ORMs Are Backwards
abe-winter.github.io
abe-winter.github.io
So I disagree with most points.
The Django ORM does map pretty neatly to SQL (in my opinion), and especially Postgres. The migration tool is anything but "half-baked". And I have observed that beginners have an easier time creating Django models than dealing with straight SQL.
On top of that, the Django ORM mitigates most SQL injection attacks out of the box, without even requiring the developer to know about SQL injections in the first place.
And the ORM enables elegant code reuse by constructing query objects that can be used in Template logic, Form validation, generic CRUD, REST endpoints and more.
I agree that most ORMs don't help you with multiple programming languages. Though both the Django ORM and SQLAlchemy allow to customize a lot of the naming conventions and can be adapted to any legacy schema.
But then again: Is it even a good idea to access the same database from two codebases at all?
Some of the concerns about "ORMs kill the SQL-star" can be boiled down to a lack of a general understanding of relational databases in developers who learn to use an ORM before diving deeper into the philosophy of relational databases.
I think I know SQL fairly well, but I don't see how I could implement things like django admin, form validation, template views etc as elegantly on top of plain SQL.
So I really don't agree with "ORMs are only useful for SQL-noobs".
This doesn't even make sense. It kills the SQL duds, stars are even more important to understand the convoluted SQL when the ORM isn't working as expected, and cases where the ORM can't handle at all (there are always some).
That's not my experience. In ~20 projects in this company, with pretty complicated GIS-related web apps, there have been about 4 cases where I need to create a raw SQL query because Django's ORM didn't do what I needed. Almost all the time, it just works.
Because for pretty obvious reasons in the first case you'd want computation on the back-end. Sure you can do a lot of spatial operations with python after passing through an ORM... but is this really more adapted than raw SQL & PostGIS? Genuinely curious.
What users want to compute can be defined by users, e.g., "take all houses in the Netherlands as polygons, compare with a DEM of the Netherlands to find the lowest point of the house's contour, compare that point to a water depth raster to see if it would be inundated" is a computation a user could define in our GUI and the actual computation would then run in (Python) background tasks.
I work in GIS field and I tend to put a lot of business logic into PostgreSQL because I can re-use all functions/view in a variety of tool from Desktop GIS, BI and even (shamefully) Excel for some our oldest App.
Whenever we'll move to a new webmap or I we'd want to serve an API it's always one connector away. All business logic put into the DB is heavily reusable because a lot of tool "speak" SQL.
I guess in the end it really depend on wether you approach is data centric vs functionality centric.
Actually I'd be OK writing SQL. It's just inconvenient having to decode the results and handle migrations manually. I did plain SQL migrations before ORMs. That's not something I want to be back to.
I also don't subscribe to the view that Django is too complicated. With other frameworks you end up implementing part of Django, and most of the time that will be worse in every conceivable way.
s/application/day/
The "ORMs are bad" articles usually boil down to "My ORM is bad" or "I tried to use an ORM where it doesn't really fit and now I believe all ORMs are bad".
It's an integration pattern. It works in some contexts.
Could you share some resources of using it properly for advanced use cases? Good cookbooks etc.
For, it's always been a huge footgun, coupled with Python's dynamic nature, it's and endless source of bug after bug... Mistype a property on a model instance - no problem, no error, it will just not get saved to the db and data will be lost. Use progres native json and array fields - good luck running your tests on sqlite, the ORM that was helpful up to here just gives up and tells you "f u buddy, you're on your own". Need to write complex hand-coded-sql queries that return complicated data, but you's still want to be able to serialize them into a graph of objects that can then be modified by regular ORM code - good luck with that, nobody cared about this scenario.
They've somehow managed to make it too simple and too complex at the same time! Oh, and don't expect to just click a few jump to definitions an make sense of the ORM's inner code easily, god forbid :|...
While I like the idea of ORMs, I found them all totally "wrong headed" And SQLAlchemy - that's just not worth the effort of learning to use it properly despite its good ideas, if you're only going to end up using 10% of its functionality and in an idiosyncratic way that was not intended.
you're not testing what you actually run in production, then what are you testing? If you just need to test in-mem behaviour, mock the DB completely; instead of expecting compatibility for vendor specific features across vendors.
Most of the code I write in Django would never need to be SQLite-compatible, even if a lot of it would be. The SQLite compatibility is almost like a relict from a time when setting up RDBMSs was difficult and slow.
Then don't run your unit tests against SQLite, run them against Postgres.
I'll definitely look into this, not a solution for existing projects bc of legacy baggage, but probably the right way to do it. Problem in practice is that you yes what you really want is to mock the db completely, but practically speaking existing code is to entangled with the db adapter specifics, so you end up with running with a "fake real db" :|
It's definitely slower than mocking, and therefore not really suitable for unit tests - but it's great for repeatable, isolated integration tests (continuous or otherwise).
Everybody prefers anything that does a barely decent job at preventing you from writing SQL manually :)
If you set the bar that low then sure, you'll have an "excellent opinion" of ANY ORM. Can you give an example of an ORM you didn't prefer to writing SQL manually for 50% of the cases? - most that were that bad are probably not in usage anymore...
No, he's not. Almost the entire Django community has a good opinion of it.
The main criticisms I see of Django's ORM seems to be from people that prefer SQLAlchemy. They have different design philosophys and the distinction seems largely a matter of taste and differing use-cases.
...then I'm in the minority of hating them both, and in the process of looking for an alternative since I'd be using a lot of python too for the foreseable future :|
I liked web.py's database abstraction layer a long time ago - know of anything similar in philosophy but more powerful?
DAL was the first Python db abstraction I used, and I liked it well enough, but I've since used both Django and SQLAlchemy, and they both seem less opaque to me, maybe because not obscured by some of the weird things about web2py itself.
I love the auto-generated backwards references you get in Django and SA when you use foreign key and many to many fields. They make life so much easier. I don't recall how that was achieved in DAL, I just remember it being... complicated.
I suspect "raw sql" to be a bigger headache and source of bugs in any moderately complex project.
Most of the use-cases of queries are actually very simple, and there the ORM allows you to simplify the code a lot.
My hints how to enjoy the ORM more: Write custom managers and QuerySets (for example for commonly used filters), write generic views that take QuerySets, take advantage of Models and QuerySet in Form Validation and REST APIs.
If you use the Django ORM like you would concatenate raw SQL, then it ain't gonna be pretty...
I write web applications / data tools with raw SQL and I find that only about a quarter of the queries are simple. Many have subtle specificity to the joins, subqueries and window functions that return for me almost exactly what I want to output.
The problem I see in most people's ORM-powered code is that they lean so heavily on application code to get all the data assembled in the right place and in the right order, resulting in scaling problems
> concatenate raw SQL
Often dangerous and almost never needed. Parameterised SQL is the right approach.
Depends on what exactly you mean here. The Django ORM fully supports some things you wouldn't initially expect if you hadn't seen it before, like passing one queryset into the filter args of another. It'll actually compose the queries and run it once on the database, instead of running the first and passing the result into the second.
> Could you share some resources of using it properly for advanced use cases? Good cookbooks etc.
The official documentation covers advanced use cases. Stackoverflow will help you shape up more complicated queries.
> Mistype a property on a model instance - no problem, no error, it will just not get saved to the db and data will be lost.
That's a drawback of most dynamic languages, not a flaw of Django's ORM.
> Need to write complex hand-coded-sql queries that return complicated data, but you's still want to be able to serialize them into a graph of objects that can then be modified by regular ORM code - good luck with that, nobody cared about this scenario.
Maybe that's the threshold Django ORM devs thought sensible to stop supporting? At some point, mixing hand-coded-SQL and ORM code introduce a whole set of hard questions and decisions. Very few people expect ORM code to properly play with hand-coded-SQL. If you need to switch back to SQL, you are generally on your own.
> They've somehow managed to make it too simple and too complex at the same time! Oh, and don't expect to just click a few jump to definitions an make sense of the ORM's inner code easily, god forbid :|...
It's simple to use, yet have complex internals. That's about what I would expect from a tool abstracting something complicated.
> That's a drawback of most dynamic languages, not a flaw of Django's ORM.
In Python, mistyping a name usually results in an exception at runtime. If Django doesn't check models for correctness, it can't be explained by being written in a dynamic language.
If you mistype a property on a nullabe or already filled property of a model instance, Django will happily save the model with the mistyped property (which will be ignored because it's not part of the model) without actually modifying anything in database.
Calling that behavior a flaw of Django's ORM is disingenuous.
Sure it does. It doesn't on assignment, as you can modify the object however you like, but it does have AttributeError for accessing a property that doesn't exist.
>>> def with_slots(**kwargs):
... class SlottedClass(object):
... __slots__ = kwargs.keys()
... def __init__(self, **kwargs):
... for k, v in kwargs.items():
... setattr(self, k, v)
... return SlottedClass(**kwargs)
...
>>> with_slots(foo=1, bar='qux').zork = 4
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'SlottedClass' object has no attribute 'zork'
Again, this can be caught by eye, by testing and probably soon by static type checkers.
Test your code. Use an IDE. PyCharm highlights these errors immediately.
> Use progres native json and array fields - good luck running your tests on sqlite,
You're using stuff from the Postgres contrib package on a different db. It's pretty clear in the docs about that
> Need to write complex hand-coded-sql queries that return complicated data, but you's still want to be able to serialize them into a graph of objects that can then be modified by regular ORM code
Make a view and manage it through the ORM? If you're updating data from this, then what you're doing seems really strange.
Its easy to use and makes inserting / updating data fast and easy. It's simple and fast for simple queries. It starts getting a bit crap when you need complex queries. Annotations / GROUP BY stuff isn't intuitive.
It does make it easy for inexperienced people to generate huge numbers of queries by looping through querysets and getting field from another table.
It lets you run raw SQL through it and prevents SQL injection, so its really a case of knowing when to switch to that.
Parameterised queries are straightforward in all competent languages. If Python can't do it natively, switch to a better language that can.
I've taken a look at a couple of Django's most popular ORM options and I suppose if I was a mid-level corporate cog writing CRUD interfaces for Stack Overflow tutorial-grade operations like customer-sale-item-product relationships it would probably work fine. I've only had passing exposure to that kind of soul-crushing drudge work.
For the way I work an ORM is baby stuff, and I'm not even doing anything particularly advanced or sophisticated. I maintain (among other things) a relatively unremarkable bespoke online discussion forum. It comprises hundreds of queries, few of which could be composed correctly by an ORM, let alone composed and run performantly. The median complexity query in my code base probably has two or three joins and two or three subqueries—at least one of which has some kind of tangential aggregation or window function.
https://www.reddit.com/r/iamverysmart/
> a relatively unremarkable bespoke online discussion forum.
>It comprises has hundreds of queries
Sounds like you have a design issue. Making the unremarkable complex.
(For the record, complexity and remarkability aren't strong correlates. And it's not particularly complex; probably 90% of the app's queries are written for indirect tasks like moderation tools, automations, statistical reports and various stratum of anti-abuse mechanisms. Most common pages only have one security-related query and one content-related query.)
[0]: https://medium.com/@iMitwe/been-working-with-django-for-over...
- data models in separate files one for each model, under a data_models/
- business logic models that may or may not map one to one with data models under a models/ that would content roughly framework-independent code
- encourage "fat apps"
- figure out some way to let the db be the source of truth and allow "multiple services, one DB" scenarios - maybe having "core models" for which all db migrations are excluded, table names fixe, and possibly extra columns that the django app could ignore but other services could use, and "app/project models" for which regular django migrations would run
I might write it up after I get the boilerplate app, and I'm really interested in hearing opinions for people with opposing viewpoints especially!
The main point is that it can result way less code, think of all the APIs between microservices that would never need to be developed or documented or maintained because they'll never exist, yet the problem they would've solved will be addressed :)
Doesn't work all the time, but if most of what you need is data-sharing, and there's a clear model of "this service produces this data, which has a clear documented fixed format, all others just consume it", you'll prevent thousands of LOCs from being written. And I do see my mission more and more as preventing (useless) code from being written nowadays...
[0]: http://docs.peewee-orm.com/en/latest/ [1]: https://stackoverflow.com/questions/57602206/mysql-gone-away...
> Need to write complex hand-coded-sql queries that return complicated data, but you's still want to be able to serialize them into a graph of objects that can then be modified by regular ORM code - good luck with that, nobody cared about this scenario.
It's been built-in since at least 1.7, and I think I remember it since 1.4: https://docs.djangoproject.com/en/2.2/topics/db/sql/
A motivation for relational algebra was different applications accessing data in different ways. This integration remains a selling point. Further, data often outlasts applications... even languages.
