Why You Should NOT Implement Layered Architectures

Abstraction layers in software are what architecture astronauts tell you to do. Instead, however, half of all applications out there would be so easy, fun, and most importantly: productive to implement if you just got rid of all those layers.

Frankly, what do you really need? You need these two:

  • Some data access
  • Some UI

Because that’s the two things that you inevitably have in most systems. Users, and data. Here’s Kyle Boon’s opinion on possible choices that you may have

Very nice choice, Kyle. Ratpack and jOOQ. You could choose any other APIs, of course. You could even choose to write JDBC directly in JSP. Why not. As long as you don’t go pile up 13 layers of abstraction:

That’s all bollocks, you’re saying? We need layers to abstract away the underlying implementation so we can change it? OK, let’s give this some serious thought. How often do you really change the implementation? Some examples:

  • SQL. You hardly change the implementation from Oracle to DB2
  • DBMS. You hardly change the model from relational to flat or XML or JSON
  • JPA. You hardly switch from Hibernate to EclipseLink
  • UI. You simply don’t replace HTML with Swing
  • Transport. You just don’t switch from HTTP to SOAP
  • Transaction layer. You just don’t substitute JavaEE with Spring, or JDBC transactions

Nope. Your architecture is probably set in stone. And if – by the incredible influence of entropy and fate – you happen to have made the wrong decision in one aspect, about 3 years ago, well you’re in for a major refactoring anyway. If SQL was the wrong choice, well good luck to you migrating everything to MongoDB (which is per se the wrong choice again, so prepare for migrating back). If HTML was the wrong choice, well even more tough luck to you. Likelihood of your layers not really helping you when a concrete incident happens: 95% (because you missed an important detail)

Layers = Insurance

If you’re still thinking about implementing an extremely nice layered architecture, ready to deal with pretty much every situation where you simply switch a complete stack with another, then what you’re really doing is filing a dozen insurance policies. Think about it this way. You can get:

  • Legal insurance
  • Third party insurance
  • Reinsurance
  • Business interruption insurance
  • Business overhead expense disability insurance
  • Key person insurance
  • Shipping insurance
  • War risk insurance
  • Payment protection insurance
  • pick a random category

You can pay and pay and pay in advance for things that probably won’t ever happen to you. Will they? Yeah, they might. But if you buy all that insurance, you pay heavily up front. And let me tell you a secret. IF any incident ever happens, chances are that you:

  • Didn’t buy that particular insurance
  • Aren’t covered appropriately
  • Didn’t read the policy
  • Got screwed

And you’re doing exactly that in every application that would otherwise already be finished and would already be adding value to your customer, while you’re still debating if on layer 37 between the business rules and transformation layers, you actually need another abstraction because the rule engine could be switched any time.

Stop doing that

You get the point. If you have infinite amounts of time and money, implement an awesome, huge architecture up front.

Your competitor’s time to market (and fun, on the way) is better than yours. But for a short period of time, you were that close to the perfect, layered architecture!

When the Java 8 Streams API is not Enough

Java 8 was – as always – a release of compromises and backwards-compatibility. A release where the JSR-335 expert group might not have agreed upon scope or feasibility of certain features with some of the audience. See some concrete explanations by Brian Goetz about why …

But today we’re going to focus on the Streams API’s “short-comings”, or as Brian Goetz would probably put it: things out of scope given the design goals.

Parallel Streams?

Parallel computing is hard, and it used to be a pain. People didn’t exactly love the new (now old) Fork / Join API, when it was first shipped with Java 7. Conversely, and clearly, the conciseness of calling Stream.parallel() is unbeatable.

But many people don’t actually need parallel computing (not to be confused with multi-threading!). In 95% of all cases, people would have probably preferred a more powerful Streams API, or perhaps a generally more powerful Collections API with lots of awesome methods on various Iterable subtypes.

Changing Iterable is dangerous, though. Even a no-brainer as transforming an Iterable into a Stream via a potential Iterable.stream() method seems to risk opening pandora’s box!.

