Apache Arrow 优点 :

    高性能数据处理： Arrow 使用列式内存布局，这特别适合于数据分析和查询操作，因为它允许对数据进行高效批量处理，减少CPU缓存未命中，从而提升处理速度。

    零拷贝数据共享： Arrow 允许不同系统和进程之间直接共享内存中的数据而无需复制，这对于提高数据密集型应用的效率至关重要，减少了内存使用和CPU开销。

    跨平台兼容性： Arrow 是一个跨语言开发平台，支持C++, Java, Python等多种编程语言，促进了不同软件组件间的互操作性。

    标准化数据格式： 定义了一套统一的数据格式规范，使得数据可以在不同系统间无缝传递，降低了数据转换的成本和复杂性。

    优化大数据处理： 特别是在与大数据框架（如Spark、Pandas）集成时，Arrow 可显著加速数据加载、处理和分析的速度，例如，与PySpark集成后数据处理速度提升高达53倍。

    集成广泛： 被众多数据处理工具和库采用，如Pandas、Parquet、Drill、Spark等，形成了强大的生态系统。

Apache Arrow 缺点 :

    内存消耗： 列式存储相对于行式存储可能需要更多的内存，尤其是在处理稀疏数据或宽表时，因为每一列都需要分配连续的内存空间。

    不适合所有场景： 对于需要频繁随机访问记录或更新操作的场景，Arrow 的列式存储可能不如传统的行式存储高效。

    学习曲线： 对于新用户来说，理解和掌握Arrow的数据结构和API可能需要一定时间，尤其是当他们习惯于使用其他数据处理模型时。

    生态成熟度： 虽然Arrow的生态系统正在快速发展，但在某些特定领域或小众技术栈中，相关支持和工具可能不够丰富或成熟。

    实现复杂性： 对于开发者来说，实现Arrow的高效利用可能涉及到复杂的内存管理和优化策略，这在某些情况下可能会增加开发难度。

<code>

#define ARROW_COMPUTE

#include <arrow/compute/api.h>

#include "arrow/pretty_print.h"

#include <arrow/api.h>

#include <arrow/csv/api.h>

#include <arrow/json/api.h>

#include <arrow/io/api.h>

#include <arrow/table.h>

#include <arrow/pretty_print.h>

#include <arrow/result.h>

#include <arrow/status.h>

#include <arrow/ipc/api.h>

#include <parquet/arrow/reader.h>

#include <parquet/arrow/writer.h>

#include <parquet/exception.h>

#include <memory>

#include <iostream>

template <typename T>

using numbuildT = arrow::NumericBuilder<T>;

struct ArrowUtil {

static arrow::Status read_csv(char const* file_name, std::shared_ptr<arrow::Table>& tb);

static arrow::Status read_ipc(char const* file_name, std::shared_ptr<arrow::Table>& tb);

static arrow::Status read_parquet(char const* file_name, std::shared_ptr<arrow::Table>& tb);

static arrow::Status read_json(char const* file_name, std::shared_ptr<arrow::Table>& tb);

static arrow::Status write_ipc(arrow::Table const& tb, char const* file_name);

static arrow::Status write_parquet(arrow::Table const& tb, char const* file_name);

template <typename T, typename buildT, typename arrayT>

inline static std::shared_ptr<arrow::Array> chunked_array_to_array(std::shared_ptr<arrow::ChunkedArray> const& array_a) {

buildT int64_builder;

int64_builder.Resize(array_a->length());

std::vector<T> int64_values;

int64_values.reserve(array_a->length());

for (int i = 0; i < array_a->num_chunks(); ++i) {

auto inner_arr = array_a->chunk(i);

auto int_a = std::static_pointer_cast<arrayT>(inner_arr);

for (int j = 0; j < int_a->length(); ++j) {

int64_values.push_back(int_a->Value(j));

}

}

int64_builder.AppendValues(int64_values);

std::shared_ptr<arrow::Array> array_a_res;

int64_builder.Finish(&array_a_res);

return array_a_res;

}

template <typename T, typename arrayT>

inline static std::vector<T> chunked_array_to_vector(std::shared_ptr<arrow::ChunkedArray> const& array_a) {

std::vector<T> int64_values;

int64_values.reserve(array_a->length());

for (int i = 0; i < array_a->num_chunks(); ++i) {

auto inner_arr = array_a->chunk(i);

auto int_a = std::static_pointer_cast<arrayT>(inner_arr);

for (int j = 0; j < int_a->length(); ++j) {

int64_values.push_back(int_a->Value(j));

