zanmato1984 commented on code in PR #45562:
URL: https://github.com/apache/arrow/pull/45562#discussion_r1977943197
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cpp/src/arrow/compute/kernels/hash_aggregate.cc:
##########
@@ -3319,9 +3324,401 @@ struct GroupedListFactory {
HashAggregateKernel kernel;
InputType argument_type;
};
-} // namespace
-namespace {
+// ----------------------------------------------------------------------
+// Pivot implementation
+
+struct GroupedPivotAccumulator {
+ Status Init(ExecContext* ctx, std::shared_ptr<DataType> value_type,
+ const PivotWiderOptions* options) {
+ ctx_ = ctx;
+ value_type_ = std::move(value_type);
+ num_keys_ = static_cast<int>(options->key_names.size());
+ num_groups_ = 0;
+ columns_.resize(num_keys_);
+ scratch_buffer_ = BufferBuilder(ctx_->memory_pool());
+ return Status::OK();
+ }
+
+ Status Consume(span<const uint32_t> groups, span<const PivotWiderKeyIndex>
keys,
+ const ArraySpan& values) {
+ // To dispatch the values into the right (group, key) coordinates,
+ // we first compute a vector of take indices for each output column.
+ //
+ // For each index #i, we set take_indices[keys[#i]][groups[#i]] = #i.
+ // Unpopulated take_indices entries are null.
+ //
+ // For example, assuming we get:
+ // groups | keys
+ // ===================
+ // 1 | 0
+ // 3 | 1
+ // 1 | 1
+ // 0 | 1
+ //
+ // We are going to compute:
+ // - take_indices[key = 0] = [null, 0, null, null]
+ // - take_indices[key = 1] = [3, 2, null, 1]
+ //
+ // Then each output column is computed by taking the values with the
+ // respective take_indices for the column's keys.
+ //
+
+ DCHECK_EQ(groups.size(), keys.size());
+ DCHECK_EQ(groups.size(), static_cast<size_t>(values.length));
+
+ std::shared_ptr<DataType> take_index_type;
+ std::vector<std::shared_ptr<Buffer>> take_indices(num_keys_);
+ std::vector<std::shared_ptr<Buffer>> take_bitmaps(num_keys_);
+
+ // A generic lambda that computes the take indices with the desired
integer width
+ auto compute_take_indices = [&](auto typed_index) {
+ ARROW_UNUSED(typed_index);
+ using TakeIndex = std::decay_t<decltype(typed_index)>;
+ take_index_type = CTypeTraits<TakeIndex>::type_singleton();
+
+ const int64_t take_indices_size =
+ bit_util::RoundUpToMultipleOf64(num_groups_ * sizeof(TakeIndex));
+ const int64_t take_bitmap_size =
+ bit_util::RoundUpToMultipleOf64(bit_util::BytesForBits(num_groups_));
+ const int64_t total_scratch_size =
+ num_keys_ * (take_indices_size + take_bitmap_size);
+ RETURN_NOT_OK(scratch_buffer_.Resize(total_scratch_size,
/*shrink_to_fit=*/false));
+
+ // Slice the scratch space into individual buffers for each output
column's
+ // take_indices array.
+ std::vector<TakeIndex*> take_indices_data(num_keys_);
+ std::vector<uint8_t*> take_bitmap_data(num_keys_);
+ int64_t offset = 0;
+ for (int i = 0; i < num_keys_; ++i) {
+ take_indices[i] = std::make_shared<MutableBuffer>(
+ scratch_buffer_.mutable_data() + offset, take_indices_size);
+ take_indices_data[i] = take_indices[i]->mutable_data_as<TakeIndex>();
+ offset += take_indices_size;
+ take_bitmaps[i] = std::make_shared<MutableBuffer>(
+ scratch_buffer_.mutable_data() + offset, take_bitmap_size);
+ take_bitmap_data[i] = take_bitmaps[i]->mutable_data();
+ memset(take_bitmap_data[i], 0, take_bitmap_size);
+ offset += take_bitmap_size;
+ }
+ DCHECK_LE(offset, scratch_buffer_.capacity());
+
+ // Populate the take_indices for each output column
+ for (int64_t i = 0; i < values.length; ++i) {
+ const PivotWiderKeyIndex key = keys[i];
+ if (key != kNullPivotKey && !values.IsNull(i)) {
+ DCHECK_LT(static_cast<int>(key), num_keys_);
+ const uint32_t group = groups[i];
+ if (bit_util::GetBit(take_bitmap_data[key], group)) {
+ return DuplicateValue();
+ }
+ // For row #group in column #key, we are going to take the value at
index #i
+ bit_util::SetBit(take_bitmap_data[key], group);
+ take_indices_data[key][group] = static_cast<TakeIndex>(i);
+ }
+ }
+ return Status::OK();
+ };
+
+ // Call compute_take_indices with the optimal integer width
+ if (values.length <=
static_cast<int64_t>(std::numeric_limits<uint8_t>::max())) {
+ RETURN_NOT_OK(compute_take_indices(uint8_t{}));
+ } else if (values.length <=
+ static_cast<int64_t>(std::numeric_limits<uint16_t>::max())) {
+ RETURN_NOT_OK(compute_take_indices(uint16_t{}));
+ } else if (values.length <=
+ static_cast<int64_t>(std::numeric_limits<uint32_t>::max())) {
+ RETURN_NOT_OK(compute_take_indices(uint32_t{}));
+ } else {
+ RETURN_NOT_OK(compute_take_indices(uint64_t{}));
+ }
+
+ // Use take_indices to compute the output columns for this batch
+ auto values_data = values.ToArrayData();
+ ArrayVector new_columns(num_keys_);
+ for (int i = 0; i < num_keys_; ++i) {
+ auto indices_data =
+ ArrayData::Make(take_index_type, num_groups_,
+ {std::move(take_bitmaps[i]),
std::move(take_indices[i])});
+ // If indices_data is all nulls, we can just ignore this column.
