Database-specific "SQL extensions" are in my experience just administration commands, e.g. `VACUUM` or `CREATE EXTENSION` in Postgres. They help operate the DB, but have little to do with the actual data manipulation.

Killing SQL is like trying to kill computer science: you better come up with something better than a synonym.

[0] https://en.wikipedia.org/wiki/Relational_algebra

The notion that nothing has changed about B-Trees or sorting algorithms doesn't, I think, actually hold up... but this question is more like changing the API for a standard-ish B-Tree library than about modifying how the B-Tree itself works.

As an aside: LINQ is the only “ORM” that is truly worth using. It is integrated into the language and you can use the same expressions and query expressions are first class types that can be passed around as expression trees and are then only translated either as code if you’re using in memory lists, database specific SQL, MongoQuery etc based on the provider you pass the query to.

I've been thinking a lot about ORMs and they almost usually fall flat at some level since they are not nearly as expressive as a first order language is. I haven't had experience with LINQ but I know the Django ORM is this way.

I'm thinking the best approach if I had time/money would be to develop a better query language that is close enough to SQL for people to learn, but then also translates back down to SQL. LINQ looks really close to this, but I'd really want it to be cross language.

One of the aspects of QUEL that I really liked was that if you couldn't describe what you wanted, you could program it out if you needed to. Say you had some complex ranking algorithm that was hard to write in SQL directly.

Have a look at EdgeQL, the query language that powers edgedb.

Cross language will still be an issue. LINQ isn’t really an “ORM” it is a method to translate C# commands used to work over related collections to expression trees and those expression trees are interpreted/translated at runtime by a provider.

You need support from the language/runtime to treat expression trees as a first class type.

expression tree parser. It’s not for the faint of heart.

Not if it uses CTEs or other advanced SQL features. It can do basic select, join, group, sure. But that's just scratching the surface of how powerful SQL is. As for requiring language support: in Elixir, Ecto.Query essentially implements a LINQ-like DSL as a library (albeit with macros). See https://hexdocs.pm/ecto/Ecto.Query.html

In C#, if you did something like this

var seniorMales = from user in users where u.Age > 65 && u.Sex == “male” select u

seniorMales is just an IQueryable<User>

Later on you could say

var gaMales = seniorMales.Where(s => s.State == “GA”)

var floridaMale = seniorMales.Where(s => s.State ==“Florida”)

All three of those are just expressions that have not hit the database yet.

Then when you want to actually run the query and limit the number of rows, you can do

var result = floridaMales.Limit(20).ToList()

It would then create a query including a limit clause just as you would expect it to.

Now if you do

floridaMales.ToList().Limit(10)

It would return all of the rows in the table to the client and it would be limited on the client side (don’t do that).

Worse case, if LINQ can’t express in query syntax, a provider can add its own extensions in function syntax and the provider can still parse the expression tree.

LINQ also can't do window functions. Like I said, it covers the basics of SQL that most applications need. But as soon as you need to go beyond that you'll be writing custom SQL.

I grew up with SQL so LINQ for a long time just reads so weird to me. I won't admit how long it was before I realized this fact you stated.

It still reads weird to me, but I get it.

I concur, SQL is a liability in many ways.

SQL is a language that expresses relational algebra, killing it is like killing any other language i.e. nearly impossible because there is still code sitting around written in it, thus needing competent programmers to maintain, competent programmers who will be asked to write more code and will choose to do it in SQL if they can get away with it, rinse and repeat and so forth.

Once a language has a certain installed base it may be eternal - what size that installed base is I don't know.

Could there be a better alternative, oh yes, most certainly.

E.g. Every company I worked for needed to do SQL migrations. Unfortunately that isn't a part of SQL. Could it be? Yes! Is it? No! So everyone re-invents the wheel...