Row-level permissions are possible, but it can get more complicated when certain users are only allowed to change a certain row in a certain kind of way but not in another.
Providing some kind of API as a library, REST, RPC or whatever would almost always be preferable, if only to centralize permissions and validation.
Yeah, if you're migrating code between languages and platforms and want to run both simultaneously so you can go live with a subset in the new platform and phase out the legacy system in stages.
I think that depends entirely on who you ask. Some orgs may have dedicated DB guys who are designing all the DB schema and stored procedures that must be vetted by the DB guys before the application guys can merge in changes. Others might decide dev teams should be responsible for their own data and store it as they see fit. I think product data should only be updated by one main API and that API exposed internally for other tools to interact with, unless it's something that is special. And in development, there are many special cases.
Also, there is a whole section called "ORMs take over connection management and migration" and not a single point as to why connection management handled by the ORM is bad, only comments on migrations. Moreover, migrations are not an unsolved problem at all.
In Ruby, ActiveRecord solves all scenarios I have encountered gracefully, and trust me, I have dealt with tons of edge case projects.
For Python, I recommend Alembic: https://pypi.org/project/alembic/
My preferred solution for existing DBs is Ruby's Sequel or ROM if you want to go nuts. But I'm a diehard Rails lover and will choose ActiveRecord over all other solutions if I can. To me there's only one decent ORM and everything else is either good for special purposes or junk.
The biggest problem for me is this "proxy" behaviour. When you start treating objects as proxies for database rows, your database logic inevitably leaks into your business logic. Someone returns an ActiveRecord `User` object from a database method and now random other parts of your application are intricately linked to your database implementation.
On top of that you suffer terrible performance problems. Even if you're smart enough to avoid the whole N+1 query issue, you lose control (and more importantly visibility) over where database updates are coming from, and your ORM is either not smart enough to efficiently apply updates, or so smart that building the query takes longer than actually running it! (I have this problem with SQLAlchemy right now, where it's taking several seconds per request CPU bound in the ORM). Undoing this is literally impossible without a near total rewrite.
And it all serves no purpose! The application-specific interface to its database is usually quite simple. You can just have it be a literal interface/trait/whatever and implement each method by executing a bit of SQL, or calling a stored procedure, or however you like. Arguments and return values should be plain-old-data (no magic proxy objects) and each method should correspond roughly to a transaction so that the rest of the application doesn't have to worry about that database-specific stuff.
There are a ton of other benefits to doing stuff this way, too many to list here, but it really helps with complex migrations (like if you need to migrate from one database to another), maintainability, testing, etc.
Sprinkling calls to order.save! or order.update(foo: bar) in random places is a recipe for disaster.
CPU time building a query is rarely the business bottleneck until you're at a huge scale - scaling up the engineering team uniformly and having business logic at abstraction level closer to the rapidly evolving Product specs is the bottleneck that ActiveRecord solves for, and does pretty well.
If you'd like something that avoids object/hidden state - check out how Elixir/Phoenix's Ecto[1] was designed - it avoids many of the shortcomings of ActiveRecord and SLQAlchemy:
Once you fetch something from the db, you have it in variables in memory. With or without an ORM. That could no longer be sync'd with the db a ms later. That you will probably re-use for more than one access (if you need it more than once), because issuing the same SQL again the second time you need to reference any data returned, in a given routine, would be insane.
But if I had to increment specifically, and were using ActiveRecord... I'd use the ActiveRecord increment! method. Which has the proper db-atomicity semantics you want, it does execute `UPDATE x SET n = n+1 WHERE pk = y ...`
MyModel.objects.filter(pk=...).update(myfield=F('myfield') + 1)
Having to think about these things because the abstraction is leaky instead of just using the SQL where the transformation is explicit is not worth the hassle.
Using Clojure and yesql was a real pleasure for this - Clojure is immutable so you're working with values all over the place - and working with SQL query is just passing values in it and getting values out - no magic mapping - it's just values - if you want the latest value - query again, if you want to store a new value - send the new value. No mutation, no magic, just data.
Nope. The DBMS is making the same transaction isolation guarantees to an ORM as to any other client.
I think the larger "source of truth" issue is how the schema is represented.
All of these systems that provide their set of schema objects that are then pushed to the database. This works fine for LAMP stacks, but as soon as you grow it becomes apparent that you've put the horse before the cart.
for the write? writes are not usually much of a performance issue except when people are inserting thousands of rows. You can replace flush with direct INSERT/UPDATE or use the bulk API. Pre-setting primary key values will also improve performance by an order of magnitude. Share some of your code and profile results on the mailing list and we can look into speeding it up.
SQLAlchemy always provides many techniques to optimize areas that have performance problems, not to mention the primary direction for most major releases is that of new performance features and improvements year after year, despite having to deal with the dog-slow cPython interpreter. No "total rewrite" of your application should be needed.
This is not just for writes, but also for loading complex relationships from the database: we've gone to great lengths to ensure we're using relationships optimally so as to cache query results and not fall into the N+1 query problem, but our application spends all its time inside SQLAlchemy.
The only way to get the performance improvements we needed was to stop using it as an ORM altogether. However, the ORM was too intricately tied with our business logic to do that without a near total rewrite.
What we've actually ended up doing to do this rewrite more incrementally is to create a Rust service which implements just the "GET" end-points: this gives us an order of magnitude performance improvement despite it using the exact same database and fetching the exact same data. Most of the business logic did not need to be duplicated as it is not used from "GET" requests, but users of our application will get much snappier loading times, and it will generally feel much better.
it most certainly does provide many techniques to reduce in-Python computation. Have you read the performance section in the FAQ and worked through the examples given ? Have you looked into baked queries , querying for columns and not objects ? These are often quick wins that make a huge difference. The baked queries concept is going to be much more cleanly integrated in an upcoming release.
> This is not just for writes, but also for loading complex relationships from the database: we've gone to great lengths to ensure we're using relationships optimally so as to cache query results and not fall into the N+1 query problem, but our application spends all its time inside SQLAlchemy.
that's typical because a CRUD application is all about its object model and SQLAlchemy is doing all of that work. if you wrote your own object layer using Core then you'd see all the time spent in your own objects<->core layer. This is just the reality of Python.
Here's a runsnakerun I make some years ago of the PyMYSQL driver loading data over a network-connected database: https://techspot.zzzeek.org/files/2015/pymysql_runsnake_netw... Notice how just the Python driver spends TWO THIRDS of the time an the actual waiting for the database 1/3rd ? that's Python. If the database is on localhost, that pink box shrinks to nothing and all of the time is spent in Python, just reading strings and building tuples. It's a very slow language.
Rust language however is blazingly fast and is one of the fastest languages you can use short of straight C code. This is not the fault of an ORM, this is just the reality of Python. You certainly wouldn't think that if you used an ORM that runs under Rust, it would be as slow as your Python application again. It would continue to be extremely fast. The Hibernate ORM is an enormous beast but runs immensely faster than SQLAlchemy because it's running on the JVM. These are platform comparisons. SQLAlchemy is really fast for the level of automation it provides under pure Python. You made the right choice rewriting in a fast compiled language for your performance critical features but that has little to do with whether or not you use an ORM. As long as your database code was in your Python application, you'd end up writing an ORM of your own in any case, it would just be vastly more code to maintain.
Most of those techniques boil down to "use SQLAlchemy as a query builder rather than an ORM" - something I wholeheartedly agree with and would love to do, but it is next to impossible because these ORM objects have leaked into the business logic of the application.
> that's typical because a CRUD application is all about its object model and SQLAlchemy is doing all of that work. if you wrote your own object layer using Core then you'd see all the time spent in your own objects<->core layer. This is just the reality of Python.
I'm not trying to say SQLAlchemy is a bad implementation - it's a great implementation with tons of powerful features. I just believe that using anything as an ORM is a bad idea and directly leads to these performance and maintainability issues. Whether that's ActiveRecord, SQLAlchemy or even if someone implemented a similar ORM in Rust.
Some of the performance issues are due to python, but that's not the whole story: if I use SQLAlchemy as a query builder, and only return plain-old-data, I do not see nearly the same performance issues. Not because SQLAlchemy ORM is badly written, just because it has to do more work, create more objects, with more "magic properties", maintain more entries in the session, etc. etc.
Indeed. Its a rookie mistake but hey, you work with rookies all the time! Orms can be useful but they certainly allow for some dangerous patterns that are hard to notice if you're a young developer.
SQLAlchemy author here. I can't imagine how someone would not consider SQLAlchemy to treat "SQL" as the "source of truth" as well. All of SQLAlchemy's constructs map directly to SQL syntactically. Our most grumpy users are the ones who don't know SQL. This blog post would certainly benefit from some actual examples. "ORM-light tools that coerce responses into native structs and allow for type-checking are less offensive to me." - that is...an ORM?
There is certainly a lot of information in a schema, and you definitely want your ORM's idea of the database and the database's idea of it to be in close alignment, but I feel like any serious attempt would find all sorts of holes like the above when it actually came to doing it.
Is multiple applications talking to a shared DB schema a common practice, especially nowadays?
In my mind, each app/service should have a DB schema which only it talks to, and other apps/services needing data in that DB go through services exposed by that app/service (REST, RPC, GraphQL, whatever) rather than talking to its DB directly. That means you can rearchitect the DB schema, change which DB you use completely, etc., and only the app/service which owns that DB needs to be modified.
>"ORM-light tools that coerce responses into native structs and allow for type-checking are less offensive to me." - that is...an ORM?
Yeah...its a light ORM that focuses on turning a result set into an object but not the syntax remapping of queries. Object mapping is almost universally liked but ORMs usually include query syntax mappings and not the addition of a transaction lifecycle into your data objects.
For some, the database is not actually a database, per se, it's just a place to persist state. Usually folks in this camp think in an object-oriented way. They haven't read Codd's paper, they don't care about the relational algebra, they just want some way to take bags of properties and stick them somewhere that offers random access with good performance and is reasonably unlikely to not spontaneously erase data. If you're in this camp, ORM works great, because there isn't really much of an object/relational impedance situation in the first place. The data store wasn't structured in a way that creates one, there's no need burn a bunch of effort on doing so, and dragging in the whole relational way of doing things is like dragging a wooden rowboat on a hike through the forest because you'd like to use it to go fishing on the off chance you find a nice pond.
Others see a lot of value in the relational model. They're willing to put a bunch of time and effort into structuring the data and organizing the indexes in ways that will allow you to answer complex, possibly unanticipated questions quickly and efficiently. They, too, hate the syntax for recursive common table expressions, but are willing to hold their nose and use them anyway, for various reasons, but mostly because people think you're really cool when you can spend 30 minutes and make something go 2-3 orders of mangnitude faster than it used to. They don't think of the data in terms of bags of properties, they think of it in terms of tuples and relations and a calculus for rearranging them in interesting and useful ways. For them, there is potentially a huge problem with object/relational impedance; the data's organized in ways that just don't fit cleanly into Beans.
The thing is, neither of these ways of using a database is wrong. Each has it strengths and weaknesses. The trick is figuring out which way fits your business needs. Well, that's the easy trick. The hard trick comes when someone on your team doesn't understand this, believes there is one universal solution that will work for everyone, and is hellbent on jamming the One Righteous and Holy Peg, which happens to be square, into a round hole.
If you're thinking in the first terms, is a relational DB the right backing store, or wouldn't it be better to back your DB with something more like Mongo?
Do they still obtain some benefit from the schema, since from time to time the semantics of a property will change, and a schema can tell them what the current shape of the data is and guide the migration. Or would they prefer to just write a new property which does a lazy conversion from the old terms? (To an extent, I suppose the answer to this question determines the answer to the first. But I guess there's other tradeoffs to Monggo I'm not aware of, since schemalessness fills me with fear and I just don't want to look there.)
Regarding your final paragraph: I suspect that, for many apps, the choice between relational vs transparent persistency is largely determined by the team who is working on it. The "hard trick" is therefore trying to balance a strong personality with a strong view who disagrees with the rest of the team who have weaker personalities and weaker views, but who all agree on the other side of the fence. This is simply a standard management question with very little technical relevance.
Certainly relational like for 90% of the cases, if not all.
The relational model is THE answer to nosql from the start (ie: it was the solution of the originals "nosql").
Is totally more flexible, powerful, dynamic, expressive... And that without talking about ACID!
You can model all "nosql" stores with tables. With limited exceptions it will work very fine for most uses...
> This is simply a standard management question with very little technical relevance.
I don't get what your are implicating here...
