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This Common API Technique is Actually an Anti-Pattern

I admit, we’ve been lured into using this technique as well. It’s just so convenient, as it allows for avoiding a seemingly unnecessary cast. It’s the following technique here:

interface SomeWrapper {
  <T> T get();

Now you can type safely assign anything from the wrapper to any type:

SomeWrapper wrapper = ...

// Obviously
Object a = wrapper.get();

// Well...
Number b = wrapper.get();

// Risky
String[][] c = wrapper.get();

// Unprobable
javax.persistence.SqlResultSetMapping d = 

This is actually the API you can use when you’re using jOOR, our reflection library that we’ve written and open sourced to improve our integration tests. With jOOR, you can write things like:

Employee[] employees = on(department)
for (Employee employee : employees) {
    Street street = on(employee)

The API is rather simple. The on() method wraps an Object or a Class. The call() methods then call a method on that object using reflection (but without requiring exact signatures, without requiring the method to be public, and without throwing any checked exceptions). And without the need for casting, you can then call get() to assign the result to any arbitrary reference type.

This is probably OK with a reflection library like jOOR, because the whole library is not really type safe. It can’t be, because it’s reflection.

But the “dirty” feeling remains. The feeling of giving the call-site a promise with respect to the resulting type, a promise that cannot be kept, and that will result in ClassCastException – a thing of the past that junior developers who have started after Java 5 and generics hardly know.

But the JDK libraries also do that…

Yes, they do. But very seldomly, and only if the generic type parameter is really irrelevant. For instance, when getting a Collection.emptyList(), whose implementation looks like this:

public static final <T> List<T> emptyList() {
    return (List<T>) EMPTY_LIST;

It’s true that the EMPTY_LIST is cast unsafely from List to List<T>, but from a semantic perspective, this is a safe cast. You cannot modify this List reference, and because it’s empty, there is no method in List<T> that will ever give you an instance of T or T[] that does not correspond to your target type. So, all of these are valid:

// perfectly fine
List<?> a = emptyList();

// yep
List<Object> b = emptyList();

// alright
List<Number> c = emptyList();

// no problem
List<String[][]> d = emptyList();

// if you must
List<javax.persistence.SqlResultSetMapping> e 
    = emptyList();

So, as always (or mostly), the JDK library designers have taken great care not to make any false promises about the generic type that you might get. This means that you will often get an Object type where you know that another type would be more suitable.

But even if YOU know this, the compiler won’t. Erasure comes at a price and the price is paid when your wrapper or collection is empty. There is no way of knowing the contained type of such an expression, so don’t pretend you do. In other words:

Do not use the just-to-avoid-casting generic method anti pattern

Let’s Review How to Insert Clob or Blob via JDBC

LOBs are a PITA in all databases, as well as in JDBC. Handling them correctly takes a couple of lines of code, and you can be sure that you’ll get it wrong eventually. Because you have to think of a couple of things:

  • Foremost, LOBs are heavy resources that need special lifecycle management. Once you’ve allocated a LOB, you better “free” it as well to decrease the pressure on your GC. This article shows more about why you need to free lobs
  • The time when you allocate and free a lob is crucial. It might have a longer life span than any of your ResultSet, PreparedStatement, or Connection / transaction. Each database manages such life spans individually, and you might have to read up the specifications in edge cases
  • While you may use String instead of Clob, or byte[] instead of Blob for small to medium size LOBs, this may not always be the case, and may even lead to some nasty errors, like Oracle’s dreaded ORA-01461: can bind a LONG value only for insert into a LONG column

So, if you’re working on a low level using JDBC (instead of abstracting JDBC via Hibernate or jOOQ), you better write a small utility that takes care of proper LOB handling.

