根据前一篇文章,正常情况下,arrow应该是已经安装好了,这个时候,就可以尝试编译运行第一个arrow程序了。

想要实现上述的效果,需要做三个步骤,第一个是编写my_example.cc文件,代码如下:
// (Doc section: Includes)
#include <arrow/api.h>
#include <iostream>
// (Doc section: Includes)
// (Doc section: RunMain Start)
arrow::Status RunMain() {
// (Doc section: RunMain Start)
// (Doc section: int8builder 1 Append)
// Builders are the main way to create Arrays in Arrow from existing values that are not
// on-disk. In this case, we'll make a simple array, and feed that in.
// Data types are important as ever, and there is a Builder for each compatible type;
// in this case, int8.
arrow::Int8Builder int8builder;
int8_t days_raw[5] = {1, 12, 17, 23, 28};
// AppendValues, as called, puts 5 values from days_raw into our Builder object.
ARROW_RETURN_NOT_OK(int8builder.AppendValues(days_raw, 5));
// (Doc section: int8builder 1 Append)
// (Doc section: int8builder 1 Finish)
// We only have a Builder though, not an Array -- the following code pushes out the
// built up data into a proper Array.
std::shared_ptr<arrow::Array> days;
ARROW_ASSIGN_OR_RAISE(days, int8builder.Finish());
// (Doc section: int8builder 1 Finish)
// (Doc section: int8builder 2)
// Builders clear their state every time they fill an Array, so if the type is the same,
// we can re-use the builder. We do that here for month values.
int8_t months_raw[5] = {1, 3, 5, 7, 1};
ARROW_RETURN_NOT_OK(int8builder.AppendValues(months_raw, 5));
std::shared_ptr<arrow::Array> months;
ARROW_ASSIGN_OR_RAISE(months, int8builder.Finish());
// (Doc section: int8builder 2)
// (Doc section: int16builder)
// Now that we change to int16, we use the Builder for that data type instead.
arrow::Int16Builder int16builder;
int16_t years_raw[5] = {1990, 2000, 1995, 2000, 1995};
ARROW_RETURN_NOT_OK(int16builder.AppendValues(years_raw, 5));
std::shared_ptr<arrow::Array> years;
ARROW_ASSIGN_OR_RAISE(years, int16builder.Finish());
// (Doc section: int16builder)
// (Doc section: Schema)
// 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});
// (Doc section: Schema)
// (Doc section: RBatch)
// 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();
// (Doc section: RBatch)
// (Doc section: More Arrays)
// Now, let's get some new arrays! It'll be the same datatypes as above, so we re-use
// Builders.
int8_t days_raw2[5] = {6, 12, 3, 30, 22};
ARROW_RETURN_NOT_OK(int8builder.AppendValues(days_raw2, 5));
std::shared_ptr<arrow::Array> days2;
ARROW_ASSIGN_OR_RAISE(days2, int8builder.Finish());
int8_t months_raw2[5] = {5, 4, 11, 3, 2};
ARROW_RETURN_NOT_OK(int8builder.AppendValues(months_raw2, 5));
std::shared_ptr<arrow::Array> months2;
ARROW_ASSIGN_OR_RAISE(months2, int8builder.Finish());
int16_t years_raw2[5] = {1980, 2001, 1915, 2020, 1996};
ARROW_RETURN_NOT_OK(int16builder.AppendValues(years_raw2, 5));
std::shared_ptr<arrow::Array> years2;
ARROW_ASSIGN_OR_RAISE(years2, int16builder.Finish());
// (Doc section: More Arrays)
// (Doc section: ArrayVector)
// ChunkedArrays let us have a list of arrays, which aren't contiguous
// with each other. First, we get a vector of arrays.
arrow::ArrayVector day_vecs{days, days2};
// (Doc section: ArrayVector)
// (Doc section: ChunkedArray Day)
// Then, we use that to initialize a ChunkedArray, which can be used with other
// functions in Arrow! This is good, since having a normal vector of arrays wouldn't
// get us far.
std::shared_ptr<arrow::ChunkedArray> day_chunks =
std::make_shared<arrow::ChunkedArray>(day_vecs);
// (Doc section: ChunkedArray Day)
// (Doc section: ChunkedArray Month Year)
// Repeat for months.
arrow::ArrayVector month_vecs{months, months2};
std::shared_ptr<arrow::ChunkedArray> month_chunks =
std::make_shared<arrow::ChunkedArray>(month_vecs);
// Repeat for years.
arrow::ArrayVector year_vecs{years, years2};
std::shared_ptr<arrow::ChunkedArray> year_chunks =
std::make_shared<arrow::ChunkedArray>(year_vecs);
// (Doc section: ChunkedArray Month Year)
// (Doc section: Table)
// A Table is the structure we need for these non-contiguous columns, and keeps them
// all in one place for us so we can use them as if they were normal arrays.
std::shared_ptr<arrow::Table> table;
table = arrow::Table::Make(schema, {day_chunks, month_chunks, year_chunks}, 10);
std::cout << table->ToString();
// (Doc section: Table)
// (Doc section: Ret)
return arrow::Status::OK();
}
// (Doc section: Ret)
// (Doc section: Main)
int main() {
arrow::Status st = RunMain();
if (!st.ok()) {
std::cerr << st << std::endl;
return 1;
}
return 0;
}
// (Doc section: Main)这个代码是官方给出的例子,后续我们会进一步深入学习,这篇主要是编译成功就ok了。
接下来就是需要实现CMakeLists.txt文件,以便后续用cmake进行编译
cmake_minimum_required(VERSION 3.16) project(MyExample) find_package(Arrow REQUIRED) add_executable(my_example my_example.cc) target_link_libraries(my_example PRIVATE Arrow::arrow_shared)cmake_minimum_required(VERSION 3.16) project(MyExample) find_package(Arrow REQUIRED) add_executable(my_example my_example.cc) target_link_libraries(my_example PRIVATE Arrow::arrow_shared)
最后,就是编译之后运行了
把生成的这两个文件放到一个文件夹下,创建一个build文件夹,然后cmake CMakeLists.txt文件,最后make编译,运行代码,如上图所示。
mkdir build cd build cmake ../ make
用build文件夹的好处就是编译的文件会放到build文件夹,如果不新建build, 直接 cmake . make也是可以的,但是生成的文件就会比较乱。

好了,第一个编译完成了,代表我们arrow已经安装成功了,接下来就去啃一下复杂的官方文档吧。幸好有chatgpt,翻译成中文,效率应该会高很多。
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