But nosql solutions are the ones to be suspected and the ones to requiere a harder qualifications and justifications to use. Is the wrong choice in the hands of the naive. "NoSql" is for experts and for niche/specific workloads.
Even if you're working under the first model, there's still a lot an RDBMS can do to help you ensure data integrity. Largely by being less flexible. Databases like MongoDB allow for a more fexible schema, at the cost of pushing a lot of the work of ensuring data integrity into the application code.
For my part, I do a fair bit of working with databases that were built on the MongoDB of the '90s, Lotus Notes, and I've seen what they can grow into over the course of 25 years. It's not pretty. That experience has left me thinking that, while there's certainly a lot of value in the document store model, I wouldn't jump to a document store just because I don't need everything an RDBMS does. I'd only do it if I actively needed a document store.
Another question I'd ask is whether you're expecting to deal with millions of rows/objects or hundreds of millions. Modelling relational data correctly can have performance impacts in orders of magnitude.
When I look at most projects, I instinctively begin by modelling the data structure; then I think about why/when/how data can move from one state to another; then I think about how an application could prod the data between these states; then I build code which runs against the data.
If your application isn't data at its core (e.g. a document-based app) then it probably makes more sense to treat data elements as objects and use a CRM (or similar) to store and retrieve the objects.
The former almost invariably spurt out inefficient queries, or too many queries, or both. They usually require you to let the ORM generate tables. If you just want to have your object oriented design persist in a database, that's great.
The latter almost invariably results in trying to reinvent the SQL syntax in a quasi-language-native, quasi-database-agnostic way. They almost never manage to replicate more than a quarter of the power of real SQL, and in order to do anything non-trivial (or performant at scale) they force you to become an expert SQL and/or how it translates its own syntax into SQL.
And once you become more expert at SQL than your ORM, it's not long before you find the ORM is a net loss to productivity—in particular by how it encourages you to write too much data manipulation logic in code rather than directly in the database.
For the projects I've worked on, I've almost never wanted to turn data into objects. And on the occasions when I've thought otherwise, it has always turned out to be a mistake; de-objectifying it consistently results in simpler, shorter code with fewer data bugs.
I tend to find that the longer data spends being sieved through layers and tossed around inside your application, the more data bugs you'll end up having. It's much better to throw all data at the database as quickly as possible and do all manipulation within the database (where possible) or keep the turnaround into application code as short as possible. It means treating read-only outputs/reports more like isolated mini-applications; the false nirvana of code reuse be damned.
And that doesn't mean replacing an OOP or ORM fetish into a stored procedure/trigger fetish. It means realising that if your application is data at its core, it's your responsibility as a programmer to become an expert at native SQL.
The problem is that far too few programmers realise how deeply complex SQL can be; it's treated like a little side-hustle like regular expressions, when for so many programmers it's the most valuable skill to level up.
I tend to hand-write almost all of my migrations and many of my queries that synthesis data from multiple tables to reach a conclusion. I can think of only a handful of times where it was worth writing custom code to persist state (usually only when there are a large number of records that need a couple of specific fields updated.)
Like many tools, it all depends on how well it is used and how well it fits its use-case.
> The trick is figuring out which way fits your business needs.
Rule of thumb - if you don't control the database, use an ORM; if you do control the database, work directly with it.
For example, let's say that your business is a software company that sells an on-prem product. Some of your customers have Postgres expertise, some have MySQL expertise, some MSSQL, some people are stuck on Oracle. Forcing customers to develop DBA expertise in a database they're not familiar with just for the privilege of buying your product is a sales disaster in the making. So you go with an ORM and set up QA testing that tests releases across all of the databases that you support, and the ORM helps you by making it much more likely that your development efforts will automagically succeed in working with each of the supported databases.
In most other situations, though, it makes much more sense to start with the data design. If your business grows, your databases are going to grow. You are almost guaranteed not to switch databases (absent overwhelming financial need, see: Oracle) over the lifetime of your company. Data analysts (data scientists now?) can extract serious value from your databases by getting into the weeds of the database schema, indexes, and queries and working with developers and DBAs to optimize them for business reporting. If you give up control to an automated tool that knows nothing about your business, your business will be less competitive as a result.
Data is far too valuable these days to refuse to develop expertise with the underlying databases.
Maybe another customer has bought Oracle and the installation still has room. Also, they have a custom backup scheme that takes their load patterns into account.
Absolutely not worth it, in my experience.
But this is the stack I often work with now:
- Postgres
- Django ORM
- Django REST Framework
- Auto-generated OpenAPI Typescript functions
- A Redux store
- My React app
In other words, four ORM-like non-relational frameworks stacked on top of each other between Postgres and my app.
A generic solution for "complex, possibly unanticipated questions" would need to work through all of those layers.
But I've worked places where dba refused to use version control.
I bought into the idea myself for a while, but when I interrogate my belief there it was just something I picked up at uni as part of the general 3-tier approach, and I'm not sure how much of it is really practically grounded. I see the same ideas held dogmatically by newer employees, and when I ask them about it in detail, I see the same fuzziness.
After all, if you want to really be pedantic, then you could claim anything beyond having a single table with two columns, "key" and "value" is pushing BL into the DB.
I.e, what practically does the separation gain you? You may want to swap out the DB in future? Almost never happens, not without some significant other refactoring going on (e.g breaking out into separate DBs because the product's grown to the point of needing distributing across services, etc). If you want to really design with that in mind, you're almost certainly giving up a lot of functionality that the particular DB is going to give you. It's on the level of holding onto the possibility that you'll want to change the language you're writing in at some stage (and that event would favour pushing more into the DB anyway).
In reality, what I've found is when there's a reliable, transactional, relational datastore at the bottom (i.e we're not talking about a larger distributed system), then for your own sanity you want to be pushing as much as possible down the stack as possible, to reduce the surface area that someone could reach through to change data in the wrong way. Data consistency issues are some of the worst when it comes to eroding customer trust, and if your data access/mutation paths are many and varied then you can do your head in or burn through a lot of dev morale trying to diagnose them.
The strongest advocates otherwise I've found are those in office cultures where there's little trust between devs and DBAs and getting things through DB review are a rigamorale that end up driving devs towards doing as much in code as possible. I've always suspected that beyond Google envy, these sort of dynamics are what drove a lot of the initial NoSQL movement...
https://www.vertabelo.com/blog/business-logic-in-the-databas...
The consequence has been catastrophic data consistency problems and the whole programme rapidly grinding to a halt.
I think the problem is the overly broad definition of 'business logic' that encompasses everything from data integrity to presentation logic.
There’s a quote from Gavin King somewhere exhorting developers that ORMs and raw sql are meant to be used concurrently, ORM for CRUD ops,sql for most everything else.
The problem I have with ORMs is lack of paging on collections; there’s always that one user that pulls in half the database.
If you have a single-user database system (e.g. webapp with database backend), there is relatively little to gain implementing data validity checking in the database, other than the declarative statements versus imperative rules discussion (which can already be a huge benefit, depending on the team).
But once you have a central database with multiple frontends (common in enterprise ERP solutions), enforcing data consistency in the backend becomes pretty much unavoidable -- otherwise a single bug or new feature in one frontend could disable entire production lines.
when there's a reliable, transactional, relational datastore at the bottom [..] you want to be pushing as much as possible down the stack, to reduce the surface area that someone could [..] change data in the wrong way
Yes, this. Very much this.
Usually there are two things:
1. Performance. Business logic is usually horizontally scalable, databases are usually not. You want to pull as much BL out of the database as possible, and put it into stateless microservices so they can be scaled.
2. Version control and deploying changes to business logic. Business logic changes frequently. Do you want to have to be making that frequent of changes to your database? Do you have practices around safe deployment and code review for stored procedures?
I've worked at a place where there was lots of business logic (millions of LOC) stored "in the database". In this case the database was MUMPS. It can be done, and it can be pleasant. The catch is that vertical scaling is the only option, and you have to spend years building your own tooling from scratch.
I think the line between database and code is destined to become even more blurred, but not by bringing the code down into the database, but by lifting more and more database concepts up into "application space" as distributed computing becomes more normalized.
The system was an enterprise-focused offering with weak product management, so in the early growth-oriented days of the company ended up saying "yes" to a lot of feature requests that we probably shouldn't have. Where this particularly impacted was the permissions system, which evolved over time from an ACL with odd warts to also include a bolted on hierarchy of users and their objects, and then a role based system, and then all sorts of enterprise-level oddities like cross-links in the hierarchy, shared subhierarchies, roles that could appear at multiple hierarchy points, virtual ACLs that behaved slightly differently etc. So potentially a large number of different access paths between a given user and resource.
Years ago, when this mess was starting to expand it was decided it was too hard to model this in the DB (and really that should've been a giant red flag), so the new approach was to load each customer's dataset up into an application server that would handle the BL, crucially including the permission logic. It very much wasn't stateless, as you mention, but I'm not sure how it could've been really, given you needed so much loaded up from the DB in order to make these permission decisions. Would've avoided a lot of headache if it had...
The consequence though of using this write-through in-memory cache was it became the source of truth.
The chief problem this led to was that shift into application land was a bell that couldn't be unrung. Everything others in this thread have complained about seeing BL spread across all sorts of SPs in a DB happened here, just in application code (which again hints I guess that the chief problem is architectural and lack of governance, not a wrong-layer problem). Nobody could properly describe the permissions system in response to support requests without a lot of code scouring, let alone hold it in their heads.
Even worse, as the application grew in size and needs, we found we still needed things we had left behind in the DB. A couple of smart devs working on the core service, because they were smart and trusted, convinced themselves that what we needed was an in-memory transaction system for managing this in-server cache (by now the core was being called the Object Cache, a name so generic that it also should've been a red flag). So a couple of years went into implementing a technically impressive reimplementation of a transaction system.
Meanwhile the system as a whole was well past the point of being needed to split up into multiple services, so a grand goal was set of moving the Object Cache into a standalone service: the Object Service. Slap an OData API on it, and then leave that API open for all internal teams to hit. By this point the core team who owned this was starting to become well aware they had fallen into a bad pit of reimplementing the Postgres DB everything still sat on: transactions, generic query API, configurable in-memory indexes for each type of object, partition loading logic for the timeseries sets, user permissions etc. Worse, the generic query API (and this is what's ultimately turned me off OData/GraphQL etc for cross-team interfaces) ran into all the same problems as an SQL interface - people in other teams would always be coming up with brand new queries your indexes hadn't anticipated, forcing new work on the keepers of the cache to support.
The way forward probably would've been to leave the permissions structure in place and pull as much as possible out of the service/cache into separate stanadlone services, i.e leave the objects in the object cache little more than just IDs you could look up elsewhere for actual attributes. We'll never really know though: it was recently decided to put the whole thing into maintenance mode, partially because nobody has the political will to fix the thing and its complexity.
I've thought a lot about the lessons of this system and where it went wrong, and I trace it ultimately to forging ahead with a permissions system (i.e core BL) that couldn't be modelled in the DB. I think that doomed us to what I'd ultimately name second system syndrome (i.e the whole stack above slowly evolved into a poor Postgres clone). Perhaps if we had been ruthless in going to a CQRS design or similar very early on we could've pulled it off (which would again demand stateless services above) we could've pulled it off.
I think a big takeway for me was to not take for granted the good things a RDBMS is giving you, especially transactionality. I think I'd bias these days towards looking for ways to scale up the powers the DB gives you (even if it involves pushing external integrations into the DB maybe via some funny FDW or transaction log integrations, or maybe using a tiered solution of DBs, one layer as the storage layer and another layer above that acting as coherent transctors, a little bit similar to Datomic's internal structure), rather than rushing toward replicating core DB functionality in application code, which can be tempting initially as the easier-looking solutions.
I think I might blog about this when the system's properly retired and the involved can look back and laugh rather than cringe...
Your either generating and submitting the queries through prepared statements or just storing them in the database. It's not "leaking" IMHO to do the later and in fact can come with benefits; you can call the procs while connected to the database through various other clients be it command line or a BI system. So in effect you are encapsulating the logic in the place with the most potential for re-use.
The problem with it is technical: the tooling for migrating and managing schemas and stored procedures is hot garbage, and there aren't good ways to enforce consistency between the invariants of the applications and the database.
Really, it should be possible, on both application deploy and when stored procedures are updated, to import library functions that the stored procedures use from the database and run unit tests against them to ensure that all clients are compatible.
I.e. ORM vs query builder.
That said, I've come around to the Repository pattern and quite like the Ecto way of doing things. It's a lot more flexible at the expense of some simplicity. It makes some really hard things easier than AR can.
