Scheduling a task in Java within a CompletableFuture

Andy Balaam from Andy Balaam's Blog

When we want to do something later in our Java code, we often turn to the ScheduledExecutorService. This class has a method called schedule(), and we can pass it some code to be run later like this:

    ScheduledExecutorService executor =
        Executors.newScheduledThreadPool(4);
    executor.schedule(
        () -> {System.out.println("..later");},
        1,
        TimeUnit.SECONDS
    );
    System.out.println("do...");
    // (Don't forget to shut down the executor later...)

The above code prints “do…” and then one second later it prints “…later”.

We can even write code that does some work and returns a result in a similar way:

    // (Make the executor as above.)
    ScheduledFuture future = executor.schedule(
        () -> 10 + 25, 1, TimeUnit.SECONDS);
    System.out.println("answer=" + future.get())

The above code prints “answer=35”. When we call get() it blocks waiting for the scheduler to run the task and mark the ScheduledFuture as complete, and then returns the answer to the sum (10 + 25) when it is ready.

This is all very well, but you may note that the Future returned from schedule() is a ScheduledFuture, and a ScheduledFuture is not a CompletableFuture.

Why do you care? Well, you might care if you want to do something after the scheduled task is completed. Of course, you can call get(), and block, and then do something, but if you want to react asynchronously without blocking, this won’t work.

The normal way to run some code after a Future has completed is to call one of the “then*” or “when*” methods on the Future, but these methods are only available on CompletableFuture, not ScheduledFuture.

Never fear, we have figured this out for you. We present a small wrapper for schedule that transforms your ScheduledFuture into a CompletableFuture. Here’s how to use it:

    CompletableFuture future =
        ScheduledCompletable.schedule(
            executor,
            () -> 10 + 25,
            1,
            TimeUnit.SECONDS
         );
    future.thenAccept(
        answer -> {System.out.println(answer);}
    );
    System.out.println("Answer coming...")

The above code prints “Answer coming…” and then “35”, so we can see that we don’t block the main thread waiting for the answer to come back.

So far, we have scheduled a synchronous task to run in the background after a delay, and wrapped its result in a CompletableFuture to allow us to chain more tasks after it.

In fact, what we often want to do is schedule a delayed task that is itself asynchronous, and already returns a CompletableFuture. In this case it feels particularly natural to get the result back as a CompletableFuture, but with the current ScheduledExecutorService interface we can’t easily do it.

It’s OK, we’ve figured that out too:

    Supplier> asyncTask = () ->
        CompletableFuture.completedFuture(10 + 25);
    CompletableFuture future =
        ScheduledCompletable.scheduleAsync(
            executor, asyncTask, 1, TimeUnit.SECONDS);
    future.thenAccept(
        answer -> {System.out.println(answer);}
    );
    System.out.println("Answer coming...")

The above code prints “Answer coming…” and then “35”, so we can see that our existing asynchronous task was scheduled in the background, and we didn’t have to block the main thread waiting for it. Also, under the hood, we are not blocking the ScheduledExecutorService‘s thread (from its pool) while the async task is running – that task just runs in whatever thread it was assigned when it was created. (Note: in our example we don’t really run an async task at all, but just immediately return a completed Future, but this does work for real async tasks.)

I know you’re wondering how we achieved all this. First, here’s how we run a simple blocking task in the background and wrap it in a CompletableFuture:

    public static  CompletableFuture schedule(
        ScheduledExecutorService executor,
        Supplier command,
        long delay,
        TimeUnit unit
    ) {
        CompletableFuture completableFuture = new CompletableFuture<>();
        executor.schedule(
            (() -> {
                try {
                    return completableFuture.complete(command.get());
                } catch (Throwable t) {
                    return completableFuture.completeExceptionally(t);
                }
            }),
            delay,
            unit
        );
        return completableFuture;
    }

And here’s how we delay execution of an async task but still return its result in a CompletableFuture:

    public static  CompletableFuture scheduleAsync(
        ScheduledExecutorService executor,
        Supplier> command,
        long delay,
        TimeUnit unit
    ) {
        CompletableFuture completableFuture = new CompletableFuture<>();
        executor.schedule(
            (() -> {
                command.get().thenAccept(
                    t -> {completableFuture.complete(t);}
                )
                .exceptionally(
                    t -> {completableFuture.completeExceptionally(t);return null;}
                );
            }),
            delay,
            unit
        );
        return completableFuture;
    }

Note that this should all work to run methods like exceptionally(), thenAccept(), whenComplete() etc.

