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.
Related
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
We are trying to implement caching for our multi-tenant application. We are planning to create new Redis DB for each tenant.
We have one scenario where we need to use Redis Transactions. While going through this post https://redis.io/topics/transactions, we found that
All the commands in a transaction are serialized and executed
sequentially. It can never happen that a request issued by another
client is served in the middle of the execution of a Redis
transaction. This guarantees that the commands are executed as a
single isolated operation.
Is this read blocking will only apply to database level or at full instance level?
The guarantee you quoted applies to the instance, not the database. A command for DB 2 will not run in the middle of a transaction for DB 1.
You can find more information about multiple databases (including an argument by the creator of Redis against using them at all) in this question.
What is the fastest way to send batch requests to Postgres database using Golang? Each request contains 500-200000 rows.
Methods I know about are-
1. Using database/sql package's transaction Begin, Prepare, Commit.
2. Sending all data in one statement.
3. Sending a list of statements using sql.Exec() method.
Is there some other way to send batch requests without making a connection at every statement? If not which is the best way of these?
This question is similar to question at- Golang how do I batch sql statements with package database.sql
There is a bit old depesz blog post on that. His programs are Perl scripts, but if you concentrate on SQL... Anyway - from DB perspective, you can use COPY, or INSERT with many rows in VALUES. It looks that around 20 is good choice, but it is worth to test that in your case. If performance is key factor, I would put around 2000-5000 rows per transaction. Also, from DB perspective transaction, and session are two separate things. So you can open session, and to many transactions in it.
For PostgreSQL starting new session per operation is really bad idea - DB spawns new process for each session. One of answers for the question you referred contains this. So you open connection, and then transaction, as it should be done.
Let's assume we have some configuration GUI that in its current form uses direct DB transactions to submit new configurations for more than one configurable component in a consistent manner.
Now let's move the data (DB) stuff behind some SOAP/WS API. The GUI has no direct DB access anymore. The transactional behaviour must remain, but the API should NOT be designed to explcitly accommodate the GUI form submissions. In fact, I don't even know how the new GUI will work or how the user input will be structured. Therefore I need to provide something like WS-AtomicTransaction on the API server side. However, there are (at least) two caveats:
The GUI is written in PHP: I don't think there is any WS-Transaction support in PHP available.
I don't want to keep DB transactions open on the server side while waiting for additional client requests.
Solutions I can think of:
using Camel's aggregation. However, that would make things more complicated in at least two ways:
You cannot use DB row ids of newly inserted rows in the subsequent calls inside the same transaction. You need to use some sort of symbolic back-referencing because there would be no communication between client and server while processing the aggregated messages.
call replies would not be immediate (or the immediate and separate reply to each single call would only be some sort of a stub, ie. not containing any useful information beyond "your message has been attached to TX xyz" -- if that's at all possible in the Camel aggregation case).
the two disadvantages of the previous solution make me think of request batches where possibly the WS standards provide means for referencing call results in subsequent calls inside the batch transaction. Is there any such thing already available? Maybe even as a PHP client?
trying to eliminate lock contention in the database by carefully using row-level locks etc. However, when inserting new elements, my guess is that usually pages and index pages need to be locked by the DB.
maybe some server-side persistence layer using optimistic locking? But again, that would not return any DB IDs back to the client before the final commit if DB writes would be postponed until the commit (don't know if that's possible at all).
What do YOU think?
Transactions are a powerful tool and we easily get into a thinking pattern in which we see every problem as a nail we hit with this big hammer. I can relate to your confusion because I've experienced it myself. Unfortunately I have no better advice for you than to try not think in terms of transactions but of atomic API calls.
When I think in terms of transactions, my thought pattern usually goes like this:
start transaction
read (repeat as required)
update (repeat as required)
commit/roll back
It takes some time to realize that we overuse this pattern. Actual conflicts are rare and there are many other ways of dealing with them. Here is a commonly used one in APIs
read and send data to client (atomic API call)
update data (on the client)
send original + updates back to the server (atomic API call)
start transaction (on server)
read
compare with original from client
if not same, return error (client should retry)
if same, update
commit
The last six points are part of the implementation of the API call.
Ferenc Mihaly
http://theamiableapi.com
I'm looking for a way to continue execution of a transaction despite errors while inserting low-priority data. It seems like real nested transaction could be a solution, but they aren't supported by SQL Server 2005/2008. Another solution would be to have logic to decide if an error is critical or not, but it would seem that's not possible either.
Here's more detail on my scenario:
Data is periodicaly inserted in the database using ADO.NET/C#, and while some of it is vital, some could also be missing without problems. When the inserts are done, some computations are made on the data. (Both vital and non-vital) This whole process is inside a transaction so everything remains in synch.
Currently, transaction save points are used, and partial rollbacks are made on exceptions which occur during non-vital inserts. However, this doesn't work for "batch-abort" errors, which automaticly rollback the entire transaction. I understand some errors are critical, but things like failed casts are considered by SQL Server to be batch-abort errors. (Info on batch errors) I'm trying to prevent these errors from bringing down the whole insert if they occur on low priority data.
If what I'm describing isn't possible, I'm willing to consider any alternative way to achieve data integrity but allow the failure of the non-vital inserts.
Thanks for your help.
Unfortunately, can't be done as you describe (full support for nested transactions would be key here). Couple things I can think of that have been used to get around this in the past:
Best option would probably be to separate the commands into important/non-important commands that could be executed distinctly, naturally this would require that they not be order-dependent on each other
Could also use a messaging based approach (see Service Broker) where you would execute the primary commands inline and push the non-primary commands onto a queue for execution later/separately. The push to the queue would be transactional within the batch, but the execution of the command when you pop off the queue would be separate. This too would require they not be order-dependent on each other.
If order-dependent, you could use the messaging approach for everything, which would ensure order and could have separate messages per operation, then grouping them together (via conversation groups) would allow you to pull them off the queue in order as well and use separate transactions for each 'type' of operation (i.e. primary vs. non-primary). This would require some special coding on your part if all the grouped messages must be a single autonomous operation, but could be done.
I hesitate to even mention this option, because it is a terrible option, but for full disclosure I suppose you could consider it at your discretion if you think it fits (but it is definitely not an architecture that would apply to almost any scenario). You could use xp_cmdshell to call out to the command line and execute sqlcmd/osql for the non-critical tasks - this sqlcmd execution would be in a separate transaction from the module you are executing from, and simply ignoring the xp_cmdshell failure should allow the primary batch to continue.
Those are some ideas...
Can you do your import into a temporary location, using transactions only for the important parts. Once the temp location loaded, having absorbed any non-critical errors, you can copy the data into its final destination in a single transaction. Depends on the nature the work you are doing, but potentially a viable option.