use { crate::{accounts_db::SnapshotStorages, ancestors::Ancestors, rent_collector::RentCollector}, log::*, rayon::prelude::*, safecoin_measure::measure::Measure, solana_sdk::{ hash::{Hash, Hasher}, pubkey::Pubkey, slot_history::Slot, sysvar::epoch_schedule::EpochSchedule, }, std::{ borrow::Borrow, convert::TryInto, sync::{ atomic::{AtomicU64, AtomicUsize, Ordering}, Mutex, }, }, }; pub const ZERO_RAW_LAMPORTS_SENTINEL: u64 = std::u64::MAX; pub const MERKLE_FANOUT: usize = 16; #[derive(Default, Debug)] pub struct PreviousPass { pub reduced_hashes: Vec>, pub remaining_unhashed: Vec, pub lamports: u64, } #[derive(Debug)] #[allow(dead_code)] pub struct FullSnapshotAccountsHashInfo { /// accounts hash over all accounts when the full snapshot was taken hash: Hash, /// slot where full snapshot was taken slot: Slot, } /// parameters to calculate accounts hash #[derive(Debug)] pub struct CalcAccountsHashConfig<'a> { /// true to use a thread pool dedicated to bg operations pub use_bg_thread_pool: bool, /// verify every hash in append vec/write cache with a recalculated hash /// this option will be removed pub check_hash: bool, /// 'ancestors' is used to get storages and also used if 'use_write_cache' is true to /// get account data from the write cache pub ancestors: Option<&'a Ancestors>, /// does hash calc need to consider account data that exists in the write cache? /// if so, 'ancestors' will be used for this purpose as well as storages. pub use_write_cache: bool, pub epoch_schedule: &'a EpochSchedule, pub rent_collector: &'a RentCollector, /// used for tracking down hash mismatches after the fact pub store_detailed_debug_info_on_failure: bool, /// `Some` if this is an incremental snapshot which only hashes slots since the base full snapshot pub full_snapshot: Option, } impl<'a> CalcAccountsHashConfig<'a> { /// return true if we should cache accounts hash intermediate data between calls pub fn get_should_cache_hash_data() -> bool { // when we are skipping rewrites, we cannot rely on the cached data from old append vecs, so we have to disable caching for now // skipping rewrites is not enabled in this branch. It requires a cli argument. true } } // smallest, 3 quartiles, largest, average pub type StorageSizeQuartileStats = [usize; 6]; #[derive(Debug, Default)] pub struct HashStats { pub scan_time_total_us: u64, pub zeros_time_total_us: u64, pub hash_time_total_us: u64, pub hash_time_pre_us: u64, pub sort_time_total_us: u64, pub hash_total: usize, pub unreduced_entries: usize, pub num_snapshot_storage: usize, pub num_slots: usize, pub collect_snapshots_us: u64, pub storage_sort_us: u64, pub min_bin_size: usize, pub max_bin_size: usize, pub storage_size_quartiles: StorageSizeQuartileStats, /// time spent hashing during rehash calls pub rehash_hash_us: AtomicU64, /// time spent determining whether to rehash during rehash calls pub rehash_calc_us: AtomicU64, /// # rehashes that took place and were necessary pub rehash_required: AtomicUsize, /// # rehashes that took place and were UNnecessary pub rehash_unnecessary: AtomicUsize, pub oldest_root: Slot, pub roots_older_than_epoch: AtomicUsize, pub accounts_in_roots_older_than_epoch: AtomicUsize, pub append_vec_sizes_older_than_epoch: AtomicUsize, /// # ancient append vecs encountered pub ancient_append_vecs: AtomicUsize, } impl HashStats { pub fn calc_storage_size_quartiles(&mut self, storages: &SnapshotStorages) { let mut sum = 0; let mut sizes = storages .iter() .flat_map(|storages| { let result = storages .iter() .map(|storage| { let cap = storage.accounts.capacity() as usize; sum += cap; cap }) .collect::>(); result }) .collect::>(); sizes.sort_unstable(); let len = sizes.len(); self.storage_size_quartiles = if len == 0 { StorageSizeQuartileStats::default() } else { [ *sizes.first().unwrap(), sizes[len / 4], sizes[len * 2 / 4], sizes[len * 3 / 4], *sizes.last().unwrap(), sum / len, ] }; } fn log(&mut self) { let total_time_us = self.scan_time_total_us + self.zeros_time_total_us + self.hash_time_total_us + self.collect_snapshots_us + self.storage_sort_us; datapoint_info!( "calculate_accounts_hash_without_index", ("accounts_scan", self.scan_time_total_us, i64), ("eliminate_zeros", self.zeros_time_total_us, i64), ("hash", self.hash_time_total_us, i64), ("hash_time_pre_us", self.hash_time_pre_us, i64), ("sort", self.sort_time_total_us, i64), ("hash_total", self.hash_total, i64), ("storage_sort_us", self.storage_sort_us, i64), ("unreduced_entries", self.unreduced_entries as i64, i64), ( "collect_snapshots_us", self.collect_snapshots_us as i64, i64 ), ( "num_snapshot_storage", self.num_snapshot_storage as i64, i64 ), ("num_slots", self.num_slots as i64, i64), ("min_bin_size", self.min_bin_size as i64, i64), ("max_bin_size", self.max_bin_size as i64, i64), ( "storage_size_min", self.storage_size_quartiles[0] as i64, i64 ), ( "storage_size_quartile_1", self.storage_size_quartiles[1] as i64, i64 ), ( "storage_size_quartile_2", self.storage_size_quartiles[2] as i64, i64 ), ( "storage_size_quartile_3", self.storage_size_quartiles[3] as i64, i64 ), ( "storage_size_max", self.storage_size_quartiles[4] as i64, i64 ), ( "storage_size_avg", self.storage_size_quartiles[5] as i64, i64 ), ("total", total_time_us as i64, i64), ( "rehashed_rewrites", self.rehash_required.load(Ordering::Relaxed) as i64, i64 ), ( "rehash_hash_us", self.rehash_hash_us.load(Ordering::Relaxed) as i64, i64 ), ( "rehash_calc_us", self.rehash_calc_us.load(Ordering::Relaxed) as i64, i64 ), ( "rehashed_rewrites_unnecessary", self.rehash_unnecessary.load(Ordering::Relaxed) as i64, i64 ), ( "roots_older_than_epoch", self.roots_older_than_epoch.load(Ordering::Relaxed) as i64, i64 ), ("oldest_root", self.oldest_root as i64, i64), ( "ancient_append_vecs", self.ancient_append_vecs.load(Ordering::Relaxed) as i64, i64 ), ( "append_vec_sizes_older_than_epoch", self.append_vec_sizes_older_than_epoch .load(Ordering::Relaxed) as i64, i64 ), ( "accounts_in_roots_older_than_epoch", self.accounts_in_roots_older_than_epoch .load(Ordering::Relaxed) as i64, i64 ), ); } } #[derive(Default, Debug, PartialEq, Eq, Clone)] pub struct CalculateHashIntermediate { pub hash: Hash, pub lamports: u64, pub pubkey: Pubkey, } impl CalculateHashIntermediate { pub fn new(hash: Hash, lamports: u64, pubkey: Pubkey) -> Self { Self { hash, lamports, pubkey, } } } #[derive(Default, Debug, PartialEq, Eq)] pub struct CumulativeOffset { pub index: Vec, pub start_offset: usize, } impl CumulativeOffset { pub fn new(index: Vec, start_offset: usize) -> CumulativeOffset { Self { index, start_offset, } } } pub trait ExtractSliceFromRawData<'b, T: 'b> { fn extract<'a>(&'b self, offset: &'a CumulativeOffset, start: usize) -> &'b [T]; } impl<'b, T: 'b> ExtractSliceFromRawData<'b, T> for Vec> { fn extract<'a>(&'b self, offset: &'a CumulativeOffset, start: usize) -> &'b [T] { &self[offset.index[0]][start..] } } impl<'b, T: 'b> ExtractSliceFromRawData<'b, T> for Vec>> { fn extract<'a>(&'b self, offset: &'a CumulativeOffset, start: usize) -> &'b [T] { &self[offset.index[0]][offset.index[1]][start..] } } // Allow retrieving &[start..end] from a logical src: Vec, where src is really Vec> (or later Vec>>) // This model prevents callers from having to flatten which saves both working memory and time. #[derive(Default, Debug)] pub struct CumulativeOffsets { cumulative_offsets: Vec, total_count: usize, } impl CumulativeOffsets { pub fn from_raw(raw: &[Vec]) -> CumulativeOffsets { let mut total_count: usize = 0; let cumulative_offsets: Vec<_> = raw .iter() .enumerate() .filter_map(|(i, v)| { let len = v.len(); if len > 0 { let result = CumulativeOffset::new(vec![i], total_count); total_count += len; Some(result) } else { None } }) .collect(); Self { cumulative_offsets, total_count, } } pub fn from_raw_2d(raw: &[Vec>]) -> CumulativeOffsets { let mut total_count: usize = 0; let mut cumulative_offsets = Vec::with_capacity(0); for (i, v_outer) in raw.iter().enumerate() { for (j, v) in v_outer.iter().enumerate() { let len = v.len(); if len > 0 { if cumulative_offsets.is_empty() { // the first inner, non-empty vector we find gives us an approximate rectangular shape cumulative_offsets = Vec::with_capacity(raw.len() * v_outer.len()); } cumulative_offsets.push(CumulativeOffset::new(vec![i, j], total_count)); total_count += len; } } } Self { cumulative_offsets, total_count, } } fn find_index(&self, start: usize) -> usize { assert!