395 lines
15 KiB
C++
395 lines
15 KiB
C++
// Copyright (c) 2012-2016 The Bitcoin Core developers
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// Distributed under the MIT software license, see the accompanying
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// file COPYING or http://www.opensource.org/licenses/mit-license.php.
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#include <boost/test/unit_test.hpp>
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#include "cuckoocache.h"
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#include "test/test_bitcoin.h"
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#include "random.h"
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#include <thread>
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#include <boost/thread.hpp>
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/** Test Suite for CuckooCache
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*
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* 1) All tests should have a deterministic result (using insecure rand
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* with deterministic seeds)
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* 2) Some test methods are templated to allow for easier testing
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* against new versions / comparing
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* 3) Results should be treated as a regression test, i.e., did the behavior
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* change significantly from what was expected. This can be OK, depending on
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* the nature of the change, but requires updating the tests to reflect the new
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* expected behavior. For example improving the hit rate may cause some tests
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* using BOOST_CHECK_CLOSE to fail.
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*
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*/
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FastRandomContext insecure_rand(true);
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BOOST_AUTO_TEST_SUITE(cuckoocache_tests);
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/** insecure_GetRandHash fills in a uint256 from insecure_rand
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*/
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void insecure_GetRandHash(uint256& t)
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{
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uint32_t* ptr = (uint32_t*)t.begin();
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for (uint8_t j = 0; j < 8; ++j)
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*(ptr++) = insecure_rand.rand32();
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}
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/** Definition copied from /src/script/sigcache.cpp
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*/
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class uint256Hasher
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{
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public:
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template <uint8_t hash_select>
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uint32_t operator()(const uint256& key) const
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{
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static_assert(hash_select <8, "SignatureCacheHasher only has 8 hashes available.");
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uint32_t u;
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std::memcpy(&u, key.begin() + 4 * hash_select, 4);
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return u;
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}
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};
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/* Test that no values not inserted into the cache are read out of it.
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*
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* There are no repeats in the first 200000 insecure_GetRandHash calls
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*/
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BOOST_AUTO_TEST_CASE(test_cuckoocache_no_fakes)
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{
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insecure_rand = FastRandomContext(true);
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CuckooCache::cache<uint256, uint256Hasher> cc{};
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cc.setup_bytes(32 << 20);
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uint256 v;
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for (int x = 0; x < 100000; ++x) {
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insecure_GetRandHash(v);
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cc.insert(v);
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}
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for (int x = 0; x < 100000; ++x) {
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insecure_GetRandHash(v);
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BOOST_CHECK(!cc.contains(v, false));
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}
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};
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/** This helper returns the hit rate when megabytes*load worth of entries are
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* inserted into a megabytes sized cache
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*/
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template <typename Cache>
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double test_cache(size_t megabytes, double load)
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{
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insecure_rand = FastRandomContext(true);
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std::vector<uint256> hashes;
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Cache set{};
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size_t bytes = megabytes * (1 << 20);
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set.setup_bytes(bytes);
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uint32_t n_insert = static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
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hashes.resize(n_insert);
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for (uint32_t i = 0; i < n_insert; ++i) {
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uint32_t* ptr = (uint32_t*)hashes[i].begin();
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for (uint8_t j = 0; j < 8; ++j)
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*(ptr++) = insecure_rand.rand32();
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}
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/** We make a copy of the hashes because future optimizations of the
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* cuckoocache may overwrite the inserted element, so the test is
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* "future proofed".
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*/
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std::vector<uint256> hashes_insert_copy = hashes;
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/** Do the insert */
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for (uint256& h : hashes_insert_copy)
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set.insert(h);
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/** Count the hits */
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uint32_t count = 0;
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for (uint256& h : hashes)
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count += set.contains(h, false);
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double hit_rate = ((double)count) / ((double)n_insert);
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return hit_rate;
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}
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/** The normalized hit rate for a given load.
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*
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* The semantics are a little confusing, so please see the below
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* explanation.
