#![expect( clippy::float_arithmetic, reason = "One should expect floating point arithmetic in statistic calculations" )] #![expect( clippy::cast_precision_loss, reason = "Operated numbers is not big enough to have precision loss" )] use std::{collections::HashMap, path::Path, sync::Arc}; use anyhow::{Context as _, Result}; use common::{HashType, transaction::LeeTransaction}; use itertools::Itertools as _; use lee_core::BlockId; use sequencer_service_rpc::{RpcClient as _, SequencerClient, SequencerClientBuilder}; use serde::{Deserialize, Serialize}; use tokio::{sync::RwLock, task::JoinSet}; use url::Url; use crate::{ ExecutionFailureKind, config::{MultiSequencerClientConfig, SequencerConnectionData}, }; #[derive(Debug, Clone, Serialize, Deserialize)] pub struct Statistics { pub latency_avg: f32, pub latency_var: f32, pub sample_size: usize, pub latest_block_id: BlockId, pub errors: u64, } #[derive(Debug, Clone)] pub enum StatisticsUpdate { Success { latency: f32, new_latest_block_id: BlockId, }, Failure, } impl Statistics { pub fn apply_updates(&mut self, updates: &[StatisticsUpdate]) { let CumulativeUpdates { failure_count, latest_block_id, cumulative_latency, cumulative_latency_squares, additional_sample_size, } = CumulativeUpdates::from_metric_updates(updates); self.errors = self.errors.saturating_add(failure_count); if let Some(latest_block_id) = latest_block_id { self.latest_block_id = latest_block_id; } #[expect(clippy::as_conversions, reason = "int to float conversion is safe")] let orig_size_f = self.sample_size as f32; #[expect(clippy::as_conversions, reason = "int to float conversion is safe")] let mod_size_f = additional_sample_size as f32; let latency_avg_old = self.latency_avg; let latency_avg_new = cumulative_avg(latency_avg_old, cumulative_latency, orig_size_f, mod_size_f); let latency_var_new = cumulative_var( latency_avg_old, latency_avg_new, self.latency_var, cumulative_latency, cumulative_latency_squares, orig_size_f, mod_size_f, ); self.latency_avg = latency_avg_new; self.latency_var = latency_var_new; self.sample_size = self.sample_size.saturating_add(additional_sample_size); } } #[derive(Clone)] pub struct MultiSequencerClient { /// Ordered list of leaders, from best to worst. leader_list: Vec<(SequencerClient, Url)>, config: MultiSequencerClientConfig, /// Wrapping statistic updates in Arc to not break interfaces too much. /// /// It is assumed, that wallet methods can be accesed via immutable reference. statistic_updates: Arc>>>, } impl MultiSequencerClient { async fn setup( conn_data: &[SequencerConnectionData], statistics: &mut HashMap, multi_sequencer_client_config: &MultiSequencerClientConfig, ) -> Result> { if !conn_data .iter() .map(|conn| &conn.sequencer_addr) .all_unique() { anyhow::bail!("All addresses must be unique"); } let mut actualization_list = vec![]; let mut calibration_list = vec![]; let mut client_list = vec![]; for SequencerConnectionData { sequencer_addr, basic_auth, } in conn_data { let sequencer_client = { let mut builder = SequencerClientBuilder::default(); if let Some(basic_auth) = &basic_auth { builder = builder.set_headers( std::iter::once(( "Authorization".parse().expect("Header name is valid"), format!("Basic {basic_auth}") .parse() .context("Invalid basic auth format")?, )) .collect(), ); } builder .build(sequencer_addr) .context("Failed to create sequencer client")? }; if statistics.contains_key(sequencer_addr) { actualization_list.push((sequencer_addr.clone(), sequencer_client.clone())); } else { calibration_list.push((sequencer_addr.clone(), sequencer_client.clone())); } client_list.push((sequencer_addr.clone(), sequencer_client)); } // This actually starts in one thread, but each exact future produces more tasks. let (actualization_res, callibration_res) = tokio::join!