forked from apache/arrow
/
hash_aggregate_test.cc
5342 lines (4866 loc) · 208 KB
/
hash_aggregate_test.cc
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// 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 <gtest/gtest.h>
#include <algorithm>
#include <limits>
#include <memory>
#include <type_traits>
#include <unordered_map>
#include <utility>
#include <vector>
#include "arrow/acero/aggregate_node.h"
#include "arrow/acero/exec_plan.h"
#include "arrow/acero/options.h"
#include "arrow/acero/test_util_internal.h"
#include "arrow/array.h"
#include "arrow/array/concatenate.h"
#include "arrow/chunked_array.h"
#include "arrow/compute/api_aggregate.h"
#include "arrow/compute/api_scalar.h"
#include "arrow/compute/api_vector.h"
#include "arrow/compute/cast.h"
#include "arrow/compute/exec.h"
#include "arrow/compute/exec_internal.h"
#include "arrow/compute/kernels/aggregate_internal.h"
#include "arrow/compute/kernels/codegen_internal.h"
#include "arrow/compute/registry.h"
#include "arrow/compute/row/grouper.h"
#include "arrow/compute/row/grouper_internal.h"
#include "arrow/table.h"
#include "arrow/testing/generator.h"
#include "arrow/testing/gtest_util.h"
#include "arrow/testing/matchers.h"
#include "arrow/testing/random.h"
#include "arrow/type.h"
#include "arrow/type_traits.h"
#include "arrow/util/async_generator.h"
#include "arrow/util/bitmap_reader.h"
#include "arrow/util/checked_cast.h"
#include "arrow/util/int_util_overflow.h"
#include "arrow/util/key_value_metadata.h"
#include "arrow/util/logging.h"
#include "arrow/util/string.h"
#include "arrow/util/thread_pool.h"
#include "arrow/util/vector.h"
using testing::Eq;
using testing::HasSubstr;
namespace arrow {
using internal::BitmapReader;
using internal::checked_cast;
using internal::checked_pointer_cast;
using internal::ToChars;
using compute::CallFunction;
using compute::CountOptions;
using compute::default_exec_context;
using compute::ExecSpan;
using compute::FunctionOptions;
using compute::Grouper;
using compute::RowSegmenter;
using compute::ScalarAggregateOptions;
using compute::Segment;
using compute::SortIndices;
using compute::SortKey;
using compute::SortOrder;
using compute::Take;
using compute::TDigestOptions;
using compute::VarianceOptions;
namespace acero {
TEST(AggregateSchema, NoKeys) {
auto input_schema = schema({field("x", int32())});
EXPECT_RAISES_WITH_MESSAGE_THAT(
Invalid, HasSubstr("is a hash aggregate function"),
aggregate::MakeOutputSchema(input_schema, {}, {},
{{"hash_count", nullptr, "x", "hash_count"}}));
ASSERT_OK_AND_ASSIGN(auto output_schema,
aggregate::MakeOutputSchema(input_schema, {}, {},
{{"count", nullptr, "x", "count"}}));
AssertSchemaEqual(schema({field("count", int64())}), output_schema);
}
TEST(AggregateSchema, SingleKey) {
auto input_schema = schema({field("x", int32()), field("y", int32())});
EXPECT_RAISES_WITH_MESSAGE_THAT(
Invalid, HasSubstr("is a scalar aggregate function"),
aggregate::MakeOutputSchema(input_schema, {FieldRef("y")}, {},
{{"count", nullptr, "x", "count"}}));
ASSERT_OK_AND_ASSIGN(
auto output_schema,
aggregate::MakeOutputSchema(input_schema, {FieldRef("y")}, {},
{{"hash_count", nullptr, "x", "hash_count"}}));
AssertSchemaEqual(schema({field("y", int32()), field("hash_count", int64())}),
output_schema);
}
TEST(AggregateSchema, DoubleKey) {
auto input_schema =
schema({field("x", int32()), field("y", int32()), field("z", int32())});
ASSERT_OK_AND_ASSIGN(
auto output_schema,
aggregate::MakeOutputSchema(input_schema, {FieldRef("z"), FieldRef("y")}, {},
{{"hash_count", nullptr, "x", "hash_count"}}));
AssertSchemaEqual(
schema({field("z", int32()), field("y", int32()), field("hash_count", int64())}),
output_schema);
}
TEST(AggregateSchema, SingleSegmentKey) {
auto input_schema = schema({field("x", int32()), field("y", int32())});
ASSERT_OK_AND_ASSIGN(auto output_schema,
aggregate::MakeOutputSchema(input_schema, {}, {FieldRef("y")},
{{"count", nullptr, "x", "count"}}));
AssertSchemaEqual(schema({field("y", int32()), field("count", int64())}),
output_schema);
}
TEST(AggregateSchema, DoubleSegmentKey) {
auto input_schema =
schema({field("x", int32()), field("y", int32()), field("z", int32())});
ASSERT_OK_AND_ASSIGN(
auto output_schema,
aggregate::MakeOutputSchema(input_schema, {}, {FieldRef("z"), FieldRef("y")},