(!self.cumulative_offsets.is_empty()); match self.cumulative_offsets[..].binary_search_by(|index| index.start_offset.cmp(&start)) { Ok(index) => index, Err(index) => index - 1, // we would insert at index so we are before the item at index } } fn find(&self, start: usize) -> (usize, &CumulativeOffset) { let index = self.find_index(start); let index = &self.cumulative_offsets[index]; let start = start - index.start_offset; (start, index) } // return the biggest slice possible that starts at 'start' pub fn get_slice<'a, 'b, T, U>(&'a self, raw: &'b U, start: usize) -> &'b [T] where U: ExtractSliceFromRawData<'b, T> + 'b, { let (start, index) = self.find(start); raw.extract(index, start) } } #[derive(Debug, Default)] pub struct AccountsHash { pub filler_account_suffix: Option, } impl AccountsHash { /// true if it is possible that there are filler accounts present pub fn filler_accounts_enabled(&self) -> bool { self.filler_account_suffix.is_some() } pub fn calculate_hash(hashes: Vec>) -> (Hash, usize) { let cumulative_offsets = CumulativeOffsets::from_raw(&hashes); let hash_total = cumulative_offsets.total_count; let result = AccountsHash::compute_merkle_root_from_slices( hash_total, MERKLE_FANOUT, None, |start: usize| cumulative_offsets.get_slice(&hashes, start), None, ); (result.0, hash_total) } pub fn compute_merkle_root(hashes: Vec<(Pubkey, Hash)>, fanout: usize) -> Hash { Self::compute_merkle_root_loop(hashes, fanout, |t| t.1) } // this function avoids an infinite recursion compiler error pub fn compute_merkle_root_recurse(hashes: Vec, fanout: usize) -> Hash { Self::compute_merkle_root_loop(hashes, fanout, |t: &Hash| *t) } pub fn div_ceil(x: usize, y: usize) -> usize { let mut result = x / y; if x % y != 0 { result += 1; } result } // For the first iteration, there could be more items in the tuple than just hash and lamports. // Using extractor allows us to avoid an unnecessary array copy on the first iteration. pub fn compute_merkle_root_loop(hashes: Vec, fanout: usize, extractor: F) -> Hash where F: Fn(&T) -> Hash + std::marker::Sync, T: std::marker::Sync, { if hashes.is_empty() { return Hasher::default().result(); } let mut time = Measure::start("time"); let total_hashes = hashes.len(); let chunks = Self::div_ceil(total_hashes, fanout); let result: Vec<_> = (0..chunks) .into_par_iter() .map(|i| { let start_index = i * fanout; let end_index = std::cmp::min(start_index + fanout, total_hashes); let mut hasher = Hasher::default(); for item in hashes.iter().take(end_index).skip(start_index) { let h = extractor(item); hasher.hash(h.as_ref()); } hasher.result() }) .collect(); time.stop(); debug!("hashing {} {}", total_hashes, time); if result.len() == 1 { result[0] } else { Self::compute_merkle_root_recurse(result, fanout) } } fn calculate_three_level_chunks( total_hashes: usize, fanout: usize, max_levels_per_pass: Option, specific_level_count: Option, ) -> (usize, usize, bool) { const THREE_LEVEL_OPTIMIZATION: usize = 3; // this '3' is dependent on the code structure below where we manually unroll let target = fanout.pow(THREE_LEVEL_OPTIMIZATION as u32); // Only use the 3 level optimization if we have at least 4 levels of data. // Otherwise, we'll be serializing a parallel operation. let threshold = target * fanout; let mut three_level = max_levels_per_pass.unwrap_or(usize::MAX) >= THREE_LEVEL_OPTIMIZATION && total_hashes >= threshold; if three_level { if let Some(specific_level_count_value) = specific_level_count { three_level = specific_level_count_value >= THREE_LEVEL_OPTIMIZATION; } } let (num_hashes_per_chunk, levels_hashed) = if three_level { (target, THREE_LEVEL_OPTIMIZATION) } else { (fanout, 1) }; (num_hashes_per_chunk, levels_hashed, three_level) } // This function is designed to allow hashes to be located in multiple, perhaps multiply deep vecs. // The caller provides a function to return a slice from the source data. pub fn compute_merkle_root_from_slices<'a, F, T>( total_hashes: usize, fanout: usize, max_levels_per_pass: Option, get_hash_slice_starting_at_index: F, specific_level_count: Option, ) -> (Hash, Vec) where F: Fn(usize) -> &'a [T] + std::marker::Sync, T: Borrow + std::marker::Sync + 'a, { if total_hashes == 0 { return (Hasher::default().result(), vec![]); } let mut time = Measure::start("time"); let (num_hashes_per_chunk, levels_hashed, three_level) = Self::calculate_three_level_chunks( total_hashes, fanout, max_levels_per_pass, specific_level_count, ); let chunks = Self::div_ceil(total_hashes, num_hashes_per_chunk); // initial fetch - could return entire slice let data = get_hash_slice_starting_at_index(0); let data_len = data.len(); let result: Vec<_> = (0..chunks) .into_par_iter() .map(|i| { // summary: // this closure computes 1 or 3 levels of merkle tree (all chunks will be 1 or all will be 3) // for a subset (our chunk) of the input data [start_index..end_index] // index into get_hash_slice_starting_at_index where this chunk's range begins let start_index = i * num_hashes_per_chunk; // index into get_hash_slice_starting_at_index where this chunk's range ends let end_index = std::cmp::min(start_index + num_hashes_per_chunk, total_hashes); // will compute the final result for this closure let mut hasher = Hasher::default(); // index into 'data' where we are currently pulling data // if we exhaust our data, then we will request a new slice, and data_index resets to 0, the beginning of the new slice let mut data_index = start_index; // source data, which we may refresh when we exhaust let mut data = data; // len of the source data let mut data_len = data_len; if !three_level { // 1 group of fanout // The result of this loop is a single hash value from fanout input hashes. for i in start_index..end_index { if data_index >= data_len { // we exhausted our data, fetch next slice starting at i data = get_hash_slice_starting_at_index(i); data_len = data.len(); data_index = 0; } hasher.hash(data[data_index].borrow().as_ref()); data_index += 1; } } else { // hash 3 levels of fanout simultaneously. // This codepath produces 1 hash value for between 1..=fanout^3 input hashes. // It is equivalent to running the normal merkle tree calculation 3 iterations on the input. // // big idea: // merkle trees usually reduce the input vector by a factor of fanout with each iteration // example with fanout 2: // start: [0,1,2,3,4,5,6,7] in our case: [...16M...] or really, 1B // iteration0 [.5, 2.5, 4.5, 6.5] [... 