It powers the best tested technology on the planets.
We've been writing relational databases for 45 years, and it has worked.
We've been writing relational databases for 45 years, and it has worked.
You mean they got better? I've been using Postgres, MySQL, and SQL Server professionally for over 20 years (Postgres for only 15), but they've only become better & faster over time. I don't see this "technology craze" you mention, but I have seen things like parallel processing, bitmap indexes, selective indexes, query plans that use multiple indexes, online index creation (no locking), support for complex data types like JSON or XML, partitioning, vastly improved resource usage (CPU+memory+disk) just to name a few.
This doesn't even include some of the cool vendor-specific features like foreign data wrappers (FDW) in Postgres, embedded language runtime support (SQL Server can do .NET, Python, R) and so on.
> so vendors add their own features
Of course they do, RDBMS' are products.
Well, I mean they support new features, which is perhaps "better", though when you continue to tack on features for decades, perhaps something was wrong with the relational algebra model to begin with. Also, you're conflating "SQL" with advancements in individual implementations of a relational database. Relational databases are great; I just think we'll find a model that is more suitable for application development than "relational algebra + the kitchen sink".
1) Hierarchical data. The relational model is fine here, but the query syntax isn't awesome.
2) Understanding remote data. This is outside the scope of the data model, but it does effect the way software is built.
Neither of these show any evidence that the relational model should be replaced.
There are many more issues, you've just gotten used to eating SQL turds. Hierarchical data is solved by CTEs, so that's not a big deal.
The bigger deal is that relations themselves are impoverished second-class citizens. This means you can't write a query, bind it to a variable and then reuse that query to build another query or store that query in a table column. Something like:
var firstQuery = select * from Foo where ...
var compositeQuery = select * from firstQuery where ...
create table StoredQuery(id int not null primary key, query relation)
insert into StoredQuery values (0, firstQuery)
Then there's pervasive NULL, inconsistent function and value semantics across implementations, and a few other issues.
How would the database engine efficiently resolve any references to the embedded relation ?
I'm also having trouble understanding how your INSERT statement would even work - is the firstQuery being inserted being treated as hierarchical data where every single relation attached to every single row has its own schema/structure ? Or is the structure fixed in the CREATE TABLE statement so that you can't use just any relation, but rather have to use a relation that conforms to the structure defined in the CREATE TABLE statement ?
Relations are functions over these sequences. The embedded relation is then a stored function over a set of nested sequences. So to add the concrete types:
var firstQuery = select Id, Name, Payment from Foo where ...
var compositeQuery = select Id, Name, Total from firstQuery where ...
create table StoredQuery(id int not null primary key, query relation{Id int, Name Text, Payment decimal})
insert into StoredQuery values (0, firstQuery)
But first-class relations would also present some optimization challenges, so a subset of a relational system with a restricted form of first-class relations corresponding to materialized views that can be stored in table columns and used in queries would get pretty close.
Apache Spark is the best example I've seen of this in that you can compose schemas on the fly and it will unroll them at the end based on physical layout.
http://farrago.sourceforge.net/design/CollectionTypes.html
However, good luck finding a database engine that necessarily supports such features. Some support array types, but I haven't seen too many that support multisets.
As for pervasive NULLs, isn't that more the fault of schema design?
There are a lot of things wrong with SQL. Little things like the fact that INSERT and UPDATE have different syntax for no good reason.
A few of its syntax quirks annoy me as they're nonstandard in today's languages, but it's only a shallow complaint: using '<>' for its inequality operator, and using single-quotes for strings.
I'm also disappointed in the implementations in various ways: the way Microsoft SQL Server sends query text over the wire unencrypted, in its default configuration. The way Firebird SQL has such a basic wire protocol that you can see significant performance enhancements by invoking TRIM on text-type fields. The way the optimisers are so damn primitive, especially compared to the baffling wizardry that goes on in today's (programming language) compilers.
Somewhat off topic further ranting:
But the core relational model makes good sense. I see little general value in the freeform graph-databases calling themselves 'NoSQL'. (Do we call functional programming languages "No-assignment"?)
Perhaps some of them can scale well, but can't SQL do that? Google Cloud Datastore, for instance - it can scale marvellously, but only because it imposes considerable constraints on its queries. Can't we do the same thing with an SQL subset?
I think NULL defaults are widely regarded as a bad thing by now. You should have to declare what's nullable, not declare what's not null.
> There are a lot of things wrong with SQL. Little things like the fact that INSERT and UPDATE have different syntax for no good reason.
Moreover, SELECT should be at the end not the beginning. Query comprehensions and LINQ did this right.
And yes, the implementation inconsistencies are seriously irritating as well.
> Google Cloud Datastore, for instance - it can scale marvellously, but only because it imposes considerable constraints on its queries. Can't we do the same thing with an SQL subset?
If you extend Map-Reduce with a Merge phase, then you can implement the relational algebra with joins [1]. That scales pretty well.
I'd one-up this and argue that all SQL statements should be in order of execution (within reason). Moving SELECT to the end is definitely a good start.
It's pretty much impossible to decide what an optimised query should look like on a different databases with different data.
Slow network? Fast disks? GPU optimised joins? No way to know what execution order should be, and that's a strength of the relational model.
Those all seem like pretty ad-hoc features, some of which were influenced by prevailing tech fashion trends at the time. For instance, the fact that you need to manually specify so many details about indices sounds like a failure to have a good theory around indexing which can be integrated into query plans. There are a lot of flaws like this in SQL stores as a whole.
> the fact that you need to manually specify so many details about indices sounds like a failure to have a good theory around indexing which can be integrated into query plans
I'm no SQL guru, but this smacks of a variant on the 'sufficiently smart compiler fallacy' to me.
That the system can't always, uh, optimally optimise, isn't necessarily an indication that the system is fundamentally flawed.
GCC permits inline assembly, but that doesn't mean GCC is a failure.
Given that this feature was added to the DBMS pretty late in the game, we can infer that it's only rarely worthwhile.
Postgresql has a handful of plugins that can tell you if you had an indice; would it be used and you can see query plans before/after without actually having to add indices until you're comfortable. This same plugin could easily automate indice creation, but most people don't.
Real life isn't about theory, it's about what works and doesn't work in production. These are not flaws, they're features. I don't need a bitmap index on my primary key, nor does it need to be selective because it will include every row. But having these features and the ability to customize based on the needs of my product is paramount.
If you were not using an RDBMS to store your data, you'd still have these same problems and you'd still end up with optimized secondary indexes based on the lookup criteria.