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JavaScript goes SQL with Nashorn and jOOQ

Lukas Eder
rhino

A look into some guaranteed premium serverside SQL scripting with Nashorn and Java 8.

This post was originally published over at jooq.org as part of a special series focusing on all things Java 8, including how take advantage of lambda expressions, extension methods, and other great stuff. You’ll find the source code on GitHub.

This week, we’ll look into some awesome serverside SQL scripting with Nashorn and Java 8. Only few things can be found on the web regarding the use of JDBC in Nashorn. But why use JDBC and take care of painful resource management and SQL string composition, when you can usejOOQ? Everything works out of the box!

Let’s set up a little sample JavaScript file as such:

var someDatabaseFun = function() {
    var Properties = Java.type("java.util.Properties");
    var Driver = Java.type("org.h2.Driver");
 
    var driver = new Driver();
    var properties = new Properties();
 
    properties.setProperty("user", "sa");
    properties.setProperty("password", "");
 
    try {
        var conn = driver.connect(
            "jdbc:h2:~/test", properties);
 
        // Database code here
    }
    finally {
        try { 
            if (conn) conn.close();
        } catch (e) {}
    }
}
 
someDatabaseFun();

This is pretty much all you need to interoperate with JDBC and a H2 database. So we could be running SQL statements with JDBC like so:

try {
    var stmt = conn.prepareStatement(
        "select table_schema, table_name " + 
        "from information_schema.tables");
    var rs = stmt.executeQuery();
 
    while (rs.next()) {
        print(rs.getString("TABLE_SCHEMA") + "."
            + rs.getString("TABLE_NAME"))
    }
}
finally {
    if (rs)
        try {
            rs.close();
        }
        catch(e) {}
 
    if (stmt)
        try {
            stmt.close();
        }
        catch(e) {}
}

Most of the bloat is JDBC resource handling as we unfortunately don’t have a try-with-resources statement in JavaScript. The above generates the following output:

INFORMATION_SCHEMA.FUNCTION_COLUMNS
INFORMATION_SCHEMA.CONSTANTS
INFORMATION_SCHEMA.SEQUENCES
INFORMATION_SCHEMA.RIGHTS
INFORMATION_SCHEMA.TRIGGERS
INFORMATION_SCHEMA.CATALOGS
INFORMATION_SCHEMA.CROSS_REFERENCES
INFORMATION_SCHEMA.SETTINGS
INFORMATION_SCHEMA.FUNCTION_ALIASES
INFORMATION_SCHEMA.VIEWS
INFORMATION_SCHEMA.TYPE_INFO
INFORMATION_SCHEMA.CONSTRAINTS
...

Let’s see if we can run the same query using jOOQ:

var DSL = Java.type("org.jooq.impl.DSL");
 
print(
    DSL.using(conn)
       .fetch("select table_schema, table_name " +
              "from information_schema.tables")
);

This is how you can execute plain SQL statements in jOOQ, with much less bloat than with JDBC. The output is roughly the same:

+------------------+--------------------+
|TABLE_SCHEMA      |TABLE_NAME          |
+------------------+--------------------+
|INFORMATION_SCHEMA|FUNCTION_COLUMNS    |
|INFORMATION_SCHEMA|CONSTANTS           |
|INFORMATION_SCHEMA|SEQUENCES           |
|INFORMATION_SCHEMA|RIGHTS              |
|INFORMATION_SCHEMA|TRIGGERS            |
|INFORMATION_SCHEMA|CATALOGS            |
|INFORMATION_SCHEMA|CROSS_REFERENCES    |
|INFORMATION_SCHEMA|SETTINGS            |
|INFORMATION_SCHEMA|FUNCTION_ALIASES    |
 ...

But jOOQ’s strength is not in its plain SQL capabilities, it lies in the DSL API, which abstracts away all the vendor-specific SQL subtleties and allows you to compose queries (and also DML) fluently. Consider the following SQL statement:

// Let's assume these objects were generated
// by the jOOQ source code generator
var Tables = Java.type(
    "org.jooq.db.h2.information_schema.Tables");
var t = Tables.TABLES;
var c = Tables.COLUMNS;
 
// This is the equivalent of Java's static imports
var count = DSL.count;
var row = DSL.row;
 
// We can now execute the following query:
print(
    DSL.using(conn)
       .select(
           t.TABLE_SCHEMA, 
           t.TABLE_NAME, 
           c.COLUMN_NAME)
       .from(t)
       .join(c)
       .on(row(t.TABLE_SCHEMA, t.TABLE_NAME)
           .eq(c.TABLE_SCHEMA, c.TABLE_NAME))
       .orderBy(
           t.TABLE_SCHEMA.asc(),
           t.TABLE_NAME.asc(),
           c.ORDINAL_POSITION.asc())
       .fetch()
);

Note that there is obviously no typesafety in the above query, as this is JavaScript. But I would imagine that the IntelliJ, Eclipse, or NetBeans creators will eventually detect Nashorn dependencies on Java programs, and provide syntax auto-completion and highlighting, as some things can be statically analysed.

Things get even better if you’re using the Java 8 Streams API from Nashorn. Let’s consider the following query:

DSL.using(conn)
   .select(
       t.TABLE_SCHEMA,
       t.TABLE_NAME,
       count().as("CNT"))
   .from(t)
   .join(c)
   .on(row(t.TABLE_SCHEMA, t.TABLE_NAME)
       .eq(c.TABLE_SCHEMA, c.TABLE_NAME))
   .groupBy(t.TABLE_SCHEMA, t.TABLE_NAME)
   .orderBy(
       t.TABLE_SCHEMA.asc(),
       t.TABLE_NAME.asc())
 
// This fetches a List<Map<String, Object>> as
// your ResultSet representation
   .fetchMaps()
 
// This is Java 8's standard Collection.stream()
   .stream()
 
// And now, r is like any other JavaScript object
// or record!
   .forEach(function (r) {
       print(r.TABLE_SCHEMA + '.' 
           + r.TABLE_NAME + ' has ' 
           + r.CNT + ' columns.');
   });

The above generates this output:

INFORMATION_SCHEMA.CATALOGS has 1 columns.
INFORMATION_SCHEMA.COLLATIONS has 2 columns.
INFORMATION_SCHEMA.COLUMNS has 23 columns.
INFORMATION_SCHEMA.COLUMN_PRIVILEGES has 8 columns.
INFORMATION_SCHEMA.CONSTANTS has 7 columns.
INFORMATION_SCHEMA.CONSTRAINTS has 13 columns.
INFORMATION_SCHEMA.CROSS_REFERENCES has 14 columns.
INFORMATION_SCHEMA.DOMAINS has 14 columns.
...

If your database supports arrays, you can even access such array columns by index, e.g.

r.COLUMN_NAME[3]

So, if you’re a server-side JavaScript aficionado, download jOOQ today, and start writing awesome SQL in JavaScript, now! For more Nashorn awesomeness, consider reading this article here.

Author
Lukas Eder
Lukas is a Java and SQL aficionado. He’s the founder and head of R&D at Data Geekery GmbH (datageekery.com), the company behind jOOQ (jooq.org) , the best way to write SQL in Java.
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