pitrou commented on code in PR #45001: URL: https://github.com/apache/arrow/pull/45001#discussion_r1890341648
########## cpp/src/arrow/compute/kernels/scalar_hash.cc: ########## @@ -0,0 +1,221 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you 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 <algorithm> + +#include "arrow/array/array_base.h" +#include "arrow/array/builder_primitive.h" +#include "arrow/compute/kernels/common_internal.h" +#include "arrow/compute/key_hash_internal.h" +#include "arrow/compute/light_array_internal.h" +#include "arrow/compute/util.h" +#include "arrow/result.h" + +namespace arrow { +namespace compute { +namespace internal { + +// Define symbols visible within `arrow::compute::internal` in this file; +// these symbols are not visible outside of this file. +namespace { + +// ------------------------------ +// Kernel implementations +// It is expected that HashArrowType is either UInt32Type or UInt64Type (default) + +template <typename ArrowType, typename Hasher> +struct FastHashScalar { + using c_type = typename ArrowType::c_type; + + static Result<KeyColumnArray> ToColumnArray( + const ArraySpan& array, LightContext* ctx, + const uint8_t* list_values_buffer = nullptr) { + KeyColumnMetadata metadata; + const uint8_t* validity_buffer = nullptr; + const uint8_t* fixed_length_buffer = nullptr; + const uint8_t* var_length_buffer = nullptr; + + if (array.GetBuffer(0) != nullptr) { + validity_buffer = array.GetBuffer(0)->data(); + } + if (array.GetBuffer(1) != nullptr) { + fixed_length_buffer = array.GetBuffer(1)->data(); + } + + auto type = array.type; + auto type_id = type->id(); + if (type_id == Type::NA) { + metadata = KeyColumnMetadata(true, 0, true); + } else if (type_id == Type::BOOL) { + metadata = KeyColumnMetadata(true, 0); + } else if (is_fixed_width(type_id)) { + metadata = KeyColumnMetadata(true, type->bit_width() / 8); + } else if (is_binary_like(type_id)) { + metadata = KeyColumnMetadata(false, sizeof(uint32_t)); + var_length_buffer = array.GetBuffer(2)->data(); + } else if (is_large_binary_like(type_id)) { + metadata = KeyColumnMetadata(false, sizeof(uint64_t)); + var_length_buffer = array.GetBuffer(2)->data(); + } else if (type_id == Type::MAP) { + metadata = KeyColumnMetadata(false, sizeof(uint32_t)); + var_length_buffer = list_values_buffer; + } else if (type_id == Type::LIST) { + metadata = KeyColumnMetadata(false, sizeof(uint32_t)); + var_length_buffer = list_values_buffer; + } else if (type_id == Type::LARGE_LIST) { + metadata = KeyColumnMetadata(false, sizeof(uint64_t)); + var_length_buffer = list_values_buffer; + } else if (type_id == Type::FIXED_SIZE_LIST) { + auto list_type = checked_cast<const FixedSizeListType*>(type); + metadata = KeyColumnMetadata(true, list_type->list_size()); + fixed_length_buffer = list_values_buffer; + } else { + return Status::TypeError("Unsupported column data type ", type->name(), + " used with hash64 compute kernel"); + } + + return KeyColumnArray(metadata, array.length, validity_buffer, fixed_length_buffer, + var_length_buffer); + } + + static Result<std::shared_ptr<ArrayData>> HashChild(const ArraySpan& array, + const ArraySpan& child, + LightContext* hash_ctx, + MemoryPool* memory_pool) { + auto arrow_type = std::make_shared<ArrowType>(); + auto buffer_size = child.length * sizeof(c_type); + ARROW_ASSIGN_OR_RAISE(auto buffer, AllocateBuffer(buffer_size, memory_pool)); + ARROW_RETURN_NOT_OK( + HashArray(child, hash_ctx, memory_pool, buffer->mutable_data_as<c_type>())); + return