We’ve recently re-discovered our own utility that we’re using for jOOQ integration testing, at least in some databases, and thought this might be very useful to a couple of our readers who operate directly with JDBC. Consider the following class:

public class LOB implements AutoCloseable {

    private final Connection connection;
    private final List<Blob> blobs;
    private final List<Clob> clobs;

    public LOB(Connection connection) {
        this.connection = connection;
        this.blobs = new ArrayList<>();
        this.clobs = new ArrayList<>();

    public final Blob blob(byte[] bytes) 
    throws SQLException {
        Blob blob;

        // You may write more robust dialect 
        // detection here
        if (connection.getMetaData()
                      .contains("oracle")) {
            blob = BLOB.createTemporary(connection, 
                       false, BLOB.DURATION_SESSION);
        else {
            blob = connection.createBlob();

        blob.setBytes(1, bytes);
        return blob;

    public final Clob clob(String string) 
    throws SQLException {
        Clob clob;

        if (connection.getMetaData()
                      .contains("oracle")) {
            clob = CLOB.createTemporary(connection, 
                       false, CLOB.DURATION_SESSION);
        else {
            clob = connection.createClob();

        clob.setString(1, string);
        return clob;

    public final void close() throws Exception {

This simple class has some nice treats:

  • It’s AutoCloseable, so you can free your lobs with the try-with-resources statement
  • It abstracts over the creation of LOBs across SQL dialects. No need to remember the Oracle way

To use this class, simply write something like the following:

try (
    LOB lob = new LOB(connection);
    PreparedStatement stmt = connection.prepareStatement(
        "insert into lobs (id, lob) values (?, ?)")
) {
    stmt.setInt(1, 1);
    stmt.setClob(2, lob.clob("abc"));

That’s it! No need to keep references to the lob, safely freeing it if it’s not null, correctly recovering from exceptions, etc. Just put the LOB container in the try-with-resources statement, along with the PreparedStatement and done.

If you’re interested in why you have to call or in the first place, read our article about it. It’ll spare you one or two OutOfMemoryErrors

Is Your Eclipse Running a Bit Slow? Just Use This Simple Trick!

You wouldn’t believe it until you try it yourself. I’ve been using the Eclipse Mars developer milestones lately, and I’ve been having some issues with slow compilation. I always thought it was because of the m2e integration, which has never been famous for working perfectly. But then, it dawned upon me when I added a JPA persistence.xml file to run some jOOQ + Hibernate tests… I ran into this issue, and googled it to learn that many people are complaining about JPA validation running forever in their Eclipses.

So I searched for how to deactivate that, and boom!

All of my Eclipse got much much faster

In fact, I didn’t just deactivate JPA validation, but all validation:

deactivate all validation in your Eclipse to boost performance

I don’t remember the last time I ever needed validation, or thought that it was a useful feature in the first place. If you want to help your whole team, you can also check in the following file in each of your projects’ .settings/org.eclipse.wst.validation.prefs files:


This has the same effect, but can be checked into version control.

Found this tip useful? See also our list of Top 5 Useful Hidden Eclipse Features

How JPA 2.1 has become the new EJB 2.0

Beauty lies in the eye of the beholder. So does “ease”:

Thorben writes very good and useful articles about JPA, and he’s recently started an excellent series about JPA 2.1’s new features. Among which: Result set mapping. You may know result set mapping from websites like CTMMC, or We can summarise this mapping procedure as follows:

a) define the mapping

    name = "BookAuthorMapping",
    entities = {
            entityClass = Book.class,
            fields = {
                @FieldResult(name = "id", column = "id"),
                @FieldResult(name = "title", column = "title"),
                @FieldResult(name = "author", column = "author_id"),
                @FieldResult(name = "version", column = "version")}),
            entityClass = Author.class,
            fields = {
                @FieldResult(name = "id", column = "authorId"),
                @FieldResult(name = "firstName", column = "firstName"),
                @FieldResult(name = "lastName", column = "lastName"),
                @FieldResult(name = "version", column = "authorVersion")})})

The above mapping is rather straight-forward. It specifies how database columns should be mapped to entity fields and to entities as a whole. Then you give this mapping a name ("BookAuthorMapping"), which you can then reuse across your application, e.g. with native JPA queries.