- .count on a collection calls SQL every time, but there is also a .count method for arrays - which means you need to remember to use .length if you just want to count the objects loaded into memory
- ‘build’ methods for an association usually only initialize a record in memory, but with has_one associations it deletes any record that already exists
So basically when it’s not clear about the fact that it’s doing database things.
'Employee.xml'
Now imagine this but with stored procedures and inserts with tens of parameters.
At some point they rewrote it as a web app with NHibernate and all that. Took them literally many hundreds of man-years, and it still runs like molasses. And profiling whatever crap NHibernate emits is rather joyless enterprise.
Then you call your alternative anti-orm and it generates... orm specs, and then you say "migration commands based on the git history"... what does that even mean? What does git history has to do with the database?
Hopefully a lot. Non-versioned database schemas are the devil.
I tried to think about it from a gitflow release cycle perspective to be generous. This would mean constant churn of migration code for each branch of the repo.
If you have complex migrations which go beyond simply renaming columns, it would require you to write special migration code on a release branch which would then need to be merged back. yuck!
Hopefully I have cleared that up.
Facebook, who operate one of the largest relational database fleets in the world, have used declarative schema management company-wide for nearly a decade. If engineered well, you end up with a system that allows developers to change schemas easily without needing any DBA intervention.
I viewed the author as targeting smaller dev shops who don't have a dedicated DBA/expert. Groups who might use an ORM's migration framework for example. In which case, it would appear that there is significant loss in migration flexibility if you remove application code from the architecture.
For example, say you need to populate a new column based on data in other columns, across an entire table. If the table is a billion rows, you can't safely run this as a single DML query. It will take too long to run and be disruptive to applications, given the locking impact; it will cause excessive journaling due to the huge transaction; there can be MVCC old-row-version-history pileups as well.
Typically companies build custom systems that handle this by splitting up DML into many smaller queries. It's then no longer atomic, which means such a system needs knowledge about application-level consistency assumptions.
Having auto-runnable migrations checked in alongside the code that depends on them allows you to review and test those migrations as part of your normal code review and CI processes.
I think there must be some misunderstanding here because I don't understand why you say either of these things:
> This would mean constant churn of migration code for each branch of the repo.
> it would require you to write special migration code on a release branch
There should only a be at most a few migrations per feature branch and merging is only an issue when you have two feature branches with inconsistent schema definitions. You should only have to add custom migrations to a release branch to fix bugs and those should be merged back into your develop branch as soon as possible to minimize effort spend merging later.
Those aren't the only alternatives. Query builders exist.
And there is an answer to that. The idealistic answer is good DB design from the start. The realistic answer is by using views (sometimes materialized views) to create a clean representation out of odd tables.
The monster query can be decomposed into simple and clean operations against these views (and if necessary, views upon views).
The only caveat is you need a SQL expert to understand the performance if you want to do this at scale.
The problem with this statement is that DB schemas like code can evolve badly over time, even if they're maintained by a DBA sometimes (since they're human afterall too).
And then the DBA starts to suggest that you use things like triggers in your DB, which may or may not be good, but often surprise developers. "I just wrote 0 into the DB, why did it turn into NULL? What line of code did that? Ohhhh... there's a trigger."
SQLAlchemy exists exactly because of the pain the "just use views" approach causes. The entire point is that you can structure a query of arbitrary complexity in a succinct manner using Python structures, while not losing any of the relational capabilities. Of course you can write queries that are too complex, if you're then lucky enough to be able to have materialized views at your disposal, you can turn the SQL you wrote into one. But that's an optimization you can use if you need it, or not.
I feel like if a DBA keeps thinking of the database as the solution to all of the problems (by implementing views, triggers, stored procedures, etc) then maybe that person should be willing to support them, otherwise they really shouldn't complain that the developers moved to an ORM.
Do I seriously make the correct assumption that the people who are writing the backend of many apps are actually separated from the people who decide how the data those apps process the data, and you may have no direct say about the database schema? that effectively just a client - perhaps the only client, but nothing more than a client.
Obviously you've described the disadvantage of this approach; what advantage does it have?
That would leave us at an impasse (he said / she said), unless we get more nuanced. So let's.
It sounds like some of your frustration is at some DBAs you knew. Firstly I've never worked with a DBA, I'm just a senior software engineer / DevOps and to me that includes an intimate knowledge of SQL perfomance, so maybe this is why I don't mind an in-database approach.
I'm fairly sure that whatever SQL Alchemy could do with a monstrously complex data-relationship views can do with better performance (the further the abstraction gets from the engine the less performance optimization it can do).
Now maybe you agree with that, and see ORMs as a tool for the kind of job where the dev is locked out of the database. If so then I can't really disagree as I haven't worked at such places much at all.
SQL Alchemy will spit out SQL query. Whatever the DB engine can do with queries hand-written on top of views, it can also do it with the query generated by SQL Alchemy.
No, on two levels. Firstly, there are many features in SQL that an ORM will not support (e.g. no substitute for a materialized view).
Secondly, once you get really good at SQL, you learn that very tiny seeming things can make the difference between a query running for an hour or a second (e.g. case-insensitive search against an indexed column). Part of being expert in DB technologies is knowing how/why some ways of writing a query are very fast, and others are very slow. The idea of an ORM is to hide that complexity, which is fine for a toy todo-mvc app. But once you start querying tables with 100k rows you need to understand that complexity and master it if you want to write a fast query.
But in any case, we are not discussing some abstract ORM whose "idea is to hide complexity", but SQL Alchemy whose idea (one of them, at least) is to allow you to write composable queries yet still retain full power of SQL.
You are incorrect in your other assertion. Sqlalchemy allows for optimising your queries at runtime in a way that just isn’t available to sql views. The beauty of sqlalchemy is that allows for arbitrary composition of blocks of sql. I too have had to fight with dbas in a previous life because they didn’t want me to construct a query in code (non sqla), but I eventually won because I was magnitudes faster because I had runtime knowledge they couldn’t use.
SQLAlchemy supports case-insensitive searches, per-column / per-expression collation settings, index and other SQL hint formats for all databases that support them, e.g. SQLAlchemy's query language supports most optimizing SQL syntaxes, with the possible exception of very esoteric / outdated Oracle things like which table you list first in the FROM clause (use index hints instead). We are adding esoteric performance features all the time to support not just SQL-level tricks but driver level tricks too which are usually much more consequential, such as the typing information applied to bound parameters matching up for indexes as well as special driver-level APIs to improve the speed of bulk operations.
SQLAlchemy has been around for thirteen years, plenty of SQL experts have come along and requested these features and we implement them all. Feel free to come up with examples of SQL-expert level performance optimizations that SQLAlchemy doesn't support and we'll look into them.
What I'm trying to challenge is the philosophy that an ORM is an adequate facade in place of learning how databases work. My point about views is that in my experience, the answer to disgusting queries is cleaning up the DB design (and views can be the tool to accomplish this). My point about case-insensitive searching (you can create a case-insensitive index if you want) is that a lot of db-performance stuff just can't be solved on the query side alone anyways.
It sounds like SqlAlchemy is designed with a lot of flexibility around how queries are run, so maybe you agree that understanding what's going on beneath the hood is important to handle these complicated cases.
Thank you for this response and I agree, we are likely on the same page. I don't know that anyone actually espouses that philosophy. This is the anxiety that ORM skeptics have, and certainly you get beginners who rush into using ORMs not knowing what they are doing, but if someone wants to be a DB expert, I'm pretty sure they go to read about databases :) These ORM beginners will fail either with the ORM or without out. I guess the idea is you'd prefer they "fail fast", e.g. the ORM covers for them while they proceed to screw up more deeply? This is arguable; if you've seen much of the non-ORM raw DB code I've seen, it too fails pretty hard. But even with this argument, if ORMs produce the problem of incompetents who are not found out fast enough, why hoist the denial of useful tools to those developers who do know what they're doing.
if you have the option to use materialized views, that can be helpful when appropriate, however materialized views have their own issues, not the least of which is that they need to be refreshed in order to have current data, they can take up a vast amount of storage, not to mention a lot of databases don't support them at all. It should not be necessary to emit CREATE MATERIALIZED VIEW with super-user privileges and to build the infrastructure to store them and keep them up-to-date for every query one needs to write or modify that happens to have some degree of composed complexity.
you don't need an ORM for these queries either, just a composable query builder (like SQLAlchemy Core) that allows the composition task to be more easily organizable in application code.
none of this means you can't use views but it's unnecessary to dictate that they should be the only tool available for dealing with large composed queries.
Having a separate "DBA" from the developers or having an adverserial relationship with said person is not really a technical problem I think.
Just like Data in a codebase ;-)
I don't feel like this answers the question. My ORM supports views.
It actually seems like a really cool idea. E.g.
* Commit A: create a schema.sql file "create table t (a INT);"
* Commit B: update the schema.sql to "create table t (a INT, b INT);"
* The tool can generate a migration from A..B "alter table t add column b INT;"
It seems like it probably would work really well for creating new tables and columns in simple projects. In more complex projects I've worked on, we usually had more complicated migrations to update the data to fit the new schema, and we often had to be careful to interleave schema, data and code updates (especially if you wanted to be able to rollback).
foos = execute_sql("SELECT * FROM foo WHERE bar = ? AND baz = ?", [bar, baz])
UPDATE sometable SET {set clause} WHERE id = ?
function update(id: ID_TYPE, value: Partial<sometable>) {
const set_clause = value.keys.map(k => k + " = ?").join(", ")
const params = value.values.concat([id])
db.query("UPDATE sometable SET " + set_clause + " WHERE = ?", params)
}
Is there some other circumstance when you want that?
UPDATE sometable SET
foo = COALESCE(?, foo),
bar = COALESCE(?, bar),
baz = COALESCE(?, baz),
bam = COALESCE(?, bam),
bat = COALESCE(?, bat)
WHERE id = ?
Confirmed that this works exactly as expected in SQLite:
$ sqlite3 optional_column_test.db
sqlite> CREATE TABLE sometable (id INTEGER PRIMARY KEY, foo, bar, baz, bam, bat);
sqlite> INSERT INTO sometable (foo,bar,baz,bam,bat) VALUES ('You', 'are', 'a', 'bold', 'one');
sqlite> SELECT * FROM sometable;
1|You|are|a|bold|one
sqlite> UPDATE sometable SET
...> foo = COALESCE(NULL, foo),
...> bar = COALESCE('ain''t', bar),
...> baz = COALESCE(NULL, baz),
...> bam = COALESCE(NULL, bam),
...> bat = COALESCE(NULL, bat)
...> WHERE id = 1;
sqlite> SELECT * FROM sometable;
1|You|ain't|a|bold|oneAre there any bad performance implications here? Like, will it still rewrite columns that don't need to be updated?
The answer to that is almost certainly "it depends on your DB". Most query planners should be smart enough to not try to write anything, but some are smarter than others. For example, SQL Server (if I'm understanding right) does perform a log write even if it should be obvious that the value won't change: https://www.sql.kiwi/2010/08/the-impact-of-non-updating-upda...
That being said, I'd skeptical of the performance impact (if any) being all that significant; even in a worst-case scenario of "yeah, it's going to stupidly write every field every time", the columns for each row should be pretty close together both in-memory and on-disk, so unless you're writing a whole bunch of giant strings or something at once (and even then) there shouldn't be a whole lot of seeking happening.
tl;dr: assuming there's a minor performance impact is reasonable, but it should probably be measured before trying to optimize it away.
Also, getting the library to properly expose the formatting interface is also a trick. We would do massive loads/dumps that we did not want to use Python for (but we worked in Python) as the DB-API pulls the entire result set into RAM, and we were pulling >RAM (and we didn't want to do something lower-level w/ the driver). We'd spawn psql, but that needs the query, as a complete, pre-templated string — it does not support any sort of positional parameters. And the Python library really doesn't (last I checked) have a public interface to the templater portion of the library. (I think this is b/c PG just sends the parameters, and the binding is done server-side. So, really, psql needs an interface to supply those values as arguments to the CLI.)
Thin SQL wrappers/dsl's like the SQLAlchemy expression library are great. IMO those thin wrappers are what most people should reach for first, over a full blown ORM. A good mimimal sql wrapper will:
* Save people from pain, problems, and security issues involved in building queries through string manipulation.
* Map very closely to raw SQL
* Make it easy to compose query expressions and queries, using language native features.
* Help devs develop their SQL skills. Learning the wrapper IS learning SQL, for the most part.
I have a hard time seeing many good reasons to add any more layers of abstraction on top. That last point is particularly important to me. My first exposure to many advanced SQL techniques, was through such libraries. Since the code maps to sql quite naturally, that learning can be applied in a much wider variety of contexts. Teach someone how to do aggregations using Django's ORM, and you've taught them to do aggregations in Django's ORM, and basically nowhere else.
