D Best Practices

Analogy categoryAnalogies

Writing Idiomatic, Maintainable D

D gives you a lot of freedom — you can write it like C, like Java, or like a scripting language — but idiomatic D code follows a handful of consistent habits: default to immutable and const data, build transformations with lazy range pipelines and UFCS rather than manual loops, manage garbage collector pressure deliberately in hot paths, and encode assumptions as contracts and unittest blocks rather than comments. Establishing these habits early keeps a codebase consistent as it grows past a single author.

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Cricket analogy: Like a national coach such as Rahul Dravid establishing a standard set of net-session drills — throw-downs, fielding circuits, video review — that every player follows before a Test series, a D style guide establishes safety, immutability, and testing habits before code ships.

Prefer immutable and const by Default

In idiomatic D, mutability is opt-in, not the default assumption. Use immutable for data that must never change through any reference (immutable(int)[] or the shorthand immutable int[]), and const for a read-only view where the underlying data might still change through another reference. Function parameters that only read their argument should be declared const or in (an alias for scope const), which both documents intent to callers and lets the compiler catch accidental mutation.

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Cricket analogy: A match's published final scorecard is immutable — once released it can never be altered — while a broadcaster's live read-only scoreboard feed (const) still updates as the actual on-field score keeps changing.

Use Ranges and UFCS for Composable Code

D's std.algorithm and std.range provide composable, lazy building blocks — map, filter, take, chain — that combine through Uniform Function Call Syntax so arr.filter!(x => x > 0).map!(x => x * 2) reads left-to-right like a pipeline. Because ranges are evaluated lazily, no intermediate array is built for each stage; elements are only computed when something (a foreach loop, or an explicit .array call) actually pulls them through the pipeline, which keeps both allocation count and peak memory low.

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Cricket analogy: Like analyzing MS Dhoni's run-chase strategy ball-by-ball as the over unfolds rather than pre-computing every possible outcome table before the innings starts, a lazy range only computes each element as it's actually needed.

import std.stdio;
import std.algorithm : filter, map;
import std.range : iota;

void main()
{
    // Lazy pipeline: no intermediate arrays are allocated until iteration forces evaluation
    auto result = iota(1, 20)
        .filter!(n => n % 2 == 0)
        .map!(n => n * n);

    foreach (n; result)
        write(n, " ");
    writeln();
}

Manage the Garbage Collector Deliberately

The GC is convenient by default but allocation-heavy loops erode performance and can trigger collection pauses. Reuse buffers instead of allocating fresh memory each iteration, prefer std.array.Appender or a pre-sized array (new int[](n)) over repeated ~= concatenation, and once a hot-path function is verified allocation-free, mark it @nogc so the compiler flags any future accidental allocation as a compile error rather than a runtime surprise.

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Cricket analogy: Like groundstaff at Eden Gardens reusing the same roller and covers for every match rather than manufacturing new equipment each time, reusing buffers in a hot loop avoids repeated allocation overhead.

Concatenating strings or appending to arrays inside a hot loop (result ~= item;) reallocates and copies the backing buffer on every growth step, which is quadratic in the worst case. Prefer std.array.Appender (auto app = appender!(int[])(); app.put(item);) or a pre-sized array (new int[](n)) to avoid this GC churn in performance-sensitive code.

Use Contracts, Unittests, and Design by Contract

D supports design-by-contract natively: an in contract block checks preconditions before a function body runs, an out contract checks postconditions on the return value, and a class invariant is checked after every public method call. Combined with unittest blocks — which live directly next to the code they test and compile in with -unittest — you get executable documentation of a function's assumptions that fails loudly the moment reality diverges from intent, instead of a comment nobody kept up to date.

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Cricket analogy: The Decision Review System acts as a runtime check verifying an umpire's call against defined rules, similar to how an in/out contract verifies a function's precondition and postcondition before and after it runs.

D's unittest blocks live directly next to the code they test and compile in with -unittest, so a call like assert(factorial(5) == 120); inside unittest { ... } runs automatically during dub test without needing a separate test framework or file layout.

Default to immutable and const for data that doesn't need to change; mutability should be an explicit, deliberate choice in D, not the default assumption.
Use ranges (map, filter, iota, and friends) with UFCS to build lazy, composable pipelines instead of manually looping and allocating intermediate arrays.
Prefer std.array.Appender or pre-sized arrays over repeated ~= concatenation to avoid quadratic reallocation costs under the GC.
Mark performance-critical functions @nogc once you've verified they don't allocate, so the compiler catches accidental future allocations.
Use in/out contracts and class invariants to document and enforce a function's assumptions, catching bugs at the boundary rather than deep inside logic.
Write unittest blocks alongside the functions they test; they compile in with -unittest and run via dub test with no separate test framework needed.
Reuse buffers and pooled objects in hot paths instead of allocating fresh memory per iteration, mirroring allocation-conscious C++ style.