Using jOOλ to Combine Several Java 8 Collectors into One

With Java 8 being mainstream now, people start using Streams for everything, even in cases where that’s a bit exaggerated (a.k.a. completely nuts, if you were expecting a hyperbole here). For instance, take mykong’s article here, showing how to collect a Map’s entry set stream into a list of keys and a list of values:

http://www.mkyong.com/java8/java-8-convert-map-to-list

The code posted on mykong.com does it in two steps:

package com.mkyong.example;

import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;

public class ConvertMapToList {
    public static void main(String[] args) {
        Map<Integer, String> map = new HashMap<>();
        map.put(10, "apple");
        map.put(20, "orange");
        map.put(30, "banana");
        map.put(40, "watermelon");
        map.put(50, "dragonfruit");

        System.out.println("\n1. Export Map Key to List...");

        List<Integer> result = map.entrySet().stream()
                .map(x -> x.getKey())
                .collect(Collectors.toList());

        result.forEach(System.out::println);

        System.out.println("\n2. Export Map Value to List...");

        List<String> result2 = map.entrySet().stream()
                .map(x -> x.getValue())
                .collect(Collectors.toList());

        result2.forEach(System.out::println);
    }
}

This is probably not what you should do in your own code. First off, if you’re OK with iterating the map twice, the simplest way to collect a map’s keys and values would be this:

List<Integer> result1 = new ArrayList<>(map.keySet());
List<String> result2 = new ArrayList<>(map.values());

There’s absolutely no need to resort to Java 8 streams for this particular example. The above is about as simple and speedy as it gets.

Don’t shoehorn Java 8 Streams into every problem

But if you really want to use streams, then I would personally prefer a solution where you do it in one go. There’s no need to iterate the Map twice in this particular case. For instance, you could do it by using jOOλ’s Tuple.collectors() method, a method that combines two collectors into a new collector that returns a tuple of the individual collections.

Code speaks for itself more clearly than the above description. Mykong.com’s code could be replaced by this:

Tuple2<List<Integer>, List<String>> result = 
map.entrySet()
    .stream()
    .collect(Tuple.collectors(
        Collectors.mapping(Entry::getKey, Collectors.toList()),
        Collectors.mapping(Entry::getValue, Collectors.toList())
    ));

The only jOOλ code put in place here is the call to Tuple.collectors(), which combines the standard JDK collectors that apply mapping on the Map entries before collecting keys and values into lists.

When printing the above result, you’ll get:

([50, 20, 40, 10, 30], [dragonfruit, orange, watermelon, apple, banana])

i.e. a tuple containing the two resulting lists.

Even simpler, don’t use the Java 8 Stream API, use jOOλ’s Seq (for sequential stream) and write this shorty instead:

Tuple2<List<Integer>, List<String>> result = 
Seq.seq(map)
   .collect(
        Collectors.mapping(Tuple2::v1, Collectors.toList()),
        Collectors.mapping(Tuple2::v2, Collectors.toList())
   );

Where Collectable.collect(Collector, Collector) provides awesome syntax sugar over the previous example

Convinced? Get jOOλ here: https://github.com/jOOQ/jOOL

The Danger of Subtype Polymorphism Applied to Tuples

Java 8 has lambdas and streams, but no tuples, which is a shame. This is why we have implemented tuples in jOOλ – Java 8’s missing parts. Tuples are really boring value type containers. Essentially, they’re just an enumeration of types like these:

public class Tuple2<T1, T2> {
    public final T1 v1;
    public final T2 v2;

    public Tuple2(T1 v1, T2 v2) {
        this.v1 = v1;
        this.v2 = v2;
    }

    // [...]
}


public class Tuple3<T1, T2, T3> {
    public final T1 v1;
    public final T2 v2;
    public final T3 v3;

    public Tuple3(T1 v1, T2 v2, T3 v3) {
        this.v1 = v1;
        this.v2 = v2;
        this.v3 = v3;
    }

    // [...]
}

Writing tuple classes is a very boring task, and it’s best done using a source code generator.