1M...] // iteration1 [1.5, 5.5] [...65k...] // iteration2 3.5 [...4k... ] // So iteration 0 consumes N elements, hashes them in groups of 'fanout' and produces a vector of N/fanout elements // and the process repeats until there is only 1 hash left. // // With the three_level code path, we make each chunk we iterate of size fanout^3 (4096) // So, the input could be 16M hashes and the output will be 4k hashes, or N/fanout^3 // The goal is to reduce the amount of data that has to be constructed and held in memory. // When we know we have enough hashes, then, in 1 pass, we hash 3 levels simultaneously, storing far fewer intermediate hashes. // // Now, some details: // The result of this loop is a single hash value from fanout^3 input hashes. // concepts: // what we're conceptually hashing: "raw_hashes"[start_index..end_index] // example: [a,b,c,d,e,f] // but... hashes[] may really be multiple vectors that are pieced together. // example: [[a,b],[c],[d,e,f]] // get_hash_slice_starting_at_index(any_index) abstracts that and returns a slice starting at raw_hashes[any_index..] // such that the end of get_hash_slice_starting_at_index may be <, >, or = end_index // example: get_hash_slice_starting_at_index(1) returns [b] // get_hash_slice_starting_at_index(3) returns [d,e,f] // This code is basically 3 iterations of merkle tree hashing occurring simultaneously. // The first fanout raw hashes are hashed in hasher_k. This is iteration0 // Once hasher_k has hashed fanout hashes, hasher_k's result hash is hashed in hasher_j and then discarded // hasher_k then starts over fresh and hashes the next fanout raw hashes. This is iteration0 again for a new set of data. // Once hasher_j has hashed fanout hashes (from k), hasher_j's result hash is hashed in hasher and then discarded // Once hasher has hashed fanout hashes (from j), then the result of hasher is the hash for fanout^3 raw hashes. // If there are < fanout^3 hashes, then this code stops when it runs out of raw hashes and returns whatever it hashed. // This is always how the very last elements work in a merkle tree. let mut i = start_index; while i < end_index { let mut hasher_j = Hasher::default(); for _j in 0..fanout { let mut hasher_k = Hasher::default(); let end = std::cmp::min(end_index - i, fanout); for _k in 0..end { if data_index >= data_len { // we exhausted our data, fetch next slice starting at i data = get_hash_slice_starting_at_index(i); data_len = data.len(); data_index = 0; } hasher_k.hash(data[data_index].borrow().as_ref()); data_index += 1; i += 1; } hasher_j.hash(hasher_k.result().as_ref()); if i >= end_index { break; } } hasher.hash(hasher_j.result().as_ref()); } } hasher.result() }) .collect(); time.stop(); debug!("hashing {} {}", total_hashes, time); if let Some(mut specific_level_count_value) = specific_level_count { specific_level_count_value -= levels_hashed; if specific_level_count_value == 0 { (Hash::default(), result) } else { assert!(specific_level_count_value > 0); // We did not hash the number of levels required by 'specific_level_count', so repeat Self::compute_merkle_root_from_slices_recurse( result, fanout, max_levels_per_pass, Some(specific_level_count_value), ) } } else { ( if result.len() == 1 { result[0] } else { Self::compute_merkle_root_recurse(result, fanout) }, vec![], // no intermediate results needed by caller ) } } pub fn compute_merkle_root_from_slices_recurse( hashes: Vec, fanout: usize, max_levels_per_pass: Option, specific_level_count: Option, ) -> (Hash, Vec) { Self::compute_merkle_root_from_slices( hashes.len(), fanout, max_levels_per_pass, |start| &hashes[start..], specific_level_count, ) } pub fn accumulate_account_hashes(mut hashes: Vec<(Pubkey, Hash)>) -> Hash { Self::sort_hashes_by_pubkey(&mut hashes); Self::compute_merkle_root_loop(hashes, MERKLE_FANOUT, |i| i.1) } pub fn sort_hashes_by_pubkey(hashes: &mut Vec<(Pubkey, Hash)>) { hashes.par_sort_unstable_by(|a, b| a.0.cmp(&b.0)); } pub fn compare_two_hash_entries( a: &CalculateHashIntermediate, b: &CalculateHashIntermediate, ) -> std::cmp::Ordering { // note partial_cmp only returns None with floating point comparisons a.pubkey.partial_cmp(&b.pubkey).unwrap() } pub fn checked_cast_for_capitalization(balance: u128) -> u64 { balance .try_into() .expect("overflow is detected while summing capitalization") } fn de_dup_and_eliminate_zeros<'a>( &self, sorted_data_by_pubkey: &'a [Vec>], stats: &mut HashStats, max_bin: usize, ) -> (Vec>, u64) { // 1. eliminate zero lamport accounts // 2. pick the highest slot or (slot = and highest version) of each pubkey // 3. produce this output: // a. vec: PUBKEY_BINS_FOR_CALCULATING_HASHES in pubkey order // vec: individual hashes in pubkey order, 1 hash per // b. lamports let mut zeros = Measure::start("eliminate zeros"); let min_max_sum_entries_hashes = Mutex::new((usize::MAX, usize::MIN, 0u64, 0usize, 0usize)); let hashes: Vec> = (0..max_bin) .into_par_iter() .map(|bin| { let (hashes, lamports_bin, unreduced_entries_count) = self.de_dup_accounts_in_parallel(sorted_data_by_pubkey, bin); { let mut lock = min_max_sum_entries_hashes.lock().unwrap(); let (mut min, mut max, mut lamports_sum, mut entries, mut hash_total) = *lock; min = std::cmp::min(min, unreduced_entries_count); max = std::cmp::max(max, unreduced_entries_count); lamports_sum = Self::checked_cast_for_capitalization( lamports_sum as u128 + lamports_bin as u128, ); entries += unreduced_entries_count; hash_total += hashes.len(); *lock = (min, max, lamports_sum, entries, hash_total); } hashes }) .collect(); zeros.stop(); stats.zeros_time_total_us += zeros.as_us(); let (min, max, lamports_sum, entries, hash_total) = *min_max_sum_entries_hashes.lock().unwrap(); stats.min_bin_size = min; stats.max_bin_size = max; stats.unreduced_entries += entries; stats.hash_total += hash_total; (hashes, lamports_sum) } // returns true if this vector was exhausted fn get_item<'a, 'b>( min_index: usize, bin: usize, first_items: &'a mut Vec, pubkey_division: &'b [Vec>], indexes: &'a mut [usize], first_item_to_pubkey_division: &'a mut Vec, ) -> &'b CalculateHashIntermediate { let first_item = first_items[min_index]; let key = &first_item; let division_index = first_item_to_pubkey_division[min_index]; let bin = &pubkey_division[division_index][bin]; let mut index = indexes[division_index]; index += 1; while index < bin.len() { // still more items where we found the previous key, so just increment the index for that slot group, skipping all pubkeys that are equal if &bin[index].pubkey == key { index += 1; continue; // duplicate entries of same pubkey, so keep skipping } // point to the next pubkey > key first_items[min_index] = bin[index].pubkey; indexes[division_index] = index; break; } if index >= bin.len() { // stop looking in this vector - we exhausted it first_items.remove(min_index); first_item_to_pubkey_division.remove(min_index); } // this is the previous first item that was requested &bin[index - 1] } // go through: [..][pubkey_bin][..] and return hashes and lamport sum // slot groups^ ^accounts found in a slot group, sorted by pubkey, higher slot, write_version // 1. eliminate zero lamport accounts // 2. pick the highest slot or (slot = and highest version) of each pubkey // 3. produce this output: // a. vec: individual hashes in pubkey order // b. lamport sum // c. unreduced count (ie. including duplicates and zero lamport) fn de_dup_accounts_in_parallel<'a>( &self, pubkey_division: &'a [Vec>], pubkey_bin: usize, ) -> (Vec<&'a Hash>, u64, usize) { let len = pubkey_division.len(); let mut item_len = 0; let mut indexes = vec![0; len]; let mut first_items = Vec::with_capacity(len); // map