Saturday, 9 March 2019

Card Table

Card Table is a multi-player web based virtual card table implemented using Java, plain JavaScript, WebSockets and Postgres.

Source Code

Code available in GitHub - card-table


This project requires a minimum of Java 8 JDK to build and a Postgres installation.

A drop/create Postgres SQL script needs to be run to create and initalise the database with default data:

Configure the Java web application's database dev configuration:

Build and Run

Build and run using Maven with an embedded Tomcat:

mvn clean install tomcat7:run-war

Browse to:


A new card table will be created with a unique URL. If this project is deployed to a publicly available host, the URL can be shared with other players to play against.

Mouse Controls

Packs of cards can be dragged from the side bar and dropped on the table to create a new deck. Currently there are 2 decks - both standard 52 card decks, one with a black back and one with a red back.

Single cards can be clicked and dragged to move them around the table. Multiple cards can be selected by clicking and dragging the mouse and drawing a selection box around the cards to be selected. Selected cards can be clicked and dragged to move more than one card.

Clicking a single card will turn the card face up/face down. Clicking multiple selected cards will shuffle the selected cards.

Moving cards to the bottom of the table, below the green line, hides them from other players. Any card actions which take place here, e.g. moving, turning and shuffling will not be broadcast to other players.

Dragging single or multiple cards off the screen removes them from the table.

See the video above for examples of all these actions.

Supported Browsers

Currently only desktop browsers are supported due to the lack of native drag-and-drop JavaScript support on mobile devices. At the time of writing, Card Table has been tested on Chrome 72, Firefox 65, Edge 42, IE 11 and Opera 58.

Wednesday, 29 August 2018

Java 9/10 Multiline String

My Java Multiline String project stopped building when compiling with Java 10 because tools.jar has been removed since Java 9.

When the tools.jar dependency is specified like this:



The build failed with output:

Total time: 0.347 s
Finished at: 2018-08-29T21:10:41+01:00
Final Memory: 6M/24M
Failed to execute goal on project multiline-string: Could not resolve dependencies for project org.adrianwalker:multiline-string:jar:0.2.1: Could not find artifact sun.jdk:tools:jar:LATEST at specified path /usr/local/jdk-10.0.1/../lib/tools.jar -> [Help 1]

Simply removing the dependency fixes the build and the project compiles without error. So where are the classes which were in the tools.jar packages?

In JDK versions 1.8 and lower:

cd /usr/local/jdk1.8.0_172
unzip -l ./lib/tools.jar | grep com/sun/tools/javac/tree/TreeMaker.class
    47366  2018-03-28 21:40   com/sun/tools/javac/tree/TreeMaker.class

In JDK version 10:

cd /usr/local/jdk-10.0.1
unzip -l ./jmods/jdk.compiler.jmod | grep com/sun/tools/javac/tree/TreeMaker.class
warning [./jmods/jdk.compiler.jmod]:  4 extra bytes at beginning or within zipfile
  (attempting to process anyway)
    64266  2018-03-26 18:16   classes/com/sun/tools/javac/tree/TreeMaker.class

I still want to be able to compile this library will all JDK versions from 1.6 onwards without creating another project for versions 9 and 10. To do this we can move the tools.jar dependency to a profile which is only activated for older JDKs:



The line <jdk>[1.6,9)</jdk> specifies a version range using the Apache Maven Enforcer range syntax. In this case, include all versions from 1.6 upto but not including 9.

Aside from pom.xml changes, the Java code and usage remains identical to the original project.