I specifically like the fact that Thorben then writes:

If you don’t like to add such a huge block of annotations to your entity, you can also define the mapping in an XML file

… So, we’re back to replacing huge blocks of annotations by huge blocks of XML – a technique that many of us wanted to avoid using annotations… :-)

b) apply the mapping

Once the mapping has been statically defined on some Java type, you can then fetch those entities by applying the above BookAuthorMapping

List<Object[]> results = this.em.createNativeQuery(
    "SELECT, b.title, b.author_id, b.version, " +
    " as authorId, a.firstName, a.lastName, " + 
    "       a.version as authorVersion " + 
    "FROM Book b " +
    "JOIN Author a ON b.author_id =", 
).getResultList(); -> {
    Book book = (Book)record[0];
    Author author = (Author)record[1];

Notice how you still have to remember the Book and Author types and cast explicitly as no verifiable type information is really attached to anything.

The definition of “complex”

Now, the article claims that this is “complex” mapping, and no doubt, I would agree. This very simple query with only a simple join already triggers such an annotation mess if you want to really map your entities via JPA. You don’t want to see Thorben’s mapping annotations, once the queries get a little more complex. And remember, @SqlResultSetMapping is about mapping (native!) SQL results, so we’re no longer in object-graph-persistence land, we’re in SQL land, where bulk fetching, denormalising, aggregating, and other “fancy” SQL stuff is king.

The problem is here:

Java 5 introduced annotations. Annotations were originally intended to be used as “artificial modifiers”, i.e. things like static, final, protected (interestingly enough, Ceylon only knows annotations, no modifiers). This makes sense. Java language designers could introduce new modifiers / “keywords” without breaking existing code – because “real” keywords are reserved words, which are hard to introduce in a language. Remember enum?

So, good use-cases for annotations (and there are only few) are:

  • @Override
  • @Deprecated (although, a comment attribute would’ve been fancy)
  • @FunctionalInterface

JPA (and other Java EE APIs, as well as Spring) have gone completely wacko on their use of annotations. Repeat after me:

No language @Before or @After Java ever abused annotations as much as Java tweet this

(the @Before / @After idea was lennoff’s, on reddit)

There is a strong déjà vu in me when reading the above. Do you remember the following?

No language before or after Java ever abused checked exceptions as much as Java

We will all deeply regret Java annotations by 2020.

Annotations are a big wart in the Java type system. They have an extremely limited justified use and what we Java Enterprise developers are doing these days is absolutely not within the limits of “justified”. We’re abusing them for configuration for things that we should really be writing code for.

Here’s how you’d run the same query with jOOQ (or any other API that leverages generics and type safety for SQL):

Book b ="b");
Author a ="a");

   .select(b.ID, b.TITLE, b.AUTHOR_ID, b.VERSION,
           a.ID, a.FIRST_NAME, a.LAST_NAME,
   .forEach(record -> {
       BookRecord book = record.into(b);
       AuthorRecord author = record.into(a);

This example combines both JPA 2.1’s annotations AND querying. All the meta information about projected “entities” is already contained in the query and thus in the Result that is produced by the fetch() method. But it doesn’t really matter, the point here is that this lambda expression …

record -> {
    BookRecord book = record.into(b);
    AuthorRecord author = record.into(a);

… it can be anything you want! Like the more sophisticated examples we’ve shown in previous blog posts:

Mapping can be defined ad-hoc, on the fly, using functions. Functions are the ideal mappers, because they take an input, produce an output, and are completely stateless. And the best thing about functions in Java 8 is, they’re compiled by the Java compiler and can be used to type-check your mapping. And you can assign functions to objects, which allows you to reuse the functions, when a given mapping algorithm can be used several times.

In fact, the SQL SELECT clause itself is such a function. A function that transforms input tuples / rows into output tuples / rows, and you can adapt that function on the fly using additional expressions.

There is absolutely no way to type-check anything in the previous JPA 2.1 native SQL statement and @SqlResultSetMapping example. Imagine changing a column name:

List<Object[]> results = this.em.createNativeQuery(
    "SELECT, b.title as book_title, " +
    "       b.author_id, b.version, " +
    " as authorId, a.firstName, a.lastName, " + 
    "       a.version as authorVersion " + 
    "FROM Book b " +
    "JOIN Author a ON b.author_id =", 

Did you notice the difference? The b.title column was renamed to book_title. In a SQL string. Which blows up at run time! How to remember that you have to also adapt

@FieldResult(name = "title", column = "title")

… to be

@FieldResult(name = "title", column = "book_title")

