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solana/runtime/src/accounts_hash.rs

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Rust
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use {
crate::{
accounts_db::{AccountStorageEntry, IncludeSlotInHash, PUBKEY_BINS_FOR_CALCULATING_HASHES},
ancestors::Ancestors,
rent_collector::RentCollector,
},
core::ops::Range,
log::*,
memmap2::MmapMut,
rayon::prelude::*,
solana_measure::measure::Measure,
solana_sdk::{
hash::{Hash, Hasher},
pubkey::Pubkey,
slot_history::Slot,
sysvar::epoch_schedule::EpochSchedule,
},
std::{
borrow::Borrow,
convert::TryInto,
fs::File,
io::{BufWriter, Write},
sync::{
atomic::{AtomicU64, AtomicUsize, Ordering},
Arc, Mutex,
},
},
tempfile::tempfile,
};
pub const MERKLE_FANOUT: usize = 16;
/// the data passed through the processing functions
pub type SortedDataByPubkey<'a> = Vec<&'a [CalculateHashIntermediate]>;
/// 1 file containing account hashes sorted by pubkey, mapped into memory
struct MmapAccountHashesFile {
mmap: MmapMut,
}
impl MmapAccountHashesFile {
/// return a slice of account hashes starting at 'index'
fn read(&self, index: usize) -> &[Hash] {
let start = std::mem::size_of::<Hash>() * index;
let item_slice: &[u8] = &self.mmap[start..];
let remaining_elements = item_slice.len() / std::mem::size_of::<Hash>();
unsafe {
let item = item_slice.as_ptr() as *const Hash;
std::slice::from_raw_parts(item, remaining_elements)
}
}
}
/// 1 file containing account hashes sorted by pubkey
#[derive(Default)]
pub struct AccountHashesFile {
/// # hashes and an open file that will be deleted on drop. None if there are zero hashes to represent, and thus, no file.
count_and_writer: Option<(usize, BufWriter<File>)>,
}
impl AccountHashesFile {
/// map the file into memory and return a reader that can access it by slice
fn get_reader(&mut self) -> Option<(usize, MmapAccountHashesFile)> {
std::mem::take(&mut self.count_and_writer).map(|(count, writer)| {
let file = Some(writer.into_inner().unwrap());
(
count,
MmapAccountHashesFile {
mmap: unsafe { MmapMut::map_mut(file.as_ref().unwrap()).unwrap() },
},
)
})
}
/// # hashes stored in this file
pub fn count(&self) -> usize {
self.count_and_writer
.as_ref()
.map(|(count, _)| *count)
.unwrap_or_default()
}
/// write 'hash' to the file
/// If the file isn't open, create it first.
pub fn write(&mut self, hash: &Hash) {
if self.count_and_writer.is_none() {
// we have hashes to write but no file yet, so create a file that will auto-delete on drop
self.count_and_writer = Some((0, BufWriter::new(tempfile().unwrap())));
}
let mut count_and_writer = self.count_and_writer.as_mut().unwrap();
assert_eq!(
std::mem::size_of::<Hash>(),
count_and_writer.1.write(hash.as_ref()).unwrap()
);
count_and_writer.0 += 1;
}
}
/// 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
pub check_hash: bool,
/// 'ancestors' is used to get storages
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 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,
pub include_slot_in_hash: IncludeSlotInHash,
}
// smallest, 3 quartiles, largest, average
pub type StorageSizeQuartileStats = [usize; 6];
#[derive(Debug, Default)]
pub struct HashStats {
pub mark_time_us: u64,
pub scan_time_total_us: u64,
pub zeros_time_total_us: u64,
pub hash_time_total_us: u64,
pub sort_time_total_us: u64,
pub hash_total: usize,
pub unreduced_entries: usize,
pub num_snapshot_storage: usize,
pub scan_chunks: usize,
pub num_slots: usize,
pub num_dirty_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,
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,