Java 9/10 module system

This all only works because the maven-compiler-plugin is configured with source and target set to 1.6:



If we want to use Java 9/10 lanuage features, setting source and target to 10 will give these errors:

Failed to execute goal org.apache.maven.plugins:maven-compiler-plugin:3.1:compile (default-compile) on project multiline-string: Compilation failure: Compilation failure:
org/adrianwalker/multilinestring/[3,27] package is not visible
(package is declared in module jdk.compiler, which does not export it to the unnamed module)
org/adrianwalker/multilinestring/[4,27] package is not visible
(package is declared in module jdk.compiler, which does not export it)
org/adrianwalker/multilinestring/[5,27] package is not visible
(package is declared in module jdk.compiler, which does not export it to the unnamed module)
org/adrianwalker/multilinestring/[6,27] package is not visible
(package is declared in module jdk.compiler, which does not export it to the unnamed module)

In this case we must correctly use the new Java Module System. To resolve the above errors first we need a in the project root specifying a module name and the module's requirements:

module org.adrianwalker.multilinestring {
  requires jdk.compiler;

Next we need to export the required packages in the jdk.compiler module and make them visible to our org.adrianwalker.multilinestring module:



And now the project should build without errors and work just as before.

Source Code

Monday, 27 August 2018

Enforcing Multi-Tier Architecture

So you've designed an application, using the principals of separation of concerns and a multi-tier architecture. It's a delight to navigate and maintain the code base, the architecture might look something like this:

The presentation layer talks to the application layer, which talks to the data access layer. The facade object provides a high-level interface for API consumers, talking to the service objects, which call objects encapsulating business logic, which operate on data provided by the data access objects. Life is good.

Eventually other programmers will have to maintain and add new features to your application, possibly in your absence. How do you communicate your design intentions to future maintainers? The above diagram, a bit of documentation, and some programming rigour should suffice. Back in the real world, programmers face time pressures which prevent them creating and updating documentation, and managers and customers don't care about code maintainability - they want their features yesterday. When getting the code into production as fast as possible is the only focus, clean code and architecture are soon forgotten.

To quote John Carmack:

"It’s just amazing how many mistakes and how bad programmers can be. Everything that is syntactically legal, that the compiler will accept, will eventually wind up in your code base."

Carmack was talking about the usefulness of static typing here, but the same problem also applies to code architecture: over time, whatever can happen, will happen. Your well designed architecture will risk turning into spaghetti code, with objects calling methods from any layer:

To address this problem I think it would be useful to have a way of documenting and enforcing which objects can invoke a method on another object. In Java this can be achieved with a couple of annotations and some aspect oriented programming. Below is an annotation named CallableFrom which can be used to annotate methods on a class indicating what classes and interface implementations the method can be called from.

package org.adrianwalker.callablefrom;

import java.lang.annotation.ElementType;
import java.lang.annotation.Retention;
import java.lang.annotation.RetentionPolicy;
import java.lang.annotation.Target;
import org.adrianwalker.callablefrom.test.TestCaller;

public @interface CallableFrom {

  CallableFromClass[] value() default {

The annotation's value method returns an array of another annotation CallableFromClass:

package org.adrianwalker.callablefrom;

import java.lang.annotation.ElementType;
import java.lang.annotation.Retention;
import java.lang.annotation.RetentionPolicy;
import java.lang.annotation.Target;

public @interface CallableFromClass {

  Class value();

  boolean subclasses() default true;

The annotation's value method returns a Class object - the class (or interface) of an object which is allowed to call the annotated method. The annotation's subclasses method returns a boolean value which flags if subclasses (or interface implementations) are allowed to call the annotated method.

At this point the annotations do nothing, we need a way of enforcing the behaviour specified by the annotations. This can be achieved using an AspectJ aspect class:

package org.adrianwalker.callablefrom;

import org.aspectj.lang.JoinPoint;
import org.aspectj.lang.annotation.Aspect;
import org.aspectj.lang.annotation.Before;

public final class CallableFromAspect {

  @Before("@annotation(callableFrom) && call(* *.*(..))")
  public void before(final JoinPoint joinPoint, final CallableFrom callableFrom) throws CallableFromError {

    Class callingClass = joinPoint.getThis().getClass();
    boolean isCallable = isCallable(callableFrom, callingClass);

    if (!isCallable) {
      Class targetClass = joinPoint.getTarget().getClass();
      throw new CallableFromError(targetClass, callingClass);

  private boolean isCallable(final CallableFrom callableFrom, final Class callingClass) {

    boolean callable = false;
    CallableFromClass[] callableFromClasses = callableFrom.value();

    for (CallableFromClass callableFromClass : callableFromClasses) {

      Class clazz = callableFromClass.value();
      boolean subclasses = callableFromClass.subclasses();

      callable = (subclasses && clazz.isAssignableFrom(callingClass))
              || (!subclasses && clazz.equals(callingClass));

      if (callable) {

    return callable;

