# Learning from Examples » Text Processing Pipeline

We study a text processing pipeline that finds the most frequent character of each string from an input source. Parallelism exhibits in the form of a three-stage pipeline that transforms the input string to a final pair type.

## Formulate the Text Processing Pipeline Problem

Given an input vector of strings, we want to compute the most frequent character for each string using a series of transform operations. For example:

    # input strings
    abade
    ddddf
    eefge
    xyzzd
    ijjjj
    jiiii
    kkijk

    # output
    a:2
    d:4
    e:3
    z:2
    j:4
    i:4
    k:3

We decompose the algorithm into three stages:

1. read a `std::string` from the input vector
2. generate a `std::unorder_map<char, size_t>` frequency map from the string
3. reduce the most frequent character to a `std::pair<char, size_t>` from the map

The first and the third stages process inputs and generate results in serial, and the second stage can run in parallel. The algorithm is a perfect fit to pipeline parallelism, as different stages can overlap with each other in time across parallel lines.

## Create a Text Processing Pipeline

We create a pipeline of three pipes (stages) and two parallel lines to solve the problem. The number of parallel lines is a tunable parameter. In most cases, we can just use `std::thread::hardware_concurrency` as the line count. The first pipe reads an input string from the vector in order, the second pipe transforms the input string from the first pipe to a frequency map in parallel, and the third pipe reduces the frequency map to find the most frequent character. The overall implementation is shown below:

    #include <taskflow/taskflow.hpp>
    #include <taskflow/algorithm/pipeline.hpp>

    // Function: format the map
    std::string format_map(const std::unordered_map<char, size_t>& map) {
      std::ostringstream oss;
      for(const auto& [i, j] : map) {
        oss << i << ':' << j << ' ';
      }
      return oss.str();
    }

    int main() {

      tf::Taskflow taskflow("text-filter pipeline");
      tf::Executor executor;

      const size_t num_lines = 2;

      // input data
      std::vector<std::string> input = {
        "abade",
        "ddddf",
        "eefge",
        "xyzzd",
        "ijjjj",
        "jiiii",
        "kkijk"
      };

      // custom data storage
      using data_type = std::variant<
        std::string, std::unordered_map<char, size_t>, std::pair<char, size_t>
      >;
      std::array<data_type, num_lines> mybuffer;

      // the pipeline consists of three pipes (serial-parallel-serial)
      // and up to two concurrent scheduling tokens
      tf::Pipeline pl(num_lines,

        // first pipe processes the input data
        tf::Pipe{tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) {
          if(pf.token() == input.size()) {
            pf.stop();
          }
          else {
            printf("stage 1: input token = %s\n", input[pf.token()].c_str());
            mybuffer[pf.line()] = input[pf.token()];
          }
        }},

        // second pipe counts the frequency of each character
        tf::Pipe{tf::PipeType::PARALLEL, [&](tf::Pipeflow& pf) {
          std::unordered_map<char, size_t> map;
          for(auto c : std::get<std::string>(mybuffer[pf.line()])) {
            map[c]++;
          }
          printf("stage 2: map = %s\n", format_map(map).c_str());
          mybuffer[pf.line()] = map;
        }},

        // third pipe reduces the most frequent character
        tf::Pipe{tf::PipeType::SERIAL, [&mybuffer](tf::Pipeflow& pf) {
          auto& map = std::get<std::unordered_map<char, size_t>>(mybuffer[pf.line()]);
          auto sol = std::max_element(map.begin(), map.end(), [](auto& a, auto& b){
            return a.second < b.second;
          });
          printf("stage 3: %c:%zu\n", sol->first, sol->second);
          // not necessary to store the last-stage data, just for demo purpose
          mybuffer[pf.line()] = *sol;
        }}
      );

      // build the pipeline graph using composition
      tf::Task init = taskflow.emplace([](){ std::cout << "ready\n"; })
                              .name("starting pipeline");
      tf::Task task = taskflow.composed_of(pl)
                              .name("pipeline");
      tf::Task stop = taskflow.emplace([](){ std::cout << "stopped\n"; })
                              .name("pipeline stopped");

      // create task dependency
      init.precede(task);
      task.precede(stop);

