Squeezing Another 10% Speed Increase out of jOOQ using JMC and JMH

In this post, we’re going to discuss a couple of recent efforts to squeeze roughly 10% in terms of speed out of jOOQ by iterating on hotspots that were detected using JMC (Java Mission Control) and then validated using JMH (Java Microbenchmark Harness). This post shows how to apply micro optimisations to algorithms where the smallest improvement can have a significant effect.

While JMH is probably without competition, JMC could easily be replaced by JProfiler, YourKit, or even your own manual jstack sampling. I’ll just use JMC because it ships with the JDK and is free for use for development as of JDK 8 and 9 (if you’re unsure whether you’re “developing”, better ask Oracle). Rumours have it that JMC might be contributed to the OpenJDK in the near future.

Micro optimisations

Micro optimisations are a cool technique to squeeze a very small improvement out of a local algorithm (e.g. a loop) that has a significant effect on the entire application / library, because of the fact that the local algorithm is called many times. This is absolutely the case in jOOQ, which is essentially a library that always runs 4 nested loops:

  1. S: A “loop” over all possible SQL statements
  2. E: A “loop” over all executions of such a statement
  3. R: A loop over all rows in the result
  4. C: A loop over all columns in a row

Such four level nested loops result in what we could call a polynomial complexity of our algorithms, even if we cannot call the complexity O(N4) (as the 4 “N” are not all the same), it is certainly of O(S x E x R x C) (I’ll call this “S-E-R-C loops” further down). Even to the untrained eye, it becomes evident that anything that happens in the inner-most “C-loop” can have devastating effects. We better not be opening any files here, that could be opened outside of, e.g. the “S-loop”

In a previous blog post, we’ve discussed common techniques of optimising such situations. In this blog post, we’ll look into a couple of concrete examples.

How to discover flaws in these loops?

We’re looking for the problems that affect all users, the kind of problem that, once fixed, will improve jOOQ’s performance for everyone by e.g. 10%. This is similar to what the JIT does, by performing things like stack allocation, inlining, which don’t drastically improve things locally, but do so globally, and for everyone. Here’s an interesting guest post by Tagir Valeev on JIT optimisation, and how good it is.

Getting a large “S-loop”

The first option is to run profiling sessions on benchmarks. We could, for example, run the entire “S-E-R-C loops” in a JMC profiling session, where the “S-loop” is a loop over all our statements, or in other words, over all our integration tests. Unfortunately, with this approach, our “E-loop” (in the case of jOOQ’s integration tests) is a single execution per statement. We’d have to run the integration tests many, many times in order to get meaningful results.

Also, while the jOOQ integration tests run thousands of distinct queries, most queries are still rather simple, each one focusing on an individual SQL feature (e.g. lateral join). In a end user application, queries might use less specific features, but are much more complex, i.e. they have a lot of ordinary joins.

This technique is useful to find problems that appear in all queries, deep down inside of jOOQ – e.g. at the JDBC interface. But we cannot use this approach to test individual features.

Getting a large “E-loop”

Another option is to write a single test that runs a few statements (small “S-loop”) many times in an explicit loop (large “E-loop”). This has the advantage that a specific bottleneck can be found with a high confidence, but the drawback is: It’s specific. For instance, if we find a small bottleneck in the string concatenation function, well, that is certainly worth fixing, but doesn’t affect most users.

This approach is useful to test individual features. It can also be useful for finding issues that affect all queries, but with a lower confidence than the previous case, where the “S-loop” is maximised.

Getting large “R-loops” and “C-loops”

Creating large result sets is easy and should definitely be part of such benchmarks, because in the case of a large result set, any flaw will multiply drastically, so fixing these things is worthwhile. However, these problems only affect actual result sets, not the query building process or the execution process. Sure, most statements are probably queries, not insertions / updates, etc. But this needs to be kept in mind.

Optimising for problems in large “E-loops”

All of the above scenarios are different optimisation sessions and deserve their own blog posts. In this post, I’m describing what has been discovered and fixed when running a single query 3 million times on an H2 database. The H2 database is chosen here, because it can run in memory of the same process and thus has the least extra overhead compared to jOOQ – so jOOQ’s overhead contributions become significant in a profiling session / benchmark. In fact, it can be shown that in such a benchmark, jOOQ (or Hibernate, etc.) appears to perform quite poorly compared to a JDBC only solution, as many have done before.

This is an important moment to remind ourselves:

Benchmarks do not reflect real-world use cases! You will never run the exact same query 3 million times on a production system, and your production system doesn’t run on H2.

A benchmark profits from so much caching, buffering, you would never perform as fast as in a benchmark.

Always be careful not to draw any wrong conclusions from a benchmark!

This needs to be said, so take every benchmark you find on the web with a grain of salt. This includes our own!

The query being profiled is:

ctx.select(
      AUTHOR.FIRST_NAME,
      AUTHOR.LAST_NAME,
      BOOK.ID,
      BOOK.TITLE)
   .from(BOOK)
   .join(AUTHOR).on(BOOK.AUTHOR_ID.eq(AUTHOR.ID))
   .where(BOOK.ID.eq(1))
   .and(BOOK.TITLE.isNull().or(BOOK.TITLE.ne(randomValue)));

The trivial query returns a ridiculous 4 rows and 4 columns, so the “R-loop” and “C-loops” are negligible. This benchmark is really testing the overhead of jOOQ query execution in a case where the database does not contribute much to the execution time. Again, in a real world scenario, you will get much more overhead from your database.

In the following sections, I’ll show a few minor bottlenecks that could be found when drilling down into these such execution scenarios. As I’ve switched between JMC versions, the screenshots will not always be the same, I’m afraid.

1. Instance allocation of constant values

A very silly mistake was easily discovered right away:

The mistake didn’t contribute a whole lot of overhead, only 1.1% to the sampled time spent, but it made me curious. In version 3.10 of jOOQ, the SelectQueryImpl‘s Limit class, which encodes the jOOQ OFFSET / LIMIT behaviour kept allocating this DSL.val() thingy, which is a bind variable. Sure, limits do work with bind variables, but this happened when SelectQueryImpl was initialised, not when the LIMIT clause is added by the jOOQ API user.

As can be seen in the sources, the following logic was there:

private static final Field<Integer> ZERO              = zero();
private static final Field<Integer> ONE               = one();
private Field<Integer>              numberOfRowsOrMax = 
    DSL.inline(Integer.MAX_VALUE);

While the “special limits” ZERO and ONE were static members, the numberOfRowsOrMax value wasn’t. That’s the instantiation we were measuring in JMC. The member is not a constant, but the default value is. It is always initialised with Integer.MAX_VALUE wrapped in an DSL.inline() call. The solution is really simple:

private static final Param<Integer> MAX               = 
    DSL.inline(Integer.MAX_VALUE);
private Field<Integer>              numberOfRowsOrMax = MAX;

This is obviously better! Not only does it avoid the allocation of the bind variable, it also avoids the boxing of Integer.MAX_VALUE (which can also be seen in the sampling screenshot).

