I run a early stage company that builds analytics infrastructure for companies. We are betting very heavily on SQL, and Craigs post rings true now more than ever.

Increasingly, more SQL is written in companies by analysts and data scientists than typical software engineers.

The advent of the MMP data warehouse (redshift, bigquery, snowflake, etc) has given companies with even the most limited budget the ability to warehouse and query an enormous amount of data just using SQL. SQL is more powerful and valuable today than it ever has been.

When you look into a typical organization, most software engineers aren't very good at SQL. Why should they be? Most complex queries are analytics queries. ORMs can handle a majority of the basic functions application code needs to handle.

Perhaps going against Craig's point is the simple fact that we've abstracted SQL away from a lot of engineers across the backend and certainly frontend and mobile. You can be a great developer and not know a lot about SQL.

On the other end of the spectrum are the influx of "data engineers" with basic to intermediate knowledge of HDFS, streaming data or various other NoSQL technologies. They often know less about raw SQL than even junior engineers because SQL is below their high-power big data tools.

But if you really understand SQL, and it seems few people truly today, you command an immense amount of power. Probably more than ever.

This is largely because of the number of non-programmers who know SQL. Add an SQL layer on top of your non-SQL database and you instantly open up a wide variety of reporting & analytics functionality to PMs, data scientists, business analysts, finance people, librarians (seriously! I have a couple librarian-as-in-dead-trees friends who know SQL), scientists, etc.

In some ways this is too bad because SQL sucks as a language for many reasons (very clumsily compositional; verbose; duplicates math expressions & string manipulation of the host language, poorly; poor support for trees & graphs; easy to write insecure code; doesn't express the full relational algebra), but if it didn't suck in those ways it probably wouldn't have proven learnable by all those other professions that make it so popular.

Unfortunately the vast majority of SQL users aren't that proficient. It's also fairly hard to learn because it's something that you only pick up with experience and specifically longer time experience with a sufficiently complex data model.

I personally would never have gotten good at SQL if I hadn't stayed in my first two jobs for 5 years each working daily with the data-models and the domain.

But as my career has gone forward, I'm touching it less and less; today, I hardly touch it at all outside of my personal usage.

Much of that has to do with the fact that I'm now employed building and maintaining an SPA using javascript and nodejs where the backend is accessed thru a RESTful API; we never get to touch the actual database.

The few times I have seen some queries for that DB - albeit not in our API, though I could probably find them somewhere - all I can hope is that the SQL engine being used does some kind of query optimization on-the-fly, because there's so many inner selects that make me cringe it ain't funny (like I wonder if they've heard of joins and such).

Before, I was involved in a lot of PHP web apps and backend server automation, where I needed to use SQL a lot; I feel like I am getting rusty in it.

To illustrate on `select` queries, you start listing the attributes then the table, while on `updates` you start by specifying the table and then the attributes on which on operate. This illustrate the grammar problem: in one case, you start by bringing what table you will use and set on which attributes, the other the attributes you need, while keeping in mind on which table name since you specify it after. It's not really a problem, but developer tends to hate any kind of cognitive load, and this one source of load.

I'm not an ORM fan, but developper often use the programming language of their application to build SQL queries string, and ie. with such tools you always start by specifying the target table.

Personally I really wished that RDMS would provide another intermediate language, or better, data structure, to interface with them.

I see it on a spectrum between declerative and imperative data structures, and where I think most people go wrong with it is trying to create a monolithic solution to a broad spectrum of problems. I think you need a graduated approach where each data layer is simplifying and satisfying the next, so you're using Tables, Procs, Views, and in-memory constructs in concert. The database is a powerful tool, and SQL is just part of that bigger puzzle :)

Updates aren't different from select statements: first you state what you want, update a table with some new column values, and then you state where this data should come from, and what data you want to update.

And this is why it sucks. Which is fine, the language comes from a different era when our understanding of computer languages was much more primitive. It is understandable that mistakes would be made. What is unfortunate is that there has been little to no progress in improving on those mistakes in this problem space.

In the procedural world, you could also say that C has some mistakes in its design. However, we've gone to great lengths to try and improve on C, for example in semi-recent times with Rust and Go. SQL could really benefit from the same. SQL gets a lot of things right. So does C. But we can do better on both fronts.

Unfortunately, it seems that people regularly confuse SQL with the underlying concepts that SQL is based on. Because there is no basically no competition in this space, it is assumed by many that this type of problem can be expressed in no other way, and that you simply do not understand SQL well enough if you do not see it as perfect. I guess it comes from the same place of those who argue that if you make memory management mistakes in C, you just don't understand C well enough.

That is, minimize the network bandwidth by putting the work on the DB engine.

This of course necessitated creating and understanding proper SQL query building practices. It was real easy to mess up if you didn't know what you were doing (ie - inner selects, improper joins, etc) and cause a combinatorial explosion that would consume all the RAM on the server and grind it to a halt.

That, or bring back a load of data that you then filtered on the "client" - better to let the DB server do that if you can. Of course, this was back when the clients were 486s and early Pentiums with maybe 8-16 MB RAM. Today it's a bit different, but you still want to minimize the network traffic.

We're a machine learning shop, and this is absolutely one of our core design principles.

EDIT: when you read process management above, perhaps it's better to think task management.

SQL is troublesome to some programming types because it seems alien to ask what you want instead of telling the computer what to do and I find most programmers, especially ASD-types (who I think have an edge for some situations, like writing certain code in a huge org like Google) find this an unfamiliar and strange way of thinking.

You're right about some of it (especially string manipulation, which is brutal) but I think you look at it like most programmers. SQL engages more of a simulative mindset—one popular with analysts—than the acquisitive mindset that most software developers outside of data science employ.

My issue with SQL is that a programming language should allow you to compose and name building blocks, and then recombine them to build ever more useful software. SQL doesn't have this structure. If you have a query that almost does what you want but you need to add one more filter, you need to reissue the query (modulo views/temporary tables, which is why I said "very clumsily compositional" rather than "not compositional"). If you have an expression that computes some quantity and then you want to use it with slight modifications in a lot of places, you usually end up copy & pasting it (modulo stored procedures). Modern programming languages have made this sort of abstraction really easy, but it's quite clunky in SQL. You can do it (by using stored procedures, views, triggers, subqueries, etc.), but then most of your application ends up written in SQL and it starts to feel like something out of the 1970s.

My preferred interface would be something like the relational algebra where relations are represented as typed values in the host programming language and operators are normal method calls (or binary operators, depending on language flavor) and importantly, intermediate results can be assigned to variables. I don't care what the particular execution strategy is of the query, but I do think it should be possible to refine, join, project, and subquery using values you've already defined.