+ if (indices_data->GetNullCount() != indices_data->length) {
+ ARROW_ASSIGN_OR_RAISE(Datum grouped_column, Take(values_data,
indices_data,
+
TakeOptions::Defaults(), ctx_));
+ new_columns[i] = grouped_column.make_array();
+ }
+ }
+ // Merge them with the previous columns
+ return MergeColumns(std::move(new_columns));
+ }
+
+ Status Consume(span<const uint32_t> groups, const PivotWiderKeyIndex key,
+ const ArraySpan& values) {
+ if (key == kNullPivotKey) {
+ // Nothing to update
+ return Status::OK();
+ }
+ DCHECK_LT(static_cast<int>(key), num_keys_);
+ DCHECK_EQ(groups.size(), static_cast<size_t>(values.length));
+
+ // The algorithm is simpler than in the array-taking version of Consume()
+ // below, since only the column #key needs to be updated.
+ std::shared_ptr<DataType> take_index_type;
+ std::shared_ptr<Buffer> take_indices;
+ std::shared_ptr<Buffer> take_bitmap;
+
+ // A generic lambda that computes the take indices with the desired
integer width
+ auto compute_take_indices = [&](auto typed_index) {
+ ARROW_UNUSED(typed_index);
+ using TakeIndex = std::decay_t<decltype(typed_index)>;
+ take_index_type = CTypeTraits<TakeIndex>::type_singleton();
+
+ const int64_t take_indices_size =
+ bit_util::RoundUpToMultipleOf64(num_groups_ * sizeof(TakeIndex));
+ const int64_t take_bitmap_size =
+ bit_util::RoundUpToMultipleOf64(bit_util::BytesForBits(num_groups_));
+ const int64_t total_scratch_size = take_indices_size + take_bitmap_size;
+ RETURN_NOT_OK(scratch_buffer_.Resize(total_scratch_size,
/*shrink_to_fit=*/false));
+
+ take_indices =
std::make_shared<MutableBuffer>(scratch_buffer_.mutable_data(),
+ take_indices_size);
+ take_bitmap = std::make_shared<MutableBuffer>(
+ scratch_buffer_.mutable_data() + take_indices_size,
take_bitmap_size);
+ auto take_indices_data = take_indices->mutable_data_as<TakeIndex>();
+ auto take_bitmap_data = take_bitmap->mutable_data();
+ memset(take_bitmap_data, 0, take_bitmap_size);
+
+ for (int64_t i = 0; i < values.length; ++i) {
+ const uint32_t group = groups[i];
+ if (!values.IsNull(i)) {
+ if (bit_util::GetBit(take_bitmap_data, group)) {
+ return DuplicateValue();
+ }
+ bit_util::SetBit(take_bitmap_data, group);
+ take_indices_data[group] = static_cast<TakeIndex>(i);
+ }
+ }
+ return Status::OK();
+ };
+
+ // Call compute_take_indices with the optimal integer width
+ if (values.length <=
static_cast<int64_t>(std::numeric_limits<uint8_t>::max())) {
+ RETURN_NOT_OK(compute_take_indices(uint8_t{}));
+ } else if (values.length <=
+ static_cast<int64_t>(std::numeric_limits<uint16_t>::max())) {
+ RETURN_NOT_OK(compute_take_indices(uint16_t{}));
+ } else if (values.length <=
+ static_cast<int64_t>(std::numeric_limits<uint32_t>::max())) {
+ RETURN_NOT_OK(compute_take_indices(uint32_t{}));
+ } else {
+ RETURN_NOT_OK(compute_take_indices(uint64_t{}));
+ }
+
+ // Use take_indices to update column #key
+ auto values_data = values.ToArrayData();
+ auto indices_data = ArrayData::Make(
+ take_index_type, num_groups_, {std::move(take_bitmap),
std::move(take_indices)});
+ ARROW_ASSIGN_OR_RAISE(Datum grouped_column,
+ Take(values_data, indices_data,
TakeOptions::Defaults(), ctx_));
+ return MergeColumn(&columns_[key], grouped_column.make_array());
+ }
+
+ Status Resize(int64_t new_num_groups) {
+ if (new_num_groups > std::numeric_limits<int32_t>::max()) {
+ return Status::NotImplemented("Pivot with more 2**31 groups");
+ }
+ return ResizeColumns(new_num_groups);
+ }
+
+ Status Merge(GroupedPivotAccumulator&& other, const ArrayData&
group_id_mapping) {
+ // To merge `other` into `*this`, we simply merge their respective columns.