Note, a similar optimisation is available in the JDK’s ArrayList. When you look at the sources, you’ll see:

/**
 * Shared empty array instance used for empty instances.
 */
private static final Object[] EMPTY_ELEMENTDATA = {};

When you initialise an ArrayList without initial capacity, it will reference this shared instance, instead of creating a new, empty (or even non-empty) array. This delays the allocation of such an array until we actually add things to the ArrayList, just in case it stays empty.

jOOQ’s LIMIT is the same. Most queries might not have a LIMIT, so better not allocate that MAX_VALUE afresh!

This is done once per “E-loop” iteration

One issue down: https://github.com/jOOQ/jOOQ/issues/6635

2. Copying lists in internals

This is really a micro optimisation that you probably shouldn’t do in ordinary business logic. But it might be worthwhile in infrastructure logic, e.g. when you’re also in an “S-E-R-C loop”:

jOOQ (unfortunately) occasionally copies data around between arrays, e.g. wrapping Strings in jOOQ wrapper types, transforming numbers to strings, etc. These loops aren’t bad per se, but remember, we’re inside some level of the “S-E-R-C loop”, so these copying operations might be run hundreds of millions of times when we run a statement 3 million times.

The above loop didn’t contribute a lot of overhead, and possible the cloned object was stack allocated or the clone call eliminated by the JIT. But maybe it wasn’t. The QualifiedName class cloned its argument prior to returning it to make sure that no accidental modifications will have any side effect:

private static final String[] nonEmpty(String[] qualifiedName) {
    String[] result;
    ...
    if (nulls > 0) {
        result = new String[qualifiedName.length - nulls];
        ...
    }
    else {
        result = qualifiedName.clone();
    }
    return result;
}

So, the implementation of the method guaranteed a new array as a result.

After a bit of analysis, it could be seen that there is only a single consumer of this method, and it doesn’t leave that consumer. So, it’s safe to remove the clone call. Probably, the utility was refactored from a more general purpose method into this local usage.

This is done several times per “E-loop” iteration

One more issue down: https://github.com/jOOQ/jOOQ/issues/6640

3. Running checks in loops

This one is too silly to be true:

There’s a costly overhead in the CombinedCondition constructor (<init> method). Notice, how the samples drop from 0.47% to 0.32% between the constructor and the next method init(), that’s the time spent inside the constructor.

A tiny amount of time, but this time is spent every time someone combines two conditions / predicates with AND and OR. Every time. We can probably save this time. The problem is this:

CombinedCondition(Operator operator, Collection<? extends Condition> conditions) {
    ...
    for (Condition condition : conditions)
        if (condition == null)
            throw new IllegalArgumentException("The argument 'conditions' must not contain null");

    ...
    init(operator, conditions);
}

There’s a loop over the arguments to give some meaningful error messages. That’s a bit too defensive, I suspect. How about we simply live with the NPE when it arises, as this should be rather unexpected (for the context, jOOQ hardly ever checks on parameters like this, so this should also be removed for consistency reasons).

This is done several times per “E-loop” iteration

One more issue down: https://github.com/jOOQ/jOOQ/issues/6666 (nice number)

4. Lazy initialisation of lists

The nature of the JDBC API forces us to work with ThreadLocal variables, very unfortunately, as it is not possible to pass arguments from parent SQLData objects to children, especially when we combine nesting of Oracle TABLE/VARRAY and OBJECT types.

In this analysis, we’re combining the profiler’s CPU sampling with its memory sampling:

In the CPU sampling view above, we can see some overhead in the DefaultExecuteContext, which is instantiated once per “E-loop” iteration. Again, not a huge overhead, but let’s look at what this constructor does. It contributes to the overall allocations of ArrayList:

When we select the type in JMC, the other view will then display all the stack traces where ArrayList instances were allocated, among which, again, our dear DefaultExecuteContext constructor:

Where are those ArrayLists allocated? Right here:

BLOBS.set(new ArrayList<Blob>());
CLOBS.set(new ArrayList<Clob>());
SQLXMLS.set(new ArrayList<SQLXML>());
ARRAYS.set(new ArrayList<Array>());

Every time we start executing a query, we initialise a list for each ones of these types. All of our variable binding logic will then register any possibly allocated BLOB or CLOB, etc. such that we can clean these up at the end of the execution (a JDBC 4.0 feature that not everyone knows of!):

static final void register(Blob blob) {
    BLOBS.get().add(blob);
}
    
static final void clean() {
    List<Blob> blobs = BLOBS.get();

    if (blobs != null) {
        for (Blob blob : blobs)
            JDBCUtils.safeFree(blob);

        BLOBS.remove();
    }
    ...
}

Don’t forget calling Blob.free() et al, if you’re working with JDBC directly!

But the truth is, in most cases, we don’t really need these things. We need them only in Oracle, and only if we’re using TABLE / VARRAY or OBJECT types, due to some JDBC restrictions. Why punish all the users of other databases with this overhead? Instead of a sophisticated refactoring, which risks introducing regressions (https://github.com/jOOQ/jOOQ/issues/4205), we can simply initialise these lists lazily. We leave the clean() method as it is, remove the initialisation in the constructor, and replace the register() logic by this:

static final void register(Blob blob) {
    List<Blob> list = BLOBS.get();

    if (list == null) {
        list = new ArrayList<Blob>();
        BLOBS.set(list);
    }

    list.add(blob);
}

That was easy. And significant. Check out the new allocation measurements:

Note that every allocation, apart from the overhead of allocating things, also incurs additional overhead when the object is garbage collected. That’s a bit trickier to measure and correlate. In general, less allocations is almost always a good thing, except if the allocation is super short lived, in case of which stack allocation can happen, or the logic can even be eliminated by the JIT.

This is done several times per “E-loop” iteration

One more issue down: https://github.com/jOOQ/jOOQ/issues/6669

6. Using String.replace()

This is mostly a problem in JDK 8 only, JDK 9 fixed string replacing by no longer relying on regular expressions internally. In JDK 8, however (and jOOQ still supports Java 6, so this is relevant), string replacement works through regular expressions as can be seen here:

The Pattern implementation allocates quite a few int[] instances, even if that’s probably not strictly needed for non-regex patterns as those of String.replace():

I’ve already analysed this in a previous blog post, which can be seen here:

https://blog.jooq.org/2017/10/11/benchmarking-jdk-string-replace-vs-apache-commons-stringutils-replace/