[0]: https://en.wikipedia.org/wiki/The_Third_Manifesto

I've been trying to get my hands on a corpus of Dataphor code, because Tutorial D is unsatisfactory for different reasons than SQL. What I've been able to glean of Dataphor is quite promising, but it isn't much.

On one hand, it might be nice to have completely portable triggers. On the other hand, the lack of standardization allowed to have approaches as differer as PL/SQL and T-SQL to emerge. It would be terrible to be stuck with some ancient COBOL-like SP / trigger syntax, required by the standard.

Granted, stored procedures and triggers aren't part of the relational model (strictly speaking). But they are the first layer built on top of the core - the one that packs multiple queries/statements together, introduces variables, and allows custom logic to be executed when the data changes.

The lack of standardization means that migrating from e.g. Oracle to Postgres/MySQL involves knowing the dialects and statements supported by each of them in order to migrate the PL/SQL or T-SQL logic - and, especially in the case of MySQL/MariaDB, some of those constructs may not be available at all.

That creates really a lot of friction when it comes to database migrations, especially for large databases. I've myself been working with this stuff for years, but I still have to regularly lookup how to write an IF or declare a variable for this or that DMBS, since we have such a proliferation of dialects and standards that one person can't keep all the variations in their mind.

Ad-hoc syntax with hard-to-compose parts,is what makes SQL arbitrary and quirky. I would like a more algebraic syntax, with uniform, composable parts.

Actually, no. Relational algebra is based on sets. SQL is not. Perhaps you are confusing SQL with QUEL, which was used by Postgres in its younger days? QUEL evolved out of Codd's original Alpha language.

SQL is only relational-like. With experience and care you can craft your queries in such a way that you do get true sets (e.g. using DISTINCT), but I see beginners – and sometimes even experts in a rush – overlook this quirk in a complex query resulting in what might seem okay in limited testing, but blows up when there is more data than the bare minimum needed to test. A language that truly follows Codd's relational model would produce sets always.

I expect SQL is considered difficult to learn exactly because of this deviation. It's quite unintuitive, especially if you go in thinking that it is actually relational. Nothing you can't work around with sufficient knowledge and experience, but that added knowledge and experience required is where the difficulty no doubt stems from and undoubtedly scares many away out of frustration when it doesn't work like you think it should in the interim.

Regarding your complaint ... those aren't identical constructions.

Insert has to have the syntax it does because it lets you insert multiple rows all with different values in a single statement:

    INSERT ... (C1, C2) VALUES (v1, v2), (v3, v4), (v5, v6)

    INSERT ... VALUES (C1=v1, C2=v2), (C1=v3, C2=v4), (C1=v5, C2=v6)

  INSERT ... VALUES (C1=v1, C2=v2), (C3=v3, C4=v4), (C5=v5, C6=v6)

  INSERT ... (C1, C2, ... C6) VALUES (v1, v2, DEFAULT, DEFAULT, DEFAULT, DEFAULT), (DEFAULT, DEFAULT, v3, v4, DEFAULT, DEFAULT), ...

Think bulk inserting into via the command why the format exists vs what you do in your language and/or framework.

You are not inserting a few dozen records, you are inserting a several thousand records each query.

In fact, sometimes insert speed is measured in MB per second, not records per second.

Having the syntax identical is inferior in every way except for the case where you want to build the string up programmatically, and that is a trivial problem to solve for the caller.

I think your proposal works better for an UPSERT keyword, as that is yet another different intended action.

I don’t know who “we” is, but, before NoSQL tried to replace RDBMS(well, starting before, these things overlapped it), there were efforts to provide alternatives to SQL for RDBMSs, some examples:

Query-by-Example. https://en.wikipedia.org/wiki/Query_by_Example

the D class of data languages. https://en.wikipedia.org/wiki/D_(data_language_specification...

https://prql-lang.org/

Every single one, without exception, lets you do incredibly powerful things compared to its complexity.

This hasn't stopped the progression of general purpose programming languages, and I think the vast majority of developers would agree that there have been huge improvements in the last 50 years.

