use std::{path::PathBuf, sync::Arc};

use tuwunel_core::{
	Server, at, debug,
	debug::INFO_SPAN_LEVEL,
	debug_info, debug_warn, expected, info, is_equal_to,
	utils::{
		BoolExt,
		math::usize_from_f64,
		result::LogDebugErr,
		stream,
		stream::{AMPLIFICATION_LIMIT, WIDTH_LIMIT},
		sys::{
			compute::{available_parallelism, cores_available, is_core_available},
			max_threads, storage,
		},
	},
};

use super::{QUEUE_LIMIT, WORKER_LIMIT};

/// Determine storage hardware capabilities of the system for configuring the
/// shape of the database frontend threadpool.
///
/// Returns a tuple of:
/// - `topology` Vector mapping hardware cores to hardware queues. Systems with
///   fewer queues than cores will see queue ID's repeated. Systems with the
///   same or more queues as cores will usually see a 1:1 association of core
///   ID's to queue ID's. Systems with sparse core assignments will see 0 for
///   core ID positions not available to the process. Systems where detection
///   failed will see a default of 1:1 core identity as a best-guess maintaining
///   core locality.
/// - `workers` Vector mapping hardware queues to the number of threads to spawn
///   in service of that queue. Systems with fewer queues than cores will set an
///   affinity mask for each thread to multiple cores based on the topology.
///   Systems with equal or more hardware queues than cores will set a single
///   affinity for each thread.
/// - `queues` Vector of software mpmc queues to create and the size of each
///   queue. Each indice is associated with a thread-pool of workers which it
///   feeds requests from various tokio tasks. When this queue reaches capacity
///   the tokio task must yield.
#[tracing::instrument(
	level = INFO_SPAN_LEVEL,
	skip_all,
	ret(level = "trace"),
)]
pub(super) fn configure(server: &Arc<Server>) -> (Vec<usize>, Vec<usize>, Vec<usize>) {
	let config = &server.config;
	let num_cores = available_parallelism();

	// Determine the maximum number of cores. The total number of cores available to
	// the process may be less on systems with sparse core assignments, but this
	// still serves as an upper-bound.
	let cores_max = cores_available()
		.last()
		.unwrap_or(0)
		.saturating_add(1);

	// This finds the block device and gathers all the properties we need.
	let path: PathBuf = config.database_path.clone();
	let device_name = storage::name_from_path(&path)
		.log_debug_err()
		.ok();

	let devices = storage::md_discover(&path);
	let topology_detected = devices.md.is_empty().is_false();
	debug!(?topology_detected, ?device_name, ?devices);

	// The default worker count is masked-on if we didn't find better information.
	let default_worker_count = topology_detected
		.is_false()
		.then_some(config.db_pool_workers)
		.map(|workers| workers.saturating_mul(num_cores));

	// Sum the total number of possible tags. When no hardware detected this will
	// default to the default_worker_count. Note well that the thread-worker model
	// we use will never approach actual NVMe capacity as with io_uring or even
	// close to userspace drivers. We still take some cues from this value which
	// does give us actual request capacity.
	let total_tags = devices
		.md
		.iter()
		.flat_map(|md| md.mq.iter())
		.filter(|mq| mq.cpu_list.iter().copied().any(is_core_available))
		.filter_map(|mq| mq.nr_tags)
		.chain(default_worker_count)
		.fold(0_usize, usize::saturating_add);

	// Determine the CPU affinities of each hardware queue. Each indice is a core
	// and each value is the associated hardware queue. On systems which share
	// queues between cores some values will be repeated; on systems with multiple
	// queues per core the affinities are assumed to match and we don't require a
	// vector of vectors. Sparse unavailable cores default to 0. Undetected hardware
	// defaults to the core identity as a best-guess.
	let topology: Vec<usize> = devices
		.md
		.iter()
		.flat_map(|md| md.mq.iter())
		.fold(vec![0; cores_max], |mut topology, mq| {
			mq.cpu_list
				.iter()
				.filter(|&&id| id < cores_max)
				.filter(|&&id| is_core_available(id))
				.for_each(|&id| {
					topology[id] = mq.id;
				});

			topology
		})
		.into_iter()
		.enumerate()
		.map(|(core_id, queue_id)| {
			topology_detected
				.then_some(queue_id)
				.unwrap_or(core_id)
		})
		.collect();

	// Query getrlimit(2) to impose any additional restriction, divide to leave room
	// for other threads in the process.
	let max_threads = max_threads()
		.map(at!(0))
		.unwrap_or(usize::MAX)
		.saturating_div(3);

	// Determine an ideal max worker count based on true capacity. As stated prior
	// the true value is rarely attainable in any thread-worker model, and clamped.
	let max_workers = devices
		.md
		.iter()
		.flat_map(|md| md.mq.iter())
		.filter_map(|mq| mq.nr_tags)
		.chain(default_worker_count.into_iter())
		.fold(0_usize, usize::saturating_add)
		.min(config.db_pool_max_workers)
		.clamp(WORKER_LIMIT.0, max_threads)
		.clamp(WORKER_LIMIT.0, WORKER_LIMIT.1);

	// Tamper for the total number of workers by reducing the count for each group.
	let chan_limit = expected!(max_workers / num_cores)
		.saturating_sub(8)
		.saturating_add(1)
		.next_multiple_of(8);