This is done several times per “E-loop” iteration

One more issue down: https://github.com/jOOQ/jOOQ/issues/6672

7. Registering an SPI that is going to be inactive

This one was a bit more tricky to solve as it relies on a deeper analysis. Unfortunately, I have no profiling screenshots available anymore, but it is easy to explain with code. There’s an internal ExecuteListeners utility, which abstracts over the ExecuteListener SPIs. Users can register such a listener and listen to query rendering, variable binding, query execution, and other lifecycle events. By default, there is no such ExecuteListener by the users, but there’s always one internal ExecuteListener:

private static ExecuteListener[] listeners(ExecuteContext ctx) {
    List<ExecuteListener> result = new ArrayList<ExecuteListener>();

    for (ExecuteListenerProvider provider : ctx.configuration()
                                               .executeListenerProviders())
        if (provider != null)
            result.add(provider.provide());

    if (!FALSE.equals(ctx.settings().isExecuteLogging()))
        result.add(new LoggerListener());

    return result.toArray(EMPTY_EXECUTE_LISTENER);
}

The LoggerListener is added by default, unless users turn off that feature. Which means:

  • We’ll pretty much always get this ArrayList
  • We’ll pretty much always loop over this list
  • We’ll pretty much always clal this LoggerListener

But what does it do? It logs stuff on DEBUG and TRACE level. For instance:

@Override
public void executeEnd(ExecuteContext ctx) {
    if (ctx.rows() >= 0)
        if (log.isDebugEnabled())
            log.debug("Affected row(s)", ctx.rows());
}

That’s what it does by definition. It’s a debug logger. So, the improved logic for initialising this thing is the following:

private static final ExecuteListener[] listeners(ExecuteContext ctx) {
    List<ExecuteListener> result = null;

    for (ExecuteListenerProvider provider : ctx.configuration()
                                               .executeListenerProviders())
        if (provider != null)
            (result = init(result)).add(provider.provide());

    if (!FALSE.equals(ctx.settings().isExecuteLogging())) {
        if (LOGGER_LISTENER_LOGGER.isDebugEnabled())
            (result = init(result)).add(new LoggerListener());
    }

    return result == null ? null : result.toArray(EMPTY_EXECUTE_LISTENER);
}

We’re no longer allocating the ArrayList (that might be premature, the JIT might have rewritten this allocation to not happen, but OK), and we’re only adding the LoggerListener if it DEBUG or TRACE logging is enabled for it, i.e. if it would do any work at all.

That’s just a couple of CPU cycles we can save on every execution. Again, I don’t have the profiling measurements anymore, but trust me. It helped.

This is done several times per “E-loop” iteration

One more issue down: https://github.com/jOOQ/jOOQ/issues/6747

8. Eager allocation where lazy allocation works

Sometimes, we need two different representations of the same information. The “raw” representation, and a more useful, pre-processed representation for some purposes. This was done, for instance, in QualifiedField:

private final Name          name;
private final Table<Record> table;

QualifiedField(Name name, DataType<T> type) {
    super(name, type);

    this.name = name;
    this.table = name.qualified()
        ? DSL.table(name.qualifier())
        : null;
}

@Override
public final void accept(Context<?> ctx) {
    ctx.visit(name);
}

@Override
public final Table<Record> getTable() {
    return table;
}

As can be seen, the name is really the beef of this class. It’s a qualified name that generates itself on the SQL string. The Table representation is useful when navigating the meta model, but this is hardly ever done by jOOQ’s internals and/or user facing code.

However, this eager initialisation it is costly:

Quite a few UnqualifiedName[] arrays are allocated by the call to Name.qualifier(). We can easily make that table reference non-final and calculate it lazily:

private final Name              name;
private Table<Record>           table;

QualifiedField(Name name, DataType<T> type) {
    super(name, type);

    this.name = name;
}

@Override
public final Table<Record> getTable() {
    if (table == null)
        table = name.qualified() ? DSL.table(name.qualifier()) : null;

    return table;
}

Because name is final, we could call table “effectively final” (in a different meaning than the Java language’s) – we won’t have any thread safety issues because these particular types are immutable inside of jOOQ.

This is done several times per “E-loop” iteration

One more issue down: https://github.com/jOOQ/jOOQ/issues/6755

Results

Now, thus far, we’ve “improved” many low hanging fruit based on a profiler session (that was run, akhem, from outside of Eclipse on a rather busy machine). This wasn’t very scientific. Just tracking down “bottlenecks” which triggered my interest by having high enough numbers to even notice. This is called “micro optimisation”, and it is only worth the trouble if you’re in a “S-E-R-C loop”, meaning that the code you’re optimising is executed many many times. For me, developing jOOQ, this is almost always the case, because jOOQ is a library used by a lot of people who all profit from these optimisations. In many other cases, this might be called “premature optimisation”

But once we’ve optimised, we shouldn’t stop. I’ve done a couple of individual JMH benchmarks for many of the above problems, to see if they were really an improvement. But sometimes, in a JMH benchmark, something that doesn’t look like an improvement will still be an improvement in the bigger picture. The JVM doesn’t inline all methods 100 levels deep. If your algorithm is complex, perhaps a micro optimisation will still have an effect that would not have any effect on a JMH benchmark.

Unfortunately this isn’t very exact science, but with enough intuition, you’ll find the right spots to optimise.

In my case, I verified progress over two patch releases: 3.10.0 -> 3.10.1 -> 3.10.2 (not yet released) by running a JMH benchmark over the entire query execution (including H2’s part). The results of applying roughly 15 of the above and similar optimisations (~2 days’ worth of effort) is:

JDK 9 (9+181)

jOOQ 3.10.0 Open Source Edition

Benchmark                          Mode   Cnt       Score      Error  Units
ExecutionBenchmark.testExecution   thrpt   21  101891.108 ± 7283.832  ops/s

jOOQ 3.10.2 Open Source Edition

Benchmark                          Mode   Cnt       Score      Error  Units
ExecutionBenchmark.testExecution   thrpt   21  110982.940 ± 2374.504  ops/s

JDK 8 (1.8.0_145)

jOOQ 3.10.0 Open Source Edition

Benchmark                          Mode   Cnt       Score      Error  Units
ExecutionBenchmark.testExecution   thrpt   21  110178.873 ± 2134.894  ops/s

jOOQ 3.10.2 Open Source Edition

Benchmark                          Mode   Cnt       Score      Error  Units
ExecutionBenchmark.testExecution   thrpt   21  118795.922 ± 2661.653  ops/s

As can be seen, in both JDK versions, we’ve gotten roughly a 10% speed increase. What’s interesting is also that JDK 8 seemed to have been also 10% faster than JDK 9 in this benchmark, although this can be due to a variety of things that I haven’t considered yet, and which are out of scope for this discussion.

Conclusion

This iterative approach to tackling performance is definitely worth it for library authors:

  • run a representative benchmark (repeat a task millions of times)
  • profile it
  • track down “bottlenecks”
  • if they’re easy to fix without regression risk, do it
  • repeat
  • after a while, verify with JMH

Individual improvements are quite hard to measure, or measure correctly. But when you do 10-15 of them, they start adding up and become significant. 10% can make a difference.

Looking forward to your comments, alternative techniques, alternative tools, etc.!

