text: // 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_eclipsim.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, 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.
   *
   */
  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();
  