Add unit tests for the CuckooCache

SQUASHME: Update Tests for other SQUASHMEs

1 files changed, 394 insertions, 0 deletions

diff --git a/src/test/cuckoocache_tests.cpp b/src/test/cuckoocache_tests.cpp
new file mode 100644
index 0000000000..1bc50d5ea9
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+++ 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();