{{"count", nullptr, "x", "count"}}));
AssertSchemaEqual(
schema({field("z", int32()), field("y", int32()), field("count", int64())}),
output_schema);
}
TEST(AggregateSchema, SingleKeyAndSegmentKey) {
auto input_schema =
schema({field("x", int32()), field("y", int32()), field("z", int32())});
ASSERT_OK_AND_ASSIGN(
auto output_schema,
aggregate::MakeOutputSchema(input_schema, {FieldRef("y")}, {FieldRef("z")},
{{"hash_count", nullptr, "x", "hash_count"}}));
AssertSchemaEqual(
schema({field("z", int32()), field("y", int32()), field("hash_count", int64())}),
output_schema);
}
namespace {
using GroupByFunction = std::function<Result<Datum>(
const std::vector<Datum>&, const std::vector<Datum>&, const std::vector<Datum>&,
const std::vector<Aggregate>&, bool, bool)>;
Result<Datum> NaiveGroupBy(std::vector<Datum> arguments, std::vector<Datum> keys,
const std::vector<Aggregate>& aggregates) {
ARROW_ASSIGN_OR_RAISE(auto key_batch, ExecBatch::Make(std::move(keys)));
ARROW_ASSIGN_OR_RAISE(auto grouper, Grouper::Make(key_batch.GetTypes()));
ARROW_ASSIGN_OR_RAISE(Datum id_batch, grouper->Consume(ExecSpan(key_batch)));
ARROW_ASSIGN_OR_RAISE(
auto groupings,
Grouper::MakeGroupings(*id_batch.array_as<UInt32Array>(), grouper->num_groups()));
ArrayVector out_columns;
std::vector<std::string> out_names;
int key_idx = 0;
ARROW_ASSIGN_OR_RAISE(auto uniques, grouper->GetUniques());
std::vector<SortKey> sort_keys;
std::vector<std::shared_ptr<Field>> sort_table_fields;
for (const Datum& key : uniques.values) {
out_columns.push_back(key.make_array());
sort_keys.emplace_back(FieldRef(key_idx));
sort_table_fields.push_back(field("key_" + ToChars(key_idx), key.type()));
out_names.push_back("key_" + ToChars(key_idx++));
}
for (size_t i = 0; i < arguments.size(); ++i) {
out_names.push_back(aggregates[i].function);
// trim "hash_" prefix
auto scalar_agg_function = aggregates[i].function.substr(5);
ARROW_ASSIGN_OR_RAISE(
auto grouped_argument,
Grouper::ApplyGroupings(*groupings, *arguments[i].make_array()));
ScalarVector aggregated_scalars;
for (int64_t i_group = 0; i_group < grouper->num_groups(); ++i_group) {
auto slice = grouped_argument->value_slice(i_group);
if (slice->length() == 0) continue;
ARROW_ASSIGN_OR_RAISE(Datum d, CallFunction(scalar_agg_function, {slice},
aggregates[i].options.get()));
aggregated_scalars.push_back(d.scalar());
}
ARROW_ASSIGN_OR_RAISE(Datum aggregated_column,
ScalarVectorToArray(aggregated_scalars));
out_columns.push_back(aggregated_column.make_array());
}
// Return a struct array sorted by the keys
SortOptions sort_options(std::move(sort_keys));
ARROW_ASSIGN_OR_RAISE(std::shared_ptr<RecordBatch> sort_batch,
uniques.ToRecordBatch(schema(std::move(sort_table_fields))));
ARROW_ASSIGN_OR_RAISE(std::shared_ptr<Array> sorted_indices,
SortIndices(sort_batch, sort_options));
ARROW_ASSIGN_OR_RAISE(auto struct_arr,
StructArray::Make(std::move(out_columns), std::move(out_names)));
return Take(struct_arr, sorted_indices);
}
Result<Datum> MakeGroupByOutput(const std::vector<ExecBatch>& output_batches,
const std::shared_ptr<Schema> output_schema,
size_t num_aggregates, size_t num_keys, bool naive) {
ArrayVector out_arrays(num_aggregates + num_keys);
for (size_t i = 0; i < out_arrays.size(); ++i) {
std::vector<std::shared_ptr<Array>> arrays(output_batches.size());
for (size_t j = 0; j < output_batches.size(); ++j) {
arrays[j] = output_batches[j].values[i].make_array();
}
if (arrays.empty()) {
ARROW_ASSIGN_OR_RAISE(
out_arrays[i],
MakeArrayOfNull(output_schema->field(static_cast<int>(i))->type(),
/*length=*/0));
} else {
ARROW_ASSIGN_OR_RAISE(out_arrays[i], Concatenate(arrays));
}
}
ARROW_ASSIGN_OR_RAISE(
std::shared_ptr<Array> struct_arr,
StructArray::Make(std::move(out_arrays), output_schema->fields()));
bool need_sort = !naive;
for (size_t i = 0; need_sort && i < num_keys; i++) {
if (output_schema->field(static_cast<int>(i))->type()->id() == Type::DICTIONARY) {
need_sort = false;
}
}
if (!need_sort) {
return struct_arr;
}
// The exec plan may reorder the output rows. The tests are all setup to expect output
// in ascending order of keys. So we need to sort the result by the key columns. To do
// that we create a table using the key columns, calculate the sort indices from that
// table (sorting on all fields) and then use those indices to calculate our result.