Sequential Streams!

So if the JDK doesn’t ship it, we create it ourselves!

Streams are quite awesome per se. They’re potentially infinite, and that’s a cool feature. Mostly – and especially with functional programming – the size of a collection doesn’t really matter that much, as we transform element by element using functions.

If we admit Streams to be purely sequential, then we could have any of these pretty cool methods as well (some of which would also be possible with parallel Streams):

  • cycle() – a guaranteed way to make every stream infinite
  • duplicate() – duplicate a stream into two equivalent streams
  • foldLeft() – a sequential and non-associative alternative to reduce()
  • foldRight() – a sequential and non-associative alternative to reduce()
  • limitUntil() – limit the stream to those records before the first one to satisfy a predicate
  • limitWhile() – limit the stream to those records before the first one not to satisfy a predicate
  • maxBy() – reduce the stream to the maximum mapped value
  • minBy() – reduce the stream to the minimum mapped value
  • partition() – partition a stream into two streams, one satisfying a predicate and the other not satisfying the same predicate
  • reverse() – produce a new stream in inverse order
  • skipUntil() – skip records until a predicate is satisified
  • skipWhile() – skip records as long as a predicate is satisfied
  • slice() – take a slice of the stream, i.e. combine skip() and limit()
  • splitAt() – split a stream into two streams at a given position
  • unzip() – split a stream of pairs into two streams
  • zip() – merge two streams into a single stream of pairs
  • zipWithIndex() – merge a stream with its corresponding stream of indexes into a single stream of pairs

jOOλ’s new Seq type does all that

All of the above is part of jOOλ. jOOλ (pronounced “jewel”, or “dju-lambda”, also written jOOL in URLs and such) is an ASL 2.0 licensed library that emerged from our own development needs when implementing jOOQ integration tests with Java 8. Java 8 is exceptionally well-suited for writing tests that reason about sets, tuples, records, and all things SQL.

But the Streams API just slightly feels insufficient, so we have wrapped JDK’s Streams into our own Seq type (Seq for sequence / sequential Stream):

// Wrap a stream in a sequence
Seq<Integer> seq1 = seq(Stream.of(1, 2, 3));

// Or create a sequence directly from values
Seq<Integer> seq2 = Seq.of(1, 2, 3);

We’ve made Seq a new interface that extends the JDK Stream interface, so you can use Seq fully interoperably with other Java APIs – leaving the existing methods unchanged:

public interface Seq<T> extends Stream<T> {

     * The underlying {@link Stream} implementation.
    Stream<T> stream();
	// [...]

Now, functional programming is only half the fun if you don’t have tuples. Unfortunately, Java doesn’t have built-in tuples and while it is easy to create a tuple library using generics, tuples are still second-class syntactic citizens when comparing Java to Scala, for instance, or C# and even VB.NET.


jOOλ also has tuples

We’ve run a code-generator to produce tuples of degree 1-8 (we might add more in the future, e.g. to match Scala’s and jOOQ’s “magical” degree 22).

And if a library has such tuples, the library also needs corresponding functions. The essence of these TupleN and FunctionN types is summarised as follows:

public class Tuple3<T1, T2, T3>
	Comparable<Tuple3<T1, T2, T3>>, 
	Serializable, Cloneable {
    public final T1 v1;
    public final T2 v2;
    public final T3 v3;
	// [...]


public interface Function3<T1, T2, T3, R> {

    default R apply(Tuple3<T1, T2, T3> args) {
        return apply(args.v1, args.v2, args.v3);

    R apply(T1 v1, T2 v2, T3 v3);

There are many more features in Tuple types, but let’s leave them out for today.

On a side note, I’ve recently had an interesting discussion with Gavin King (the creator of Hibernate) on reddit. From an ORM perspective, Java classes seem like a suitable implementation for SQL / relational tuples, and they are indeed. From an ORM perspective.

But classes and tuples are fundamentally different, which is a very subtle issue with most ORMs – e.g. as explained here by Vlad Mihalcea.