}

}

return int64_values;

}

inline static std::vector<std::string> chunked_array_to_str_vector(std::shared_ptr<arrow::ChunkedArray> const& array_a) {

std::vector<std::string> int64_values;

int64_values.reserve(array_a->length());

for (int i = 0; i < array_a->num_chunks(); ++i) {

auto inner_arr = array_a->chunk(i);

auto int_a = std::static_pointer_cast<arrow::StringArray>(inner_arr);

for (int j = 0; j < int_a->length(); ++j) {

int64_values.push_back(int_a->Value(j).data());

}

}

return int64_values;

}

inline static std::shared_ptr<arrow::Array> chunked_array_to_str_array(std::shared_ptr<arrow::ChunkedArray> const& array_a) {

arrow::StringBuilder int64_builder;

int64_builder.Resize(array_a->length());

std::vector<std::string> int64_values;

int64_values.reserve(array_a->length());

for (int i = 0; i < array_a->num_chunks(); ++i) {

auto inner_arr = array_a->chunk(i);

auto int_a = std::static_pointer_cast<arrow::StringArray>(inner_arr);

for (int j = 0; j < int_a->length(); ++j) {

int64_values.push_back(int_a->Value(j).data());

}

}

int64_builder.AppendValues(int64_values);

std::shared_ptr<arrow::Array> array_a_res;

int64_builder.Finish(&array_a_res);

return array_a_res;

}

};

arrow::Status ArrowUtil::read_csv(char const* file_name, std::shared_ptr<arrow::Table>& tb) {

ARROW_ASSIGN_OR_RAISE(auto input_file,

arrow::io::ReadableFile::Open(file_name));

ARROW_ASSIGN_OR_RAISE(auto csv_reader,

arrow::csv::TableReader::Make(

arrow::io::default_io_context(), input_file,

arrow::csv::ReadOptions::Defaults(),

arrow::csv::ParseOptions::Defaults(),

arrow::csv::ConvertOptions::Defaults()));

ARROW_ASSIGN_OR_RAISE(auto table, csv_reader->Read());

tb = table;

return arrow::Status::OK();

}

arrow::Status ArrowUtil::read_ipc(char const* file_name, std::shared_ptr<arrow::Table>& tb) {

ARROW_ASSIGN_OR_RAISE(auto input_file,

arrow::io::ReadableFile::Open(file_name));

ARROW_ASSIGN_OR_RAISE(auto ipc_reader, arrow::ipc::RecordBatchFileReader::Open(input_file));

std::vector<std::shared_ptr<arrow::RecordBatch>> batches;

batches.reserve(ipc_reader->num_record_batches());

for (int i = 0; i < ipc_reader->num_record_batches(); ++i) {

ARROW_ASSIGN_OR_RAISE(auto a_record, ipc_reader->ReadRecordBatch(i));

batches.emplace_back(std::move(a_record));

}

arrow::Table::FromRecordBatches(ipc_reader->schema(), std::move(batches)).Value(&tb);

return arrow::Status::OK();

}

arrow::Status ArrowUtil::read_parquet(char const* file_name, std::shared_ptr<arrow::Table>& tb) {

std::shared_ptr<arrow::io::ReadableFile> infile;

PARQUET_ASSIGN_OR_THROW(infile,

arrow::io::ReadableFile::Open(file_name,

arrow::default_memory_pool()));

std::unique_ptr<parquet::arrow::FileReader> reader;

PARQUET_THROW_NOT_OK(

parquet::arrow::OpenFile(infile, arrow::default_memory_pool(), &reader));

std::shared_ptr<arrow::Table> table;

PARQUET_THROW_NOT_OK(reader->ReadTable(&table));

tb = table;

return arrow::Status::OK();

}

arrow::Status ArrowUtil::read_json(char const* file_name, std::shared_ptr<arrow::Table>& tb) {

std::shared_ptr<arrow::io::ReadableFile> infile;

PARQUET_ASSIGN_OR_THROW(infile,

arrow::io::ReadableFile::Open(file_name,

arrow::default_memory_pool()));