Tuples in other languages and APIs

jOOλ‘s current version features tuples of degrees 0 – 16. C# and other .NET languages have tuple types between 1 – 8. There’s a special library just for tuples called Javatuples with tuples between degrees 1 and 10, and the authors went the extra mile and gave the tuples individual English names:

Unit<A> // (1 element)
Pair<A,B> // (2 elements)
Triplet<A,B,C> // (3 elements)
Quartet<A,B,C,D> // (4 elements)
Quintet<A,B,C,D,E> // (5 elements)
Sextet<A,B,C,D,E,F> // (6 elements)
Septet<A,B,C,D,E,F,G> // (7 elements)
Octet<A,B,C,D,E,F,G,H> // (8 elements)
Ennead<A,B,C,D,E,F,G,H,I> // (9 elements)
Decade<A,B,C,D,E,F,G,H,I,J> // (10 elements)

Why?

because Ennead really rings that sweet bell when I see it

Last, but not least, jOOQ also has a built-in tuple-like type, the org.jooq.Record, which serves as a base type for nice subtypes like Record7<T1, T2, T3, T4, T5, T6, T7>. jOOQ follows Scala and defines records up to a degree of 22.

Watch out when defining tuple type hierarchies

As we have seen in the previous example, Tuple3 has much code in common with Tuple2.

As we’re all massively brain-damaged by decades of object orientation and polymorphic design anti-patters, we might think that it would be a good idea to let Tuple3<T1, T2, T3> extend Tuple2<T1, T2>, as Tuple3 just adds one more attribute to the right of Tuple2, right? So…

public class Tuple3<T1, T2, T3> extends Tuple2<T1, T2> {
    public final T3 v3;

    public Tuple3(T1 v1, T2 v2, T3 v3) {
        super(v1, v2);
        this.v3 = v3;
    }

    // [...]
}

The truth is: That’s about the worst thing you could do, for several reasons. First off, yes. Both Tuple2 and Tuple3 are tuples, so they do have some common features. It’s not a bad idea to group those features in a common super type, such as:

public class Tuple2<T1, T2> implements Tuple {
    // [...]
}

But the degree is not one of those things. Here’s why:

Permutations

Think about all the possible tuples that you can form. If you let tuples extend each other, then a Tuple5 would also be assignment-compatible with a Tuple2, for instance. The following would compile perfectly:

Tuple2<String, Integer> t2 = tuple("A", 1, 2, 3, "B");

When letting Tuple3 extend Tuple2, it may have seemed like a good default choice to just drop the right-most attribute from the tuple in the extension chain.

But in the above example, why don’t I want to re-assign (v2, v4) such that the result is (1, 3), or maybe (v1, v3), such that the result is ("A", 2)?

There are a tremendous amount of permutations of possible attributes that could be of interest when “reducing” a higher degree tuple to a lower degree one. No way a default of dropping the right-most attribute will be sufficiently general for all use-cases

Type systems

Much worse than the above, there would be drastic implications for the type system, if Tuple3 extended Tuple2. Check out the jOOQ API, for instance. In jOOQ, you can safely assume the following:

// Compiles:
TABLE1.COL1.in(select(TABLE2.COL1).from(TABLE2))

// Must not compile:
TABLE1.COL1.in(select(TABLE2.COL1, TABLE2.COL2).from(TABLE2))

The first IN predicate is correct. The left hand side of the predicate has a single column (as opposed to being a row value expression). This means that the right hand side of the predicate must also operate on single-column expressions, e.g. a SELECT subquery that selects a single column (of the same type).

The second example selects too many columns, and the jOOQ API will tell the Java compiler that this is wrong.

This is guaranteed by jOOQ via the Field.in(Select) method, whose signature reads:

public interface Field<T> {
    ...
    Condition in(Select<? extends Record1<T>> select);
    ...
}

So, you can provide a SELECT statement that produces any subtype of the Record1<T> type.

Luckily, Record2 does not extend Record1

If now Record2 extended Record1, which might have seemed like a good idea at first, the second query would suddenly compile:

// This would now compile
TABLE1.COL1.in(select(TABLE2.COL1, TABLE2.COL2).from(TABLE2))

… even if it forms an invalid SQL statement. It would compile because it would generate a Select<Record2<Type1, Type2>> type, which would be a subtype of the expected Select<Record1<Type1>> from the Field.in(Select) method.

Conclusion

Tuple2 and Tuple5 types are fundamentally incompatible types. In strong type systems, you mustn’t be lured into thinking that similar types, or related types should also be compatible types.