If you liked this article, you will also like Top 10 Easy Performance Optimisations in Java

jOOQ Tuesdays: Nicolai Parlog Talks About Java 9

Welcome to the jOOQ Tuesdays series. In this series, we’ll publish an article on the third Tuesday every other month where we interview someone we find exciting in our industry from a jOOQ perspective. This includes people who work with SQL, Java, Open Source, and a variety of other related topics.

I’m very excited to feature today Nicolai Parlog, author of The Java Module System

Nicolai, your blog is an “archeological” treasure trove for everyone who wants to learn about why Java expert group decisions were made. What made you dig out all these interesting discussions on the mailing lists?

Ha, thank you, didn’t know I was sitting on a treasure.

It all started with everyone’s favorite bikeshed: Optional. After using it for a few months, I was curious to learn more about the reason behind its introduction to Java and why it was designed the way it was, so I started digging and learned a few things:

  • Piperman, the JDK mailing list archive, is a horrible place to peruse and search.
  • Mailing list discussions are often lengthy, fragmented, and thus hard to revisit.
  • Brian Goetz was absolutely right: Everything related to Optional seems to take 300 messages.

Consequently, researching that post about Optional’s design took a week or so. But as you say, it’s interesting to peek behind the curtain and once a discussion is condensed to its most relevant positions and peppered with some context it really appeals to the wider Java community.

I actually think there’s a niche to be filled, here. Imagine there were a site that did regularly (at least once a week) what I did with a few selected topics: Follow the JDK mailing list, summarize ongoing discussions, and make them accessible to a wide audience. That would be a great service to the Java community as it would make it much easier to follow what is going on and to chime in with an informed opinion when you feel you have something to contribute. Now we just need to find someone with a lot of free time on their hands.

By the way, I think it’s awesome that the comparitively open development of the JDK makes that possible.

I had followed your blog after Java 8 came out, where you explained expert group decisions in retrospect. Now, you’re mostly covering what’s new in Java 9. What are your favourite “hidden” (i.e. non-Jigsaw) Java 9 features and why?

From the few language changes, it’s easy pickings: definitely private interface methods. I’ve been in the situation more than once that I wanted to share code between default methods but found no good place to put it without making it part of the public API. With private mehods in interfaces, that’s a thing of the past.

When it comes to API changes, the decision is much harder as there is more to choose from. People definitely like collection factory methods and I do, too, but I think I’ll go with the changes to Stream and Optional. I really enjoy using those Java 8 features and think it’s great that they’ve been improved in 9.

A JVM feature I really like are multi-release JARs. The ability to ship a JAR that uses the newest APIs, but degrades gracefully on older JVMs will come in very handy. Some projects, Spring for example, already do this, but without JVM support it’s not exactly pleasant.

Can I go on? Because there’s so much more! Just two: Unified logging makes it much easier to tease out JVM log messages without having to configure logging for different subsystems and compact strings and indified string concatenation make working with strings faster, reduce garbage and conserve heap space (on average, 10% to 15% less memory!). Ok, that were three, but there you go.

You’re writing a book on the Java 9 module system that can already be pre-ordered on Manning. What will readers get out of your book?

All they need to become module system experts. Of course it explains all the basics (delcaring, compiling, packaging, and running modular applications) and advanced features (services, implied readability, optional dependencies, etc), but it goes far beyond that. More than how to use a feature it also explains when and why to use it, which nuances to consider, and what are good defaults if you’re not sure which way to go.

It’s also full of practical advice. I migrated two large applications to Java 9 (compiling and running on the new release, not turning them into modules) and that experience as well as the many discussions on the mailing list informed a big chapter on migration. If readers are interested in a preview, I condensed it into a post on the most common Java 9 migration challenges. I also show how to debug modules and the module system with various tools (JDeps for example) and logging (that’s when I started using uniform logging), Last but not least, I plan to include a chapter that simply lists error messages and what to do about them.

In your opinion, what are the good parts and the bad parts about  Jigsaw? Do you think Jigsaw will be adopted quickly?

The good, the bad, and the ugly, eh? My favorite feature (of all of Java 9 actually) is strong encapsulation. The ability to have types that are public only within a module is incredibly valuable! This adds another option to the private-to-public-axis and once people internalize that feature we will wonder how we ever lived without it. Can you imagine giving up private? We will think the same about exported.

I hope the worst aspect of the module system will be the compatibility challenges. That’s a weird way to phrase it, but let me explain. These challenges definitely exist and they will require a non-neglectable investmement from the Java community as a whole to get everything working on Java 9, in the long run as modules. (As an aside: This is well invested time – much of it pays back technical debt.)

My hope is that no other aspect of the module system turns out to be worse. One thing I’m a little concerned about is the strictness of reliable configuration. I like the general principle and I’m definitely one for enforcing good practices, but just think about all those POMs that busily exclude transitive dependencies. Once all those JARs are modules, that won’t work – the module system will not let you launch without all dependencies present.

Generally speaking, the module system makes it harder to go against the maintainers’ decisions. Making internal APIs available via reflection or altering dependencies now goes against the grain of a mechanism that is built deeply into the compiler and JVM. There are of course a number of command line flags to affect the module system but they don’t cover everything. To come back to exclusing dependencies, maybe–ignore-missing-modules ${modules} would be a good idea…

Regarding adoption rate, I expect it to be slower than Java 8. But leaving those projects aside that see every new version as insurmountable and are still on Java 6, I’m sure the vast majority will migrate eventually. If not for Java 9’s features than surely for future ones. As a friend and colleague once said: “I’ll do everything to get to value types.”

Now that Java 9 is out and “legacy”, what Java projects will you cover next in your blog and your work?

Oh boy, I’m still busy with Java 9. First I have to finish the book (November hopefully) and then I want to do a few more migrations because I actually like doing that for some weird and maybe not entirely healthy reason (the things you see…). FYI, I’m for hire, so if readers are stuck with their migration they should reach out.

Beyond that, I’m already looking forward to primitive specialization, e.g. ArrayList<int>, and value types (both from Project Valhalla) as well as the changes Project Amber will bring to Java. I’m sure I’ll start discussing those in 2018.

Another thing I’ll keep myself busy with and which I would love your readers to check out is my YouTube channel. It’s still very young and until the book’s done I won’t do a lot of videos (hope to record one next week), but I’m really thrilled about the whole endavour!

Are Java 8 Streams Truly Lazy? Not Completely!

In a recent article, I’ve shown that programmers should always apply a filter first, map later strategy with streams. The example I made there was this one:

hugeCollection
    .stream()
    .limit(2)
    .map(e -> superExpensiveMapping(e))
    .collect(Collectors.toList());

In this case, the limit() operation implements the filtering, which should take place before the mapping.

Several readers correctly mentioned that in this case, it doesn’t matter what order we’re putting the limit() and map() operations, because most operations are evaluated lazily in the Java 8 Stream API.

