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// to you under the Apache License, Version 2.0 (the
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//
//   http://www.apache.org/licenses/LICENSE-2.0
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// Unless required by applicable law or agreed to in writing,
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#ifndef IMPALA_UTIL_BLOOM_H
#define IMPALA_UTIL_BLOOM_H

#include <math.h>
#include <stdint.h>

#include <limits>

#include <immintrin.h>

#include "common/compiler-util.h"
#include "gen-cpp/ImpalaInternalService_types.h"
#include "gutil/macros.h"
#include "runtime/bufferpool/buffer-pool.h"
#include "util/cpu-info.h"
#include "util/hash-util.h"

namespace impala {

/// A BloomFilter stores sets of items and offers a query operation indicating whether or
/// not that item is in the set.  BloomFilters use much less space than other compact data
/// structures, but they are less accurate: for a small percentage of elements, the query
/// operation incorrectly returns true even when the item is not in the set.
///
/// When talking about Bloom filter size, rather than talking about 'size', which might be
/// ambiguous, we distinguish two different quantities:
///
/// 1. Space: the amount of buffer pool memory used
///
/// 2. NDV: the number of unique items that have been inserted
///
/// BloomFilter is implemented using block Bloom filters from Putze et al.'s "Cache-,
/// Hash- and Space-Efficient Bloom Filters". The basic idea is to hash the item to a tiny
/// Bloom filter the size of a single cache line or smaller. This implementation sets 8
/// bits in each tiny Bloom filter. This provides a false positive rate near optimal for
/// between 5 and 15 bits per distinct value, which corresponds to false positive
/// probabilities between 0.1% (for 15 bits) and 10% (for 5 bits).
///
/// Our tiny BloomFilters are 32 bytes to take advantage of 32-byte SIMD in newer Intel
/// machines. 'noexcept' is added to various functions called from the cross-compiled code
/// so LLVM will not generate exception related code at their call sites.
class BloomFilter {
 public:
  /// Consumes at most (1 << log_bufferpool_space) bytes from the buffer pool client.
  /// 'client' should be a valid registered BufferPool Client and should have enough
  /// reservation to fulfill allocation for 'directory_'.
  explicit BloomFilter(BufferPool::ClientHandle* client);
  ~BloomFilter();

  /// Reset the filter state, allocate/reallocate and initialize the 'directory_'. All
  /// calls to Insert() and Find() should only be done between the calls to Init() and
  /// Close().Init and Close are safe to call multiple times.
  Status Init(const int log_bufferpool_space);
  Status Init(const TBloomFilter& thrift);
  void Close();

  /// Representation of a filter which allows all elements to pass.
  static constexpr BloomFilter* const ALWAYS_TRUE_FILTER = NULL;

  /// Converts 'filter' to its corresponding Thrift representation. If the first argument
  /// is NULL, it is interpreted as a complete filter which contains all elements.
  static void ToThrift(const BloomFilter* filter, TBloomFilter* thrift);

  bool AlwaysFalse() const { return always_false_; }

  /// Adds an element to the BloomFilter. The function used to generate 'hash' need not
  /// have good uniformity, but it should have low collision probability. For instance, if
  /// the set of values is 32-bit ints, the identity function is a valid hash function for
  /// this Bloom filter, since the collision probability (the probability that two
  /// non-equal values will have the same hash value) is 0.
  void Insert(const uint32_t hash) noexcept;

  /// Finds an element in the BloomFilter, returning true if it is found and false (with
  /// high probabilty) if it is not.
  bool Find(const uint32_t hash) const noexcept;

  /// Computes the logical OR of 'in' with 'out' and stores the result in 'out'.
  static void Or(const TBloomFilter& in, TBloomFilter* out);

  /// As more distinct items are inserted into a BloomFilter, the false positive rate
  /// rises. MaxNdv() returns the NDV (number of distinct values) at which a BloomFilter
  /// constructed with (1 << log_bufferpool_space) bytes of heap space hits false positive
  /// probabilty fpp.
  static size_t MaxNdv(const int log_bufferpool_space, const double fpp);

  /// If we expect to fill a Bloom filter with 'ndv' different unique elements and we
  /// want a false positive probabilty of less than 'fpp', then this is the log (base 2)
  /// of the minimum number of bytes we need.
  static int MinLogSpace(const size_t ndv, const double fpp);

  /// Returns the expected false positive rate for the given ndv and log_bufferpool_space
  static double FalsePositiveProb(const size_t ndv, const int log_bufferpool_space);

  /// Returns the amount of buffer pool space used (in bytes). A value of -1 means that
  /// 'directory_' has not been allocated which can happen if the object was just created
  /// and Init() hasn't been called or Init() failed or Close() was called on the object.
  int64_t GetBufferPoolSpaceUsed() const {
    return directory_ == nullptr ? -1 : sizeof(Bucket) * (1LL << log_num_buckets_);
  }

  static int64_t GetExpectedMemoryUsed(int log_heap_size) {
    return sizeof(Bucket) * (1LL << std::max(1, log_heap_size - LOG_BUCKET_WORD_BITS));
  }

 private:
  // always_false_ is true when the bloom filter hasn't had any elements inserted.
  bool always_false_ = true;