Type hierarchies are something very object-oriented, and by object-oriented, I mean the flawed and over-engineered notion of object orientation that we’re still suffering from since the 90s. Even in “the Enterprise”, most people have learned to favour Composition over Inheritance. Composition in the case of tuples means that you can well transform a Tuple5 to a Tuple2. But you cannot assign it.

In jOOλ, such a transformation can be done very easily as follows:

// Produces (1, 3)
Tuple2<String, Integer> t2_4 = 
    tuple("A", 1, 2, 3, "B")
    .map((v1, v2, v3, v4, v5) -> tuple(v2, v4));

// Produces ("A", 2)
Tuple2<String, Integer> t1_3 = 
    tuple("A", 1, 2, 3, "B")
    .map((v1, v2, v3, v4, v5) -> tuple(v1, v3));

The idea is that you operate on immutable values, and that you can easily extract parts of those values and map / recombine them to new values.

Read more

If you’ve enjoyed reading this article, you might also like to learn why recursive generics are a terrible idea (in many situations).

A Typesafety Comparison of SQL Access APIs

SQL is a very expressive and distinct language. It is one of the few declarative languages which are used by a broad audience in everyday work. As a declarative language, SQL allows to specify what we’re expecting as output, not how this output should be produced. As a side-effect of this, ad-hoc record data types are created by every statement. An example:

-- A (id: integer, title: varchar) type is created
SELECT id, title
FROM book;

The above statement generates a cursor whose records have a well-defined record type with these properties:

  • The degree of the record is 2
  • The column names are id and title
  • The column types are integer and varchar
  • The column id can be accessed at index 1. The column title can be accessed at index 2

In other words, SQL records combine features from records (access by name) and tuples (access by index). They can be seen like typesafe associative “map-arrays”, where map keys are formally bound to array indexes and their associated key/index type.

Another, more complex example shows how these ad-hoc record types can be reused within a SQL statement!

-- A (c: bigint) type is created
SELECT count(*) c
FROM book

-- A (..: integer, ..: integer) type is created and compared with...
WHERE (author_id, language_id) IN (

  -- ... another, compatible (..: integer, ..: integer) type
  SELECT a.id, a.language_id
  FROM author a
)

This query counts books written by authors in their native language.

In the above example, the projected record type is a bit simpler. It contains only one column. The interesting part is the row value expression IN comparison predicate, which compares two compatible (integer, integer) types. In SQL, you can typesafely create ad-hoc record types and immediately compare them with other ad-hoc record types. In these comparisons, column names are not important, but column indexes (and associated column types) are.

Comparing various SQL access APIs

The previous examples show how SQL allows for the formal declaration of record types including record degree, column names, column indexes, column types. While SQL is very expressive in that matter, many client languages accessing SQL are less expressive. When comparing expressiveness and typesafety, two features should be taken into consideration:

  1. Are the records produced into the client language typesafe?
  2. Are the SQL statements produced from the client language typesafe and syntax-safe?

Let’s have a look at various accessing techniques, and how expressive they are in terms of the above typesafety requirements:

JDBC: Least typesafety

JDBC offers the least expressiveness and typesafety. This isn’t surprising, as JDBC is a very low-level API. It offers:

  1. No typesafety whatsoever when accessing result records.
  2. No typesafety or syntax-safety whatsoever when producing SQL statements.

Here is an example:

PreparedStatement stmt = null;
ResultSet rs = null;

try {

  // SQL statements are just strings. Constructing them is not
  // typesafe or syntax-safe
  stmt = connection.prepareStatement(
    "SELECT id, title FROM book WHERE id = ?");

  // Bind values are set by index. There is no typesafety or
  // "index safety"
  stmt.setInt(1, 15);

  rs = stmt.executeQuery();
  while (rs.next()) {

    // There is no typesafety or "index safety" when accessing
    // result record values
    System.out.println(
      "ID: " + rs.getInt(1) + ", TITLE: " + rs.getString(2));
  }
}
finally {
  closeSafely(stmt, rs);
}

Now, this wasn’t surprising. JDBC makes up for the lack of typesafety by being absolutely general. It is possible to implement a JDBC driver for any type of relational database, no matter what kinds of SQL and JDBC features they really support.