Conversely, how to remember that once you rename the column in your @FieldResult, you’ll also have to go check wherever this "BookAuthorMapping" is used, and also change the column names in those queries.

    name = "BookAuthorMapping",

Annotations are evil

You may or may not agree with some of the above. You may or may not like jOOQ as an alternative to JPA, that’s perfectly fine. But it is really hard to disagree with the fact that:

  • Java 5 introduced very useful annotations
  • Java EE / Spring heavily abused those annotations to replace XML
  • We now have a parallel universe type system in Java
  • This parallel universe type system is completely useless because the compiler cannot introspect it
  • Java SE 8 introduces functional programming and lots of type inference
  • Java SE 9-10 will introduce more awesome language features
  • It now becomes clear that what was configuration (XML or annotations) should have been code in the first place
  • JPA 2.1 has become the new EJB 2.0: Obsolete

As I said. Hard to disagree. Or in other words:

Code is much better at expressing algorithms than configuration tweet this

I’ve met Thorben personally on a number of occasions at conferences. This rant here wasn’t meant personally, Thorben :-) Your articles about JPA are very interesting. If you readers of this article are using JPA, please check out Thorben’s blog:

In the meantime, I would love to nominate Thorben for the respected title “The Annotatiomaniac of the Year 2015

How to FlatMap a JDBC ResultSet with Java 8?

You’re not into the functional mood yet? Then the title might not resonate with you – but the article will! Trust me.

Essentially, we want this:

| col1 | col2 | col3 |
| A    | B    | C    | row 1
| D    | E    | F    | row 2
| G    | H    | I    | row 3

to be “flat mapped” into this:

| cols |
| A    |\ 
| B    | | row 1
| C    |/
| D    |\
| E    | | row 2
| F    |/
| G    |\
| H    | | row 3
| I    |/

How to do it with Java 8?

It’s easy, when you’re using jOOQ. Let’s create the database first:

  col1 VARCHAR2(1),
  col2 VARCHAR2(1),
  col3 VARCHAR2(1)


Now let’s add some jOOQ and Java 8!

List<String> list =
   .fetch("SELECT col1, col2, col3 FROM t")
   .flatMap(r ->[].class)))


… and that’s it! The output is:

[A, B, C, D, E, F, G, H, I]

(I’ve also given this solution to this Stack Overflow question)

How do you read the above? Simply like this:

List<String> list =

// Get a Result<Record>, which is essentially a List
// from the database query
   .fetch("SELECT col1, col2, col3 FROM t")

// Stream its records

// And generate a new stream of each record's String[]
// representation, "flat mapping" that again into a
// single stream
   .flatMap(r ->[].class)))

Note that if you’re not using jOOQ to render and execute your query, you can still use jOOQ to transform the JDBC ResultSet into a jOOQ Result to produce the same output:

try (ResultSet rs = ...) {
    List<String> list =
       .fetch(rs) // unwind the ResultSet here
       .flatMap(r ->[].class)))


Bonus: The SQL way

The SQL way to produce the same result is trivial:


Or, of course, if you’re using Oracle or SQL Server, you can use the magic UNPIVOT clause (the opposite of the PIVOT clause):

  c FOR col in (col1, col2, col3)

How to Use Java 8 Streams to Swiftly Replace Elements in a List

Imagine you have a list of items:

List<String> books = Arrays.asList(
    "The Holy Cow: The Bovine Testament",
    "True Hip Hop",
    "Truth and Existence",
    "The Big Book of Green Design"

(Don’t judge me. Books from this random book generator)

Now you’d like to create a new list where the third item only is replaced by some new value:

List<String> books = Arrays.asList(
    "The Holy Cow: The Bovine Testament",
    "True Hip Hop",
    "Pregnancy For Dummies", // New book at index 2
    "The Big Book of Green Design"

Of course, you could go and either modify the original list:

books.set(2, "Pregnancy For Dummies");

… or create a copy of the original list and then modify that copy:

List<String> copy = new ArrayList<>(books);
copy.set(2, "Pregnancy For Dummies");

But if you want to write a one-liner to do the same in a functional style, you’ll write the following, using jOOλ

    .map(t -> t.v2 == 2 
            ? "Pregnancy For Dummies"
            : t.v1)

With the JDK standard Streams API, things get a bit harder. You could write:

        Stream.of("Pregnancy For Dummies")

That would be a bit unfortunate, though, as the first part of the stream would need to be traversed twice – once for the limit and once for the skipping (see also our post about the caveats of OFFSET pagination in SQL)

Swift or not?