Besides, SQL’s notion of row value expressions (i.e. tuples) is quite different from what can be modelled with Java classes. This topic will be covered in a subsequent blog post.

Some jOOλ examples

With the aforementioned goals in mind, let’s see how the above API can be put to work by example:


// (tuple(1, "a"), tuple(2, "b"), tuple(3, "c"))
Seq.of(1, 2, 3).zip(Seq.of("a", "b", "c"));

// ("1:a", "2:b", "3:c")
Seq.of(1, 2, 3).zip(
    Seq.of("a", "b", "c"), 
    (x, y) -> x + ":" + y

// (tuple("a", 0), tuple("b", 1), tuple("c", 2))
Seq.of("a", "b", "c").zipWithIndex();

// tuple((1, 2, 3), (a, b, c))
    tuple(1, "a"),
    tuple(2, "b"),
    tuple(3, "c")

This is already a case where tuples have become very handy. When we “zip” two streams into one, we want a wrapper value type that combines both values. Classically, people might’ve used Object[] for quick-and-dirty solutions, but an array doesn’t indicate attribute types or degree.

Unfortunately, the Java compiler cannot reason about the effective bound of the <T> type in Seq<T>. This is why we can only have a static unzip() method (instead of an instance one), whose signature looks like this:

// This works
static <T1, T2> Tuple2<Seq<T1>, Seq<T2>> 
    unzip(Stream<Tuple2<T1, T2>> stream) { ... }
// This doesn't work:
interface Seq<T> extends Stream<T> {
    Tuple2<Seq<???>, Seq<???>> unzip();

Skipping and limiting

// (3, 4, 5)
Seq.of(1, 2, 3, 4, 5).skipWhile(i -> i < 3);

// (3, 4, 5)
Seq.of(1, 2, 3, 4, 5).skipUntil(i -> i == 3);

// (1, 2)
Seq.of(1, 2, 3, 4, 5).limitWhile(i -> i < 3);

// (1, 2)
Seq.of(1, 2, 3, 4, 5).limitUntil(i -> i == 3);

Other functional libraries probably use different terms than skip (e.g. drop) and limit (e.g. take). It doesn’t really matter in the end. We opted for the terms that are already present in the existing Stream API: Stream.skip() and Stream.limit()


// "abc"
Seq.of("a", "b", "c").foldLeft("", (u, t) -> t + u);

// "cba"
Seq.of("a", "b", "c").foldRight("", (t, u) -> t + u);

The Stream.reduce() operations are designed for parallelisation. This means that the functions passed to it must have these important attributes:

But sometimes, you really want to “reduce” a stream with functions that do not have the above attributes, and consequently, you probably don’t care about the reduction being parallelisable. This is where “folding” comes in.

A nice explanation about the various differences between reducing and folding (in Scala) can be seen here.


// tuple((1, 2, 3), (1, 2, 3))
Seq.of(1, 2, 3).duplicate();

// tuple((1, 3, 5), (2, 4, 6))
Seq.of(1, 2, 3, 4, 5, 6).partition(i -> i % 2 != 0)

// tuple((1, 2), (3, 4, 5))
Seq.of(1, 2, 3, 4, 5).splitAt(2);

The above functions all have one thing in common: They operate on a single stream in order to produce two new streams, that can be consumed independently.

Obviously, this means that internally, some memory must be consumed to keep buffers of partially consumed streams. E.g.

  • duplication needs to keep track of all values that have been consumed in one stream, but not in the other
  • partitioning needs to fast forward to the next value that satisfies (or doesn’t satisfy) the predicate, without losing all the dropped values
  • splitting might need to fast forward to the split index

For some real functional fun, let’s have a look at a possible splitAt() implementation:

static <T> Tuple2<Seq<T>, Seq<T>> 
splitAt(Stream<T> stream, long position) {
    return seq(stream)
          .partition(t -> t.v2 < position)
          .map((v1, v2) -> tuple(
              v1.map(t -> t.v1),
              v2.map(t -> t.v1)

