What are Software Developers Doing All Day?

6. November, 2021

Translate.

Programmer sitting at desk
https://openclipart.org/detail/328692/programming-coding

Mathematics? Nope. I use the trigonometric functions like sin(x) to draw nice graphics in my spare time but I never use them at work. I used logarithmic last year to round a number but that’s about it. Add, multiply, subtract and divide is all the math that I ever do and most of that is “x = x + 1”. If I have to do statistics, I use a library. No need to find the maximum of a list of values myself.

So what do we do? Really?

We translate mumble into Programming Language of the Year(TM).

Or more diplomatic: We try to translate the raw and unpolished rambling of clients into the strict and unforgiving rules of a programming language.

We’re translators. Like those people who translate between human languages. We know all the little tricks how to express ourselves and what you can and can’t easily express. After a while, we can distinguish between badly written code and the other kind, just like an experienced journalist.

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Jazoon 2012: CQRS – Trauma treatment for architects

4. July, 2012

A few years ago, concurrency and scalability were a hype. Today, it’s a must. But how do you write applications that scale painlessly?

Command and Query Responsibility Segregation (CQRS) is an architectural pattern to address these problems. In his talk, Allard Buijze gave a good introduction. First, some of the problems of the standard approach. Your database, everyone says, must be normalized.

That can lead to a couple of problems:

  • Historic data changes
  • The data model is neither optimized for writes nor for queries

The first problem can result in a scenario like this. Imagine you have a report that tells you the annual turnover. You run the report for 2009 in January, 2010. You run the same report again in 2011 and 2012 and each time, the annual turnover of 2009 gets bigger. What is going on?

The data model is in third normal form. This is great, no data duplication. It’s not so great when data can change over time. So if your invoices point to the products and the products point to the prices, any change of a price will also change all the existing invoices. Or when customers move, all the addresses on the invoices change. There is no way to tell where you sent something.

The solution is to add “valid time range” to each price, address, …, which makes your SQL hideous and helps to keep your bug tracker filled.

It will also make your queries slow since you will need lots and lots of joins. These joins will eventually get in conflict with your updates. Deadlocks occur.

On the architectural side, some problems will be much easier to solve if you ignore the layer boundaries. You will end up business logic in the persistence layer.

Don’t get me wrong. All these problems can be solved but the question here is: Is this amount of pain really necessary?

CQRS to the rescue. The basic idea is to use two domain models instead of one. Sounds like more work? That depends.

With CQRS, you will have more code to maintain but the code will be much more simple. There will be more tables and data will be duplicated in the database but there will never be deadlocks, queries won’t need joins in the usual case (you could get rid of all joins if you wanted). So you trade bugs for code.

How does it work? Split your application into two main parts. One part takes user input and turns that into events which are published. Listeners will then process the events.

Some listeners will write the events into the database. If you need to, you will be able to replay these later. Imagine your customer calls you because of some bug. Instead of asking your customer to explain what happened, you go to the database, copy the events into a test system and replay them. It might take a few minutes but eventually, you will have a system which is in the exact same state as when the bug happened.

Some other listeners will process the events and generate more events (which will also be written to the database). Imagine the event “checkout”. It will contain the current content of the shopping cart. You write that into the database. You need to know what was in the shopping basket? Look for this event.

The trick here is that the event is “independent”. It doesn’t contain foreign keys but immutables or value objects. The value objects are written into a new table. That makes sure that when you come back 10 years later, you will see the exact same shopping cart as the customer saw when she ordered.

When you need to display the shopping cart, you won’t need to join 8 tables. Instead, you’ll need to query 1-2 tables for the ID of the shopping cart. One table will have the header with the customer address, the order number, the date, the total and the second table will contain the items. If you wanted, you could add the foreign keys to the product definition tables but you don’t have to. If that’s enough for you, those two tables could be completely independent of any other table in your database.

The code to fill the database gets the event as input (no database access to read anything from anywhere) and it will only write to those two tables. Minimum amount of dependencies.

The code to display the cart will only need to read those two tables. No deadlocks possible.

The code will be incredibly simple.

If you make a mistake somewhere, you can always replay all the events with the fixed code.