      // dump the pipeline graph structure (with composition)
      taskflow.dump(std::cout);

      // run the pipeline
      executor.run(taskflow).wait();

      return 0;
    }

### Define the Data Buffer

Taskflow does not provide any data abstraction to perform pipeline scheduling, but give users full control over data management in their applications. In this example, we create an one-dimensional buffer of a std::variant data type to store the output of each pipe in a uniform storage:

    using data_type = std::variant<
      std::string, std::unordered_map<char, size_t>, std::pair<char, size_t>
    >;
    std::array<std::array<data_type, num_pipes>, num_lines> mybuffer;

#### Note

One-dimensional buffer is sufficient because Taskflow enables only one scheduling token per line at a time.

### Define the Pipes

The first pipe reads one string and puts it in the corresponding entry at the buffer, `mybuffer[pf.line()]`. Since we read in each string in order, we declare the pipe as a serial type:

    tf::Pipe{tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) {
      if(pf.token() == input.size()) {
        pf.stop();
      }
      else {
        mybuffer[pf.line()] = input[pf.token()];
        printf("stage 1: input token = %s\n", input[pf.token()].c_str());
      }
    }},

The second pipe needs to get the input string from the previous pipe and then transforms that input string into a frequency map that records the occurrence of each character in the string. As multiple transforms can operate simultaneously, we declare the pipe as a parallel type:

    tf::Pipe{tf::PipeType::PARALLEL, [&](tf::Pipeflow& pf) {
      std::unordered_map<char, size_t> map;
      for(auto c : std::get<std::string>(mybuffer[pf.line()])) {
        map[c]++;
      }
      mybuffer[pf.line()] = map;
      printf("stage 2: map = %s\n", format_map(map).c_str());
    }}

Similarly, the third pipe needs to get the input frequency map from the previous pipe and then reduces the result to find the most frequent character. We may not need to store the result in the buffer but other places defined by the application (e.g., an output file). As we want to output the result in the same order as the input, we declare the pipe as a serial type:

    tf::Pipe{tf::PipeType::SERIAL, [&mybuffer](tf::Pipeflow& pf) {
      auto& map = std::get<std::unordered_map<char, size_t>>(mybuffer[pf.line()]);
      auto sol = std::max_element(map.begin(), map.end(), [](auto& a, auto& b){
        return a.second < b.second;
      });
      printf("stage 3: %c:%zu\n", sol->first, sol->second);
    }}

### Define the Task Graph

To build up the taskflow graph for the pipeline, we create a module task out of the pipeline structure and connect it with two tasks that outputs messages before and after the pipeline:

    tf::Task init = taskflow.emplace([](){ std::cout << "ready\n"; })
                            .name("starting pipeline");
    tf::Task task = taskflow.composed_of(pl)
                            .name("pipeline");
    tf::Task stop = taskflow.emplace([](){ std::cout << "stopped\n"; })
                            .name("pipeline stopped");
    init.precede(task);
    task.precede(stop);

### Submit the Task Graph

Finally, we submit the taskflow to the execution and run it once:

    executor.run(taskflow).wait();

As the second stage is a parallel pipe, the output may interleave. One possible result is shown below:

    ready
    stage 1: input token = abade
    stage 1: input token = ddddf
    stage 2: map = f:1 d:4
    stage 2: map = e:1 d:1 a:2 b:1
    stage 3: a:2
    stage 1: input token = eefge
    stage 2: map = g:1 e:3 f:1
    stage 3: d:4
    stage 1: input token = xyzzd
    stage 3: e:3
    stage 1: input token = ijjjj
    stage 2: map = z:2 x:1 d:1 y:1
    stage 3: z:2
    stage 1: input token = jiiii
    stage 2: map = j:4 i:1
    stage 3: j:4
    stage 2: map = i:4 j:1
    stage 1: input token = kkijk
    stage 3: i:4
    stage 2: map = j:1 k:3 i:1
    stage 3: k:3
    stopped

We can see seven outputs at the third stage that show the most frequent character for each of the seven strings in order (`a:2`, `d:4`, `e:3`, `z:2`, `j:4`, `i:4`, `k:3`). The taskflow graph of this pipeline workload is shown below:

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