… or with comments:

static <T> Tuple2<Seq<T>, Seq<T>> 
splitAt(Stream<T> stream, long position) {
    // Add jOOλ functionality to the stream
    // -> local Type: Seq<T>
    return seq(stream)
    // Keep track of stream positions
    // with each element in the stream
    // -> local Type: Seq<Tuple2<T, Long>>
    // Split the streams at position
    // -> local Type: Tuple2<Seq<Tuple2<T, Long>>,
    //                       Seq<Tuple2<T, Long>>>
          .partition(t -> t.v2 < position)
    // Remove the indexes from zipWithIndex again
    // -> local Type: Tuple2<Seq<T>, Seq<T>>
          .map((v1, v2) -> tuple(
              v1.map(t -> t.v1),
              v2.map(t -> t.v1)

Nice, isn’t it? A possible implementation for partition(), on the other hand, is a bit more complex. Here trivially with Iterator instead of the new Spliterator:

static <T> Tuple2<Seq<T>, Seq<T>> partition(
        Stream<T> stream, 
        Predicate<? super T> predicate
) {
    final Iterator<T> it = stream.iterator();
    final LinkedList<T> buffer1 = new LinkedList<>();
    final LinkedList<T> buffer2 = new LinkedList<>();

    class Partition implements Iterator<T> {

        final boolean b;

        Partition(boolean b) {
            this.b = b;

        void fetch() {
            while (buffer(b).isEmpty() && it.hasNext()) {
                T next = it.next();

        LinkedList<T> buffer(boolean test) {
            return test ? buffer1 : buffer2;

        public boolean hasNext() {
            return !buffer(b).isEmpty();

        public T next() {
            return buffer(b).poll();

    return tuple(
        seq(new Partition(true)), 
        seq(new Partition(false))

I’ll let you do the exercise and verify the above code.

Get and contribute to jOOλ, now!

All of the above is part of jOOλ, available for free from GitHub. There is already a partially Java-8-ready, full-blown library called functionaljava, which goes much further than jOOλ.

Yet, we believe that all what’s missing from Java 8’s Streams API is really just a couple of methods that are very useful for sequential streams.

In a previous post, we’ve shown how we can bring lambdas to String-based SQL using a simple wrapper for JDBC (of course, we still believe that you should use jOOQ instead).

Today, we’ve shown how we can write awesome functional and sequential Stream processing very easily, with jOOλ.

Stay tuned for even more jOOλ goodness in the near future (and pull requests are very welcome, of course!)

Look no Further! The Final Answer to “Where to Put Generated Code?”

This recent question on Stack Overflow made me think.

Why does jOOQ suggest to put generated code under “/target” and not under “/src”?

… and I’m about to give you the final answer to “Where to Put Generated Code?”

This isn’t only about jOOQ

Even if you’re not using jOOQ, or if you’re using jOOQ but without the code generator, there might be some generated source code in your project. There are many tools that generate source code from other data, such as:

  • The Java compiler (ok, byte code, not strictly source code. But still code generation)
  • XJC, from XSD files
  • Hibernate from .hbm.xml files, or from your schema
  • Xtend translates Xtend code to Java code
  • You could even consider data transformations, like XSLT
  • many more…

In this article, we’re going to look at how to deal with jOOQ-generated code, but the same thoughts apply also to any other type of code generated from other code or data.

Now, the very very interesting strategic question that we need to ask ourselves is: Where to put that code? Under version control, like the original data? Or should we consider generated code to be derived code that must be re-generated all the time?

The answer is nigh…

It depends!

Nope, unfortunately, as with many other flame-wary discussions, this one doesn’t have a completely correct or wrong answer, either. There are essentially two approaches:

Considering generated code as part of your code base

When you consider generated code as part of your code base, you will want to:

  • Check in generated sources in your version control system
  • Use manual source code generation
  • Possibly use even partial source code generation

This approach is particularly useful when your Java developers are not in full control of or do not have full access to your database schema (or your XSD or your Java code, etc.), or if you have many developers that work simultaneously on the same database schema, which changes all the time. It is also useful to be able to track side-effects of database changes, as your checked-in database schema can be considered when you want to analyse the history of your schema.