( multi_actualize_clients(actualization_list), multi_calibrate_clients( calibration_list, multi_sequencer_client_config.calibration_limit ) ); for (addr, statistic_opt) in callibration_res { if let Some(statistic) = statistic_opt { statistics.insert(addr, statistic); } } for (addr, statistic_update_opt) in actualization_res { if let Some(statistic_mut) = statistics.get_mut(&addr) && let Some(statistic_update) = statistic_update_opt { statistic_mut.apply_updates(&[statistic_update]); } } let leader_list = choose_leaders( client_list, statistics, multi_sequencer_client_config.distribution_limit, ) .ok_or_else(|| anyhow::anyhow!("Failed to find leader"))?; if leader_list.is_empty() { anyhow::bail!( "Leader search algorithm failure: Sorted out all clients during leader search" ); } log::info!( "Chosen leaders is {:?}", leader_list.iter().map(|(_, addr)| addr).collect::>() ); Ok(leader_list) } pub async fn new( conn_data: &[SequencerConnectionData], statistics: &mut HashMap, multi_sequencer_client_config: MultiSequencerClientConfig, ) -> Result { let leader_list = Self::setup(conn_data, statistics, &multi_sequencer_client_config).await?; Ok(Self { leader_list, config: multi_sequencer_client_config, statistic_updates: Arc::new(RwLock::new(HashMap::new())), }) } /// Re-choose leader, `statistic_updates` must be empty. pub async fn rotate( &mut self, conn_data: &[SequencerConnectionData], statistics: &mut HashMap, multi_sequencer_client_config: &MultiSequencerClientConfig, ) -> Result<()> { let leader_list = Self::setup(conn_data, statistics, multi_sequencer_client_config).await?; log::info!( "Chosen leaders is {:?}", leader_list.iter().map(|(_, addr)| addr).collect::>() ); self.leader_list = leader_list; Ok(()) } #[must_use] pub fn leaders(&self) -> &[(SequencerClient, Url)] { self.leader_list.as_ref() } #[must_use] pub fn helm(&self) -> &(SequencerClient, Url) { self.leader_list .first() .expect("At least one leader must be set") } #[must_use] pub const fn config(&self) -> &MultiSequencerClientConfig { &self.config } /// Helperfunction for the `metered_get`. async fn metered_get_helper Result>( &self, call: &I, leader: &SequencerClient, leader_url: &Url, statistic_map: &mut HashMap>, ) -> Result { let (resp, statistics_update) = tokio::join!(call(leader), actualize_client(leader.clone())); log::debug!("Metered call for {leader_url:?}, statistic updates is {statistics_update:?}",); statistic_map .entry(leader_url.clone()) .or_default() .push(statistics_update); resp } /// Metered call for main leader(helm), to get data, necessary for send call. /// /// If current leader errors, we ask next one in list up to a `self.config.distribution_limit`. pub async fn metered_get Result>( &self, call: I, ) -> Result { // Collecting all statistics into one map to lock updates only once let mut statistic_map: HashMap> = HashMap::new(); // We need helm response to avoid constructing guard Result, which we can not do with // generics. let (helm, helm_url) = self.helm(); let mut resp = self .metered_get_helper(&call, helm, helm_url, &mut statistic_map) .await; // Not the cleanest approach, but I am not sure how to have it both clean and async. if resp.is_err() { for (leader, leader_url) in self.leaders().iter().skip(1) { resp = self .metered_get_helper(&call, leader, leader_url, &mut statistic_map) .await; if resp.is_ok() { break; } } } { let mut statistic_updates_guard = self.statistic_updates.write().await; #[expect( clippy::iter_over_hash_type, reason = "Ordering of map updates is not important" )] for (url, updates) in statistic_map { statistic_updates_guard .entry(url) .or_insert_with(Vec::new) .extend(updates); } } resp } /// Metered `send_transaction` for `distribution_limit` amount of leaders. /// /// Less abstract than it could be, clean way to implement it in a more general way is /// "return-type notation". /// /// `ToDo`: Return to it, when "return-type notation" is stable. pub async fn metered_send_transaction( &self, tx: LeeTransaction, ) -> Vec> { // Collecting all statistics into one map to lock updates only once let mut statistic_map: HashMap> = HashMap::new(); let mut results = vec![]; let mut join_set = JoinSet::new(); for (leader, leader_url) in self.leaders() { let curr_leader = leader.clone(); let curr_tx = tx.clone(); let curr_url = leader_url.clone(); join_set.spawn(async move { ( tokio::join!