Feedback and improvements welcome!

The Perils of Multi-Phase Construction

Chris Oldwood from The OldWood Thing

I’ve never really been a fan of C#’s object initializer syntax. Yes, it’s a little more convenient to write but it has a big downside which is it forces you to make your types mutable by default. Okay, that’s a bit strong, it doesn’t force you to do anything, but it does promote that way of thinking and allows people to take advantage of mutability outside the initialisation block [1].

This post is inspired by some buggy code I encountered where my suspicion is that the subtleties of the object initialisation syntax got lost along the way and partially constructed objects eventually found their way into the wild.

No Dragons Yet

The method, which was to get the next message from a message queue, was originally written something like this:

Message result = null;
RawMessage message = queue.Receive();

if (message != null)
{
  result = new Message
  {
    Priority = message.Priority,
    Type = GetHeader(message, “MessageType”),
    Body = message.Body, 
  };
}

return result;

This was effectively correct. I say “effectively correct” because it doesn’t contain the bug which came later but still relies on mutability which we know can be dangerous.

For example, what would happen if the GetHeader() method threw an exception? At the moment there is no error handling and so the exception propagates out the method and back up the stack. Because we make no effort to recover we let the caller decide what happens when a duff message comes in.

The Dragons Begin Circling

Presumably the behaviour when a malformed message arrived was undesirable because the method was changed slightly to include some recovery fairly soon after:

Message result = null;
RawMessage message = queue.Receive();

if (message != null)
{
  try
  {
    result = new Message
    {
      Priority = message.Priority,
      Type = GetHeader(message, “MessageType”),
      Body = message.Body,  
    };
  }
  catch (Exception e)
  {
    Log.Error(“Invalid message. Skipping.”);
  }
}

return result;

Still no bug yet, but that catch handler falling through to the return at the bottom is somewhat questionable; we are making the reader work hard to track what happens to result under the happy / sad paths to ensure it remains correct under further change.

Object Initialisation Syntax

Before showing the bug, here’s a brief refresher on how the object initialisation syntax works under the covers [2] in the context of our example code. Essentially it invokes the default constructor first and then performs assignments on the various other properties, e.g.

var __m = new Message();
__m.Priority = message.Priority;
__m.Type = GetHeader(message, “MessageType”);
__m.Body = message.Body,  
result = __m;

Notice how the compiler introduces a hidden temporary variable during the construction which it then assigns to the target at the end? This ensures that any exceptions during construction won’t create partially constructed objects that are bound to variables by accident. (This assumes you don’t use the constructor or property setter to attach itself to any global variables either.)

Hence, with respect to our example, if any part of the initialization fails then result will be left as null and therefore the message is indeed discarded and the caller gets a null reference back.

The Dragons Surface

Time passes and the code is then updated to support a new property which is also passed via a header. And then another, and another. However, being more complicated than a simple string value the logic to parse it is placed outside the object initialisation block, like this:

Message result = null;
RawMessage message = queue.Receive();

if (message != null)
{
  try
  {
    result = new Message
    {
      Priority = message.Priority,
      Type = GetHeader(message, “MessageType”),
      Body = message.Body,  
    };

    var str = GetHeader(message, “SomeIntValue”);
    if (str != null && TryParseInt(str, out var value))
      result.IntValue = value;

    // ... more of the same ...
  }
  catch (Exception e)
  {
    Log.Error(“Invalid message. Skipping.”);
  }
}

return result;

Now the problems start. With the latter header parsing code outside the initialisation block result is assigned a partially constructed object while the remaining parsing code runs. Any exceptions that occur [3] mean that result will be left only partially constructed and the caller will be returned the duff object because the exception handler falls out the bottom.