The aspect intercepts any calls to methods annotated with @CallableFrom, gets the calling object's class and compares it to the class objects specified by the @CallableFromClass's class values. If subclasses is true (the default), the calling class can be a subclass (or implementation) of the class object specified by @CallableFromClass. If subclasses is false the calling class must be equal to the class object specified by @CallableFromClass.

If the above conditions are not met, for any of the @CallableFromClass annotations, the method is not callable from the calling class and a CallableFromError error is thrown. CallableFromError extends Error rather than Exception as it is not expected that application code should ever to attempt to catch it.

package org.adrianwalker.callablefrom;

public final class CallableFromError extends Error {

  private static final String EXCEPTION_MESSAGE = "%s is not callable from %s";

  public CallableFromError(final Class targetClass, final Class callingClass) {


For example, if you have a class named Callable and you only want to be able to call it from another class named CallableCaller, no subclasses:

package org.adrianwalker.callablefrom;

public final class Callable {

    @CallableFromClass(value=CallableCaller.class, subclasses=false)
  public void doStuff() {

    System.out.println("Callable doing stuff");

Another example, if you had some business logic encapsulated in an object which should only be called by a service object and test classes:

package org.adrianwalker.callablefrom.example.application;

import org.adrianwalker.callablefrom.CallableFrom;
import org.adrianwalker.callablefrom.CallableFromClass;
import org.adrianwalker.callablefrom.test.TestCaller;

public final class UpperCaseBusinessObject implements ApplicationLayer {

    @CallableFromClass(value = MessageService.class, subclasses = false),
    @CallableFromClass(value = TestCaller.class, subclasses = true)
  public String uppercaseMessage(final String message) {

    if (null == message) {
      return null;

    return message.toUpperCase();


To make classes callable from JUnit tests, the unit test class should implement the TestCaller interface. This interface is the default value for the CallableFrom annotation:

package org.adrianwalker.callablefrom;

import org.adrianwalker.callablefrom.test.TestCaller;
import static org.junit.Assert.assertEquals;
import static;
import org.junit.Test;

public final class CallableFromTest implements TestCaller {

  public void testCallableFromTestCaller() {

    CallableCaller cc = new CallableCaller(new Callable());

  public void testCallableFromError() {

    ErrorCaller er = new ErrorCaller(new CallableCaller(new Callable()));

    try {

      fail("Expected CallableFromError to be thrown");

    } catch (final CallableFromError cfe) {

      String expectedMessage
              = "org.adrianwalker.callablefrom.Callable "
              + "is not callable from "
              + "org.adrianwalker.callablefrom.ErrorCaller";
      String actualMessage = cfe.getMessage();

      assertEquals(expectedMessage, actualMessage);

  public void testNotCallableFromSubclass() {

    CallableCallerSubclass ccs = new CallableCallerSubclass(new Callable());

    try {

      fail("Expected CallableFromError to be thrown");

    } catch (final CallableFromError cfe) {

      String expectedMessage
              = "org.adrianwalker.callablefrom.Callable "
              + "is not callable from "
              + "org.adrianwalker.callablefrom.CallableCallerSubclass";
      String actualMessage = cfe.getMessage();

      assertEquals(expectedMessage, actualMessage);

Where CallableCaller can be called from implementations of TestCaller:

package org.adrianwalker.callablefrom;

import org.adrianwalker.callablefrom.test.TestCaller;

public class CallableCaller {

  private final Callable callable;

  public CallableCaller(final Callable callable) {

    this.callable = callable;

    @CallableFromClass(value=ErrorCaller.class, subclasses = false),
    @CallableFromClass(value=TestCaller.class, subclasses = true)
  public void doStuff() {

