For the past few months, I have been working on an experimental functional/data language called Morel.

SQL has several deficiences relating to nested collections, higher-order functions and type system. After several months trying to figure out how to add these features to SQL, I noticed that they were basically the defining characteristics of a functional programming language.

I figured, rather than fixing SQL, why not dig the tunnel from the other end? Start with a functional programming language, then add what makes SQL a good query language.

Morel’s design goals:

Seamless integration of collections (relations) and relational operators into a functional programming language

As concise as SQL

Access external data

Retain the computational power and sophisticated type system of the functional programming language

Allow query planning via relational algebra, even in hybrid programs that are mixture of relational algebra

Suitable for interactive queries from a REPL (read-eval-print loop) and also larger scale programs

Conciseness

SQL is concise. Many useful queries are only a few lines long.

Functional programming languages also tend to be concise, for similar reasons to SQL: they are strongly typed, and the language has good type inference. Type inference means that you don’t need to explicitly specify types often, if ever.

When you write a query in Morel, you are writing a short expression in a functional programming language, but its structure looks very similar to the equivalent SQL query.

For example, here is a query in Morel:

from e in hr . emps , d in hr . depts where e . deptno = d . deptno yield { e . id , e . deptno , ename = e . name , dname = d . name };

The equivalent query in SQL looks very similar:

SELECT e . id , e . deptno , e . name AS ename , d . name AS dname FROM hr . emps AS e , hr . depts AS d WHERE e . deptno = d . deptno ;

External data

In the above example, the hr data structure looks like a record in memory (with fields emps and depts that are collections of records) but actually maps to a schema in a DBMS.

Virtualized data is essential to bringing large-scale data sets into the programming model, and is implemented by calling out to Apache Calcite schema adapters. I expect to make more use of Calcite for query planning.

Interactivity

People tend to write SQL queries in a REPL (read-eval-print loop). Small functional programs can be written in the same way, so there is a good fit there.

A modest project

I chose to extend Standard ML because it is a small, simple language, well known in the academic community.

This made it feasible for me to write a parser and interpreter as a solo project. (By the way, the interpreter is written in Java, and is quite a nice implementation of Standard ML for the JVM, even if you don’t care for the relational extensions.)

If the Morel experiment is successful, the ideas can be carried into more complex and powerful languages such as Haskell and Scala.

Relationally complete vs. Turing complete

Query languages are, by design, powerful but not too powerful.

One reason for this is that, if we add extra power (for example, function values and arrays) then we have to add extra syntax for these features. The extra syntax makes the language harder to use for simple taskes, and also harder to learn.

More important, query languages rely on a query planner. Many details can be left out of the program (such as whether to use a hash-join or sort-merge-join algorithm to perform a join) because the planner can make these decisions for us. But if we give the language too much power, we make the planner’s job difficult or impossible.

Why is this? As soon as a language has sufficient power – if it can loop, or call functions recursively – it becomes Turing complete, and not all programs in such a language can be reasoned about. (See, for example, the Halting Problem.)

SQL is not Turing complete (if you ignore the WITH RECURSIVE construct), as evidenced by the fact that any query with finite input relations eventually terminates. It is equivalent in power to relational algebra and relational calculus, which Edgar F. Codd called relationally complete.

Morel, on the other hand, crosses that line. This is necessary, because all functional languages are Turing complete, but are we giving the planner an impossible task?

Limiting the power

I believe that we can solve the problem by separating the “query” parts of a program, that consist only of relational operators, from the “looping” parts of a program. This is unproven at this point, but is bolstered by the observation that many data-oriented programs fall into one of the following patterns:

Pure queries consist only of relational operators. Such programs are often small, are structurally similar to the equivalent SQL, and can be planned in a similar fashion.

consist only of relational operators. Such programs are often small, are structurally similar to the equivalent SQL, and can be planned in a similar fashion. Queries with locally defined scalar functions can be separated into a pure query that makes calls into user-defined functions. The user-defined functions do not invoke relational operators and therefore the query can be planned as normal.

can be separated into a pure query that makes calls into user-defined functions. The user-defined functions do not invoke relational operators and therefore the query can be planned as normal. Queries in loops can be converted into a parameterized query that is executed under the control of a functional program.

can be converted into a parameterized query that is executed under the control of a functional program. Iterative queries , for example queries that add to a set until it reaches a fixed point, can be planned and executed using techniques such as stratified recursion.

, for example queries that add to a set until it reaches a fixed point, can be planned and executed using techniques such as stratified recursion. User-defined table functions connected by relational operators. This is essentially the MapReduce pattern. What happens in the table functions is beyond our control, but if we know something about their inputs and outputs (for example, that an aggregate function can be computed by examining only rows with the same key) then the framework can assist in running the query reliably and in parallel.

In all of these patterns, if we can recognize the ‘query’ parts we can optimize them using conventional techniques. If we cannot recognize any ‘query’ parts, nothing is lost; we can still execute the whole as a functional program.

Conclusion

Morel is an exciting experimental language that combines the best aspects of database query languages and functional programming.

In this brief introduction, I have not gone into the details of Morel’s syntax, semantics or implementation, but examples can be found on the Morel site and in Morel’s test suite, and I plan to write more blog posts over the following months.

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