WET Dilutes Performance Bottlenecks

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The importance of the DRY principle (Don't Repeat Yourself) is that codifies the idea that every piece of knowledge in a system should have a singular representation. In the world of code that means we should have a single implementation. On the other hand, WET (Write Every Time) leads to multiple implementations. The performance implications of DRY versus WET become very clear when you consider their effects on a performance profile. In short, their effects are many.
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The importance of the DRY principle (Don't Repeat Yourself) is that codifies the idea that every piece of knowledge in a system should have a singular representation. This translates to; knowledge should be contained in a single implementation. The antitheses of DRY is WET (Write Every Time). Our code is WET when knowledge is codified in several different implementations. The performance implications of DRY versus WET become very clear when you consider their numerous effects on a performance profile.
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To see the first effect let's consider a feature of our system (say ''X'') that is a CPU bottleneck. Let's say ''X'' consumes 30% of the CPU. Now let's consider that feature ''X'' has 10 different implementations. On average, each implementation will consume 3% of the CPU. Hardly a level of CPU utilization worth considering if we are looking for a big gain. In this scenario it is unlikely that we'd recognize that feature as being a bottleneck. That said, let's move to the second point by saying, magic has happened and we recognize feature ''X'' as the source of our problem. Now we are left with the problem of finding, recognizing, and fixing every single implementation. In our example we have 10 different implementations that we need to find and fix and all because we didn't follow the DRY principle. Following DRY we'd clearly see the 30% CPU utilization and we'd have 1/10 the code to fix, not to mention the time saved by not having to find each implementation.
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Lets start by considering a feature of our system, say ''X'', that is a CPU bottleneck. Let's say feature ''X'' consumes 30% of the CPU. Now let's say that feature ''X'' has 10 different implementations. On average, each implementation will consume 3% of the CPU. As this is level of CPU utilization isn't worth considering if we are looking for a quick win, it is unlikely that we'd miss that this feature is our bottleneck. Let say that for some magical reason we have recognized feature ''X'' as a bottleneck. Now we are left with the problem of finding and fixing every single implementation. With WET, we have 10 different implementations that we need to find and fix. With DRY we'd clearly see the 30% CPU utilization and we'd have 1/10 the code to fix. Was it mentioned that we don't have to spend time looking for each implementation?
There is one use case where we are often guilty of violating DRY. That is in our use of collections. Let's say we are working with customer data. A common technique to implement a query would be to iterate over the collection and then apply the query in turn to each element.
There is one use case where we are often guilty of violating DRY. That is in our use of collections. Let's say we are working with customer data. A common technique to implement a query would be to iterate over the collection and then apply the query in turn to each element.
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By exposing this raw collection to clients, we have violated encapsulation. This limits our ability to refactor and we have forced our clients to violate DRY by having each of them implement potentially the same query. One solution is to not expose raw collections in any API. In this example we can introduce a new collection called <code>CustomerList</code>. This new class is more semantically in line with our domain. It will act as a natural home for all our queries.
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By exposing this raw collection to clients, we have violated encapsulation. This not only limits our ability to refactor, it forces users of our code to violate DRY by having each of them re-implement potentially the same query. This situation can easily be avoided by removing the exposed raw collections from the API. In this example we can introduce a new collection called <code>CustomerList</code>. This new class is more semantically in line with our domain. It will act as a natural home for all our queries.
Having this new collection type will also allows to easily see if these queries are a performance bottleneck. By incorporating the queries into the class we eliminate the need to expose internal representations to our clients. This gives us the freedom to alter these implementations without fear of violating client contracts.
Having this new collection type will also allows to easily see if these queries are a performance bottleneck. By incorporating the queries into the class we eliminate the need to expose internal representations to our clients. This gives us the freedom to alter these implementations without fear of violating client contracts.

Revision as of 10:06, 3 November 2008

The importance of the DRY principle (Don't Repeat Yourself) is that codifies the idea that every piece of knowledge in a system should have a singular representation. This translates to; knowledge should be contained in a single implementation. The antitheses of DRY is WET (Write Every Time). Our code is WET when knowledge is codified in several different implementations. The performance implications of DRY versus WET become very clear when you consider their numerous effects on a performance profile.

Lets start by considering a feature of our system, say X, that is a CPU bottleneck. Let's say feature X consumes 30% of the CPU. Now let's say that feature X has 10 different implementations. On average, each implementation will consume 3% of the CPU. As this is level of CPU utilization isn't worth considering if we are looking for a quick win, it is unlikely that we'd miss that this feature is our bottleneck. Let say that for some magical reason we have recognized feature X as a bottleneck. Now we are left with the problem of finding and fixing every single implementation. With WET, we have 10 different implementations that we need to find and fix. With DRY we'd clearly see the 30% CPU utilization and we'd have 1/10 the code to fix. Was it mentioned that we don't have to spend time looking for each implementation?

There is one use case where we are often guilty of violating DRY. That is in our use of collections. Let's say we are working with customer data. A common technique to implement a query would be to iterate over the collection and then apply the query in turn to each element.

public class UsageExample {

    private ArrayList<Customer> allCustomers = new ArrayList<Customer>();

    public ArrayList<Customer> findCustomersThatSpendAtLeast(float amount) {
        ArrayList<Customer> customersOfInterest = new ArrayList<Customer>();
        for (Customer customer: allCustomers) {
            if (customer.spendsAtLeast(amount))
               customersOfInterest.add(customer);
        }
        return customersOfInterest;
    }
}

By exposing this raw collection to clients, we have violated encapsulation. This not only limits our ability to refactor, it forces users of our code to violate DRY by having each of them re-implement potentially the same query. This situation can easily be avoided by removing the exposed raw collections from the API. In this example we can introduce a new collection called CustomerList. This new class is more semantically in line with our domain. It will act as a natural home for all our queries.

Having this new collection type will also allows to easily see if these queries are a performance bottleneck. By incorporating the queries into the class we eliminate the need to expose internal representations to our clients. This gives us the freedom to alter these implementations without fear of violating client contracts.

public class CustomerList {

    private ArrayList<Customer> customers = new ArrayList<Customer>();
    private SortedList<Customer> customersSortedBySpendingLevel = new SortedList<Customer)();

    public CustomerList findCustomersThatSpendAtLeast(float amount) {
        return customersSortedBySpendingLevel.elementsLargerThan(amount);
    }
}
public class UsageExample {

    public static void main(String[] args) {
        CustomerList customers = new CustomerList();
        // ...
        CustomerList customersOfInterest = customers.findCustomersThatSpendAtLeast(500.00);
    }
}
           

In this example, adherence to DRY allowed us to introduced an alternate indexing scheme with SortedList keyed on our customers level of spending. More important than the specific details of this particular example that following DRY helped us to find and repair a performance bottleneck that would have been more difficult to find were the code to be WET.


By Kirk Pepperdine

This work is licensed under a Creative Commons Attribution 3


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