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//! A benchmarking harness for concurrent key-value collections. //! //! Say you have a concurrent collection (like a `HashMap`) and you want to measure how well it //! performs across different workloads. Does it collapse when there are many writes? Or when there //! are many threads? Or if there are concurrent removals? This crate tries to give you answers. //! //! `bustle` runs a concurrent mix of operations (a "workload") against your collection, measuring //! statistics as it goes, and gives you a report at the end about how you did. There are many //! parameters to tweak, but hopefully the documentation for each element will help you decide. You //! probably want to measure your collection against many different workloads, rather than just a //! single one. //! //! To run the benchmark, just implement [`Collection`] for your collection (`CollectionHandle` may //! end up just being a call to `clone`), build a [`Workload`], and call [`Workload::run`] //! parameterized by your collection type. You may want to look at the benchmarks for //! lock-protected collections from the standard library in `benches/` for inspiration. //! //! The crate is, at the time of writing, a pretty direct port of the [Universal Benchmark] from //! `libcuckoo`, though that may change over time. //! //! [Universal Benchmark]: https://github.com/efficient/libcuckoo/tree/master/tests/universal-benchmark #![deny(missing_docs)] #![warn( rust_2018_idioms, missing_debug_implementations, unreachable_pub, intra_doc_link_resolution_failure )] use rand::prelude::*; use std::sync::Arc; use tracing::{debug, info, info_span}; /// A workload mix configration. /// /// The sum of the fields must add to 100. #[derive(Clone, Copy, Debug)] pub struct Mix { /// The percentage of operations in the mix that are reads. pub read: u8, /// The percentage of operations in the mix that are inserts. pub insert: u8, /// The percentage of operations in the mix that are removals. pub remove: u8, /// The percentage of operations in the mix that are updates. pub update: u8, /// The percentage of operations in the mix that are update-or-inserts. pub upsert: u8, } impl Mix { /// Constructs a very read-heavy workload (~95%), with limited concurrent modifications. pub fn read_heavy() -> Self { Self { read: 94, insert: 2, update: 3, remove: 1, upsert: 0, } } /// Constructs a very insert-heavy workload (~80%), with some reads and updates. pub fn insert_heavy() -> Self { Self { read: 10, insert: 80, update: 10, remove: 0, upsert: 0, } } /// Constructs a very update-heavy workload (~50%), with some other modifications and the rest /// reads. pub fn update_heavy() -> Self { Self { read: 35, insert: 5, update: 50, remove: 5, upsert: 5, } } /// Constructs a workload where all operations occur with equal probability. pub fn uniform() -> Self { Self { read: 20, insert: 20, update: 20, remove: 20, upsert: 20, } } } /// A benchmark workload builder. #[derive(Clone, Copy, Debug)] pub struct Workload { /// The mix of operations to run. mix: Mix, /// The initial capacity of the table, specified as a power of 2. initial_cap_log2: u8, /// The fraction of the initial table capacity should we populate before running the benchmark. prefill_f: f64, /// Total number of operations as a multiple of the initial capacity. ops_f: f64, /// Number of threads to run the benchmark with. threads: usize, /// Random seed to randomize the workload. /// /// If `None`, the seed is picked randomly. /// If `Some`, the workload is deterministic if `threads == 1`. seed: Option<[u8; 16]>, } /// A collection that can be benchmarked by bustle. /// /// Any thread that performs operations on the collection will first call `pin` and then perform /// collection operations on the `Handle` that is returned. `pin` will not be called in the hot /// loop of the benchmark. pub trait Collection: Send + Sync + 'static { /// A thread-local handle to the concurrent collection under test. type Handle: CollectionHandle; /// Allocate a new instance of the benchmark target with the given capacity. fn with_capacity(capacity: usize) -> Self; /// Pin a thread-local handle to the concurrent collection under test. fn pin(&self) -> Self::Handle; } /// A handle to a key-value collection. /// /// Note that for all these methods, the benchmarker does not dictate what the values are. Feel /// free to use the same value for all operations, or use distinct ones and check that your /// retrievals indeed return the right results. pub trait CollectionHandle { /// The `u64` seeds used to construct `Key` (through `From<u64>`) are distinct. /// The returned keys must be as well. type Key: From<u64>; /// Perform a lookup for `key`. /// /// Should return `true` if the key is found. fn get(&mut self, key: &Self::Key) -> bool; /// Insert `key` into the collection. /// /// Should return `true` if no value previously existed for the key. fn insert(&mut self, key: &Self::Key) -> bool; /// Remove `key` from the collection. /// /// Should return `true` if the key existed and was removed. fn remove(&mut self, key: &Self::Key) -> bool; /// Update the value for `key` in the collection, if it exists. /// /// Should return `true` if the key existed and was updated. /// /// Should **not** insert the key if it did not exist. fn update(&mut self, key: &Self::Key) -> bool; } impl Workload { /// Start building a new benchmark workload. pub fn new(threads: usize, mix: Mix) -> Self { Self { mix, initial_cap_log2: 25, prefill_f: 0.0, ops_f: 0.75, threads, seed: None, } } /// Set the initial capacity for the map. /// /// Note that the capacity will be `2^` the given capacity! /// /// The number of operations and the number of pre-filled keys are determined based on the /// computed initial capacity, so keep that in mind if you change this parameter. /// /// Defaults to 25 (so `2^25 ~= 34M`). pub fn initial_capacity_log2(&mut self, capacity: u8) -> &mut Self { self.initial_cap_log2 = capacity; self } /// Set the fraction of the initial table capacity we should populate before running the /// benchmark. /// /// Defaults to 0%. pub fn prefill_fraction(&mut self, fraction: f64) -> &mut Self { assert!(fraction >= 0.0); assert!(fraction <= 1.0); self.prefill_f = fraction; self } /// Set the number of operations to run as a multiple of the initial capacity. /// /// This value can exceed 1.0. /// /// Defaults to 0.75 (75%). pub fn operations(&mut self, multiple: f64) -> &mut Self { assert!(multiple >= 0.0); self.ops_f = multiple; self } /// Set the seed used to randomize the workload. /// /// The seed does _not_ dictate thread interleaving, so you will only observe the exact same /// workload if you run the benchmark with `nthreads == 1`. pub fn seed(&mut self, seed: [u8; 16]) -> &mut Self { self.seed = Some(seed); self } /// Execute this workload against the collection type given by `T`. /// /// The key type must be `Send` since we generate the keys on a different thread than the one /// we do the benchmarks on. /// /// The key type must be `Debug` so that we can print meaningful errors if an assertion is /// violated during the benchmark. /// /// Returns the seed used for the run. #[allow(clippy::cognitive_complexity)] pub fn run<T: Collection>(&self) -> [u8; 16] where <T::Handle as CollectionHandle>::Key: Send + std::fmt::Debug, { assert_eq!( self.mix.read + self.mix.insert + self.mix.remove + self.mix.update + self.mix.upsert, 100, "mix fractions do not add up to 100%" ); let initial_capacity = 1 << self.initial_cap_log2; let total_ops = (initial_capacity as f64 * self.ops_f) as usize; let seed = self.seed.unwrap_or_else(rand::random); let mut rng: rand::rngs::SmallRng = rand::SeedableRng::from_seed(seed); // NOTE: it'd be nice to include std::intrinsics::type_name::<T> here let span = info_span!("benchmark", mix = ?self.mix, threads = self.threads); let _guard = span.enter(); debug!(initial_capacity, total_ops, ?seed, "workload parameters"); info!("generating operation mix"); let mut op_mix = Vec::with_capacity(100); op_mix.append(&mut vec![Operation::Read; usize::from(self.mix.read)]); op_mix.append(&mut vec![Operation::Insert; usize::from(self.mix.insert)]); op_mix.append(&mut vec![Operation::Remove; usize::from(self.mix.remove)]); op_mix.append(&mut vec![Operation::Update; usize::from(self.mix.update)]); op_mix.append(&mut vec![Operation::Upsert; usize::from(self.mix.upsert)]); op_mix.shuffle(&mut rng); info!("generating key space"); let prefill = (initial_capacity as f64 * self.prefill_f) as usize; // We won't be running through `op_mix` more than ceil(total_ops / 100), so calculate that // ceiling and multiply by the number of inserts and upserts to get an upper bound on how // many elements we'll be inserting. let max_insert_ops = (total_ops + 99) / 100 * usize::from(self.mix.insert + self.mix.upsert); let insert_keys = std::cmp::max(initial_capacity, max_insert_ops) + prefill; // Round this quantity up to a power of 2, so that we can use an LCG to cycle over the // array "randomly". let insert_keys_per_thread = insert_keys.next_power_of_two(); let mut generators = Vec::new(); for _ in 0..self.threads { let mut thread_seed = [0u8; 16]; rng.fill_bytes(&mut thread_seed[..]); generators.push(std::thread::spawn(move || { let mut rng: rand::rngs::SmallRng = rand::SeedableRng::from_seed(thread_seed); let mut keys: Vec<<T::Handle as CollectionHandle>::Key> = Vec::with_capacity(insert_keys_per_thread); keys.extend((0..insert_keys_per_thread).map(|_| rng.next_u64().into())); keys })); } let keys: Vec<_> = generators .into_iter() .map(|jh| jh.join().unwrap()) .collect(); info!