Or rather: The collect() terminal operation pulls values from the stream lazily, and as the limit(5) operation reaches the end, it will no longer produce new values, regardless whether map() came before or after. This can be proven easily as follows:

import java.util.stream.Stream;

public class LazyStream {
    public static void main(String[] args) {
        Stream.iterate(0, i -> i + 1)
              .map(i -> i + 1)
              .peek(i -> System.out.println("Map: " + i))
              .limit(5)
              .forEach(i -> {});

        System.out.println();
        System.out.println();

        Stream.iterate(0, i -> i + 1)
              .limit(5)
              .map(i -> i + 1)
              .peek(i -> System.out.println("Map: " + i))
              .forEach(i -> {});
    }
}

The output of the above is:

Map: 1
Map: 2
Map: 3
Map: 4
Map: 5


Map: 1
Map: 2
Map: 3
Map: 4
Map: 5

But this isn’t always the case!

This optimisation is an implementation detail, and in general, it is not unwise to really apply the filter first, map later rule thoroughly, not relying on such an optimisation. In particular, the Java 8 implementation of flatMap() is not lazy. Consider the following logic, where we put a flatMap() operation in the middle of the stream:

import java.util.stream.Stream;

public class LazyStream {
    public static void main(String[] args) {
        Stream.iterate(0, i -> i + 1)
              .flatMap(i -> Stream.of(i, i, i, i))
              .map(i -> i + 1)
              .peek(i -> System.out.println("Map: " + i))
              .limit(5)
              .forEach(i -> {});

        System.out.println();
        System.out.println();

        Stream.iterate(0, i -> i + 1)
              .flatMap(i -> Stream.of(i, i, i, i))
              .limit(5)
              .map(i -> i + 1)
              .peek(i -> System.out.println("Map: " + i))
              .forEach(i -> {});
    }
}

The result is now:

Map: 1
Map: 1
Map: 1
Map: 1
Map: 2
Map: 2
Map: 2
Map: 2


Map: 1
Map: 1
Map: 1
Map: 1
Map: 2

So, the first Stream pipeline will map all the 8 flatmapped values prior to applying the limit, whereas the second Stream pipeline really limits the stream to 5 elements first, and then maps only those.

The reason for this is in the flatMap() implementation:

// In ReferencePipeline.flatMap()
try (Stream<? extends R> result = mapper.apply(u)) {
    if (result != null)
        result.sequential().forEach(downstream);
}

As you can see, the result of the flatMap() operation is consumed eagerly with a terminal forEach() operation, which will always produce all the four values in our case and send them to the next operation. So, flatMap() isn’t lazy, and thus the next operation after it will get all of its results. This is true for Java 8. Future Java versions might improve this, of course.

We better filter them first. And map later.

A Nice API Design Gem: Strategy Pattern With Lambdas

With Java 8 lambdas being available to us as a programming tool, there is a “new” and elegant way of constructing objects. I put “new” in quotes, because it’s not new. It used to be called the strategy pattern, but as I’ve written on this blog before, many GoF patterns will no longer be implemented in their classic OO way, now that we have lambdas.

A simple example from jOOQ

jOOQ knows a simple type called Converter. It’s a simple SPI, which allows users to implement custom data types and inject data type conversion into jOOQ’s type system. The interface looks like this:

public interface Converter<T, U> {
    U from(T databaseObject);
    T to(U userObject);
    Class<T> fromType();
    Class<U> toType();
}

Users will have to implement 4 methods:

  • Conversion from a database (JDBC) type T to the user type U
  • Conversion from the user type U to the database (JDBC) type T
  • Two methods providing a Class reference, to work around generic type erasure

Now, an implementation that converts hex strings (database) to integers (user type):

public class HexConverter implements Converter<String, Integer> {

    @Override
    public Integer from(String hexString) {
        return hexString == null 
            ? null 
            : Integer.parseInt(hexString, 16);
    }

    @Override
    public String to(Integer number) {
        return number == null 
            ? null 
            : Integer.toHexString(number);
    }

    @Override
    public Class<String> fromType() {
        return String.class;
    }

    @Override
    public Class<Integer> toType() {
        return Integer.class;
    }
}

That wasn’t difficult to write, but it’s quite boring to write this much boilerplate:

  • Why do we need to give this class a name?
  • Why do we need to override methods?
  • Why do we need to handle nulls ourselves?

Now, we could write some object oriented libraries, e.g. abstract base classes that take care at least of the fromType() and toType() methods, but much better: The API designer can provide a “constructor API”, which allows users to provide “strategies”, which is just a fancy name for “function”. One function (i.e. lambda) for each of the four methods. For example:

public interface Converter<T, U> {
    ...

    static <T, U> Converter<T, U> of(
        Class<T> fromType,
        Class<U> toType,
        Function<? super T, ? extends U> from,
        Function<? super U, ? extends T> to
    ) {
        return new Converter<T, U>() { ... boring code here ... }
    }

    static <T, U> Converter<T, U> ofNullable(
        Class<T> fromType,
        Class<U> toType,
        Function<? super T, ? extends U> from,
        Function<? super U, ? extends T> to
    ) {
        return of(
            fromType,
            toType,

            // Boring null handling code here
            t -> t == null ? null : from.apply(t),
            u -> u == null ? null : to.apply(u)
        );
    }
}

From now on, we can easily write converters in a functional way. For example, our HexConverter would become:

Converter<String, Integer> converter =
Converter.ofNullable(
    String.class,
    Integer.class,
    s -> Integer.parseInt(s, 16),
    Integer::toHexString
);

Wow! This is really nice, isn’t it? This is the pure essence of what it means to write a Converter. No more overriding, null handling, type juggling, just the bidirectional conversion logic.

Other examples

A more famous example is the JDK 8 Collector.of() constructor, without which it would be much more tedious to implement a collector. For example, if we want to find the second largest element in a stream… easy!

for (int i : Stream.of(1, 8, 3, 5, 6, 2, 4, 7)
                   .collect(Collector.of(
    () -> new int[] { Integer.MIN_VALUE, Integer.MIN_VALUE },
    (a, t) -> {
        if (a[0] < t) {
            a[1] = a[0];
            a[0] = t;
        }
        else if (a[1] < t)
            a[1] = t;
    },
    (a1, a2) -> {
        throw new UnsupportedOperationException(
            "Say no to parallel streams");
    }
)))
    System.out.println(i);

Run this, and you get:

8
7

Bonus exercise: Make the collector parallel capable by implementing the combiner correctly. In a sequential-only scenario, we don’t need it (until we do, of course…).

