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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.

Yak Shaving is a Good Way to Improve an API

Yak Shaving (uncountable):

  1. (idiomatic) Any apparently useless activity which, by allowing you to overcome intermediate difficulties, allows you to solve a larger problem.
  2. (idiomatic) A less useful activity done to consciously or unconsciously procrastinate about a larger but more useful task.

Both interpretations of the term Yak Shaving as explained by Wiktionary are absolutely accurate descriptions of most refactoring jobs. The Yak Shaving in refactoring itself can be described by this gif showing what happens when you want to change a light bulb:


However, when developing an API, it’s not such a bad idea to perform actual Yak Shaving (only the first interpretation, of course). Let’s look at an example why, from the daily work maintaining jOOQ.

The Task

For jOOQ 3.6, I wanted to implement a very simple feature. Feature #2639: Add stored procedure OUT values to DEBUG log output. This is not an important feature at all, but certainly very useful to a lot of jOOQ users. The idea is that every time you run a stored procedure with DEBUG logging activated, you’ll get the OUT parameters logged along with the procedure call. Here’s a visualisation:


Now, the actual implementation would have been very easy. Just about 10 lines of code in the existing LoggerListener that already takes care of logging all the other things. But there were a couple of caveats, which reminded me of the above lightbulb changing gif:

The apparently useless activities

  1. There was no way to access the RETURN_VALUE meta information of a jOOQ Routine
  2. There was no easy way to access Routine IN and OUT values generically
  3. There was lifecycle event that modelled the moment when OUT parameters are fetched in jOOQ
  4. There was no way to format Routine OUT parameters in a nice way

Does this feel familiar? There is need for refactoring!

Now, this whole implementation is hidden in jOOQ’s internals. It wouldn’t matter too much for users, if this had been hacked together in one way or another. For instance, obviously the RETURN_VALUE meta information could be accessed through internal refactorings, the same is true for IN and OUT values. There are other lifecycle events that might have worked just as well, and formatting is easy to re-implement.

But this is a popular API that is used by many users who might profit from a cleaner solution. Thus, why don’t we simply refactor and implement:

  1. Add a public Routine.getReturnParameter() method
  2. Add public Routine.getValue() and setValue() methods
  3. Add ExecuteListener.outStart(ExecuteContext) and outEnd(ExecuteContext) to capture fetching of Routine OUT parameters
  4. Add Routine.outRecord() and Routine.inRecord() to view a Routine as a Record”

The thing is:

The API implementor is the first API consumer

It’s hard to foresee what API users really want. But if you’re implementing an API (or just a feature), and you discover that something is missing, always consider adding that missing thing to the public API. If it could be useful to yourself, internally, it could be even more useful to many others. This way, you turn one little nice feature into 5, amplifying the user love.

Don’t get me wrong. This doesn’t mean that every little piece of functionality needs to be exposed publicly, au contraire. But the fact that something is keeping you – as the maintainer from writing clean code might indicate that others implement the same hacky workarounds as you. And they won’t ask you explicitly for it!

Don’t believe it? Here’s an entirely subjective analysis of user feedback:

  • 0.2% – Hey, this is a cool product, I want to help the owner make it better, I’ll provide a very descriptive, constructive feature request and engage for the next 5 weeks to help implement it.
  • 0.8% – Whatever dudes. Make this work. Please.
  • 1.3% – Whatever dudes. Make this work. ASAP!
  • 4.0% – WTF is wrong with you guys? Didn’t you at least think about this once??
  • 4.7% – OK, I’m going to write this completely uninformed rant about this product now, which I hate so much. It makes my life completely miserable
  • 9.0% – Oh well, this doesn’t work. Let’s go home, it’s 17:00 anyways
  • 80.0% – Oh well, this didn’t work yesterday already. Let’s go home. It’s Friday, 16:00 anyways

Now, most of this list wasn’t meant entirely seriously, but you get the point. There may be those 0.2% of users / customers that love you and that actively engage with you. Others may still love you or at least like you, but they won’t engage. You have to guesstimate what they need.