from index of an item in first_items[] to index of the corresponding item in pubkey_division[] // this will change as items in pubkey_division[] are exhausted let mut first_item_to_pubkey_division = Vec::with_capacity(len); // initialize 'first_items', which holds the current lowest item in each slot group pubkey_division.iter().enumerate().for_each(|(i, bins)| { // check to make sure we can do bins[pubkey_bin] if bins.len() > pubkey_bin { let sub = &bins[pubkey_bin]; if !sub.is_empty() { item_len += bins[pubkey_bin].len(); // sum for metrics first_items.push(bins[pubkey_bin][0].pubkey); first_item_to_pubkey_division.push(i); } } }); let mut overall_sum = 0; let mut hashes: Vec<&Hash> = Vec::with_capacity(item_len); let mut duplicate_pubkey_indexes = Vec::with_capacity(len); let filler_accounts_enabled = self.filler_accounts_enabled(); // this loop runs once per unique pubkey contained in any slot group while !first_items.is_empty() { let loop_stop = { first_items.len() - 1 }; // we increment at the beginning of the loop let mut min_index = 0; let mut min_pubkey = first_items[min_index]; let mut first_item_index = 0; // we will start iterating at item 1. +=1 is first instruction in loop // this loop iterates over each slot group to find the minimum pubkey at the maximum slot // it also identifies duplicate pubkey entries at lower slots and remembers those to skip them after while first_item_index < loop_stop { first_item_index += 1; let key = &first_items[first_item_index]; let cmp = min_pubkey.cmp(key); match cmp { std::cmp::Ordering::Less => { continue; // we still have the min item } std::cmp::Ordering::Equal => { // we found the same pubkey in a later slot, so remember the lower slot as a duplicate duplicate_pubkey_indexes.push(min_index); } std::cmp::Ordering::Greater => { // this is the new min pubkey min_pubkey = *key; } } // this is the new index of the min entry min_index = first_item_index; } // get the min item, add lamports, get hash let item = Self::get_item( min_index, pubkey_bin, &mut first_items, pubkey_division, &mut indexes, &mut first_item_to_pubkey_division, ); // add lamports, get hash as long as the lamports are > 0 if item.lamports != ZERO_RAW_LAMPORTS_SENTINEL && (!filler_accounts_enabled || !self.is_filler_account(&item.pubkey)) { overall_sum = Self::checked_cast_for_capitalization( item.lamports as u128 + overall_sum as u128, ); hashes.push(&item.hash); } if !duplicate_pubkey_indexes.is_empty() { // skip past duplicate keys in earlier slots // reverse this list because get_item can remove first_items[*i] when *i is exhausted // and that would mess up subsequent *i values duplicate_pubkey_indexes.iter().rev().for_each(|i| { Self::get_item( *i, pubkey_bin, &mut first_items, pubkey_division, &mut indexes, &mut first_item_to_pubkey_division, ); }); duplicate_pubkey_indexes.clear(); } } (hashes, overall_sum, item_len) } fn is_filler_account(&self, pubkey: &Pubkey) -> bool { crate::accounts_db::AccountsDb::is_filler_account_helper( pubkey, self.filler_account_suffix.as_ref(), ) } // input: // vec: group of slot data, ordered by Slot (low to high) // vec: [0..bins] - where bins are pubkey ranges (these are ordered by Pubkey range) // vec: [..] - items which fit in the containing bin. Sorted by: Pubkey, higher Slot, higher Write version (if pubkey =) pub fn rest_of_hash_calculation( &self, data_sections_by_pubkey: Vec>>, mut stats: &mut HashStats, is_last_pass: bool, mut previous_state: PreviousPass, max_bin: usize, ) -> (Hash, u64, PreviousPass) { let (mut hashes, mut total_lamports) = self.de_dup_and_eliminate_zeros(&data_sections_by_pubkey, stats, max_bin); total_lamports += previous_state.lamports; let mut _remaining_unhashed = None; if !previous_state.remaining_unhashed.is_empty() { // These items were not hashed last iteration because they didn't divide evenly. // These are hashes for pubkeys that are < the pubkeys we are looking at now, so their hashes go first in order. _remaining_unhashed = Some(previous_state.remaining_unhashed); hashes.insert( 0, _remaining_unhashed .as_ref() .unwrap() .iter() .collect::>(), ); previous_state.remaining_unhashed = Vec::new(); } let mut next_pass = PreviousPass::default(); let cumulative = CumulativeOffsets::from_raw(&hashes); let mut hash_total = cumulative.total_count; next_pass.reduced_hashes = previous_state.reduced_hashes; const TARGET_FANOUT_LEVEL: usize = 3; let target_fanout = MERKLE_FANOUT.pow(TARGET_FANOUT_LEVEL as u32); if !is_last_pass { next_pass.lamports = total_lamports; total_lamports = 0; // Save hashes that don't evenly hash. They will be combined with hashes from the next pass. let left_over_hashes = hash_total % target_fanout; // move tail hashes that don't evenly hash into a 1d vector for next time let mut i = hash_total - left_over_hashes; while i < hash_total { let data = cumulative.get_slice(&hashes, i); next_pass.remaining_unhashed.extend(data.iter().cloned()); i += data.len(); } hash_total -= left_over_hashes; // this is enough to cause the hashes at the end of the data set to be ignored } // if we have raw hashes to process and // we are not the last pass (we already modded against target_fanout) OR // we have previously surpassed target_fanout and hashed some already to the target_fanout level. In that case, we know // we need to hash whatever is left here to the target_fanout level. if hash_total != 0 && (!is_last_pass || !next_pass.reduced_hashes.is_empty()) { let mut hash_time = Measure::start("hash"); let partial_hashes = Self::compute_merkle_root_from_slices( hash_total, // note this does not include the ones that didn't divide evenly, unless we're in the last iteration MERKLE_FANOUT, Some(TARGET_FANOUT_LEVEL), |start| cumulative.get_slice(&hashes, start), Some(TARGET_FANOUT_LEVEL), ) .1; hash_time.stop(); stats.hash_time_total_us += hash_time.as_us(); stats.hash_time_pre_us += hash_time.as_us(); next_pass.reduced_hashes.push(partial_hashes); } let no_progress = is_last_pass && next_pass.reduced_hashes.is_empty() && !hashes.is_empty(); if no_progress { // we never made partial progress, so hash everything now hashes.into_iter().for_each(|v| { if !v.is_empty() { next_pass .reduced_hashes .push(v.into_iter().cloned().collect()); } }); } let hash = if is_last_pass { let cumulative = CumulativeOffsets::from_raw(&next_pass.reduced_hashes); let hash = if cumulative.total_count == 1 && !no_progress { // all the passes resulted in a single hash, that means we're done, so we had <= MERKLE_ROOT total hashes cumulative.get_slice(&next_pass.reduced_hashes, 0)[0] } else { let mut hash_time = Measure::start("hash"); // hash all the rest and combine and hash until we have only 1 hash left let (hash, _) = Self::compute_merkle_root_from_slices( cumulative.total_count, MERKLE_FANOUT, None, |start| cumulative.get_slice(&next_pass.reduced_hashes, start), None, ); hash_time.stop(); stats.hash_time_total_us += hash_time.as_us(); hash }; next_pass.reduced_hashes = Vec::new(); hash } else { Hash::default() }; if is_last_pass { stats.log(); } (hash, total_lamports, next_pass) } } #[cfg(test)] pub mod tests { use {super::*, std::str::FromStr}; #[test] fn test_accountsdb_div_ceil() { assert_eq!(AccountsHash::div_ceil(10, 3), 4); assert_eq!(AccountsHash::div_ceil(0, 1), 0); assert_eq!(AccountsHash::div_ceil(0, 5), 0); assert_eq!(AccountsHash::div_ceil(9, 3), 3); assert_eq!(AccountsHash::div_ceil(9, 9), 1); } #[test] #[should_panic(expected = "attempt to divide by zero")] fn test_accountsdb_div_ceil_fail() { assert_eq!