For tests, you can replay the events. No need to a human to click buttons in a web browser (not more than once, anyway).

Since you don’t need foreign keys unless you want to, you can spread the data model over different databases, computers, data centers. Some data would be better in a NoSQL repository? No problem.

Something crashes? Fix the problem, replay the events which got lost.

Instead of developing one huge monster model where each change possibly dirties some existing feature, you can imagine CQRS as developing thousands of mini-applications that work together.

And the best feature: It allows you to retroactively add features. Imagine you want to give users credits for some action. The idea is born one year after the action was added. In a traditional application, it will be hard to assign credit to the existing users. With CQRS, you simply implement the feature, set up the listeners, disable the listeners which already ran (so the action isn’t executed again) and replay the events. Presto, all the existing users will have their credit.

Related:

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Jazoon 2012: Improving system development using traceability

4. July, 2012

When you develop a software, you will ask yourself these questions (quoted from here):

  • Is it still possible to accept a late change request? What would be the impact?
  • What is the overall level of completion of the system or a component?
  • Which components are ready for testing?
  • A failure occurs because the system is erroneous. What parts of the system should I check?

In his talk “Improving system development using traceability“, Ömer Gürsoy shows an approach to answer these. The idea is to trace changes end-to-end: From the idea over requirements to design, implementation, tests, bug reports and the product manual. For this to work, you’ll need to

  • Analyze
  • Document
  • Validate
  • Manage

At itemis, they developed tooling support. A plug-in for Eclipse can track changes in all kinds of sources (text documents, UML diagrams, requirement DSLs) and “keep them together”. It can answer questions like “who uses this piece of code?”

The answer will tell you where you need to look to estimate the impact of a change. That helps to avoid traps like underestimation or missing surveillance.

Today, the plug-in shows some promise but there are rough edges left. The main problem is integration with other tools. The plug-in supports extension points to add any kind of data source but that only helps if the data source is willing to share. The second problem is that it doesn’t support versioning right now. It’s on the feature list.

On the positive side, it can create dependencies from a piece of text (say a paragraph in a text file). If you edit other parts of the text file, the tool will make sure the dependency still points to the right part of the text. So you can make notes during a meeting. Afterwards, you can click on the paragraphs and link them to (new) requirements or parts of the code (like modules) that will be affected. Over time, a graph of dependencies will be created that helps you to keep track of everything that is related to some change and how it is related: Where did the request come from? Which code was changed?

Always keep in mind that tracking everything isn’t possible – it would simply too expensive today. But you can track your most important or most dangerous changes. That would give you the most bang for the buck. To do that, you must know what you must track and why.

A feature that I’d like to see is automatic discovery. Especially Java source code should be easy to analyze for dependencies.

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Jazoon 2012: Divide&Conquer: Efficient Java for Multicore World

29. June, 2012

Not much new in the talk “Divide&Conquer: Efficient Java for Multicore World” by Sunil Mundluri and Velmurugan Periasamy.

Amdahl’s law shows that you can’t get an arbitrary speed-up when running part of your code in parallel. In practice, you can expect serial code to execute 2-4 times faster if you run it with, say, the fork/join framework of Java 7. This is due to setup + join cost and the fact that the tasks themselves don’t get faster – you just execute more of them at the same time. So if a task takes 10 seconds and you can run all of them in parallel, the total execution time will be a bit over 10s.

If you want to use fork/join with Java 6, you can add the jsr166y.jar to your classpath.

Again, functional programming makes everything more simple. With Java 8 and lambda expressions, syntactic sugar will make things even more readable but at a price.

You might want to check one of today’s new languages like Xtend, Scala or Groovy to get these features today with Java 6.

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Jazoon 2012: Syntactic Salt and Sugar

29. June, 2012

Syntactic Salt and Sugar was a presentation given by James Gould and Alex Holmes. They were talking about some recent developments and whether they are good (sugar) or bad (salt).

DSLs

DSLs are becoming ubiquitous. Everyone wants, needs and does DSLs today. But think of this for a moment: Is SQL a DSL?

Scary thought, eh? It’s certainly a limited language but since it’s Turing complete, the limits are more in the pain writing queries and not in the fact that it’s a language designed to query data sets.