+1 for Tests

The reason I spotted the bug was because I was writing some tests around the code for a new header which also temporarily needed to be optional, like the others, to decouple the deployments. When running the tests there was an error displayed on the console output [4] telling me the message was being discarded, which I didn’t twig at first. It was when I added a retrospective test for the previous optional fields and I found my new one wasn’t be parsed correctly that I realised something funky was going on.

Alternatives

So, what’s the answer? Well, I can think of a number of approaches that would fix this particular code, ranging from small to large in terms of the amount of code that needs changing and our appetite for it.

Firstly we could avoid falling through in the exception handler and make it easier on the reader to comprehend what would be returned in the face of a parsing error:

catch (Exception e)  
{  
  Log.Error(“Invalid message. Skipping.”);
  return null;
}

Secondly we could reduce the scope of the result variable and return that at the end of the parsing block so it’s also clearer about what the happy path returns:

var result = new Message  
{  
  // . . .  
};

var str = GetHeader(message, “SomeIntValue”);
if (str != null && TryParseInt(str, out var value)
  result.IntValue = value;

return result;

We could also short circuit the original check too and remove the longer lived result variable altogether with:

RawMessage message = queue.Receive();

if (message == null)
    return null;

These are all quite simple changes which are also safe going forward should someone add more header values in the same way. Of course, if we were truly perverse and wanted to show how clever we were, we could fold the extra values back into the initialisation block by doing an Extract Function on the logic instead and leave the original dragons in place, e.g.

try
{  
  result = new Message  
  {  
    Priority = message.Priority,  
    Type = GetHeader(message, “MessageType”),  
    Body = message.Body,
    IntValue = GetIntHeader(message, “SomeIntValue”),
    // ... more of the same ...  
  };
}  
catch (Exception e)  
{  
  Log.Error(“Invalid message. Skipping.”);  
}

But we would never do that because the aim is to write code that helps stop people making these kinds of mistakes in the first place. If we want to be clever we should make it easier for the maintainers to fall into The Pit of Success.

Other Alternatives 

I said at the beginning that I was not a fan of mutability by default and therefore it would be remiss of me not to suggest that the entire Message type be made immutable and all properties set via the constructor instead:

result = new Message  
(  
  priority: message.Priority,  
  type: GetHeader(message, “MessageType”),  
  body: message.Body,
  IntValue: GetIntHeader(message, “SomeIntValue”),
  // ... more of the same ...  
);

Yes, adding a new property is a little more work but, as always, writing the tests to make sure it all works correctly will dominate here.

I would also prefer to see use of an Optional<> type instead of a null reference for signalling “no message” but that’s a different discussion.

Epilogue

While this bug was merely “theoretical” at the time I discovered it [5] it quickly came back to bite. A bug fix I made on the sending side got deployed before the receiving end and so the misleading error popped up in the logs after all.

Although the system appeared to be functioning correctly it had slowed down noticeably which we quickly discovered was down to the receiving process continually restarting. What I hadn’t twigged just from reading this nugget of code was that due to the catch handler falling through and passing the message on it was being acknowledged on the queue twice –– once in that catch handler, and again after processing it. This second acknowledgment attempt generated a fatal error that caused the process to restart. Deploying the fixed receiver code as well sorted the issue out.

Ironically the impetus for my blog post “Black Hole - The Fail Fast Anti-Pattern” way back in 2012 was also triggered by two-phase construction problems that caused a process to go into a nasty failure mode, but that time it processed messages much too quickly and stayed alive failing them all.

 

[1] Generally speaking the setting of multiple properties implies it’s multi-phase construction. The more common term Two-Phase Construction comes (I presume) from explicit constructor methods names like Initialise() or Create() which take multiple arguments, like the constructor, rather than setting properties one-by-one.

[2] This is based on my copy of The C# Programming Language: The Annotated Edition.

[3] When the header was missing it was passing a null byte[] reference into a UTF8 decoder which caused it to throw an ArgumentNullException.