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*
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* Examples:
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*
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* 1) at load 0.5, we expect a perfect hit rate, so we multiply by
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* 1.0
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* 2) at load 2.0, we expect to see half the entries, so a perfect hit rate
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* would be 0.5. Therefore, if we see a hit rate of 0.4, 0.4*2.0 = 0.8 is the
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* normalized hit rate.
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*
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* This is basically the right semantics, but has a bit of a glitch depending on
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* how you measure around load 1.0 as after load 1.0 your normalized hit rate
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* becomes effectively perfect, ignoring freshness.
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*/
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double normalize_hit_rate(double hits, double load)
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{
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return hits * std::max(load, 1.0);
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}
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/** Check the hit rate on loads ranging from 0.1 to 2.0 */
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BOOST_AUTO_TEST_CASE(cuckoocache_hit_rate_ok)
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{
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/** Arbitrarily selected Hit Rate threshold that happens to work for this test
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* as a lower bound on performance.
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*/
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double HitRateThresh = 0.98;
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size_t megabytes = 32;
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for (double load = 0.1; load < 2; load *= 2) {
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double hits = test_cache<CuckooCache::cache<uint256, uint256Hasher>>(megabytes, load);
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BOOST_CHECK(normalize_hit_rate(hits, load) > HitRateThresh);
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}
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}
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/** This helper checks that erased elements are preferentially inserted onto and
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* that the hit rate of "fresher" keys is reasonable*/
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template <typename Cache>
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void test_cache_erase(size_t megabytes)
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{
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double load = 1;
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insecure_rand = FastRandomContext(true);
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std::vector<uint256> hashes;
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Cache set{};
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size_t bytes = megabytes * (1 << 20);
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set.setup_bytes(bytes);
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uint32_t n_insert = static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
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hashes.resize(n_insert);
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for (uint32_t i = 0; i < n_insert; ++i) {
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uint32_t* ptr = (uint32_t*)hashes[i].begin();
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for (uint8_t j = 0; j < 8; ++j)
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*(ptr++) = insecure_rand.rand32();
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}
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/** We make a copy of the hashes because future optimizations of the
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* cuckoocache may overwrite the inserted element, so the test is
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* "future proofed".
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*/
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std::vector<uint256> hashes_insert_copy = hashes;
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/** Insert the first half */
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for (uint32_t i = 0; i < (n_insert / 2); ++i)
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set.insert(hashes_insert_copy[i]);
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/** Erase the first quarter */
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for (uint32_t i = 0; i < (n_insert / 4); ++i)
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set.contains(hashes[i], true);
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/** Insert the second half */
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for (uint32_t i = (n_insert / 2); i < n_insert; ++i)
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set.insert(hashes_insert_copy[i]);
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/** elements that we marked erased but that are still there */
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size_t count_erased_but_contained = 0;
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/** elements that we did not erase but are older */
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size_t count_stale = 0;
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/** elements that were most recently inserted */
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size_t count_fresh = 0;
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for (uint32_t i = 0; i < (n_insert / 4); ++i)
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count_erased_but_contained += set.contains(hashes[i], false);
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for (uint32_t i = (n_insert / 4); i < (n_insert / 2); ++i)
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count_stale += set.contains(hashes[i], false);
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for (uint32_t i = (n_insert / 2); i < n_insert; ++i)
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count_fresh += set.contains(hashes[i], false);
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double hit_rate_erased_but_contained = double(count_erased_but_contained) / (double(n_insert) / 4.0);
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double hit_rate_stale = double(count_stale) / (double(n_insert) / 4.0);
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double hit_rate_fresh = double(count_fresh) / (double(n_insert) / 2.0);
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// Check that our hit_rate_fresh is perfect
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BOOST_CHECK_EQUAL(hit_rate_fresh, 1.0);
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// Check that we have a more than 2x better hit rate on stale elements than
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// erased elements.