( curr_leader.send_transaction(curr_tx), actualize_client(curr_leader.clone()) ), curr_url, ) }); } while let Some(resp) = join_set.join_next().await { let res = resp .inspect_err(|j_err| log::warn!("Task failed with join error: {j_err:?}")) .map_err(Into::into) .and_then(|((resp, statistics_update), leader_url)| { log::debug!( "Metered call for {leader_url:?}, statistic updates is {statistics_update:?}", ); statistic_map .entry(leader_url) .or_default() .push(statistics_update); resp.map_err(Into::into) }); results.push(res); } { let mut statistic_updates_guard = self.statistic_updates.write().await; #[expect( clippy::iter_over_hash_type, reason = "Ordering of map updates is not important" )] for (url, updates) in statistic_map { statistic_updates_guard .entry(url) .or_insert_with(Vec::new) .extend(updates); } } results } /// Update statistics of a leader, clear statistic updates log. pub async fn update_statistics(&self, statistics: &mut HashMap) -> Result<()> { let mut statistic_updates = self.statistic_updates.write().await; #[expect(clippy::iter_over_hash_type, reason = "Ordering is unnecesary here")] for (addr, statistic_updates_vec) in statistic_updates.iter() { let leader_statistic = statistics .get_mut(addr) .ok_or_else(|| anyhow::anyhow!("Leader statistic must be present after setup"))?; leader_statistic.apply_updates(statistic_updates_vec.as_slice()); } statistic_updates.clear(); Ok(()) } } struct CumulativeUpdates { pub failure_count: u64, pub latest_block_id: Option, /// Necessary for cumulative average calculation. pub cumulative_latency: f32, /// Necessary for cumulative variance calculation. pub cumulative_latency_squares: f32, pub additional_sample_size: usize, } impl CumulativeUpdates { fn from_metric_updates(metric_updates: &[StatisticsUpdate]) -> Self { let (failure_count, latest_block_id, cumulative_latency, cumulative_latency_squares) = metric_updates .iter() .fold((0_u64, None, 0_f32, 0_f32), |mut acc, x| { match x { StatisticsUpdate::Success { latency, new_latest_block_id, } => { match acc.1 { Some(val_old) => { acc.1 = Some(std::cmp::max(val_old, *new_latest_block_id)); } None => { acc.1 = Some(*new_latest_block_id); } } acc.2 += latency; acc.3 = latency.mul_add(*latency, acc.3); } StatisticsUpdate::Failure => { acc.0 = acc.0.saturating_add(1); } } acc }); Self { failure_count, latest_block_id, cumulative_latency, cumulative_latency_squares, additional_sample_size: metric_updates.len().saturating_sub( usize::try_from(failure_count).expect("Sample size should fit usize"), ), } } } pub fn extract_statistics_from_path( path: &Path, ) -> Result, anyhow::Error> { match std::fs::File::open(path) { Ok(file) => { let reader = std::io::BufReader::new(file); Ok(serde_json::from_reader(reader)?) } Err(err) if err.kind() == std::io::ErrorKind::NotFound => { println!("Statistics not found, choosing empty"); Ok(HashMap::new()) } Err(err) => Err(err).context("IO error"), } } /// Measuring `get_last_block_id` as it should be the fastest request on sequencer. async fn measure_request_duration(client: &SequencerClient) -> (u128, Option) { let now = tokio::time::Instant::now(); let block_id = client.get_last_block_id().await.ok(); ( tokio::time::Instant::now().duration_since(now).as_millis(), block_id, ) } /// Calibrate statistics for one client. Takes `client` by value deliberately, cloning /// `SequencerClient` is cheap. async fn calibrate_client(client: SequencerClient, calibration_limit: usize) -> Option { let mut latencies = vec![]; let mut latest_block_id = 0; let mut errors: u64 = 0; // ToDo: Add some DDoS adaptation for _ in 0..calibration_limit { let (latency, block_id) = measure_request_duration(&client).await; let Some(block_id) = block_id else { errors = errors.saturating_add(1); continue; }; latest_block_id = block_id; latencies.push(latency); } // There is no point in guard numbers, exclude client if it fails all requests. if latencies.is_empty() { return None; } // Precision loss is fine there let sample_size = latencies.len(); #[expect(clippy::as_conversions, reason = "int to float conversion is