(AccountsHash::div_ceil(10, 0), 0); } fn for_rest( original: Vec, ) -> Vec>> { vec![vec![original]] } #[test] fn test_accountsdb_rest_of_hash_calculation() { solana_logger::setup(); let mut account_maps = Vec::new(); let key = Pubkey::from([11u8; 32]); let hash = Hash::new(&[1u8; 32]); let val = CalculateHashIntermediate::new(hash, 88, key); account_maps.push(val); // 2nd key - zero lamports, so will be removed let key = Pubkey::from([12u8; 32]); let hash = Hash::new(&[2u8; 32]); let val = CalculateHashIntermediate::new(hash, ZERO_RAW_LAMPORTS_SENTINEL, key); account_maps.push(val); let accounts_hash = AccountsHash::default(); let result = accounts_hash.rest_of_hash_calculation( for_rest(account_maps.clone()), &mut HashStats::default(), true, PreviousPass::default(), one_range(), ); let expected_hash = Hash::from_str("8j9ARGFv4W2GfML7d3sVJK2MePwrikqYnu6yqer28cCa").unwrap(); assert_eq!((result.0, result.1), (expected_hash, 88)); // 3rd key - with pubkey value before 1st key so it will be sorted first let key = Pubkey::from([10u8; 32]); let hash = Hash::new(&[2u8; 32]); let val = CalculateHashIntermediate::new(hash, 20, key); account_maps.insert(0, val); let result = accounts_hash.rest_of_hash_calculation( for_rest(account_maps.clone()), &mut HashStats::default(), true, PreviousPass::default(), one_range(), ); let expected_hash = Hash::from_str("EHv9C5vX7xQjjMpsJMzudnDTzoTSRwYkqLzY8tVMihGj").unwrap(); assert_eq!((result.0, result.1), (expected_hash, 108)); // 3rd key - with later slot let key = Pubkey::from([10u8; 32]); let hash = Hash::new(&[99u8; 32]); let val = CalculateHashIntermediate::new(hash, 30, key); account_maps.insert(1, val); let result = accounts_hash.rest_of_hash_calculation( for_rest(account_maps), &mut HashStats::default(), true, PreviousPass::default(), one_range(), ); let expected_hash = Hash::from_str("7NNPg5A8Xsg1uv4UFm6KZNwsipyyUnmgCrznP6MBWoBZ").unwrap(); assert_eq!((result.0, result.1), (expected_hash, 118)); } fn one_range() -> usize { 1 } fn zero_range() -> usize { 0 } #[test] fn test_accountsdb_multi_pass_rest_of_hash_calculation() { solana_logger::setup(); // passes: // 0: empty, NON-empty, empty, empty final // 1: NON-empty, empty final // 2: NON-empty, empty, empty final for pass in 0..3 { let mut account_maps = Vec::new(); let key = Pubkey::from([11u8; 32]); let hash = Hash::new(&[1u8; 32]); let val = CalculateHashIntermediate::new(hash, 88, key); account_maps.push(val); // 2nd key - zero lamports, so will be removed let key = Pubkey::from([12u8; 32]); let hash = Hash::new(&[2u8; 32]); let val = CalculateHashIntermediate::new(hash, ZERO_RAW_LAMPORTS_SENTINEL, key); account_maps.push(val); let mut previous_pass = PreviousPass::default(); let accounts_index = AccountsHash::default(); if pass == 0 { // first pass that is not last and is empty let result = accounts_index.rest_of_hash_calculation( vec![vec![vec![]]], &mut HashStats::default(), false, // not last pass previous_pass, one_range(), ); assert_eq!(result.0, Hash::default()); assert_eq!(result.1, 0); previous_pass = result.2; assert_eq!(previous_pass.remaining_unhashed.len(), 0); assert_eq!(previous_pass.reduced_hashes.len(), 0); assert_eq!(previous_pass.lamports, 0); } let result = accounts_index.rest_of_hash_calculation( for_rest(account_maps.clone()), &mut HashStats::default(), false, // not last pass previous_pass, one_range(), ); assert_eq!(result.0, Hash::default()); assert_eq!(result.1, 0); let mut previous_pass = result.2; assert_eq!(previous_pass.remaining_unhashed, vec![account_maps[0].hash]); assert_eq!(previous_pass.reduced_hashes.len(), 0); assert_eq!(previous_pass.lamports, account_maps[0].lamports); let expected_hash = Hash::from_str("8j9ARGFv4W2GfML7d3sVJK2MePwrikqYnu6yqer28cCa").unwrap(); let accounts_index = AccountsHash::default(); if pass == 2 { let result = accounts_index.rest_of_hash_calculation( vec![vec![vec![]]], &mut HashStats::default(), false, previous_pass, one_range(), ); previous_pass = result.2; assert_eq!(previous_pass.remaining_unhashed, vec![account_maps[0].hash]); assert_eq!(previous_pass.reduced_hashes.len(), 0); assert_eq!(previous_pass.lamports, account_maps[0].lamports); } let result = accounts_index.rest_of_hash_calculation( vec![vec![vec![]]], &mut HashStats::default(), true, // finally, last pass previous_pass, one_range(), ); let previous_pass = result.2; assert_eq!(previous_pass.remaining_unhashed.len(), 0); assert_eq!(previous_pass.reduced_hashes.len(), 0); assert_eq!(previous_pass.lamports, 0); assert_eq!((result.0, result.1), (expected_hash, 88)); } } #[test] fn test_accountsdb_multi_pass_rest_of_hash_calculation_partial() { solana_logger::setup(); let mut account_maps = Vec::new(); let key = Pubkey::from([11u8; 32]); let hash = Hash::new(&[1u8; 32]); let val = CalculateHashIntermediate::new(hash, 88, key); account_maps.push(val); let key = Pubkey::from([12u8; 32]); let hash = Hash::new(&[2u8; 32]); let val = CalculateHashIntermediate::new(hash, 20, key); account_maps.push(val); let accounts_hash = AccountsHash::default(); let result = accounts_hash.rest_of_hash_calculation( for_rest(vec![account_maps[0].clone()]), &mut HashStats::default(), false, // not last pass PreviousPass::default(), one_range(), ); assert_eq!(result.0, Hash::default()); assert_eq!(result.1, 0); let previous_pass = result.2; assert_eq!(previous_pass.remaining_unhashed, vec![account_maps[0].hash]); assert_eq!(previous_pass.reduced_hashes.len(), 0); assert_eq!(previous_pass.lamports, account_maps[0].lamports); let result = accounts_hash.rest_of_hash_calculation( for_rest(vec![account_maps[1].clone()]), &mut HashStats::default(), false, // not last pass previous_pass, one_range(), ); assert_eq!(result.0, Hash::default()); assert_eq!(result.1, 0); let previous_pass = result.2; assert_eq!( previous_pass.remaining_unhashed, vec![account_maps[0].hash, account_maps[1].hash] ); assert_eq!(previous_pass.reduced_hashes.len(), 0); let total_lamports_expected = account_maps[0].lamports + account_maps[1].lamports; assert_eq!(previous_pass.lamports, total_lamports_expected); let result = accounts_hash.rest_of_hash_calculation( vec![vec![vec![]]], &mut HashStats::default(), true, previous_pass, one_range(), ); let previous_pass = result.2; assert_eq!(previous_pass.remaining_unhashed.len(), 0); assert_eq!(previous_pass.reduced_hashes.len(), 0); assert_eq!(previous_pass.lamports, 0); let expected_hash = AccountsHash::compute_merkle_root( account_maps .iter() .map(|a| (a.pubkey, a.hash)) .collect::>(), MERKLE_FANOUT, ); assert_eq!( (result.0, result.1), (expected_hash, total_lamports_expected) ); } #[test] fn test_accountsdb_multi_pass_rest_of_hash_calculation_partial_hashes() { solana_logger::setup(); let mut account_maps = Vec::new(); let accounts_hash = AccountsHash::default(); const TARGET_FANOUT_LEVEL: usize = 3; let target_fanout = MERKLE_FANOUT.pow(TARGET_FANOUT_LEVEL as u32); let mut total_lamports_expected = 0; let plus1 = target_fanout + 1; for i in 0..plus1 * 2 { let lamports = (i + 1) as u64; total_lamports_expected += lamports; let key = Pubkey::new_unique(); let hash = Hash::new_unique(); let val = CalculateHashIntermediate::new(hash, lamports, key); account_maps.push(val); } let mut chunk = account_maps[0..plus1].to_vec(); chunk.sort_by(AccountsHash::compare_two_hash_entries); let sorted = chunk.clone(); // first 4097 hashes (1 left over) let result = accounts_hash.rest_of_hash_calculation( for_rest(chunk), &mut HashStats::default(), false, // not last pass PreviousPass::default(), one_range(), ); assert_eq!