With this approach, you can also keep track of the change of behaviour in the jOOQ code generator, e.g. when upgrading jOOQ, or when modifying the code generation configuration.

When you use this approach, you will treat your generated code as an external library with its own lifecycle.

The drawback of this approach is that it is more error-prone and possibly a bit more work as the actual schema may go out of sync with the generated schema.

Considering generated code as derived artefacts

When you consider generated code to be derived artefacts, you will want to:

  • Check in only the actual DDL, i.e. the “original source of truth” (e.g. controlled via Flyway)
  • Regenerate jOOQ code every time the schema changes
  • Regenerate jOOQ code on every machine – including continuous integration machines, and possibly, if you’re crazy enough, on production

This approach is particularly useful when you have a smaller database schema that is under full control by your Java developers, who want to profit from the increased quality of being able to regenerate all derived artefacts in every step of your build.

This approach is fully supported by Maven, for instance, which foresees special directories (e.g. target/generated-sources), and phases (e.g. <phase>generate-sources</phase>) specifically for source code generation.

The drawback of this approach is that the build may break in perfectly “acceptable” situations, when parts of your database are temporarily unavailable.

Pragmatic approach

Some of you might not like that answer, but there is also a pragmatic approach, a combination of both. You can consider some code as part of your code base, and some code as derived. For instance, jOOQ-meta’s generated sources (used to query the dictionary views / INFORMATION_SCHEMA when generating jOOQ code) are put under version control as few jOOQ contributors will be able to run the jOOQ-meta code generator against all supported databases. But in many integration tests, we re-generate the sources every time to be sure the code generator works correctly.



I’m sorry to disappoint you. There is no final answer to whether one approach or the other is better. Pick the one that offers you more value in your specific situation.

In case you’re choosing your generated code to be part of the code base, read this interesting experience report on the jOOQ User Group by Witold Szczerba about how to best achieve this.

Stop Manually Importing Your ERD Export into jOOQ

ERD (Entity Relationship Diagrams) are a great way of designing and visualising your database model. There is a variety of vendors offering free and commercial ERD tools. Vertabelo by E-Point is a SaaS product where you can design and manage your database schema online. For instance, the jOOQ example database can be modelled as such:

jOOQ Sample Database

jOOQ Sample Database


The most important aspect of such an ERD tool, however, is its import / export functionality. Not only can an existing schema be reverse-engineered, but you can also export it in SQL or XML format. This is great news for early adopters of the upcoming jOOQ 3.5, which will finally support importing file-based schema definitions, e.g. using an XML representation of your INFORMATION_SCHEMA.

XSLT to the rescue

In “modern” times where people put JSON everywhere and XML has become evil, people might have forgotten the power of XML and its tool chain. In this case, it will be very trivial to transform the Vertabelo export format into the jOOQ import format:


    <Table Id="t1">
            <Column Id="c1">
            <!-- ... -->

A full export file can be seen here.


        <!-- ... -->
        <!-- ... -->
            <constraint_type>PRIMARY KEY</constraint_type>
        <!-- ... -->

While the Vertabelo export format organised hierarchically, the jOOQ import format is a flat XML representation of the relevant SQL standard INFORMATION SCHEMA tables, as implemented by a variety of databases such as H2, HSQLDB, MySQL, PostgreSQL, SQL Server. The transformation can be done easily with XSLT, looking something like this:

<xsl:template match="/">
    <xsl:key name="schema" 
        match="/DatabaseModel/Tables/Table/Properties/Property[Name = 'Schema']" use="." />

    <information_schema xmlns="http://www.jooq.org/xsd/jooq-meta-3.5.0.xsd">
                select="/DatabaseModel/Tables/Table/Properties/Property[Name = 'Schema'][generate-id() = generate-id(key('schema', .)[1])]" 