Conclusion

The concrete examples are nice examples of API usage, but the key message is this:

If you have an interface of the form:

interface MyInterface {
    void myMethod1();
    String myMethod2();
    void myMethod3(String value);
    String myMethod4(String value);
}

Then, just add a convenience constructor to the interface, accepting Java 8 functional interfaces like this:

// You write this boring stuff
interface MyInterface {
    static MyInterface of(
        Runnable function1,
        Supplier<String> function2,
        Consumer<String> function3,
        Function<String, String> function4
    ) {
        return new MyInterface() {
            @Override
            public void myMethod1() {
                function1.run();
            }

            @Override
            public String myMethod2() {
                return function2.get();
            }

            @Override
            public void myMethod3(String value) {
                function3.accept(value);
            }

            @Override
            public String myMethod4(String value) {
                return function4.apply(value);
            }
        }
    }
}

As an API designer, you write this boilerplate only once. And your users can then easily write things like these:

// Your users write this awesome stuff
MyInterface.of(
    () -> { ... },
    () -> "hello",
    v -> { ... },
    v -> "world"
);

Easy! And your users will love you forever for this.

jOOQ Tuesdays: Mario Fusco Talks About Functional and Declarative Programming

Welcome to the jOOQ Tuesdays series. In this series, we’ll publish an article on the third Tuesday every other month where we interview someone we find exciting in our industry from a jOOQ perspective. This includes people who work with SQL, Java, Open Source, and a variety of other related topics.

mariofusco

I’m very excited to feature today Mario Fusco, author of LambdaJ, working on Red Hat’s drools, a Java Champion and frequent speaker at Java conferences on all topics functional programming.

Mario, a long time ago, I have already stumbled upon your name when looking up the author of Lambdaj – a library that went to the extreme to bring lambdas to Java 5 or earlier. How does it work? And what’s the most peculiar hack you implemented to make it work?

When I started developing Lambdaj in 2007 I thought to it just as a proof-of-concept to check how far I could push Java 5. I never expected that it could become something that somebody else other than myself may actually want to use. In reality, given the limited, or I should say non-existing, capabilities of Java 5 as a functional language, Lambdaj was entirely a big hack. Despite this, people started using and somewhat loving it, and this made me (and possibly somebody else) realize that Java developers, or at least part of them, were tired of the pure imperative paradigm imposed by the language and ready to experiment with something more functional.

The main feature of Lambdaj, and what made its DSL quite nice to use, was the possibility to reference the method of a class in a static and type safe way and pass it to another method. In this way you could for example sort a list of persons by their age doing something like:

sort(persons, on(Person.class).getAge());

As anticipated what happened under the hood was a big hack: the on() method created a proxy of the Person class so you could safely call the getAge() method on it. The proxy didn’t do anything useful other than registering the method call. However it had to return something of the same type of the value returned by the actual method to avoid a ClassCastException. To this purpose it had a mechanism to generate a reasonably unique instance of that type, an int in my example. Before returning that value it also associated it, using a WeakHashMap, to the invoked method. In this way the sort() method was actually invoked with a list and the value generated by my proxy. It then retrieved from the map the Java method associated with that value and invoked it on all the items of the list performing the operation, a sorting in this case, that it was supposed to execute.

That’s crazy 🙂 I’m sure you’re happy that a lot of Lambdaj features are now deprecated. You’re now touring the world with your functional programming talks. What makes you so excited about this topic?

The whole Lambdaj project is now deprecated and abandoned. The new functional features introduced with Java 8 just made it obsolete. Nevertheless it not only had the merit to make developers become curious and interested about functional programming, but also to experiment with new patterns and ideas that in the end also influenced the Java 8 syntax. Take for instance how you can sort a Stream of persons by age using a method reference

persons.sort(Person::getAge)

It looks evident how the method references have been at least inspired by the Lambdaj‘s on() method.

There is a number of things that I love of functional programming:

  1. The readability: a snippet of code written in functional style looks like a story while too often the equivalent code in imperative style resembles a puzzle.
  2. The declarative nature: in functional programming is enough to declare the result that you want to achieve rather than specifying the steps to obtain it. You only care about the what without getting lost in the details of the how.
  3. The possibility of treating data and behaviors uniformly: functional programming allows you to pass to a method both data (the list of persons to be sorted) and computation (the function to be applied to each person in the list). This idea is fundamental for many algorithms like for example the map/reduce: since data and computation are the same thing and the second is typically orders of magnitude smaller you are free to send them to the machine holding the data instead of the opposite.
  4. The higher level of abstraction: the possibility of encapsulating computations in functions and pass them around to other functions allows both a dramatic reduction of code duplication and the design of more generic and expressive API.
  5. Immutability and referential transparency: using immutable values and having side-effects programs makes far easier to reason on your code, test it and ensure its correctness.
  6. The parallelism friendliness: all the features listed above also enable the parallelization of your software in a simpler and more reliable way. It is not coincidence that functional programming started becoming more popular around 10 years ago that is also when multicore CPUs began to be available on commodity hardware.

Our readers love SQL (or at least, they use it frequently). How does functional programming compare to SQL?

The most evident thing that FP and SQL have in common is their declarative paradigm. To some extent SQL, or at least the data selection part, can be seen as a functional language specialized to manipulate data in tabular format.

The data modification part is a totally different story though. The biggest part of SQL users normally change data in a destructive way, overwriting or even deleting the existing data. This is clearly in contrast with the immutability mantra of functional programming. However this is only how SQL is most commonly used, but nothing dictates that it couldn’t be also employed in a non-destructive append-only way. I wish to see SQL used more often in this way in future.

In your day job, you’re working for Red Hat, on drools. Business rules sound enterprisey. How does that get along with your fondness of functional programming?

Under an user point of view a rule engine in general and drools in particular are the extreme form of declarative programming, second only to Prolog. For this reason developers who are only familiar with the imperative paradigm struggle to use it, because they also try to enforce it to work in an imperative way. Conversely programmers more used to think in functional (and then declarative) terms are more often able to use it correctly when they approach it for the first time.

For what regards me, my work as developer of both the core engine and the compiler of drools allows me to experiment every day in both fields of language design and algorithmic invention and optimization. To cut it short it’s a challenging job and there’s lot’s of fun in it: don’t tell this to my employer but I cannot stop being surprised that they allow me to play with this everyday and they also pay me for that.

You’re also on the board of VoxxedDays Ticino, Zurich, and CERN (wow, how geeky is that? A large hadron collider Java conference!). Why is Voxxed such a big success for you?

I must admit that, before being involved in this, I didn’t imagine the amount of work that organizing a conference requires. However this effort is totally rewarded. In particular the great advantage of VoxxedDays is the fact of being local 1-day events made by developers for developers that practically anybody can afford.

I remember that the most common feedback I received after the first VoxxedDays Ticino that we did 2 years ago was some like: “This has been the very first conference I attended in my life and I didn’t imagine it could have been a so amazing experience both under a technical and even more a social point of view. Thanks a lot for that, I eagerly wait to attend even next year”. Can you imagine something more rewarding for a conference organizer?

The other important thing for me is giving the possibility to speakers that aren’t rock stars (yet) to talk in public and share their experience with a competent audience. I know that for at least some of them this is only the first step to let themselves and others discover their capabilities as public speakers and launch them toward bigger conferences like the Devoxx.

