diff --git a/src/test/cuckoocache_tests.cpp b/src/test/cuckoocache_tests.cpp
index 1721ef217..d65bcff49 100644
--- a/src/test/cuckoocache_tests.cpp
+++ b/src/test/cuckoocache_tests.cpp
@@ -1,526 +1,528 @@
// Copyright (c) 2012-2019 The Bitcoin Core developers
// Distributed under the MIT software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
#include <cuckoocache.h>
#include <random.h>
#include <script/sigcache.h>
+#include <deque>
+
#include <test/util/setup_common.h>
#include <boost/test/unit_test.hpp>
#include <boost/thread/shared_mutex.hpp>
/**
* Test Suite for CuckooCache
*
* 1. All tests should have a deterministic result (using insecure rand
* with deterministic seeds)
* 2. Some test methods are templated to allow for easier testing
* against new versions / comparing
* 3. Results should be treated as a regression test, i.e., did the behavior
* change significantly from what was expected. This can be OK, depending on
* the nature of the change, but requires updating the tests to reflect the new
* expected behavior. For example improving the hit rate may cause some tests
* using BOOST_CHECK_CLOSE to fail.
*/
BOOST_AUTO_TEST_SUITE(cuckoocache_tests)
/**
* Example key/value element. The key is 28 bytes long and the value 4, for a
* total of 32 bytes.
*/
struct TestMapElement {
struct KeyType {
std::array<uint8_t, 28> data;
KeyType() = default;
KeyType(const KeyType &rhs) = default;
KeyType(const uint256 &k) {
std::copy(k.begin() + 4, k.end(), data.begin());
}
bool operator==(const KeyType &rhs) const { return rhs.data == data; }
};
private:
KeyType key;
uint32_t value;
public:
TestMapElement() = default;
TestMapElement(const TestMapElement &rhs) = default;
TestMapElement(const uint256 &data)
: TestMapElement(data, data.GetUint64(0)) {}
TestMapElement(const KeyType &keyIn, uint32_t valueIn)
: key(keyIn), value(valueIn) {}
const KeyType &getKey() const { return key; }
uint32_t getValue() const { return value; }
class CacheHasher {
public:
template <uint8_t hash_select>
uint32_t operator()(const KeyType &k) const {
static_assert(hash_select < 8, "only has 8 hashes available.");
const auto &d = k.data;
uint32_t u;
if (hash_select < 7) {
std::memcpy(&u, d.begin() + 4 * hash_select, 4);
} else {
// We are required to produce 8 subhashes but all key bytes have
// been used once already. So, we grab a mix of bits from each
// of the other blobs. We try to ensure more entropy on the
// higher bits, as these are what primarily get used to
// determine the index.
u = (uint32_t(d[0] & 0x07) << 29) +
(uint32_t(d[4] & 0x07) << 26) +
(uint32_t(d[8] & 0x0f) << 22) +
(uint32_t(d[16] & 0x3f) << 16) +
(uint32_t(d[20] & 0xff) << 8) +
(uint32_t(d[24] & 0xff) << 0);
}
return u;
}
};
friend class CuckooCache::cache<TestMapElement, CacheHasher>;
};
static_assert(sizeof(TestMapElement) == 32, "TestMapElement must be 32 bytes");
// For convenience.
using CuckooCacheSet =
CuckooCache::cache<CuckooCache::KeyOnly<uint256>, SignatureCacheHasher>;
using CuckooCacheMap =
CuckooCache::cache<TestMapElement, TestMapElement::CacheHasher>;
using TestMapKey = TestMapElement::KeyType;
/**
* Test that no values not inserted into the cache are read out of it.