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BOOST_CHECK(hit_rate_stale > 2 * hit_rate_erased_but_contained);
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}
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BOOST_AUTO_TEST_CASE(cuckoocache_erase_ok)
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{
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size_t megabytes = 32;
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test_cache_erase<CuckooCache::cache<uint256, uint256Hasher>>(megabytes);
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}
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template <typename Cache>
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void test_cache_erase_parallel(size_t megabytes)
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{
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double load = 1;
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insecure_rand = FastRandomContext(true);
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std::vector<uint256> hashes;
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Cache set{};
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size_t bytes = megabytes * (1 << 20);
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set.setup_bytes(bytes);
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uint32_t n_insert = static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
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hashes.resize(n_insert);
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for (uint32_t i = 0; i < n_insert; ++i) {
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uint32_t* ptr = (uint32_t*)hashes[i].begin();
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for (uint8_t j = 0; j < 8; ++j)
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*(ptr++) = insecure_rand.rand32();
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}
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/** We make a copy of the hashes because future optimizations of the
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* cuckoocache may overwrite the inserted element, so the test is
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* "future proofed".
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*/
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std::vector<uint256> hashes_insert_copy = hashes;
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boost::shared_mutex mtx;
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{
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/** Grab lock to make sure we release inserts */
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boost::unique_lock<boost::shared_mutex> l(mtx);
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/** Insert the first half */
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for (uint32_t i = 0; i < (n_insert / 2); ++i)
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set.insert(hashes_insert_copy[i]);
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}
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/** Spin up 3 threads to run contains with erase.
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*/
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std::vector<std::thread> threads;
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/** Erase the first quarter */
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for (uint32_t x = 0; x < 3; ++x)
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/** Each thread is emplaced with x copy-by-value
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*/
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threads.emplace_back([&, x] {
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boost::shared_lock<boost::shared_mutex> l(mtx);
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size_t ntodo = (n_insert/4)/3;
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size_t start = ntodo*x;
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size_t end = ntodo*(x+1);
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for (uint32_t i = start; i < end; ++i)
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set.contains(hashes[i], true);
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});
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/** Wait for all threads to finish
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*/
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for (std::thread& t : threads)
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t.join();
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/** Grab lock to make sure we observe erases */
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boost::unique_lock<boost::shared_mutex> l(mtx);
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/** Insert the second half */
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for (uint32_t i = (n_insert / 2); i < n_insert; ++i)
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set.insert(hashes_insert_copy[i]);
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/** elements that we marked erased but that are still there */
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size_t count_erased_but_contained = 0;
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/** elements that we did not erase but are older */
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size_t count_stale = 0;
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/** elements that were most recently inserted */
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size_t count_fresh = 0;
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for (uint32_t i = 0; i < (n_insert / 4); ++i)
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count_erased_but_contained += set.contains(hashes[i], false);
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for (uint32_t i = (n_insert / 4); i < (n_insert / 2); ++i)
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count_stale += set.contains(hashes[i], false);
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for (uint32_t i = (n_insert / 2); i < n_insert; ++i)
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count_fresh += set.contains(hashes[i], false);
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double hit_rate_erased_but_contained = double(count_erased_but_contained) / (double(n_insert) / 4.0);
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double hit_rate_stale = double(count_stale) / (double(n_insert) / 4.0);
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double hit_rate_fresh = double(count_fresh) / (double(n_insert) / 2.0);
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// Check that our hit_rate_fresh is perfect
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BOOST_CHECK_EQUAL(hit_rate_fresh, 1.0);
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// Check that we have a more than 2x better hit rate on stale elements than
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// erased elements.
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BOOST_CHECK(hit_rate_stale > 2 * hit_rate_erased_but_contained);
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}
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BOOST_AUTO_TEST_CASE(cuckoocache_erase_parallel_ok)
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{
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size_t megabytes = 32;
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test_cache_erase_parallel<CuckooCache::cache<uint256, uint256Hasher>>(megabytes);
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}
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template <typename Cache>
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void test_cache_generations()
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{
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// This test checks that for a simulation of network activity, the fresh hit
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// rate is never below 99%, and the number of times that it is worse than
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// 99.9% are less than 1% of the time.