ARROW_ASSIGN_OR_RAISE(auto reader, arrow::json::TableReader::Make(arrow::default_memory_pool(), infile, arrow::json::ReadOptions::Defaults(), arrow::json::ParseOptions::Defaults()));

ARROW_ASSIGN_OR_RAISE(auto res_tb, reader->Read());

tb = res_tb;

return arrow::Status::OK();

}

arrow::Status ArrowUtil::write_ipc(arrow::Table const& tb, char const* file_name) {

ARROW_ASSIGN_OR_RAISE(auto output_file,

arrow::io::FileOutputStream::Open(file_name));

ARROW_ASSIGN_OR_RAISE(auto batch_writer,

arrow::ipc::MakeFileWriter(output_file, tb.schema()));

ARROW_RETURN_NOT_OK(batch_writer->WriteTable(tb));

ARROW_RETURN_NOT_OK(batch_writer->Close());

return arrow::Status::OK();

}

arrow::Status ArrowUtil::write_parquet(arrow::Table const& tb, char const* file_name) {

std::shared_ptr<arrow::io::FileOutputStream> outfile;

PARQUET_ASSIGN_OR_THROW(

outfile, arrow::io::FileOutputStream::Open(file_name));

// The last argument to the function call is the size of the RowGroup in

// the parquet file. Normally you would choose this to be rather large but

// for the example, we use a small value to have multiple RowGroups.

PARQUET_THROW_NOT_OK(

parquet::arrow::WriteTable(tb, arrow::default_memory_pool(), outfile, 3));

return arrow::Status::OK();

}

void testReadCSV() {

// 读取CSV文件

char const* csv_path = "./test.csv";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_csv(csv_path, tb);

auto const& tb_ = *tb;

arrow::PrettyPrint(tb_, {}, &std::cerr);

assert(tb_.num_rows() == 2);

}

void testWriteIpc() {

// 读取CSV文件并写入IPC文件

char const* csv_path = "./test.csv";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_csv(csv_path, tb);

auto const& tb_ = *tb;

char const* write_csv_path = "./test_dst.arrow";

arrow::PrettyPrint(tb_, {}, &std::cerr);

auto write_res = ArrowUtil::write_ipc(tb_, write_csv_path);

assert(write_res == arrow::Status::OK());

}

void testReadIPC() {

// 读取Arrow IPC 文件

char const* ipc_path = "./test_dst.arrow";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_ipc(ipc_path, tb);

auto const& tb_ = *tb;

arrow::PrettyPrint(tb_, {}, &std::cerr);

assert(tb_.num_rows() == 2);

}

void testWriteParquet() {

// 写入Parquet文件

char const* csv_path = "./test.csv";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_csv(csv_path, tb);

auto const& tb_ = *tb;

char const* write_parquet_path = "./test_dst.parquet";

arrow::PrettyPrint(tb_, {}, &std::cerr);

auto write_res = ArrowUtil::write_parquet(tb_, write_parquet_path);

assert(write_res == arrow::Status::OK());

}

void testReadParquet() {

// 读取 Parquet

char const* parquet_path = "./test_dst.parquet";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_parquet(parquet_path, tb);

auto const& tb_ = *tb;

arrow::PrettyPrint(tb_, {}, &std::cerr);

assert(tb_.num_rows() == 2);

}

void testReadJson() {

// 读取Json文件

char const* json_path = "./test.json";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_json(json_path, tb);

auto const& tb_ = *tb;

arrow::PrettyPrint(tb_, {}, &std::cerr);

assert(tb_.num_rows() == 2);

}

void testComputeGreater() {

// 比较两列 int 值中 int1 > int2的值， greater函数

char const* json_path = "./comp_gt.csv";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_csv(json_path, tb);

auto const& tb_ = *tb;

arrow::PrettyPrint(tb_, {}, &std::cerr);

auto array_a = tb_.GetColumnByName("int1");

auto array_b = tb_.GetColumnByName("int2");

auto array_a_res = ArrowUtil::chunked_array_to_array<int64_t, numbuildT<arrow::Int64Type>, arrow::Int64Array>(array_a);

auto array_b_res = ArrowUtil::chunked_array_to_array<int64_t, numbuildT<arrow::Int64Type>, arrow::Int64Array>(array_b);

auto compared_datum = arrow::compute::CallFunction("greater", { array_a_res, array_b_res });

auto array_a_gt_b_compute = compared_datum->make_array();

arrow::PrettyPrint(*array_a_gt_b_compute, {}, &std::cerr);

auto schema =

arrow::schema({ arrow::field("int1", arrow::int64()), arrow::field("int2", arrow::int64()),

arrow::field("a>b? (arrow)", arrow::boolean()) });

std::shared_ptr<arrow::Table> my_table = arrow::Table::Make(

schema, { array_a_res, array_b_res, array_a_gt_b_compute }, tb_.num_rows());

arrow::PrettyPrint(*my_table, {}, &std::cerr);