pub longest_ancient_scan_us: AtomicU64,
pub sum_ancient_scans_us: AtomicU64,
pub count_ancient_scans: AtomicU64,
}
impl HashStats {
pub fn calc_storage_size_quartiles(&mut self, storages: &[Arc<AccountStorageEntry>]) {
let mut sum = 0;
let mut sizes = storages
.iter()
.map(|storage| {
let cap = storage.accounts.capacity() as usize;
sum += cap;
cap
})
.collect::<Vec<_>>();
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,
]
};
}
pub 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_from_storages",
("mark_time_us", self.mark_time_us, i64),
("accounts_scan_us", self.scan_time_total_us, i64),
("eliminate_zeros_us", self.zeros_time_total_us, i64),
("hash_us", self.hash_time_total_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
),
("scan_chunks", self.scan_chunks as i64, i64),
("num_slots", self.num_slots as i64, i64),
("num_dirty_slots", self.num_dirty_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_us", total_time_us 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
),
(
"longest_ancient_scan_us",
self.longest_ancient_scan_us.load(Ordering::Relaxed) as i64,
i64
),
(
"sum_ancient_scans_us",
self.sum_ancient_scans_us.load(Ordering::Relaxed) as i64,
i64
),
(
"count_ancient_scans",
self.count_ancient_scans.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
),
);
}
}
/// While scanning appendvecs, this is the info that needs to be extracted, de-duped, and sorted from what is stored in an append vec.
/// Note this can be saved/loaded during hash calculation to a memory mapped file whose contents are
/// [CalculateHashIntermediate]
#[repr(C)]
#[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<usize>,
pub start_offset: usize,
}
impl CumulativeOffset {
pub fn new(index: Vec<usize>, 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<Vec<T>> {
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<Vec<Vec<T>>> {
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<T>, where src is really Vec<Vec<T>> (or later Vec<Vec<Vec<T>>>)
// This model prevents callers from having to flatten which saves both working memory and time.
#[derive(Default, Debug)]
pub struct CumulativeOffsets {
cumulative_offsets: Vec<CumulativeOffset>,
total_count: usize,
}
/// used by merkle tree calculation to lookup account hashes by overall index
#[derive(Default)]
pub struct CumulativeHashesFromFiles {
/// source of hashes in order
readers: Vec<MmapAccountHashesFile>,
/// look up reader index and offset by overall index
cumulative: CumulativeOffsets,
}
impl CumulativeHashesFromFiles {
/// Calculate offset from overall index to which file and offset within that file based on the length of each hash file.
/// Also collect readers to access the data.
pub fn from_files(hashes: Vec<AccountHashesFile>) -> Self {
let mut readers = Vec::with_capacity(hashes.len());
let cumulative = CumulativeOffsets::new(hashes.into_iter().filter_map(|mut hash_file| {
// ignores all hashfiles that have zero entries
hash_file.get_reader().map(|(count, reader)| {
readers.push(reader);
count
})
}));
Self {
cumulative,
readers,
}
}
/// total # of items referenced
pub fn total_count(&self) -> usize {
self.cumulative.total_count
}
// return the biggest slice possible that starts at the overall index 'start'
pub fn get_slice(&self, start: usize) -> &[Hash] {
let (start, offset) = self.cumulative.find(start);
let data_source_index = offset.index[0];
let data = &self.readers[data_source_index];
// unwrap here because we should never ask for data that doesn't exist. If we do, then cumulative calculated incorrectly.