So. Bottom line:

If you need it, they probably need it. Start Yak Shaving!

The Java Legacy is Constantly Growing

I’ve recently stumbled upon a very interesting caveat of the JDK APIs, the Class.getConstructors() method. Its method signature is this:

Constructor<?>[] getConstructors()

The interesting thing here is that Class.getConstructor(Class...) returns a Constructor<T>, with <T> being maintained:

Constructor<T> getConstructor(Class<?>... parameterTypes)

Why is there a difference, i.e. why doesn’t the first method return Constructor<T>[]?

Let’s consider the Javadoc:

Note that while this method returns an array of Constructor<T> objects (that is an array of constructors from this class), the return type of this method is Constructor<?>[] and not Constructor<T>[] as might be expected. This less informative return type is necessary since after being returned from this method, the array could be modified to hold Constructor objects for different classes, which would violate the type guarantees of Constructor<T>[].


That’s a tough one. Historically, here’s how this happened:

Java 1.0 / Oak: Arrays

In Java 1.0 (the immediate successor of the Oak programming language), arrays were already introduced. In fact, they have been introduced before the collections API, which was introduced in Java 1.2. Arrays suffer from all the problems that we know today, including them being covariant, which leads to a lot of problems at runtime, that cannot be checked at compile time:

Object[] objects = new String[1];
objects[0] = Integer.valueOf(1); // Ouch

Java 1.1: Reflection API

Short of a “decent” collections API, the only possible return type of the Class.getConstructors() method was Constructor[]. A reasonable decision at the time. Of course, you could do the same mistake as above:

Object[] objects = String.class.getConstructors();
objects[0] = Integer.valueOf(1); // Ouch

but in the addition to the above, you could also, rightfully, write this:

Constructor[] constructors  = String.class.getConstructors();
constructors[0] = Object.class.getConstructor();

// Muahahahahahahaha

Java 1.2: Collections API

Java has been backwards-compatible from the very early days, even from Oak onwards. There’s a very interesting piece of historic research about some of Oak’s backwards-compatibility having leaked into Java to this date in this Stack Overflow question.

While it would have been natural to design the reflection API using collections, now, it was already too late. A better solution might’ve been:

List getConstructors()

However, note that we didn’t have generics yet, so the array actually conveys more type information than the collection.

Java 1.5: Generics

In Java 5, the change from

Constructor[] getConstructors()


Constructor<?>[] getConstructors()

has been made for the reasons mentioned above. Now, the alternative API using a collection would definitely have been better:

List<Constructor<T>> getConstructors()

But the ship has sailed.

Java, the ugly wart

Java is full of these little caveats. They’re all documented in the Javadocs, and often on Stack Overflow. Just yesterday, we’ve documented a new caveat related to completely new API in Map and ConcurrentHashMap.

“Stewardship: the Sobering Parts,” a very good talk about all those caveats and how hard it is to maintain them by Brian Goetz can be seen here:

The summary of the talk:

When language designers talk about the language they're designing

When language designers talk about the language they’re designing

jOOQ – Ein alternativer Weg mit Java und SQL zu arbeiten

We’ve published an article in the German magazine, which is published by the iJUG e.V..

You can read and download the article free of charge from our blog!

In Java gibt es kein Standard-API, das die Ausdrucksstärke und Mächtigkeit von SQL direkt unterstützt. Alle Aufmerksamkeit ist auf objekt-relationales Mapping und andere höhere Abstraktionslevel gerichtet, beispielsweise OQL, HQL, JPQL, CriteriaQuery. jOOQ ist ein dual-lizenziertes Open-Source-Produkt, das diese Lücke füllt. Es implementiert SQL als typsichere domänen-spezifische Sprache direkt in Java und ist eine gute Wahl für Java-Applikationen, in denen SQL und herstellerspezifische Datenbankfunktionalität wichtig sind. Es zeigt, wie eine moderne domänenspezifische Sprache die Entwicklerproduktivität stark erhöhen kann, indem SQL direkt in Java eingebettet ist.

jOOQ vs. Slick – Pros and Cons of Each Approach

Every framework introduces a new compromise. A compromise that is introduced because the framework makes some assumptions about how you’d like to interact with your software infrastructure.