I haven't tried this before, so caveat emptor, but per psql's manpage this should be supported:
QUERY="SELECT * FROM sometable WHERE name = :'foo'"
echo $QUERY | psql -v foo=asdf # ... other args ...
This also happens to work for table names, at least per the example in the manpage:
testdb=> \set foo 'my_table'
testdb=> SELECT * FROM :"foo";
echo 'SELECT * FROM :"foo";' | psql -v foo=my_table # ... other args ...
I'm not sure what git-specific interaction the author is envisioning, but more generally: declarative schema management tools are based on the notion of using a repo to store only CREATE statements. It's an infrastructure-as-code style approach, where to modify an existing table, you just modify the CREATE definition. The tool figures out how to translate this to an ALTER.
I'm the author of an existing tool in this space, Skeema [1], which supports MySQL and MariaDB. Other recent tools in this space include migra [2] and sqldef [3]. From what I understand, in the SQL Server world there are a number of others.
Nobody suggests that ActiveRecord is perfect, but it is pretty amazing at what it does. I think a big part of the reason it's more reliably excellent is that Rails in general embraces the notion of "convention over configuration" - there's typically a correct naming scheme (with opinionated pluralization) that starts in the table scheme and flows straight through the models to the controllers and finally the view layer. I can join your Rails project and make guesses about where your logical components are located and what they are called that would beat any TV psychic cold 100:1.
Plus, much like with SQLAlchemy and basically every other modern ORM... you are always one method away from writing raw SQL if you want to be.
As always: use the tools that work best for you and for the vast majority of web development cases, you'd have to pry ActiveRecord out of my hands.
SQLAlchemy uses the data mapper + unit of work patterns. This means that your connections and your record objects have the same lifecycle. If your request takes 200ms, then you are likely holding open your connections for 190ms, even though the only SQL query takes 20ms. If connections are scarce, and they usually are, you will not utilize them well at all.
There are two ways that I know of to solve this, neither of which is pretty: (1) run pgbouncer as a sidecar on your python processes. This has the effect of making connections a far cheaper resource. (2) After every single query, manually close the related transaction/connection/cursor in your code.
ActiveRecord defaults to giving up the connection after every query, which in a high-traffic webapp, is much less of a footgun.
I’m lucky in that I have low requests per second so I never have to worry about any of that too much. I don’t envy you having to deal with that sort of load.
"give up connection after every query" is, IMO, not _less_ of a footgun, but a _more subtle_ footgun. That is to say, sure, for the most part, it will work ok.
_But when it doesn't_... like when your database goes down for a couple hours, or your rack loses network, or your proxy to whatever cloud provider is running your database, or maybe some other dependency had an outage between the queries... you end up not really knowing what executed and what didn't, with some fraction of your 10k req/sec leaving behind some dangling state -- a partially processed $whatsit -- and no clear/easy way to fix it or even necessarily identify how many $whatsits were affected. At least a few times, I've needed to either wait until records make their way into some OLAP system for offline analysis, or deploy some fixup code, or emulate either/both to fix stuff up after something weird happens.
At least that's what I am constantly afraid of. Do you also worry about this kind of stuff? If not, what prevents it from being a problem in your environment?
Signed,
The guy constantly badgering seemingly everyone about minding their transaction boundaries.
Fun story: A coworker and I are in the process of tracking down exactly violations of this in a misbehaving app, due to an accumulation of RPC within transactions across the entire app. (I used to be responsible for it as a greenhorn ~5years ago, but walked away for the last several years to work in a different part of the org.)
The approach is actually kinda interesting: we add an interceptor in the RPC layer for every outgoing call that asks the DB machinery “am I currently in a transaction?”, and emits a warning (containing at least the RPC being made, perhaps a (partial) stack trace) if so. We had so many occurrences of those warnings that our logging framework proactively started dropping log lines! So the next step is to only emit the warnings once per $duration per message.
Another day in the life...
What this translates to is that things should be impressively stable when your DB nodes go down, the backhoe shows up to party, or your BGP is routed to North Korea.
SQLAlchemy is probably close to the worst experience and that's likely largely due to python and pythons tooling state.. And the docs. Just something about the docs. Still, other than bulk insert performance I got it to do just about everything I needed and came to the conclusion it was the best python ORM for my needs.
result = db.engine.execute("<sql here>")
I don't think there's anything wrong with using tools to help you generate good SQL queries, but you should see them clearly and own what the database is doing. MySQL and Postgres have a myriad of useful functions and extensions that ORMs don't utilize or hide from the developer. That leaves the application to do multiple queries or a lot of logic that isn't needed.
It doesn't have to be gnarly; any SQL query that can't be handwritten on an index card is probably too cumbersome to maintain. A 1000 line SQL query is a code smell that means the data model could probably be improved, perhaps with views.
Here's a datapoint I experienced:
The company I worked for didn't trust the Django ORM for DB migrations. They thought they could do it by hand. After lots of deployment failures where the migration script worked in the dev environment but failed in the prod environment, we switched to using django migrations. Haven't had an issue since.
The author even admits that migrations are a hard problem, but yet, he has no solutions other than just using raw SQL.
Edited to Add:
In Django, these migrations are handled pretty well with a single command to make the migrations. In SQL, someone would probably have to spend some time testing, and then would need to be reviewed in the PR.
This post reads to me as the author not having the proper ability to structure and plan their projects so they blame their tools instead of themselves. So instead of understanding limitations and tradeoffs, they try to build their own magic lamp that solves every problem under the sun.
It was a matter of setting up the database, writing a prepared statement, and calling that prepared statement with the relevant arguments. Bringing in an ORM for these cases is absolutely overkill.
As your requirements constantly balloon, perhaps adding an ORM (or just a simple query builder) is appropriate. But none of the services I mentioned above have grown in that manner, and most are still up and have been running (and modified) for several years now.
I've been on projects where an ORM and/or query builder was likely the right answer, especially for automated migration and rollback scripts. But the way you're presenting it seems to be all or nothing, and I couldn't disagree more.
If you consider a web project, with many developers of different levels of experience, having an ORM is a life saver. Because not everyone is constantly mindful of SQL injections. Because not everyone is constantly mindful of transaction management in requests. Those take experience.
In my experience ORMs are great 95% of the time. Those 5% can be extremely painful (I've gotten bitten by ORM Rails transaction nesting problems, and sometimes it frustrates me when I need to make something more complex). But let's not throw away the baby with the bath water.
I have had great experiences with Django ORM and SQL Alchemy. Rails ORM goes a long way, but it feels eons behind Django's.
I really don't like using a tool as a crutch to a systemic deficiency of a team, in the long run you are only compounding the problem, the more complex the system gets the more workarounds and gotchas you have with ORMs so it's better to educate developers on how to not depend on them from the get go.
Yup, ORMs have their place in some projects but in the majority of what I've worked they brought more pain and confusion than just properly effing learning SQL.
Very valid point, but then this becomes a discussion of how much you want to create mitigations in the system vs. educating developers to topics of, for example security. The larger a team becomes, the harder it is to police that every single query in your system is sanitized.
Still on the security side, Software architecture tell us not to repeat ourselves, and if sanitizing query strings can be done centrally through an ORM, then why shift the responsibility to code review processes that are especially prone to failure due to their human nature?
As you said, these kind of become a crutch to people who don't know SQL. People begin to rely on those instead of knowing the underlying mechanisms -- but hey, that's the price of any abstraction. Probably this same argument was used when high level language compilers (analogous to the ORM in this discussion) were introduced.
As with anything related to software design and architecture, it depends, depends on the size of the project, how many teams work on the same codebase, etc. I don't think using an ORM as the sanitisation layer of your SQL queries to be a good enough feature compared to its drawbacks on complexity and abstraction leakage. The object model doesn't serve relational data very well, that is already a first sign and smell for me that ORM should be used in very specific cases instead of as the default go-to solution.
Having to deal later on the application lifecycle with partial entities and the likes (anything to do with partial data access compared to the models' defined fields) is really painful when done improperly, which is easy to do, and then we are back on square one having to teach developers how to use an ORM framework properly. Also one thing I really dislike (and that I believe will always increase a system's complexity) is making code less explicit, an ORM layer will make your code flow less accessible, you'll need to know where the layer takes over and where it leaves you to implement more complex use-cases and then you are back to writing SQL queries manually or depending on some set of features from the library implementing the specific use-case you are looking for.
I don't hate ORMs but I have mea culpa in using them improperly and learning from my past experiences, nowadays it's really hard for me to justify its usage aside from very simple data access patterns. I say that because in the end you'll always need to reason about what the ORM layer is doing in terms of SQL concepts, translating between those layers in my head is adding another complexity: cognitive load.
I'm reasonably proficient in SQL. My practical blocker with ORMs in general has been that you still have to understand your application obviously, and you have to understand SQL to fix most things, and now you're adding effectively a new language and framework between them.
> I have had great experiences with Django ORM and SQL Alchemy.
I think the best ORMs aren't ORMs at all, but larger frameworks like Django and Fails, as you're making the investment to learn that framework anyway.
And in fairness, I started with Hibernate which is particularly complex, having its own query language. Many ORMs have gone the no-object route and try to directly represent relational concepts, especially jOOQ[1]. That's smart engineering to recognize that problem and minimize it, but at that point it's less of an ORM and more like numpy for the relational algebra.
[1]: http://www.jooq.org/
I'm a senior developer but just started using SQL seriously a few months ago, and only for my own projects, so don't have much experience; but it's difficult for me to understand this. Both in Python and in Go, every single tutorial tells you how to use parameterized queries, and every answer on StackOverflow has warnings of building SQL queries from normal string operations. Fifteen years ago I could see this being an issue; but is it really possible that in 2019 "inexperienced web developers" manage to write code vulnerable to SQL injection? Is it really only my hard-won paranoia, gained from decades of building operating systems in C, that made me immediately take these warnings to heart?
(That's a genuine question, not a rhetorical one.)
But, as always, "use the right tool for the job." I have gotten tremendous value from ORM's over the years, but I'm not going to insist on using one for everything just "because".
My favourite neglected feature is JPQL with constructor expressions, which lets you write queries in an SQL-like language, but in terms of objects rather than tables, which is slightly easier, and materialise results directly into objects of your choice.
However, there are things that JPQL can’t do, but it’s easy enough to create a native SQL query and have its results map to JPA entities. I use Spring Boot (JPA, Spring Data, Spring Data REST) for the sheer convenience of it and speed of development, not to abstract away the database, which is always PostgreSQL. I am very comfortable writing SQL queries and making use of PostgreSQL-specific features and Spring Boot with JPA certainly lets me do that when I want to.
A statically typed binding to the database with 'find all references' etc.. The ability to write a few lines of simple understandable code that turns into a much more verbose SQL query aids in readability, maintainability, and understand ability of the code base.
I have used other ORMs and feel that EF/LINQ is way simpler in that the same query syntax is used for in-memory and database operations. They can be mixed and matched for doing really intensive data processing with relatively few lines of very easy to read code.
My only comment would be that using an ORM - you do need to understand how it works, and how the code you write turns into SQL and a lot of the details of contexts and tracking.
It's like flying a plane - big, complicated, it will get you where you need to go very fast and effectively, but if you don't know what you're doing you'll crash right into a mountainside.
Foo.withSession { session ->
session.createSQLQuery("INSERT INTO FOO (bar, baz) VALUES (:bar, :baz)")
.setParameter("bar", "bar")
.setParameter("baz", "baz")
.executeUpdate()
}
new Foo(bar: "bar", baz:"baz").save()
When you write a technical paper in English, you switch to math equations when appropriate. Math isn't assembly language, its the correct language. It's the native language.
Yes, you can always describe your equations with English prose—and if you're doing simple addition and subtraction, it's probably nicer to write that in English. But once you want to do anything remotely complex, writing it as a math equation is more terse and less ambiguous.
Right! But 99% of the time, I do not want to do something complex - I just want to load a few rows based on simple search parameters, and save a changed values (which may involve heavy data processing, but not relational). Hence only using SQL rarely, and therefore getting rusty.
That said, based on my experience analysing applications, "loading a few rows" to perform "heavy data processing" and then "save a changed value" smells suspiciously like "the entire task can be rewritten as a single, moderate complexity query and the data never even had to leave the database server."
I'm not saying that's the case in your own generic hypothetical, as there are indeed forms of business logic and advanced manipulations that exceed the scope of a database server. But you might be amazed at what you can do within the SQL language. For example if you aren't intimately familiar with window functions like DENSE_RANK() then do yourself a favour and learn about those—and then contemplate how you could use them within a subquery joined to a table that you're updating.