data.read(start)
}
}
impl CumulativeOffsets {
pub fn new<I>(iter: I) -> Self
where
I: Iterator<Item = usize>,
{
let mut total_count: usize = 0;
let cumulative_offsets: Vec<_> = iter
.enumerate()
.filter_map(|(i, 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<T>(raw: &[Vec<T>]) -> Self {
Self::new(raw.iter().map(|v| v.len()))
}
pub fn from_raw_2d<T>(raw: &[Vec<Vec<T>>]) -> Self {
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,
}
}
/// find the index of the data source that contains 'start'
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
}
}
/// given overall start index 'start'
/// return ('start', which is the offset into the data source at 'index',
/// and 'index', which is the data source to use)
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)]
pub struct AccountsHasher {
pub filler_account_suffix: Option<Pubkey>,
pub zero_lamport_accounts: ZeroLamportAccounts,
}
impl Default for AccountsHasher {
fn default() -> Self {
Self {
filler_account_suffix: None,
zero_lamport_accounts: ZeroLamportAccounts::Excluded,
}
}
}
impl AccountsHasher {
/// 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<Vec<Hash>>) -> (Hash, usize) {
let cumulative_offsets = CumulativeOffsets::from_raw(&hashes);
let hash_total = cumulative_offsets.total_count;
let result = AccountsHasher::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<Hash>, fanout: usize) -> Hash {
Self::compute_merkle_root_loop(hashes, fanout, |t| 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<T, F>(hashes: Vec<T>, 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<usize>,
specific_level_count: Option<usize>,
) -> (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<usize>,
get_hash_slice_starting_at_index: F,
specific_level_count: Option<usize>,
) -> (Hash, Vec<Hash>)
where
// returns a slice of hashes starting at the given overall index
F: Fn(usize) -> &'a [T] + std::marker::Sync,
T: Borrow<Hash> + 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<Hash>,
fanout: usize,
max_levels_per_pass: Option<usize>,
specific_level_count: Option<usize>,
) -> (Hash, Vec<Hash>) {
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().unwrap_or_else(|_| {
panic!("overflow is detected while summing capitalization: {balance}")
})
}
/// return references to cache hash data, grouped by bin, sourced from 'sorted_data_by_pubkey',
/// which is probably a mmapped file.
pub(crate) fn get_binned_data<'a>(
sorted_data_by_pubkey: &'a Vec<&'a [CalculateHashIntermediate]>,
bins: usize,
bin_range: &Range<usize>,
) -> Vec<Vec<&'a [CalculateHashIntermediate]>> {
// get slices per bin from each slice
use crate::pubkey_bins::PubkeyBinCalculator24;
let binner = PubkeyBinCalculator24::new(bins);
sorted_data_by_pubkey
.par_iter()
.map(|all_bins| {
let mut last_start_index = 0;
let mut result = Vec::with_capacity(bin_range.len());
let mut current_bin = bin_range.start;
let max_inclusive = all_bins.len();
for i in 0..=max_inclusive {
let this_bin = if i != max_inclusive {
let entry = &all_bins[i];
let this_bin = binner.bin_from_pubkey(&entry.pubkey);
if this_bin == current_bin {
// this pk is in the same bin as we're currently investigating, so keep iterating
continue;
}
this_bin
} else {
// we exhausted the source data, so 'this bin' is now the end (exclusive) bin
// this case exists to handle the +1 case
bin_range.end
};
// we found the first pubkey in the bin after the bin we were investigating
// or we passed the end of the input list.
// So, the bin we were investigating is now complete.
result.push(&all_bins[last_start_index..i]);
last_start_index = i;
((current_bin + 1)..this_bin).for_each(|_| {
// the source data could contain a pubey from bin 1, then bin 5, skipping the bins in between.
// In that case, fill in 2..5 with empty
result.push(&all_bins[0..0]); // empty slice
});
current_bin = this_bin;
}
result
})
.collect::<Vec<_>>()
}
/// returns:
/// Vec, with one entry per bin
/// for each entry, Vec<Hash> in pubkey order
/// If return Vec<AccountHashesFile> was flattened, it would be all hashes, in pubkey order.