JPA: Some typesafety

JPA has implemented quite a bit of typesafety mostly on top of JPQL, but also slightly on top of SQL. With JPA, you can have:

  1. Some typesafety when accessing records.
  2. Some typesafety and syntax-safety when producing JPQL statements through the CriteriaQuery API (not SQL statements).

Record access typesafety can be guaranteed when you project the outcome of your statements onto your JPA-annotated entities. While the mapping itself isn’t really typesafe, the outcome is, as a Java class is the closest match to a SQL record. A Java class, much like a SQL record, has:

  • A degree, expressed in the number of properties
  • Column names, expressed as property names
  • Column types, expressed as property types
  • But: No column indexes. Properties have no explicit order

JPA record mapping has additional features that exceed the expressiveness of SQL, as “flat”, tabular result sets can be mapped onto object hierarchies. In any case, you will have to create one record / entity type per query to profit from this typesafety. If you’re not projecting all columns from every table, but ad-hoc records (including values derived from functions), you will lose this typesafety again.

When it comes to statement typesafety, JPA offers the CriteriaQuery API to produce typesafe JPQL statements. The CriteriaQuery API is often criticised for its verboseness and for the fact that resulting client code is hard to read. Here is an example taken from the CriteriaQuery API docs:

CriteriaQuery<String> q = cb.createQuery(String.class);
Root<Order> order = q.from(Order.class);
q.select(order.get("shippingAddress").<String>get("state"));
 
CriteriaQuery<Product> q2 = cb.createQuery(Product.class);
q2.select(q2.from(Order.class)
            .join("items")
            .<Item,Product>join("product"));

It can be seen that there is only a limited amount of typesafety in the above query construction:

  • Columns are accessed by string literals, such as "shippingAddress".
  • Generic entity types are not really checked. The <Item,Product> generic parameters might as well be wrong.

Of course, there are more typesafe API parts in JPA’s CriteriaQuery API. Using those API parts quickly lead to the aforementioned verbosity, though, as can be seen in this Stack Overflow question, or in the Java EE 6 Tutorials.

LINQ: Much typesafety (in .NET)

LINQ goes very far in offering typesafety in both dimensions:

  1. Much typesafety when accessing records or tuples.
  2. Much typesafety when producing LINQ-to-SQL statements (not SQL statements).

As LINQ is formally integrated into various .NET languages, it has the advantage of being able to produce formally defined record types, directly into the target language (e.g. C#). Not only can typesafe records be produced, the LINQ-to-SQL statement is formally verified by the compiler as well. An example

// Typesafe renaming (aliasing with "AS" in SQL)
From p In db.Products
// Typesafe (named!) variable binding
Where p.UnitsInStock <= ReorderLevel AndAlso Not p.Discontinued
// The typesafe projection will produce a Products record
Select p

Another example from Stack Overflow can be seen here:

// Producing a C# tuple
var r = from u in db.Users
        join s in db.Staffs on u.Id equals s.UserId
        select new Tuple<User, Staff>(u, s);

// Producing an anonymous record type
var r = from u in db.Users
    select new { u.Name, 
                 u.Address,
                 ...,
                 (from s in db.Staffs 
                  select s.Password where u.Id == s.UserId) 
               };

LINQ has many obvious advantages when it comes to typesafety. In the case of LINQ, this comes at the price of losing actual SQL expressivity and syntax, as LINQ-to-SQL is not really SQL (just as JPQL is not really SQL either). The SQL querying API is partially shared with other, heterogeneous querying targets, such as LINQ-to-Entities, LINQ-to-Collections, LINQ-to-XML. This will reduce LINQ’s feature scope (see also a previous blog post, and I will soon blog about this again).

But C# offers all typesafety aspects that a SQL record offers as well: degree, column name (anonymous types), column index (tuples), column types (both types and tuples).

SLICK: Much typesafety (in Scala)

SLICK has been inspired by LINQ, and can thus offer a lot of typesafety as well. It offers:

  1. Much typesafety when accessing tuples (not records).
  2. Much typesafety when producing SLICK statements (not SQL statements).