    System.out.println("CallableCaller doing stuff");

    callable.doStuff(); // callable from here


Using the callable-from library in a project requires the aspect to be weaved into your code at build time. Using Apache Maven, this means using the AspectJ plugin and specifying callable-from as a weave dependency:






Checking every annotated method introduces significant overhead, I've bench-marked the same code compiled an run with and without the aspect weaved at compile time:

package org.adrianwalker.callablefrom.example;

import java.util.Random;
import org.adrianwalker.callablefrom.CallableFrom;
import org.adrianwalker.callablefrom.CallableFromClass;
import org.junit.Test;

public final class BenchmarkTest {

  private static class CallableFromRandomNumberGenerator {

    private static final Random RANDOM = new Random(System.currentTimeMillis());

      @CallableFromClass(value = BenchmarkTest.class, subclasses = false)
    public int nextInt() {

      return RANDOM.nextInt();

  public void testBenchmarkCallableFrom() {

    long elapsed = generateRandomNumbers(1_000_000_000);

    System.out.printf("%s milliseconds\n", elapsed);

  private long generateRandomNumbers(final int n) {

    CallableFromRandomNumberGenerator cfrng = new CallableFromRandomNumberGenerator();

    long start = System.currentTimeMillis();

    for (long i = 0; i < n; i++) {

    long end = System.currentTimeMillis();

    return end - start;

Without aspect weaving:

 T E S T S
Running org.adrianwalker.callablefrom.example.BenchmarkTest
13075 milliseconds

With aspect weaving:

 T E S T S
Running org.adrianwalker.callablefrom.example.BenchmarkTest
81951 milliseconds

13075 milliseconds vs 81951 milliseconds means the above code took 6.3 times longer to execute with @CallableFrom checking enabled. For this reason, if execution speed is important to you, I'd recommend only weaving the aspect for a test build profile and using another build profile, without the AspectJ plugin, for building your release artifacts (see the callable-from-usage project pom.xml for an example).


So is this the worst idea ever in the history of programming? Speed issues aside, it probably is because:

  1. I've never seen a language that offers this sort of method call enforcement as standard.
  2. An object in layer n, called by an object in layer n+1 should ideally contain no knowledge of the layer above it. The code could be changed to compare class object canonical name strings rather than the class object itself, so imports for calling classes are not needed in the callable class - but this creates a maintenance problem as refactoring tools won't automatically change the full class names in the string values and the compiler can't tell you if a class name does not exist.

That said, I still think something like this could help stop the proliferation of spaghetti code.

Source Code

The annotations and aspect code are provided in the callable-from project, with an example usage project similar to the diagram at the start of this post provided in the callable-from-usage project.

Sunday, 22 April 2018

Dynamically Typed Stacks Make Me Nervous

Ten years ago Ted Dziuba wrote Python Makes Me Nervous, I agree with everything he wrote back then - I suppose I'm what Steve Yegge would call a Software Conservative. Ten years on, the static vs dynamic language debate is no closer to being over and now what makes *me* really nervous is entire dynamically typed system stacks.

To be more accurate, what I mean by dynamically typed stacks is: systems built with dynamically typed languages and composed of schema-less services, end-to-end. Let me explain ...

When I was a young programmer, if you wanted to created a web service you used XML-RPC or SOAP. I liked SOAP (yeah, I said it!), with a well defined WSDL and some XSD you knew exactly what your client/server was going to send/receive. You generated client code and server side stub classes with Apache Axis and you got serialisation, de-serialisation, parsing, validation and error handling all for free.

Now everyone uses REST and JSON. Instead of well defined XML services, RESTful web services have to try and shoehorn requests into a HTTP GET/POST/PUT/DELETE method along with some path parameters and/or query parameters and/or request/response headers. Serialisation and validation for RESTful web services are often made an implementation concern of the application with custom serialisation/de-serialisation handlers and bespoke validation code.

I like Relational databases (You heard me!). With a well defined schema you know exactly what data you're going to store and retrieve. Database constraints enforce data correctness and referential integrity and it all gets managed for free in one place.

