This series is another approach of https://github.com/scylladb/scylladb/pull/18646 and https://github.com/scylladb/scylladb/pull/19181. In this series we only change where the view backlog gets
updated - we do not assure that the view update backlog returned in a response is necessarily the backlog
that increased due to the corresponding write, the returned backlog may be outdated up to 10ms. Because
this series does not include this change, it's considerably less complex and it doesn't modify the common
write patch, so no particular performance considerations were needed in that context. The issue being fixed
is still the same, the full description can be seen below.
When a replica applies a write on a table which has a materialized view
it generates view updates. These updates take memory which is tracked
by `database::_view_update_concurrency_sem`, separate on each shard.
The fraction of units taken from the semaphore to the semaphore limit
is the shard's view update backlog. Based on these backlogs, we want
to estimate how busy a node is with its view updates work. We do that
by taking the max backlog across all shards.
To avoid excessive cross-shard operations, the node's (max) backlog isn't
calculated each time we need it, but up to 1 time per 10ms (the `_interval`) with an optimization where the backlog of the calculating shard is immediately up-to-date (we don't need cross-shard operations for it):
```
update_backlog node_update_backlog::fetch() {
auto now = clock::now();
if (now >= _last_update.load(std::memory_order_relaxed) + _interval) {
_last_update.store(now, std::memory_order_relaxed);
auto new_max = boost::accumulate(
_backlogs,
update_backlog::no_backlog(),
[] (const update_backlog& lhs, const per_shard_backlog& rhs) {
return std::max(lhs, rhs.load());
});
_max.store(new_max, std::memory_order_relaxed);
return new_max;
}
return std::max(fetch_shard(this_shard_id()), _max.load(std::memory_order_relaxed));
}
```
For the same reason, even when we do calculate the new node's backlog,
we don't read from the `_view_update_concurrency_sem`. Instead, for
each shard we also store a update_backlog atomic which we use for
calculation:
```
struct per_shard_backlog {
// Multiply by 2 to defeat the prefetcher
alignas(seastar::cache_line_size * 2) std::atomic<update_backlog> backlog = update_backlog::no_backlog();
need_publishing need_publishing = need_publishing::no;
update_backlog load() const {
return backlog.load(std::memory_order_relaxed);
}
};
std::vector<per_shard_backlog> _backlogs;
```
Due to this distinction, the update_backlog atomic need to be updated
separately, when the `_view_update_concurrency_sem` changes.
This is done by calling `storage_proxy::update_view_update_backlog`, which reads the `_view_update_concurrency_sem` of the shard (in `database::get_view_update_backlog`)
and then calls node`_update_backlog::add` where the read backlog
is stored in the atomic:
```
void storage_proxy::update_view_update_backlog() {
_max_view_update_backlog.add(get_db().local().get_view_update_backlog());
}
void node_update_backlog::add(update_backlog backlog) {
_backlogs[this_shard_id()].backlog.store(backlog, std::memory_order_relaxed);
_backlogs[this_shard_id()].need_publishing = need_publishing::yes;
}
```
For this implementation of calculating the node's view update backlog to work,
we need the atomics to be updated correctly when the semaphores of corresponding
shards change.
The main event where the view update backlog changes is an incoming write
request. That's why when handling the request and preparing a response
we update the backlog calling `storage_proxy::get_view_update_backlog` (also
because we want to read the backlog and send it in the response):
backlog update after local view updates (`storage_proxy::send_to_live_endpoints` in `mutate_begin`)
```
auto lmutate = [handler_ptr, response_id, this, my_address, timeout] () mutable {
return handler_ptr->apply_locally(timeout, handler_ptr->get_trace_state())
.then([response_id, this, my_address, h = std::move(handler_ptr), p = shared_from_this()] {
// make mutation alive until it is processed locally, otherwise it
// may disappear if write timeouts before this future is ready
got_response(response_id, my_address, get_view_update_backlog());
});
};
backlog update after remote view updates (storage_proxy::remote::handle_write)
auto f = co_await coroutine::as_future(send_mutation_done(netw::messaging_service::msg_addr{reply_to, shard}, trace_state_ptr,
shard, response_id, p->get_view_update_backlog()));
```
Now assume that on a certain node we have a write request received on shard A,
which updates a row on shard B (A!=B). As a result, shard B will generate view
updates and consume units from its `_view_update_concurrency_sem`, but will
not update its atomic in `_backlogs` yet. Because both shards in the example
are on the same node, shard A will perform a local write calling `lmutate` shown
above. In the `lmutate` call, the `apply_locally` will initiate the actual write on
shard B and the `storage_proxy::update_view_update_backlog` will be called back
on shard A. In no place will the backlog atomic on shard B get updated even
though it increased in size due to the view updates generated there.
Currently, what we calculate there doesn't really matter - it's only used for the
MV flow control delays, so currently, in this scenario, we may only overload
a replica causing failed replica writes which will be later retried as hints. However,
when we add MV admission control, the calculated backlog will be the difference
between an accepted and a rejected request.
Fixes: https://github.com/scylladb/scylladb/issues/18542
Without admission control (https://github.com/scylladb/scylladb/pull/18334), this patch doesn't affect much, so I'm marking it as backport/none
Closesscylladb/scylladb#19341
* github.com:scylladb/scylladb:
test: add test for view backlog not being updated on correct shard
test: move auxiliary methods for waiting until a view is built to util
mv: update view update backlog when it increases on correct shard
96dff367f8