My experience over my career has been this: there's CRUD, lots of IO, and a bunch of data processing that just needs specialized software (image, video, weather simulation, etc). I try to offload what I can to the database - and yeah, I know about window functions - but other than for the occasional report, it just doesn't move the needle.
ORM's are a bad case of this, because they try to bridge the impedance mismatch between how data is stored and linked in the database and the programming language you are using. This creates a forced model and a whole lot of assumptions and in the end it is still not good enough. Fixing the part of your project that does not fit the ORM's assumptions gets magnitudes harder because the bridge they built is very complex.
Yes, most ORM's allow you to write direct SQL queries, but that does not really solve the problem. You work the way it was intended, or you do not not. If the latter is the case, you will encounter difficulties, no matter the way break the 'rules'.
Right off the bat, its obvious how to do a transaction in the first example but not the second. Its also obvious to use complex SQL functions in the first style and not the second.
Here's your query in knex, a query builder for JS:
knex.insert({ bar: 'bar', baz: 'baz' }).into('foo')I think it's a mistake to think you can't write good efficient SQL for well-normalized schemas with AR. You can. Usually anyway, for a great many use cases.
[Arel is _really nice_, but sadly Rails absorbed it and considers it "private API", especially in it's most sophisticated features. I use em anyway, they generally don't break... but if I wasn't using ActiveRecord as a whole, I'd probably use Sequel instead.]
When it was first added to Rails, improving the query building possibilities, it was a separate gem that Rails depended on. I'm not sure if it was written specifically for Rails originally just maintained in a separate gem, or intended by it's original writers to be an independent project. But it was later absorbed into the Rails repo and we were later told by Rails maintainers that it's more sophisticated manual API (look up how to make a subquery with Arel for instance) were not intended as public API and had no backwards compat commitment.
In any sufficiently complex codebase you'll be doing more than this, however, and the work to support the ORM properly then outweighs the work of simply coding the raw SQL and preparing statements as appropriate.
Raw SQL also tends to be much more performant.
And for those special (and IMO pretty rare) occasions, you can drop down to arel or raw SQL anyway. Why throw away the consistency and readability of something like AR for edge cases, when you can just treat your edge case as an edge case with raw sql and still keep AR for your other 95% of queries.
That's not my experience.
I maintain a relatively unremarkable but bespoke online discussion forum, which has hundreds of queries, few of which could be composed by an ORM, let alone composed and run performantly. The median complexity query in my code base probably has two or three joins, two or three subqueries, and some kind of aggregation or window function.
The result is a typical page runs around two or three queries total—one query to authenticate the user and load everything about their profile and permissions, one to load the entirety of the data being output on that page, and occasionally one to update a statistic somewhere.
(The authentication query runs on every page because there's absolutely no persistence in the application layer. The authentication query goes three layers deep in subqueries and includes half a dozen joins. It hits perfect indexes when it runs and takes only a few msec round trip.)
> People hugely exaggerate this IMO.
In my experience, people who think an ORM can do most things are simply under-experienced with SQL and set theory.
Well, just from that description, Django ORM could do it. Can you post an example of a median query? I'm curious to see why it can't be ORM'ed.
SELECT
b.field, b.field, b.field,
group_concat(concat(y.field, useful_thing)) as useful_things
FROM (
SELECT t.field, f.field, w.field,
(case when w.wid is null then 0 else 1 end) as has_watched,
exists(select id from posts p where p.tid = t.tid and p.uid = :uid) as has_posted
FROM (
SELECT tid
DENSE_RANK() OVER (PARTITION BY foo ORDER BY bar DESC) AS useful_pseudo_id
FROM editorial_things
WHERE thing = :thing
ORDER BY something
LIMIT number
) as a
INNER JOIN thread as t ON t.tid = a.tid AND t.last > Now()-INTERVAL 2 DAY
INNER JOIN forum as f ON f.fid = t.fid AND f.fid in (:security)
LEFT JOIN watched as w ON w.tid = t.tid AND w.uid = :uid
ORDER BY something
) as b
GROUP BY something;
What language are you using for that?
CFML has a "query" datatype that represents the rows and columns returned from a database combined with useful metadata and really neat features like n-level iteration where values repeat. To the programmer it's like a dictionary that magically changes its values by passing it to an iterator. Without the iterator it works like a dictionary of the first row. Or if you treat it like an array you can manually read any column in any row directly.
There's plenty of ways to manage complexity in software. Two or three different databases in a single app doesn't kill them, nor will two different DB interfaces.
What I did find interesting was the pushback I got from my team when I told them that I wanted to drop the Java ORM (JPA). One guy, who I respect, was super critical of the decision. He was telling me that SQL was the reason we had a performance problem, but when I investigated I was astounded by how fast SQL was, even over JDBC. It was clearly the ORM that was slowing us down.
In his defence, when I was able to show him literally 100x performance improvement after moving a service to PL/PGSQL and MyBatis (Java), he changed his mind.
Sometimes you have to concatenate, e.g. to specify order by clauses. This is something database vendors need to fix. There need to be ways to tell the database to modify a prepared statement without changing it's SQL. You might be able to do it with a crazy IIF, but then it's likely not to use indexes well.
I have hit this problem in just about any system using prepared statements. The performance overhead of query planning is minimal compared to the headaches it prevents.
> Sometimes you have to concatenate, e.g. to specify order by clauses.
So, should I concatenate or not?
> This is something database vendors need to fix.
How long do you propose I wait before giving up and using a library that allows me to safely and sanely compose queries?
deleted_at TIMESTAMP
created_at TIMESTAMP
user_id INTEGER
body TEXT
That you view the situation as ORM or concatenation terrifies me. No wonder SQL injection remains so prevalent.
Prepared statements are what you use here. You provide a query with placeholders for the values you want to fill in, and then you call that prepared statement with the arguments you need. These are typically type-checked and hardened against SQL injection, but of course you should verify with your particular language/library combination.
With an ORM you can construct the prepared statements programmatically.
You're still working with SQL as a language, but you're manipulating it directly instead of trying to go via string formatting.
A quick search for "query builder golang" (We are on HN after all) provides the following library:
https://github.com/fragmenta/query
An example from the README:
// In your handlers, construct queries and ask your models for the data
// Find a simple model by id
page, err := pages.Find(1)
// Start querying the database using chained finders
q := page.Query().Where("id IN (?,?)",4,5).Order("id desc").Limit(30)
// Build up chains depending on other app logic, still no db requests
if shouldRestrict {
q.Where("id > ?",3).OrWhere("keywords ~* ?","Page")
}Absolutely, prepared statements would not be appropriate for that.
> With an ORM you can construct the prepared statements programmatically.
That's not limited to ORMs though. You can also just use a query builder. In fact I would argue that an ORM that provides that functionality is a query builder with additional ORM features on top.
You can use functions to create basic "getters" for application data structures. Layering these through further functions for limits/offsets/etc is simple (and you can write those layers in a generic way).
You just have to get used to the mental mindshift that all your data manipulation code lives in the database, and your application simply consumes data from single function calls.
I don't think others mean concatenation of values where placeholders should be, but the fact that the query itself is a big string. Unless some sort of query builder is used to literally build this big string for you.
They explicitly contrast using an ORM to the practice of concatenating strings here:
> > SQL is backward to a programming language. Creating statements by concatenating strings?
> Unless some sort of query builder is used to literally build this big string for you.
I'm not entirely sure they're aware of the existence of query builders outside of an ORM given the dichotomy presented.
What would be great to have first class support in the language (or perhaps IDE) that would understand database schema and database types so you could have error highlighting and autocompletion.
Something like JOOQ for Java seems to do that. Seems like a good idea at the surface, though I don't currently program in Java and don't see anything similar in other languages, so I'm wondering what's wrong with it
-the only project where ORM was worth was a simple backend admin project with many tables and very little load: ORM avoids having to write tedious simple CRUD queries.
-for recruiters, it feels more simple to find people with language X than to find people with language X + SQL. OK but ...
-to understand the intricacies of ORMs takes a lot of time and effort, probably as much if not more as writing proper SQL.
-illustration of the previous point: it is hard to know the amount of data the ORM is really carrying, hence the common situation where half of your DB ends up in memory.
-developers tend to forget performance improvements. For instance, in C#, IQueryable objects are used everywhere, meaning that in any layer of your app, the query that will be pushed to your server might be modified: hard to tweak your performance in such context. More generally, this additional layer makes everybody postpone the time when you have to think about DB performance.
-it prevents you to use advanced DB features (ex.: built-in audit trailig and versioning), either because nobody ends up knowing SQL DB anymore, or because ORM makes it harder if not impossible.
-queries sent by the ORM to the DB seem always huge and be returning more than they should. -for performance or complicated requests, you always end up adding some SQL to your ORM.
-because of the previous point, the maintenance advantage of ORM, namely being able to automatically migrate your DB with your code is lost: you end up having to maintain more or less manually some SQL.
-letting the ORM automatically update the SQL DB during migration on real data always make people sweat: if any problem occurs during that process, welcome in hell. I feel always more comfortable being able to write some SQL fix in worse case.
-for my side projects, I never use ORM, and enjoy it.
Anyone wanting to set up something similar just watch Mosh's Repository Pattern
https://www.youtube.com/watch?v=rtXpYpZdOzM
I modified this pattern to use dapper instead of entity framework, but he already decouples entity framework to the point where you still have control.. so you can do either.
ORMs are necessarily language-specific, and as a single DB interfaces with multiple languages it’s impossible to do as you suggest (that’s what SQL is anyway).
An object in JavaScript has different semantics and conventions around it (everything is a prototype and a dictionary simultaneously) compared to C# or Java (everything is statically defined)
Storing data is a hard problem and likely to be a leaky abstractions. "Save my object" is OK until you need to ask questions like how to find such objects, by which attributes. If I update this and another object, do I care that they are done in a single transaction?
An "I JUST WANT IT SAVED" friendly abstraction would be JSON as a value in a key/value store. As a bonus you can choose your consistency/availability trade offs by going "nosql". For example Azure table storage. Nice!
But now what if your object is an invoice with a link to an account. Is than an ID or is the account info embedded in the invoice. Etc. There is a lot of design thought that needs to go into data. You can stop using SQL, Tables or whatever but these problems don't disappear.
ORM allows you to almost forget about these issues for simple CRUD-like tables. And for the start up style SaaS doing a bunch of boilerplate crud stuff (to begin with) something like ActiveRecord saves the day. But anyone using such conveniences should also learn database design, query optimization and such things too.
No, they do not. If yours does, throw it away.
I've personally settled on using a "light" ORM, which does the mapping of result sets to objects, variable replacement, and connection handling, and I write the SQL myself. It's still not ideal, because for statically types languages I need to write ad-hoc object types for query results, or deal with untyped data, but at least I don't get any surprises.
Try a good ORM (eg Django).
Is this really a good example? Sqlalchemy allows you to use raw queries when you want to. Just like almost every orm out there.
Bookmarking it so I can reference it.
http://blogs.tedneward.com/post/the-vietnam-of-computer-scie...
The worst part is that you often find out that ORM is affecting performance when you start hitting real world loads in production.
That said. . . yeah, I've also had my share of cases where the app ended up seeing higher load than anyone anticipated, and it's hard to even see the source of the performance problem, because, with an ORM, it takes a little bit (or, with the worse ones, a lot) more work to find out what the actual queries being executed are so that you can ask the DBMS to `EXPLAIN` its query plans.
Currently using migra and some custom scripts for migration management - will check out automigrate.
The main issue I've had with developing a large backend in pure SQL is that you end up having to piece together custom scripts, migration tools, etc. to add features your database/SQL doesn't provide, like modules and higher order abstractions. So I'm actually considering switching to something like SQLAlchemy simply for that
There are frameworks like JOOQ or MyBatis that work this way.
Also now that we need auditing there’s a pile of work we could have have gotten mostly free with an Envers annotation.
In fact, an important part of our transition away from ORM was to invert the ORM-centric relationship between the application and the database. We wanted our tech people to be able to manipulate the database natively via SQL, which is generally much easier and more efficient than writing and deploying Java code to do the same thing.
For us, this meant moving most data manipulation logic into the database using PL/PGSQL. Doing so has empowered a team of non Java admins, massively improved performance, and significantly reduced LOC. I’d say that we now have about 30% (1/3rd) of the LOC in PL/PGSQL than we had in Java+JPA. Obviously this means less bugs, but performance is literally 100x in some cases.