}

void testComputeMinMax() {

// 计算int1列的最大值和最小值

char const* json_path = "./comp_gt.csv";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_csv(json_path, tb);

auto const& tb_ = *tb;

arrow::PrettyPrint(tb_, {}, &std::cerr);

auto array_a = tb_.GetColumnByName("int1");

auto array_a_res = ArrowUtil::chunked_array_to_array<int64_t, numbuildT<arrow::Int64Type>, arrow::Int64Array>(array_a);

arrow::compute::ScalarAggregateOptions scalar_aggregate_options;

scalar_aggregate_options.skip_nulls = false;

auto min_max = arrow::compute::CallFunction("min_max", { array_a_res }, &scalar_aggregate_options);

// Unpack struct scalar result (a two-field {"min", "max"} scalar)

auto min_value = min_max->scalar_as<arrow::StructScalar>().value[0];

auto max_value = min_max->scalar_as<arrow::StructScalar>().value[1];

assert(min_value->ToString() == "1");

assert(max_value->ToString() == "8");

}

#define GTEST_TEST(a, b) void a##_##b()

#define ASSERT_EQ(a, b) assert(a == b)

GTEST_TEST(RWTests, ComputeMean) {

// 计算int1列的平均值

char const* json_path = "../data/comp_gt.csv";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_csv(json_path, tb);

auto const& tb_ = *tb;

arrow::PrettyPrint(tb_, {}, &std::cerr);

auto array_a = tb_.GetColumnByName("int1");

auto array_a_res = ArrowUtil::chunked_array_to_array<int64_t, numbuildT<arrow::Int64Type>, arrow::Int64Array>(array_a);

arrow::compute::ScalarAggregateOptions scalar_aggregate_options;

scalar_aggregate_options.skip_nulls = false;

auto mean = arrow::compute::CallFunction("mean", { array_a_res }, &scalar_aggregate_options);

auto const& mean_value = mean->scalar_as<arrow::Scalar>();

ASSERT_EQ(mean_value.ToString(), "4.5");

}

GTEST_TEST(RWTests, ComputeAdd) {

// 将第一列的值加3

char const* json_path = "../data/comp_gt.csv";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_csv(json_path, tb);

auto const& tb_ = *tb;

arrow::PrettyPrint(tb_, {}, &std::cerr);

auto array_a = tb_.GetColumnByName("int1");

auto array_a_res = ArrowUtil::chunked_array_to_array<int64_t, numbuildT<arrow::Int64Type>, arrow::Int64Array>(array_a);

arrow::compute::ScalarAggregateOptions scalar_aggregate_options;

scalar_aggregate_options.skip_nulls = false;

std::shared_ptr<arrow::Scalar> increment = std::make_shared<arrow::Int64Scalar>(3);

auto add = arrow::compute::CallFunction("add", { array_a_res, increment }, &scalar_aggregate_options);

std::shared_ptr<arrow::Array> incremented_array = add->array_as<arrow::Array>();

arrow::PrettyPrint(*incremented_array, {}, &std::cerr);

}

GTEST_TEST(RWTests, ComputeAddArray) {

// int1和int2两列相加

char const* json_path = "../data/comp_gt.csv";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_csv(json_path, tb);

auto const& tb_ = *tb;

arrow::PrettyPrint(tb_, {}, &std::cerr);

auto array_a = tb_.GetColumnByName("int1");

auto array_a_res = ArrowUtil::chunked_array_to_array<int64_t, numbuildT<arrow::Int64Type>, arrow::Int64Array>(array_a);

auto array_b = tb_.GetColumnByName("int2");

auto array_b_res = ArrowUtil::chunked_array_to_array<int64_t, numbuildT<arrow::Int64Type>, arrow::Int64Array>(array_b);

arrow::compute::ScalarAggregateOptions scalar_aggregate_options;

scalar_aggregate_options.skip_nulls = false;

auto add = arrow::compute::CallFunction("add", { array_a_res, array_b_res }, &scalar_aggregate_options);

std::shared_ptr<arrow::Array> incremented_array = add->array_as<arrow::Array>();

arrow::PrettyPrint(*incremented_array, {}, &std::cerr);