std::vector<std::shared_ptr<Field>> key_fields;
std::vector<std::shared_ptr<Array>> key_columns;
std::vector<SortKey> sort_keys;
for (std::size_t i = 0; i < num_keys; i++) {
const std::shared_ptr<Array>& arr = out_arrays[i];
key_columns.push_back(arr);
key_fields.push_back(field("name_does_not_matter", arr->type()));
sort_keys.emplace_back(static_cast<int>(i));
}
std::shared_ptr<Schema> key_schema = schema(std::move(key_fields));
std::shared_ptr<Table> key_table = Table::Make(std::move(key_schema), key_columns);
SortOptions sort_options(std::move(sort_keys));
ARROW_ASSIGN_OR_RAISE(std::shared_ptr<Array> sort_indices,
SortIndices(key_table, sort_options));
return Take(struct_arr, sort_indices);
}
Result<Datum> RunGroupBy(const BatchesWithSchema& input,
const std::vector<std::string>& key_names,
const std::vector<std::string>& segment_key_names,
const std::vector<Aggregate>& aggregates, ExecContext* ctx,
bool use_threads, bool segmented = false, bool naive = false) {
// The `use_threads` flag determines whether threads are used in generating the input to
// the group-by.
//
// When segment_keys is non-empty the `segmented` flag is always true; otherwise (when
// empty), it may still be set to true. In this case, the tester restructures (without
// changing the data of) the result of RunGroupBy from `std::vector<ExecBatch>`
// (output_batches) to `std::vector<ArrayVector>` (out_arrays), which have the structure
// typical of the case of a non-empty segment_keys (with multiple arrays per column, one
// array per segment) but only one array per column (because, technically, there is only
// one segment in this case). Thus, this case focuses on the structure of the result.
//
// The `naive` flag means that the output is expected to be like that of `NaiveGroupBy`,
// which in particular doesn't require sorting. The reason for the naive flag is that
// the expected output of some test-cases is naive and of some others it is not. The
// current `RunGroupBy` function deals with both kinds of expected output.
std::vector<FieldRef> keys(key_names.size());
for (size_t i = 0; i < key_names.size(); ++i) {
keys[i] = FieldRef(key_names[i]);
}
std::vector<FieldRef> segment_keys(segment_key_names.size());
for (size_t i = 0; i < segment_key_names.size(); ++i) {
segment_keys[i] = FieldRef(segment_key_names[i]);
}
ARROW_ASSIGN_OR_RAISE(auto plan, ExecPlan::Make(*ctx));
AsyncGenerator<std::optional<ExecBatch>> sink_gen;
RETURN_NOT_OK(
Declaration::Sequence(
{
{"source",
SourceNodeOptions{input.schema, input.gen(use_threads, /*slow=*/false)}},
{"aggregate", AggregateNodeOptions{std::move(aggregates), std::move(keys),
std::move(segment_keys)}},
{"sink", SinkNodeOptions{&sink_gen}},
})
.AddToPlan(plan.get()));
RETURN_NOT_OK(plan->Validate());
plan->StartProducing();
auto collected_fut = CollectAsyncGenerator(sink_gen);
auto start_and_collect =
AllFinished({plan->finished(), Future<>(collected_fut)})
.Then([collected_fut]() -> Result<std::vector<ExecBatch>> {
ARROW_ASSIGN_OR_RAISE(auto collected, collected_fut.result());
return ::arrow::internal::MapVector(
[](std::optional<ExecBatch> batch) {
return batch.value_or(ExecBatch());
},
std::move(collected));
});
ARROW_ASSIGN_OR_RAISE(std::vector<ExecBatch> output_batches,
start_and_collect.MoveResult());
const auto& output_schema = plan->nodes()[0]->output()->output_schema();
if (!segmented) {
return MakeGroupByOutput(output_batches, output_schema, aggregates.size(),
key_names.size(), naive);
}
std::vector<ArrayVector> out_arrays(aggregates.size() + key_names.size() +
segment_key_names.size());
for (size_t i = 0; i < out_arrays.size(); ++i) {
std::vector<std::shared_ptr<Array>> arrays(output_batches.size());
for (size_t j = 0; j < output_batches.size(); ++j) {
auto& value = output_batches[j].values[i];
if (value.is_scalar()) {
ARROW_ASSIGN_OR_RAISE(
arrays[j], MakeArrayFromScalar(*value.scalar(), output_batches[j].length));
} else if (value.is_array()) {
arrays[j] = value.make_array();
} else {
return Status::Invalid("GroupByUsingExecPlan unsupported value kind ",
ToString(value.kind()));
}
}
if (arrays.empty()) {
arrays.resize(1);
ARROW_ASSIGN_OR_RAISE(