When I write a complex query it's kind of like this.

I start with one table (viewed in my head as an excel style grid of results). I join another table then filter on the join / where clause. Again the output is effectively another table. Rinse repeat.

The difference is that SQL is very powerful at what it does, so you don't need to compose blocks in the way that you would in other languages. Just think of everything as a table. The result of every join or filtering clause is another table. That's your building block.

You are going to need significantly less SQL than Java / Python to get the same results from your data.

On the serious side: It's not a bad idea to contain critical business logic in the database. It's shared by any app using the database. Foreign keys esp link and let you cascade changes with no extra code.

Less overall code. Higher guarantees.

Everyone agrees it's a good idea to use a `datetime` field for a `created` field instead of `varchar` and letting the application parse/extract it's own format in there.

It's also a good idea to add triggers that do specific things to keep your db in a consistent state upon actions.

Maybe adding/deleting a row needs to update a counter in a statistics table for quick access because count(*) on that table takes too long.

You could do that it in your app - but what happens if someone changes the data without going through your app.

Instead you add it as a trigger. Now you can be sure that the number in the stats table is always the same as the actual number.

The counter to that is that it can be very difficult to get an API that is as comprehensive and flexible as SQL is, and eventually you'd find yourself re-implementing SQL if you ever tried. Certain reporting scenarios or mass data manipulation scenarios really require actual data store access in order to perform remotely well.

I've found moderate success in doing the reverse and using communication channels from SQL => services either via NOTIFY or queues.

ie. Dispatching an event that signals a non SQL service to do an action. eg saving a file to S3, then updating the database when the action is complete.

This doesn't really work when retrieving data synchronously but in most cases if the data is stored elsewhere - then you probably want to access it via non db methods anyway (ie. for an S3 file, you'd use the url)

WRT JSON, please for the love of god no. Someone made the same argument a month ago, my reply here: https://news.ycombinator.com/item?id=18870838

I don't like it either, but for most scenarios that this sort of thing works for a RESTful API or microservice or equivalent (or whatever you want to call it these days since I think the expiration date on those terms has elapsed) and not all data can be meaningfully coded without a structure like JSON or XML. Or, rather, you can, but, you're reinventing the wheel just like you would with an API trying to be as flexible as SQL. You may run into various system limits on URL length, too.

Events and queues are great, but not all requests work that way. If you're USPS and you're providing an address and ZIP code resolver, you've got different requirements than getting data from one system to cascade across a series of systems with a dozen different asynchronous widgets. You'd have a burden of using a format that your customers would prefer, too.

Not saying microservices don't work, they are great for specific use cases, but 9/10 people just want to microservice everything just to say they use microservices.

> If you're USPS and you're providing an address and ZIP code resolver

Not sure I see your point with this example - isn't a resolver like this just a fixed database of entries?

Until you get to how well it handles misspellings and incomplete information. Addresses are also notoriously difficult to parse. There is some logic and ranking at work behind the scenes. The only times I've seen it consistently fail are when the city is incorrect, or it's a genuinely new address (new construction or address renumbering).

In any event, is a read-only service somehow less of a service? I'd wager read-only services see a lot higher demand than anything.

I've be very much in the market for something more modular than SQL, which had a much more customisable query planner so you could say "I don't care if you go away and compile this for an hour as long as you make it as quick as physically possible and then save the query plan forever".

is this even possible? Afaik, the query plan can depend on the data distribution and needs to be re-optimized when the data distribution changes.

    > My preferred interface would be something like the 
    > relational algebra where relations are represented 
    > as typed values in the host programming language and 
    > operators are normal method calls (or binary operators, 
    > depending on language flavor) and importantly, 
    > intermediate results can be assigned to variables.

There's not a host programming language database library that exposes those things in quite the manner you seem to be asking for, but that's not a limitation of these SQL-based RDBMSs themselves.

In the realm of what's possible right now, you could of course write your stored procedures directly in .NET languages (MSSQL) Python (Postgres) etc.

Your preferred interface sounds like Linq. An IQueryable<T> interface represent a query and can be composed and assigned to variables, and the type T represent the type of the relation or projection. Linq composes a lot more elegantly than SQL itself. The instances does not represent intermediate results though, they represent composed queries, which are executed in one go on demand. But I think that is what you want anyway.

These are great, but their implementation varies pretty significantly. Notably, MySQL didn't have them at all until v8.0, and PostgreSQL's CTEs are... wonky, for lack of a better term (I believe they use an "evaluate and store" method that's closer to a temp table, and as a result they don't optimize well and have been known to have strange side effects). Not all RDBMSs support recursive CTEs, either.

As you and others cite, there's a variety of features that have crept up on the idea over the years, but they're all bodging things on to the side of something they can't really change. I'd love to see the Rust of database querying to SQL's C++. I draw that comparison because I think a lot of the same structural problems are in play preventing it; SQL is just so good that it is actively prevented from being fundamentally improved. It can be incrementally improved, but not fundamentally.

For a few of our customers, that meant that a 15 minute DB change (adding a column) turned into a several hour DB change (rebuilding materialized view).

If they're refreshed immediately, they also incur a penalty on the underlying tables, since any data changes essentially runs a trigger to see if the materialized view needs changes as well.

In our case we had one such immediate refresh materialized view which caused such a performance degradation of an underlying table that we had to find an alternate solution.

Writing a program that makes a temporary table somewhere, and if there's a mistake you have to start all over again?

For manual refresh, I agree, either non-sql or just plain temp tables is a decent alternative. In our case the views do a lot of joins and subqueries, so we've mostly used temp tables.

It is an ugly, verbose language. You can be very familiar with thinking in sets, and still not like SQL. It's what we've got, and the SQL databases available are very, very good products. But I do wish a cleaner language could have won. As you know, anyone who's used LINQ in C#, particularly by directly calling the extension methods .Select(...).Where(...).OrderBy(...), sees how much better it is from a composability standpoint.

SQL is the anti-Lisp. Lisp's design is about 4 pieces of syntax and a few fundamental operations from which all else is built.

Conversely, nearly every operation in SQL is a tacked-on special case to the ridiculously complex SELECT syntax. Filtering results? That's a clause of SELECT. Ordering? Clause of SELECT. Filtering after aggregating? Oh, that's a different clause of SELECT.

Once this philosophy has infected the brain of a SQL implementer, it spreads like wildfire. That's why you even see custom syntax pop up even in good'ol function calls sometimes, like in Postgres: overlay('abcdef' placing 'wt' from 3 for 2).