Clearly, the JDK APIs won’t help you to write concise functional logic, as can be seen above and the “imperative” style is more straight-forward. We’ve written about this before. This has also been our main motivation to create jOOλ.

If you’re looking for even more functional bliss, do also have a look at the following libraries:

Don’t be Fooled by Generics and Backwards-Compatibility. Use Generic Generic Types

I’ve recently had a very interesting discussion with Sebastian Gruber from Ergon, a very early jOOQ customer, whom we’re in close touch with. Talking to Sebastian has lead our engineering team to the conclusion that we should completely rewrite the jOOQ API. Right now, we already have lots of generics for various purposes, e.g.

  • Generics for column types, such as
    interface Field<T> { ... }
    Field<String> field = BOOK.TITLE;
  • Generics for table types, such as
    interface Table<R extends Record> { ... }
    Table<BookRecord> books = BOOK;
  • Combined generics where both <T> and <R> are used
  • … and much more

Sometimes, you just cannot anticipate how many different generic types you’ll need on your classes and interfaces two years down the line, and the problem with Java is: You can generify your classes only exactly once. Let’s assume that you’ve always had a type like this:

class Foo {}

Now you happen to know that you need two generic type parameters right now:

// Still compatible
class Foo<Bar, Baz> {}

That’ll work and all the existing client code will still compile, with a rawtype warning. But once you’ve published Foo<Bar, Baz>, you can no longer add more type variables to it, or remove them. Every modification will break client code!

// Breaking change
class Foo<Bar, Baz, Fizz> {}

The solution: Generic generic types

We don’t want to place that burden upon our customers, the heavy burden of backwards-incompatibility. This is why we’re now publishing our next release of jOOQ with a new feature that we call generic generic types. How does it work? It’s easy. We’ve learned from the best database designers who have already been using generic column types all along. In SQL, if you run into this kind of problem, you’d simply write:

    bar int,
    baz int,
    fizz int,

    generic_1 varchar(4000),
    generic_2 varchar(4000),
    generic_3 varchar(4000),
    generic_4 varchar(4000),
    -- [...]

Now your SQL schema is safe for ages to come. We’ll do the same in Java:

class Foo<

    // [...]
> {}

We’ll thus generify all our types to have exactly 256 generic type parameters. 256 was the sensible limit that MS Access chose for the number of possible columns. That way, our customers will only have to upgrade to the new version of jOOQ once and from then on, generic type backwards-compatibility will be guaranteed forever.

Happy coding!

Hack up a Simple JDBC ResultSet Cache Using jOOQ’s MockDataProvider

Some queries shouldn’t hit the database all the time. When you query for master data (such as system settings, languages, translations, etc.), for instance, you may want to avoid sending the same silly query (and the results) over the wire all the time. For example:

SELECT * FROM languages

Most databases maintain buffer caches to accelerate these queries, so you don’t always hit the disk. Some databases maintain result set caches per cursor, or their JDBC drivers might even implement result set caches directly in the driver – a little known feature in Oracle, for instance:

SELECT /*+ RESULT_CACHE */ * FROM languages

But you may not be using Oracle, and because patching JDBC is a pain, you might have resorted to implementing the cache one or two layers up in the data access or service layer:

class LanguageService {
    private Cache cache;

    List<Language> getLanguages() {
        List<Language> result = cache.get();

        if (result == null) {
            result = doGetLanguages();

        return result;

Doing it in the JDBC layer, instead

While this might work fine on a per-service-and-method level, it might quickly become tedious when you query only parts of those results. E.g. what happens when you add an additional filter? Should you cache that query as well? Should you perform the filter on the cache, or hit the database at least once per filter?

class LanguageService {
    private Cache cache;

    List<Language> getLanguages() { ... }
    List<Language> getLanguages(Country country) {
        // Another cache?
        // Query the cache only and delegate to
        //     getLanguages()?
        // Or don't cache this at all?

wouldn’t it be nice if we had a cache of the form:

Map<String, ResultSet> cache;

… which caches re-usable JDBC ResultSets (or better: jOOQ Results) and returns the same results every time an identical query string is encountered.