I think that if you take a Java-first view of your application then all the complexity of JPA is part and parcel of any solution, and that’s fine for you. But we’ve found that taking an SQL-first approach has been great for our use cases, and has had lots of ongoing benefits.
Of course, we needed to build a bunch of tools to help with this. For example, we built a gradle plugin that treats SQL code like Java code so we can write libraries in SQL with transitive dependencies. We also needed to build testing tools and tools to automate schema migrations for CD.
I do think the lack of tooling around managing large SQL-based projects is a blocker to wider adoption of our approach, but the benefits of going against the ORM grain have been very significant for us.
I will also concede that under some circumstances it's too slow to bring the data to the code, and instead you have to bring the code to the data (i.e. PL/PGSQL). The sort of speedup is mostly from eliminating round trips and data marshaling, however. That's not quite a like-for-like comparison of an ORM vs a mapper.
The reason I stick to ORMs is mostly because of RAD tools that save me so much time. For instance, JHipster generates liquibase migrations and JPA entities. This gives me a relational schema very quickly. To avoid any "surprise" queries that tank performance, I do turn Hibernate's statement logging on when developing new features.
Next time if I can find some more tools to help with an SQL-only + stored procedure approach, I'll give it a deeper consideration. Maybe convince your company to open source some of tools it has developed!
I definitely agree with you that tooling is a big deal. We did spend a lot of time manually proving it out before we spent the time on the tools. It was a bit of a leap of faith but it quickly became obvious that we were onto something.
And you’re right, the point of our approach was to move the code to the data. We deal with reasonably large, complex real time data sets Sotheby’s round trips and marshalling become the dominating contributor to total time. Hence our ability to improve some operations by 100x.
I can see that if you have a familiar and reliable tool chain and a compatible use case then ORMs could be great. I think our problem was that we had neither, so it was never going to work out too well for us!
Putting an orm layer on top of this refusal to do one's job and design the database is the second nightmare of an orm. Adding a new language and new paradigms that don't make much sense just to translate data into objects adds a ton of completely unnecessary complexity. Not to mention many orms are slow either at fetching or hydrating data. They are just unnecessary solutions for nonexistent problems. It's too bad most people have no experience with database design or even sql to understand and see this. Most people prefer to make apps more complex unnecessarily rather than reduce complexity and think that makes them experts. They are usually the ones advocating for the unnecessary orm layer. On the other hand, this problem mostly goes away when you no longer deal with objects in oop languages, so there is hope.
Here's the deal: if you design a decent entity manager, you shouldn't have to worry about how the database works.
A database was created to ingest and query user data. Programs pretty much ingest and query user data, the amount of regular (non tech) people using SQL is too damn low.
So, IMHO, programming the data ingestion and query should be easy, and a good ORM framework should handle it. Of course, to make a programmers life easy, they should start following mindful and clean practices - Otherwise any tool will be a gateway to hell.
- SQL alone will never help you understand if your underlying table is hashed/unordered, column/indexed or tree/ordered either.
- ORMs need not generate SQL statements. An optimized ORM can directly send the data to be inserted in a native protocol to the DB.
- The deal is that data tuning / access optimizations have no semblance with SQL or the ORM.
- Bad ORM usage / anti patterns does not mean ORMs are bad. It means people use them badly.
- SQL is not an artificial layer, an ORM however is
- SQL is a language, it's up to you as a developer to tune your queries against your database (e.g. create and use partitioning, indexes, statistics gathering etc.)
- If your using an ORM just to send data via a native protocol then why use the ORM in the first place?
- Performance tuning is very closely related to how you write your SQL
- This is my opinion but I think almost any usage of an ORM will mostly be bad, no real arguments to support otherwise. Let the database do the work, that's what it's there for.That is still no reason for me to use an ORM instead. Learn SQL then, that's way more valuable than to learn the next ORM library with all it's issues.
* SQLA can infer joins, but sometimes it can't and needs hints - then you may worry that you miss some combination of table join it doesn't have enough information for.
* In one case, SQL inferred a join incorrectly and I ended up with dupe results of a cross join. this is possibly because of a certain primary key duplicated on a few tables. The way SQLA infers joins is a bit magical, and it's not always obvious how to modify that behavior.
* dealing with joining a table on itself can be complicated, when you need to distinguish between those two versions of itself (using an alias?)
* how about this use case: modify a set of filters to apply at two levels: one in filtering rows before a select query, then again to filter the result of joining those original results.
* If you operate with objects/models, for saving/retrieving things, it can often be hard to mix with raw SQL which can't easily be modeled (at least without committing everything, and then executing the SQL). That means you need to communicate that SQL in object syntax, which is far more complicated.
* for migrations, I need to redefine some models, because I need two - one representing the old DB structure, and another representing the new structure.
It's possible some of these have better solutions than I've found, but I find it's often hard to figure them out.
I just use ORM for OLTP and raw SQL for OLAP (Better for being more expressive).
Migrations saves a lot of time.
ORMs are generally not good at or even capable of efficient set-based operations spanning entitles.
Building a dashboard with an ORM results in pain and wildly oversized DB instances in my experience.
SQLAlchemy for Python is that for me. I find it incredibly expressive and allows me to write complex queries that are readable, maintainable, and perfomant. Yes, it requires learning, but it’s the right tool for the job, personally.
It's exactly what you want. It blew my mind, and taught me that python syntax can natively express SQL semantics. This gives me all that ORMs are really good at -- letting you avoid having to write one language in string-form inside of another language.
Templating engines (like Razor), UI formatting engines (Bootstrap), and routing engines (URL path translation) are also BDGB's with a similar great-until-broken profile.
I most cases it seems we can could have simpler versions that are only a few hundred lines of code. If this few hundred can generate 90% of our SQL or whatnot, then we get most of the time savings without the complexity: we can study or fix a few hundreds lines of code. Chasing that last 10% has created these bloated BDGB monstrosities. Leave it at 90% and enjoy simplicity. It seems components have a handling-curve something like this:
80%: 100 LOC (LOC = lines of code)
90%: 250 LOC
95%: 1000 LOC
98%: 3000 LOC
99%: 6000 LOC
We should strive for components that assist us with grunt-work, not hide the process of doing it behind too much code to grok. If it's simple enough, we can also tune it to better fit shop conventions.
For big development shops, it may make sense to assign specialists to master BDGB. If they work with it all day, they'll eventually figure out most of its quirks and oddities. It's specialization at work. But smaller shops often waste too much time trying to troubleshoot irksome BDGB's because they don't have time to focus on any one.
For example, Django's ORM protections against SQL injections: https://docs.djangoproject.com/en/2.2/topics/security/#sql-i.... Of course, not all ORMs have this feature.
pymysql / psycopg2 is doing this piece of it
Is there a driver out there that doesn't support parameter substitution?
Does no one teach prepared statements in schools/online classes/textbooks? Does no one go digging underneath their abstractions to learn what they're providing of value and what is easily available without that abstraction?
> ORM protections against SQL injection attacks are worth the clunky syntax. Change my mind.
You can create what's called a prepared statement in most databases. This is a statement that contains placeholders for parts of the query that you want to change at runtime. You then query against this prepared statement, providing arguments for each placeholder. These arguments are typically typechecked and protected against SQL injection.
(I only say "typically" because I'm sure someone out there can provide a database/driver combo that does the wrong thing, but I'm equally sure you can find a misbehaving ORM that does the wrong thing as well.)
I’ve worked at places that have taken both approaches and each has advantages and disadvantages but I would take writing my query over an ORM any day. I am just too paranoid too leave any performance on the table and don’t want the cognitive overhead of an ORM getting in the way of my queries. That being said SQLAlchemy is pretty awesome when used as a query builder — I think that’s a good compromise if you must use an ORM and it is SQLAlchemy then I’d use it like that.
The social problems imo are:
- (contrary to blog's assertion) most programmers are scared to death by SQL, and are far more comfortable with iterative programming than declarative/functional programming in SQL.
- ORMs in part also addressed organizational issues. Databases used to be the domain of DBAs. ORMs to a large extent wrested the control over the "Domain Model" from DBAs to Programmers.
From a purely technical pov I believe that the facility of user defined functions (c.f. Postgres) permit for complete and coherent structural and semantic description of a domain model in the database. However this is nearly universally hated by programmers as it requires programming in the database.
HTTP adpater receives request -> object is loaded from database as immutable object -> business operation creates a modified copy -> DB adapter persists the new object.
The last step could be improved if the DB adapter would look what was actually changed. Since everything is immutable it is as easy and fast as comparing references (like React does btw). This works even with Domain Driven Design, what is usually used with mutable entity objects.
[1] https://blog.ploeh.dk/2016/03/18/functional-architecture-is-...
No, ORMs are usually solving the wrong problem entirely. Your code objects and your schema are solving different problems and should not be directly mapped.
As for other things they do...In my opinion, they usually ruin the declarative beauty of SQL by creating a lot of imperative methods, but I admit this is _mostly_ a preference thing.
That said, _I've_ never seen transactions or complex queries done properly in an ORM without simply giving up and using SQL.
But I still use a thin ORM. Who wants to write a result set parser? You just need to reason about and be aware of when you want to create an object for a custom result columns that aren't mapped to a table vs when you want to do the default ORM thing and nest table mapped objects.
None of the ORM/ODMs out there come even close to satisfying half of these requirements... so I'd rather implement an app-specific one that does 50% of this for the 1% restricted subset of functionality of the app I have...
I think tackling the most general problem would lead to a way more interesting solution but I have to time to work on this :P
Bonus points for open source examples.
The SQL variant used by SQL Server and Sybase, called Transact SQL, also has elements of procedural programming such as variables, flow control with IF/THEN/ELSE/switches/etc, looping, calling other SQL procedures or user defined functions, exception handling and so on.
These features have been part of SQL Server since the 90s. It's extremely powerful and if used well, can be much more elegant and flexible than an ORM in my opinion. It's a shame that PostgreSQL doesn't support stored procedures quite as well or have an SQL variant that's as well crafted as Transact SQL because I'd love to be able to write apps in that style again. Somewhat recently I believe PG got something like stored procedures that can return multiple heterogeneous result sets and although I can't remember the specifics, I don't think it's quite as robust as what you can do with TSQL and it lacked client/driver side support maybe... (Also PG has some support for running TSQL itself, but not with all the same features.)
Here's an example of how you might call a stored procedure in C#, using a library written by Marc Gravell of StackOverflow - https://stackoverflow.com/questions/5962117/is-there-a-way-t...
But I do have a pain point around migrations - I have to either painfully hand-write migrations for the deploy code to deal with, or hand-alter the production database to make it fit the new schema. Neither of which are good solutions.
Totally stoked to see the automigrate tool. It completely fits my pain point. Ideally I can have a line in my make file that compares the current schema with the last-known production schema and automagically generates a migration for it.
I'll have a play with this, and contribute if I can.
It can make what you built slow if you don't know how to use, if it doesn't allow you to do optimize find an alternative or use SQL if you have to.
ps: I love ORMs and haven't find a better one than Django ORM when working with Django, everything seems integrated tons of flexibility and database specific features are included as well. Forms, Admin, Search, Sessions, Auth, REST Framework and many other things are well integrated with the ORM so things works smoothly as possible.
You might have some 'data access layer' class/module but if you're using an ORM, it more than likely accepts as arguments and returns as results some ORM objects. The ORM objects returned from the data access layer invariably have methods like `save()` or `delete()` or `validate()` or some magical property when called ends up issuing another query (think: `customer.orders` or `order.customer`). The objects you're supposed to pass to your data access layer as arguments probably also have constructors that, under the hood, grab static references to singletons (often the connection factory).
In this way, the data access layer is leaking the opportunity to (if not the responsibility of) invoking save/delete/validate functionality to any and every part of the code base, when it should really be contained in one module. Moreover, you cannot reason about whether a particular data object is persisted, could be persisted, or was persisted just from its type.
That is, your Customer object always exposes `save`/`delete`/`validate` even if you got it from a data access layer method called `getCustomersWithRecentOrders`. (Idem for the elements of `getCustomersWithRecentOrder()[0].orders`.) There's nothing stopping you from pulling out `getCustomersWithRecentOrder()[0]`, mutating it, then calling `save()` (or `validate`, or `delete`)—which never-ever makes sense.
Instead, `getCustomersWithRecentOrders` should return behavior-less structs, and your data access layer should provide an `updateCustomer` method. Moreover it should be impossible to construct the type that goes into `updateCustomer` with data that does not validate.
When you implement this kind of data access layer, maybe you use a low level DB driver, maybe you use an ORM for some niceties, but either way, you shouldn't leak types from these dependencies out to the consuming module. However I rarely see this kind of discipline in 'naive' ORM apps (rails/django/laravel/CoreData/Hibernate/etc).