Apart from SQL, all of these languages are imperative, and most of them derive from C in some shape or form; K&R C was published in 1978, 44 years ago.

So, while I'd agree that there have certainly been huge incremental improvements in imperative programming in the last ~50 years, when we look at what people use on a daily basis, it's not really clear to me that the changes have been any greater in significance than the changes that SQL has gone through during the same period.

[0] https://www.tiobe.com/tiobe-index/

SQL is really good at its in-place one-time this-specific-case relational querying.

Things SQL is not even mediocre at: programming.

Lots of stuff untilizing SQL and the ability to do lots of cool stuff with SQL, doesn't actually mean that the Developer Experience or ergonomics around using SQL are good.

Much like C code say, it's not a feature of C the language that gcc, for example, can optimise your crappy code for whatever target platform.

For instance

var seniors = query(from u in Users where u.Age > 65 && u.Sex == “male” select u)

IEnumerable<Users> query(IQueryable<Users>)

I know people hate listing the fields before the tables, but when I deal with data that's how I tend to think. I need X, Y, Z, now where do I get it from and do I need to filter.

From a vendor standpoint, it can take time getting new standards implemented, but it's definitely not half. One of the big issues here is that MySQL was and still is in some ways woefully inadequate. It's shortcomings are also often make people think they are relational database shortcomings in general.

I think SQL is so popular and has staying power for the same reason that our ridiculous gregorian calendar has staying power.

You can't change this by providing a better query language in one of the datasource products, or by providing a better ORM. Because of the network effects it will be very hard to come up with something better that is universally adopted. Many have tried though but it's always limited to a small stack: MDX, DAX, Linq, Dplyr, Graphql, etc.

There may be opportunities to replace SQL as soon as we need something that moves away from relational algebra. Currently you see al lot of adoption on graph storage and graph query languages in the Data Fabric space, as users need to build queries reasoning about relationships between different datasets in the enterprise.

The other reason Data Fabrics could offer an opportunity here is that they're basically adding an layer of abstraction between all data sources and data consumers, and they the possibility to translate SQL into something else, e.g. graphql.

That's irrelevant and a fallacy. Plenty of amazing things are old and have stood the test of time. Vim and SQL have empowered me my entire career.

There is an alternative INSERT syntax matching the UPDATE SET one:

  INSERT INTO table SET col1='val1', col2='val2';

I do like SQL the syntax but it's hard to discard the ecosystem around SQL.

For instance, I made better-sql [1] recently which generate SQL from a language similar to GraphQL and EdgeDB query language.

[1] https://better-sql.surge.sh

SQL and dbs are fine, they work, we could discuss beautifying sql for sure, but frankly we shouldnt solving data structuring problems that were already solved 50 years ago: we have real business to support instead.

Also, surely allowing FROM and SELECT clauses to be swapped in order shouldn't stress out any parser too much.

Not that this is particularly relevant to the discussion, but GraphQL is actually much closer to SQL than most NoSQL solutions because it's statically typed and the schema is statically defined. The GraphQL schema acts as the contract of what the API can serve to the client, much like a schema in a relational database.

https://en.wikipedia.org/wiki/Object_database

I also wanted to kill old, obsolete, crummy SQL when I first learned it as a teenager. It reminded me of FORTRAN or COBAL or something.

It took quite a lot of theory and maturity to understand why RDBMS is the right paradigm. SQL is imperfect, but it's good enough to not be worth replacing.

Whenever I've seen benchmarks of postgresql managing JSON versus mongodb, postgresql won (at least comparing apples-to-apples; postgresql is much more conservative by default, in terms of reliability and data consistency, than mongodb). There's literally no reason to going with a lot of NoSQL databases over using a SQL database as a key-value store.

Great way to put it.

> There's literally no reason to going with a lot of NoSQL databases over using a SQL database as a key-value store.