(result.0, Hash::default()); assert_eq!(result.1, 0); let previous_pass = result.2; let left_over_1 = sorted[plus1 - 1].hash; assert_eq!(previous_pass.remaining_unhashed, vec![left_over_1]); assert_eq!(previous_pass.reduced_hashes.len(), 1); let expected_hash = AccountsHash::compute_merkle_root( sorted[0..target_fanout] .iter() .map(|a| (a.pubkey, a.hash)) .collect::>(), MERKLE_FANOUT, ); assert_eq!(previous_pass.reduced_hashes[0], vec![expected_hash]); assert_eq!( previous_pass.lamports, account_maps[0..plus1] .iter() .map(|i| i.lamports) .sum::() ); let mut chunk = account_maps[plus1..plus1 * 2].to_vec(); chunk.sort_by(AccountsHash::compare_two_hash_entries); let sorted2 = chunk.clone(); let mut with_left_over = vec![left_over_1]; with_left_over.extend(sorted2[0..plus1 - 2].iter().cloned().map(|i| i.hash)); let expected_hash2 = AccountsHash::compute_merkle_root( with_left_over[0..target_fanout] .iter() .map(|a| (Pubkey::default(), *a)) .collect::>(), MERKLE_FANOUT, ); // second 4097 hashes (2 left over) let result = accounts_hash.rest_of_hash_calculation( for_rest(chunk), &mut HashStats::default(), false, // not last pass previous_pass, one_range(), ); assert_eq!(result.0, Hash::default()); assert_eq!(result.1, 0); let previous_pass = result.2; assert_eq!( previous_pass.remaining_unhashed, vec![sorted2[plus1 - 2].hash, sorted2[plus1 - 1].hash] ); assert_eq!(previous_pass.reduced_hashes.len(), 2); assert_eq!( previous_pass.reduced_hashes, vec![vec![expected_hash], vec![expected_hash2]] ); assert_eq!( previous_pass.lamports, account_maps[0..plus1 * 2] .iter() .map(|i| i.lamports) .sum::() ); let result = accounts_hash.rest_of_hash_calculation( vec![vec![vec![]]], &mut HashStats::default(), true, previous_pass, one_range(), ); let previous_pass = result.2; assert_eq!(previous_pass.remaining_unhashed.len(), 0); assert_eq!(previous_pass.reduced_hashes.len(), 0); assert_eq!(previous_pass.lamports, 0); let mut combined = sorted; combined.extend(sorted2); let expected_hash = AccountsHash::compute_merkle_root( combined .iter() .map(|a| (a.pubkey, a.hash)) .collect::>(), MERKLE_FANOUT, ); assert_eq!( (result.0, result.1), (expected_hash, total_lamports_expected) ); } #[test] fn test_accountsdb_de_dup_accounts_zero_chunks() { let vec = [vec![vec![CalculateHashIntermediate::default()]]]; let (hashes, lamports, _) = AccountsHash::default().de_dup_accounts_in_parallel(&vec, 0); assert_eq!(vec![&Hash::default()], hashes); assert_eq!(lamports, 0); } #[test] fn test_accountsdb_de_dup_accounts_empty() { solana_logger::setup(); let accounts_hash = AccountsHash::default(); let vec = vec![vec![], vec![]]; let (hashes, lamports) = accounts_hash.de_dup_and_eliminate_zeros(&vec, &mut HashStats::default(), one_range()); assert_eq!( vec![&Hash::default(); 0], hashes.into_iter().flatten().collect::>() ); assert_eq!(lamports, 0); let vec = vec![]; let (hashes, lamports) = accounts_hash.de_dup_and_eliminate_zeros(&vec, &mut HashStats::default(), zero_range()); let empty: Vec> = Vec::default(); assert_eq!(empty, hashes); assert_eq!(lamports, 0); let (hashes, lamports, _) = accounts_hash.de_dup_accounts_in_parallel(&[], 1); assert_eq!(vec![&Hash::default(); 0], hashes); assert_eq!(lamports, 0); let (hashes, lamports, _) = accounts_hash.de_dup_accounts_in_parallel(&[], 2); assert_eq!(vec![&Hash::default(); 0], hashes); assert_eq!(lamports, 0); } #[test] fn test_accountsdb_de_dup_accounts_from_stores() { solana_logger::setup(); let key_a = Pubkey::from([1u8; 32]); let key_b = Pubkey::from([2u8; 32]); let key_c = Pubkey::from([3u8; 32]); const COUNT: usize = 6; let hashes = (0..COUNT).into_iter().map(|i| Hash::new(&[i as u8; 32])); // create this vector // abbbcc let keys = [key_a, key_b, key_b, key_b, key_c, key_c]; let accounts: Vec<_> = hashes .zip(keys.iter()) .enumerate() .map(|(i, (hash, key))| CalculateHashIntermediate::new(hash, (i + 1) as u64, *key)) .collect(); type ExpectedType = (String, bool, u64, String); let expected:Vec = vec![ // ("key/lamports key2/lamports ...", // is_last_slice // result lamports // result hashes) // "a5" = key_a, 5 lamports ("a1", false, 1, "[11111111111111111111111111111111]"), ("a1b2", false, 3, "[11111111111111111111111111111111, 4vJ9JU1bJJE96FWSJKvHsmmFADCg4gpZQff4P3bkLKi]"), ("a1b2b3", false, 4, "[11111111111111111111111111111111, 8qbHbw2BbbTHBW1sbeqakYXVKRQM8Ne7pLK7m6CVfeR]"), ("a1b2b3b4", false, 5, "[11111111111111111111111111111111, CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"), ("a1b2b3b4c5", false, 10, "[11111111111111111111111111111111, CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"), ("b2", false, 2, "[4vJ9JU1bJJE96FWSJKvHsmmFADCg4gpZQff4P3bkLKi]"), ("b2b3", false, 3, "[8qbHbw2BbbTHBW1sbeqakYXVKRQM8Ne7pLK7m6CVfeR]"), ("b2b3b4", false, 4, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"), ("b2b3b4c5", false, 9, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"), ("b3", false, 3, "[8qbHbw2BbbTHBW1sbeqakYXVKRQM8Ne7pLK7m6CVfeR]"), ("b3b4", false, 4, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"), ("b3b4c5", false, 9, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"), ("b4", false, 4, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"), ("b4c5", false, 9, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"), ("c5", false, 5, "[GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"), ("a1", true, 1, "[11111111111111111111111111111111]"), ("a1b2", true, 3, "[11111111111111111111111111111111, 4vJ9JU1bJJE96FWSJKvHsmmFADCg4gpZQff4P3bkLKi]"), ("a1b2b3", true, 4, "[11111111111111111111111111111111, 8qbHbw2BbbTHBW1sbeqakYXVKRQM8Ne7pLK7m6CVfeR]"), ("a1b2b3b4", true, 5, "[11111111111111111111111111111111, CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"), ("a1b2b3b4c5", true, 10, "[11111111111111111111111111111111, CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"), ("b2", true, 2, "[4vJ9JU1bJJE96FWSJKvHsmmFADCg4gpZQff4P3bkLKi]"), ("b2b3", true, 3, "[8qbHbw2BbbTHBW1sbeqakYXVKRQM8Ne7pLK7m6CVfeR]"), ("b2b3b4", true, 4, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"), ("b2b3b4c5", true, 9, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"), ("b3", true, 3, "[8qbHbw2BbbTHBW1sbeqakYXVKRQM8Ne7pLK7m6CVfeR]"), ("b3b4", true, 4, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"), ("b3b4c5", true, 9, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"), ("b4", true, 4, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"), ("b4c5", true, 9, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"), ("c5", true, 5, "[GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"), ].into_iter().map(|item| { let result: ExpectedType = ( item.0.to_string(), item.1, item.2, item.3.to_string(), ); result }).collect(); let hash = AccountsHash::default(); let mut expected_index = 0; for last_slice in 0..2 { for start in 0..COUNT { for end in start + 1..COUNT { let is_last_slice = last_slice == 1; let accounts = accounts.clone(); let slice = &accounts[start..end]; let slice2 = vec![vec![slice.to_vec()]]; let slice = &slice2[..]; let (hashes2, lamports2, _) = hash.de_dup_accounts_in_parallel(slice, 0); let (hashes3, lamports3, _) = hash.de_dup_accounts_in_parallel(slice, 0); let vec = slice.to_vec(); let (hashes4, lamports4) = hash.de_dup_and_eliminate_zeros( &vec, &mut HashStats::default(), end - start, ); let vec = slice.to_vec(); let (hashes5, lamports5) = hash.de_dup_and_eliminate_zeros( &vec, &mut HashStats::default(), end - start, ); let vec = slice.to_vec(); let (hashes6, lamports6) = hash.de_dup_and_eliminate_zeros( &vec, &mut HashStats::default(), end - start, ); assert_eq!