Why not replace "vs" with "and"?
On the other hand I can not stand, no, really really really cannot stand clever 1000+ line SQL queries. Writing an entire program in one dense line doesn't show how smart you are, it shows you don't have a clue about the bigger picture or else lack respect for the time and mental health of others.
For most cases on a team and for maintainability I've finally decided the advantages of a good ORM and migration tool where you can commit migrations in a rational and standard pattern far outweigh the disadvantages. But this is not because I don't know or respect SQL.
Maybe don’t use a half baked ORM.
I’m experiencing schadenfreude that someone is having ORM problems in 2019 long after the NoSQL hype died.
I can remember on zero hands the number of times my not half baked ORM was the source of my problems such that I wanted to say “fuck it lets just write SQL”. ORM patterns are pretty well laid. Hell they haven’t changed much in at least a decade or more and they work great for the problem they solve. Which makes me think maybe a relational store is not right for whatever you’re using it for.
tl;dr If you have ORM problems I feel bad for you son, I got 99 problems but SQL ain’t one.
SQL has the ability to target multiple backends.
I think that our goal as enterprise software developers is to automate business logic (business constraints) in a way that more-or-less a domain expert can read and understand. To create a precise language that facilitates fast and robust communication between experts and developers. New hire to the team should be able to learn domain expertise from the codebase because your codebase should contain easy to read domain model.
Relational algebra and SQL is not a very expressive natural language. SQL limits your vocabulary to 4-5 verbs unless you start writing procedural code in procedures etc. but SQL is not a good procedural language. Relational databases are a solution to specific technical problems of execution speed, atomicity, consistency, isolation, and durability (ACID). They excel at that, not at communicating intention.
You should use ORMs (preferably Data Mapper) if your goal is to solve the first problem while using off the shelf solution for the second problem. Data Mapper allows you to isolate your domain model from technical aspects of data storage. This will work very well as long as you will actually be able to ignore technical aspects in your domain model.
You can do the data mapping, querying, migrations, and all this technical cruft manually with a handwritten SQL if you want, but SQL certainly will not address very well the first goal of creating an expressive domain model that facilitates robust communication between developers and business experts.
And to address Active Record. It's a solution in the middle, you don't get full isolation, because database details creep into your model. The tight coupling of the domain model to ORM also makes it difficult to swap it for a handwritten mapper. But it's a very good tool for fast iteration and prototyping.
And to address the only point from the article that is not moot based on my explanation above.
"We can’t have nice things until we move our schemas out of our app languages and into declarative spec languages like SQL, proto, or even jsonschema / swagger (though I’m not a great fan of the last two)."
SQL, jsonschema / swagger can't express business constraints. I'm working for a university. How do you express that "a student internship in some company A can not fall in time between some two other internships if the two other are in some company B" in a declarative non-domain specific language and make it still readable? You can write a declarative domain-specific language, but I wish you luck on your adventure trough parsers, interpreters, planners etc. to create that declarative domain-specific language in the first place.
- Heavy ORMs: Object-oriented mapping layers that map classes to tables. A record is represented as an object that not only carries data but also implements various behaviours for storage, retrieval, serialization and deserialization of its own data, sometimes it also implements business/domain logic. Examples: ActiveRecord, EntityFramework, Hibernate, Sequelize, ...
- Lightweight query builders: Lightweight, programmatic abstractions on top of SQL. Examples: knex.js, pypika, ...
There are issues with both approaches.
Heavy ORMs
Heavy ORMs make it seem like a developer doesn't need to know SQL to work with a database. They help to get a project off the ground quickly, but as a project grows and requirements become more complex, they often generate slow queries or generally lack capabilities to express the right queries. After all, a developer will be forced to understand what's going on in the SQL layer underneath, spending a lot of time debugging and understanding the generated queries, often without the possibility to optimize them. The premise of "not knowing SQL" and productively working with database longterm has just been proven wrong in so many cases (I feel like everyone has been burnt by an ORM at least once in their dev career...).
Lightweight query builders
Lightweight query builders on the other hand don't try to abstract too much away from SQL. Their APIs typically use SQL terminology and they're not trying to hide the fact that they're working with a database. Coming from an application developers perspective, there still is the drawback that you must have a solid understanding of SQL to work with them efficiently. Another issue that these query builder APIs typically aren't type-safe. There's no static analysis that can help identify typos or other issues in the DB queries you're building. In terms of developer experience, they lack features like autocompletion for DB queries. Personally, I still prefer query builders over heave ORMs since I get more control.
Time for a plug: I work at Prisma where our mission is to make it easier for application developers to work with databases by providing developer-friendly, efficient and performant abstractions and tools for working with database! We're currently building the Prisma Framework [1] which consists of two main tools:
- Photon [2]: A lightweight, auto-generated and type-safe database client
- Lift [3]: A tool for declarative data modeling and database migrations
You can learn more about both tools in this blog post: https://www.prisma.io/blog/announcing-prisma-2-zq1s745db8i5/
Both tools are based on the Prisma schema [4], a declarative abstraction over your database schema that serves as foundation for the generated Photon API. Lift migrations work by mapping the Prisma schema to your database schema. We also build a ton of other nice tools and libraries that simplify database workflows (such as Prisma Studio, a database GUI) or integrations with API layers, such as GraphQL Nexus [5] for building type-safe GraphQL APIs without boilerplate.
To get started, check out this tutorial: https://github.com/prisma/prisma2/blob/master/docs/tutorial....
[1] https://github.com/prisma/prisma2
[2] https://photonjs.prisma.io/
[4] https://github.com/prisma/prisma2/blob/master/docs/prisma-sc...
It makes it much easier to avoid errors and tab-complete relations across the object graph.
> Some ORMs offer to ‘mirror your live DB’ into a schema. That’s insane to me. It makes any type-checking or linting impossible. You have no idea what you’re getting.
Hm, I believe the author is talking about SQLAlchemy's sqlalchmey.ext.automap: https://docs.sqlalchemy.org/en/13/orm/extensions/automap.htm.... This is a relatively new feature in SQLAlchemy, no need to use it if you don't want (but it is pretty cool).
I can't speak for everyone here, but I doubt most ORM users are using it that way.
> Your test suite will be useless because your local DB probably is getting spun up from a schema.
Most likely a test database is spun up (if it's not a sqlite in-memory db). In the case of django it'll automatically prefix test_{databasename}, run migration files, and go to town.
> If not, then you have another hard problem of doing regular prod dumps to your dev environment. Yuck.
Which leads me to another benefit of ORM, adding fixture data to simulate very intricate states is much easier to do and maintain. Between tests, most likely, transactions are being rolled back.
Since you are using python, you'll be able to use pytest fixtures: https://docs.pytest.org/en/latest/fixture.html. I really like pytest-django, but I'm not sure how well the sqlalchemy equivalent is though. Maybe that's the area you could have having issues with, since when they have bugs, they're notoriously hard to figure out (is it how I'm bootstrapping sqlalchemy? is it my models? is it my fixtures? is it a pytest bug? or the pytest extension?)
> There’s always some surprise about the connection or pool type. It’s never straightforward, it’s always badly documented, it has twice as many layers as necessary because it has to manage bespoke session tracking and table registries. Transactions are undocumented or mysterious, autocommit is implied or hidden.
I recommend Django's ORM. I've used SQLAlchemy for years and couldn't tell you the default behavior cold. I believe autocommit is off by default unless you specify otherwise (https://docs.sqlalchemy.org/en/13/orm/session_api.html, https://docs.sqlalchemy.org/en/13/core/connections.html)
> We can’t have nice things until we move our schemas out of our app languages and into declarative spec languages like SQL, proto, or even jsonschema / swagger (though I’m not a great fan of the last two).
You could have your django (or whatever is using the ORM) server / etc. run an API. What are you building exactly? graphql will get you a typed API and have other languages use it via whatever you like (OAuth2?)
You're probably going to a main programming language of some sort doing the CRUD/migrations. Why? There's normally business logic / layer stuff on top, that's another +1 having other languages access via API may be used so there isn't duplication.
Are there cases where making API wouldn't work? If that's the case you're probably well beyond the ORM questions, and probably learned to just keep ORM to the basics - simple ForeignKey relations, staying away from polymorphism / stuff that doesn't map well to other languages where you may have to access SQL.
Of course it's backwards.
It's like one of the biggest legacies of web development next to javascript, html and CSS. SQL is a very specific way to think about data and a very high level and leaky abstraction. So high and leaky, that you literally need to look at the query plan in order to optimize sql queries. If Database IO is the bottleneck for web development the best abstraction for this area is most likely a zero cost abstraction (rust for example). Instead we have SQL and we create a generation of SQL hackers who memorize a bunch of technology specific hacks to try to get things to work. Why is SELECT * bad? It's a hack you memorized, a leaky abstraction.
I'm not saying schema-less is better hear me out. I'm saying SQL is a bad API. You can probably make some api language that's more explicit and imperative on top of something like postgres. Change the API itself don't write something on top of it.
Either way, an ORM on top of SQL is like what type script is to javascript. You know when they make an API on top of an API it's to cover up something that doesn't work well. Unless it's a zero cost compilation such fixes only make performance worse.
If you insist on using some abstraction on top of sql the way to do it with zero cost is an api that is bijective to the syntax of SQL itself. Type script I'm guessing is relatively isomorphic to javascript, so it works out.
Something like VSAM or IMS?
The relational model and declarative query languages were invented because the previously existing imperative navigational model was far too inflexible and difficult to use. Access paths were baked into the data structures, you had to be your own optimizer, and if you optimized for the wrong usage patterns, you had to refactor everything or live with poor performance.
For example:
query1 = select(table, columns, optimization_method="auto")
query3 = join(query1, query2, joinOptimizationMethod="auto"...)
Optimizer hints is a good idea. As far as I know this isn't a well known feature (if it even is a feature) for postgresql, which is the DB I have the most experience with.
Raw SQL is great at what it does.
ORMs are typically good at what they do.
Depending on the use case both have their pain points. Typically I see a mixture of ORM for most CRUD based stuff and raw SQL for cases where the ORM isn't suitable.
No need to throw the baby out with the bathwater.
It means you can literally have the API do exactly what SQL does. Automatically specify the query plan. But you explicitly do it.
Meaning that sometimes the auto planner screws up. When it screws up, in those cases you explicitly make it use another algorithm rather then modify SQL to be slightly different and hope that it compiles into something more reasonable.
The baby is rosemary's baby.
A bunch of performance problems aren't even solved by tweaking the query either. They're solved by changing the database structure. Adding the appropriate indexes and such. Making sure datatypes match between joined data and no implicit conversions are happening. Right-sizing columns.
No amount of specifying your own query plan will do anything to affect those kinds of issues.
In practice I find mostly it comes down to cardinality estimate issues as a very common source of problems as the database either over or under provisions enough memory for the query. If it estimates it's going to get back a lot more data than it actually does and it grants too much memory that will reduce parallelism because that memory can't be used by other queries. If it under-estimates it doesn't grant enough memory and when it gets back more rows that will fit into memory it has to write them temporarily to disk taking a massive hit in I/O performance.
How does your scheme work with figuring out how much memory the database should grant to a query when specifying a plan by hand?
What's more is plans change over time as statistics change. Leaving it up to the query optimizer means it's adaptive. Having to specify yourself means you have to know the optimizer isn't giving you the best plan at design time. There are some cases where you know that. There are some cases where you don't.
You can already specify query hints etc. I think SQL just has this covered already. I have no qualms if the query changes slightly.
https://github.com/Microsoft/TypeScript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
E.g. I have nodes that can be listed and sorted in various ways. That might mean having to join and/or select on additional tables (taxonomies, tags, sources, auhtors). I have a lot of code to generate exactly the query I need for any given listing, but how do I do that without string concenation? Make a dedicated query for each permutation of additional tables I need or don't need?
I'm not so much looking for an explanation in your own words, but just a point on what to search for, or maybe some articles that skip right to such "advanced" stuff.
In addition, ORMs greatly increase the surface area for troubleshooting. Instead of just being able to run and profile a query, you now need someone who is experienced in the internals of the ORM and how it "plans" a certain query. Of course, because ORMs do not understand how the data is actually laid out, when they say "plan" they mean create the SQL string.
So in place of a query you will need to learn the conventions of the particular ORM, leaks and all, and how that maps to the SQL. Then you will still need to understand how that SQL actually interacts with the table schema.
The end result is normally one backend person who is very territorial over their code, and who acts as a bottleneck for the rest of the team. If you're using an ORM I'm sure you already know who this guy is...