<!-- ... -->
<xsl:template match="Table" mode="table">
            <xsl:value-of select="Properties/Property[Name = 'Schema']/Value"/>
            <xsl:value-of select="Name"/>

<xsl:template match="Column" mode="column">
    <xsl:variable name="Id" select="@Id"/>
            <xsl:value-of select="ancestor::Table/Properties/Property[Name = 'Schema']/Value"/>
            <xsl:value-of select="ancestor::Table/Name"/>
            <xsl:value-of select="Name"/>
            <xsl:value-of select="Type"/>
            <xsl:value-of select="1 + count(preceding-sibling::Column)"/>
            <xsl:value-of select="Nullable"/>

The full XSL file can be seen here.

Configuring Maven

All we need now is to put the Vertabelo export file somewhere in src/main/resources, and transform it with the Codehaus xml-maven-plugin like so:

<!-- The XSLT plugin transforming XML files prior to code generation -->

The output is then available to the jOOQ code generator in the target directory:


                        <!-- We're using the new jOOQ 3.5 XMLDatabase here -->

That’s it!

This obviously works with other ERD tools than Vertabelo as well. It should be just as easy to write your own XSL file for your own tool. All you need to do is generate valid XML according to the new http://www.jooq.org/xsd/jooq-meta-3.5.0.xsd schema.

See the pom.xml file here, for details, or download the full example project from GitHub.

Awesome SQL Trick: Constraints on Views

CHECK constraints are already pretty great when you want to sanitize your data. But there are some limitations to CHECK constraints, including the fact that they are applied to the table itself, when sometimes, you want to specify constraints that only apply in certain situations.

This can be done with the SQL standard WITH CHECK OPTION clause, which is implemented by at least Oracle and SQL Server. Here’s how to do that:

  id    NUMBER(10)         NOT NULL,
  title VARCHAR2(100 CHAR) NOT NULL,
  price NUMBER(10, 2)      NOT NULL,

CREATE VIEW expensive_books
SELECT id, title, price
FROM books
WHERE price > 100

VALUES (1, '1984', 35.90);

  'The Answer to Life, the Universe, and Everything',

As you can see, expensive_books are all those books whose price is more than 100.00. This view will only report the second book:

SELECT * FROM expensive_books;

The above query yields:

ID TITLE                                       PRICE
-- ----------------------------------------- -------
 2 The Answer to Life, the Universe, and ...   999.9 

But now, that we have that CHECK OPTION, we can also prevent users from inserting “expensive books” that aren’t really expensive. For instance, let’s run this query:

INSERT INTO expensive_books 
VALUES (3, '10 Reasons why jOOQ is Awesome', 9.99);

This query won’t work now. We’re getting:

ORA-01402: view WITH CHECK OPTION where-clause violation

We also cannot update any of the “expensive books” to be non-expensive:

UPDATE expensive_books
SET price = 9.99;

This query results in the same ORA-01402 error message.


In case you need to locally prevent bogus data from being inserted into a table, you can also use inline WITH CHECK OPTION clauses like so:

  FROM expensive_books
  WHERE price > 1000
) really_expensive_books
VALUES (3, 'Modern Enterprise Software', 999.99);

And the above query again resutls in an ORA-01402 error.

Using SQL transformation to generate ad-hoc constraints

While CHECK OPTION is very useful for stored views, which can have proper grants for those users that may not access the underlying table directly, the inline CHECK OPTION is mainly useful when you transform dynamic SQL in an intermediate SQL transformation layer in your applciation.

This can be done with jOOQ’s SQL transformation capabilities, for instance, where you can watch out for a certain table in your SQL statements, and then centrally prevent bogus DML from being executed. This is a great way to implement multi-tenancy, if your database doesn’t natively support row-level security.

Stay tuned for a future blog post explaining how to transform your SQL with jOOQ to implement row-level security for any database.

jOOQ Newsletter: September 02, 2014 – Do You Really Need Support?

Do you really need support?

Our apologies. We hadn’t realised that we didn’t advertise the support-free jOOQ licenses, which we had been offering for quite a while now well enough on our website. So we have fixed that now.

