C++ SparseArrayValueMap类代码示例

void JSArray::push(ExecState* exec, JSValue value)
{
    checkConsistency();

    if (m_storage->m_length < m_vectorLength) {
        m_storage->m_vector[m_storage->m_length] = value;
        ++m_storage->m_numValuesInVector;
        ++m_storage->m_length;
        checkConsistency();
        return;
    }

    if (m_storage->m_length < MIN_SPARSE_ARRAY_INDEX) {
        SparseArrayValueMap* map = m_storage->m_sparseValueMap;
        if (!map || map->isEmpty()) {
            if (increaseVectorLength(m_storage->m_length + 1)) {
                m_storage->m_vector[m_storage->m_length] = value;
                ++m_storage->m_numValuesInVector;
                ++m_storage->m_length;
                checkConsistency();
                return;
            }
            checkConsistency();
            throwOutOfMemoryError(exec);
            return;
        }
    }

    putSlowCase(exec, m_storage->m_length++, value);
}

void JSArray::setLengthWritable(ExecState* exec, bool writable)
{
    ASSERT(isLengthWritable() || !writable);
    if (!isLengthWritable() || writable)
        return;

    enterDictionaryIndexingMode(exec->vm());

    SparseArrayValueMap* map = arrayStorage()->m_sparseMap.get();
    ASSERT(map);
    map->setLengthIsReadOnly();
}

NEVER_INLINE void JSArray::putSlowCase(ExecState* exec, unsigned i, JSValue value)
{
    ArrayStorage* storage = m_storage;
    SparseArrayValueMap* map = storage->m_sparseValueMap;

    if (i >= MIN_SPARSE_ARRAY_INDEX) {
        if (i > MAX_ARRAY_INDEX) {
            PutPropertySlot slot;
            put(exec, Identifier::from(exec, i), value, slot);
            return;
        }

        // We miss some cases where we could compact the storage, such as a large array that is being filled from the end
        // (which will only be compacted as we reach indices that are less than MIN_SPARSE_ARRAY_INDEX) - but this makes the check much faster.
        if ((i > MAX_STORAGE_VECTOR_INDEX) || !isDenseEnoughForVector(i + 1, storage->m_numValuesInVector + 1)) {
            if (!map) {
                map = new SparseArrayValueMap;
                storage->m_sparseValueMap = map;
            }

            pair<SparseArrayValueMap::iterator, bool> result = map->add(i, value);
            if (!result.second) { // pre-existing entry
                result.first->second = value;
                return;
            }

            size_t capacity = map->capacity();
            if (capacity != storage->reportedMapCapacity) {
                Heap::heap(this)->reportExtraMemoryCost((capacity - storage->reportedMapCapacity) * (sizeof(unsigned) + sizeof(JSValue)));
                storage->reportedMapCapacity = capacity;
            }
            return;
        }
    }

    // We have decided that we'll put the new item into the vector.
    // Fast case is when there is no sparse map, so we can increase the vector size without moving values from it.
    if (!map || map->isEmpty()) {
        if (increaseVectorLength(i + 1)) {
            storage = m_storage;
            storage->m_vector[i] = value;
            ++storage->m_numValuesInVector;
            checkConsistency();
        } else
            throwOutOfMemoryError(exec);
        return;
    }

    // Decide how many values it would be best to move from the map.
    unsigned newNumValuesInVector = storage->m_numValuesInVector + 1;
    unsigned newVectorLength = increasedVectorLength(i + 1);
    for (unsigned j = max(m_vectorLength, MIN_SPARSE_ARRAY_INDEX); j < newVectorLength; ++j)
        newNumValuesInVector += map->contains(j);
    if (i >= MIN_SPARSE_ARRAY_INDEX)
        newNumValuesInVector -= map->contains(i);
    if (isDenseEnoughForVector(newVectorLength, newNumValuesInVector)) {
        unsigned proposedNewNumValuesInVector = newNumValuesInVector;
        // If newVectorLength is already the maximum - MAX_STORAGE_VECTOR_LENGTH - then do not attempt to grow any further.
        while (newVectorLength < MAX_STORAGE_VECTOR_LENGTH) {
            unsigned proposedNewVectorLength = increasedVectorLength(newVectorLength + 1);
            for (unsigned j = max(newVectorLength, MIN_SPARSE_ARRAY_INDEX); j < proposedNewVectorLength; ++j)
                proposedNewNumValuesInVector += map->contains(j);
            if (!isDenseEnoughForVector(proposedNewVectorLength, proposedNewNumValuesInVector))
                break;
            newVectorLength = proposedNewVectorLength;
            newNumValuesInVector = proposedNewNumValuesInVector;
        }
    }