Now we have schema-less NoSQL databases. These types of data stores are supposedly popular because of their horizontal scalability and fault tolerance across network partitions, but in reality, they are popular because they can be used as a data dumping ground with no need for data modelling, schema design, normalisation/de-normalisation, transaction handling, index design, query plan analysis or need to learn a query language. Data consistency, typing, referential integrity, transactions etc. are all concerns pushed on to the application to implement.

Over the last ten years, knowing fuck all about the data your system operates on until run-time has become trendy.

Enough ranting. Lets look at some code, here's a (contrived) example. Let's say we have an existing Java code base, with a PersonController class for persisting a person's contact details, for use in a contacts list application or something. How do you use this API? Well, the classes method signatures and a good IDE tell you everything you need to know with a minimum of key strokes:

I know I need to pass a Person object to the save method. My IDE will tell me what properties I can set on the Person object. The method throws a checked exception if anything goes wrong, or returns a UUID if the entity is persisted correctly. Awesome, I've got everything I need to use this API in my application, I don't need to care about the implementation details.

Now let's do the same thing with Python:

The save method takes one argument, that's all I know. I'd better go have a look at the code...

class PersonController(object):
    URL = 'http://%s:%s/person'

    def __init__(self, host='localhost', port=8888):
        self.url = self.URL % (host, port)

    def save(self, person):
        data = person if isinstance(person, dict) else person.__dict__
        response =, data=json.dumps(data))
        if response.status_code != 201:
            raise ControllerSaveException(response.status_code, response.json()['error'])

        return uuid.UUID(response.json()['id'])

... it makes a REST call. person can be anything that can be serialised to JSON and posted to the /person URL. I'd better go try and find the code for the web service...

class Application(tornado.web.Application):

    def __init__(self):
        handlers = [
            (r'/person/?', Handler)
        tornado.web.Application.__init__(self, handlers)

    def listen(self, address='localhost', port=8888, **kwargs):
        super(Application, self).listen(port, address, **kwargs)

... it's a Tornado REST web service, lets go check the handler class...

class Handler(tornado.web.RequestHandler):

    def __init__(self, application, request, **kwargs):
        super(Handler, self).__init__(application, request, **kwargs)
        self.publisher = Publisher()

    def set_default_headers(self):
        self.set_header('Content-Type', 'application/json')

    def prepare(self):
        except ValueError:
            self.send_error(400, message='Error parsing JSON')

    def post(self):
        response = json.loads(self.publisher.publish(self.request.body.decode('utf-8')))

... this tells me nothing about what the person object's JSON representation should contain, WTF is Publisher for. I'd better go find that code and take a look...

class Publisher(object):

    def __init__(self, host='localhost', queue='person'):

        self.connection = pika.BlockingConnection(pika.ConnectionParameters(host=host)) =
        result =
        self.callback_queue = result.method.queue, no_ack=True, queue=self.callback_queue)
        self.response = None
        self.correlation_id = None
        self.queue = queue

    def on_response(self, channel, method, properties, body):
        if self.correlation_id == properties.correlation_id:
            self.response = body

    def publish(self, data):

        self.correlation_id = str(uuid.uuid4())'',
        while self.response is None:

        return self.response

... FFS, it publishes the JSON to a RabbitMQ message queue. I'd better go find the code for the possible consumers ...

class Consumer(object):

    def __init__(self, host='localhost', queue='person', bucket='person'):

        self.connection = pika.BlockingConnection(pika.ConnectionParameters(host)) =, queue=queue)
        self.dataStore = datastore.DataStore(bucket)

    def on_request(self, channel, method, properties, body):

        request = json.loads(body)
        errors = self.validate(request)
        if errors:
            response = {
                'status': 400,
                'error': ', '.join(errors)
            response ='',

    def consume(self):

    def validate(self, request):

        errors = []

        if 'first_name' not in request or not request['first_name']:
            errors.append('Invalid or missing first name')

        if 'last_name' not in request or not request['last_name']:
            errors.append('Invalid or missing last name')

        return errors

    def save(self, request):

        id = str(uuid.uuid4())
  , request)
            response = {
                'id': id,
                'status': 201,
        except Exception as e:
            response = {
                'status': 500,
                'error': str(e)

        return response

... some bespoke validation code tells me I have to have first_name and last_name keys in my JSON object. Then the object gets saved to the person bucket in a Riak database. But, what else should be in my object? Let's curl an existing record and have a look...