SQL fans often talk about the beauty of relational algebra. Once you achieve relational enlightenment, SQL is supposed to be beautiful. But if we wrote math like SQL, you wouldn't say 2 * 3 + 4. There would be a grand COMPUTE statement with clauses for each operation you could wish to perform. So you'd write COMPUTE MULTIPLY 2 ADD 4 FROM 3. Of course, the COMPUTE statement is a pipeline, and multiplication comes after addition in the pipeline, so if you wanted to represent 2 * (3 + 4) you'll push that into a sub-compute, like COMPUTE MULTIPLY 2 FROM (COMPUTE MULTIPLY 1 ADD 4 FROM 3).

SQL clauses could have been "functions" with well-defined input and output types, if the language designers had come up with a type system to match the relational algebra.

    WHERE - table<row type> -> predicate<row type> -> table<row type>
    JOIN - table<row x> -> table<row y> -> predicate<row x, row y> -> table<row x, row y>
    ORDER - table<row type> -> list<expression<row type>> -> table<row type>

You understand the idea. Again, as you've mentioned, the LINQ methods[1] are a great resource for people not familiar with this style. But, counterargument. Languages with minimal syntax and great power are often claimed to be unreadable. Sometimes it's nice to have special syntax, to help give a recognizable shape to what you're reading, instead of it being operator / function call soup.

So what did SQL accomplish by making everything a special case of SELECT?

Well, it's got this sensible flow to every statement. You see, the execution of SELECT logically flows as I've numbered the lines below.

      SELECT
    8 DISTINCT
    7 TOP n
    5 column expressions
    1 FROM tables
    2 WHERE predicate
    3 GROUP BY expressions
    4 HAVING expressions
    6 ORDER BY ...

Ignoring completely the syntactical design, the lack of basic operations is a pain too. Why can't I declare variables within queries? I often would like to do something like:

    select
        let x = compute_something(...)
        in
        x + q as column1
        x + r as column2
    from ...

Then you have the sloppy design of the type system in virtually all SQL dialects. SQL Server doesn't even have a boolean type. There is no type you can declare for `@x` that will let you `set @x = (1 <> 2)`.

And don't even get me started on the GROUP BY clause, the design of which contorts the whole rest of the language. If you GROUP BY some columns, you must not refer to any non-grouped columns in the later parts of your query (refer to my table above for which parts are "later"). Unless, that is, you are referring to them in aggregates. Then the HAVING clause was tacked on so that you'd have a way to do a filter -- the same thing as WHERE -- after the GROUP BY. Does it all make sense once you understand it? Yes, in that you can see how you'd end up with this system if you were adding things piece by piece and never went back to redesign from square one.

Wow, I have a lot of ranting to do about SQL. I feel like I haven't even scratched the surface. And hell, I still pick SQL databases every time I start a project! The damn language is useful enough and the products work great. But it has all the design elegance of the US tax code.

[0] https://github.com/rspeele/Rezoom.SQL [1] https://docs.microsoft.com/en-us/dotnet/api/system.linq.enum...

While you can't declare new variables within a SELECT statement, you can modify them with functions:

  DECLARE @foo INT;

  SELECT
      @foo = dbo.ComputeSomething(...),
      @foo + @bar AS Val1
  FROM
      ...

To modify your example to use proper SQL:

  DECLARE @x BIT;
  SELECT @x = 'TRUE' WHERE 1 <> 2

  SELECT
    @x = CASE WHEN 1 <> 2 THEN 1 ELSE 0 END

Finally, your GROUP BY / HAVING / WHERE example lacks an important distinction. HAVING <> WHERE. HAVING is specifically meant to deal with aggregate functions, while WHERE is a simple filter. You wouldn't use "HAVING PostId = @foo" any more than you might use "WHERE COUNT(*) > @bar". This is why it comes after the GROUP BY clause -- which you should think of as a "key" for the data that's being aggregated.

Look, SQL is not perfect or near perfect. As with any language, it could use improvements. But to criticize a language based on ignorance, misleading statements, or use cases that are not meant for the language is fairly dishonest, in my opinion.

I'm assuming there is a way to make it work, but then it seems like it'd be kind of a messy imperative-style approach. Would the value of @foo persist between each row's evaluation, so now my query would be capable of (intentionally or not) stateful execution? Would that interfere with the optimizer's ability to re-order evaluation or would it be undefined behavior?

Other than the special cased string value conversion, SQL server treats bit as just a very short integer type. You cannot supply a bit value alone to WHERE or CASE, you must use a comparison operator. And likewise, you cannot assign the result of a comparison expression to a bit, you must wrap it in CASE as you have demonstrated. In fact my own project, in an attempt to squeeze various dialects of SQL into a common type system, fakes booleans in T-SQL by inserting CASE expressions and (<> 0) comparisons as needed See example:

http://rzsql.net/#80273F1AF9F7DB7776102BFA0814D4A7F01F2397

If a normal programming language had special places where boolean expressions could appear (like in if and while loops), and boolean expressions did not produce values in and of themselves, it would stand out as being extremely hacky.

Of course that is a complaint only for T-SQL, other dialects do have true bools. But it is emblematic of design undertaken without effort to unify fundamentals (The question not asked by T-SQL's designers: "What if predicate expressions were just like any other expression, and had a value?") and that is what I find distasteful about SQL in general.

HAVING is another example of that lazy, slap-more-syntax-on design. As you correctly point out, HAVING evaluates after the GROUP BY, WHERE before, and that's why HAVING can refer to aggregate results. But if "WHERE" was just an operator it could be used in the order it was needed without hassle. In LINQ, you write:

    users
    .Where(u => u.AccountIsActivated)
    .GroupBy(u => u.Name)
    .Select(g => new { Name = g.Key, NumberOfUsersWithName = g.Count() })
    .Where(g => g.NumberOfUsersWithName > 1)
    .Select(g => $"The name {g.Name} is shared by {g.NumberOfUsersWithName} users")

Of course you could do the same thing in SQL without HAVING using a subquery:

    select 'The name ' + sq.Name + ' is shared by ' + sq.NumberOfUsersWithName + ' users'
    from
        (select u.Name, count(*) as NumberOfUsersWithName
         from Users u
         where u.AccountIsActivated = 1
         group by u.Name) sq
    where sq.NumberOfUsersWithName > 1

    select 'The name ' + u.Name + ' is shared by ' + count(*) + ' users'
    from Users u
    where u.AccountIsActivated = 1
    group by u.Name
    having count(*) > 1

