Prototype — Professional Level¶

Source: refactoring.guru/design-patterns/prototype Prerequisites: Junior · Middle · Senior

Introduction¶

At the professional level, Prototype intersects with memory model semantics, JVM/Go runtime, serialization, and persistent data structures. You should be able to:

• Explain why Java's Object.clone() skips the constructor and what that means.
• Implement Copy-on-Write correctly under multi-threading.
• Reason about clone performance for large object graphs.
• Choose between deep clone, structural sharing, and persistent data structures.

Java's Object.clone() Internals¶

The native Object.clone():

1. Allocates new object of the same class via Class.newInstance0 (bypassing constructor).
2. Performs a field-by-field shallow copy at the bytecode level.
3. Returns the new object.

Implications: - No constructor runs. Final field initializers, side effects in constructors — skipped. - Shallow. Object references are copied; pointed-to objects shared. - Throws CloneNotSupportedException unless Cloneable is implemented. Marker interface only.

This is why Bloch advises "Override clone judiciously" (Effective Java Item 13). Most modern Java code uses copy constructors:

public final class Foo {
    public Foo(Foo other) { this.x = other.x; ... }
    public Foo clone() { return new Foo(this); }
}

Cloneable is bypassed entirely.

Memory Model & Visibility¶

A clone operation creates a new object. Other threads observing the clone need a happens-before edge to see fully-initialized state.

public Foo clone() {
    Foo copy = new Foo();
    copy.x = this.x;
    copy.list = new ArrayList<>(this.list);
    return copy;   // safe publication via return
}

The return statement is a synchronization action — the calling thread sees the assigned fields. Within a single thread, this is automatic. Across threads, ensure publication via:

• final fields (JMM guarantees safe publication after constructor).
• Synchronized publication.
• volatile references.

Copy-on-Write (COW) Correctness¶

A correct COW implementation under concurrency:

public final class CowList<T> {
    private volatile List<T> items;          // immutable snapshot
    private volatile boolean owned;

    public CowList(List<T> initial)          { this.items = List.copyOf(initial); this.owned = true; }
    public CowList<T> clone()                 { return new CowList<>(this.items, false); }
    private CowList(List<T> items, boolean owned) { this.items = items; this.owned = owned; }

    public synchronized void add(T item) {
        if (!owned) {
            this.items = new ArrayList<>(items);
            this.owned = true;
        }
        ((ArrayList<T>) items).add(item);
    }
}

Reads (items.get(...)) are lock-free thanks to volatile. Writes synchronize.

Key insight: until first mutation, two CowLists share the same List reference. Mutation triggers ownership transfer.

Used in java.util.concurrent.CopyOnWriteArrayList.

Performance: Deep Clone Cost¶

For an object graph with N nodes: - Time: O(N) — must visit every node. - Memory: O(N) — fresh copies. - GC pressure: N allocations.

For large graphs, this is expensive. Mitigations:

1. Selective deep copy¶

Identify which fields really need fresh copies:

public Doc clone() {
    Doc d = new Doc();
    d.title = this.title;          // String is immutable — share
    d.cache = this.cache;          // Read-only — share
    d.sections = deepCopy(this.sections);   // Mutable — clone
    return d;
}

2. Structural sharing¶

Persistent data structures (Scala immutable.Map, Clojure HAMTs):

val m1 = Map("a" -> 1)
val m2 = m1 + ("b" -> 2)   // O(log n), shares most internal nodes

m2 and m1 share most of their internal trie. "Copying" a 1M-entry map: ~20 allocations vs 1M.

3. Pre-clone caching¶

If many clones are made of the same prototype:

public final class CachedClone {
    private final Foo prototype = expensiveLoad();
    public Foo create() { return prototype.clone(); }
}

Loaded once; cloned many times.