We think that jOOQ is such a high quality, intuitive piece of software with a vibrant community that our customers might not even need us at Data Geekery to support them! That is why we have been offering support-less subscriptions where customers get to use the jOOQ Professional Edition or the jOOQ Enterprise Edition for 20% less than if they had our guaranteed reaction times.

All you need to do is enter the “NO SUPPORT” discount code with your next purchase, and start coding. More details here. Note that this will only remove our support guarantees, not the warranty. All upgrades and bugfixes are still included.

And while we’re at it, if you’re planning on purchasing 10 licenses or more, please contact us to learn about our high-volume tiered pricing model to further increase the value you’re getting out of jOOQ.

Data Geekery 1 Year Anniversary


One year ago, on August 15 2013, Data Geekery GmbH was founded to provide commercial licensing and support for jOOQ. We’ve had exciting times behind us, and even more exciting times ahead of us. Here’s a quick wrap-up of what happend in the last year:

  • 2013-08-15: Data Geekery enters the Zurich trade register
  • 2013-10-09: jOOQ 3.2 is released under the new dual licensing strategy
  • 2013-10-29: jOOQ gets roughly 10% votes on this InfoQ poll
  • 2013-12-18: We’re having the 8th conference or JUG talk about jOOQ
  • 2014-12-31: Data Geekery is profitable. A Happy New Year, indeed!
  • 2014-01-01: Our monthly downloads have recovered from dual licensing
  • 2014-01-17: Our articles reach 1M reads on DZone
  • 2014-02-14: jOOQ 3.3 is released with Keyset pagination support
  • 2014-02-19: The 200th Stack Overflow question about jOOQ was asked
  • 2014-05-21: jOOQ is referenced from the RebelLabs reports
  • 2014-06-12: We’re having the 21st conference or JUG talk about jOOQ
  • 2014-06-20: jOOQ 3.4 is released with CTE, transactions, and DDL support
  • 2014-06-23: The 500th GitHub Star was added
  • 2014-07-01: Our monthly downloads have doubled compared to last year
  • 2014-08-08: The 400th blog post was published bringing the 650’000th hit

So, what’s next?

jOOQ is a big success story. Many minor frameworks by other “data geeks” copy jOOQ’s approach to writing internal domain-specific languages for a subset of SQL or of another query language. Examples are:

Being the industry’s leading type safe embedded SQL API, we’re going to continue pushing embedded SQL in Java, and SQL in general. Stay tuned for a very exciting second year of Data Geekery!

Tweet of the Day

Our customers, users, and followers are sharing their love for jOOQ with the world and we can hardly catch up with them! Here are:

Thanks for the shouts, guys! You make the jOOQ experience rock!

SQL Zone – The Dreaded COUNT(*) Function

COUNT(*) seems to be a practical way for many SQL developers to ensure that there is exactly one result record. No more, no less. But often, if you want exactly one record, you can achieve the same thing using a CASE expression along with anEXISTS predicate, which is likely to be much faster than the COUNT(*) alternative, because you probably don’t care about the exact number of records, only about the existence of such records.

Does that sound too abstract? Read this article here, and decide for yourself, if you find potential for optimisation in your code.

SQL Zone – Constraints on Views

If you’re using Oracle or SQL Server (or another standards-compliant database), you can put constraints (“CHECK OPTIONS”) on your database views. This can be extremely useful when you want to prevent users from inserting data into views that don’t match the view itself. Take this view for instance:

CREATE VIEW expensive_books
SELECT id, title, price
FROM books
WHERE price > 100

This view will not allow you to insert any books with a price lower than 100, because of the CHECK OPTION. An incredibly useful feature that will also be supported by the upcoming jOOQ 3.5.

Read this blog post for more information.

Upcoming Events

After a summer break, we’re back on the road!

Have you missed any of our previous jOOQ talks? Soon you’ll get another chance to hear us talk about jOOQ or SQL in general in any of these upcoming events:

Stay informed about 2014 events on www.jooq.org/news.