Add distinct key inner join

cpp/CMakeLists.txt

@@ -446,6 +446,7 @@ add_library(
   src/join/mixed_join_size_kernel_nulls.cu
   src/join/mixed_join_size_kernels_semi.cu
   src/join/semi_join.cu
+  src/join/unique_hash_join.cu
   src/json/json_path.cu
   src/lists/contains.cu
   src/lists/combine/concatenate_list_elements.cu

cpp/benchmarks/CMakeLists.txt

@@ -164,7 +164,7 @@ ConfigureNVBench(
 # ##################################################################################################
 # * join benchmark --------------------------------------------------------------------------------
 ConfigureBench(JOIN_BENCH join/left_join.cu join/conditional_join.cu)
-ConfigureNVBench(JOIN_NVBENCH join/join.cu join/mixed_join.cu)
+ConfigureNVBench(JOIN_NVBENCH join/join.cu join/mixed_join.cu join/unique_join.cu)
 
 # ##################################################################################################
 # * iterator benchmark ----------------------------------------------------------------------------

cpp/benchmarks/join/unique_join.cu

@@ -0,0 +1,77 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "join_common.hpp"
+
+template <typename key_type, typename payload_type, bool Nullable>
+void unique_inner_join(nvbench::state& state,
+                       nvbench::type_list<key_type, payload_type, nvbench::enum_type<Nullable>>)
+{
+  skip_helper(state);
+
+  auto join = [](cudf::table_view const& left_input,
+                 cudf::table_view const& right_input,
+                 cudf::null_equality compare_nulls,
+                 rmm::cuda_stream_view stream) {
+    auto const has_nulls = cudf::has_nested_nulls(left_input) || cudf::has_nested_nulls(right_input)
+                             ? cudf::nullable_join::YES
+                             : cudf::nullable_join::NO;
+    auto hj_obj          = cudf::unique_hash_join<cudf::has_nested::NO>{
+      left_input, right_input, has_nulls, compare_nulls, stream};
+    return hj_obj.inner_join(stream);
+  };
+
+  BM_join<key_type, payload_type, Nullable>(state, join);
+}
+
+// inner join -----------------------------------------------------------------------
+NVBENCH_BENCH_TYPES(unique_inner_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("unique_inner_join_32bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(unique_inner_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("unique_inner_join_64bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
+
+NVBENCH_BENCH_TYPES(unique_inner_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("unique_inner_join_32bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(unique_inner_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("unique_inner_join_64bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});

cpp/include/cudf/detail/unique_hash_join.cuh

@@ -0,0 +1,154 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/hashing/detail/helper_functions.cuh>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <cuco/static_set.cuh>
+
+#include <cstddef>
+#include <memory>
+#include <type_traits>
+#include <utility>
+
+namespace cudf::detail {
+
+using cudf::experimental::row::lhs_index_type;
+using cudf::experimental::row::rhs_index_type;
+
+/**
+ * @brief An comparator adapter wrapping both self comparator and two table comparator
+ */
+template <typename Equal>
+struct comparator_adapter {
+  comparator_adapter(Equal const& d_equal) : _d_equal{d_equal} {}
+
+  __device__ constexpr auto operator()(
+    cuco::pair<hash_value_type, lhs_index_type> const&,
+    cuco::pair<hash_value_type, lhs_index_type> const&) const noexcept
+  {
+    // All build table keys are unique thus `false` no matter what
+    return false;
+  }
+
+  __device__ constexpr auto operator()(
+    cuco::pair<hash_value_type, lhs_index_type> const& lhs,
+    cuco::pair<hash_value_type, rhs_index_type> const& rhs) const noexcept
+  {
+    if (lhs.first != rhs.first) { return false; }
+    return _d_equal(lhs.second, rhs.second);
+  }
+
+ private:
+  Equal _d_equal;
+};
+
+template <typename Hasher>
+struct hasher_adapter {
+  hasher_adapter(Hasher const& d_hasher = {}) : _d_hasher{d_hasher} {}
+
+  template <typename T>
+  __device__ constexpr auto operator()(cuco::pair<hash_value_type, T> const& key) const noexcept
+  {
+    return _d_hasher(key.first);
+  }
+
+ private:
+  Hasher _d_hasher;
+};
+
+/**
+ * @brief Unique hash join that builds hash table in creation and probes results in subsequent
+ * `*_join` member functions.
+ *
+ * @tparam HasNested Flag indicating whether there are nested columns in build/probe table
+ */
+template <has_nested HasNested = has_nested::NO>
+struct unique_hash_join {
+ private:
+  /// Row equality type for nested columns
+  using nested_row_equal = cudf::experimental::row::equality::strong_index_comparator_adapter<
+    cudf::experimental::row::equality::device_row_comparator<true, cudf::nullate::DYNAMIC>>;
+  /// Row equality type for flat columns
+  using flat_row_equal = cudf::experimental::row::equality::strong_index_comparator_adapter<
+    cudf::experimental::row::equality::device_row_comparator<false, cudf::nullate::DYNAMIC>>;
+
+  /// Device row equal type
+  using d_equal_type =
+    std::conditional_t<HasNested == has_nested::YES, nested_row_equal, flat_row_equal>;
+  using hasher              = hasher_adapter<thrust::identity<hash_value_type>>;
+  using probing_scheme_type = cuco::experimental::linear_probing<1, hasher>;
+  using cuco_storge_type    = cuco::experimental::storage<1>;
+
+  /// Hash table type
+  using hash_table_type =
+    cuco::experimental::static_set<cuco::pair<hash_value_type, lhs_index_type>,
+                                   cuco::experimental::extent<std::size_t>,
+                                   cuda::thread_scope_device,
+                                   comparator_adapter<d_equal_type>,
+                                   probing_scheme_type,
+                                   cudf::detail::cuco_allocator,
+                                   cuco_storge_type>;
+
+  bool _has_nulls;  ///< true if nulls are present in either build table or probe table
+  cudf::null_equality _nulls_equal;  ///< whether to consider nulls as equal
+  cudf::table_view _build;           ///< input table to build the hash map
+  cudf::table_view _probe;           ///< input table to probe the hash map
+  std::shared_ptr<cudf::experimental::row::equality::preprocessed_table>
+    _preprocessed_build;  ///< input table preprocssed for row operators
+  std::shared_ptr<cudf::experimental::row::equality::preprocessed_table>
+    _preprocessed_probe;        ///< input table preprocssed for row operators
+  hash_table_type _hash_table;  ///< hash table built on `_build`
+
+ public:
+  unique_hash_join()                                   = delete;
+  ~unique_hash_join()                                  = default;
+  unique_hash_join(unique_hash_join const&)            = delete;
+  unique_hash_join(unique_hash_join&&)                 = delete;
+  unique_hash_join& operator=(unique_hash_join const&) = delete;
+  unique_hash_join& operator=(unique_hash_join&&)      = delete;
+
+  /**
+   * @brief Constructor that internally builds the hash table based on the given `build` table.
+   *
+   * @throw cudf::logic_error if the number of columns in `build` table is 0.
+   *
+   * @param build The build table, from which the hash table is built
+   * @param probe The probe table
+   * @param has_nulls Flag to indicate if any nulls exist in the `build` table or
+   *        any `probe` table that will be used later for join.
+   * @param compare_nulls Controls whether null join-key values should match or not.
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   */
+  unique_hash_join(cudf::table_view const& build,
+                   cudf::table_view const& probe,
+                   bool has_nulls,
+                   cudf::null_equality compare_nulls,
+                   rmm::cuda_stream_view stream);
+
+  /**
+   * @copydoc cudf::unique_hash_join::inner_join
+   */
+  std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+            std::unique_ptr<rmm::device_uvector<size_type>>>
+  inner_join(rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr) const;
+};
+}  // namespace cudf::detail