+ // However, we must first transpose `other`'s rows using
`group_id_mapping`.
+ // This is a logical "scatter" operation.
+ //
+ // Since `scatter(indices)` is implemented as
`take(inverse_permutation(indices))`,
+ // we can save time by computing `inverse_permutation(indices)` once for
all
+ // columns.
+
+ // Scatter/InversePermutation only accept signed indices. We checked
+ // in Resize() above that we were inside the limites for int32.
+ auto scatter_indices = group_id_mapping.Copy();
+ scatter_indices->type = int32();
+ std::shared_ptr<DataType> take_indices_type;
+ if (num_groups_ - 1 <= std::numeric_limits<int8_t>::max()) {
+ take_indices_type = int8();
+ } else if (num_groups_ - 1 <= std::numeric_limits<int16_t>::max()) {
+ take_indices_type = int16();
+ } else {
+ DCHECK_GE(num_groups_ - 1, std::numeric_limits<int32_t>::max());
+ take_indices_type = int32();
+ }
+ InversePermutationOptions options(/*max_index=*/num_groups_ - 1,
take_indices_type);
+ ARROW_ASSIGN_OR_RAISE(auto take_indices,
+ InversePermutation(scatter_indices, options, ctx_));
+ auto scatter_column =
+ [&](const std::shared_ptr<Array>& column) ->
Result<std::shared_ptr<Array>> {
+ ARROW_ASSIGN_OR_RAISE(auto scattered,
+ CallFunction("take", {column, take_indices},
&options, ctx_));
Review Comment:
Why is this not using `Take(...)` like the other two invocations?
##########
cpp/src/arrow/compute/kernels/aggregate_pivot.cc:
##########
@@ -0,0 +1,186 @@
+// 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 "arrow/compute/api_aggregate.h"
+#include "arrow/compute/kernels/aggregate_internal.h"
+#include "arrow/compute/kernels/common_internal.h"
+#include "arrow/compute/kernels/pivot_internal.h"
+#include "arrow/scalar.h"
+#include "arrow/util/bit_run_reader.h"
+#include "arrow/util/logging.h"
+
+namespace arrow::compute::internal {
+namespace {
+
+using arrow::internal::VisitSetBitRunsVoid;
+using arrow::util::span;
+
+struct PivotImpl : public ScalarAggregator {
+ Status Init(const PivotWiderOptions& options, const std::vector<TypeHolder>&
in_types) {
+ options_ = &options;
+ key_type_ = in_types[0].GetSharedPtr();
+ auto value_type = in_types[1].GetSharedPtr();
+ FieldVector fields;
+ fields.reserve(options_->key_names.size());
+ values_.reserve(options_->key_names.size());
+ for (const auto& key_name : options_->key_names) {
+ fields.push_back(field(key_name, value_type));
+ values_.push_back(MakeNullScalar(value_type));
+ }
+ out_type_ = struct_(std::move(fields));
+ ARROW_ASSIGN_OR_RAISE(key_mapper_, PivotWiderKeyMapper::Make(*key_type_,
options_));
+ return Status::OK();
+ }
+
+ Status Consume(KernelContext*, const ExecSpan& batch) override {
+ DCHECK_EQ(batch.num_values(), 2);
+ if (batch[0].is_array()) {
+ ARROW_ASSIGN_OR_RAISE(span<const PivotWiderKeyIndex> keys,
+ key_mapper_->MapKeys(batch[0].array));
+ if (batch[1].is_array()) {
+ // Array keys, array values
+ auto values = batch[1].array.ToArray();
+ for (int64_t i = 0; i < batch.length; ++i) {
+ PivotWiderKeyIndex key = keys[i];
+ if (key != kNullPivotKey && !values->IsNull(i)) {
+ if (ARROW_PREDICT_FALSE(values_[key]->is_valid)) {
+ return DuplicateValue();
+ }
+ ARROW_ASSIGN_OR_RAISE(values_[key], values->GetScalar(i));
+ DCHECK(values_[key]->is_valid);
+ }
+ }
+ } else {
+ // Array keys, scalar value
+ const Scalar* value = batch[1].scalar;