*
* There are no repeats in the first 400000 insecure_GetRandHash calls
*/
BOOST_AUTO_TEST_CASE(test_cuckoocache_no_fakes) {
SeedInsecureRand(SeedRand::ZEROS);
CuckooCacheSet cc{};
size_t megabytes = 4;
cc.setup_bytes(megabytes << 20);
for (int x = 0; x < 100000; ++x) {
cc.insert(InsecureRand256());
}
for (int x = 0; x < 100000; ++x) {
BOOST_CHECK(!cc.contains(InsecureRand256(), false));
}
CuckooCacheMap cm{};
cm.setup_bytes(megabytes << 20);
for (int x = 0; x < 100000; ++x) {
cm.insert(TestMapElement(InsecureRand256()));
}
for (int x = 0; x < 100000; ++x) {
BOOST_CHECK(!cm.contains(TestMapKey(InsecureRand256()), false));
}
};
/**
* This helper returns the hit rate when megabytes*load worth of entries are
* inserted into a megabytes sized cache
*/
template <typename Cache>
static double test_cache(size_t megabytes, double load) {
SeedInsecureRand(SeedRand::ZEROS);
std::vector<uint256> hashes;
Cache set{};
size_t bytes = megabytes * (1 << 20);
set.setup_bytes(bytes);
uint32_t n_insert = static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
hashes.reserve(n_insert);
for (uint32_t i = 0; i < n_insert; ++i) {
hashes.emplace_back(InsecureRand256());
}
/**
* We make a copy of the hashes because future optimizations of the
* cuckoocache may overwrite the inserted element, so the test is "future
* proofed".
*/
std::vector<uint256> hashes_insert_copy = hashes;
/** Do the insert */
for (const uint256 &h : hashes_insert_copy) {
set.insert(h);
}
/** Count the hits */
uint32_t count = 0;
for (const uint256 &h : hashes) {
count += set.contains(h, false);
}
double hit_rate = double(count) / double(n_insert);
return hit_rate;
}
/**
* The normalized hit rate for a given load.
*
* The semantics are a little confusing, so please see the below
* explanation.
*
* Examples:
*
* 1. at load 0.5, we expect a perfect hit rate, so we multiply by
* 1.0
* 2. at load 2.0, we expect to see half the entries, so a perfect hit rate
* would be 0.5. Therefore, if we see a hit rate of 0.4, 0.4*2.0 = 0.8 is the
* normalized hit rate.
*
* This is basically the right semantics, but has a bit of a glitch depending on
* how you measure around load 1.0 as after load 1.0 your normalized hit rate
* becomes effectively perfect, ignoring freshness.
*/
static double normalize_hit_rate(double hits, double load) {
return hits * std::max(load, 1.0);
}
/** Check the hit rate on loads ranging from 0.1 to 1.6 */
BOOST_AUTO_TEST_CASE(cuckoocache_hit_rate_ok) {
/**
* Arbitrarily selected Hit Rate threshold that happens to work for this
* test as a lower bound on performance.
*/
double HitRateThresh = 0.98;
size_t megabytes = 4;
for (double load = 0.1; load < 2; load *= 2) {
double hits = test_cache<CuckooCacheSet>(megabytes, load);
BOOST_CHECK(normalize_hit_rate(hits, load) > HitRateThresh);
}
for (double load = 0.1; load < 2; load *= 2) {
double hits = test_cache<CuckooCacheMap>(megabytes, load);
BOOST_CHECK(normalize_hit_rate(hits, load) > HitRateThresh);
}
}
/**
* This helper checks that erased elements are preferentially inserted onto and
* that the hit rate of "fresher" keys is reasonable.
*/
template <typename Cache> static void test_cache_erase(size_t megabytes) {
double load = 1;
SeedInsecureRand(SeedRand::ZEROS);
std::vector<uint256> hashes;
Cache set{};
size_t bytes = megabytes * (1 << 20);
set.setup_bytes(bytes);
uint32_t n_insert = static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
hashes.resize(n_insert);
for (uint32_t i = 0; i < n_insert; ++i) {
hashes[i] = InsecureRand256();
}
/**
* We make a copy of the hashes because future optimizations of the
* cuckoocache may overwrite the inserted element, so the test is
* "future proofed".