[4] Internally it created a logger on-the-fly so it wasn’t an obvious dependency that initially needed mocking.

[5] It’s old, so possibly it did bite in the past but nobody knew why or it magically fixed itself when both ends where upgraded close enough together.

Convert a video to a GIF with reasonable colours

Andy Balaam from Andy Balaam&#039;s Blog

Here’s a little script I wrote to avoid copy-pasting the ffmpeg command from superuser every time I needed it.

It converts a video to a GIF file by pre-calculating a good palette, then using that palette.

Usage:

./to_gif input.mp4 output.gif

The file to_gif (which should be executable):

#!/bin/bash

set -e
set -u

# Credit: https://superuser.com/questions/556029/how-do-i-convert-a-video-to-gif-using-ffmpeg-with-reasonable-quality

INPUT="$1"
OUTPUT="$2"

PALETTE=$(tempfile --suffix=.png)

ffmpeg -y -i "${INPUT}" -vf palettegen "${PALETTE}"
ffmpeg -y -i "${INPUT}" -i "${PALETTE}" \
    -filter_complex "fps=15,paletteuse" "${OUTPUT}"

rm -f "${PALETTE}"

Note: you might want to modify the number after fps= to adjust how fast the video plays.

Keybase chat bot in 10 lines of bash

Andy Balaam from Andy Balaam&#039;s Blog

I’ve been getting very excited about keybase.io recently, not least because it offers secure conversation, and you can have bots.

I wrote a quick bot to simulate Arnold Schwarzenegger which I thought I’d share to demonstrate how easy it is. It is based on the keybase command line tool (which comes with the desktop client as standard) and jq, the brilliant command-line JSON manipulator.

For this to work, you need to have the keybase command installed and working, and you need jq.

Here’s the bot:

#!/bin/bash
CHANNEL=mychannel
keybase chat api-listen | while read L; do
{
    OUT=$(jq --raw-output 'select(.type == "chat")|select(.msg.content.text.body|startswith("!arnie "))| .msg.content.text.body | "*" + ltrimstr("!arnie ") + "*"' <<< "$L")
    if [ "${OUT}" != "" ]; then
    {
        keybase chat send "${CHANNEL}" "${OUT}"
    }; fi
}; done

and here's it working:

andy> !arnie Do eet do eet now!!!
andy> Do eet do eet now!!!

Note: here the bot is pretending to be me. To do this nicely, you will want a different account for the bot, but you get the idea.

Obviously, I am now working on a comprehensive bot framework in Rust. Watch this space.

Performance of Java 2D drawing operations (part 3: image opacity)

Andy Balaam from Andy Balaam&#039;s Blog

Series: operations, images, opacity

Not because I was enjoying it, I seemed compelled to continue my quest to understand the performance of various Java 2D drawing operations. I’m hoping to make my game Rabbit Escape faster, especially on the Raspberry Pi, so you may see another post sometime actually trying this stuff out on a Pi.

But for now, here are the results of my investigation into how different patterns of opacity in images affects rendering performance.

You can find the code here: gitlab.com/andybalaam/java-2d-performance.

Results

  • Images with partially-opaque pixels are no slower than those with fully-opaque pixels
  • Large transparent areas in images are drawn quite quickly, but transparent pixels mixed with non-transparent are slow

Advice

  • Still avoid any transparency whenever possible
  • It’s relatively OK to use large transparent areas on images (e.g. a fixed-size animation where a character moves through the image)
  • Don’t bother restricting pixels to be either fully transparent or fully opaque – partially-opaque is fine

Opacity patterns in images

Non-transparent images drew at 76 FPS, and transparent ones dropped to 45 FPS.

I went further into investigating transparency by creating images that were:

  • All pixels 50% opacity (34 FPS)
  • Half pixels 0% opacity, half 100%, mixed up (34 FPS)
  • Double the size of the original image, but the extra area is fully transparent, and the original area is non-transparent (41 FPS)

I concluded that partial-opacity is not important to performance compared with full-opacity, but that large areas of transparency are relatively fast compared with images with complex patterns of transparency and opacity.