    storage = static_cast<ArrayStorage*>(tryFastRealloc(storage, storageSize(newVectorLength)));
    if (!storage) {
        throwOutOfMemoryError(exec);
        return;
    }

    unsigned vectorLength = m_vectorLength;

    if (newNumValuesInVector == storage->m_numValuesInVector + 1) {
        for (unsigned j = vectorLength; j < newVectorLength; ++j)
            storage->m_vector[j] = JSValue();
        if (i > MIN_SPARSE_ARRAY_INDEX)
            map->remove(i);
    } else {
        for (unsigned j = vectorLength; j < max(vectorLength, MIN_SPARSE_ARRAY_INDEX); ++j)
            storage->m_vector[j] = JSValue();
        for (unsigned j = max(vectorLength, MIN_SPARSE_ARRAY_INDEX); j < newVectorLength; ++j)
            storage->m_vector[j] = map->take(j);
    }

    storage->m_vector[i] = value;

    m_vectorLength = newVectorLength;
    storage->m_numValuesInVector = newNumValuesInVector;

    m_storage = storage;

    checkConsistency();

    Heap::heap(this)->reportExtraMemoryCost(storageSize(newVectorLength) - storageSize(vectorLength));
}

SparseArrayValueMap* SparseArrayValueMap::create(VM& vm)
{
    SparseArrayValueMap* result = new (NotNull, allocateCell<SparseArrayValueMap>(vm.heap)) SparseArrayValueMap(vm);
    result->finishCreation(vm);
    return result;
}

NEVER_INLINE void JSArray::putSlowCase(ExecState* exec, unsigned i, JSValue* value)
{
    ArrayStorage* storage = m_storage;
    SparseArrayValueMap* map = storage->m_sparseValueMap;

    if (i >= sparseArrayCutoff) {
        if (i > maxArrayIndex) {
            put(exec, Identifier::from(exec, i), value);
            return;
        }

        // We miss some cases where we could compact the storage, such as a large array that is being filled from the end
        // (which will only be compacted as we reach indices that are less than cutoff) - but this makes the check much faster.
        if (!isDenseEnoughForVector(i + 1, storage->m_numValuesInVector + 1)) {
            if (!map) {
                map = new SparseArrayValueMap;
                storage->m_sparseValueMap = map;
            }
            map->set(i, value);
            return;
        }
    }

    // We have decided that we'll put the new item into the vector.
    // Fast case is when there is no sparse map, so we can increase the vector size without moving values from it.
    if (!map || map->isEmpty()) {
        increaseVectorLength(i + 1);
        storage = m_storage;
        ++storage->m_numValuesInVector;
        storage->m_vector[i] = value;
        checkConsistency();
        return;
    }

    // Decide how many values it would be best to move from the map.
    unsigned newNumValuesInVector = storage->m_numValuesInVector + 1;
    unsigned newVectorLength = increasedVectorLength(i + 1);
    for (unsigned j = max(storage->m_vectorLength, sparseArrayCutoff); j < newVectorLength; ++j)
        newNumValuesInVector += map->contains(j);
    if (i >= sparseArrayCutoff)
        newNumValuesInVector -= map->contains(i);
    if (isDenseEnoughForVector(newVectorLength, newNumValuesInVector)) {
        unsigned proposedNewNumValuesInVector = newNumValuesInVector;
        while (true) {
            unsigned proposedNewVectorLength = increasedVectorLength(newVectorLength + 1);
            for (unsigned j = max(newVectorLength, sparseArrayCutoff); j < proposedNewVectorLength; ++j)
                proposedNewNumValuesInVector += map->contains(j);
            if (!isDenseEnoughForVector(proposedNewVectorLength, proposedNewNumValuesInVector))
                break;
            newVectorLength = proposedNewVectorLength;
            newNumValuesInVector = proposedNewNumValuesInVector;
        }
    }

    storage = static_cast<ArrayStorage*>(fastRealloc(storage, storageSize(newVectorLength)));

    unsigned vectorLength = storage->m_vectorLength;
    if (newNumValuesInVector == storage->m_numValuesInVector + 1) {
        for (unsigned j = vectorLength; j < newVectorLength; ++j)
            storage->m_vector[j] = 0;
        if (i > sparseArrayCutoff)
            map->remove(i);
    } else {
        for (unsigned j = vectorLength; j < max(vectorLength, sparseArrayCutoff); ++j)
            storage->m_vector[j] = 0;
        for (unsigned j = max(vectorLength, sparseArrayCutoff); j < newVectorLength; ++j)
            storage->m_vector[j] = map->take(j);
    }

    storage->m_vector[i] = value;

    storage->m_vectorLength = newVectorLength;
    storage->m_numValuesInVector = newNumValuesInVector;

    m_storage = storage;

    checkConsistency();
}