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double min_hit_rate = 0.99;
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double tight_hit_rate = 0.999;
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double max_rate_less_than_tight_hit_rate = 0.01;
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// A cache that meets this specification is therefore shown to have a hit
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// rate of at least tight_hit_rate * (1 - max_rate_less_than_tight_hit_rate) +
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// min_hit_rate*max_rate_less_than_tight_hit_rate = 0.999*99%+0.99*1% == 99.89%
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// hit rate with low variance.
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// We use deterministic values, but this test has also passed on many
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// iterations with non-deterministic values, so it isn't "overfit" to the
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// specific entropy in FastRandomContext(true) and implementation of the
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// cache.
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insecure_rand = FastRandomContext(true);
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// block_activity models a chunk of network activity. n_insert elements are
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// adde to the cache. The first and last n/4 are stored for removal later
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// and the middle n/2 are not stored. This models a network which uses half
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// the signatures of recently (since the last block) added transactions
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// immediately and never uses the other half.
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struct block_activity {
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std::vector<uint256> reads;
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block_activity(uint32_t n_insert, Cache& c) : reads()
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{
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std::vector<uint256> inserts;
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inserts.resize(n_insert);
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reads.reserve(n_insert / 2);
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for (uint32_t i = 0; i < n_insert; ++i) {
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uint32_t* ptr = (uint32_t*)inserts[i].begin();
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for (uint8_t j = 0; j < 8; ++j)
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*(ptr++) = insecure_rand.rand32();
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}
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for (uint32_t i = 0; i < n_insert / 4; ++i)
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reads.push_back(inserts[i]);
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for (uint32_t i = n_insert - (n_insert / 4); i < n_insert; ++i)
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reads.push_back(inserts[i]);
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for (auto h : inserts)
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c.insert(h);
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}
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};
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const uint32_t BLOCK_SIZE = 10000;
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// We expect window size 60 to perform reasonably given that each epoch
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// stores 45% of the cache size (~472k).
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const uint32_t WINDOW_SIZE = 60;
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const uint32_t POP_AMOUNT = (BLOCK_SIZE / WINDOW_SIZE) / 2;
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const double load = 10;
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const size_t megabytes = 32;
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const size_t bytes = megabytes * (1 << 20);
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const uint32_t n_insert = static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
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std::vector<block_activity> hashes;
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Cache set{};
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set.setup_bytes(bytes);
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hashes.reserve(n_insert / BLOCK_SIZE);
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std::deque<block_activity> last_few;
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uint32_t out_of_tight_tolerance = 0;
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uint32_t total = n_insert / BLOCK_SIZE;
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// we use the deque last_few to model a sliding window of blocks. at each
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// step, each of the last WINDOW_SIZE block_activities checks the cache for
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// POP_AMOUNT of the hashes that they inserted, and marks these erased.
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for (uint32_t i = 0; i < total; ++i) {
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if (last_few.size() == WINDOW_SIZE)
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last_few.pop_front();
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last_few.emplace_back(BLOCK_SIZE, set);
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uint32_t count = 0;
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for (auto& act : last_few)
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for (uint32_t k = 0; k < POP_AMOUNT; ++k) {
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count += set.contains(act.reads.back(), true);
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act.reads.pop_back();
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}
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// We use last_few.size() rather than WINDOW_SIZE for the correct
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// behavior on the first WINDOW_SIZE iterations where the deque is not
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// full yet.
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double hit = (double(count)) / (last_few.size() * POP_AMOUNT);
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// Loose Check that hit rate is above min_hit_rate
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BOOST_CHECK(hit > min_hit_rate);
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// Tighter check, count number of times we are less than tight_hit_rate
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// (and implicityly, greater than min_hit_rate)
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out_of_tight_tolerance += hit < tight_hit_rate;
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}
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// Check that being out of tolerance happens less than
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// max_rate_less_than_tight_hit_rate of the time
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BOOST_CHECK(double(out_of_tight_tolerance) / double(total) < max_rate_less_than_tight_hit_rate);
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}
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BOOST_AUTO_TEST_CASE(cuckoocache_generations)
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{
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test_cache_generations<CuckooCache::cache<uint256, uint256Hasher>>();
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}
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BOOST_AUTO_TEST_SUITE_END();
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