To be devil's advocate, the arguments I hear range more on ease of ops/scalability for noSQL, apparently some do it better than SQL implementations, and some drivers can make trivial operations easier as well. That said, I'd pick PostgreSQL for pretty much any new project I get to work on.

You run into issues with things like JOINs across shards, but there's literally no upside even there to NoSQL versus using a SQL databased in a disciplined way. I've designed systems with the same type of infinitely horizontally scalable KVS-type storage for all the changing state in SQL.

SQL also means you can do things like:

- Do local joins (if e.g. all the data for a user is in the same shard)

- Keeping a small set of relational data (not the stuff which needs to scale) and have one technology

- Use read replicas to scale some of the stuff which doesn't fit in the "infinitely horizontally scalable KVS" model

.... and so on. You can't not think about it, but if you do think about it, it's all upside and no downside.

> It took quite a lot of theory and maturity to understand why RDBMS is the right paradigm.

You and the guy you are replying to are doing it again, conflating SQL and relational databases. :(

1) "RDBMS is the right paradigm." That's high praise. RDBMS is exactly the Right Thing, with a trademark and all.

2) "SQL is imperfect, but it's good enough to not be worth replacing." That's definitely not high praise. It means that this implementation has all sorts of warts and annoyance. However, that doesn't rise over the bar of replacing.

As a teenager, I definitely did conflate the two. Perhaps that's where your misread came from?

SQL is here to stay, if only because few can be convinced that another language could query the RDBMS directly (without first compiling down to SQL). Peolle also keep persisting in this belief it’s standardized, for god knows what reason

In practice:

1) Most attempts to do this historically have missed the point of an RDBMS (e.g. most ORMs).

2) SQL is cumbersome, but it's not quite cumbersome enough I'd want to bother with something else.

As a footnote, I find SQL-grade standards to be incredibly helpful. In 2022, I wish there were a strict standard, but in new domains, standards like this mean:

1) I can read code for virtually any database, with maybe gentle use of search engines, unless I get into really hairy corners. Ultimately, those, I can look up too.

2) If I write code for a new database, I don't need to learn anything; I can understand syntax with a few web searches.

3) For 90+% of SQL, I can write automatic scripts quickly and easily to translate between databases (when migrating, the remaining few percent, someone can do by hand, and it's a very tractable chore).

That's not the case jumping into a graph database or other stuff.

At the same time, they don't over-constrain things. If you want your database to have a BCD type, JavaScript stored routines, or some wonky form of virtual tables, you can.

Now, in 2022, we know enough to standardize all of this stuff, but I'm not sure we did when these tools were coming out.

As a footnote: I'm working in a domain with no standards, and if everyone could just pick JSON (or XML, or just about any one thing), we'd already have 50% of the benefit of a full standard. If people standardized a few nouns and verbs (e.g. 'user' versus 'user_id' versus 'actor' versus 'agent' versus ...), we'd be another 50% of the way there. Systems do different things, and I don't think going 100% of the way makes sense until we understand the domain better, but partial standardization is a huge win. I've been pushing hard for having partial standards, and hard for not having full standards yet.

Tuning SQL on the other hand is a guessing game with the query planner. You can express a lot more but 1% of those expressions could down your DB.

I like both, but if you want performance NoSQL is the winner.

Performance for which use cases?

It matters to clarify this because if you get rid of every use case SQL+RDBMS optimize for, NoSQL is obviously going to be the winner. A car with no safety measures and without most consumer-oriented features is probably going to be "faster" than one that has them.

  INSERT INTO table1 (col_1, col_2, col3)
  SELECT COUNT(col_6), col_4, col_5 FROM table2 INNER JOIN...

    INSERT INTO table1
        col_1 = COUNT(table2.col_6),
        col_2 = table2.col_4,
        col3 = table2.col_5
    FROM table2 INNER JOIN ...

    update t set (x, y) = (n, m);

/s