$ curl
{"first_name": "Adrian", "last_name": "Walker"}

... and I'm no closer to knowing exactly what should or shouldn't be in a person object.

What a waste of time.

Source Code

Sunday, 1 April 2018

Riak - Building a Development Environment From Source

Building a Riak development environment, like anything involving Linux, is needlessly complicated for no good reason. This method to build from source worked for me from a clean install of Lubuntu 17.10.1:

First, update your package index and install the dependencies and utilities you will need:

$ sudo apt-get update
$ sudo apt-get install build-essential autoconf libncurses5-dev libpam0g-dev openssl libssl-dev fop xsltproc unixodbc-dev git curl

Next, navigate to user home, download kerl and use it to build and install the Basho version of Erlang (WHY?!?!). These steps took a while to complete on my machine, bear with it:

$ cd ~
$ curl -O
$ chmod a+x kerl
$ ./kerl build git git:// OTP_R16B02_basho10 R16B02-basho10
$ ./kerl install R16B02-basho10 ~/erlang/R16B02-basho10
$ . ~/erlang/R16B02-basho10/activate

With Erlang installed, clone the Riak source repository from GitHub and build:

$ git clone
$ cd riak
$ make rel

Finally, create 8 separate copies of Riak to use in a cluster:

$ make devrel

Start 3 (or more) Riak instances:

$ dev/dev1/bin/riak start
$ dev/dev2/bin/riak start
$ dev/dev3/bin/riak start

Then join instances 2 and 3 with instance 1 to form a cluster:

$ dev/dev2/bin/riak-admin cluster join dev1@
$ dev/dev3/bin/riak-admin cluster join dev1@

Check and commit the cluster plan:

$ dev/dev3/bin/riak-admin cluster plan
$ dev/dev3/bin/riak-admin cluster commit

Monitor the cluster status until all pending changes are complete:

$ dev/dev3/bin/riak-admin cluster status
---- Cluster Status ----
Ring ready: false

|        node        |status| avail |ring |pending|
| (C) dev1@ |valid |  up   |100.0|  34.4 |
|     dev2@ |valid |  up   |  0.0|  32.8 |
|     dev3@ |valid |  up   |  0.0|  32.8 |

$ dev/dev3/bin/riak-admin cluster status
---- Cluster Status ----
Ring ready: true

|        node        |status| avail |ring |pending|
| (C) dev1@ |valid |  up   | 34.4|  --   |
|     dev2@ |valid |  up   | 32.8|  --   |
|     dev3@ |valid |  up   | 32.8|  --   |

Check the cluster member status:

$ dev/dev3/bin/riak-admin member-status
================================= Membership ==================================
Status     Ring    Pending    Node
valid      34.4%      --      'dev1@'
valid      32.8%      --      'dev2@'
valid      32.8%      --      'dev3@'
Valid:3 / Leaving:0 / Exiting:0 / Joining:0 / Down:0

Congratulations, you have a development Riak cluster. Test the cluster by writing some data to a node:

$ curl -XPUT -H "Content-type: application/json" --data-binary "Hello World!"

Use a browser to read the data from each node:

Thursday, 8 February 2018

Tell 'em Steve-Dave!

SoundCloud's web interface is rubbish for downloading podcasts, but their API is pretty good, so here's a handy Python script for downloading all of your favourite Tell 'em Steve-Dave! episodes:

import os.path
import re

import requests

API_URL = ""
TRACKS_URL = API_URL + "/users/%(USER_ID)s/tracks" \
                       "?client_id=%(CLIENT_ID)s" \
                       "&offset=%(OFFSET)s" \
                       "&limit=%(LIMIT)s" \
CHUNK_SIZE = 16 * 1024
TESD_USER_ID = "79299245"
CLIENT_ID = "3b6b877942303cb49ff687b6facb0270"
LIMIT = 10
offset = 0

while True:

    url = TRACKS_URL % {
        "USER_ID": TESD_USER_ID,
        "LIMIT": LIMIT,
        "OFFSET": offset

    tracks = requests.get(url).json()

    if not tracks:

    tracks = [(track["id"], track["title"]) for track in tracks]

    for (id, title) in tracks:

        title = str(re.sub('[^A-Za-z0-9]+', '_', title)).strip('_')
        url = DOWNLOAD_URL % {"TRACK_ID": id, "CLIENT_ID": CLIENT_ID}

        filename = "%s.mp3" % title
        print "downloading: %s from %s" % (filename, url)

        if os.path.exists(filename):

        request = requests.get(url, stream=True)

        with open(filename + ".tmp", 'wb') as fd:
            chunks = request.iter_content(chunk_size=CHUNK_SIZE)
            for chunk in chunks:
        os.rename(filename + ".tmp", filename)

    offset = offset + LIMIT

4 colors 4 life

Saturday, 27 January 2018

Overengineering Shit

I’ve had enough of Flickr, for all the standard reasons.

So I set out to build a scalable, secure, distributed, image sharing platform of my own, using open source components, tried and tested tech, with no bullshit.

It would be great, I thought, I could start small, just hosting my own photos; then I could open it up to friends and family, working out the bugs as I go, seamlessly scaling up the hardware as required. Then, who knows, I could open it up to the internet! It was going to be awesome!

I wanted nothing fancy for the implementation - only mature tech, battle tested stuff, which wasn’t going to become unsupported any time soon. And only the right tools for the job:
  1. Bulk uploading files over HTTP is bollocks, transferring files is a solved problem, the system should use FTP.
  2. No custom user database, no hand rolled permissions pseudo-framework, don’t re-invent the wheel, the authentication and authorisation should be handled by LDAP.
  3. Image storage should be implemented using a distributed, scalable filesystem, I want to just add more nodes when disk starts running low.
  4. Image processing, such as thumbnail generation, should be asynchronous with jobs taken from a scalable message queue, that way I can add more message processors when I need to.
  5. Simple REST webservices should be used by a client to fetch images and image metadata from the server.
  6. And finally the web UI should be simple, responsive and avoid JavaScript framework bloat.

My chosen implementations to satisfy the above included:

Also using imgscalr-lib for preview generation, Apache Avro and Apache commons-lang for serialization, Apache Tika for image format detection, SLF4J and Logback for logging and finally Ansible for deployment.

The components logically hang together something like this:

The code was looking good enough to get something up and running - it was time to investigate some hosting costs. I figured I would need a big-ish box to put Apache Cassandra and Apache Directory Server on, a small-ish box for the Apache Tomcat and Apache Webserver, another small-ish box for Apache FTP Server, and a medium sized box for Apache Kafka and the consumer processes.

Time to check out some recommended production hardware requirements for Cassandra:

a minimal production server requires at least 2 cores, and at least 8GB of RAM. Typical production servers have 8 or more cores and at least 32GB of RAM

And for Kafka:

A machine with 64 GB of RAM is a decent choice, but 32 GB machines are not uncommon. Less than 32 GB tends to be counterproductive (you end up needing many, many small machines).

Are you fucking kidding me? When did minimum requirements for a database and a queue become 32GB of fucking RAM each?!

At DigitalOcean's current prices, an 8GB droplet is $40 a month and a 32GB droplet is $160 a month, and the smaller droplets anything between $5 and $20 a month.

Memory vCPUs SSD Disk Transfer Price
1 GB 1 vCPU 25 GB 1 TB $5/mo
2 GB 1 vCPU 50 GB 2 TB $10/mo
4 GB 2 vCPUs 80 GB 4 TB $20/mo
8 GB 4 vCPUs 160 GB 5 TB $40/mo
16 GB 6 vCPUs 320 GB 6 TB $80/mo
32 GB 8 vCPUs 640 GB 7 TB $160/mo
... ... ... ... ...

I guess writing and running scalable systems requires a scalable bank balance - mine does not scale to over $200 a month just to upload some photos. Time to scrap this idea.