safe")] let latency_avg = (latencies.iter().sum::() as f32) / (sample_size as f32); #[expect(clippy::as_conversions, reason = "int to float conversion is safe")] let latency_var = latencies.iter().fold(0_f32, |acc, x| { ((*x as f32) - latency_avg).mul_add((*x as f32) - latency_avg, acc) }) / (sample_size as f32); Some(Statistics { latency_avg, latency_var, sample_size, latest_block_id, errors, }) } /// Actualize statistics for one client. Takes `client` by value deliberately, cloning /// `SequencerClient` is cheap. async fn actualize_client(client: SequencerClient) -> StatisticsUpdate { let (latency, block_id) = measure_request_duration(&client).await; #[expect(clippy::as_conversions, reason = "int to float conversion is safe")] let latency = latency as f32; block_id.map_or(StatisticsUpdate::Failure, |new_latest_block_id| { StatisticsUpdate::Success { latency, new_latest_block_id, } }) } // Next 2 functions can be done in uniform way right now, but it will be incredibly cursed. // ToDo: Return to it when "return-type notation" is stable async fn multi_actualize_clients( clients: Vec<(Url, SequencerClient)>, ) -> Vec<(Url, Option)> { let mut handle_map = HashMap::new(); for (url, client) in clients { // `client` here must have 'static lifetime, so we can not use reference let actualization_task = tokio::task::spawn(actualize_client(client)); handle_map.insert(url, actualization_task); } let mut statistic_updates = vec![]; // We anyhow need results of each one #[expect( clippy::iter_over_hash_type, reason = "Ordering of map updates is not important" )] for (url, task) in handle_map { let task_opt = task.await.ok(); statistic_updates.push((url, task_opt)); } statistic_updates } async fn multi_calibrate_clients( clients: Vec<(Url, SequencerClient)>, calibration_limit: usize, ) -> Vec<(Url, Option)> { let mut handle_map = HashMap::new(); for (url, client) in clients { // `client` here must have 'static lifetime, so we can not use reference let calibration_task = tokio::task::spawn(calibrate_client(client, calibration_limit)); handle_map.insert(url, calibration_task); } let mut statistics = vec![]; // We anyhow need results of each one #[expect( clippy::iter_over_hash_type, reason = "Ordering of map updates is not important" )] for (url, task) in handle_map { let task_opt = task .await .ok() .inspect(|val| { if val.is_none() { log::warn!( "Client {url:?} failed all {calibration_limit} calibration attempts, it may be unhealthy. \n Consider bumping calibration_limit or remove this client altogether" ); } }) .flatten(); statistics.push((url, task_opt)); } statistics } /// Choosing leaders up to a `distribution_limit`. /// /// Assumes that all clients have their statistics in `statistics`. #[must_use] fn choose_leaders( client_vec: Vec<(Url, SequencerClient)>, statistics: &HashMap, distribution_limit: usize, ) -> Option> { // Sort out all unmetered clients let client_vec: Vec<_> = client_vec .into_iter() .filter(|item| statistics.contains_key(&item.0)) .collect(); if client_vec.is_empty() { return None; } // Considering the nature of our requests, the latest_block_id is the dominant characteristic let max_block_id_addr = client_vec .iter() .fold(client_vec.first().unwrap(), |acc, x| { let old_latest_block_id = statistics.get(&acc.0).unwrap().latest_block_id; let new_latest_block_id = statistics.get(&x.0).unwrap().latest_block_id; if new_latest_block_id > old_latest_block_id { x } else { acc } }); let max_block_id = statistics .get(&max_block_id_addr.0) .unwrap() .latest_block_id; // Sort out all clients running late let mut res_vec: Vec<_> = client_vec .into_iter() .filter(|x| { let latest_block_id = statistics.get(&x.0).unwrap().latest_block_id; latest_block_id == max_block_id }) .collect(); // Get the clients with lesser or equal to average error ratio #[expect(clippy::as_conversions, reason = "int to float conversion is safe")] let avg_err_ratio = res_vec.iter().fold(0_f32, |acc, x| { acc + error_ratio( statistics.get(&x.0).unwrap().errors, statistics.get(&x.0).unwrap().sample_size, ) }) / (res_vec.len() as