An example of where this compromise has struck users recently is the discussion “Are Slick queries generally isomorphic to the SQL queries?“. And, of course, the answer is: No. What appears to be a simple Slick query:

val salesJoin = sales 
      join purchasers 
      join products 
      join suppliers on {
  case (((sale, purchaser), product), supplier) =>
    sale.productId === &&
    sale.purchaserId === &&
    product.supplierId ===

… turns into a rather large monster with tons of derived tables that are totally unnecessary, given the original query (formatting is mine):

select x2.x3, x4.x5, x2.x6, x2.x7 
from (
    select x8.x9 as x10, 
           x8.x11 as x12, 
           x8.x13 as x14, 
           x8.x15 as x7, 
           x8.x16 as x17, 
           x8.x18 as x3, 
           x8.x19 as x20, 
           x21.x22 as x23, 
           x21.x24 as x25, 
           x21.x26 as x6 
    from (
        select x27.x28 as x9,
               x27.x29 as x11, 
               x27.x30 as x13, 
               x27.x31 as x15, 
               x32.x33 as x16, 
               x32.x34 as x18, 
               x32.x35 as x19 
        from (
            select x36."id" as x28, 
                   x36."purchaser_id" as x29, 
                   x36."product_id" as x30, 
                   x36."total" as x31 
            from "sale" x36
        ) x27 
        inner join (
            select x37."id" as x33, 
                   x37."name" as x34, 
                   x37."address" as x35 
	    from "purchaser" x37
        ) x32 
        on 1=1
    ) x8 
    inner join (
        select x38."id" as x22, 
               x38."supplier_id" as x24, 
               x38."name" as x26 
        from "product" x38
    ) x21
    on 1=1
) x2 
inner join (
    select x39."id" as x40, 
           x39."name" as x5, 
           x39."address" as x41 
    from "supplier" x39
) x4 
on ((x2.x14 = x2.x23) 
and (x2.x12 = x2.x17)) 
and (x2.x25 = x4.x40) 
where x2.x7 >= ?

Christopher Vogt, a former Slick maintainer and still actively involved member of the Slick community, explains the above in the following words:

This means that Slick relies on your database’s query optimizer to be able to execute the sql query that Slick produced efficiently. Currently that is not always the case in MySQL

One of the main ideas behind Slick, according to Christopher, is:

Slick is not a DSL that allows you to build exactly specified SQL strings. Slick’s Scala query translation allows for re-use and composition and using Scala as the language to write your queries. It does not allow you to predict the exact sql query, only the semantics and the rough structure.

Slick vs. jOOQ

Since Christopher later on also compared Slick with jOOQ, I allowed myself to chime in and to add my two cents:

From a high level (without actual Slick experience) I’d say that Slick and jOOQ embrace compositionality equally well. I’ve seen crazy queries of several 100s of lines of [jOOQ] SQL in customer code, composed over several methods. You can do that with both APIs.

On the other hand, as Chris said: Slick has a focus on Scala collections, jOOQ on SQL tables.

  • From a conceptual perspective (= in theory), this focus shouldn’t matter.
  • From a type safety perspective, Scala collections are easier to type-check than SQL tables and queries because SQL as a language itself is rather hard to type-check given that the semantics of various of the advanced SQL clauses alter type configurations rather implicitly (e.g. outer joins, grouping sets, pivot clauses, unions, group by, etc.).
  • From a practical perspective, SQL itself is only an approximation of the original relational theories and has attained a life of its own. This may or may not matter to you.