Let's go back to my needlessly cruel and exaggerated example of "SQL math", in which all expressions are written as a fill-in-the-clauses COMPUTE statement with each operation being its own optional clause, in a fixed order. Suppose that it was decided that it's helpful to be able to MULTIPLY after adding, which normally comes _before_ multiplying in the fixed order, but people were sick of writing:

    COMPUTE MULTIPLY 3 FROM (COMPUTE MULTIPLY 2 ADD 1 FROM 3)

    COMPUTE MULTIPLY 2 ADD 1 PRODUCT WITH 3

I get that SQL works and is extremely useful. Remember, I spent quite a bit of time implementing a library for working with it. But there are languages that make you think things like, "Oh, that's simpler than I thought. I can use this kind of construct to iterate over anything that implements the right interface. That was smart of the language designers". Then there are languages that make you think, "Huh. Another special case. Couldn't they have thought this through a bit more carefully before speccing out the language?".

SQL feels like the latter and yet I see people call it a beautiful language or their favorite language and I get a strong "stop liking what I don't like" impulse.

It allows writing things like -

    type PersonRel = Relation (name :: String, age :: Int)
    type EmployeeRel = Relation (employeeName :: String, managerName :: String)

    -- Get all managers older than 60
    oldManagers :: PersonRel
    oldManagers =
      employeeTable
        # renameField (SProxy :: SProxy "managerName") (SProxy :: SProxy "name")
        # join (RProxy :: RProxy (name :: String)) personTable
        # filter (\r -> r.age > 60)
        # project (RProxy :: RProxy (name :: String, age :: Int))

The code snippet I posted doesn't adequately demonstrate it but the DSL is strongly typed with full type inference at every step. So for example, it knows that the type of the table after the "join" is has a field "age" of type "int", and that filtering on it is okay. Trying to filter on a non-existent field will cause a compile (not runtime) error.

Second, the "steps" are actually plain functions. `#` is simply the reverse function application operator defined as `a # f = f a`. Which means you can abstract operations. For example, you can define a generic function that can take any relation with an age field and filters for age > 60. Thanks to strong types, using it incorrectly (i.e. on a relation without an age field of type integer) would cause an compile error.

Also, the language itself is a deeply embedded DSL. So "ordinary" code also compiles down to SQL. You can do things like loops etc. (for example, create a function that filters over a list of age limits rather than just one) and it will try to generate the best SQL possible.

of course the SQL generation is currently not implemented, so all this is theoretical, but I'm getting there!

In postgres i've created financial year reports, with monthly summaries per category just by having a few layers of views.

I think it's really valuable i can think in logical sets, and the database will takes all those layers of views and combine those into one optimized query plan.

I can see the appeal of such a construct in SQL. The requirement that reusable objects are persisted in the database requires a top-down design approach, and that doesn't really blend well with modern coding practices.

That's basically how most tools work outside of software development. When you use Google, you aren't telling it how to find your result; you're telling it what you want.

Even with myself, who has worked with Oracle writing PL/SQL (which is an abomination imo) in past jobs still has to sit down and get into the right mindset before writing efficient SQL for more complex queries.

Conceptually SQL is much more simple than programming, it basically reads like english:

  SELECT customer, SUM(total)
  FROM orders
  GROUP BY customer
  WHERE created BETWEEN '2018-01-01' AND '2018-12-31'`
  ORDER BY SUM(total) DESC
  

  totals = {}
  for row in rows:
      if row.created > '2018-12-31':
          continue
      if row.created < '2018-01-01':
          continue
      if row.customer not in customer_totals_2018:
          totals[customer] = 0
      totals[customer] = totals[customer] + row.total
  
  def _sorter((customer, total)):
      return -total
  
  for customer, total in sorted(totals.items(), key=_sorter):
      print(customer, total)

Now imagine adding an AVG(total) to both the SQL and programming version...

I know a lot about data, but I can't solve that problem. To anyone out there, much smarter than me: this is a real problem. If you solve it, you'll cement yourself the history of computer science.

I have no doubt it will get done, though.

I always ask a basic SQL question (involving sorting/grouping and one simple join) in my interviews for backend and/or data. IMO if you are calling yourself a Data Engineer and do not know SQL, then you haven't really worked with data, you've mostly just worked on data infrastructure.

I interview a lot of data engineers/analytics engineers and I always start with SQL. If you can't grasp SQL, you're in for a very bad time.

I love it. If you're looking to learn it and fundamentals, check out Jennifer Widom's course from Harvard. I can safely credit her w/ my career.

https://lagunita.stanford.edu/courses/Engineering/db/2014_1/...

There are plenty of use-cases for SQL with developers: especially in batch processes such as invoicing. A well crafted SQL query can execute exponentially faster than iterative code takes a lot less time to implement.

This tells us that people think SQL is good, but it's unfortunate that this is attempted, because the good thing about SQL is how well it corresponds to its data structures. Attempting to imitate SQL's form, rather than its principal of correspondence, has been counter-productive and prevented the reproduction of its merits. Case in point: Cypher.

Its syntax is a bit old fashioned and I do think efforts to make it more native to programming environments rather than a bolt on might be fruitful, but its concepts are timeless.

It seems to me that graph databases are far more efficient than relational ones for most tasks.

That’s because all lookups are O(1) instead of O(log N). That adds up. Also, copying a subgraph is far easier, and so is joining.

Think about it, when you shard you are essentially approaching graph databases because your hash or range by which you find your shard is basically the “pointer” half of the way there. And then you do a bunch of O(log N) lookups.

Also, data locality and caching would be better in graph databases. Especially when you have stuff distributed around the world, you aren’t going to want to do relational index lookups. You shard — in the limit you have a graph database. Why not just use one from the start?

So it seems to me that something like Neo4J and Cypher would have taken off and beat SQL. Why is it hardly heard of, and NoSQL is instead relegated to documents without schemas?

First, a database can have hash indexes instead of btree indexes, so lookups can be O(1) too, but it turns out that btrees are often better because they can return range results efficiently, and finding the range in a btree is only logarithmic for the first lookup. If your index is clustered - if it covers the fields needed downstream in the plan - no further indirections are needed and locality is far better than a hash lookup. For example, a filter could be efficiently evaluated along with the index scan. And if your final results need to be sorted, major bonus if the index also covers this.

