from my understanding you get the ability to put the DAL into a "pause" mode where it queues all the api requests allowing you do to updates / upgrades to the database with no downtime.
It also gives you a way of controlling what queries are used by the API servers preventing a developer from doing silly things and creating a production outage
There are easier database-user level solutions to these problems than increasing ops-architecture with yet another layer of indirection. Personally this is why I hate dev-ops culture, no one knows how to use databases properly anymore.
> It also gives you a way of controlling what queries are used by the API servers preventing a developer from doing silly things and creating a production outage
Also called database roles. Do your DALs have full database admin credentials?!
> mode where it queues all the api requests allowing you do to updates / upgrades to the database with no downtime.
This is just a bad idea. Better idea: unless you are rewriting your entire schema from scratch, you should be able to use database views, database triggers, extra/duplicated columns and tables as you make schema swaps.
Is that a performance burden? Yes, though it is temporary and a lot less of a burden than a whole 'nother layer of indirection. Does this also allow the really nice feature of not stopping your entire system to change schemas? Yes. How about allowing testing new schemas in production piecemeal? Yes.
I think it's due to the trend against hiring DBAs. Nobody wants to put so much work into their databases anymore. I feel like that was half of the NoSQL craze, let's stop thinking about how to use databases because it's a pain.
After a while of slowly modifying and lumping more crap on a database, and it becomes a slow PITA that everyone is too afraid to touch, the usual result is to lift everything onto a new DB. DALs make this easier, but I agree that this should in no way be the point of a DAL. The point should just be to simplify & improve access to the database.
I think that's a result of the systems getting more complex and development models changing over time. You can either design one monolithic behemoth that can do everything by itself, or you use so many abstractions that you can assemble a different kind of behemoth piece by piece.
This is just a bad idea. Better idea: unless you are rewriting your entire schema from scratch, you should be able to use database views, database triggers, extra/duplicated columns and tables as you make schema swaps.
Even with tools like Liquibase, the more functionality you put in the database (views, stored procedures, triggers, etc.) the harder it is to do deployments and rollbacks and keep the code and the database functionality in sync.
Temporary development functionality, not permanent (I said "as you make schema swaps").
> keep the code and the database functionality in sync.
At any time there should only be a fixed number of versions of the code (ideally two: Production and Stage; and maybe a half, Development, if things go really sour). Hence the overhead for supporting that fixed number of versions should then be relatively constant. When a system is done and moved to maintenance mode you remove all of your temporary functionality and get the database back to it's optimal form for the current code.
Obviously it gets tricky when you are doing multiple products on the same database, or a very large database. But I don't see how a DAL delivers anything to those that just having clean well documented code and using existing database features doesn't.
Stable App w/ old schema -> add functionality to support new schema -> add new code w/ new schema -> add functionality to support old schema, migrate to new schema -> remove old app w/ old schema -> remove now vestigial functionality supporting old schema. Supports multiple versions of the code and supports rolling updates. If it's hard to keep track of that I don't know how to help you.
Like all things, it depends. Certain features/abstractions have more value at different stages of an application.
I LOVE using views early on in a new application's schema as it allows me to evolve the logical model separately from the physical model, and once I've coalesced on something I like it's easy enough to swap the view with a real table and my application code higher in the stack is none the wiser.
Even Facebook at one point relied on MySQL triggers to keep its memcache fleet synced.
Views aren’t quite as bad as stored procedures. I could see doing a view being decently easy to manage - you can always add s column without breaking backwards compatibility. But you can’t imaging the number of times I’ve seen things like...
It also makes changing your DB a lot easier since APIs using the DAL don't need to be updated since they're DB agnostic -- you "only" need to update the DAL API.
I've done it twice. If you're experiencing significant growth or change in access patterns, you may for example go from Postgres to a KV store.
In one of the cases where I had to switch, we swapped from Cassandra to S3 for 100x OpEx savings since C* couldn't scale cost effectively to our needs, so we rolled a database on top of S3 instead that well out performed C* for our use case (e.g. need to export a 3B row CSV in a minute?).
I'm sure there are rare exceptions but I would imagine if you dug deeply into the business rules around "I need to export a 3,000,000,000 row CSV file" and into what the users are actually trying to accomplish at the end of that workflow, you could find a solution that meets those goals better while also obviating the need to export a 3,000,000,000 row CSV file.
> you may for example go from Postgres to a KV store.
If its easy to do this then you are using a tiny fraction of Postgres.
If you want it to be easy to switch your database then you need to code to the lowest common denominator. I would rather use my databases to their fullest potential, rather than purposefully handicap myself because I might have to change it in the future.
I used to design systems so this was possible, but eventually realised it just wasn't needed - I was adding more abstraction and complexity for no reason.
I also don't think it's a good idea. If you don't use the database specific functions out of fear you aren't able to switch anymore, you are probably wasting a lot of potential performance.
It also gives you a way of controlling what queries are used by the API servers preventing a developer from doing silly things and creating a production outage