If you’re modeling relational data, it doesn’t seem like you can get around using a DB that uses SQL, which to me is the worst: most programmers aren’t DB experts and the SQL they output is quite often terrible.
Not to dunk on the lemmy devs, they do a good job, but they themselves know that their SQL is bad. Luckily there are community members who stepped up and are doing a great job at fixing the numerous performance issues and tuning the DB settings, but not everybody has that kind of support, nor time.
Also, the translation step from binary (program) -> text (SQL) -> binary (server), just feels quite wrong. For HTML and CSS, it’s fine, but for SQL, where injection is still in the top 10 security risks, is there something better?
Yes, there are ORMs, but some languages don’t have them (rust has diesel for example, which still requires you to write SQL) and it would be great to “just” have a DB with a binary protocol that makes it unnecessary to write an ORM.
Does such a thing exist? Is there something better than SQL out there?
I used to be full on the ORM train. Now I’m a little less enthusiastic. What I actually think people need most of the time is something closer to ActiveRecord. Something that can easily map a result set into a collection of typed objects. You still generally write parameterized SQL, but the work of translating a db decimal into the correct target type on a record object in your language is handled for you (for example). In .net, Dapper is a good example.
I also think most people overemphasize or talk about how other programmers “suck at SQL” waaayy too much.
IMO, for most situations, these are the few high-level things that devs should be vigilant about:
parameterize all sql.
consider the big-o of the app-side lookup/write methods (sometimes an app join or pulling a larger set and filtering in memory is better than crafting very complex projections in sql). This is a little harder to analyze with an ORM, but not by much if you keep the mappings simple and understand the loading semantics of the ORM.
understand the index coverage of queries and model table keys properly to maintain insert performance (monotonically increasing keys).
stop fixating on optimizing queries that run in a few seconds, a few times a day. Optimize the stuff that you run on every transaction - if you need to.
On most of those points, if you don’t have aggregate query counts/metrics on query performance on your clusters, starting to get cute with complex queries is flying blind, and there’s no way to prioritize what to optimize.
For the vast majority of cases, simple, obvious selects that don’t involve special db features are going to do the job for most applications. When the database becomes a bottleneck, there are usually much more effective ways to handle them than to try to hand optimize all the queries.
Lastly, I have a little bit of a theory that part of the reason people do/do not like looking at SQL in code is because it’s a hard context switch from one language to another, often requiring the programmer to switch to “stringly-typed” mode, something we all learn causes huge numbers of headaches in our first few months of programming. Some developers accept that there’s going to be different languages/contexts and not all of them are going to be as fluent or familiar, but accept that this is par for the job. Others recoil from the unfamiliar and want to burn it down. IMO, the former attitude is a lot more productive.
Without a DSL for writing SQL, any sufficiently complex program will end up with string concatinating all over the place. Basically, writing a language with ZERO checks or highlighting or anything. That’s asking for trouble.
But coming from Java, I agree that some ORMs go way too far.
It’s necessarily complexity that is easily encapsulated in methods.
If those methods are under test to verify their behavior, trivial typos can be detected instantly, without adding another dialect and more conceptual overhead to a project.
If those methods are not under test, then there’s a tiny bit of help by using a DSL if it can be compile-time checked.
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I used to be full on the ORM train. Now I’m a little less enthusiastic. What I actually think people need most of the time is something closer to ActiveRecord. Something that can easily map a result set into a collection of typed objects. You still generally write parameterized SQL, but the work of translating a db decimal into the correct target type on a record object in your language is handled for you (for example). In .net, Dapper is a good example.
I also think most people overemphasize or talk about how other programmers “suck at SQL” waaayy too much.
IMO, for most situations, these are the few high-level things that devs should be vigilant about:
On most of those points, if you don’t have aggregate query counts/metrics on query performance on your clusters, starting to get cute with complex queries is flying blind, and there’s no way to prioritize what to optimize.
For the vast majority of cases, simple, obvious selects that don’t involve special db features are going to do the job for most applications. When the database becomes a bottleneck, there are usually much more effective ways to handle them than to try to hand optimize all the queries.
Lastly, I have a little bit of a theory that part of the reason people do/do not like looking at SQL in code is because it’s a hard context switch from one language to another, often requiring the programmer to switch to “stringly-typed” mode, something we all learn causes huge numbers of headaches in our first few months of programming. Some developers accept that there’s going to be different languages/contexts and not all of them are going to be as fluent or familiar, but accept that this is par for the job. Others recoil from the unfamiliar and want to burn it down. IMO, the former attitude is a lot more productive.
Without a DSL for writing SQL, any sufficiently complex program will end up with string concatinating all over the place. Basically, writing a language with ZERO checks or highlighting or anything. That’s asking for trouble.
But coming from Java, I agree that some ORMs go way too far.
It’s necessarily complexity that is easily encapsulated in methods.
If those methods are under test to verify their behavior, trivial typos can be detected instantly, without adding another dialect and more conceptual overhead to a project.
If those methods are not under test, then there’s a tiny bit of help by using a DSL if it can be compile-time checked.