Second, it's best to think of databases doing operations on batches of data at a time. Depending on your database planner, different queries will tend to result in more row-based lookups (MySQL generally does everything that isn't a derived table, uncorrelated subquery or a filesort in a nested loop fashion) but others build lookups for more efficiency (a derived table in MySQL, or a hash join in Postgres). The flexibility to mix and match hash lookups with scans and sequential access - which are usually a few orders of magnitude faster than iterated indirection - means it can outperform graph traversal that is constantly getting caches misses at every level of the hierarchy.

The reason NoSQL and document databases suck from a relational standpoint is that they are bad at joins, and work better if you denormalize your joins and embed child structures in your rows and documents. Add decent indexes to NoSQL and they gradually turn back into relational databases - indexes are a denormalization of your data that is automatically handled by the database, but have nonlocality and write amplification consequences which can slow things down.

In terms of distribution, a relational plan can be parallelized and distributed somewhat easily if your data is also replicated - sometimes join results need to be redundantly recalculated or shuffled / copied around - and most analytics databases use this approach, though usually with column stores rather than row stores, again because scanning sequential access is so much faster than indirection. Joins don't always distribute well, is the main catch.

Many recent web & mobile apps have a lot of screens where you just want to grab one blob of heterogenous data and format it with the UI toolkit of choice. Or if they do display multiple results, it's O(10) rather than O(1000) or O(1M). Chasing pointers is fine for use-cases like this, because you do it once and you have all the information you're looking for.

This is also behind the recent popularity of key/value stores and document databases. If all you need is a key/value lookup, well, just do a key/value lookup and don't pay the overhead of query parsing, query planning, predicate matching, joins, etc. When I was working on Google Search > 50% of features could get by with read-only datasets that supported only key/value lookup. You don't need a database for that, just binary search or hash indexes over a big file.

The application I work on in my day job does not match the key/value lookup idiom at all. User-defined sorts and filters over user-defined schema, and mass automated operations over data matching certain criteria. If you squint a bit, the app even looks a bit like a database in terms of user actions.

And even relational databases (at least row-oriented with primarily disk storage) have their limit here. With increasing volumes of data, it can't keep up. We can't index all the columns, and indexes can't span multiple tables. We increasingly need more denormalization solutions that convert hotter bits of data into e.g. in-memory caches that are faster for ad-hoc sorts and filters. Database first is a decent place to start, though having a first-class event feed for updates would certainly be nice...

Yup. Thing is: with RDBMSs you are in control of both the storage patterns and the access patterns of your data. That's where a large part of the performance benefit comes from.

> 50% of features could get by with read-only datasets that supported only key/value lookup

Did you implement a storage pattern that was ordered by key (or hash(key) if you used hashing)?

Well, I didn't implement it, Jeff Dean did, but yes, that's what we used for a lot of features.

https://www.quora.com/What-is-an-SSTable-in-Googles-internal...

https://www.igvita.com/2012/02/06/sstable-and-log-structured...

The power of SQL comes from the fact that you can easily create new information out of the data: create new sets, group by certain features, aggregates on certain features.

It's a lot more powerful than just store and retrieve.

Checkout that platform that a full-blown Oracle license can provide to your DBA... It's definitely more than just CRUD.

-

[0] https://www.oreilly.com/library/view/discovering-sql-a/97811...

It also doesn't bother with tons of consistency guarantees and such that you (may) get from, say, PostgreSQL. Yet is still slower for many purposes.

Graph DBs might be valuable for a lot of problems, and it does feel like something like Neo4J would make a lot of sense for stuff like social networks, but for business records stuff isn't really that spread out

It's not SQL that's the concept. The concept there is set theory/intersection/union and predicates.

That's why you think you are "recreating" those, because they can be mapped using the same concept

SQL is only one way of expressing that concept.

SQL gets you tables and joins, sure. But it also gets you queries that (some) non-programmers can write, outputs that are always a table, a variety of GUI programs to compose queries and display the results, analytic functions to do things like find percentiles, and tools to connect from MS Excel. And it often means you get transactions, ACID compliance, compound indexes, explicit table schemas, multiple join types, arbitrary-precision decimal data types, constraints, views, and a statistics-based query optimiser.

Of course it also gets you weird null handling, stored procedures, and triggers.

The latter group often didn't want to learn SQL and went with NoSQL's as a shortcut, not because they had an intimate understanding of the tradeoffs between the approaches and decided NoSQL was the better option.

https://www.amazon.com/Art-SQL-Stephane-Faroult/dp/059600894...

'Being great' usually helps a product gain traction. When SQL came about, it was very useful because hey 'I can query data!'.

But it's usually other reasons that drive incumbency.

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

1. You believe in your interpretation. This is true.

2. The parent poster has their interpretation. This is true.

3. The statement "They are surely talking about the specific syntax" is true if and only if their interpretation is the same as your interpretation.

4. This is a semantic argument now.

What about wikipedia entry that lists a specific syntax?

That's true for any piece of software. SQL is not done yet, the fact that you can put in neat little features still into it is a proof to how well it was designed.

Heck we still have Lisp evolving today. A lot of things got done right in the early days.

Data cleaning? SQL. Feature engineering? SQL.

Pipelines of stored procedures, stored in other stored procedures. Some of these procedures were so convoluted that they outputted tables with over 700 features, and had queries that were hundreds of lines long.

Every input, stored procedure, and output timestamped, so a change to one script involved changing every procedure downstream of it. My cries to use git were unheeded (would have required upskilling everyone on the team).

It was probably the worst year of my life. By the end of it I built a framework in T-SQL that would generate T-SQL scripts. In the final week of a project (which had been consistent 60-70 hour weeks), the partner on the project came in, saw the procedures written in my framework and demanded that they all be converted back into raw SQL. I moved teams a few weeks later.

The only good bit looking at it, is that now I'm REALLY good with SQL. It's incredibly powerful stuff, and more devs should work on it.

My bulk of experience in Sql was in MsSql. Recently I got into an Oracle shop and the dialect is a bit different though it didnt take too long to be productive in. However, being productive and such, if you ask me, oracle smells clunky and has way too many features, let's say it's not my cop of tea...

Also MS SQL let you throw a data object (say XML) at a stored procedure, convert it to a table structure with some XPath (another useful black art like regex) and use that as the input to a single INSERT.

You’re giving me flashbacks to stored procs that directly invoke java methods, and batch scrips that load client supplied data from and FTP server into external tables.

A legacy application I once worked at used this extensively, and good grief it gave me nightmares.

How anyone ever thought this is a good idea to use is beyond me; that stuff is not maintainable at all.

