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authorJeremy Rubin <jeremy.l.rubin@gmail.com>2016-10-05 16:59:18 -0400
committerJeremy Rubin <jeremy.l.rubin@gmail.com>2016-12-14 16:02:22 -0500
commit67dac4e1937b9835d2c09402d35e0050467fbc6d (patch)
tree7bb00ce68b10a6e65c44516595df3861675125c5
parentc9e69fbf3915fe1187b4c2e77be5ae6b16121194 (diff)
Add unit tests for the CuckooCache
SQUASHME: Update Tests for other SQUASHMEs
-rw-r--r--src/Makefile.test.include1
-rw-r--r--src/test/cuckoocache_tests.cpp394
2 files changed, 395 insertions, 0 deletions
diff --git a/src/Makefile.test.include b/src/Makefile.test.include
index 5ce1bbb896..8178fb7429 100644
--- a/src/Makefile.test.include
+++ b/src/Makefile.test.include
@@ -54,6 +54,7 @@ BITCOIN_TESTS =\
test/coins_tests.cpp \
test/compress_tests.cpp \
test/crypto_tests.cpp \
+ test/cuckoocache_tests.cpp \
test/DoS_tests.cpp \
test/getarg_tests.cpp \
test/hash_tests.cpp \
diff --git a/src/test/cuckoocache_tests.cpp b/src/test/cuckoocache_tests.cpp
new file mode 100644
index 0000000000..1bc50d5ea9
--- /dev/null
+++ b/src/test/cuckoocache_tests.cpp
@@ -0,0 +1,394 @@
+// Copyright (c) 2012-2016 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 <boost/test/unit_test.hpp>
+#include "cuckoocache.h"
+#include "test/test_bitcoin.h"
+#include "random.h"
+#include <thread>
+#include <boost/thread.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, ie, 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.
+ *
+ */
+FastRandomContext insecure_rand(true);
+
+BOOST_AUTO_TEST_SUITE(cuckoocache_tests);
+
+
+/** insecure_GetRandHash fills in a uint256 from insecure_rand
+ */
+void insecure_GetRandHash(uint256& t)
+{
+ uint32_t* ptr = (uint32_t*)t.begin();
+ for (uint8_t j = 0; j < 8; ++j)
+ *(ptr++) = insecure_rand.rand32();
+}
+
+/** Definition copied from /src/script/sigcache.cpp
+ */
+class uint256Hasher
+{
+public:
+ template <uint8_t hash_select>
+ uint32_t operator()(const uint256& key) const
+ {
+ static_assert(hash_select <8, "SignatureCacheHasher only has 8 hashes available.");
+ uint32_t u;
+ std::memcpy(&u, key.begin() + 4 * hash_select, 4);
+ return u;
+ }
+};
+
+
+/* Test that no values not inserted into the cache are read out of it.
+ *
+ * There are no repeats in the first 200000 insecure_GetRandHash calls
+ */
+BOOST_AUTO_TEST_CASE(test_cuckoocache_no_fakes)
+{
+ insecure_rand = FastRandomContext(true);
+ CuckooCache::cache<uint256, uint256Hasher> cc{};
+ cc.setup_bytes(32 << 20);
+ uint256 v;
+ for (int x = 0; x < 100000; ++x) {
+ insecure_GetRandHash(v);
+ cc.insert(v);
+ }
+ for (int x = 0; x < 100000; ++x) {
+ insecure_GetRandHash(v);
+ BOOST_CHECK(!cc.contains(v, false));
+ }
+};
+
+/** This helper returns the hit rate when megabytes*load worth of entries are
+ * inserted into a megabytes sized cache
+ */
+template <typename Cache>
+double test_cache(size_t megabytes, double load)
+{
+ insecure_rand = FastRandomContext(true);
+ 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) {
+ uint32_t* ptr = (uint32_t*)hashes[i].begin();
+ for (uint8_t j = 0; j < 8; ++j)
+ *(ptr++) = insecure_rand.rand32();
+ }
+ /** 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 (uint256& h : hashes_insert_copy)
+ set.insert(h);
+ /** Count the hits */
+ uint32_t count = 0;
+ for (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.
+ */
+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 2.0 */
+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 = 32;
+ for (double load = 0.1; load < 2; load *= 2) {
+ double hits = test_cache<CuckooCache::cache<uint256, uint256Hasher>>(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>
+void test_cache_erase(size_t megabytes)
+{
+ double load = 1;
+ insecure_rand = FastRandomContext(true);
+ 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) {
+ uint32_t* ptr = (uint32_t*)hashes[i].begin();
+ for (uint8_t j = 0; j < 8; ++j)
+ *(ptr++) = insecure_rand.rand32();
+ }
+ /** 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)
+ 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 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_ok)
+{
+ size_t megabytes = 32;
+ test_cache_erase<CuckooCache::cache<uint256, uint256Hasher>>(megabytes);
+}
+
+template <typename Cache>
+void test_cache_erase_parallel(size_t megabytes)
+{
+ double load = 1;
+ insecure_rand = FastRandomContext(true);
+ 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) {
+ uint32_t* ptr = (uint32_t*)hashes[i].begin();
+ for (uint8_t j = 0; j < 8; ++j)
+ *(ptr++) = insecure_rand.rand32();
+ }
+ /** 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)
+ set.contains(hashes[i], true);
+ });
+
+ /** 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 = 32;
+ test_cache_erase_parallel<CuckooCache::cache<uint256, uint256Hasher>>(megabytes);
+}
+
+
+template <typename Cache>
+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.
+ insecure_rand = FastRandomContext(true);
+
+ // block_activity models a chunk of network activity. n_insert elements are
+ // adde 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) {
+ uint32_t* ptr = (uint32_t*)inserts[i].begin();
+ for (uint8_t j = 0; j < 8; ++j)
+ *(ptr++) = insecure_rand.rand32();
+ }
+ 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 (auto h : inserts)
+ c.insert(h);
+ }
+ };
+
+ const uint32_t BLOCK_SIZE = 10000;
+ // 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 = 32;
+ 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 implicityly, 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<CuckooCache::cache<uint256, uint256Hasher>>();
+}
+
+BOOST_AUTO_TEST_SUITE_END();