+ if (value->is_valid) {
+ for (int64_t i = 0; i < batch.length; ++i) {
+ PivotWiderKeyIndex key = keys[i];
+ if (key != kNullPivotKey) {
+ if (ARROW_PREDICT_FALSE(values_[key]->is_valid)) {
+ return DuplicateValue();
+ }
+ values_[key] = value->GetSharedPtr();
+ }
+ }
+ }
+ }
+ } else {
+ ARROW_ASSIGN_OR_RAISE(PivotWiderKeyIndex key,
+ key_mapper_->MapKey(*batch[0].scalar));
+ if (key != kNullPivotKey) {
+ if (batch[1].is_array()) {
+ // Scalar key, array values
+ auto values = batch[1].array.ToArray();
+ for (int64_t i = 0; i < batch.length; ++i) {
+ if (!values->IsNull(i)) {
+ if (ARROW_PREDICT_FALSE(values_[key]->is_valid)) {
+ return DuplicateValue();
+ }
+ ARROW_ASSIGN_OR_RAISE(values_[key], values->GetScalar(i));
+ DCHECK(values_[key]->is_valid);
+ }
+ }
+ } else {
+ // Scalar key, scalar value
+ const Scalar* value = batch[1].scalar;
+ if (value->is_valid) {
+ if (batch.length > 1 || values_[key]->is_valid) {
+ return DuplicateValue();
+ }
+ values_[key] = value->GetSharedPtr();
+ }
+ }
+ }
+ }
+ return Status::OK();
+ }
+
+ Status MergeFrom(KernelContext*, KernelState&& src) override {
+ const auto& other_state = checked_cast<const PivotImpl&>(src);
+ for (int64_t key = 0; key < static_cast<int64_t>(values_.size()); ++key) {
+ if (other_state.values_[key]->is_valid) {
+ if (ARROW_PREDICT_FALSE(values_[key]->is_valid)) {
+ return DuplicateValue();
+ }
+ values_[key] = other_state.values_[key];
+ }
+ }
+ return Status::OK();
+ }
+
+ Status Finalize(KernelContext* ctx, Datum* out) override {
+ *out = std::make_shared<StructScalar>(std::move(values_), out_type_);
+ return Status::OK();
+ }
+
+ Status DuplicateValue() {
+ return Status::Invalid(
+ "Encountered more than one non-null value for the same pivot key");
+ }
+
+ std::shared_ptr<DataType> out_type() const { return out_type_; }
+
+ std::shared_ptr<DataType> key_type_;
+ std::shared_ptr<DataType> out_type_;
+ const PivotWiderOptions* options_;
+ std::unique_ptr<PivotWiderKeyMapper> key_mapper_;
+ ScalarVector values_;
+};
+
+Result<std::unique_ptr<KernelState>> PivotInit(KernelContext* ctx,
+ const KernelInitArgs& args) {
+ const auto& options = checked_cast<const PivotWiderOptions&>(*args.options);
+ DCHECK_EQ(args.inputs.size(), 2);
+ DCHECK(is_base_binary_like(args.inputs[0].id()));
+ auto state = std::make_unique<PivotImpl>();
+ RETURN_NOT_OK(state->Init(options, args.inputs));
+ return state;
+}
+
+Result<TypeHolder> ResolveOutputType(KernelContext* ctx, const
std::vector<TypeHolder>&) {
+ return checked_cast<PivotImpl*>(ctx->state())->out_type();
+}
+
+const FunctionDoc pivot_doc{
+ "Pivot values according to a pivot key column",
+ ("Output is a struct with as many fields as
`PivotWiderOptions.key_names`.\n"
+ "All output struct fields have the same type as `pivot_values`.\n"
+ "Each pivot key decides in which output field the corresponding pivot
value\n"
+ "is emitted. If a pivot key doesn't appear, null is emitted.\n"
+ "If a pivot key appears twice, KeyError is raised.\n"
+ "Behavior of unexpected pivot keys is controlled by PivotWiderOptions."),
Review Comment:
Well, that's what I originally expected, until I saw `Invalid` not
`KeyError` is actually thrown in `DuplicateValue`, so I think this description
is not accurate enough.
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