Use jOOQ’s MockDataProvider for this

jOOQ ships with a MockConnection, which implements the JDBC Connection API for you, mocking all other objects, such as PreparedStatement, ResultSet, etc. We’ve already introduced this useful tool for unit testing in a previous blog post.

But you can “mock” your connection also in order to implement a cache! Consider the following, very simple MockDataProvider:

class ResultCache implements MockDataProvider {
    final Map<String, Result<?>> cache = 
        new ConcurrentHashMap<>();
    final Connection connection;

    ResultCache(Connection connection) {
        this.connection = connection;

    public MockResult[] execute(MockExecuteContext ctx)
    throws SQLException {
        Result<?> result;

        // Add more sophisticated caching criteria
        if (ctx.sql().contains("from language")) {

            // We're using this very useful new Java 8
            // API for atomic cache value calculation
            result = cache.computeIfAbsent(
                sql -> DSL.using(connection).fetch(

        // All other queries go to the database
        else {
            result = DSL.using(connection).fetch(

        return new MockResult[] { 
            new MockResult(result.size(), result)

Obviously, this is a very simplistic example. A real cache would involve invalidation (time-based, update-based, etc.) as well as more selective caching criteria than just matching on from language.

But the fact is that using the above ResultCache, we can now wrap all JDBC connections and prevent hitting the database more than once for all queries that query from the language table! An example using jOOQ API:

DSLContext normal = DSL.using(connection);
DSLContext cached = DSL.using(
    new MockConnection(new ResultCache(connection))

// This executs a select count(*) from language query
assertEquals(4, cached.fetchCount(LANGUAGE));
assertEquals(4, normal.fetchCount(LANGUAGE));

// Let's add another language (using normal config):
LanguageRecord lang = normal.newRecord(LANGUAGE);

// Checking again on the language table:
assertEquals(4, cached.fetchCount(LANGUAGE));
assertEquals(5, normal.fetchCount(LANGUAGE));

The cache works like a charm! Note that the current cache implementation is merely SQL string based (as it should be). If you modify the SQL string even only slightly, you’ll experience another cache miss and the query goes back to the database:

// This query is not the same as the cached one, it
// fetches two count(*) expressions. Thus we go back
// to the database and get the latest result.
assertEquals(5, (int) cached

// This still has the "stale" previous result
assertEquals(4, cached.fetchCount(LANGUAGE));


Caching is hard. Very hard. Apart from concurrency, naming things and off-by-one errors, it’s one of the three hardest problems in software.

This article doesn’t recommend to implement a cache at the JDBC level. You may or may not make that decision yourself. But when you do, then you can see how easy it is to implement such a cache using jOOQ.

jOOQ is the best way to write SQL in Java

And the best thing is that you don’t have to use jOOQ in all of your application. You can use it just for this particular use-case (and for mocking JDBC), and continue using JDBC, MyBatis, Hibernate, etc, as long as you patch other framework’s JDBC Connections with the jOOQ MockConnection.

jOOQ vs. Hibernate: When to Choose Which

Hibernate has become a de-facto standard in the Java ecosystem, and after the fact, also an actual JavaEE standard implementation if standards matter to you, and if you put the JCP on the same level with ISO, ANSI, IEEE, etc.

This article does not intended to discuss standards, but visions. Hibernate shares JPA’s vision of ORM. jOOQ shares SQL’s vision of powerful querying, so for the sake of the argument, let’s use Hibernate / JPA / ORM interchangeably, much like jOOQ / JDBC / SQL.

The question why should anyone not use Hibernate these days always shows up frequently – precisely because Hibernate is a de-facto standard, and the first framework choice in many other frameworks such as Grails (which uses GORM, which again uses Hibernate).

However, even Gavin King, the creator of Hibernate, doesn’t believe that Hibernate should be used for everything:


If that’s the case, are there any objective decision helping points that you could consider, when to use an ORM and when to use SQL?