fn de_dup_accounts<'a>(
&self,
sorted_data_by_pubkey: &'a [SortedDataByPubkey<'a>],
stats: &mut HashStats,
max_bin: usize,
) -> (Vec<AccountHashesFile>, 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_file, 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_file.count();
*lock = (min, max, lamports_sum, entries, hash_total);
}
hashes_file
})
.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>,
pubkey_division: &'b [SortedDataByPubkey<'b>],
indexes: &'a mut [usize],
first_item_to_pubkey_division: &'a mut Vec<usize>,
) -> &'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. handle zero lamport accounts
// 2. pick the highest slot or (slot = and highest version) of each pubkey
// 3. produce this output:
// a. AccountHashesFile: individual account 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 [SortedDataByPubkey<'a>],
pubkey_bin: usize,
) -> (AccountHashesFile, u64, usize) {
let len = pubkey_division.len();
let mut unreduced_count = 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);
let mut hashes = AccountHashesFile::default();
// 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() {
unreduced_count += 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 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 and get hash
if item.lamports != 0 {
// do not include filler accounts in the hash
if !(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.write(&item.hash);
}
} else {
// if lamports == 0, check if they should be included
if self.zero_lamport_accounts == ZeroLamportAccounts::Included {
// For incremental accounts hash, the hash of a zero lamport account is
// the hash of its pubkey
let hash = blake3::hash(bytemuck::bytes_of(&item.pubkey));
let hash = Hash::new_from_array(hash.into());
hashes.write(&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, unreduced_count)
}
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<SortedDataByPubkey<'_>>,
mut stats: &mut HashStats,
) -> (Hash, u64) {
let (hashes, total_lamports) = self.de_dup_accounts(
&data_sections_by_pubkey,
stats,
PUBKEY_BINS_FOR_CALCULATING_HASHES,
);
let cumulative = CumulativeHashesFromFiles::from_files(hashes);
let mut hash_time = Measure::start("hash");
let (hash, _) = Self::compute_merkle_root_from_slices(
cumulative.total_count(),
MERKLE_FANOUT,
None,
|start| cumulative.get_slice(start),
None,
);
hash_time.stop();
stats.hash_time_total_us += hash_time.as_us();
(hash, total_lamports)
}
}
/// How should zero-lamport accounts be treated by the accounts hasher?
#[derive(Debug, Copy, Clone, Eq, PartialEq)]
pub enum ZeroLamportAccounts {
Excluded,
Included,
}
/// Hash of accounts
#[derive(Debug, Copy, Clone, Eq, PartialEq)]
pub enum AccountsHashEnum {
Full(AccountsHash),
Incremental(IncrementalAccountsHash),
}
impl AccountsHashEnum {
pub fn as_hash(&self) -> &Hash {
match self {
AccountsHashEnum::Full(AccountsHash(hash))
| AccountsHashEnum::Incremental(IncrementalAccountsHash(hash)) => hash,
}
}
}
impl From<AccountsHash> for AccountsHashEnum {
fn from(accounts_hash: AccountsHash) -> Self {
AccountsHashEnum::Full(accounts_hash)
}
}
impl From<IncrementalAccountsHash> for AccountsHashEnum {
fn from(incremental_accounts_hash: IncrementalAccountsHash) -> Self {
AccountsHashEnum::Incremental(incremental_accounts_hash)
}
}
/// Hash of accounts
#[derive(Debug, Copy, Clone, Eq, PartialEq)]
pub struct AccountsHash(pub Hash);
/// Hash of accounts that includes zero-lamport accounts
/// Used with incremental snapshots
#[derive(Debug, Copy, Clone, Eq, PartialEq)]
pub struct IncrementalAccountsHash(pub Hash);
/// Hash of accounts written in a single slot
#[derive(Debug, Copy, Clone, Eq, PartialEq)]
