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ℹ️ New Book: Build Your Own Database

14. The Thread Pool & Asynchronous Tasks

14.1 Queues

There is a flaw in our server since the introduction of the sorted set data type: the deletion of keys. If the size of a sorted set is huge, it can take a long time to free its nodes and the server is stalled during the destruction of the key. This can be easily fixed by using multi-threading to move the destructor away from the main thread.

Firstly, we introduce the “thread pool”, which is literally a pool of threads. The thread from the pool consumes tasks from a queue and executes them. It is trivial to code a multi-producer multi-consumer queue using pthread APIs. (Although there is only a single producer in our case.)

The relevant pthread primitives are pthread_mutex_t and pthread_cond_t; they are called the mutex and the condition variable respectively. If you are unfamiliar with them, it is advised to get some education on multi-threading after reading this chapter. (Such as manpages of the pthread APIs, textbooks on operating systems, online courses, etc.)

Here is a really short introduction to the two pthread primitives:

14.2 The Implementation

The thread pool data type is defined as follows:

struct Work {
    void (*f)(void *) = NULL;
    void *arg = NULL;

struct TheadPool {
    std::vector<pthread_t> threads;
    std::deque<Work> queue;
    pthread_mutex_t mu;
    pthread_cond_t not_empty;

The thread_pool_init is for initialization and starting threads. pthread types are initialized by pthread_xxx_init functions and the pthread_create starts a thread with the target function worker.

void thread_pool_init(TheadPool *tp, size_t num_threads) {
    assert(num_threads > 0);

    int rv = pthread_mutex_init(&tp->mu, NULL);
    assert(rv == 0);
    rv = pthread_cond_init(&tp->not_empty, NULL);
    assert(rv == 0);

    for (size_t i = 0; i < num_threads; ++i) {
        int rv = pthread_create(&tp->threads[i], NULL, &worker, tp);
        assert(rv == 0);

The consumer code:

static void *worker(void *arg) {
    TheadPool *tp = (TheadPool *)arg;
    while (true) {
        // wait for the condition: a non-empty queue
        while (tp->queue.empty()) {
            pthread_cond_wait(&tp->not_empty, &tp->mu);

        // got the job
        Work w = tp->queue.front();

        // do the work
    return NULL;

The producer code:

void thread_pool_queue(TheadPool *tp, void (*f)(void *), void *arg) {
    Work w;
    w.f = f;
    w.arg = arg;


14.3 pthread APIs

The explanation:

  1. For both the producer and consumers, the queue access code is surrounded by the pthread_mutex_lock and the pthread_mutex_unlock, only one thread can access the queue at once.
  2. After a consumer acquired the mutex, check the queue:
    • If the queue is not empty, grab a job from the queue, release the mutex and do the work.
    • Otherwise, release the mutex and go to sleep, the sleep can be wakened later by the condition variable. This is accomplished via a single pthread_cond_wait call.
  3. After the producer puts a job into the queue, the producer calls the pthread_cond_signal to wake up a potentially sleeping consumer.
  4. After a consumer woken up from the pthread_cond_wait, the mutex is held again automatically. The consumer must check for the condition again after waking up, if the condition (a non-empty queue) is not satisfied, go back to sleep.

The use of the condition variable needs some more explanations: The pthread_cond_wait function is always inside a loop checking for the condition. This is because the condition could be changed by other consumers before the wakening consumer grabs the mutex; the mutex is not transferred from the signaler to the to-be-waked consumer! It is probably a mistake if you see a condition variable used without a loop.

A concrete sequence to help you understand the use of condition variables:

  1. The producer signals.
  2. The producer releases the mutex.
  3. Some consumer grabs the mutex and empties the queue.
  4. A consumer wakes up from the producer’s signal and grabs the mutex, but the queue is empty!

Note that the pthread_cond_signal doesn’t need to be protected by the mutex, singaling after releasing the mutex is also correct.

14.4 Integrating with the Server

The thread pool is done. Let’s add that to our server:

// global variables
static struct {
    HMap db;
    // a map of all client connections, keyed by fd
    std::vector<Conn *> fd2conn;
    // timers for idle connections
    DList idle_list;
    // timers for TTLs
    std::vector<HeapItem> heap;
    // the thread pool
    TheadPool tp;
} g_data;

Inside the main function:

    // some initializations
    thread_pool_init(&, 4);

The entry_del function is modified: It will put the destruction of large sorted sets into the thread pool. And the thread pool is only for the large ones since multi-threading has some overheads too.

// deallocate the key immediately
static void entry_destroy(Entry *ent) {
    switch (ent->type) {
    case T_ZSET:
        delete ent->zset;
    delete ent;

static void entry_del_async(void *arg) {
    entry_destroy((Entry *)arg);

// dispose the entry after it got detached from the key space
static void entry_del(Entry *ent) {
    entry_set_ttl(ent, -1);

    const size_t k_large_container_size = 10000;
    bool too_big = false;
    switch (ent->type) {
    case T_ZSET:
        too_big = hm_size(&ent->zset->hmap) > k_large_container_size;

    if (too_big) {
        thread_pool_queue(&, &entry_del_async, ent);
    } else {


  1. The semaphore is often introduced as a multi-threading primitive instead of the condition variable and the mutex. Try to implement the thread pool using the semaphore.
  2. Some fun exercises to help you understand these primitives further:
    1. Implement the mutex using the semaphore. (Trivial)
    2. Implement the semaphore using the condition variable. (Easy)
    3. Implement the condition variable using only mutexes. (Intermediate)
    4. Now that you know these primitives are somewhat equivalent, why should you prefer one to another?

Source code:

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