Discussing on a high level

First off, let’s bring this discussion to a higher level. Instead of deciding between Hibernate and jOOQ as concrete implementations of their own domains, let’s think about ORM vs. SQL, and their different use-cases.

When deciding between an ORM (e.g. Hibernate) and SQL (e.g. jOOQ), the driving question that you should ask yourself is not the question of project complexity. Some of our most demanding customers are using jOOQ on medium-sized schemas with thousands of tables / views. Often, those schemas are extremely normalised and sometimes even deployed on as many as six different RDBMS. jOOQ was specifically designed to work in these scenarios, while keeping the simple use-case in mind as well.

So, instead of thinking about project complexity, ask yourself the following questions:

  • 1. Will your data model drive your application design, or will your application design drive your data model(s)?

    A main aspect here is the question whether you “care” about your database in the sense of whether it might survive your application. Very often, applications come and go. They may be re-written in Python / JavaScript, etc. 5 years down the line. Or you have multiple applications accessing the same database: Your Java application, some Perl scripts, stored procedures, etc. If this is the case, database design is a priority in your project, and jOOQ works extremely well in these setups.

    If you don’t necessarily “care” about your database in the sense that you just want to “persist” your Java domain somewhere, and this happens to be a relational database, then Hibernate might be a better choice – at least in early stages of your project, because you can easily generate your database schema from your Entity model.

  • 2. Will you do mostly complex reading and simple writing, or will you engage in complex writing?

    SQL really shines when reading is complex. When you join many tables, when you aggregate data in your database, when you do reporting, when you do bulk reading and writing. You think of your data in terms of set theory, e.g. your data as a whole. Writing CRUD with SQL is boring, though. This is why jOOQ also provides you with an ActiveRecord-style API that handles the boring parts, when you’re operating on single tables (Jason mentioned this).

    If, however, your writing becomes complex, i.e. you have to load a complex object graph with 20 entities involved into memory, perform optimistic locking on it, modify it in many different ways and then persist it again in one go, then SQL / jOOQ will not help you. This is what Hibernate has originally been created for.


I believe that data is forever. You should *always* assume that your database survives your application. It is much easier to rewrite (parts of) an application than to migrate a database. Having a clean and well-designed database schema will always pay off down the line of a project, specifically of a complex project. See also our previous article about the fallacy of “schemaless” databases.

Also, most projects really do 90% reading and 10% writing, writing often not being complex (2-3 tables modified within a transaction). This means that most of the time, the complexity solved by Hibernate / JPA’s first and second level caches is not needed. People often misunderstand these features and simply turn off caching, flushing Hibernate’s cache to the server all the time, and thus using Hibernate in the wrong way.

If, however, you’re undecided about the above two axes of decision, you can go the middle way and use jOOQ only for reporting, batch processing, etc. and use Hibernate for your CRUD – in a CQRS (Command Query Responsibility Segregation: style. There are also quite a few jOOQ users who have chosen this path.

Further reading

10 Java Articles Everyone Must Read

One month ago, we’ve published a list of 10 SQL Articles Everyone Must Read. A list of articles that we believe would add exceptional value to our readers on the jOOQ blog. The jOOQ blog is a blog focusing on both Java and SQL, so it is only natural that today, one month later, we’re publishing an equally exciting list of 10 Java articles everyone must read.

Note that by “must read”, we may not specifically mean the particular linked article only, but also other works from the same authors, who have been regular bloggers over the past years and never failed to produce new interesting content!

Here goes…

1. Brian Goetz: “Stewardship: the Sobering Parts”

The first blog post is actually not a blog post but a recording of a very interesting talk by Brian Goetz on Oracle’s stewardship of Java. On the jOOQ blog, we’ve been slightly critical about 1-2 features of the Java language in the past, e.g. when comparing it to Scala, or Ceylon.