*/
std::vector<uint256> hashes_insert_copy = hashes;
/** Insert the first half */
for (uint32_t i = 0; i < (n_insert / 2); ++i) {
set.insert(hashes_insert_copy[i]);
}
/** Erase the first quarter */
for (uint32_t i = 0; i < (n_insert / 4); ++i) {
BOOST_CHECK(set.contains(hashes[i], true));
}
/** Insert the second half */
for (uint32_t i = (n_insert / 2); i < n_insert; ++i) {
set.insert(hashes_insert_copy[i]);
}
/** elements that we marked as erased but are still there */
size_t count_erased_but_contained = 0;
/** elements that we did not erase but are older */
size_t count_stale = 0;
/** elements that were most recently inserted */
size_t count_fresh = 0;
for (uint32_t i = 0; i < (n_insert / 4); ++i) {
count_erased_but_contained += set.contains(hashes[i], false);
}
for (uint32_t i = (n_insert / 4); i < (n_insert / 2); ++i) {
count_stale += set.contains(hashes[i], false);
}
for (uint32_t i = (n_insert / 2); i < n_insert; ++i) {
count_fresh += set.contains(hashes[i], false);
}
double hit_rate_erased_but_contained =
double(count_erased_but_contained) / (double(n_insert) / 4.0);
double hit_rate_stale = double(count_stale) / (double(n_insert) / 4.0);
double hit_rate_fresh = double(count_fresh) / (double(n_insert) / 2.0);
// Check that our hit_rate_fresh is perfect
BOOST_CHECK_EQUAL(hit_rate_fresh, 1.0);
// Check that we have a more than 2x better hit rate on stale elements than
// erased elements.
BOOST_CHECK(hit_rate_stale > 2 * hit_rate_erased_but_contained);
}
BOOST_AUTO_TEST_CASE(cuckoocache_erase_ok) {
size_t megabytes = 4;
test_cache_erase<CuckooCacheSet>(megabytes);
test_cache_erase<CuckooCacheMap>(megabytes);
}
template <typename Cache>
static void test_cache_erase_parallel(size_t megabytes) {
double load = 1;
SeedInsecureRand(SeedRand::ZEROS);
std::vector<uint256> hashes;
Cache set{};
size_t bytes = megabytes * (1 << 20);
set.setup_bytes(bytes);
uint32_t n_insert = static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
hashes.resize(n_insert);
for (uint32_t i = 0; i < n_insert; ++i) {
hashes[i] = InsecureRand256();
}
/**
* We make a copy of the hashes because future optimizations of the
* cuckoocache may overwrite the inserted element, so the test is
* "future proofed".
*/
std::vector<uint256> hashes_insert_copy = hashes;
boost::shared_mutex mtx;
{
/** Grab lock to make sure we release inserts */
boost::unique_lock<boost::shared_mutex> l(mtx);
/** Insert the first half */
for (uint32_t i = 0; i < (n_insert / 2); ++i) {
set.insert(hashes_insert_copy[i]);
}
}
/**
* Spin up 3 threads to run contains with erase.