Thank you very much Mario

If you want to learn more about Mario’s insights on functional programming, please do visit his interesting talks at Devoxx from the recent past:

The Java Ecosystem’s Obsession with NonNull Annotations

I’m not well known for my love of annotations. While I do recognise that they can serve a very limited purpose in some areas (e.g. hinting stuff to the compiler or extending the language where we don’t want new keywords), I certainly don’t think they were ever meant to be used for API design.

“unfortunately” (but this is a matter of taste), Java 8 introduced type annotations. An entirely new extension to the annotation type system, which allows you to do things like:

@Positive int positive = 1;

Thus far, I’ve seen such common type restriction features only in the Ada or PL/SQL languages in a much more rigid way, but others may have similar features.

The nice thing about Java 8’s implementation is the fact that the meaning of the type of the above local variable (@Positive int) is unknown to the Java compiler (and to the runtime), until you write and activate a specific compiler plugin to enforce your custom meaning. The easiest way to do that is by using the Checker Framework (and yes, we’re guilty at jOOQ. We have our own checker for SQL dialect validation). You can implement any semantics, for instance:

// This compiles because @Positive int is a subtype of int
int number = positive;

// Doesn't compile, because number might be negative
@Positive int positive2 = number;

// Doesn't compile, because -1 is negative
@Positive int positive3 = -1;

As you can see, using type annotations is a very strategic decision. Either you want to create hundreds of types in this parallel universe as in this example:

Or, in my opinion, you better leave this set of features alone, because probably: YAGNI

Unfortunately, and to the disappointment of Mike Ernst, the author of the Checker Framework (whom I’ve talked to about this some years ago), most people abuse this new JSR-308 feature for boring and simple null checking. For instance, just recently, there had been a feature request on the popular Javaslang library to add support for such annotations that help users and IDEs guarantee that Javaslang API methods return non-null results.

Please no. Don’t use this atomic bomb for boring null checks

Let me make this very clear:

Type annotations are the wrong tool to enforce nullability

– Lukas Eder, timeless

You may quote me on that. The only exception to the above is if you strategically embrace JSR-308 type annotations in every possible way and start adding annotations for all sorts of type restrictions, including the @Positive example that I’ve given, then yes, adding nullability annotations won’t hurt you much anymore, as your types will take 50 lines of code to declare and reference anyway. But frankly, this is an extremely niche approach to type systems that only few general purpose programs, let alone publicly available APIs can profit from. If in doubt, don’t use type annotations.

One important reason why a library like Javaslang shouldn’t add such annotations is the fact that in a library like Javaslang, you can be very sure that you will hardly ever encounter null, because references in Javaslang are mostly one of three things:

  • A collection, which is never null but empty
  • An Option which replaces null (it is in fact a collection of cardinality 0..1)
  • A non-null reference (because in the presence of Option, all references can be expected to be non-null)

Of course, these rules aren’t valid for every API. There are some low quality APIs out there that return “unexpected” null values, or leak “internal” null values (and historically, some of the JDK APIs, unfortunately, are part of these “low quality APIs”). But Javaslang is not one of them, and the APIs you are designing also shouldn’t be one of them.

So, let go of your null fear. null is not a problem in well-designed software. You can spare yourself the work of adding a @NonNull annotation on 99% of all of your types just to shut up your IDE, in case you turned on those warnings. Focus on writing high-quality software rather than bikeshedding null.

Because: YAGNI.

And, if you haven’t had enough bikeshedding already, consider watching this entertaining talk by Stuart Marks:

jOOQ Tuesdays: Daniel Dietrich Explains the Benefits of Object-Functional Programming

Welcome to the jOOQ Tuesdays series. In this series, we’ll publish an article on the third Tuesday every other month where we interview someone we find exciting in our industry from a jOOQ perspective. This includes people who work with SQL, Java, Open Source, and a variety of other related topics.

danieldietrich

I’m very excited to feature today Daniel Dietrich whose popular library JΛVΛSLΛNG is picking up a lot of momentum among functional programming afictionados working with Java.

Daniel, you created JΛVΛSLΛNG – Object-Functional Programming in Java, a library that is becoming more and more popular among functional programmers. Why is Javaslang so popular?

Thank you Lukas for giving me the opportunity to share my thoughts.

I think that many users were disappointed about Java 8 in the whole, especially those who are already familiar with more advanced languages. The Java language architects did an awesome job. Java 8 brought groundbreaking new features like Lambdas, the new Stream API and CompletableFuture. But the new abstractions were only poorly integrated into the language from an API perspective.

There is already an increasing amount of write-ups about the disadvantages of Java 8, starting with the drawbacks of the Optional type. We read that we have to take care when using parallel Streams. These are self-made problems that keep us busy, stealing our expensive time. Javaslang provides us with alternatives.

There is no reason to reinvent the wheel. My vision is to bring as much as possible of the Scala goodness to Java. In fact Scala emerged from Java in the form of the Pizza language. Back in 2001 it had features like generics, function pointers (aka lambdas), case classes (aka value types) and pattern matching. In 2004 Java got generics, in 2014 came lambdas, and hopefully Java 10 will include value types. Scala left Java far behind. It used the last 15 year to evolve.

Object-functional programming is nothing new. It is the best of both worlds, object-oriented programming and functional programming. Scala is one of the better choices to do it on the JVM. Java’s Lambdas are an enabling feature. They allowed us to create a Javaslang API that is similar to Scala.

Java developers who get their hands on Javaslang often react in a way that I call the nice-effect: “Wow that’s nice, it feels like Scala”.

You have published a guest post on the jOOQ blog about Javaslang more than one year ago. Since then, Javaslang has moved forward quite a bit and you’ve recently published the roadmap for version 3.0. What have you done since then and where are you going?

Yes, that is true, it has changed a lot since then. We released Javaslang 1.2.2 two weeks before the first jOOQ guest post went online. Beside enriched functions that release offered popular Scala features like Option for null-safety, Try for performing computations headache-free in the presence of exceptions and a fluent pattern matching DSL. Also notably we shipped two common persistent collections, an eagerly evaluated linked List and the lazy form of it, also called Stream.

Roughly one year later we released Javaslang 2.0.0. We hardened the existing features and most notably included Future and Promise for concurrent programming and a full-fledged, Scala-like persistent collection library. Beside that, we replaced the pattern matching DSL with a more powerful pattern matching API that allows us to recursively match arbitrary object trees.

I spent a significant amount of time and energy abstracting on the type level over the mentioned features, as far as this is possible in Java. For Java developers it is not important to call things monads, sum-types or products. For example we do not need to know group theory in order to calculate 1 + 1. My duty as library developer is to make it as simple as possible for users of Javaslang to reach their goals. The need to learn new APIs and DSLs should be reduced to the minimum. This is the main reason for aligning Javaslang to Scala.

Our efforts for the next release concentrate on adding more syntactic sugar and missing persistent collections beyond those of Scala. It will be sufficient to add one import to reach 90% of Javaslang’s API. There will be new persistent collections BitSet, several MultiMaps and a PriorityQueue. We are improving the performance of our collections, most notably our persistent Vector. It will be faster than Java’s Stream for some operations and have a smaller memory footprint than Java’s ArrayList for primitive elements.