	// Default workers vector without detection.
	let default_workers = default_worker_count
		.into_iter()
		.cycle()
		.enumerate()
		.map(|(core_id, count)| {
			is_core_available(core_id)
				.then_some(count)
				.unwrap_or(0)
				.min(chan_limit)
		});

	// Determine the worker groupings. Each indice represents a hardware queue and
	// contains the number of workers which will service it. This vector is
	// truncated to the number of cores on systems which have multiple hardware
	// queues per core. The number of workers is then truncated to a maximum for
	// each pool; as stated prior, this will usually be less than NVMe capacity.
	let workers: Vec<usize> = devices
		.md
		.iter()
		.inspect(|md| debug!(?md))
		.flat_map(|md| md.mq.iter())
		.map(|mq| {
			let shares = mq
				.cpu_list
				.iter()
				.filter(|&&id| is_core_available(id))
				.count();

			let conf_limit = config
				.db_pool_workers_limit
				.saturating_mul(shares);

			let hard_limit = devices
				.md
				.iter()
				.filter(|_| shares > 0)
				.fold(0_usize, |acc, mq| {
					mq.nr_requests
						.map(|nr| nr.min(conf_limit))
						.or(Some(conf_limit))
						.map(|nr| acc.saturating_add(nr))
						.unwrap_or(acc)
				});

			let tags = mq
				.nr_tags
				.unwrap_or(WORKER_LIMIT.0)
				.min(hard_limit)
				.min(chan_limit);

			debug!(?mq, ?shares, ?tags, ?conf_limit, ?hard_limit, ?chan_limit);

			tags
		})
		.chain(default_workers)
		.take(topology.len())
		.collect();

	// Determine our software queue size for each hardware queue. This is the mpmc
	// between the tokio worker and the pool worker.
	let queues: Vec<usize> = workers
		.iter()
		.map(|count| {
			count
				.saturating_mul(config.db_pool_queue_mult)
				.min(QUEUE_LIMIT.1)
		})
		.collect();

	// Total number of workers to spawn.
	let total_workers = workers.iter().sum::<usize>();

	// Total capacity of all software queues.
	let total_capacity = queues.iter().sum::<usize>();

	// Discount queues with zero capacity for a proper denominator.
	let num_queues = queues.iter().filter(|&&cap| cap > 0).count();

	// After computing all of the above we can update the global automatic stream
	// width, hopefully with a better value tailored to this system.
	if config.stream_width_scale > 0.0 {
		update_stream_width(server, num_queues, total_workers, total_capacity);
	}

	if topology_detected {
		debug_info!(?num_cores, ?topology, ?workers, ?queues, "Frontend topology",);
		info!(
			device_name = ?device_name.as_deref().unwrap_or("None"),
			?num_queues,
			?total_workers,
			?total_tags,
			?total_capacity,
			stream_width = ?stream::automatic_width(),
			amplification = ?stream::automatic_amplification(),
			"Frontend topology",
		);
	} else {
		debug_info!(?num_cores, ?topology, ?workers, ?queues, "Frontend topology (defaults)");
		debug_warn!(
			device_name = ?device_name.as_deref().unwrap_or("None"),
			?total_workers,
			?total_capacity,
			stream_width = ?stream::automatic_width(),
			amplification = ?stream::automatic_amplification(),
			"Storage hardware not detected for database directory; assuming defaults.",
		);
	}

	assert!(total_workers > 0, "some workers expected");
	debug_assert!(
		total_workers <= max_workers || !topology_detected,
		"spawning too many workers"
	);

	assert!(!queues.is_empty(), "some queues expected");
	assert!(!queues.iter().copied().all(is_equal_to!(0)), "positive queue capacity expected");

	(topology, workers, queues)
}

#[expect(clippy::as_conversions, clippy::cast_precision_loss)]
fn update_stream_width(
	server: &Arc<Server>,
	num_queues: usize,
	total_workers: usize,
	_total_capacity: usize,
) {
	assert!(num_queues > 0, "Expected at least one queue.");
	assert!(total_workers > 0, "Expected some workers.");

	let config = &server.config;
	let scale: f64 = config.stream_width_scale.min(100.0).into();
	let max_width = expected!(total_workers / num_queues);

	let old_width = stream::automatic_width();
	let old_scale_width = expected!(old_width * num_queues);

	let new_scale = total_workers as f64 / old_scale_width as f64;
	let new_scale = new_scale.clamp(1.0, 4.0);
	let new_scale_width = new_scale * old_width as f64;
	let new_scale_width = usize_from_f64(new_scale_width)
		.expect("failed to convert f64 to usize")
		.next_multiple_of(8);

	let req_width = usize_from_f64(scale * new_scale_width as f64)
		.expect("failed to convert f64 to usize")
		.next_multiple_of(4)
		.min(max_width)
		.clamp(WIDTH_LIMIT.0, WIDTH_LIMIT.1);

	let req_amp = new_scale * config.stream_amplification as f64;
	let req_amp = usize_from_f64(req_amp * scale)
		.expect("failed to convert f64 to usize")
		.next_multiple_of(64)
		.clamp(AMPLIFICATION_LIMIT.0, AMPLIFICATION_LIMIT.1);

	let (old_width, new_width) = stream::set_width(req_width);
	let (old_amp, new_amp) = stream::set_amplification(req_amp);
	debug!(
		config_scale = ?config.stream_width_scale,
		?old_width,
		?new_scale,
		?new_width,
		?old_amp,
		?new_amp,
		"Updated global stream width"
	);
}