To make it worse, the app had about 300 scheduled tasks that were a mixture of batch files, SQL scripts and java classes. None of which had source code, most of which were slightly different and essentially operated as mysterious black boxes. We ended up having a copy of JD-GUI on all the production servers because we needed to decompile for debugging so often.

The sql server agent was easy and reliable for scheduling so it made sense to let it run the whole process.

We were doing this 20+ years ago, all code was in csv (we upgraded from rcs to csv), and we had a productized distributed scheduling system that would deploy all the sql scripts every night on a number of oracle databases running from aix, to solaris, to vms, to hpux, to irix, and later linux and windows NT.

Similar like you would now use jenkins to build, deploy and test your java apps.

Developers would never touch the test/production databases, only commit sql to csv. Develop local, sql text files, test on a local or shared development database, and then commit to csv.

Jump-to-definition when something calls something else. Unit tests. Atomic build/install.

> this can be a sql or shell script, or a gradle file or even make.

Precisely the problem. There's too many different ways to do it, and no consistency.

___ 1: Replace with C, C++, Basica, FORTRAN, COBOL, JavaScript, Ruby, Closure...

Debugging leaves more to wish, but still available in some limited form.

What is the best practice workflow using git with SQL server views/procedures? Can you actually somehow track changes in the views/procs themselves so that if someone happens to run ALTER VIEW, git diff is going to show something?

Outside of commercial tools dedicated to the purpose: you can query the DB for the content of the procedures/tables and compare them to a given set of scripts, or the most recent expected version. Auto-generated ORM models can be used to validate table/view composition for a given DB/App version, as well. Having these capacities baked into the versioning and upgrade process can do a lot over time to correct deviating schemas and train developers away from meddling with DBs outside the normal update procedure :)

If you are asking about SQL text then a good formatter would make it easy to see the physical diffs. Otherwise there are various vendors and tools that parse SQL and show the logical diffs in queries and schemas, along with doing deployments, backups, syncing changes, etc.

You basically shouldn't allow anyone to modify anything without it being scripted (bonus points if it comes with a rollback and is repeatable for testing). Your scripts then all go into Git.

I second the rollback and repeatability bonus. Every script should leave the database in either the new state or the previous good state no matter how many times it's run.

I wonder if there were somewhere a website to describe good idioms to achieve this?

Having a second person run a script is the best way to ensure no mistakes.

I've moved to writing backend code. I'm surprised most of my peers cannot write anything more complicated than a join. Most people are perfectly happy to let the orm do all the work, and never care to dig into the data directly. Every once in a while my sql skills save the day and several people in other departments contact me directly when they need excel files of data in our database we don't have UIs to pull yet.

I wrote this post to introduce the idea:

What Is a Data Frame? (In Python, R, and SQL) https://www.oilshell.org/blog/2018/11/30.html

It's often useful to treat SQL as an extraction/filtering language, and then use R or Pandas as a computation/reporting language.

I think of it as separating I/O and computation. SQL does enough filtering to cut the data down to a reasonable size, and maybe some preliminary logic. And then you compute on that smaller data set in R or Pandas -- iterating in SECONDS instead of hours. The code will likely be shorter as well, so it's a win-win (see examples in my blog post).

I can't think of many situations where hours of runtime is "reasonable" for an SQL query. In 2 hours you could probably do a linear scan over every table in most production databases 10-100 times.

For example, if your database is 10 GB, you could cat all of its files in less than 5 minutes (probably much less on a modern SSD). In 2 hours, you can do a five minute operation 24 times. I can't think of many reports that should take longer than 24 full passes over all the data in the database (i.e. pretending that you're not bothering to use indices in the most basic way). If it takes longer than that, the joins should be expressible with something orders of magnitude more efficient.

I've mainly worked with big data frameworks, but I think that almost any SQL database (sqlite, MySQL, Postgres) should be able to do a scan of a single table with some trivial predicates within 10x the speed of 'cat' (should be within 2x really). They can probably do better on realistic workloads because of caching.

I might reach for that kind of tooling at the hundreds of TB to PB scale, but in our production applications we have _tables_ that are multiple terabytes. SQL is just fine.

Yes, we also have have queries that run in the timescale of hours and they are always of the reporting/scheduled task variety and absolutely vital to our customers. Long running reporting queries are pretty acceptable (and pretty much the norm since forever) outside of the tech industry and your customers won't balk at it.

Big data was a reference to thinking about the problem in terms of the speed of the hardware. If it's 1000x slower than what the hardware can do, that's a sign you're using the wrong tool for the job.

Getting within 10x is reasonable, but not 100x or 1000x, which is VERY COMMON in my experience. These two situations are very common:

1) SQL queries that are orders of magnitude slower than a simple offline computation in Python or R (let alone C++). The underlying cause is usually due to bad query planning of joins / lack of indices.

You might not have the ability to add indices easily, and even if you did, that has its drawbacks for one-off queries.

2) You need to do some computation that's awkward inside SQL. Statistics beyond basic aggregations, iterative computations (loops), and tree structures are common problems.

From the Enterprise side I think too many developers have an unfounded expectations around data storage technology. There's this unchallenged belief that monolithic datastorage that will solve thier problems across the entire time/storage/complexity spectrum. By bringing multiple tools to bear, instead, you end up with more purpose built storage but far less domain impedence.

Slapping a denormalized NoSQL front-end for webscale onto a legacy RDBMS can be a cheap win/win to maximize the capabilities of both. SQL + R is oodles better than R or SQL in isolation.

If you are writing SQL regularly, understanding the basics of how the queries you write is not that hard for you engine of choice, and everyone should be required to understand the basics of reading an execution plan so they can find the right inflection points between data gathering and processing.

I regularly sigh write and maintain SQL procedures that are >10k LOC, and their runtime never would exceed minutes, much less hours.

I think we have a misunderstanding about the scale of data.

EDIT: Reading your post you mention that dataframes stores data in memory. Working with data in ram would provide a significant speedup. It just wasn't possible.

Some more detail here: https://news.ycombinator.com/item?id=19150971

There are many interesting queries that don't touch all the data in the database. The 'cat' thing was basically assuming the worst case, e.g. as a sanity check.

Reducing a highly used query's execution time by several orders of magnitude can be quite gratifying.

I also learned a crapload about obscure SQL since I would go to extreme lengths to achieve this. There was a lot of meta-SQL programming, where I would use SQL to generate more SQL and execute that within my statement, sometimes multiple layers deep. It was beautiful in its own way, expanding out in intricate patterns.

Asking from wanting to make use of such a place, and haven't seen anything like it. So, probably need to bootstrap one instead (etc).