Serialization-Based Cloning¶

A common deep-clone trick:

public Foo deepClone(Foo orig) throws IOException, ClassNotFoundException {
    ByteArrayOutputStream bos = new ByteArrayOutputStream();
    new ObjectOutputStream(bos).writeObject(orig);
    return (Foo) new ObjectInputStream(new ByteArrayInputStream(bos.toByteArray())).readObject();
}

Pros: - Handles deep object graphs automatically. - Handles cycles (via reference IDs). - Less code than manual clone().

Cons: - ~10× slower than manual clone. - Requires Serializable. - Doesn't work for transient/external resources. - ObjectInputStream has security implications (gadget chains).

Modern alternative: Jackson / Gson:

public <T> T deepClone(T orig, Class<T> type) {
    String json = mapper.writeValueAsString(orig);
    return mapper.readValue(json, type);
}

Slow but reliable for POJOs.

Python copy Module Internals¶

copy.deepcopy(obj, memo=None):

1. Check memo (default empty dict) for existing copy.
2. If obj is in memo, return memoized copy (handles cycles).
3. If obj has __deepcopy__, call obj.__deepcopy__(memo).
4. If obj is a primitive (int, str, frozenset, etc.), return it (immutable, share).
5. Otherwise, fall back to pickle protocol (__reduce_ex__) for unknown types.

The pickle fallback is slow but works for most user classes.

Custom __deepcopy__ performance¶

class Doc:
    def __deepcopy__(self, memo):
        new = Doc()
        memo[id(self)] = new   # before recursing
        new.children = [copy.deepcopy(c, memo) for c in self.children]
        return new

vs. relying on pickle fallback: - Custom: ~5× faster for simple structures. - Custom: handles non-picklable types. - Custom: explicit about what's copied vs shared.

Go: Reflection-Based Deep Clone¶

For generic deep clone in Go:

import "reflect"

func deepClone(src any) any {
    v := reflect.ValueOf(src)
    return reflect.New(v.Type()).Elem().Interface()   // simplified; real version recurses
}

Reflection is slow (~100× direct field copy). Libraries like mohae/deepcopy exist but are rarely used in production. Prefer hand-written Clone() methods.

Benchmarks¶

Apple M2 Pro, single thread.

Java¶

DirectConstructor                500M ops/s
CopyConstructor (handwritten)    400M ops/s
ObjectClone (Cloneable)          250M ops/s   (shallow)
ObjectStreamClone (Serializable) 50K ops/s
JacksonClone (JSON roundtrip)    100K ops/s

Manual copy constructor is ~80% the speed of direct construction.

Python¶

copy.copy (shallow)              200 ns
copy.deepcopy (custom __deepcopy__)  600 ns
copy.deepcopy (pickle fallback)  3 µs

Custom __deepcopy__ for hot paths.

Go¶

DirectStructCopy                 500M ops/s
HandwrittenClone                 200M ops/s   (with explicit slice copy)
ReflectionDeepCopy                3M ops/s

Hand-written clone for hot paths.

Persistent Data Structures (When Possible)¶

Languages with first-class persistent structures (Clojure, Scala) sidestep most clone concerns:

(def m1 {:a 1 :b 2})
(def m2 (assoc m1 :c 3))   ; m1 unchanged; m2 shares with m1

assoc is O(log n). No clone needed.

Vavr brings this to Java:

import io.vavr.collection.Map;

Map<String, Integer> m1 = HashMap.of("a", 1, "b", 2);
Map<String, Integer> m2 = m1.put("c", 3);   // immutable, structural sharing

For data-heavy clones, Vavr eliminates the deep clone problem.

Diagrams¶

Object.clone vs Copy Constructor¶

COW¶

Related Topics¶

• JIT internals: Java Performance (Scott Oaks).
• JMM: Java Concurrency in Practice (Goetz et al.), Chapter 16.
• Persistent data structures: Okasaki's "Purely Functional Data Structures."
• Clojure data structures: Bagwell's HAMT papers.

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