*/
std::vector<std::thread> threads;
/** Erase the first quarter */
for (uint32_t x = 0; x < 3; ++x) {
/** Each thread is emplaced with x copy-by-value */
threads.emplace_back([&, x] {
boost::shared_lock<boost::shared_mutex> l(mtx);
size_t ntodo = (n_insert / 4) / 3;
size_t start = ntodo * x;
size_t end = ntodo * (x + 1);
for (uint32_t i = start; i < end; ++i) {
bool contains = set.contains(hashes[i], true);
assert(contains);
}
});
}
/** Wait for all threads to finish */
for (std::thread &t : threads) {
t.join();
}
/** Grab lock to make sure we observe erases */
boost::unique_lock<boost::shared_mutex> l(mtx);
/** Insert the second half */
for (uint32_t i = (n_insert / 2); i < n_insert; ++i) {
set.insert(hashes_insert_copy[i]);
}
/** elements that we marked erased but that are still there */
size_t count_erased_but_contained = 0;
/** elements that we did not erase but are older */
size_t count_stale = 0;
/** elements that were most recently inserted */
size_t count_fresh = 0;
for (uint32_t i = 0; i < (n_insert / 4); ++i) {
count_erased_but_contained += set.contains(hashes[i], false);
}
for (uint32_t i = (n_insert / 4); i < (n_insert / 2); ++i) {
count_stale += set.contains(hashes[i], false);
}
for (uint32_t i = (n_insert / 2); i < n_insert; ++i) {
count_fresh += set.contains(hashes[i], false);
}
double hit_rate_erased_but_contained =
double(count_erased_but_contained) / (double(n_insert) / 4.0);
double hit_rate_stale = double(count_stale) / (double(n_insert) / 4.0);
double hit_rate_fresh = double(count_fresh) / (double(n_insert) / 2.0);
// Check that our hit_rate_fresh is perfect
BOOST_CHECK_EQUAL(hit_rate_fresh, 1.0);
// Check that we have a more than 2x better hit rate on stale elements than
// erased elements.
BOOST_CHECK(hit_rate_stale > 2 * hit_rate_erased_but_contained);
}
BOOST_AUTO_TEST_CASE(cuckoocache_erase_parallel_ok) {
size_t megabytes = 4;
test_cache_erase_parallel<CuckooCacheSet>(megabytes);
test_cache_erase_parallel<CuckooCacheMap>(megabytes);
}
template <typename Cache> static void test_cache_generations() {
// This test checks that for a simulation of network activity, the fresh hit
// rate is never below 99%, and the number of times that it is worse than
// 99.9% are less than 1% of the time.
double min_hit_rate = 0.99;
double tight_hit_rate = 0.999;
double max_rate_less_than_tight_hit_rate = 0.01;
// A cache that meets this specification is therefore shown to have a hit
// rate of at least tight_hit_rate * (1 - max_rate_less_than_tight_hit_rate)
// +
// min_hit_rate*max_rate_less_than_tight_hit_rate = 0.999*99%+0.99*1% ==
// 99.89%
// hit rate with low variance.
// We use deterministic values, but this test has also passed on many
// iterations with non-deterministic values, so it isn't "overfit" to the
// specific entropy in FastRandomContext(true) and implementation of the
// cache.
SeedInsecureRand(SeedRand::ZEROS);
// block_activity models a chunk of network activity. n_insert elements are
// added to the cache. The first and last n/4 are stored for removal later
// and the middle n/2 are not stored. This models a network which uses half
// the signatures of recently (since the last block) added transactions
// immediately and never uses the other half.
struct block_activity {
std::vector<uint256> reads;
block_activity(uint32_t n_insert, Cache &c) : reads() {
std::vector<uint256> inserts;
inserts.resize(n_insert);
reads.reserve(n_insert / 2);
for (uint32_t i = 0; i < n_insert; ++i) {
inserts[i] = InsecureRand256();
}
for (uint32_t i = 0; i < n_insert / 4; ++i) {
reads.push_back(inserts[i]);
}
for (uint32_t i = n_insert - (n_insert / 4); i < n_insert; ++i) {
reads.push_back(inserts[i]);
}
for (const auto &h : inserts) {
c.insert(h);
}
}
};
const uint32_t BLOCK_SIZE = 1000;
// We expect window size 60 to perform reasonably given that each epoch
// stores 45% of the cache size (~472k).