I would think there already is a Stack Exchange for SQL, possibly several (for different RDBMSes/ dialects); go have a look there, if this is what you meant.

In my experience, if you can grok lateral joins (aka cross apply), recursive CTEs, window functions, and fully understand all the join types, that's a gold star for understanding SQL!

ORM all-too-often defines data structures from code.

Linus Torvalds wrote,

> I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships.

https://lwn.net/Articles/193245/

I'd wager more don't even know what a join is.

It's a powershell module that allows you to easily dump things directly to excel files, does pretty decent datatables, multi-tabs, etc.

- Step 1 - Step 2 - Loop through results in Step 2 - Curate and finish output.

I try very hard to transform the above steps into an SQL statement spanning multiple tables, but always fail and I usually fallback to python for manually extracting and processing the data.

Does anyone else face this problem?

Any suggested guides / books to make me think more SQL'ley ?

Basically, instead of thinking in terms of how to get to the result, think instead of the result and figure out how to get there. For example, let's say I need to get a mapping of students to their classes. One way would be to get the students, then loop through that to get classes for each one. Another way to do it would be to ask the database to combine those sets of data for us instead and return the result (join students and class tables on the student ID).

Basically, find the data you want from each table, tell the database about the relationship between them (join on <field>), and set up your constraints. I guess you could think about the query as a function, and you're just writing the unit tests for it (when I call X with parameters Y, I expect Z).

I have the reverse problem: every time I motivate myself to learn Prolog I feel like I could just input the constraints in a db and use SQL to get my results.

(1) Think of a database as a "big pile of stuff in a room". Some of it's ordered in a sane way, some of it's not. There is "a person at the door" to the room preventing you from entering - this is the Database Engine, not to be confused with the Database itself. You need to get things out of the room, and you need to put things in the room. You're not allowed to enter. If you want something out of the room, you must ask for it (query) by specifying what the thing looks like and which part of the pile you think it's in. If you want to put something in the room, you must provide explicit instructions for where it must go. When you write SQL, you are either asking a question of or giving instructions to the database engine. The DB Engine is distinct from the Database (the big pile of stuff...which you never have direct access to).

(2) As others have pointed out, SQL is very much the mathematics of Set Theory put into a programming language. Stanford has a free class on Databases. I took it a few years ago (it may have changed), and most of it had no SQL at all. It was fairly very straight forward - things you can do with pencil and paper. It's free, so no pressure. Go sign up and do some of the homework assignments. Do them until you get them right (you can re-take wrongly answered problems immediately). It'll help break your procedural mindset. https://online.stanford.edu/courses/soe-ydatabases-databases

Of course you can always screw up and write suboptimal SQL that will take forever to finish, but it's just harder to do unless you're really trying to be dumb about it.

So I do think there's still a lot of merit in trying to learn and use SQL more.

There are certain situations where an SQL query is all that I can work with.

I find myself having the opposite problem I probably use (abuse?) SQL more than should.

My world (industrial plant) is very DB/historian heavy everything speaks SQL it's pretty much the common tongue connecting everything. I think this is slowly changing some PLC/historians now offer a webservice which returns JSON objects via an ajax query - personally I vastly prefer SQL to ajax.

I'd estimate for a typical problem I'm working with maybe 90% is done in SQL vs 10% in R/SAS code.

When I need Regression, Principal Component Analysis, Time Series manipulation, Plots etc I have to break into dedicated language.

For most other things - extract, merge, filtering, high level aggregation (Count sum etc) type of operations using SQL feels more natural and expressive to me.

But over time, and very heavy SQL reporting, I realized the procedural mindset still applies, it's just not expressed as such.

You need to think through the steps of how you want to retrieve, transform, aggregate etc. the data and build up the query to accomplish those steps. The resulting query doesn't look procedural, but the thought process was. Of course you need to know the sometimes opaque constructs/tricks/syntax to express the steps, which again, look like magic at face value or just reading docs.

I think this is why people struggle with understanding SQL. The thought process is still procedural, but the query isn't. You need to translate the query back into procedural terms for your own understanding, which is a PITA.

Just the basics, very clear and easy to understand.

It also helps to change the language you use in your inner monologue. Instead of thinking, "For each row in table A...", you should think, "For all the rows in table A that match on...".

EDIT: A CTE (Common Table Expression) might be what you're thinking you need. You can do some fun recursive queries with them.

It's like all my fears concentrated, like the power of a thousand suns, onto a single sentence.

Some problems can to be assembled from CTEs / subqueries when you might have reached for a procedural solution to 'finish' the problem. Grasping CTEs was a big thing for me and now I'm wrestling with issues of style mixing CTEs with tiny subqueries and wondering if the increase in readability (for me) is correct.

I'd love to see suggestions for digging deeper on this too.

1) Think in terms of excel spreadsheets. At each step in your SQL you are creating a set of data. Doesn't usually matter how many rites it is, you just want the right columns. 2) Once you have a recordset, decide how the next recordset goes together. I would mentally visualize putting one worksheet next to the other and think through what columns I had to join together to get the rows I wanted. 3) Make sure you know what your first recordset should be. This is the base data that everything can be joined into. I refer to this as the domain of records.

Using another tool for processing data often results in recreating SQL mechanics at application level. E.g. select this data, retrieve it, loop and if this, then set that, etc. SQL does it way better, guaranteed.

Of course that's often required for technical reasons (scalability etc.) or processing that's too complex to implement at data layer, or just for cleaner design.

But SQL is amazing at processing data!

I think it influences the mindset of the developer. As you say, “retrieve ... if this, then ... loop”. If you're in a “data processing” mindset, then you'll think of a problem like “Get the total number of car widgets in the warehouse” as fetch a widget row; if it's of type car, add number to total; loop until you've processed every row; there you have your total. If, OTOH, you're in an “asking questions” mindset, you'll go: What was the question again, exactly? Oh yes, get the sum of the number for all the widgets which are of type car widgets. Which is almost exactly the same as SELECT SUM(NUMBER) FROM WIDGET WHERE TYPE = 'CAR';.

Processing data is when you do it (in code); answering questions is when the RDBMS (i.e, its code) does it for you. :-)

(At least that's what I think the difference is _in terms of vvkumar's original question._)

We do >95% of our transformations with pure SQL and the queries are primarily in Looker.

DBs like Kafka who recognize this and instead offer SQL on top of their things take the right approach IMO KSQL.

No, its Datalog. Joking aside, they are equally powerful but one could argue about explicit vs implicit joins.