("constructing initial table"); let table = Arc::new(T::with_capacity(initial_capacity)); // And fill it let prefill_per_thread = prefill / self.threads; let mut prefillers = Vec::new(); for keys in keys { let table = Arc::clone(&table); prefillers.push(std::thread::spawn(move || { let mut table = table.pin(); for key in &keys[0..prefill_per_thread] { let inserted = table.insert(key); assert!(inserted); } keys })); } let keys: Vec<_> = prefillers .into_iter() .map(|jh| jh.join().unwrap()) .collect(); info!("start workload mix"); let ops_per_thread = total_ops / self.threads; let op_mix: &'static [_] = Box::leak(op_mix.into_boxed_slice()); let start = std::time::Instant::now(); let mut mix_threads = Vec::with_capacity(self.threads); for keys in keys { let table = Arc::clone(&table); mix_threads.push(std::thread::spawn(move || { let mut table = table.pin(); mix( &mut table, &keys, op_mix, ops_per_thread, prefill_per_thread, ) })); } let _samples: Vec<_> = mix_threads .into_iter() .map(|jh| jh.join().unwrap()) .collect(); let took = start.elapsed(); let avg = took / total_ops as u32; info!(?took, ops = total_ops, ?avg, "workload mix finished"); // TODO: do more with this information // TODO: collect statistics per operation type eprintln!( "{} operations across {} thread(s) in {:?}; time/op = {:?}", total_ops, self.threads, took, avg ); seed } } #[derive(Clone, Copy, Debug, Eq, PartialEq)] enum Operation { Read, Insert, Remove, Update, Upsert, } fn mix<H: CollectionHandle>( tbl: &mut H, keys: &[H::Key], op_mix: &'static [Operation], ops: usize, prefilled: usize, ) where H::Key: std::fmt::Debug, { // Invariant: erase_seq <= insert_seq // Invariant: insert_seq < numkeys let nkeys = keys.len(); let mut erase_seq = 0; let mut insert_seq = prefilled; let mut find_seq = 0; // We're going to use a very simple LCG to pick random keys. // We want it to be _super_ fast so it doesn't add any overhead. assert!(nkeys.is_power_of_two()); assert!(nkeys > 4); assert_eq!(op_mix.len(), 100); let a = nkeys / 2 + 1; let c = nkeys / 4 - 1; let find_seq_mask = nkeys - 1; for (i, op) in (0..(ops / op_mix.len())) .flat_map(|_| op_mix.iter()) .enumerate() { if i == ops { break; } match op { Operation::Read => { let should_find = find_seq >= erase_seq && find_seq < insert_seq; let found = tbl.get(&keys[find_seq]); if find_seq >= erase_seq { assert_eq!( should_find, found, "get({:?}) {} {} {}", &keys[find_seq], find_seq, erase_seq, insert_seq ); } else { // due to upserts, we may _or may not_ find the key } // Twist the LCG since we used find_seq find_seq = (a * find_seq + c) & find_seq_mask; } Operation::Insert => { let new_key = tbl.insert(&keys[insert_seq]); assert!( new_key, "insert({:?}) should insert a new value", &keys[insert_seq] ); insert_seq += 1; } Operation::Remove => { if erase_seq == insert_seq { // If `erase_seq` == `insert_eq`, the table should be empty. let removed = tbl.remove(&keys[find_seq]); assert!( !removed, "remove({:?}) succeeded on empty table", &keys[find_seq] ); // Twist the LCG since we used find_seq find_seq = (a * find_seq + c) & find_seq_mask; } else { let removed = tbl.remove(&keys[erase_seq]); assert!(removed, "remove({:?}) should succeed", &keys[erase_seq]); erase_seq += 1; } } Operation::Update => { // Same as find, except we update to the same default value let should_exist = find_seq >= erase_seq && find_seq < insert_seq; let updated = tbl.update(&keys[find_seq]); if find_seq >= erase_seq { assert_eq!(should_exist, updated, "update({:?})", &keys[find_seq]); } else { // due to upserts, we may or may not have updated an existing key } // Twist the LCG since we used find_seq find_seq = (a * find_seq + c) & find_seq_mask; } Operation::Upsert => { // Pick a number from the full distribution, but cap it to the insert_seq, so we // don't insert a number greater than insert_seq. let n = std::cmp::min(find_seq, insert_seq); // Twist the LCG since we used find_seq find_seq = (a * find_seq + c) & find_seq_mask; let _inserted = tbl.insert(&keys[n]); if n == insert_seq { insert_seq += 1; } } } } }