}

GTEST_TEST(RWTests, ComputeStringEqual) {

// 比较s1和s2两列是否相等

char const* json_path = "../data/comp_s_eq.csv";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_csv(json_path, tb);

auto const& tb_ = *tb;

arrow::PrettyPrint(tb_, {}, &std::cerr);

auto array_a = tb_.GetColumnByName("s1");

auto array_a_res = ArrowUtil::chunked_array_to_str_array(array_a);

auto array_b = tb_.GetColumnByName("s2");

auto array_b_res = ArrowUtil::chunked_array_to_str_array(array_b);

arrow::compute::ScalarAggregateOptions scalar_aggregate_options;

scalar_aggregate_options.skip_nulls = false;

auto eq_ = arrow::compute::CallFunction("equal", { array_a_res, array_b_res }, &scalar_aggregate_options);

std::shared_ptr<arrow::Array> eq_array = eq_->array_as<arrow::Array>();

arrow::PrettyPrint(*eq_array, {}, &std::cerr);

}

GTEST_TEST(RWTests, ComputeCustom) {

// 自己写算法逐个比较相等

char const* json_path = "../data/comp_s_eq.csv";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_csv(json_path, tb);

auto const& tb_ = *tb;

arrow::PrettyPrint(tb_, {}, &std::cerr);

auto arr1 = tb_.GetColumnByName("s1");

auto arr2 = tb_.GetColumnByName("s2");

auto v1 = ArrowUtil::chunked_array_to_str_vector(arr1);

auto v2 = ArrowUtil::chunked_array_to_str_vector(arr2);

for (std::size_t i = 0; i < v1.size(); ++i) {

if (v1[i] != v2[i]) {

std::cerr << v1[i] << "!=" << v2[i] << "\n";

}

}

}

GTEST_TEST(RWTests, ComputeCustomDbl) {

// 自己写算法比较double值

char const* json_path = "../data/custom_dbl.csv";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_csv(json_path, tb);

auto const& tb_ = *tb;

arrow::PrettyPrint(tb_, {}, &std::cerr);

auto arr1 = tb_.GetColumnByName("dbl1");

auto arr2 = tb_.GetColumnByName("dbl2");

auto v1 = ArrowUtil::chunked_array_to_vector<double, arrow::DoubleArray>(arr1);

auto v2 = ArrowUtil::chunked_array_to_vector<double, arrow::DoubleArray>(arr2);

for (std::size_t i = 0; i < v1.size(); ++i) {

if (v1[i] != v2[i]) {

std::cerr << v1[i] << "!=" << v2[i] << "\n";

}

}

}

GTEST_TEST(RWTests, ComputeEqualDbl) {

// 使用equal函数比较double值

char const* json_path = "../data/custom_dbl.csv";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_csv(json_path, tb);

auto const& tb_ = *tb;

arrow::PrettyPrint(tb_, {}, &std::cerr);

auto arr1 = tb_.GetColumnByName("dbl1");

auto arr2 = tb_.GetColumnByName("dbl2");

auto dbl_arr1 = ArrowUtil::chunked_array_to_array<double, numbuildT<arrow::DoubleType>, arrow::DoubleArray>(arr1);

auto dbl_arr2 = ArrowUtil::chunked_array_to_array<double, numbuildT<arrow::DoubleType>, arrow::DoubleArray>(arr2);

arrow::compute::ScalarAggregateOptions scalar_aggregate_options;

scalar_aggregate_options.skip_nulls = false;

auto eq_ = arrow::compute::CallFunction("equal", { dbl_arr1, dbl_arr2 }, &scalar_aggregate_options);

std::shared_ptr<arrow::Array> eq_array = eq_->array_as<arrow::Array>();

arrow::PrettyPrint(*eq_array, {}, &std::cerr);

}

GTEST_TEST(RWTests, StrStartsWith) {

// 计算s1列以是否以 Zha开头的值

char const* json_path = "../data/comp_s_eq.csv";

std::shared_ptr<arrow::Table> tb;

