with before/after middleware) every client request with an SQL transaction and a Redis multi command (pipes commands until exec or discard command is invoked), and allow both transactions to occur only if the request was processed successfully.
The problem is that once you start a Redis multi command, you are not able to preform any reads/writes and actually use their values while processing a request. I reduced the problem just for reads since depending on just-now written values can be optimized out.
My (simple) solution: split the Redis connection into two - a writer and a reader. The writer connection will be the one to be initialized with the multi command and executed/discarded at the end. Of course, all writing will be preformed through it, while reading is done using the reader (executed instantly).
The down side: as opposed to SQL, you can't rely on values written in the same request (transaction). Then again, usually quite easy to overcome.
I used to use PostgreSQL sequence SELECT nextval('number'); to make sure I get a new number even if there are several clients since nextval is guaranteed to return distinct and increasing values.
I wanted to use the same mechanism with Redis.
I thought using the INCR command but I want to make sure I well understand that it will give the same guarantee than the nextval command?
There is no risk of getting the same number when several processes run this command or do I have to use a Redis lock?
Thanks
Redis is a single-threaded engine, so the execution of all commands is serialized. It always provides atomicity and isolation (in the sense of ACID) for each individual command.
The consequence is applying a single INCR command and exploiting its result is safe (even if multiple connections do it concurrently).
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).