cpp/include/cudf/join.hpp

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2019-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -33,6 +33,11 @@
 
 namespace cudf {
 
+/**
+ * @brief Enum to indicate whether the unique join table has nested columns or not
+ */
+enum class has_nested : bool { YES, NO };
+
 // forward declaration
 namespace hashing::detail {
 template <typename T>
@@ -41,6 +46,9 @@ class MurmurHash3_x86_32;
 namespace detail {
 template <typename T>
 class hash_join;
+
+template <has_nested>
+class unique_hash_join;
 }  // namespace detail
 
 /**
@@ -438,6 +446,64 @@ class hash_join {
   const std::unique_ptr<impl_type const> _impl;
 };
 
+/**
+ * @brief Unique hash join that builds hash table in creation and probes results in subsequent
+ * `*_join` member functions
+ *
+ * @note Behavior is undefined if the build table contains duplicates.
+ * @note All NaNs are considered as equal
+ *
+ * @tparam HasNested Flag indicating whether there are nested columns in build/probe table
+ */
+// TODO: `HasNested` to be removed via dispatching
+template <has_nested HasNested = has_nested::NO>
+class unique_hash_join {
+ public:
+  unique_hash_join() = delete;
+  ~unique_hash_join();
+  unique_hash_join(unique_hash_join const&)            = delete;
+  unique_hash_join(unique_hash_join&&)                 = delete;
+  unique_hash_join& operator=(unique_hash_join const&) = delete;
+  unique_hash_join& operator=(unique_hash_join&&)      = delete;
+
+  /**
+   * @brief Constructs a unique hash join object for subsequent probe calls
+   *
+   * @param build The build table that contains unique elements
+   * @param probe The probe table, from which the keys are probed
+   * @param has_nulls Flag to indicate if there exists any nulls in the `build` table or
+   *        any `probe` table that will be used later for join
+   * @param compare_nulls Controls whether null join-key values should match or not
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   */
+  unique_hash_join(cudf::table_view const& build,
+                   cudf::table_view const& probe,
+                   nullable_join has_nulls      = nullable_join::YES,
+                   null_equality compare_nulls  = null_equality::EQUAL,
+                   rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+  /**
+   * Returns the row indices that can be used to construct the result of performing
+   * an inner join between two tables. @see cudf::inner_join().
+   *
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @param mr Device memory resource used to allocate the returned indices' device memory.
+   *
+   * @return A pair of columns [`build_indices`, `probe_indices`] that can be used to construct
+   * the result of performing an inner join between two tables with `build` and `probe`
+   * as the join keys.
+   */
+  std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+            std::unique_ptr<rmm::device_uvector<size_type>>>
+  inner_join(rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+             rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource()) const;
+
+ private:
+  using impl_type = typename cudf::detail::unique_hash_join<HasNested>;  ///< Implementation type
+
+  std::unique_ptr<impl_type> _impl;  ///< Unique hash join implementation
+};
+
 /**
  * @brief Returns a pair of row index vectors corresponding to all pairs
  * of rows between the specified tables where the predicate evaluates to true.