const uint32_t WINDOW_SIZE = 60;
const uint32_t POP_AMOUNT = (BLOCK_SIZE / WINDOW_SIZE) / 2;
const double load = 10;
const size_t megabytes = 4;
const size_t bytes = megabytes * (1 << 20);
const uint32_t n_insert =
static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
std::vector<block_activity> hashes;
Cache set{};
set.setup_bytes(bytes);
hashes.reserve(n_insert / BLOCK_SIZE);
std::deque<block_activity> last_few;
uint32_t out_of_tight_tolerance = 0;
uint32_t total = n_insert / BLOCK_SIZE;
// we use the deque last_few to model a sliding window of blocks. at each
// step, each of the last WINDOW_SIZE block_activities checks the cache for
// POP_AMOUNT of the hashes that they inserted, and marks these erased.
for (uint32_t i = 0; i < total; ++i) {
if (last_few.size() == WINDOW_SIZE) {
last_few.pop_front();
}
last_few.emplace_back(BLOCK_SIZE, set);
uint32_t count = 0;
for (auto &act : last_few) {
for (uint32_t k = 0; k < POP_AMOUNT; ++k) {
count += set.contains(act.reads.back(), true);
act.reads.pop_back();
}
}
// We use last_few.size() rather than WINDOW_SIZE for the correct
// behavior on the first WINDOW_SIZE iterations where the deque is not
// full yet.
double hit = double(count) / (last_few.size() * POP_AMOUNT);
// Loose Check that hit rate is above min_hit_rate
BOOST_CHECK(hit > min_hit_rate);
// Tighter check, count number of times we are less than tight_hit_rate
// (and implicitly, greater than min_hit_rate)
out_of_tight_tolerance += hit < tight_hit_rate;
}
// Check that being out of tolerance happens less than
// max_rate_less_than_tight_hit_rate of the time
BOOST_CHECK(double(out_of_tight_tolerance) / double(total) <
max_rate_less_than_tight_hit_rate);
}
BOOST_AUTO_TEST_CASE(cuckoocache_generations) {
test_cache_generations<CuckooCacheSet>();
test_cache_generations<CuckooCacheMap>();
}
BOOST_AUTO_TEST_CASE(cuckoocache_map_element) {
// Check the hash is parsed properly.
uint256 hash = uint256S(
"baadf00dcafebeef00000000bada550ff1ce00000000000000000000deadc0de");
uint256 key = uint256S(
"baadf00dcafebeef00000000bada550ff1ce00000000000000000000abadba5e");
const TestMapElement e(hash);
BOOST_CHECK_EQUAL(e.getValue(), 0xdeadc0de);
BOOST_CHECK(e.getKey() == TestMapElement(key).getKey());
}
BOOST_AUTO_TEST_CASE(cuckoocache_map) {
SeedInsecureRand(SeedRand::ZEROS);
// 4k cache.
CuckooCacheMap cm{};
cm.setup_bytes(4 * 1024);
for (int x = 0; x < 100000; ++x) {
const TestMapElement e1(InsecureRand256());
const TestMapElement e2(e1.getKey(), e1.getValue() ^ 0xbabe);
BOOST_CHECK(e1.getKey() == e2.getKey());
BOOST_CHECK(e1.getValue() != e2.getValue());
// Insert a KV pair in the map and checks that it is effectively
// inserted.
BOOST_CHECK(!cm.contains(e1.getKey(), false));
cm.insert(e1);
BOOST_CHECK(cm.contains(e1.getKey(), false));
// Check that we recover the proper value from the map.
TestMapElement e(e1.getKey(), 0);
BOOST_CHECK(cm.get(e, false));
BOOST_CHECK(e.getKey() == e1.getKey());
BOOST_CHECK(e.getValue() == e1.getValue());
// Insert without replace will not change the value.
e = e2;
cm.insert(e2);
BOOST_CHECK(cm.get(e, false));
BOOST_CHECK(e.getKey() == e1.getKey());
BOOST_CHECK(e.getValue() == e1.getValue());
// Insert and replace.
cm.insert(e2, true);
BOOST_CHECK(cm.get(e, false));
BOOST_CHECK(e.getKey() == e2.getKey());
BOOST_CHECK(e.getValue() == e2.getValue());
}
}
BOOST_AUTO_TEST_SUITE_END();