SLICK takes advantage of Scala’s integrated tuple expressions. This is best shown by example:

// "for" is the "entry-point" to the DSL
val q = for {

    // FROM clause   WHERE clause
    c <- Coffees     if c.supID === 101

// SELECT clause and projection to a tuple
} yield (c.name, c.price)

The above example shows that the projection onto a (String, Int) tuple is done typesafely by the yield method. At the same time, the whole query expression is formally validated by the compiler, as SLICK makes heavy use of Scala’s language features in order to introduce an internal DSL for querying. Much more than LINQ, SLICK has a unique syntax that doesn’t remind of SQL any more. It is not obvious how subqueries, complex joins, grouping and aggregation can be expressed.

jOOQ: Much typesafety

jOOQ is mainly inspired by SQL itself and embraces all the features that SQL offers. It has thus:

  1. Much typesafety when accessing records or tuples.
  2. Much typesafety when producing SQL statements.

jOOQ offers similar capabilities as JPA when it comes to mapping SQL result sets onto records, although JPA’s mapping type hierarchies are not supported by jOOQ. But jOOQ also allows for typesafe tuple access, the way SLICK has implemented it. Ad-hoc records produced by arbitrary query projections will maintain their various column types through generic Record1<T1>, Record2<T1, T2>, Record3<T1, T2, T3>, … record types. Unlike in Java, this can be leveraged extensively in Scala, where these typesafe Record[N] types can be used just like Scala’s tuples.

On the other hand, just like LINQ-to-SQL, which has formally integrated querying as a first-class citizen into .NET languages, jOOQ allows for heavy type-checking and syntax-checking, when writing SQL statements in Java.

In SQL, you can typesafely write things like:

SELECT * FROM t WHERE (t.a, t.b) = (1, 2)
SELECT * FROM t WHERE (t.a, t.b) OVERLAPS (date1, date2)
SELECT * FROM t WHERE (t.a, t.b) IN (SELECT x, y FROM t2)
UPDATE t SET (a, b) = (SELECT x, y FROM t2 WHERE ...)
INSERT INTO t (a, b) VALUES (1, 2)

In jOOQ 3.0, you can (also typesafely!) write

select().from(t).where(row(t.a, t.b).eq(1, 2));
// Type-check here: ----------------->  ^^^^
 
select().from(t).where(row(t.a, t.b).overlaps(date1, date2));
// Type-check here: ------------------------> ^^^^^^^^^^^^
 
select().from(t).where(row(t.a, t.b).in(select(t2.x, t2.y).from(t2)));
// Type-check here: -------------------------> ^^^^^^^^^^
 
update(t).set(row(t.a, t.b), select(t2.x, t2.y).where(...));
// Type-check here: --------------> ^^^^^^^^^^

insertInto(t, t.a, t.b).values(1, 2);
// Type-check here: ---------> ^^^^

This also applies for existing API, which doesn’t involve row value expressions:

select().from(t).where(t.a.eq(select(t2.x).from(t2));
// Type-check here: ---------------> ^^^^
 
select().from(t).where(t.a.eq(any(select(t2.x).from(t2)));
// Type-check here: -------------------> ^^^^
 
select().from(t).where(t.a.in(select(t2.x).from(t2));
// Type-check here: ---------------> ^^^^

select(t1.a, t1.b).from(t1).union(select(t2.a, t2.b).from(t2));
// Type-check here: -------------------> ^^^^^^^^^^

jOOQ is not SQL, but unlike other attempts of introducing SQL as an internal domain-specific language into host languages like Java, Scala, C#, jOOQ looks very much like SQL thanks to its unique fluent API technique, which informally follows an underlying BNF notation.

Even if Java offers less expressiveness than other languages like C# or Scala, jOOQ probably comes closest to both result record typesafety and SQL syntax safety in the Java world.

Row value expressions and the BETWEEN predicate

Now this is a simple example of how SQL clause simulation can get nasty if you want to make use of some more advanced SQL clauses that aren’t supported in all databases. Consider the following predicate and equivalent transformations thereof:

The BETWEEN predicate

The BETWEEN predicate is a convenient form of expressing the fact that one expression A should be in BETWEEN two other expressions B and C. This predicate was defined already in §8.4 of SQL-1992, and then refined in SQL-1999 (adding ASYMMETRIC/SYMMETRIC):

8.3 <between predicate>

Function
    Specify a range comparison.