  /// The BloomFilter is divided up into Buckets
  static const uint64_t BUCKET_WORDS = 8;
  typedef uint32_t BucketWord;

  // log2(number of bits in a BucketWord)
  static const int LOG_BUCKET_WORD_BITS = 5;
  static const BucketWord BUCKET_WORD_MASK = (1 << LOG_BUCKET_WORD_BITS) - 1;

  /// log2(number of bytes in a bucket)
  static const int LOG_BUCKET_BYTE_SIZE = 5;

  static_assert((1 << LOG_BUCKET_WORD_BITS) == std::numeric_limits<BucketWord>::digits,
      "BucketWord must have a bit-width that is be a power of 2, like 64 for uint64_t.");

  typedef BucketWord Bucket[BUCKET_WORDS];

  /// log_num_buckets_ is the log (base 2) of the number of buckets in the directory.
  int log_num_buckets_ = 0;

  /// directory_mask_ is (1 << log_num_buckets_) - 1. It is precomputed for
  /// efficiency reasons.
  uint32_t directory_mask_ = 0;

  Bucket* directory_ = nullptr;

  /// Bufferpool client and handle used for allocating and freeing directory memory.
  /// Client is not owned by the filter.
  BufferPool::ClientHandle* buffer_pool_client_;
  BufferPool::BufferHandle buffer_handle_;

  // Same as Insert(), but skips the CPU check and assumes that AVX is not available.
  void InsertNoAvx2(const uint32_t hash) noexcept;

  // Same as Insert(), but skips the CPU check and assumes that AVX is available.
  void InsertAvx2(const uint32_t hash) noexcept;

  /// Does the actual work of Insert(). bucket_idx is the index of the bucket to insert
  /// into and 'hash' is the value passed to Insert().
  void BucketInsert(const uint32_t bucket_idx, const uint32_t hash) noexcept;

  /// A faster SIMD version of BucketInsert().
  void BucketInsertAVX2(const uint32_t bucket_idx, const uint32_t hash) noexcept
      __attribute__((__target__("avx2")));

  /// BucketFind() and BucketFindAVX2() are just like BucketInsert() and
  /// BucketInsertAVX2(), but for Find().
  bool BucketFind(const uint32_t bucket_idx, const uint32_t hash) const noexcept;
  bool BucketFindAVX2(const uint32_t bucket_idx, const uint32_t hash) const noexcept
      __attribute__((__target__("avx2")));

  /// A helper function for the AVX2 methods. Turns a 32-bit hash into a 256-bit Bucket
  /// with 1 single 1-bit set in each 32-bit lane.
  static inline ALWAYS_INLINE __m256i MakeMask(const uint32_t hash)
      __attribute__((__target__("avx2")));

  int64_t directory_size() const {
    return 1uLL << (log_num_buckets_ + LOG_BUCKET_BYTE_SIZE);
  }

  /// Serializes this filter as Thrift.
  void ToThrift(TBloomFilter* thrift) const;

/// Some constants used in hashing. #defined for efficiency reasons.
#define IMPALA_BLOOM_HASH_CONSTANTS                                             \
  0x47b6137bU, 0x44974d91U, 0x8824ad5bU, 0xa2b7289dU, 0x705495c7U, 0x2df1424bU, \
      0x9efc4947U, 0x5c6bfb31U

  /// REHASH is used as 8 odd 32-bit unsigned ints.  See Dietzfelbinger et al.'s "A
  /// reliable randomized algorithm for the closest-pair problem".
  static constexpr uint32_t REHASH[8]
      __attribute__((aligned(32))) = {IMPALA_BLOOM_HASH_CONSTANTS};

  DISALLOW_COPY_AND_ASSIGN(BloomFilter);
};

// To set 8 bits in an 32-byte Bloom filter, we set one bit in each 32-bit uint32_t. This
// is a "split Bloom filter", and it has approximately the same false positive probability
// as standard a Bloom filter; See Mitzenmacher's "Bloom Filters and Such". It also has
// the advantage of requiring fewer random bits: log2(32) * 8 = 5 * 8 = 40 random bits for
// a split Bloom filter, but log2(256) * 8 = 64 random bits for a standard Bloom filter.

inline void ALWAYS_INLINE BloomFilter::Insert(const uint32_t hash) noexcept {
  DCHECK(directory_ != nullptr);
  always_false_ = false;
  const uint32_t bucket_idx = HashUtil::Rehash32to32(hash) & directory_mask_;
  if (CpuInfo::IsSupported(CpuInfo::AVX2)) {
    BucketInsertAVX2(bucket_idx, hash);
  } else {
    BucketInsert(bucket_idx, hash);
  }
}

inline bool ALWAYS_INLINE BloomFilter::Find(const uint32_t hash) const noexcept {
  if (always_false_) return false;
  DCHECK(directory_ != nullptr);
  const uint32_t bucket_idx = HashUtil::Rehash32to32(hash) & directory_mask_;
  if (CpuInfo::IsSupported(CpuInfo::AVX2)) {
    return BucketFindAVX2(bucket_idx, hash);
  } else {
    return BucketFind(bucket_idx, hash);
  }
}

} // namespace impala

#endif // IMPALA_UTIL_BLOOM_H