The advantage of DSLs is that you can fine tune them to your domain. That can help to avoid a lot of confusion.

But …

  • There are five people on this planet who can develop a nice syntax that is easy to use, easy to read, easy to understand and mostly consistent. Guido van Rossum is one of them. You’re not.
  • It’s easy to fall for the “one more feature” trap in a DSL. The most important property of a DSL is that it’s limited. It’s not a general purpose programming language.
  • Getting the syntax right is very, very hard. It’s easy to define syntax in the Xtext grammar editor – as long as you blissfully ignore the consumers of your DSL. As soon as you try to make their lives easier, all hell will break loose. Do you allow trailing commas? How do you handle ambiguities? Did you make sure all error messages make sense? Is it still readable? Can you add features without breaking all existing code?
  • YALTL – Yet another language to learn

Default Methods in Java 8

In Java 8, you can add method bodies to methods defined in interfaces:

public interface Foo {
String getName() default { return "Foo"; }
}

Finally, you can have mixins in Java. Yay ^_^

Now, some people will wonder: Isn’t that multiple inhertiance?

Yup. And as usual, because of some “features” of Java, they had to implement this in a … surprising way. What does this code print?

public interface A {
    String getName() default { return "A"; }
}

public interface B {
    String getName() default { return "B"; }
}

public class C implements A, B {
    public void main() {
        System.out.println(new C().getName());
    }
}
Nothing – it doesn’t compile because the compiler can’t decide which method to call. But this one compiles:
public interface A {
    String getName() default { return "A"; }
}

public interface B {
    String getName() default { return "B"; }
}

public interface C extends B {}

public class D implements A, C {
    public void main() {
        System.out.println(new C().getName());
    }
}

If you’re wondering: Instead of inheriting directly from “B”, I added a new interface “C”. Now, “A” is “closer” and it will print “A”.

That means changes in A or C can modify the behavior of D. If you’re lucky, the compiler will refuse to compile it. *sigh*

No Free Lunch

Again, it’s easy to see that each feature comes with a cost attached.

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Jazoon 2012: Building Scalable, Highly Concurrent and Fault-Tolerant Systems: Lessons Learned

29. June, 2012

What do Cloud Computing, multi-core processors and Big Data have in common?

Parallelism.

In his presentation, Jonas Bonér showed what you should care about:

  • Always prefer immutable
  • Separate concerns in different layers with the minimum amount of dependencies
  • Separate error handling from the business logic
  • There is no free lunch: For every feature, you will have to pay a price
  • Avoid using RPC/RMI. Try lure you into “convenience over correctness”
  • Make sure you handle timeouts correctly
  • Use CALM if you can
  • Not all your data needs ACID.
  • Know about CAP and BASE: Drop ACID And Think About Data
  • Get rid of dependencies by using event sourcing/CQS/CQRS
  • Frameworks like Hibernate always leak in places where you can’t have it. KISS.

Longer explanation:

Immutables can always be shared between threads. Usually, they are also simple to share between processes, even when they run on different computers. Trying locks and clever concurrency will only get you more bugs, unmaintainable code and a heart attack.

Dependencies kill a project faster and more efficiently than almost any other technique. Avoid them. Split your projects into Maven modules. You can’t import what you don’t have on the classpath.

Error handling in your business logic (BL) will bloat the code and make it harder to maintain. Business logic can’t handle database failures. Parameters should have been validated before they were passed to business logic. Business logic should produce a result and the caller should then decide what to do with it (instead of mixing persistence code into your business layer). The BL shouldn’t be aware that the data comes from a database or that the result goes back into a database. What would your unit tests say? See also Akka 2.0 and “parental supervision.”

Obvious programming has a value: You can see what happens. It has a price: Boiler plate code. You can try to hide this but it will still leak. Hibernate is a prefect example for this. Yes, it hides the fact that getChildren() needs to run a query against the database – unless the entity leaks outside of your transaction. It does generate proxies to save you from seeing the query but that can break equals().

Same applies to RMI. When RMI decides that you can’t handle the message, then you won’t even see it. In many cases, a slightly “unusual” message (like one with additional fields) wouldn’t hurt.