ArrowUtil::read_csv(json_path, tb);

auto const& tb_ = *tb;

arrow::PrettyPrint(tb_, {}, &std::cerr);

auto array_a = tb_.GetColumnByName("s1");

auto array_a_res = ArrowUtil::chunked_array_to_str_array(array_a);

arrow::compute::MatchSubstringOptions options("Zha");

auto eq_ = arrow::compute::CallFunction("starts_with", { array_a_res }, &options);

std::shared_ptr<arrow::Array> eq_array = eq_->array_as<arrow::Array>();

arrow::PrettyPrint(*eq_array, {}, &std::cerr);

}

using arrow::Int32Builder;

using arrow::Int64Builder;

using arrow::DoubleBuilder;

using arrow::StringBuilder;

struct row_data {

int32_t col1;

int64_t col2;

double col3;

std::string col4;

};//行结构

#define EXIT_ON_FAILURE(expr) \

do { \

arrow::Status status_ = (expr); \

if (!status_.ok()) { \

std::cerr << status_.message() << std::endl; \

return EXIT_FAILURE; \

} \

} while (0);

arrow::Status CreateTable(const std::vector<struct row_data>& rows, std::shared_ptr<arrow::Table>* table) {

//使用arrow::jemalloc::MemoryPool::default_pool()构建器更有效，因为这可以适当增加底层内存区域的大小.

arrow::MemoryPool* pool = arrow::default_memory_pool();

Int32Builder col1_builder(pool);

Int64Builder col2_builder(pool);

DoubleBuilder col3_builder(pool);

StringBuilder col4_builder(pool);

//现在我们可以循环我们现有的数据，并将其插入到构建器中。这里的' Append '调用可能会失败(例如，我们无法分配足够的额外内存)。因此我们需要检查它们的返回值。

for (const row_data& row : rows) {

ARROW_RETURN_NOT_OK(col1_builder.Append(row.col1));

ARROW_RETURN_NOT_OK(col2_builder.Append(row.col2));

ARROW_RETURN_NOT_OK(col3_builder.Append(row.col3));

ARROW_RETURN_NOT_OK(col4_builder.Append(row.col4));

}

//添加空值,末尾值的元素为空

ARROW_RETURN_NOT_OK(col1_builder.AppendNull());

ARROW_RETURN_NOT_OK(col2_builder.AppendNull());

ARROW_RETURN_NOT_OK(col3_builder.AppendNull());

ARROW_RETURN_NOT_OK(col4_builder.AppendNull());

std::shared_ptr<arrow::Array> col1_array;

ARROW_RETURN_NOT_OK(col1_builder.Finish(&col1_array));

std::shared_ptr<arrow::Array> col2_array;

ARROW_RETURN_NOT_OK(col2_builder.Finish(&col2_array));

std::shared_ptr<arrow::Array> col3_array;

ARROW_RETURN_NOT_OK(col3_builder.Finish(&col3_array));

std::shared_ptr<arrow::Array> col4_array;

ARROW_RETURN_NOT_OK(col4_builder.Finish(&col4_array));

std::vector<std::shared_ptr<arrow::Field>> schema_vector = {

arrow::field("col1", arrow::int32()), arrow::field("col2", arrow::int64()), arrow::field("col3", arrow::float64()),

arrow::field("col4", arrow::utf8()) };

auto schema = std::make_shared<arrow::Schema>(schema_vector);

//最终的' table '变量是我们可以传递给其他可以使用Apache Arrow内存结构的函数的变量。这个对象拥有所有引用数据的所有权，

//因此一旦我们离开构建表及其底层数组的函数的作用域，就不必关心未定义的引用。

*table = arrow::Table::Make(schema, { col1_array, col2_array, col3_array,col4_array });

return arrow::Status::OK();

}

arrow::Status TableToVector(const std::shared_ptr<arrow::Table>& table,

std::vector<struct row_data>* rows) {

//检查表结构是否一致

std::vector<std::shared_ptr<arrow::Field>> schema_vector = {

arrow::field("col1", arrow::int32()), arrow::field("col2", arrow::int64()), arrow::field("col3", arrow::float64()),

arrow::field("col4", arrow::utf8()) };

auto expected_schema = std::make_shared<arrow::Schema>(schema_vector);

if (!expected_schema->Equals(*table->schema())) {

// The table doesn't have the expected schema thus we cannot directly

// convert it to our target representation.