Numbers

Transparency and opacity

Test FPS
large nothing 90
large images20 largeimages 76
large images20 largeimages transparentimages 45
large images20 largeimages transparent50pcimages 34
large images20 largeimages transparent0pc100pcimages 34
large images20 largeimages transparentareaimages 41

Feedback please

Please do get back to me with tips about how to improve the performance of my experimental code.

Feel free to log issues, make merge requests or add comments to the blog post.

Performance of Java 2D drawing operations (part 2: image issues)

Andy Balaam from Andy Balaam&#039;s Blog

Series: operations, images

In my previous post I examined the performance of various drawing operations in Java 2D rendering. Here I look at some specifics around rendering images, with an eye to finding optimisations I can apply to my game Rabbit Escape.

You can find the code here: gitlab.com/andybalaam/java-2d-performance.

Results

  • Drawing images with transparent sections is very slow
  • Drawing one large image is slower than drawing many small images covering the same area(!)
  • Drawing images outside the screen is slower than not drawing them at all (but faster than drawing them onto a visible area)

Advice

  • Avoid transparent images where possible
  • Don’t bother pre-rendering your background tiles onto a single image
  • Don’t draw images that are off-screen

Images with transparency

All the images I used were PNG files with a transparency layer, but in most of my experiments there were no transparent pixels. When I used images with transparent pixels the frame rate was much slower, dropping from 78 to 46 FPS. So using images with transparent pixels causes a significant performance hit.

I’d be grateful if someone who knows more about it can recommend how to improve my program to reduce this impact – I suspect there may be tricks I can do around setComposite or setRenderingHint or enabling/encouraging hardware acceleration.

Composite images

I assumed that drawing a single image would be much faster than covering the same area of the screen by drawing lots of small images. In fact, the result was the opposite: drawing lots of small images was much faster than drawing a single image covering the same area.

The code for a single image is:

g2d.drawImage(
    singleLargeImage,
    10,
    10,
    null
)

and for the small images it is:

for (y in 0 until 40)
{
    for (x in 0 until 60)
    {
        g2d.drawImage(
            compositeImages[(y*20 + x) % compositeImages.size],
            10 + (20 * x),
            10 + (20 * y),
            null
        )
    }
}

The single large image was rendered at 74 FPS, whereas covering the same area using repeated copies of 100 images was rendered at 80 FPS. I ran this test several times because I found the result surprising, and it was consistent every time.

I have to assume some caching (possibly via accelerated graphics) of the small images is the explanation.

Drawing images off the side of the screen

Drawing images off the side of the screen was faster than drawing them in a visible area, but slower than not drawing them at all. I tested this by adding 10,000 to the x and y positions of the images being drawn (I also tested subtracting 10,000 with similar results). Not drawing any images ran at 93 FPS, drawing images on-screen at 80 FPS, and drawing them off-screen only 83 FPS, meaning drawing images off the side takes significant time.

Advice: check whether images are on-screen, and avoid drawing them if not.

Numbers

Transparency

Test FPS
large nothing 95
large images20 largeimages 78
large images20 largeimages transparentimages 46

Composite images

(Lots of small images covering an area, or a single larger image.)

Test FPS
large nothing 87
large largesingleimage 74
large compositeimage 80

Offscreen images

Test FPS
large nothing 93
large images20 largeimages 80
large images20 largeimages offscreenimages 83

Feedback please

Please do get back to me with tips about how to improve the performance of my experimental code.

Feel free to log issues, make merge requests or add comments to the blog post.

Performance of Java 2D drawing operations

Andy Balaam from Andy Balaam&#039;s Blog

I want to remodel the desktop UI of my game Rabbit Escape to be more
convenient and nicer looking, so I took a new look at game-loop-style graphics rendering onto a canvas in a Java 2D (Swing) UI.

Specifically, how fast can it be, and what pitfalls should I avoid when I’m doing it?