I guess in the end it really boils down to whether you want to reason about Scala collections (queries are better integrated / more idiomatic with your client code) or about SQL tables (queries are better integrated / more idiomatic with your database).

At this point, I’d like to add another two cents to the discussion. Customers don’t buy the product that you’re selling. They never do. In the case of Hibernate, customers and users were hoping to be able to forget SQL forever. The opposite is true. As Gavin King himself (the creator of Hibernate) had told me:


Because customers and users had never listened to Gavin (and to other ORM creators), we now have what many call the object-relational impedance mismatch. A lot of unjustified criticism has been expressed against Hibernate and JPA, APIs which are simply too popular for the limited scope they really cover.

With Slick (or C#’s LINQ, for that matter), a similar mismatch is impeding integrations, if users abuse these tools for what they believe to be a replacement for SQL. Slick does a great job at modelling the relational model directly in the Scala language. This is wonderful if you want to reason about relations just like you reason about collections. But it is not a SQL API. To illustrate how difficult it is to overcome these limitations, you can browse the issue tracker or user group to learn about:

We’ll simply call this:

The Functional-Relational Impedance Mismatch

SQL is much more

Markus Winand (the author of the popular SQL Performance Explained) has recently published a very good presentation about “modern SQL”, an idea that we fully embrace at jOOQ:

We believe that APIs that have been trying to hide the SQL language from general purpose languages like Java, Scala, C# are missing out on a lot of the very nice features that can add tremendous value to your application. jOOQ is an API that fully embraces the SQL language, with all its awesome features (and with all its quirks). You obviously may or may not agree with that.

We’ll leave this article open ended, hoping you’ll chime in to discuss the benefits and caveats of each approach. Of staying close to Scala vs. staying close to SQL.

As a small teaser, however, I’d like to announce a follow-up article showing that there is no such thing as an object-relational impedance mismatch. You (and your ORM) are just not using SQL correctly. Stay tuned!

Thou Shalt Not Name Thy Method “Equals”

(unless you really override Object.equals(), of course).

I’ve stumbled upon a rather curious Stack Overflow question by user Frank:

Why does Java’s Area#equals method not override Object#equals?

Interestingly, there is a Area.equals(Area) method which really takes an Area argument, instead of a Object argument as declared in Object.equals(). This leads to rather nasty behaviour, as discovered by Frank:

public void testEquals() {
    java.awt.geom.Area a = new java.awt.geom.Area();
    java.awt.geom.Area b = new java.awt.geom.Area();
    assertTrue(a.equals(b)); // -> true

    java.lang.Object o = b;
    assertTrue(a.equals(o)); // -> false

Technically, it is correct for AWT’s Area to have been implemented this way (as hashCode() isn’t implemented either), but the way Java resolves methods, and the way programmers digest code that has been written like the above code, it is really a terrible idea to overload the equals method.

No static equals, either

These rules also hold true for static equals() methods, such as for instance Apache Commons Lang‘s

ObjectUtils.equals(Object o1, Object o2)

The confusion here arises by the fact that you cannot static-import this equals method:

import static org.apache.commons.lang.ObjectUtils.equals;

When you now type the following:

equals(obj1, obj2);

You will get a compiler error:

The method equals(Object) in the type Object is not applicable for the arguments (…, …)

The reason for this is that methods that are in the scope of the current class and its super types will always shadow anything that you import this way. The following doesn’t work either:

import static org.apache.commons.lang.ObjectUtils.defaultIfNull;

public class Test {
  void test() {
    defaultIfNull(null, null);
    // ^^ compilation error here

  void defaultIfNull() {

Details in this Stack Overflow question.


The conclusion is simple. never overload any of the methods declared in Object (overriding is fine, of course). This includes:

  • clone()
  • equals()
  • finalize()
  • getClass()
  • hashCode()
  • notify()
  • notifyAll()
  • toString()
  • wait()

Of course, it would be great if those methods weren’t declared in Object in the first place, but that ship has sailed 20 years ago.


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