pub struct AccountsDeltaHash(pub Hash);
#[cfg(test)]
pub mod tests {
use {super::*, std::str::FromStr};
#[test]
fn test_account_hashes_file() {
// 0 hashes
let mut file = AccountHashesFile::default();
assert!(file.get_reader().is_none());
let hashes = (0..2).map(|i| Hash::new(&[i; 32])).collect::<Vec<_>>();
// 1 hash
file.write(&hashes[0]);
let reader = file.get_reader().unwrap();
assert_eq!(&[hashes[0]][..], reader.1.read(0));
assert!(reader.1.read(1).is_empty());
// multiple hashes
let mut file = AccountHashesFile::default();
assert!(file.get_reader().is_none());
hashes.iter().for_each(|hash| file.write(hash));
let reader = file.get_reader().unwrap();
(0..2).for_each(|i| assert_eq!(&hashes[i..], reader.1.read(i)));
assert!(reader.1.read(2).is_empty());
}
#[test]
fn test_cumulative_hashes_from_files() {
(0..4).for_each(|permutation| {
let hashes = (0..2).map(|i| Hash::new(&[i + 1; 32])).collect::<Vec<_>>();
let mut combined = Vec::default();
// 0 hashes
let file0 = AccountHashesFile::default();
// 1 hash
let mut file1 = AccountHashesFile::default();
file1.write(&hashes[0]);
combined.push(hashes[0]);
// multiple hashes
let mut file2 = AccountHashesFile::default();
hashes.iter().for_each(|hash| {
file2.write(hash);
combined.push(*hash);
});
let hashes = if permutation == 0 {
vec![file0, file1, file2]
} else if permutation == 1 {
// include more empty files
vec![
file0,
file1,
AccountHashesFile::default(),
file2,
AccountHashesFile::default(),
]
} else if permutation == 2 {
vec![file1, file2]
} else {
// swap file2 and 1
let one = combined.remove(0);
combined.push(one);
vec![
file2,
AccountHashesFile::default(),
AccountHashesFile::default(),
file1,
]
};
let cumulative = CumulativeHashesFromFiles::from_files(hashes);
let len = combined.len();
assert_eq!(cumulative.total_count(), len);
(0..combined.len()).for_each(|start| {
let mut retreived = Vec::default();
let mut cumulative_start = start;
// read all data
while retreived.len() < (len - start) {
let this_one = cumulative.get_slice(cumulative_start);
retreived.extend(this_one.iter());
cumulative_start += this_one.len();
assert_ne!(0, this_one.len());
}
assert_eq!(
&combined[start..],
&retreived[..],
"permutation: {permutation}"
);
});
});
}
#[test]
fn test_accountsdb_div_ceil() {
assert_eq!(AccountsHasher::div_ceil(10, 3), 4);
assert_eq!(AccountsHasher::div_ceil(0, 1), 0);
assert_eq!(AccountsHasher::div_ceil(0, 5), 0);
assert_eq!(AccountsHasher::div_ceil(9, 3), 3);
assert_eq!(AccountsHasher::div_ceil(9, 9), 1);
}
#[test]
#[should_panic(expected = "attempt to divide by zero")]
fn test_accountsdb_div_ceil_fail() {
assert_eq!(AccountsHasher::div_ceil(10, 0), 0);
}
fn for_rest(original: &[CalculateHashIntermediate]) -> Vec<SortedDataByPubkey<'_>> {
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, 0, key);
account_maps.push(val);
let accounts_hash = AccountsHasher::default();
let result = accounts_hash
.rest_of_hash_calculation(for_rest(&account_maps), &mut HashStats::default());
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), &mut HashStats::default());
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());
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_de_dup_accounts_zero_chunks() {
let vec = [vec![vec![CalculateHashIntermediate {
lamports: 1,
..CalculateHashIntermediate::default()
}]]];
let temp_vec = vec.to_vec();
let slice = convert_to_slice2(&temp_vec);
let (mut hashes, lamports, _) =
AccountsHasher::default().de_dup_accounts_in_parallel(&slice, 0);
assert_eq!(&[Hash::default()], hashes.get_reader().unwrap().1.read(0));
assert_eq!(lamports, 1);
}
fn get_vec_vec(hashes: Vec<AccountHashesFile>) -> Vec<Vec<Hash>> {