Format
    <between predicate> ::=
        <row value expression> [ NOT ] BETWEEN 
          [ ASYMMETRIC | SYMMETRIC ]
          <row value expression> AND <row value expression>

While ASYMMETRIC is just a verbose way of expressing the default behaviour of the BETWEEN predicate, SYMMETRIC has the useful property of indicating that the order of B and C is irrelevant. Knowing this, the following transformations can be established:

BETWEEN predicate transformations

The following statements are all equivalent:

A BETWEEN SYMMETRIC B AND C
(A BETWEEN B AND C) OR (A BETWEEN C AND B)
(A >= B AND A <= C) OR (A >= C AND A <= B)

While this is still somewhat readable, try adding row value expressions:

-- The original statement
(A1, A2) BETWEEN SYMMETRIC (B1, B2) AND (C1, C2)

-- Transforming away BETWEEN SYMMETRIC
   (     (A1, A2) >= (B1, B2) 
     AND (A1, A2) <= (C1, C2) )
OR (     (A1, A2) >= (C1, C2) 
     AND (A1, A2) <= (B1, B2) )

-- Transforming away the row value expressions
   (     ((A1 > B1) OR (A1 = B1 AND A2 > B2) OR (A1 = B1 AND A2 = B2))
     AND ((A1 < C1) OR (A1 = C1 AND A2 < C2) OR (A1 = C1 AND A2 = C2)) )
OR (     ((A1 > C1) OR (A1 = C1 AND A2 > C2) OR (A1 = C1 AND A2 = C2))
     AND ((A1 < B1) OR (A1 = B1 AND A2 < B2) OR (A1 = B1 AND A2 = B2)) )

In the lowest expression, some parts could’ve been factored out for “simplicity”. The example is just to give you a picture of what the BETWEEN [SYMMETRIC] predicate really does to row value expressions.

Native SQL support for row value expressions and BETWEEN SYMMETRIC

Here’s a comprehensive list of the 14 SQL dialects supported by jOOQ, and what is natively supported by them:

Database BETWEEN SYMMETRIC RVE = RVE RVE < RVE RVE BETWEEN
CUBRID [1] no yes no no
DB2 no yes yes yes
Derby no no no no
Firebird no no no no
H2 [2] no yes yes yes
HSQLDB yes yes yes yes
Ingres yes no no no
MySQL no yes yes no
Oracle no yes no no
Postgres yes yes yes yes
SQL Server no no no no
SQLite no no no no
Sybase ASE no no no no
Sybase SQL Anywhere no no no no

Explanation:

  • The BETWEEN SYMMETRIC column indicates, whether the database supports the SYMMETRIC keyword in general
  • The RVE = RVE column indicates, whether the database supports row value expressions in general (e.g. in equal comparison predicates)
  • The RVE < RVE column indicates, whether the database supports “ordering” comparison predicates (<, <=, >, >=) along with row value expressions
  • The RVE BETWEEN column indicates, whether the database supports the BETWEEN predicates along with row value expressions

Footnotes:

  • [1]: CUBRID doesn’t really support row value expressions. What looks like a RVE is in fact a SET in CUBRID
  • [2]: H2 doesn’t really support row value expressions. What looks like a RVE is in fact an ARRAY in H2

Row value expressions and the NULL predicate

Row value expressions are something very powerful in SQL. They have been around since the early days of standard SQL, e.g. in SQL 1992, even if not all databases implement them correctly, still today.

As always, the NULL predicate is one that is a bit tricky to understand, also in the context of row value expressions. Have a look at the following expressions:

(A, B) IS NULL
(A, B) IS NOT NULL

The SQL 1992 standard defines that:

8.6  

General Rules

1) Let R be the value of the <row value constructor>.

2) If all the values in R are the null value, then "R IS NULL" is
   true; otherwise, it is false.

3) If none of the values in R are the null value, then "R IS NOT
   NULL" is true; otherwise, it is false.

   Note: For all R, "R IS NOT NULL" has the same result as "NOT
   R IS NULL" if and only if R is of degree 1. Table 12, "<null
   predicate> semantics", specifies this behavior.

Pay some special attention to paragraph 3). Yes, the following two predicates are NOT equivalent!