arrays[0], MakeArrayOfNull(output_schema->field(static_cast<int>(i))->type(),
/*length=*/0));
}
out_arrays[i] = {std::move(arrays)};
}
if (segmented && segment_key_names.size() > 0) {
ArrayVector struct_arrays;
struct_arrays.reserve(output_batches.size());
for (size_t j = 0; j < output_batches.size(); ++j) {
ArrayVector struct_fields;
struct_fields.reserve(out_arrays.size());
for (auto out_array : out_arrays) {
struct_fields.push_back(out_array[j]);
}
ARROW_ASSIGN_OR_RAISE(auto struct_array,
StructArray::Make(struct_fields, output_schema->fields()));
struct_arrays.push_back(struct_array);
}
return ChunkedArray::Make(struct_arrays);
} else {
ArrayVector struct_fields(out_arrays.size());
for (size_t i = 0; i < out_arrays.size(); ++i) {
ARROW_ASSIGN_OR_RAISE(struct_fields[i], Concatenate(out_arrays[i]));
}
return StructArray::Make(std::move(struct_fields), output_schema->fields());
}
}
Result<Datum> RunGroupBy(const BatchesWithSchema& input,
const std::vector<std::string>& key_names,
const std::vector<std::string>& segment_key_names,
const std::vector<Aggregate>& aggregates, bool use_threads,
bool segmented = false, bool naive = false) {
if (!use_threads) {
ARROW_ASSIGN_OR_RAISE(auto thread_pool, arrow::internal::ThreadPool::Make(1));
ExecContext seq_ctx(default_memory_pool(), thread_pool.get());
return RunGroupBy(input, key_names, segment_key_names, aggregates, &seq_ctx,
use_threads, segmented, naive);
} else {
return RunGroupBy(input, key_names, segment_key_names, aggregates,
threaded_exec_context(), use_threads, segmented, naive);
}
}
Result<Datum> RunGroupBy(const BatchesWithSchema& input,
const std::vector<std::string>& key_names,
const std::vector<Aggregate>& aggregates, bool use_threads,
bool segmented = false, bool naive = false) {
return RunGroupBy(input, key_names, {}, aggregates, use_threads, segmented);
}
/// Simpler overload where you can give the columns as datums
Result<Datum> RunGroupBy(const std::vector<Datum>& arguments,
const std::vector<Datum>& keys,
const std::vector<Datum>& segment_keys,
const std::vector<Aggregate>& aggregates, bool use_threads,
bool segmented = false, bool naive = false) {
using arrow::compute::detail::ExecSpanIterator;
FieldVector scan_fields(arguments.size() + keys.size() + segment_keys.size());
std::vector<std::string> key_names(keys.size());
std::vector<std::string> segment_key_names(segment_keys.size());
for (size_t i = 0; i < arguments.size(); ++i) {
auto name = std::string("agg_") + ToChars(i);
scan_fields[i] = field(name, arguments[i].type());
}
size_t base = arguments.size();
for (size_t i = 0; i < keys.size(); ++i) {
auto name = std::string("key_") + ToChars(i);
scan_fields[base + i] = field(name, keys[i].type());
key_names[i] = std::move(name);
}
base += keys.size();
size_t j = keys.size();
std::string prefix("key_");
for (size_t i = 0; i < segment_keys.size(); ++i) {
auto name = prefix + std::to_string(j++);
scan_fields[base + i] = field(name, segment_keys[i].type());
segment_key_names[i] = std::move(name);
}
std::vector<Datum> inputs = arguments;
inputs.reserve(inputs.size() + keys.size() + segment_keys.size());
inputs.insert(inputs.end(), keys.begin(), keys.end());
inputs.insert(inputs.end(), segment_keys.begin(), segment_keys.end());
ExecSpanIterator span_iterator;
ARROW_ASSIGN_OR_RAISE(auto batch, ExecBatch::Make(inputs));
RETURN_NOT_OK(span_iterator.Init(batch));
BatchesWithSchema input;
input.schema = schema(std::move(scan_fields));
ExecSpan span;
while (span_iterator.Next(&span)) {
if (span.length == 0) continue;
input.batches.push_back(span.ToExecBatch());
}
return RunGroupBy(input, key_names, segment_key_names, aggregates, use_threads,
segmented, naive);
}
Result<Datum> RunGroupByImpl(const std::vector<Datum>& arguments,
const std::vector<Datum>& keys,
const std::vector<Datum>& segment_keys,
const std::vector<Aggregate>& aggregates, bool use_threads,
bool naive = false) {
return RunGroupBy(arguments, keys, segment_keys, aggregates, use_threads,
/*segmented=*/false, naive);
}
Result<Datum> RunSegmentedGroupByImpl(const std::vector<Datum>& arguments,
const std::vector<Datum>& keys,
const std::vector<Datum>& segment_keys,