ArrayData::Make(arrow_type, child.length, + {array.GetBuffer(0), std::move(buffer)}, array.null_count); + } + + static Status HashArray(const ArraySpan& array, LightContext* hash_ctx, + MemoryPool* memory_pool, c_type* out) { + // KeyColumnArray objects are being passed to the hashing utility + std::vector<KeyColumnArray> columns(1); + + auto type_id = array.type->id(); + if (type_id == Type::EXTENSION) { + auto extension_type = checked_cast<const ExtensionType*>(array.type); + auto storage_array = array; + storage_array.type = extension_type->storage_type().get(); + return HashArray(storage_array, hash_ctx, memory_pool, out); + } + + if (type_id == Type::STRUCT) { + std::vector<std::shared_ptr<ArrayData>> child_hashes(array.child_data.size()); + columns.resize(array.child_data.size()); + for (size_t i = 0; i < array.child_data.size(); i++) { + auto child = array.child_data[i]; + if (is_nested(child.type->id())) { + ARROW_ASSIGN_OR_RAISE(child_hashes[i], + HashChild(array, child, hash_ctx, memory_pool)); + ARROW_ASSIGN_OR_RAISE(columns[i], ToColumnArray(*child_hashes[i], hash_ctx)); + } else { + ARROW_ASSIGN_OR_RAISE(columns[i], ToColumnArray(child, hash_ctx)); + } + } + Hasher::HashMultiColumn(columns, hash_ctx, out); + } else if (is_list_like(type_id)) { + auto values = array.child_data[0]; + ARROW_ASSIGN_OR_RAISE(auto value_hashes, + HashChild(array, values, hash_ctx, memory_pool)); + ARROW_ASSIGN_OR_RAISE( + columns[0], ToColumnArray(array, hash_ctx, value_hashes->buffers[1]->data())); + Hasher::HashMultiColumn(columns, hash_ctx, out); + } else { + ARROW_ASSIGN_OR_RAISE(columns[0], ToColumnArray(array, hash_ctx)); + Hasher::HashMultiColumn(columns, hash_ctx, out); + } + return Status::OK(); + } + + static Status Exec(KernelContext* ctx, const ExecSpan& input_arg, ExecResult* out) { + if (input_arg.num_values() != 1 || !input_arg[0].is_array()) { + return Status::Invalid("FastHash currently supports a single array input"); + } + ArraySpan hash_input = input_arg[0].array; + + auto exec_ctx = default_exec_context(); + if (ctx && ctx->exec_context()) { + exec_ctx = ctx->exec_context(); + } + + // Initialize stack-based memory allocator used by Hashing32 and Hashing64 + util::TempVectorStack stack_memallocator; + ARROW_RETURN_NOT_OK( + stack_memallocator.Init(exec_ctx->memory_pool(), + 3 * sizeof(int32_t) * util::MiniBatch::kMiniBatchLength)); + + // Prepare context used by Hashing32 and Hashing64 + LightContext hash_ctx; + hash_ctx.hardware_flags = exec_ctx->cpu_info()->hardware_flags(); + hash_ctx.stack = &stack_memallocator; + + // Call the hashing function, overloaded based on OutputCType + ArraySpan* result_span = out->array_span_mutable(); + c_type* result_ptr = result_span->GetValues<c_type>(1); + ARROW_RETURN_NOT_OK( + HashArray(hash_input, &hash_ctx, exec_ctx->memory_pool(), result_ptr)); + + return Status::OK(); + } +}; + +const FunctionDoc hash32_doc{ + "Construct a hash for every element of the input argument", + ("An element-wise function that uses an xxHash-like algorithm.\n" + "This function is not suitable for cryptographic purposes.\n" + "Hash results are 32-bit and emitted for each valid row.\n" + "Null (or invalid) rows emit a `0` in the output."), Review Comment: Agreed. We should just ensure that the hash function has good dispersion properties, and that 0 and null hash to different values. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: github-unsubscr...@arrow.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