Results

  • Larger windows are (much) slower
  • Resizing images on-the-fly is very slow, even if they are the same size every time
  • Drawing small images is fast, but drawing large images is slow
  • Drawing rectangles is fast
  • Drawing text is fast
  • Drawing Swing widgets in front of a canvas is fast
  • Creating fonts on-the-fly is a tiny bit slow

Code

You can find the full code (written in Kotlin) at gitlab.com/andybalaam/java-2d-performance.

Basically, we make a JFrame and a Canvas and tell them not to listen to repaints (i.e. we control their drawing).

val app = JFrame()
app.ignoreRepaint = true
val canvas = Canvas()
canvas.ignoreRepaint = true

Then we add any buttons to the JFrame, and the canvas last (so it displays behind):

app.add(button)
app.add(canvas)

Now we make the canvas double-buffered and get hold of a buffer image for it:

app.isVisible = true
canvas.createBufferStrategy(2)
val bufferStrategy = canvas.bufferStrategy
val bufferedImage = GraphicsEnvironment
    .getLocalGraphicsEnvironment()
    .defaultScreenDevice
    .defaultConfiguration
    .createCompatibleImage(config.width, config.height)

Then inside a tight loop we draw onto the buffer image:

val g2d = bufferedImage.createGraphics()
try
{
    g2d.color = backgroundColor
    g2d.fillRect(0, 0, config.width, config.height)

    ... the different drawing operations go here ...

and then swap the buffers:

    val graphics = bufferStrategy.drawGraphics
    try {
        graphics.drawImage(bufferedImage, 0, 0, null)
        if (!bufferStrategy.contentsLost()) {
            bufferStrategy.show()
        }
    } finally {
        graphics.dispose()
    }
} finally {
    g2d.dispose()
}

Results

Baseline: some rectangles

I decided to compare everything against drawing 20 rectangles at random points on the screen, since that seems like a minimal requirement for a game.

My test machine is an Intel Core 2 Duo E6550 2.33GHz with 6GB RAM and a GeForce GT 740 graphics card (I have no idea whether it is being used here – I assume not). I am running Ubuntu 18.04.1 Linux, OpenJDK Java 1.8.0_191, and Kotlin 1.3.20-release-116. (I expect the results would be identical if I were using Java rather than Kotlin.)

I ran all the tests in two window sizes: 1600×900 and 640×480. 640.×480 was embarrassingly fast for all tests, but 1600×900 struggled with some of the tasks.

Drawing rectangles looks like this:

g2d.color = Color(
    rand.nextInt(256),
    rand.nextInt(256),
    rand.nextInt(256)
)
g2d.fillRect(
    rand.nextInt(config.width / 2),
    rand.nextInt(config.height / 2),
    rand.nextInt(config.width / 2),
    rand.nextInt(config.height / 2)
)

In the small window, the baseline (20 rectangles) ran at 553 FPS. In the large window it ran at 87 FPS.

I didn’t do any statistics on these numbers because I am too lazy. Feel free to do it properly and let me know the results – I will happily update the article.

Fewer rectangles

When I reduced the number of rectangles to do less drawing work, I saw small improvements in performance. In the small window, drawing 2 rectangles instead of 20 increased the frame rate from 553 to 639, but there is a lot of noise in those results, and other runs were much closer. In the large window, the same reduction improved the frame rate from 87 to 92. This is not a huge speed-up, showing that drawing rectangles is pretty fast.

Adding fixed-size images

Drawing pre-scaled images looks like this:

g2d.drawImage(
    image,
    rand.nextInt(config.width),
    rand.nextInt(config.height),
    null
)

When I added 20 small images (40×40 pixels) to be drawn in each frame, the performance was almost unchanged. In the small window, the run showing 20 images per frame (as well as rectangle) actually ran faster than the one without (561 FPS versus 553), suggesting the difference is negligible and I should do some statistics. In the large window, the 20 images version ran at exactly the same speed (87 FPS).

So, it looks like drawing small images costs almost nothing.

When I moved to large images (400×400 pixels), the small window slowed down from 553 to 446 FPS, and the large window slowed from 87 to 73 FPS, so larger images clearly have an impact, and we will need to limit the number and size of images to keep the frame rate acceptable.