const std::vector<Aggregate>& aggregates,
bool use_threads, bool naive = false) {
return RunGroupBy(arguments, keys, segment_keys, aggregates, use_threads,
/*segmented=*/true, naive);
}
void ValidateGroupBy(GroupByFunction group_by, const std::vector<Aggregate>& aggregates,
std::vector<Datum> arguments, std::vector<Datum> keys,
bool naive = true) {
ASSERT_OK_AND_ASSIGN(Datum expected, NaiveGroupBy(arguments, keys, aggregates));
ASSERT_OK_AND_ASSIGN(Datum actual, group_by(arguments, keys, {}, aggregates,
/*use_threads=*/false, naive));
ASSERT_OK(expected.make_array()->ValidateFull());
ValidateOutput(actual);
AssertDatumsEqual(expected, actual, /*verbose=*/true);
}
ExecContext* small_chunksize_context(bool use_threads = false) {
static ExecContext ctx,
ctx_with_threads{default_memory_pool(), arrow::internal::GetCpuThreadPool()};
ctx.set_exec_chunksize(2);
ctx_with_threads.set_exec_chunksize(2);
return use_threads ? &ctx_with_threads : &ctx;
}
struct TestAggregate {
std::string function;
std::shared_ptr<FunctionOptions> options;
};
Result<Datum> GroupByTest(GroupByFunction group_by, const std::vector<Datum>& arguments,
const std::vector<Datum>& keys,
const std::vector<Datum>& segment_keys,
const std::vector<TestAggregate>& aggregates,
bool use_threads) {
std::vector<Aggregate> internal_aggregates;
int idx = 0;
for (auto t_agg : aggregates) {
internal_aggregates.push_back(
{t_agg.function, t_agg.options, "agg_" + ToChars(idx), t_agg.function});
idx = idx + 1;
}
return group_by(arguments, keys, segment_keys, internal_aggregates, use_threads,
/*naive=*/false);
}
Result<Datum> GroupByTest(GroupByFunction group_by, const std::vector<Datum>& arguments,
const std::vector<Datum>& keys,
const std::vector<TestAggregate>& aggregates,
bool use_threads) {
return GroupByTest(group_by, arguments, keys, {}, aggregates, use_threads);
}
template <typename GroupClass>
void TestGroupClassSupportedKeys(
std::function<Result<std::unique_ptr<GroupClass>>(const std::vector<TypeHolder>&)>
make_func) {
ASSERT_OK(make_func({boolean()}));
ASSERT_OK(make_func({int8(), uint16(), int32(), uint64()}));
ASSERT_OK(make_func({dictionary(int64(), utf8())}));
ASSERT_OK(make_func({float16(), float32(), float64()}));
ASSERT_OK(make_func({utf8(), binary(), large_utf8(), large_binary()}));
ASSERT_OK(make_func({fixed_size_binary(16), fixed_size_binary(32)}));
ASSERT_OK(make_func({decimal128(32, 10), decimal256(76, 20)}));
ASSERT_OK(make_func({date32(), date64()}));
for (auto unit : {
TimeUnit::SECOND,
TimeUnit::MILLI,
TimeUnit::MICRO,
TimeUnit::NANO,
}) {
ASSERT_OK(make_func({timestamp(unit), duration(unit)}));
}
ASSERT_OK(
make_func({day_time_interval(), month_interval(), month_day_nano_interval()}));
ASSERT_OK(make_func({null()}));
ASSERT_RAISES(NotImplemented, make_func({struct_({field("", int64())})}));
ASSERT_RAISES(NotImplemented, make_func({struct_({})}));
ASSERT_RAISES(NotImplemented, make_func({list(int32())}));
ASSERT_RAISES(NotImplemented, make_func({fixed_size_list(int32(), 5)}));
ASSERT_RAISES(NotImplemented, make_func({dense_union({field("", int32())})}));
}
void TestSegments(std::unique_ptr<RowSegmenter>& segmenter, const ExecSpan& batch,
std::vector<Segment> expected_segments) {
int64_t offset = 0, segment_num = 0;
for (auto expected_segment : expected_segments) {
SCOPED_TRACE("segment #" + ToChars(segment_num++));
ASSERT_OK_AND_ASSIGN(auto segment, segmenter->GetNextSegment(batch, offset));
ASSERT_EQ(expected_segment, segment);
offset = segment.offset + segment.length;
}
}
Result<std::unique_ptr<Grouper>> MakeGrouper(const std::vector<TypeHolder>& key_types) {
return Grouper::Make(key_types, default_exec_context());
}
Result<std::unique_ptr<RowSegmenter>> MakeRowSegmenter(
const std::vector<TypeHolder>& key_types) {
return RowSegmenter::Make(key_types, /*nullable_leys=*/false, default_exec_context());
}
Result<std::unique_ptr<RowSegmenter>> MakeGenericSegmenter(
const std::vector<TypeHolder>& key_types) {
return MakeAnyKeysSegmenter(key_types, default_exec_context());
}
} // namespace
TEST(RowSegmenter, SupportedKeys) {
TestGroupClassSupportedKeys<RowSegmenter>(MakeRowSegmenter);