f32); res_vec.sort_by(|a, b| { let err_ratio_a = error_ratio( statistics.get(&a.0).unwrap().errors, statistics.get(&a.0).unwrap().sample_size, ); let err_ratio_b = error_ratio( statistics.get(&b.0).unwrap().errors, statistics.get(&b.0).unwrap().sample_size, ); err_ratio_a .partial_cmp(&err_ratio_b) .expect("Ratios must be a valid numbers") }); res_vec.truncate( res_vec .iter() .position(|item| { error_ratio( statistics.get(&item.0).unwrap().errors, statistics.get(&item.0).unwrap().sample_size, ) > avg_err_ratio }) .unwrap_or(res_vec.len()), ); // Choose clients with least latency and variance res_vec.sort_by(|a, b| { let left = statistics.get(&a.0).unwrap(); let (left_lat, left_var) = (left.latency_avg, left.latency_var); let right = statistics.get(&b.0).unwrap(); let (right_lat, right_var) = (right.latency_avg, right.latency_var); let right_std = right_var.sqrt(); let left_std = left_var.sqrt(); // Client is better if its average + std is lesser: // [-right_std < right_lat < +left_std < +right_std] (left_lat + left_std).total_cmp(&(right_lat + right_std)) }); Some( res_vec .into_iter() .map(|(a, b)| (b, a)) .take(distribution_limit) .collect(), ) } /// Helperfunction to calculate cumulative average. /// /// Cumulative average calculation is the following problem: /// /// We want to calculate avarage of a sample of size `N + N_1` /// where average for `N` is known. /// /// To do so we need: /// - old average value /// - sum_{`i=1}^{N_1}{n_i`} /// - `N` /// - `N_1` fn cumulative_avg( latency_avg_old: f32, cumulative_latency: f32, orig_size_f: f32, mod_size_f: f32, ) -> f32 { latency_avg_old.mul_add(orig_size_f, cumulative_latency) / (orig_size_f + mod_size_f) } /// Helperfunction to calculate cumulative variance. /// /// Cumulative variance calculation is the following problem: /// /// We want to calculate variance of a sample of size `N + N_1` /// where average for `N` is known. /// /// To do so we need: /// - old average value /// - new average value /// - old variance /// - sum_{`i=1}^{N_1}{n_i`} /// - sum_{`i=1}^{N_1}{n_i^2`} /// - `N` /// - `N_1` fn cumulative_var( latency_avg_old: f32, latency_avg_new: f32, latency_var: f32, cumulative_latency: f32, cumulative_latency_squares: f32, orig_size_f: f32, mod_size_f: f32, ) -> f32 { // The formula was atrocious before. // `mul_add` function have less precision loss with drawback of being absolutely unreadable ((2_f32 * cumulative_latency).mul_add( -latency_avg_new, mod_size_f.mul_add( latency_avg_new * latency_avg_new, latency_var.mul_add( orig_size_f, (latency_avg_new - latency_avg_old) * (latency_avg_new - latency_avg_old) * orig_size_f, ), ), ) + cumulative_latency_squares) / (orig_size_f + mod_size_f) } fn error_ratio(errors: u64, size: usize) -> f32 { #[expect(clippy::as_conversions, reason = "int to float conversion is safe")] let errors_f = errors as f32; #[expect(clippy::as_conversions, reason = "int to float conversion is safe")] let size_f = size as f32; errors_f / (errors_f + size_f) } #[cfg(test)] mod tests { use std::collections::{HashMap, HashSet}; use sequencer_service_rpc::{SequencerClient, SequencerClientBuilder}; use url::Url; use crate::multi_client::{ CumulativeUpdates, Statistics, StatisticsUpdate, choose_leaders, cumulative_avg, cumulative_var, }; fn update_statistics( statistics: &mut HashMap, leader_url: &Url, metric_updates: &[StatisticsUpdate], ) -> Result<(), anyhow::Error> { let leader_metric = statistics .get_mut(leader_url) .ok_or_else(|| anyhow::anyhow!