What I’ve ended up with is a $10 a month DigitalOcean droplet running nginx and SFTP and a half arsed Python script which uses ImageMagick to generate thumbnails and some static HTML - and you know what? It’s absolutely perfect for me.

Time to reflect on some almost-ten-year-old, but still relevant, wisdom from Ted Dziuba: I'm Going To Scale My Foot Up Your Ass

Python script to generate HTML and thumbnails:

import os
from subprocess import call

CONVERT_CMD = "convert"
PREVIEW_SIZE = "150x150"
PREVIEW_PREFIX = "preview_"
INDEX = "index.html"
ALBUM_IMG = "/album.png"

<!DOCTYPE html>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <link rel="stylesheet" type="text/css" href="/taffnaidphotos.css">
    <div id="list" class="list">
      <div id="list-nav" class="nav">
      <div id="list-previews" class="previews">

<a href="{0}" class="parent">☷</a>

<div class="preview">
  <a href="{0}">
    <img src="{1}" alt=":-("/>
    <div class="name">{2}</div>

<!DOCTYPE html>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <link rel="stylesheet" type="text/css" href="/taffnaidphotos.css">
    <div id="view" class="view">
      <div id="view-nav" class="nav">
      <div id="view-image" class="image">
        <img src="{1}" alt=":-("/>
        <link rel="prefetch" href="{2}">
        <link rel="prefetch" href="{3}">

<a href="{0}" class="previous">⟨</a>
<a href="{1}" class="parent">☷</a>
<a href="{2}" class="next">⟩</a>

cwd = os.getcwd()

for root, dirs, files in os.walk(cwd):

    dirs = sorted(dirs, reverse=True)

    files = filter(lambda file: file.lower().endswith(IMG_EXTENSION), files)
    files = filter(lambda file: not file.startswith(PREVIEW_PREFIX), files)
    files = sorted(files)

    preview_html = ""

    for dir in dirs:
        preview_html = PREVIEW_TEMPLATE.format(
            os.path.join(dir, INDEX),
            dir) + preview_html

    for i, file in enumerate(files):
        previous = files[i - 1]
        parent = os.path.join(root.replace(cwd, ""), INDEX)
        next = files[(i + 1) % len(files)]

        nav_html = VIEW_NAV_TEMPLATE.format(
            previous + HTML_EXTENSION,
            next + HTML_EXTENSION

        preview_html = preview_html + PREVIEW_TEMPLATE.format(
            file + HTML_EXTENSION,
            PREVIEW_PREFIX + file,

        view_html = VIEW_TEMPLATE.format(

        image = os.path.abspath(os.path.join(root, file))
        preview = os.path.abspath(os.path.join(os.path.dirname(image), PREVIEW_PREFIX + os.path.basename(image)))

        if not os.path.exists(preview):

            cmd = [
                "-define", "jpeg:size=%s" % PREVIEW_SIZE,
                "-thumbnail", "%s^" % PREVIEW_SIZE,
                "-gravity", "center",
                "-extent", PREVIEW_SIZE,


        view_file = image + HTML_EXTENSION

        with open(view_file, 'w') as view_file:

    parent = os.path.join(os.path.dirname(root.replace(cwd, "")), INDEX)
    nav_html = LIST_NAV_TEMPLATE.format(parent)

    list_html = LIST_TEMPLATE.format(
    index_file = os.path.join(root, INDEX)

    with open(index_file, 'w') as index_file:

nginx config to resize and cache images:

proxy_cache_path /var/www/html/cache levels=1:2 keys_zone=resized;

server {
        listen 80 default_server;
        listen [::]:80 default_server;

        root /var/www/html;

        index index.html index.html;

        server_name _;

        location / {
                try_files $uri $uri/ =404;

        location ~ ^/.*(\.jpg|\.JPG)$ {
                proxy_cache resized;
                proxy_cache_valid 200 10d;

server {
        listen 9001;
        deny all;

        root /var/www/html;

        location ~ ^/.*(\.jpg|\.JPG)$ {
                image_filter_buffer 10M;
                image_filter resize 1920 1080;

Source Code