Adding upstream version 5.7.1.upstream/5.7.1

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 189 insertions, 0 deletions

diff --git a/modules/predict/predict.lua b/modules/predict/predict.lua
new file mode 100644
index 0000000..0117fd5
--- /dev/null
+++ b/modules/predict/predict.lua
@@ -0,0 +1,189 @@
+-- SPDX-License-Identifier: GPL-3.0-or-later
+-- Speculative prefetching for repetitive and soon-expiring records to reduce latency.
+-- @module predict
+-- @field queue queue of scheduled queries
+-- @field queue_len number of scheduled queries
+-- @field period length of prediction history (number of windows)
+-- @field window length of the prediction window
+local predict = {
+	queue = {},
+	queue_len = 0,
+	batch = 0,
+	period = 24,
+	window = 15,
+	log = {},
+}
+
+
+-- Calculate next sample with jitter [1-2/5 of window]
+local function next_event()
+	local jitter = (predict.window * minute) / 5;
+	return math.random(jitter, 2 * jitter)
+end
+
+-- Calculate current epoch (which window fits current time)
+function predict.epoch()
+	if not predict.period or predict.period <= 1 then return nil end
+	return (os.date('%H')*(60/predict.window) +
+		math.floor(os.date('%M')/predict.window)) % predict.period + 1
+end
+
+-- Resolve queued records and flush the queue
+function predict.drain()
+	local deleted = 0
+	for key, _ in pairs(predict.queue) do
+		local qtype, qname = key:match('(%S*)%s(.*)')
+		resolve(qname, kres.type[qtype], kres.class.IN, {'NO_CACHE'})
+		predict.queue[key] = nil
+		deleted = deleted + 1
+		-- Resolve smaller batches at a time
+		if predict.batch > 0 and deleted >= predict.batch then
+			break
+		end
+	end
+	-- Schedule prefetch of another batch if not complete
+	if predict.ev_drain then event.cancel(predict.ev_drain) end
+	predict.ev_drain = nil
+	if deleted > 0 then
+		predict.ev_drain = event.after((predict.window * 3) * sec, predict.drain)
+	end
+	predict.queue_len = predict.queue_len - deleted
+	stats['predict.queue'] = predict.queue_len
+	collectgarbage('step')
+	return 0
+end
+
+-- Enqueue queries from same format as predict.queue or predict.log
+local function enqueue_from_log(current)
+	if not current then return 0 end
+	local queued = 0
+	for key, val in pairs(current) do
+		if val and not predict.queue[key] then
+			predict.queue[key] = val
+			queued = queued + 1
+		end
+	end
+	return queued
+end
+
+-- Sample current epoch, return number of sampled queries
+function predict.sample(epoch_now)
+	if not epoch_now then return 0, 0 end
+	local current = predict.log[epoch_now] or {}
+	local queries = stats.frequent()
+	stats.clear_frequent()
+	local nr_samples = #queries
+	for i = 1, nr_samples do
+		local entry = queries[i]
+		local key = string.format('%s %s', entry.type, entry.name)
+		current[key] = 1
+	end
+	predict.log[epoch_now] = current
+	return nr_samples
+end
+
+-- Predict queries for the upcoming epoch
+local function generate(epoch_now)
+	if not epoch_now then return 0 end
+	local queued = 0
+	for i = 1, predict.period / 2 - 1 do
+		local current = predict.log[(epoch_now - i - 1) % predict.period + 1]
+		local past = predict.log[(epoch_now - 2*i - 1) % predict.period + 1]
+		if current and past then
+			for k, _ in pairs(current) do
+				if past[k] ~= nil and not predict.queue[k] then
+					queued = queued + 1
+					predict.queue[k] = 1
+				end
+			end
+		end
+	end
+	return queued
+end
+
+function predict.process()
+	-- Start a new epoch, or continue sampling
+	local epoch_now = predict.epoch()
+	local nr_queued = 0
+
+	-- End of epoch
+	if predict.current_epoch ~= epoch_now then
+		stats['predict.epoch'] = epoch_now
+		predict.current_epoch = epoch_now
+		-- enqueue records from upcoming epoch
+		nr_queued = enqueue_from_log(predict.log[epoch_now])
+		-- predict next epoch
+		nr_queued = nr_queued + generate(epoch_now)
+		-- clear log for new epoch
+		predict.log[epoch_now] = {}
+	end
+
+	-- Sample current epoch
+	local nr_learned = predict.sample(epoch_now)
+
+	-- Dispatch predicted queries
+	if nr_queued > 0 then
+		predict.queue_len = predict.queue_len + nr_queued
+		predict.batch = predict.queue_len / 5
+		if not predict.ev_drain then
+			predict.ev_drain = event.after(0, predict.drain)
+		end
+	end
+
+	if predict.ev_sample then event.cancel(predict.ev_sample) end
+	predict.ev_sample = event.after(next_event(), predict.process)
+	if stats then
+		stats['predict.queue'] = predict.queue_len
+		stats['predict.learned'] = nr_learned
+	end
+	collectgarbage()
+end
+
+function predict.init()
+	if predict.window > 0 and predict.period > 0 then
+		predict.current_epoch = predict.epoch()
+		predict.ev_sample = event.after(next_event(), predict.process)
+	end
+end
+
+function predict.deinit()
+	if predict.ev_sample then event.cancel(predict.ev_sample) end
+	if predict.ev_drain then event.cancel(predict.ev_drain) end
+	predict.ev_sample = nil
+	predict.ev_drain = nil
+	predict.log = {}
+	predict.queue = {}
+	predict.queue_len = 0
+	collectgarbage()
+end
+
+function predict.config(config)
+	-- Reconfigure
+	config = config or {}
+	if type(config) ~= 'table' then
+		error('[predict] configuration must be a table or nil')
+	end
+	if config.window then predict.window = config.window end
+	if config.period then predict.period = config.period end
+	-- Load dependent modules
+	if (predict.period or 0) ~= 0 and not stats then modules.load('stats') end
+	-- Reinitialize to reset timers
+	predict.deinit()
+	predict.init()
+end
+
+predict.layer = {
+	-- Prefetch all expiring (sub-)queries immediately after the request finishes.
+	-- Doing that immediately is simplest and avoids creating (new) large bursts of activity.
+	finish = function (_, req)
+		local qrys = req.rplan.resolved
+		for i = 0, (tonumber(qrys.len) - 1) do -- size_t doesn't work for some reason
+			local qry = qrys.at[i]
+			if qry.flags.EXPIRING == true then
+				resolve(kres.dname2str(qry.sname), qry.stype, qry.sclass, {'NO_CACHE'})
+			end
+		end
+	end
+}
+
+return predict