As soon as you add RMI or clustering, you add an invisible network in your method calls. Make sure you have the correct timeouts (so your callers don’t block forever) and that you handle them correctly. New error sources that are caused adding the network:

  1. Failure to serialize the message
  2. Host unreachable
  3. Packet drops
  4. Network lag
  5. Destination doesn’t accept message because of configuration error
  6. Message is sent to the wrong destination
  7. Destination can’t read message
Claim checks allow to resend a message again after a timeout without having it processed twice by the consumer.

CALM and BASE refer to the fact that you can only have two of the tree CAP characteristics: Consistency, Availability and Partition Tolerance. Since Partition Tolerance (necessary for scaling) and Availability (what’s the point of having a consistent but dead database?) are most important, you have to sacrifice consistency. CALM and BASE show ways to eventually reach consistency, even without manual intervention. For all data related to money, you will want consistency as well but think about it: How many accounts are there in your database? And how many comments? Is ACID really necessary for each comment?

Solution: Put your important data (when money is involved) into an old school relational database. Single instance. Feed that database with queues, so it doesn’t hurt (much) when it goes down once in a while. Put comments, recommendations, shopping carts into a NoSQL database. So what if a shopping cart isn’t synchronized over all your partitions? Just make sure that users stay on one shard and they will only notice when the shard dies and you can’t restore the shopping cart quickly enough from the event stream.

Which event stream? The one which your CQRS design created. More on that in another post. You might also want to look at Akka 2.0 which comes with a new EventBus.

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Commenting Code

1. March, 2012

A lot of people way “you must comment your code.”

Kevlin Henney wrote an excellent piece on this topic in 97 Things Every Programmer Should Know: Comment Only What the Code Cannot Say

It really boils down to the last sentence: “Comment what the code cannot say, not simply what it does not say.”

There are various reasons why people demand comments:

  1. They are not fluent in the programming language or don’t know enough to read the code. There is nothing wrong with the code – the readers simply don’t know enough to understand it.
  2. The code is broken in some way and you need the comment to make sure people don’t break it even more.
  3. The comment explains something that no one will see from the code.

Only #3 is a valid reason for comments. #1 is just adding noise for people who shouldn’t touch the code anyway. #2 means you should refactor the code to make its intent clear – adding comments will only make things worse.

Related articles:

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Open Source As Good As Proprietary Software

28. February, 2012

The Coverity Scan 2011 Open Source Integrity Report (registration necessary) says: “Open source quality is on par with proprietary code quality, particularly in cases where codebases are of similar size.”

Which isn’t that surprising considering that it’s the same people who write both.

But there are a couple of hard number in the report which are interesting:

Linux 2.6 has about 0.62 defects per 1000 lines of code (KLOC) which Coverity says “is roughly identical to that of its proprietary codebase counterparts.” They can’t tell names but I guess the counterparts are Windows and Mac OS X. They have 0.64 defects per KLOC.

The industry average is 1.0 defects per KLOC which matches well with my (more anecdotal) knowledge that the best software developers make about 3-4 mistakes per KLOC of which 75% are found during development.

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Using Maven to Patch Third Party Code

26. October, 2011

If you have the source for the dependency, patching the code is simple: Just create a small Maven project that compiles the source with your changes. Since your changes are probably small, you need only a few tests: What’s the point to test the code that you didn’t touch? Also the build will be simple (just compile the sources for your need, no fancy resource processing/filtering).

But what if you don’t have the sources? Jakub Holý has a solution: Hacking A Maven Dependency with Javassist to Fix It

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Testing Requirements

30. August, 2011

When developing software, you’re constantly faced with two issues:

  1. Get all the requirements
  2. Make sure the requirements are correct

When writing code, we use tests to make sure we meet all the goals (completeness and correctness) but how do you test requirements?

By writing unit tests against a model of your requirements. There are two approaches to model requirements in a way useful for both the business and the development: Requirements Modeling Framework (RMF) and AlphaSimple.

See rafael.chaves’s blog post “Modeling requirements the pragmatic way (or When xUML meets xUnit)” for a more detailed introduction to the idea.

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