hashes.into_iter().map(get_vec).collect()
}
fn get_vec(mut hashes: AccountHashesFile) -> Vec<Hash> {
hashes
.get_reader()
.map(|r| r.1.read(0).to_vec())
.unwrap_or_default()
}
#[test]
fn test_accountsdb_de_dup_accounts_empty() {
solana_logger::setup();
let accounts_hash = AccountsHasher::default();
let vec = vec![vec![], vec![]];
let (hashes, lamports) =
accounts_hash.de_dup_accounts(&vec, &mut HashStats::default(), one_range());
assert_eq!(
vec![Hash::default(); 0],
get_vec_vec(hashes)
.into_iter()
.flatten()
.collect::<Vec<_>>(),
);
assert_eq!(lamports, 0);
let vec = vec![];
let (hashes, lamports) =
accounts_hash.de_dup_accounts(&vec, &mut HashStats::default(), zero_range());
let empty: Vec<Vec<Hash>> = Vec::default();
assert_eq!(empty, get_vec_vec(hashes));
assert_eq!(lamports, 0);
let (hashes, lamports, _) = accounts_hash.de_dup_accounts_in_parallel(&[], 1);
assert_eq!(vec![Hash::default(); 0], get_vec(hashes));
assert_eq!(lamports, 0);
let (hashes, lamports, _) = accounts_hash.de_dup_accounts_in_parallel(&[], 2);
assert_eq!(vec![Hash::default(); 0], get_vec(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).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<ExpectedType> = 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 = AccountsHasher::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 slice_temp = convert_to_slice2(&slice2);
let (hashes2, lamports2, _) = hash.de_dup_accounts_in_parallel(&slice_temp, 0);
let slice3 = convert_to_slice2(&slice2);
let (hashes3, lamports3, _) = hash.de_dup_accounts_in_parallel(&slice3, 0);
let vec = slice.to_vec();
let slice4 = convert_to_slice2(&vec);
let (hashes4, lamports4) =
hash.de_dup_accounts(&slice4, &mut HashStats::default(), end - start);
let vec = slice.to_vec();
let slice5 = convert_to_slice2(&vec);
let (hashes5, lamports5) =
hash.de_dup_accounts(&slice5, &mut HashStats::default(), end - start);
let vec = slice.to_vec();
let slice5 = convert_to_slice2(&vec);
let (hashes6, lamports6) =
hash.de_dup_accounts(&slice5, &mut HashStats::default(), end - start);
let hashes2 = get_vec(hashes2);
let hashes3 = get_vec(hashes3);
let hashes4 = get_vec_vec(hashes4);
let hashes5 = get_vec_vec(hashes5);
let hashes6 = get_vec_vec(hashes6);
assert_eq!(hashes2, hashes3);
let expected2 = hashes2.clone();
assert_eq!(
expected2,
hashes4.into_iter().flatten().collect::<Vec<_>>(),
"last_slice: {last_slice}, start: {start}, end: {end}, slice: {slice:?}"
);
assert_eq!(
expected2.clone(),
hashes5.iter().flatten().copied().collect::<Vec<_>>(),
"last_slice: {last_slice}, start: {start}, end: {end}, slice: {slice:?}"
);
assert_eq!(
expected2.clone(),
hashes6.iter().flatten().copied().collect::<Vec<_>>()
);
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::<String>();
let hash_result_as_string = format!("{hashes2:?}");
let packaged_result: ExpectedType = (
human_readable,
is_last_slice,
lamports2,
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
AccountsHasher::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,
AccountsHasher::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
AccountsHasher::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
AccountsHasher::compare_two_hash_entries(&val, &val5)
);
}
fn test_de_dup_accounts_in_parallel<'a>(
account_maps: &'a [SortedDataByPubkey<'a>],
) -> (AccountHashesFile, u64, usize) {
AccountsHasher::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 slice = convert_to_slice2(&vecs);
let (hashfile, lamports, count) = test_de_dup_accounts_in_parallel(&slice);
assert_eq!(
(get_vec(hashfile), lamports, count),
(vec![val.hash], val.lamports, 1)
);
// zero original lamports, higher version