Brian makes good points about why it would not be a good idea for Java to become just as “modern” as quickly as other languages. A must-watch for every Java developer (around 1h)

2. Aleksey Shipilёv: The Black Magic of (Java) Method Dispatch

In recent years, the JVM has seen quite a few improvements, including invokedynamic that arrived in Java 7 as a prerequisite for Java 8 lambdas, as well as a great tool for other, more dynamic languages built on top of the JVM, such as Nashorn.

invokedynamic is only a small, “high level” puzzle piece in the advanced trickery performed by the JVM. What really happens under the hood when you call methods? How are they resolved, optimised by the JIT? Aleksey’s article sub-title reveals what the article is really about:

“Everything you wanted to know about Black Deviously Surreptitious Magic in low-level performance engineering”

Definitely not a simple read, but a great post to learn about the power of the JVM.

Read Aleksey’s “The Black Magic of (Java) Method Dispatch

3. Oliver White: Java Tools and Technologies Landscape for 2014

We’re already in 2015, but this report by Oliver White (at the time head of ZeroTurnaround’s RebelLabs) had been exceptionally well executed and touches pretty much everything related to the Java ecosystem.

Read Oliver’s “Java Tools and Technologies Landscape for 2014

4. Peter Lawrey: Java Lambdas and Low Latency

When Aleksey has introduced us to some performance semantics in the JVM, Peter takes this one step further, talking about low latency in Java 8. We could have picked many other useful little blog posts from Peter’s blog, which is all about low-latency, high performance computing on the JVM, sometimes even doing advanced off-heap trickery.

Read Peter’s “Java Lambdas and Low Latency

5. Nicolai Parlog: Everything You Need To Know About Default Methods

Nicolai is a newcomer in the Java blogosphere, and a very promising one, too. His well-researched articles go in-depth about some interesting facts related to Java 8, digging out old e-mails from the expert group’s mailing list, explaining the decisions they made to conclude with what we call Java 8 today.

Read Nicolai’s “Everything You Need To Know About Default Methods

6. Lukas Eder: 10 Things You Didn’t Know About Java

This list wouldn’t be complete without listing another list that we wrote ourselves on the jOOQ blog. Java is an old beast with 20 years of history this year in 2015. This old beast has a lot of secrets and caveats that many people have forgotten or never thought about. We’ve uncovered them for you:

Read Lukas’s “10 Things You Didn’t Know About Java

7. Edwin Dalorzo: Why There Is Interface Pollution in Java 8

Edwin has been responding to our own blog posts a couple of times in the past with very well researched and thoroughly thought through articles, in particular about Java 8 related features, e.g. comparing Java 8 Streams with LINQ (something that we’ve done ourselves, as well).

This particular article explains why there are so many different and differently named functional interfaces in Java 8.

Read Edwin’s “Why There Is Interface Pollution in Java 8

8. Vlad Mihalcea: How Does PESSIMISTIC_FORCE_INCREMENT Lock Mode Work

When Java talks to databases, many people default to using Hibernate for convenience (see also 3. Oliver White: Java Tools and Technologies Landscape for 2014). Hibernate’s main vision, however, is not to add convenience – you can get that in many other ways as well. Hibernate’s main vision is to provide powerful means of navigating and persisting an object graph representation of your RDBMS’s data model, including various ways of locking.

Vlad is an extremely proficient Hibernate user, who has a whole blog series on how Hibernate works going. We’ve picked a recent, well-researched article about locking, but we strongly suggest you read the other articles as well:

Read Vlad’s “How Does PESSIMISTIC_FORCE_INCREMENT Lock Mode Work

9. Petri Kainulainen: Writing Clean Tests

This isn’t a purely Java-related blog post, although it is written from the perspective of a Java developer. Modern development involves testing – automatic testing – and lots of it. Petri has written an interesting blog series about writing clean tests in Java – you shouldn’t miss his articles!

Read Petri’s “Writing Clean Tests

10. Eugen Paraschiv: Java 8 Resources Collection

If you don’t already have at least 9 open tabs with interesting stuff to read after this list, get ready for a browser tab explosion! Eugen Paraschiv who maintains has been collecting all sorts of very interesting resources related to Java 8 in a single link collection. You should definitely bookmark this collection and check back frequently for interesting changes:

Read Eugen’s “Java 8 Resources Collection

Many other articles

There are, of course, many other very good articles providing deep insight into useful Java tricks. If you find you’ve encountered an article that would nicely complement this list, please leave a link and description in the comments section. Future readers will appreciate the additional insight.


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