}
TEST(RowSegmenter, Basics) {
std::vector<TypeHolder> bad_types2 = {int32(), float32()};
std::vector<TypeHolder> types2 = {int32(), int32()};
std::vector<TypeHolder> bad_types1 = {float32()};
std::vector<TypeHolder> types1 = {int32()};
std::vector<TypeHolder> types0 = {};
auto batch2 = ExecBatchFromJSON(types2, "[[1, 1], [1, 2], [2, 2]]");
auto batch1 = ExecBatchFromJSON(types1, "[[1], [1], [2]]");
ExecBatch batch0({}, 3);
{
SCOPED_TRACE("offset");
ASSERT_OK_AND_ASSIGN(auto segmenter, MakeRowSegmenter(types0));
ExecSpan span0(batch0);
for (int64_t offset : {-1, 4}) {
EXPECT_RAISES_WITH_MESSAGE_THAT(Invalid,
HasSubstr("invalid grouping segmenter offset"),
segmenter->GetNextSegment(span0, offset));
}
}
{
SCOPED_TRACE("types0 segmenting of batch2");
ASSERT_OK_AND_ASSIGN(auto segmenter, MakeRowSegmenter(types0));
ExecSpan span2(batch2);
EXPECT_RAISES_WITH_MESSAGE_THAT(Invalid, HasSubstr("expected batch size 0 "),
segmenter->GetNextSegment(span2, 0));
ExecSpan span0(batch0);
TestSegments(segmenter, span0, {{0, 3, true, true}, {3, 0, true, true}});
}
{
SCOPED_TRACE("bad_types1 segmenting of batch1");
ASSERT_OK_AND_ASSIGN(auto segmenter, MakeRowSegmenter(bad_types1));
ExecSpan span1(batch1);
EXPECT_RAISES_WITH_MESSAGE_THAT(Invalid, HasSubstr("expected batch value 0 of type "),
segmenter->GetNextSegment(span1, 0));
}
{
SCOPED_TRACE("types1 segmenting of batch2");
ASSERT_OK_AND_ASSIGN(auto segmenter, MakeRowSegmenter(types1));
ExecSpan span2(batch2);
EXPECT_RAISES_WITH_MESSAGE_THAT(Invalid, HasSubstr("expected batch size 1 "),
segmenter->GetNextSegment(span2, 0));
ExecSpan span1(batch1);
TestSegments(segmenter, span1,
{{0, 2, false, true}, {2, 1, true, false}, {3, 0, true, true}});
}
{
SCOPED_TRACE("bad_types2 segmenting of batch2");
ASSERT_OK_AND_ASSIGN(auto segmenter, MakeRowSegmenter(bad_types2));
ExecSpan span2(batch2);
EXPECT_RAISES_WITH_MESSAGE_THAT(Invalid, HasSubstr("expected batch value 1 of type "),
segmenter->GetNextSegment(span2, 0));
}
{
SCOPED_TRACE("types2 segmenting of batch1");
ASSERT_OK_AND_ASSIGN(auto segmenter, MakeRowSegmenter(types2));
ExecSpan span1(batch1);
EXPECT_RAISES_WITH_MESSAGE_THAT(Invalid, HasSubstr("expected batch size 2 "),
segmenter->GetNextSegment(span1, 0));
ExecSpan span2(batch2);
TestSegments(segmenter, span2,
{{0, 1, false, true},
{1, 1, false, false},
{2, 1, true, false},
{3, 0, true, true}});
}
}
TEST(RowSegmenter, NonOrdered) {
std::vector<TypeHolder> types = {int32()};
auto batch = ExecBatchFromJSON(types, "[[1], [1], [2], [1], [2]]");
ASSERT_OK_AND_ASSIGN(auto segmenter, MakeRowSegmenter(types));
TestSegments(segmenter, ExecSpan(batch),
{{0, 2, false, true},
{2, 1, false, false},
{3, 1, false, false},
{4, 1, true, false},
{5, 0, true, true}});
}
TEST(RowSegmenter, EmptyBatches) {
std::vector<TypeHolder> types = {int32()};
std::vector<ExecBatch> batches = {
ExecBatchFromJSON(types, "[]"), ExecBatchFromJSON(types, "[]"),
ExecBatchFromJSON(types, "[[1]]"), ExecBatchFromJSON(types, "[]"),
ExecBatchFromJSON(types, "[[1]]"), ExecBatchFromJSON(types, "[]"),
ExecBatchFromJSON(types, "[[2], [2]]"), ExecBatchFromJSON(types, "[]"),
};
ASSERT_OK_AND_ASSIGN(auto segmenter, MakeRowSegmenter(types));
TestSegments(segmenter, ExecSpan(batches[0]), {});
TestSegments(segmenter, ExecSpan(batches[1]), {});
TestSegments(segmenter, ExecSpan(batches[2]), {{0, 1, true, true}});
TestSegments(segmenter, ExecSpan(batches[3]), {});
TestSegments(segmenter, ExecSpan(batches[4]), {{0, 1, true, true}});
TestSegments(segmenter, ExecSpan(batches[5]), {});
TestSegments(segmenter, ExecSpan(batches[6]), {{0, 2, true, false}});
TestSegments(segmenter, ExecSpan(batches[7]), {});
}
TEST(RowSegmenter, MultipleSegments) {
std::vector<TypeHolder> types = {int32()};
auto batch = ExecBatchFromJSON(types, "[[1], [1], [2], [5], [3], [3], [5], [5], [4]]");
ASSERT_OK_AND_ASSIGN(auto segmenter, MakeRowSegmenter(types));
TestSegments(segmenter, ExecSpan(batch),
{{0, 2, false, true},
{2, 1, false, false},
{3, 1, false, false},
{4, 2, false, false},
{6, 2, false, false},
{8, 1, true, false},
{9, 0, true, true}});
}
namespace {
void TestRowSegmenterConstantBatch(
std::function<ArgShape(size_t i)> shape_func,