let val = CalculateHashIntermediate::new(hash, 0, 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 slice = convert_to_slice2(&vecs);
let (hashfile, lamports, count) = test_de_dup_accounts_in_parallel(&slice);
assert_eq!((get_vec(hashfile), lamports, count), (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<u32>> = 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<u64>>> = 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().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().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<u32>>> = 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().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 = AccountsHasher::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<Hash>, fanout: usize) -> Hash {
let temp: Vec<_> = hashes.iter().map(|h| (Pubkey::default(), *h)).collect();
let result = AccountsHasher::compute_merkle_root(temp, fanout);
let reduced: Vec<_> = hashes.clone();
let result2 = AccountsHasher::compute_merkle_root_from_slices(
hashes.len(),
fanout,
None,
|start| &reduced[start..],
None,
);
assert_eq!(result, result2.0, "len: {}", hashes.len());
let result2 = AccountsHasher::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 = AccountsHasher::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).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 = AccountsHasher::accumulate_account_hashes(
input.iter().map(|i| (i.0, i.1)).collect::<Vec<_>>(),
);
AccountsHasher::sort_hashes_by_pubkey(&mut input);
let result = AccountsHasher::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()),
];
AccountsHasher::default().de_dup_accounts_in_parallel(&[convert_to_slice(&[input])], 0);
}
fn convert_to_slice(
input: &[Vec<CalculateHashIntermediate>],
) -> Vec<&[CalculateHashIntermediate]> {
input.iter().map(|v| &v[..]).collect::<Vec<_>>()
}
fn convert_to_slice2(
input: &[Vec<Vec<CalculateHashIntermediate>>],
) -> Vec<Vec<&[CalculateHashIntermediate]>> {
input
.iter()
.map(|v| v.iter().map(|v| &v[..]).collect::<Vec<_>>())
.collect::<Vec<_>>()
}
#[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(),
)],
];
AccountsHasher::default().de_dup_accounts(
&[convert_to_slice(&input)],
&mut HashStats::default(),
2, // accounts above are in 2 groups
);
}
#[test]
fn test_get_binned_data() {
let data = [CalculateHashIntermediate::new(
Hash::default(),
1,
Pubkey::from([1u8; 32]),
)];
let data2 = vec![&data[..]];
let bins = 1;
let result = AccountsHasher::get_binned_data(&data2, bins, &(0..bins));
assert_eq!(result, vec![vec![&data[..]]]);
let bins = 2;
let result = AccountsHasher::get_binned_data(&data2, bins, &(0..bins));
assert_eq!(result, vec![vec![&data[..], &data[0..0]]]);
let data = [CalculateHashIntermediate::new(
Hash::default(),
1,
Pubkey::from([255u8; 32]),
)];
let data2 = vec![&data[..]];
let result = AccountsHasher::get_binned_data(&data2, bins, &(0..bins));
assert_eq!(result, vec![vec![&data[0..0], &data[..]]]);
let data = [
CalculateHashIntermediate::new(Hash::default(), 1, Pubkey::from([254u8; 32])),
CalculateHashIntermediate::new(Hash::default(), 1, Pubkey::from([255u8; 32])),
];
let data2 = vec![&data[..]];
let result = AccountsHasher::get_binned_data(&data2, bins, &(0..bins));
assert_eq!(result, vec![vec![&data[0..0], &data[..]]]);
let data = [
CalculateHashIntermediate::new(Hash::default(), 1, Pubkey::from([1u8; 32])),
CalculateHashIntermediate::new(Hash::default(), 1, Pubkey::from([255u8; 32])),
];
let data2 = vec![&data[..]];
let result = AccountsHasher::get_binned_data(&data2, bins, &(0..bins));
assert_eq!(result, vec![vec![&data[0..1], &data[1..2]]]);
}
}
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