("Leader URL is not present in statistics"))?; leader_metric.apply_updates(metric_updates); Ok(()) } fn client_from_url_unchecked(url: &Url) -> SequencerClient { let builder = SequencerClientBuilder::default(); builder.build(url).unwrap() } fn four_client_list() -> (Vec<(Url, SequencerClient)>, [Url; 4]) { let addr_leader = Url::parse("http://127.0.0.1:3040").unwrap(); let addr_1 = Url::parse("http://127.0.0.1:3041").unwrap(); let addr_2 = Url::parse("http://127.0.0.1:3042").unwrap(); let addr_3 = Url::parse("http://127.0.0.1:3043").unwrap(); let leader = client_from_url_unchecked(&addr_leader); let client_1 = client_from_url_unchecked(&addr_1); let client_2 = client_from_url_unchecked(&addr_2); let client_3 = client_from_url_unchecked(&addr_3); let client_list = vec![ (addr_leader.clone(), leader), (addr_1.clone(), client_1), (addr_2.clone(), client_2), (addr_3.clone(), client_3), ]; (client_list, [addr_leader, addr_1, addr_2, addr_3]) } #[test] fn cumulative_updates_test() { let statistics_updates_vec = vec![ StatisticsUpdate::Success { latency: 100_f32, new_latest_block_id: 15, }, StatisticsUpdate::Success { latency: 115_f32, new_latest_block_id: 16, }, StatisticsUpdate::Failure, ]; let CumulativeUpdates { failure_count, latest_block_id, cumulative_latency, cumulative_latency_squares, additional_sample_size, } = CumulativeUpdates::from_metric_updates(&statistics_updates_vec); let epsilon = 0.01_f32; let sum_squared_manual = 100_f32.mul_add(100_f32, 115_f32 * 115_f32); assert_eq!(additional_sample_size, 2); assert_eq!(failure_count, 1); assert_eq!(latest_block_id, Some(16)); assert!((cumulative_latency - 215_f32).abs() < epsilon); assert!((cumulative_latency_squares - sum_squared_manual).abs() < epsilon); } #[test] fn cumulative_avg_test() { let mut sample = vec![100_f32; 40]; #[expect(clippy::as_conversions, reason = "int to float conversion is safe")] let old_sample_size_f = sample.len() as f32; let old_avg = sample.iter().sum::() / old_sample_size_f; let new_samples = vec![ 101_f32, 110_f32, 112_f32, 97_f32, 78_f32, 25_f32, 75_f32, 189_f32, 120_f32, 50_f32, ]; #[expect(clippy::as_conversions, reason = "int to float conversion is safe")] let mod_sample_size_f = new_samples.len() as f32; let cumulative = new_samples.iter().sum(); sample.extend_from_slice(&new_samples); #[expect(clippy::as_conversions, reason = "int to float conversion is safe")] let new_sample_size_f = sample.len() as f32; let new_avg_1 = sample.iter().sum::() / new_sample_size_f; let new_avg_2 = cumulative_avg(old_avg, cumulative, old_sample_size_f, mod_sample_size_f); let epsilon = 0.01_f32; assert!((new_avg_1 - new_avg_2).abs() < epsilon); } #[test] fn cumulative_var_test() { let mut sample = vec![100_f32; 40]; #[expect(clippy::as_conversions, reason = "int to float conversion is safe")] let old_sample_size_f = sample.len() as f32; let old_avg = sample.iter().sum::() / old_sample_size_f; let old_var = sample .iter() .fold(0_f32, |acc, x| (x - old_avg).mul_add(x - old_avg, acc)) / old_sample_size_f; let new_samples = vec![ 101_f32, 110_f32, 112_f32, 97_f32, 78_f32, 25_f32, 75_f32, 189_f32, 120_f32, 50_f32, ]; #[expect(clippy::as_conversions, reason = "int to float conversion is safe")] let mod_sample_size_f = new_samples.len() as f32; let cumulative = new_samples.iter().sum(); let cumulative_squares = new_samples .iter() .fold(0_f32, |acc, x| (*x).mul_add(*x, acc)); let new_avg = cumulative_avg(old_avg, cumulative, old_sample_size_f, mod_sample_size_f); sample.extend_from_slice(&new_samples); #[expect(clippy::as_conversions, reason = "int to float conversion is safe")] let new_var_1 = sample .iter() .fold(0_f32, |acc, x| (x - new_avg).mul_add(x - new_avg, acc)) / (sample.len() as f32); let new_var_2 = cumulative_var( old_avg, new_avg, old_var, cumulative, cumulative_squares, old_sample_size_f, mod_sample_size_f, ); let epsilon = 0.01_f32; assert!((new_var_1 - new_var_2).abs() < epsilon); } #[test] fn metric_updates_correctness() { let statistics_updates_vec = vec![ StatisticsUpdate::Success { latency: 100_f32, new_latest_block_id: 105, }, StatisticsUpdate::Success { latency: 115_f32, new_latest_block_id: 106, }, StatisticsUpdate::Failure, ]; let addr_leader = Url::parse("https://127.0.0.1:3040").unwrap(); let leader_statistics = Statistics { latency_avg: 100_f32, latency_var: 25_f32, sample_size: 10, latest_block_id: 100, errors: 5, }; let cumulative_latency = 100_f32 + 115_f32; let cumulative_latency_squares = 100_f32.mul_add(100_f32, 115_f32 * 115_f32); let avg_manual = cumulative_avg( leader_statistics.latency_avg, cumulative_latency, 10_f32, 2_f32, ); let var_manual = cumulative_var( leader_statistics.latency_avg, avg_manual, leader_statistics.latency_var, cumulative_latency, cumulative_latency_squares, 10_f32, 2_f32, ); let mut metric_map = HashMap::new(); metric_map.insert(addr_leader.clone(), leader_statistics); update_statistics(&mut metric_map, &addr_leader, &statistics_updates_vec).unwrap(); let Statistics { latency_avg, latency_var, sample_size, latest_block_id, errors, } = metric_map[&addr_leader]; let epsilon = 0.01_f32; assert_eq!