A recent up-and-coming software tool is the Open Policy Agent[0] and it uses Datalog -- though it's a custom implementation. This comment felt familiar so I went back and looked and it's come up on HN before[1].

[0]: https://www.openpolicyagent.org/

[1]: https://news.ycombinator.com/item?id=18156144

If I'm going to bet my career on one or the other, it'd be SQL.

Slight aside, I think there are good lessons to be learned from LINQ still.

We just might happen to live in a world where all the RDBMSes have that same failing, but I'd argue Postgres doesn't fail as bad in the are with things like row level security[0].

[0]: https://www.postgresql.org/docs/current/ddl-rowsecurity.html

- It doesn't feel very interoperable nor flexible (it's a different "ecosystem") with regards to other software -- meaning that if it's much less "integrate with GraphQL" and more "build on top of GraphQL" or find the "GraphQL" way to do things. I find this to be different from approaches like OpenAPI/Swagger.

- Complicated GraphQL queries start to look like SQL to me, and I suspect that will become even more the case as time goes on, and people decide they want advanced things that are in SQL, like window functions.

- GraphQL locks you in to knowing the backend design from the frontend -- though this was mostly always the case with REST (need to know which endpoint to call), "true" REST/HATEOAS compliant backends held the promise of you just being able to ask for a shape of an object, and make decisions about whether to dive deeper as you go in a principled way. This kind of gets into the semantic web concepts -- but that's mostly a seas of never delivered promises so...

- It's features are mostly offered by tools like Postgrest (vertical/horizontal filtering[0], resource embedding[1]), which offers less complexity.

- GraphQL is going to very likely spend the next few years running into issues/features that the REST/HATEOAS has already solved (albeit not in a standardized way). I looked at a random issue[2] and this is definitely something that seems quite solved in the RESTish HATEOAS world (at the very least you don't have to wait for GraphQL to do something to solve it).

Basically, I wish someone had built the ease-of-use GraphQL offers on top of the HTTP+REST+HATEOAS model -- because it's wonderfully extensible. Excellent solutions often lose out to "good enough" solutions, and I just... don't want that to happen this time (I mean I don't think it will, it's pretty hard to beat out good ol' http).

We just got to a really good place with Swagger/OpenAPIv3 + jsonschema/hyperschema, in being able to create the standardized abstractions on top of HTTP (and bring with it all the benefits of HTTP1/2/3 as they arrive, and it feels like GraphQL is a distraction. The "entity" graph is just a rehashing of the relational database model and it doesn't feel like it offers much outside of a standardized way of presenting your DB -- if you want to present your DB to frontends why not just let them send SQL queries directly? No I'm not tone deaf, I know most devs don't want to write SQL, but it seems like they're about to get into bed with GraphQL despite the likelihood that it's just going to be another SQL once it has enough features.

That said, GraphQL is doing amazing things for developer velocity, and it deserves praise for that -- tools that grasp mind share this quickly usually offer a large amount of real benefits to the people using it, and I've seen that GraphQL does that.

[0]: https://postgrest.org/en/v5.1/api.html#vertical-filtering-co...

[1]: http://postgrest.org/en/v5.1/api.html#resource-embedding

[2]: https://github.com/hasura/graphql-engine/issues/1598

Having a working intuition for relational databases is valuable on a deep level. I mean having a sense of how to organize the tables, what sizes are large and small, when to add what kind of index and what the size and speed limits are likely to be for a given data structure. That's extremely valuable.

BTW, we're preparing to move a postgres database that's a few TB from Heroku to AWS RDS. The catch is that we can't afford more than a few minutes of downtime. If this is in your wheelhouse, reach out! We'd like to talk.

We had just a few minutes of downtime.

I did have few issues around permissions, but an AWS support person walked me through the process and we got it up and running. The support was great, and we just have standard business support.

EDIT: DMS might be a no go, as jpatokal points out. While Heroku allows external connections, it doesn't allow super user access which, AFAIK, DMS requires.

https://devcenter.heroku.com/articles/heroku-postgresql#conn...

Would it be ok to serve stale data for an extended amount of time? If so then you could modify your application to write to AWS RDS but to keep reading from Heroku. Then you start copying over the data from Heroku to AWS RDS, and once the copying is done then you point your application to read from AWS RDS.

Another thing: If your tables have foreign key constraints then you need to create the tables in the new DB without them first, since the writes coming to the new DB could be referencing data from the old DB which have not yet been copied over. Once you’ve completed copying the data over you add the FK constraints to the new DB. Depending on how your application was made this could be a problem or it could be ok.

[1] https://bucardo.org/Bucardo/

And oddly enough, now I'm a data engineer. Everything I do, even when I'm not doing pure SQL, is influenced by years of experience in SQL. Either the languages of my big tools are still based on SQL in some fashion, or it simply helps to have an understanding of the ecosystem to figure out what's going on at scale.

Everything else has come and gone and come again, but SQL has help up pretty nicely. The only other skills that come close for me are working in languages that branched out of C (because the syntax isn't so different over time) and years of on again off again procedural languages (ColdFusion, vanilla Javascript, etc. leads to it being way easier to pick up Python).

- Common Table Expressions (CTEs). They will equip you to write clean, organized, and expressive SQL.

- CREATE TABLE AS SELECT. This command powers SQL-driven data transformation.

- Window functions, particularly SUM, LEAD, LAG, and ROW_NUMBER. These enable complex calculations without messy self-joins.

Learning those three will change your relationship with SQL and blow the door open of problems you can solve with the language.

For those unaware of LATERAL JOINs, it’s like a for each loop in SQL. Blew my mind and opened up so many possibilities.

You may call me an extremist but from server side programming point of view with Postgres and FDW (Foreign Data Wrappers) which has ton of features other than SQL only thing i miss is HTTP server. :)

On one hand, you write what you want to find in fairly common language and you almost always get back what you want if you do it correctly. In many ways, it’s like a precursor to Alexa but in written form. It’s super easy to pick up for non technical people.

On the other hand, it’s extremely difficult to code review, and on a very complicated piece of business logic, errors could mean the difference between hiring 10 more people or laying off 100. So almost always it’s just easier to re-write.

Imagine if engineers couldn’t understand each other’s work, and had to rewrite code every time someone leaves the team. It’s insane to me that this is standard practice.

I haven't found that particularly true. I've trained people in SQL and yes, they can do the basics pretty well, but it requires a particular skill to actually write effect complex SQL statements. Non-technical people, being non-technical, generally cannot do that.

> it’s extremely difficult to code review

I don't see why it's any more difficult than any other logic? It's always important to test.