Beyond library features we pay special attention on three things: backward compatibility, controlled growth and integration aspects. Web is important. Our Jackson module ensures that all Javaslang types can be sent over the wire as serialized JSON. The next release will include a GWT module, first tests already run Javaslang in the browser. However, the Javaslang core will stay thin. It will not depend on any other libraries than the JDK.

Towards the next major release 3.0.0 I’m starting to adjust the roadmap I sketched in a previous blog post. I’ve learned that it is most important to our users that they can rely on backward compatibility. Major releases should not appear often, following the 2.x line is a better strategy. We will start to deprecate a few APIs that will be removed in a future major release. Also I keep an eye on some interesting developments that will influence the next major release. For example a new major Scala release is in the works and there are new interesting Java features that will appear in Java 10.

Looking at the current issues I don’t have to be an oracle to foresee that the next minor release 2.1.0 will take some more time. I understand that users want to start using the new Javaslang features but we need the time and the flexibility to get things right. Therefore we target a first beta release of 2.1.0 in Q4 2016.

In the meantime, there is a variety of functional(-ish) libraries for Java 8, like our own jOOλ, StreamEx, Cyclops, or the much older FunctionalJλvλ. How do all these libraries compare and how is yours different?

This question goes a little bit in the philosophical direction, maybe it is also political. These are my subjective thoughts, please treat them as such.

Humans have the ability to abstract over things. They express themselves in various ways, e.g. with painting and music. These areas split into different fields. For example in literature things are expressed in manifold ways like rhythmic prose and poetry. Furthermore different styles can be applied within these fields, like the iambic trimeter in poetry. The styles across different areas are often embossed by outer circumstances, bound to time, like an epoch.

In the area of mathematics there are also several fields, like algebra and mathematical analysis. Both have a notion of functions. Which field should I take when I want to express myself in a functional style?

Personally, I’m not able to afford the time to write non-trivial applications in each of the mentioned libraries. But I took a look at the source code and followed discussions. I see that nearly all libraries are embossed by the outer circumstance that lambdas finally made it to all curly-braces languages, especially to Java in our case. Library designers are keen to modernize their APIs in order to keep pace. But library designers are also interested in staying independent of 3rd party libraries for reasons like stability and progression.

The field of jOOQ is SQL in Java, the field of Cyclops is asynchronous systems. Both libraries are similar in the way that they adapted the new Java Lambda feature. I already mentioned that the new Java features are only poorly integrated into the language. This is the reason why we see a variety of new libraries that try to close this gap.

jOOQ needs jOOλ in order to stay independent. On the technical level StreamEx is similar to jOOλ in the way that both sit on top of Java’s Stream. They augment it with additional functionality that can be accessed using a fluent API. The biggest difference between them is that StreamEx supports parallel computations while jOOλ concentrates on sequential computations only. Looking at the SQL-ish method names it shows that jOOλ is tailored to be used with jOOQ.

Cyclops states to be the answer to the cambrian explosion of functional(-ish) libraries. It offers a facade that is backed by one of several integration modules. From the developer perspective I see this with skepticism. The one-size-fits-all approach did not work well for me in the past because it does not cover all features of the backing libraries. An abstraction layer adds another source of errors, which is unnecessary.

Many names of Cyclops look unfamiliar to me, maybe because of the huge amount of types. Looking at the API, the library seems to be a black hole, a cambrian implosion of reactive and functional features. John McClean did a great job abstracting over all the different libraries and providing a common API but I prefer to use a library directly.

FunctionalJλvλ is different. It existed long before the other libraries and has the noble goal of purely functional programming: If it does compile, it is correct. FunctionalJλvλ was originally driven by people well known from the Scala community, more specifically from the Scalaz community. Scalaz is highly influenced by Haskell, a purely functional language.

Haskell and Scala are much more expressive than Java. Porting the algebraic abstractions from Scalaz to Java turned out to be awkward. Java’s type system isn’t powerful enough, it does not allow us to reach that goal in a practical way. The committers seem to be disillusioned to me. Some state that Java is not the right language for functional programming.

Javaslang is a fresh take on porting Scala functionality to Java. At its core it is not as highly influenced by Scalaz and Haskell as FunctionalJλvλ is. However, for purely functional abstractions it offers an algebra module that depends on the core. The relation algebra/core can be compared to Scalaz/Scala.

Javaslang is similar to StreamEx in the way that it is not bound to a specific domain, in contrast to jOOλ and Cyclops. It is different from StreamEx in the sense that it does not build on top of Java’s Stream. I understand Javaslang as language addition that integrates well with existing Java features.

You have never spoken at conferences, you let other people do that for you. What’s your secret? 🙂

In fact I never attended a conference at all. My secret is to delegate the real work to others.

Joking aside, I feel more comfortable spending my time on the Javaslang source code than preparing conferences and travelling. Currently I am working on Javaslang beside my job but I’m still looking for opportunities to do it full-time.

It is awesome to see other people jumping on the Javaslang train. We receive help from all over the world. Beside IntelliJ and YourKit we recently got TouK as new sponsor and produced Javaslang stickers that are handed out at conferences.

Because of the increasing popularity of Javaslang there is also an increasing amount of questions and pull requests. Beside the conception and development I concentrate on code-reviews, discussions and managing the project.

Where do you see Java’s future with projects like Valhalla?

Java stands for stability and safety. New language features are moderately added, like salt to a soup. This is what we can expect from a future Java.

In his recent mission statement Brian Goetz gives us a great overview about the goals of Project Valhalla. From the developer point of view I really love to see that the Java language architects attach great importance to improve the expressiveness of Java. Value types for example will reduce a lot of redundant code and ceremony we are currently confronted with. It is also nice to see that value types will be immutable.

Another feature I’m really looking forward to is the extension of generics. It will allow us to remove several specializations that exist only for primitive types and void. Popular functional interfaces like Predicate, Consumer, Supplier and Runnable will be equivalent to Function. In Javaslang we currently provide additional API for performing side-effects. Having extended generics that API can be reduced to the general case, like it should have been from the beginning.

There are two more features I’m really interested in: local variable type inference, that will come to Java, and reified generics, that might come. Reified generics are needed when we want to get the type of a generic parameter at runtime. We already have type inference for lambdas. Extending it to local variables will increase conciseness and readability of method and lambda bodies while preserving type-safety. I think it is a good idea that we will still have to specify the return type of methods. It is a clear documentation of the API of an application.

I’m deeply impressed how Java and the JVM evolve over time without breaking backward compatibility. It is a safe platform we can rely on. The gap between Java and other, more modern languages is getting smaller but Java is still behind. Some popular features might never come and most probably outdated API will not get a complete refresh or a replacement. This is a field where libraries such as Javaslang can help.