(errors, 6); assert_eq!(latest_block_id, 106); assert_eq!(sample_size, 12); assert!((latency_avg - avg_manual).abs() < epsilon); assert!((latency_var - var_manual).abs() < epsilon); } #[test] fn choose_leader_latest_block() { let (client_list, [addr_leader, addr_1, addr_2, addr_3]) = four_client_list(); let mut statistics = HashMap::new(); statistics.insert( addr_3, Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 97, errors: 5, }, ); statistics.insert( addr_2, Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 98, errors: 5, }, ); statistics.insert( addr_1, Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 99, errors: 5, }, ); statistics.insert( addr_leader.clone(), Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 5, }, ); let leaders = choose_leaders(client_list, &statistics, 1).unwrap(); let (_, leader_url) = leaders.first().unwrap(); assert_eq!(leader_url, &addr_leader); } #[test] fn choose_leader_least_errors() { let (client_list, [addr_leader, addr_1, addr_2, addr_3]) = four_client_list(); let mut statistics = HashMap::new(); statistics.insert( addr_3, Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 5, }, ); statistics.insert( addr_2, Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 4, }, ); statistics.insert( addr_1, Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 3, }, ); statistics.insert( addr_leader.clone(), Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 2, }, ); let leaders = choose_leaders(client_list, &statistics, 1).unwrap(); let (_, leader_url) = leaders.first().unwrap(); assert_eq!(leader_url, &addr_leader); } #[test] fn choose_leader_simple_latency_check() { let (client_list, [addr_leader, addr_1, addr_2, addr_3]) = four_client_list(); let mut statistics = HashMap::new(); statistics.insert( addr_3, Statistics { latency_avg: 103_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 5, }, ); statistics.insert( addr_2, Statistics { latency_avg: 102_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 5, }, ); statistics.insert( addr_1, Statistics { latency_avg: 101_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 5, }, ); statistics.insert( addr_leader.clone(), Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 5, }, ); let leaders = choose_leaders(client_list, &statistics, 1).unwrap(); let (_, leader_url) = leaders.first().unwrap(); assert_eq!(leader_url, &addr_leader); } #[test] fn choose_leader_latency_var_check() { let (client_list, [addr_leader, addr_1, addr_2, addr_3]) = four_client_list(); let mut statistics = HashMap::new(); statistics.insert( addr_3, Statistics { latency_avg: 100_f32, latency_var: 13_f32, sample_size: 10, latest_block_id: 100, errors: 5, }, ); statistics.insert( addr_2, Statistics { latency_avg: 100_f32, latency_var: 12_f32, sample_size: 10, latest_block_id: 100, errors: 5, }, ); statistics.insert( addr_1, Statistics { latency_avg: 100_f32, latency_var: 11_f32, sample_size: 10, latest_block_id: 100, errors: 5, }, ); statistics.insert( addr_leader.clone(), Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 5, }, ); let leaders = choose_leaders(client_list, &statistics, 1).unwrap(); let (_, leader_url) = leaders.first().unwrap(); assert_eq!