(hashes2, hashes3); let expected2 = hashes2.clone(); assert_eq!( expected2, hashes4.into_iter().flatten().collect::>(), "last_slice: {}, start: {}, end: {}, slice: {:?}", last_slice, start, end, slice ); assert_eq!( expected2.clone(), hashes5.iter().flatten().copied().collect::>(), "last_slice: {}, start: {}, end: {}, slice: {:?}", last_slice, start, end, slice ); assert_eq!( expected2.clone(), hashes6.iter().flatten().copied().collect::>() ); assert_eq!(lamports2, lamports3); assert_eq!(lamports2, lamports4); assert_eq!(lamports2, lamports5); assert_eq!(lamports2, lamports6); let human_readable = slice[0][0] .iter() .map(|v| { let mut s = (if v.pubkey == key_a { "a" } else if v.pubkey == key_b { "b" } else { "c" }) .to_string(); s.push_str(&v.lamports.to_string()); s }) .collect::(); let hash_result_as_string = format!("{:?}", hashes2); let packaged_result: ExpectedType = ( human_readable, is_last_slice, lamports2 as u64, hash_result_as_string, ); assert_eq!(expected[expected_index], packaged_result); // for generating expected results // error!("{:?},", packaged_result); expected_index += 1; } } } } #[test] fn test_accountsdb_compare_two_hash_entries() { solana_logger::setup(); let key = Pubkey::new_unique(); let hash = Hash::new_unique(); let val = CalculateHashIntermediate::new(hash, 1, key); // slot same, version < let hash2 = Hash::new_unique(); let val2 = CalculateHashIntermediate::new(hash2, 4, key); assert_eq!( std::cmp::Ordering::Equal, // no longer comparing slots or versions AccountsHash::compare_two_hash_entries(&val, &val2) ); // slot same, vers = let hash3 = Hash::new_unique(); let val3 = CalculateHashIntermediate::new(hash3, 2, key); assert_eq!( std::cmp::Ordering::Equal, AccountsHash::compare_two_hash_entries(&val, &val3) ); // slot same, vers > let hash4 = Hash::new_unique(); let val4 = CalculateHashIntermediate::new(hash4, 6, key); assert_eq!( std::cmp::Ordering::Equal, // no longer comparing slots or versions AccountsHash::compare_two_hash_entries(&val, &val4) ); // slot >, version < let hash5 = Hash::new_unique(); let val5 = CalculateHashIntermediate::new(hash5, 8, key); assert_eq!( std::cmp::Ordering::Equal, // no longer comparing slots or versions AccountsHash::compare_two_hash_entries(&val, &val5) ); } fn test_de_dup_accounts_in_parallel( account_maps: &[Vec>], ) -> (Vec<&Hash>, u64, usize) { AccountsHash::default().de_dup_accounts_in_parallel(account_maps, 0) } #[test] fn test_accountsdb_remove_zero_balance_accounts() { solana_logger::setup(); let key = Pubkey::new_unique(); let hash = Hash::new_unique(); let mut account_maps = Vec::new(); let val = CalculateHashIntermediate::new(hash, 1, key); account_maps.push(val.clone()); let vecs = vec![vec![account_maps.to_vec()]]; let result = test_de_dup_accounts_in_parallel(&vecs); assert_eq!(result, (vec![&val.hash], val.lamports as u64, 1)); // zero original lamports, higher version let val = CalculateHashIntermediate::new(hash, ZERO_RAW_LAMPORTS_SENTINEL, key); account_maps.push(val); // has to be after previous entry since account_maps are in slot order let vecs = vec![vec![account_maps.to_vec()]]; let result = test_de_dup_accounts_in_parallel(&vecs); assert_eq!(result, (vec![], 0, 2)); } #[test] fn test_accountsdb_cumulative_offsets1_d() { let input = vec![vec![0, 1], vec![], vec![2, 3, 4], vec![]]; let cumulative = CumulativeOffsets::from_raw(&input); let src: Vec<_> = input.clone().into_iter().flatten().collect(); let len = src.len(); assert_eq!(cumulative.total_count, len); assert_eq!(cumulative.cumulative_offsets.len(), 2); // 2 non-empty vectors const DIMENSION: usize = 0; assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION], 0); assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION], 2); assert_eq!(cumulative.cumulative_offsets[0].start_offset, 0); assert_eq!(cumulative.cumulative_offsets[1].start_offset, 2); for start in 0..len { let slice = cumulative.get_slice(&input, start); let len = slice.len(); assert!(len > 0); assert_eq!(&src[start..(start + len)], slice); } let input = vec![vec![], vec![0, 1], vec![], vec![2, 3, 4], vec![]]; let cumulative = CumulativeOffsets::from_raw(&input); let src: Vec<_> = input.clone().into_iter().flatten().collect(); let len = src.len(); assert_eq!(cumulative.total_count, len); assert_eq!(cumulative.cumulative_offsets.len(), 2); // 2 non-empty vectors assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION], 1); assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION], 3); assert_eq!(cumulative.cumulative_offsets[0].start_offset, 0); assert_eq!(cumulative.cumulative_offsets[1].start_offset, 2); for start in 0..len { let slice = cumulative.get_slice(&input, start); let len = slice.len(); assert!(len > 0); assert_eq!(&src[start..(start + len)], slice); } let input: Vec> = vec![vec![]]; let cumulative = CumulativeOffsets::from_raw(&input); let len = input.into_iter().flatten().count(); assert_eq!(cumulative.total_count, len); assert_eq!(cumulative.cumulative_offsets.len(), 0); // 2 non-empty vectors } #[should_panic(expected = "is_empty")] #[test] fn test_accountsdb_cumulative_find_empty() { let input = CumulativeOffsets { cumulative_offsets: vec![], total_count: 0, }; input.find(0); } #[test] fn test_accountsdb_cumulative_find() { let input = CumulativeOffsets { cumulative_offsets: vec![CumulativeOffset { index: vec![0], start_offset: 0, }], total_count: 0, }; assert_eq!(input.find(0), (0, &input.cumulative_offsets[0])); let input = CumulativeOffsets { cumulative_offsets: vec![ CumulativeOffset { index: vec![0], start_offset: 0, }, CumulativeOffset { index: vec![1], start_offset: 2, }, ], total_count: 0, }; assert_eq!(input.find(0), (0, &input.cumulative_offsets[0])); // = first start_offset assert_eq!(input.find(1), (1, &input.cumulative_offsets[0])); // > first start_offset assert_eq!(input.find(2), (0, &input.cumulative_offsets[1])); // = last start_offset assert_eq!(input.find(3), (1, &input.cumulative_offsets[1])); // > last start_offset } #[test] fn test_accountsdb_cumulative_offsets2_d() { let input: Vec>> = vec![vec![vec![0, 1], vec![], vec![2, 3, 4], vec![]]]; let cumulative = CumulativeOffsets::from_raw_2d(&input); let src: Vec<_> = input .clone() .into_iter() .flatten() .into_iter() .flatten() .collect(); let len = src.len(); assert_eq!(cumulative.total_count, len); assert_eq!(cumulative.cumulative_offsets.len(), 2); // 2 non-empty vectors const DIMENSION_0: usize = 0; const DIMENSION_1: usize = 1; assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION_0], 0); assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION_1], 0); assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION_0], 0); assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION_1], 2); assert_eq!(cumulative.cumulative_offsets[0].start_offset, 0); assert_eq!(cumulative.cumulative_offsets[1].start_offset, 2); for start in 0..len { let slice: &[u64] = cumulative.get_slice(&input, start); let len = slice.len(); assert!(len > 0); assert_eq!(&src[start..(start + len)], slice); } let input = vec![vec![vec![], vec![0, 1], vec![], vec![2, 3, 4], vec![]]]; let cumulative = CumulativeOffsets::from_raw_2d(&input); let src: Vec<_> = input .clone() .into_iter() .flatten() .into_iter() .flatten() .collect(); let len = src.len(); assert_eq!(cumulative.total_count, len); assert_eq!