std::function<Result<std::unique_ptr<RowSegmenter>>(const std::vector<TypeHolder>&)>
make_segmenter) {
constexpr size_t n = 3, repetitions = 3;
std::vector<TypeHolder> types = {int32(), int32(), int32()};
std::vector<ArgShape> shapes(n);
for (size_t i = 0; i < n; i++) shapes[i] = shape_func(i);
auto full_batch = ExecBatchFromJSON(types, shapes, "[[1, 1, 1], [1, 1, 1], [1, 1, 1]]");
auto test_by_size = [&](size_t size) -> Status {
SCOPED_TRACE("constant-batch with " + ToChars(size) + " key(s)");
std::vector<Datum> values(full_batch.values.begin(),
full_batch.values.begin() + size);
ExecBatch batch(values, full_batch.length);
std::vector<TypeHolder> key_types(types.begin(), types.begin() + size);
ARROW_ASSIGN_OR_RAISE(auto segmenter, make_segmenter(key_types));
for (size_t i = 0; i < repetitions; i++) {
TestSegments(segmenter, ExecSpan(batch), {{0, 3, true, true}, {3, 0, true, true}});
ARROW_RETURN_NOT_OK(segmenter->Reset());
}
return Status::OK();
};
for (size_t i = 0; i <= 3; i++) {
ASSERT_OK(test_by_size(i));
}
}
} // namespace
TEST(RowSegmenter, ConstantArrayBatch) {
TestRowSegmenterConstantBatch([](size_t i) { return ArgShape::ARRAY; },
MakeRowSegmenter);
}
TEST(RowSegmenter, ConstantScalarBatch) {
TestRowSegmenterConstantBatch([](size_t i) { return ArgShape::SCALAR; },
MakeRowSegmenter);
}
TEST(RowSegmenter, ConstantMixedBatch) {
TestRowSegmenterConstantBatch(
[](size_t i) { return i % 2 == 0 ? ArgShape::SCALAR : ArgShape::ARRAY; },
MakeRowSegmenter);
}
TEST(RowSegmenter, ConstantArrayBatchWithAnyKeysSegmenter) {
TestRowSegmenterConstantBatch([](size_t i) { return ArgShape::ARRAY; },
MakeGenericSegmenter);
}
TEST(RowSegmenter, ConstantScalarBatchWithAnyKeysSegmenter) {
TestRowSegmenterConstantBatch([](size_t i) { return ArgShape::SCALAR; },
MakeGenericSegmenter);
}
TEST(RowSegmenter, ConstantMixedBatchWithAnyKeysSegmenter) {
TestRowSegmenterConstantBatch(
[](size_t i) { return i % 2 == 0 ? ArgShape::SCALAR : ArgShape::ARRAY; },
MakeGenericSegmenter);
}
TEST(RowSegmenter, RowConstantBatch) {
constexpr size_t n = 3;
std::vector<TypeHolder> types = {int32(), int32(), int32()};
auto full_batch = ExecBatchFromJSON(types, "[[1, 1, 1], [2, 2, 2], [3, 3, 3]]");
std::vector<Segment> expected_segments_for_size_0 = {{0, 3, true, true},
{3, 0, true, true}};
std::vector<Segment> expected_segments = {
{0, 1, false, true}, {1, 1, false, false}, {2, 1, true, false}, {3, 0, true, true}};
auto test_by_size = [&](size_t size) -> Status {
SCOPED_TRACE("constant-batch with " + ToChars(size) + " key(s)");
std::vector<Datum> values(full_batch.values.begin(),
full_batch.values.begin() + size);
ExecBatch batch(values, full_batch.length);
std::vector<TypeHolder> key_types(types.begin(), types.begin() + size);
ARROW_ASSIGN_OR_RAISE(auto segmenter, MakeRowSegmenter(key_types));
TestSegments(segmenter, ExecSpan(batch),
size == 0 ? expected_segments_for_size_0 : expected_segments);
return Status::OK();
};
for (size_t i = 0; i <= n; i++) {
ASSERT_OK(test_by_size(i));
}
}
TEST(Grouper, SupportedKeys) { TestGroupClassSupportedKeys<Grouper>(MakeGrouper); }
struct TestGrouper {
explicit TestGrouper(std::vector<TypeHolder> types, std::vector<ArgShape> shapes = {})
: types_(std::move(types)), shapes_(std::move(shapes)) {
grouper_ = Grouper::Make(types_).ValueOrDie();
FieldVector fields;
for (const auto& type : types_) {
fields.push_back(field("", type.GetSharedPtr()));
}
key_schema_ = schema(std::move(fields));
}
void ExpectConsume(const std::string& key_json, const std::string& expected) {
auto expected_arr = ArrayFromJSON(uint32(), expected);
if (shapes_.size() > 0) {
ExpectConsume(ExecBatchFromJSON(types_, shapes_, key_json), expected_arr);
} else {
ExpectConsume(ExecBatchFromJSON(types_, key_json), expected_arr);
}
}
void ExpectConsume(const std::vector<Datum>& key_values, Datum expected) {
ASSERT_OK_AND_ASSIGN(auto key_batch, ExecBatch::Make(key_values));
ExpectConsume(key_batch, expected);
}
void ExpectConsume(const ExecBatch& key_batch, Datum expected) {
Datum ids;
ConsumeAndValidate(key_batch, &ids);
AssertEquivalentIds(expected, ids);
}
void ExpectUniques(const ExecBatch& uniques) {
