My favorite implementation of segment trees has always been "Easy and Efficient Segment Trees, by Al.Cash. You can implement standard segtree with it, you can implement lazy propagation, etc. It suffices for the vast majority of segtree problems. However, there are some types of segtree that you can't implement in that fashion, namely dynamic segtrees and persistent segtrees. See here for criticism. With the advent of policy hash tables, however, one can now implement dynamic segtrees in Al.Cash's style with somewhat comparable performance to a custom dynamic segtree.

Standard segtree

This is how a standard segtree looks like. You can set a single element, and query for ranges. It's nice and simple, and I think it's a great implementation.

int N;
int seg[2 * MAXN];

void modify(int p, int val) {
    for (seg[p += N] = val; p > 0; p >>= 1)
        seg[p >> 1] = seg[p] + seg[p ^ 1];
}

int query(int l, int r) {
    int res = 0;
    for (l += N, r += N; l < r; l >>= 1, r >>= 1) {
        if (l & 1)
            res += seg[l++];
        if (r & 1)
            res += seg[--r];
    }
    return res;
}

Dynamic Segtree

However, say your underlying array had 1e9 possible locations, but it only contained 1e5 elements. For example, take a look at this post. Obviously, you can't store all 2e9 elements in your segtree, so what should you do? Here's one solution, replace the array with a hash table. However, as adamant mentions, unordered_map has too much overhead. We'll be benchmarking against the dynamic segtree provided here. I'll also be using a custom hash function. So to be clear, the implementation now looks like:

int N;
unordered_map<int, int, chash> seg;

void modify(int p, int val) {
    for (seg[p += N] = val; p > 0; p >>= 1)
        seg[p >> 1] = seg[p] + seg[p ^ 1];
}

int query(int l, int r) {
    int res = 0;
    for (l += N, r += N; l < r; l >>= 1, r >>= 1) {
        if (l & 1)
            res += seg[l++];
        if (r & 1)
            res += seg[--r];
    }
    return res;
}

And benchmarking it with 1e5 random insertions and 1e5 random queries.

pointer: 0.171485
unordered_map: 2.0646

Wow. The unordered_map is nearly 12x slower. That's not really feasible for a lot of contests. What if we replace it with a policy hash table, though?

int N;
unordered_map<int, int, chash> seg;

void modify(int p, int val) {
    for (seg[p += N] = val; p > 0; p >>= 1)
        seg[p >> 1] = seg[p] + seg[p ^ 1];
}

int query(int l, int r) {
    int res = 0;
    for (l += N, r += N; l < r; l >>= 1, r >>= 1) {
        if (l & 1)
            res += seg[l++];
        if (r & 1)
            res += seg[--r];
    }
    return res;
}

pointer: 0.202186
policy hash table: 0.384312

Only a 2x decrease in speed. That's already very feasible. However, one might notice that since maps in C++ create elements if you try to access a key that doesn't exist, we're creating a lot of useless elements. Thus, we can simply wrap a check to make sure the element is in the array before we try to access it.

gp_hash_table<ll, ll, chash> seg;

ll get(ll x) { return (seg.find(x) == seg.end()) ? 0 : seg[x]; }
void modify(ll p, ll val) {
    for (seg[p += N] = val; p > 0; p >>= 1) {
        seg[p >> 1] = get(p) + get(p ^ 1);
    }
}

ll query(ll l, ll r) {
    ll res = 0;
    for (l += N, r += N; l < r; l >>= 1, r >>= 1) {
        if (l & 1)
            res += get(l++);
        if (r & 1)
            res += get(--r);
    }
    return res;
}

Results:

policy hash table: 0.310953
pointer: 0.208667

Only 1.5x slower!

One more thing. Although it may seem like this implementation is just 1.5x slower, that's a little bit misleading. If we break down the numbers between insertion/querying, we see this:

policy hash table insertion: 0.24153
pointer insertion: 0.048696

policy hash table queries: 0.08687
pointer queries: 0.133678

So the custom implementation is actually 50% slower than the policy hash table one for queries. Not too bad.