(cumulative.cumulative_offsets.len(), 2); // 2 non-empty vectors assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION_0], 0); assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION_1], 1); assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION_0], 0); assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION_1], 3); assert_eq!(cumulative.cumulative_offsets[0].start_offset, 0); assert_eq!(cumulative.cumulative_offsets[1].start_offset, 2); for start in 0..len { let slice: &[u64] = cumulative.get_slice(&input, start); let len = slice.len(); assert!(len > 0); assert_eq!(&src[start..(start + len)], slice); } let input: Vec>> = vec![vec![]]; let cumulative = CumulativeOffsets::from_raw_2d(&input); let len = input.into_iter().flatten().count(); assert_eq!(cumulative.total_count, len); assert_eq!(cumulative.cumulative_offsets.len(), 0); // 2 non-empty vectors let input = vec![ vec![vec![0, 1]], vec![vec![]], vec![vec![], vec![2, 3, 4], vec![]], ]; let cumulative = CumulativeOffsets::from_raw_2d(&input); let src: Vec<_> = input .clone() .into_iter() .flatten() .into_iter() .flatten() .collect(); let len = src.len(); assert_eq!(cumulative.total_count, len); assert_eq!(cumulative.cumulative_offsets.len(), 2); // 2 non-empty vectors assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION_0], 0); assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION_1], 0); assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION_0], 2); assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION_1], 1); assert_eq!(cumulative.cumulative_offsets[0].start_offset, 0); assert_eq!(cumulative.cumulative_offsets[1].start_offset, 2); for start in 0..len { let slice: &[u64] = cumulative.get_slice(&input, start); let len = slice.len(); assert!(len > 0); assert_eq!(&src[start..(start + len)], slice); } } fn test_hashing_larger(hashes: Vec<(Pubkey, Hash)>, fanout: usize) -> Hash { let result = AccountsHash::compute_merkle_root(hashes.clone(), fanout); let reduced: Vec<_> = hashes.iter().map(|x| x.1).collect(); let result2 = test_hashing(reduced, fanout); assert_eq!(result, result2, "len: {}", hashes.len()); result } fn test_hashing(hashes: Vec, fanout: usize) -> Hash { let temp: Vec<_> = hashes.iter().map(|h| (Pubkey::default(), *h)).collect(); let result = AccountsHash::compute_merkle_root(temp, fanout); let reduced: Vec<_> = hashes.clone(); let result2 = AccountsHash::compute_merkle_root_from_slices( hashes.len(), fanout, None, |start| &reduced[start..], None, ); assert_eq!(result, result2.0, "len: {}", hashes.len()); let result2 = AccountsHash::compute_merkle_root_from_slices( hashes.len(), fanout, Some(1), |start| &reduced[start..], None, ); assert_eq!(result, result2.0, "len: {}", hashes.len()); let max = std::cmp::min(reduced.len(), fanout * 2); for left in 0..max { for right in left + 1..max { let src = vec![ vec![reduced[0..left].to_vec(), reduced[left..right].to_vec()], vec![reduced[right..].to_vec()], ]; let offsets = CumulativeOffsets::from_raw_2d(&src); let get_slice = |start: usize| -> &[Hash] { offsets.get_slice(&src, start) }; let result2 = AccountsHash::compute_merkle_root_from_slices( offsets.total_count, fanout, None, get_slice, None, ); assert_eq!(result, result2.0); } } result } #[test] fn test_accountsdb_compute_merkle_root_large() { solana_logger::setup(); // handle fanout^x -1, +0, +1 for a few 'x's const FANOUT: usize = 3; let mut hash_counts: Vec<_> = (1..6) .flat_map(|x| { let mark = FANOUT.pow(x); vec![mark - 1, mark, mark + 1] }) .collect(); // saturate the test space for threshold to threshold + target // this hits right before we use the 3 deep optimization and all the way through all possible partial last chunks let target = FANOUT.pow(3); let threshold = target * FANOUT; hash_counts.extend(threshold - 1..=threshold + target); for hash_count in hash_counts { let hashes: Vec<_> = (0..hash_count) .into_iter() .map(|_| Hash::new_unique()) .collect(); test_hashing(hashes, FANOUT); } } #[test] fn test_accountsdb_compute_merkle_root() { solana_logger::setup(); let expected_results = vec![ (0, 0, "GKot5hBsd81kMupNCXHaqbhv3huEbxAFMLnpcX2hniwn", 0), (0, 1, "8unXKJYTxrR423HgQxbDmx29mFri1QNrzVKKDxEfc6bj", 0), (0, 2, "6QfkevXLLqbfAaR1kVjvMLFtEXvNUVrpmkwXqgsYtCFW", 1), (0, 3, "G3FrJd9JrXcMiqChTSfvEdBL2sCPny3ebiUy9Xxbn7a2", 3), (0, 4, "G3sZXHhwoCFuNyWy7Efffr47RBW33ibEp7b2hqNDmXdu", 6), (0, 5, "78atJJYpokAPKMJwHxUW8SBDvPkkSpTBV7GiB27HwosJ", 10), (0, 6, "7c9SM2BmCRVVXdrEdKcMK91MviPqXqQMd8QAb77tgLEy", 15), (0, 7, "3hsmnZPhf22UvBLiZ4dVa21Qsdh65CCrtYXsb8MxoVAa", 21), (0, 8, "5bwXUiC6RCRhb8fqvjvUXT6waU25str3UXA3a6Aq1jux", 28), (0, 9, "3NNtQKH6PaYpCnFBtyi2icK9eYX3YM5pqA3SKaXtUNzu", 36), (1, 0, "GKot5hBsd81kMupNCXHaqbhv3huEbxAFMLnpcX2hniwn", 0), (1, 1, "4GWVCsnEu1iRyxjAB3F7J7C4MMvcoxFWtP9ihvwvDgxY", 0), (1, 2, "8ML8Te6Uw2mipFr2v9sMZDcziXzhVqJo2qeMJohg1CJx", 1), (1, 3, "AMEuC3AgqAeRBGBhSfTmuMdfbAiXJnGmKv99kHmcAE1H", 3), (1, 4, "HEnDuJLHpsQfrApimGrovTqPEF6Vkrx2dKFr3BDtYzWx", 6), (1, 5, "6rH69iP2yM1o565noZN1EqjySW4PhYUskz3c5tXePUfV", 10), (1, 6, "7qEQMEXdfSPjbZ3q4cuuZwebDMvTvuaQ3dBiHoDUKo9a", 15), (1, 7, "GDJz7LSKYjqqz6ujCaaQRJRmQ7TLNCwYJhdT84qT4qwk", 21), (1, 8, "HT9krPLVTo3rr5WZQBQFrbqWs8SbYScXfnt8EVuobboM", 28), (1, 9, "8y2pMgqMdRsvqw6BQXm6wtz3qxGPss72i6H6gVpPyeda", 36), ]; let mut expected_index = 0; let start = 0; let default_fanout = 2; // test 0..3 recursions (at fanout = 2) and 1 item remainder. The internals have 1 special case first loop and subsequent loops are the same types. let iterations = default_fanout * default_fanout * default_fanout + 2; for pass in 0..2 { let fanout = if pass == 0 { default_fanout } else { MERKLE_FANOUT }; for count in start..iterations { let mut input: Vec<_> = (0..count) .map(|i| { let key = Pubkey::from([(pass * iterations + count) as u8; 32]); let hash = Hash::new(&[(pass * iterations + count + i + 1) as u8; 32]); (key, hash) }) .collect(); let result = if pass == 0 { test_hashing_larger(input.clone(), fanout) } else { // this sorts inside let early_result = AccountsHash::accumulate_account_hashes( input.iter().map(|i| (i.0, i.1)).collect::>(), ); AccountsHash::sort_hashes_by_pubkey(&mut input); let result = AccountsHash::compute_merkle_root(input.clone(), fanout); assert_eq!(early_result, result); result }; // compare against captured, expected results for hash (and lamports) assert_eq!( ( pass, count, &*(result.to_string()), expected_results[expected_index].3 ), // we no longer calculate lamports expected_results[expected_index] ); expected_index += 1; } } } #[test] #[should_panic(expected = "overflow is detected while summing capitalization")] fn test_accountsdb_lamport_overflow() { solana_logger::setup(); let offset = 2; let input = vec![ CalculateHashIntermediate::new( Hash::new(&[1u8; 32]), u64::MAX - offset, Pubkey::new_unique(), ), CalculateHashIntermediate::new(Hash::new(&[2u8; 32]), offset + 1, Pubkey::new_unique()), ]; AccountsHash::default().de_dup_accounts_in_parallel(&[vec![input]], 0); } #[test] #[should_panic(expected = "overflow is detected while summing capitalization")] fn test_accountsdb_lamport_overflow2() { solana_logger::setup(); let offset = 2; let input = vec![ vec![CalculateHashIntermediate::new( Hash::new(&[1u8; 32]), u64::MAX - offset, Pubkey::new_unique(), )], vec![CalculateHashIntermediate::new( Hash::new(&[2u8; 32]), offset + 1, Pubkey::new_unique(), )], ]; AccountsHash::default().de_dup_and_eliminate_zeros( &[input], &mut HashStats::default(), 2, // accounts above are in 2 groups ); } }