Scaling images on the fly

You can scale an image on the fly as you draw onto a Canvas. (Spoiler: don’t do this!)

My code looks like:

val s = config.imageSize
val x1 = rand.nextInt(config.width)
val y1 = rand.nextInt(config.height)
val x2 = x1 + s
val y2 = y1 + s
g2d.drawImage(
    unscaledImage,
    x1, y1, x2, y2,
    0, 0, unscaledImageWidth, unscaledImageHeight,
    null
)

Note the 10-argument form of drawImage is being used. You can be sure you have avoided this situation if you use the 4-argument form from the previous section.

Note: the resulting image is the same size every time, and the Java documentation implies that scaled images may be cached by the system, but I saw a huge slow-down when using the 10-argument form of drawImage above.

On-the-fly scaled images slowed the small window from 446 to 67 FPS(!), and the large window from 73 to 31 FPS, meaning the exact same rendering took over twice as long.

Advice: check you are not using one of the drawImage overloads that scales images! Pre-scale them yourself (e.g. with getScaledInstance as I did here).

Displaying text

Drawing text on the canvas like this:

g2d.font = Font("Courier New", Font.PLAIN, 12)
g2d.color = Color.GREEN
g2d.drawString("FPS: $fpsLastSecond", 20, 20 + i * 14)

had a similar impact to drawing small images – i.e. it only affected the performance very slightly and is generally quite fast. The small window slowed from 553 to 581 FPS, and the large window from 87 to 88.

Creating the font every time (as shown above) slowed the process a little more, so it is worth moving the font creating out of the game loop and only doing it once. The slowdown just for creating the font was 581 to 572 FPS in the small window, and 88 to 86 FPS in the large.

Swing widgets

By adding Button widgets to the JFrame before the Canvas, I was able to display them in front. Their rendering and focus worked as expected, and they had no impact at all on performance.

The same was true when I tried adding these widgets in front of images rendered on the canvas (instead of rectangles).

Turning everything up to 11

When I added everything I had tested all at the same time: rectangles, text with a new font every time, large unscaled images, and large window, the frame rate reduced to 30 FPS. This is a little slow for a game already, and if we had more images to draw it could get even worse. However, when I pre-scaled the images the frame rate went up to 72 FPS, showing that Java is capable of running a game at an acceptable frame rate on my machine, so long as we are careful how we use it.

Numbers

Small window (640×480)

Test FPS
nothing 661
rectangles2 639
rectangles20 553
rectangles20 images2 538
rectangles20 images20 561
rectangles20 images20 largeimages 446
rectangles20 images20 unscaledimages 343
rectangles20 images20 largeimages unscaledimages 67
rectangles20 text2 582
rectangles20 text20 581
rectangles20 text20 newfont 572
rectangles20 buttons2 598
rectangles20 buttons20 612

Large window (1200×900)

Test FPS
large nothing 93
large rectangles2 92
large rectangles20 87
large rectangles20 images2 87
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Feedback please

Please do get back to me with tips about how to improve the performance of my experimental code.

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London Python Meetup January 2019 – Async Python and GeoPandas

Andy Balaam from Andy Balaam&#039;s Blog

It was a pleasure to go to the London Python Meetup organised by @python_london. There were plenty of friendly people and interesting conversations.

I gave a talk “Making 100 million requests with Python aiohttp” (slides) explaining the basics of writing async code in Python 3 and how I used that to make a very large number of HTTP requests.

Andy giving the presentation

(Photo by CB Bailey.)

Hopefully it was helpful – there were several good questions, so I am optimistic that people were engaged with it.

After that, there was an excellent talk by Gareth Lloyd called “GeoPandas, the geospatial extension for Pandas” in which he explained how to use the very well-developed geo-spatial data tools available in the Python ecosphere to transform, combine, plot and analyse data which includes location information. I was really impressed with how easy the libraries looked to use, and also with the cool Jupyter notebook Gareth used to explain the ideas using live demos.

London Python Meetups seem like a cool place to meet Pythonistas of all levels of experience in a nice, low-pressure environment!

Meetup link: aiohttp / GeoPandas