(leader_url, &addr_leader); } #[test] fn choose_multiple_leaders_latest_block() { let (client_list, [addr_leader, addr_1, addr_2, addr_3]) = four_client_list(); let mut statistics = HashMap::new(); statistics.insert( addr_3, Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 97, errors: 5, }, ); statistics.insert( addr_2, Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 98, errors: 5, }, ); statistics.insert( addr_1.clone(), Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 5, }, ); statistics.insert( addr_leader.clone(), Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 5, }, ); let leaders = choose_leaders(client_list, &statistics, 2).unwrap(); let mut url_set_origin = HashSet::new(); let mut url_set_res = HashSet::new(); let (_, leader_url_first) = leaders[0].clone(); let (_, leader_url_second) = leaders[1].clone(); url_set_origin.insert(addr_leader); url_set_origin.insert(addr_1); url_set_res.insert(leader_url_first); url_set_res.insert(leader_url_second); assert_eq!(url_set_origin, url_set_res); assert_eq!(leaders.len(), 2); } #[test] fn choose_multiple_leaders_latest_block_still_chooses_one_best() { let (client_list, [addr_leader, addr_1, addr_2, addr_3]) = four_client_list(); let mut statistics = HashMap::new(); statistics.insert( addr_3, Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 97, errors: 5, }, ); statistics.insert( addr_2, Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 98, errors: 5, }, ); statistics.insert( addr_1, Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 99, errors: 5, }, ); statistics.insert( addr_leader.clone(), Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 5, }, ); let leaders = choose_leaders(client_list, &statistics, 2).unwrap(); let (_, helm) = leaders.first().unwrap(); assert_eq!(&addr_leader, helm); assert_eq!(leaders.len(), 1); } #[test] fn choose_multiple_leaders_least_errors() { let (client_list, [addr_leader, addr_1, addr_2, addr_3]) = four_client_list(); let mut statistics = HashMap::new(); statistics.insert( addr_3, Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 6, }, ); statistics.insert( addr_2, Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 6, }, ); statistics.insert( addr_1.clone(), Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 3, }, ); statistics.insert( addr_leader.clone(), Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 2, }, ); let leaders = choose_leaders(client_list, &statistics, 2).unwrap(); let (_, leader_url_first) = leaders[0].clone(); let (_, leader_url_second) = leaders[1].clone(); assert_eq!(addr_leader, leader_url_first); assert_eq!(addr_1, leader_url_second); assert_eq!(leaders.len(), 2); } #[test] fn choose_multiple_leaders_simple_latency_check() { let (client_list, [addr_leader, addr_1, addr_2, addr_3]) = four_client_list(); let mut statistics = HashMap::new(); statistics.insert( addr_3, Statistics { latency_avg: 103_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 6, }, ); statistics.insert( addr_2, Statistics { latency_avg: 102_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 4, }, ); statistics.insert( addr_1.clone(), Statistics { latency_avg: 101_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 4, }, ); statistics.insert( addr_leader.clone(), Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 4, }, ); let leaders = choose_leaders(client_list, &statistics, 2).unwrap(); let (_, leader_url_first) = leaders[0].clone(); let (_, leader_url_second) = leaders[1].clone(); assert_eq!(addr_leader, leader_url_first); assert_eq!(addr_1, leader_url_second); assert_eq!(leaders.len(), 2); } #[test] fn choose_multiple_leaders_var_check() { let (client_list, [addr_leader, addr_1, addr_2, addr_3]) = four_client_list(); let mut statistics = HashMap::new(); statistics.insert( addr_3, Statistics { latency_avg: 103_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 6, }, ); statistics.insert( addr_2, Statistics { latency_avg: 100_f32, latency_var: 12_f32, sample_size: 10, latest_block_id: 100, errors: 4, }, ); statistics.insert( addr_1.clone(), Statistics { latency_avg: 100_f32, latency_var: 11_f32, sample_size: 10, latest_block_id: 100, errors: 4, }, ); statistics.insert( addr_leader.clone(), Statistics { latency_avg: 100_f32, latency_var: 10_f32, sample_size: 10, latest_block_id: 100, errors: 4, }, ); let leaders = choose_leaders(client_list, &statistics, 2).unwrap(); let (_, leader_url_first) = leaders[0].clone(); let (_, leader_url_second) = leaders[1].clone(); assert_eq!(addr_leader, leader_url_first); assert_eq!(addr_1, leader_url_second); assert_eq!(leaders.len(), 2); } }