return arrow::Status::Invalid("Schemas are not matching!");

}

//获取对应列数据指针

auto col1s =

std::static_pointer_cast<arrow::Int32Array>(table->column(0)->chunk(0));

auto col2s =

std::static_pointer_cast<arrow::Int64Array>(table->column(1)->chunk(0));

auto col3s =

std::static_pointer_cast<arrow::DoubleArray>(table->column(2)->chunk(0));

auto col4s =

std::static_pointer_cast<arrow::StringArray>(table->column(3)->chunk(0));

for (int64_t i = 0; i < table->num_rows(); i++) {

if (col1s->IsNull(i)) {

assert(i == 3);//第四行为null

}

else {

int32_t col1 = col1s->Value(i);

int64_t col2 = col2s->Value(i);

double col3 = col3s->Value(i);

std::string col4 = col4s->GetString(i);

rows->push_back({ col1, col2, col3,col4 });

}

}

return arrow::Status::OK();

}

// 行数组和列数组相互转换

int testTableConvertSTL() {

//行数组

std::vector<row_data> rows = {

{1, 11,1.0, "John"}, {2, 22,2.0, "Tom"}, {3,33, 3.0,"Susan"} };

std::shared_ptr<arrow::Table> table;

EXIT_ON_FAILURE(CreateTable(rows, &table));

std::vector<row_data> expected_rows;

EXIT_ON_FAILURE(TableToVector(table, &expected_rows));

std::cout << expected_rows.size() << std::endl;

assert(rows.size() == expected_rows.size());

return 0;

}

void test() {

// 构建一个int8数组

arrow::Int8Builder builder;

arrow::Int16Builder int16builder;

int8_t days_raw[5] = { 1, 12, 17, 23, 28 };

int8_t months_raw[5] = { 1, 3, 5, 7, 1 };

int16_t years_raw[5] = { 1990, 2000, 1995, 2000, 1995 };

builder.AppendValues(days_raw, 5);

std::shared_ptr<arrow::Array> days = builder.Finish().MoveValueUnsafe();

builder.AppendValues(months_raw, 5);

std::shared_ptr<arrow::Array> months = builder.Finish().MoveValueUnsafe();

int16builder.AppendValues(years_raw, 5);

std::shared_ptr<arrow::Array> years = int16builder.Finish().MoveValueUnsafe();

// Schema 自定义table

// Now, we want a RecordBatch, which has columns and labels for said columns.

// This gets us to the 2d data structures we want in Arrow.

// These are defined by schema, which have fields -- here we get both those object types

// ready.

std::shared_ptr<arrow::Field> field_day, field_month, field_year;

std::shared_ptr<arrow::Schema> schema;

// Every field needs its name and data type.

field_day = arrow::field("Day", arrow::int8());

field_month = arrow::field("Month", arrow::int8());

field_year = arrow::field("Year", arrow::int16());

// The schema can be built from a vector of fields, and we do so here.

schema = arrow::schema({ field_day, field_month, field_year });

// 打印

// With the schema and Arrays full of data, we can make our RecordBatch! Here,

// each column is internally contiguous. This is in opposition to Tables, which we'll

// see next.

std::shared_ptr<arrow::RecordBatch> rbatch;

// The RecordBatch needs the schema, length for columns, which all must match,

// and the actual data itself.

rbatch = arrow::RecordBatch::Make(schema, days->length(), { days, months, years });

std::cout << rbatch->ToString();

/*

Day: [

1,

12,

17,

23,

28

]

Month: [

1,

3,

5,

7,

1

]

Year: [

1990,

2000,

1995,

2000,

1995

]

*/

// stl vector容器

arrow::ArrayVector day_vecs{days};

std::shared_ptr<arrow::ChunkedArray> day_chunks =

std::make_shared<arrow::ChunkedArray>(day_vecs);

testTableConvertSTL();

testReadCSV();

/*

col1: string

col2: string

col3: string

----

col1:

[

[

"val1",

"val1"

]

]

col2:

[

[

"val2",

"val2"

]

]

col3:

[

[

"val3",

"val3"

]

]

*/

testWriteIpc();

testReadIPC();

//testComputeGreater();

//testComputeMinMax();

}

Compute Functions — Apache Arrow v17.0.0

GitHub - apache/arrow: Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing

创作不易，小小的支持一下吧！