    (A, B) IS NOT NULL
NOT((A, B) IS     NULL)

This is easy to understand, when factoring out the rules of equivalency:

(A, B) IS NOT NULL                -- equivalent to...
A IS NOT NULL AND B IS NOT NULL   -- equivalent to...
NOT(A IS NULL) AND NOT(B IS NULL) -- equivalent to...
NOT(A IS NULL OR B IS NULL)

-- whereas...
NOT((A, B) IS NULL)               -- equivalent to...
NOT(A IS NULL AND B IS NULL)

The truth table also nicely documents this:

+----------------+-------+-------------+------------+--------------+
|                | R IS  | R IS NOT    | NOT R IS   | NOT R IS NOT |
| Expression     | NULL  | NULL        | NULL       | NULL         |
+----------------+-------+-------------+------------+--------------+
| degree 1: null | true  | false       | false      |  true        |
| degree 1: not  | false | true        | true       |  false       |
| null           |       |             |            |              |
| degree > 1:    | true  | false       | false      |  true        |
| all null       |       |             |            |              |
| degree > 1:    | false | false       | true       |  true        |
| some null      |       |             |            |              |
| degree > 1:    | false | true        | true       |  false       |
| none null      |       |             |            |              |
+----------------+-------+-------------+------------+--------------+

More on row value expressions

jOOQ 3.0 will introduce formal and typesafe support for row value expressions and predicates based thereupon. Stay tuned for more interesting insight and articles about row vlaue expressions and how they’re supported (and/or simulated) in various SQL dialects

Array, list, set, map, tuple, record literals in Java

Occasionally, when I’m thrilled by the power and expressiveness of JavaScript, I find myself missing one or two features in the Java world. Apart from lambda expressions / closures or whatever you want to call “anonymous functions”, it’s the use of advanced literals for common data types, such as arrays, lists, sets, maps, etc. In JavaScript, no one would think about constructing a constant Map like this:

var map = new Object();
map["a"] = 1;
map["b"] = 2;
map["c"] = 3;

Instead, you’d probably write

var map = { "a":1, "b":2, "c":3 };

Specifically, when passing complex parameters to an API function, this turns out to be a very handy syntax.

What about these things in Java?

I’ve recently posted about a workaround that you can use for creating a “List literal” using Arrays.asList(…) here:

https://blog.jooq.org/2011/10/28/javas-arrays-aslist-is-underused/

This is somewhat OK. You can also construct arrays when you assign them, using array literals. But you cannot pass an array literal to a method:

// This will work:
int[] array = { 1, 2, 3 };

// This won't:
class Test {
  public void callee(int[] array) {}
  public void caller() {
    // Compilation error here:
    callee({1, 2, 3});
  }
}

Brian Goetz’s mentioning of various literals on lambda-dev

Missing this feature for quite a while, I was very thrilled to read Brian Goetz’s mentioning of them on the lambda-dev mailing list:

http://mail.openjdk.java.net/pipermail/lambda-dev/2012-May/004979.html

The ideas he was listing were these:

#[ 1, 2, 3 ]                          // Array, list, set
#{ "foo" : "bar", "blah" : "wooga" }  // Map literals
#/(\d+)$/                             // Regex
#(a, b)                               // Tuple
#(a: 3, b: 4)                         // Record
#"There are {foo.size()} foos"        // String literal

Unfortunately, he also added the following disclaimer:

Not that we’d embrace all of these immediately (or ever)

Obviously, at this stage of current Java language evolvements for Java 8, he cannot make any guarantee whatsoever about what might be added in the future. But from a jOOQ perspective, the idea of being able to declare tuple and record literals (with the appropriate backing language-support for such types!) is quite thrilling. Imagine selecting arbitrary tuples / records with their associated index/type, column/type pairs. Imagine a construct like this one in Java or Scala (using jOOQ):

// For simplicity, I'm using Scala's val operator here,
// indicating type inference. It's hard to guess what true
// record support in the java language should look like
for (val record : create.select(
                           BOOK.AUTHOR_ID.as("author"), 
                           count().as("books"))
                        .from(BOOK)
                        .groupBy(BOOK.AUTHOR_ID)
                        .fetch()) {
  
   // With true record support, you could now formally extract
   // values from the result set being iterated on. In other
   // words, the formal column alias and type is available to
   // the compiler:
   int author = record.author;
   int books = record.books;
}

Obviously, this is only speculation, but you can see that with true tuple / record support in the Java language, a lot of features would be unleashed in the Java universe with a very high impact on all existing libraries and APIs

Stay tuned! :-)