EXPECT_THAT(grouper_->GetUniques(), ResultWith(Eq(uniques)));
}
void ExpectUniques(const std::string& uniques_json) {
if (shapes_.size() > 0) {
ExpectUniques(ExecBatchFromJSON(types_, shapes_, uniques_json));
} else {
ExpectUniques(ExecBatchFromJSON(types_, uniques_json));
}
}
void AssertEquivalentIds(const Datum& expected, const Datum& actual) {
auto left = expected.make_array();
auto right = actual.make_array();
ASSERT_EQ(left->length(), right->length()) << "#ids unequal";
int64_t num_ids = left->length();
auto left_data = left->data();
auto right_data = right->data();
auto left_ids = reinterpret_cast<const uint32_t*>(left_data->buffers[1]->data());
auto right_ids = reinterpret_cast<const uint32_t*>(right_data->buffers[1]->data());
uint32_t max_left_id = 0;
uint32_t max_right_id = 0;
for (int64_t i = 0; i < num_ids; ++i) {
if (left_ids[i] > max_left_id) {
max_left_id = left_ids[i];
}
if (right_ids[i] > max_right_id) {
max_right_id = right_ids[i];
}
}
std::vector<bool> right_to_left_present(max_right_id + 1, false);
std::vector<bool> left_to_right_present(max_left_id + 1, false);
std::vector<uint32_t> right_to_left(max_right_id + 1);
std::vector<uint32_t> left_to_right(max_left_id + 1);
for (int64_t i = 0; i < num_ids; ++i) {
uint32_t left_id = left_ids[i];
uint32_t right_id = right_ids[i];
if (!left_to_right_present[left_id]) {
left_to_right[left_id] = right_id;
left_to_right_present[left_id] = true;
}
if (!right_to_left_present[right_id]) {
right_to_left[right_id] = left_id;
right_to_left_present[right_id] = true;
}
ASSERT_EQ(left_id, right_to_left[right_id]);
ASSERT_EQ(right_id, left_to_right[left_id]);
}
}
void ConsumeAndValidate(const ExecBatch& key_batch, Datum* ids = nullptr) {
ASSERT_OK_AND_ASSIGN(Datum id_batch, grouper_->Consume(ExecSpan(key_batch)));
ValidateConsume(key_batch, id_batch);
if (ids) {
*ids = std::move(id_batch);
}
}
void ValidateConsume(const ExecBatch& key_batch, const Datum& id_batch) {
if (uniques_.length == -1) {
ASSERT_OK_AND_ASSIGN(uniques_, grouper_->GetUniques());
} else if (static_cast<int64_t>(grouper_->num_groups()) > uniques_.length) {
ASSERT_OK_AND_ASSIGN(ExecBatch new_uniques, grouper_->GetUniques());
// check that uniques_ are prefixes of new_uniques
for (int i = 0; i < uniques_.num_values(); ++i) {
auto new_unique = new_uniques[i].make_array();
ValidateOutput(*new_unique);
AssertDatumsEqual(uniques_[i], new_unique->Slice(0, uniques_.length),
/*verbose=*/true);
}
uniques_ = std::move(new_uniques);
}
// check that the ids encode an equivalent key sequence
auto ids = id_batch.make_array();
ValidateOutput(*ids);
for (int i = 0; i < key_batch.num_values(); ++i) {
SCOPED_TRACE(ToChars(i) + "th key array");
auto original =
key_batch[i].is_array()
? key_batch[i].make_array()
: *MakeArrayFromScalar(*key_batch[i].scalar(), key_batch.length);
ASSERT_OK_AND_ASSIGN(auto encoded, Take(*uniques_[i].make_array(), *ids));
AssertArraysEqual(*original, *encoded, /*verbose=*/true,
EqualOptions().nans_equal(true));
}
}
std::vector<TypeHolder> types_;
std::vector<ArgShape> shapes_;
std::shared_ptr<Schema> key_schema_;
std::unique_ptr<Grouper> grouper_;
ExecBatch uniques_ = ExecBatch({}, -1);
};
TEST(Grouper, BooleanKey) {
TestGrouper g({boolean()});
g.ExpectConsume("[[true], [true]]", "[0, 0]");
g.ExpectConsume("[[true], [true]]", "[0, 0]");
g.ExpectConsume("[[false], [null]]", "[1, 2]");
g.ExpectConsume("[[true], [false], [true], [false], [null], [false], [null]]",
"[0, 1, 0, 1, 2, 1, 2]");
}
TEST(Grouper, NumericKey) {
for (auto ty : {
uint8(),
int8(),
uint16(),
int16(),
uint32(),
int32(),
uint64(),
int64(),
float16(),
float32(),
float64(),
}) {
SCOPED_TRACE("key type: " + ty->ToString());
TestGrouper g({ty});
g.ExpectConsume("[[3], [3]]", "[0, 0]");
g.ExpectUniques("[[3]]");
g.ExpectConsume("[[3], [3]]", "[0, 0]");
g.ExpectUniques("[[3]]");
g.ExpectConsume("[[27], [81], [81]]", "[1, 2, 2]");
g.ExpectUniques("[[3], [27], [81]]");
g.ExpectConsume("[[3], [27], [3], [27], [null], [81], [27], [81]]",
"[0, 1, 0, 1, 3, 2, 1, 2]");
g.ExpectUniques("[[3], [27], [81], [null]]");
}
}
TEST(Grouper, FloatingPointKey) {
TestGrouper g({float32()});