flyingtrain - Document

Use an iterative parser to retrieve transport models and total passenger capacity from long JSON transport list in a .txt file

Installation

This project is packaged with Python 2, and can be installed with pip. Copy-paste and run this command in the terminal:

pip install flyingtrain

Docker (supplementary solution)

• This project is also dockerized. Docker needs to be installed to run this project in containerization method.
• The Dockerfile uses ​python:2​​ as base image.
• There are some feasible commands as indicated in ​Makefile​, or simply execute ​ make help, it will show the Make commands that can be used. (We will go through more in detail later)

Tool

This project uses ijson as an iterative JSON parser to avoid dumping the entire data file into memory

Usage

After installation, the following snippet can be used inside a virtual environment to extract the data

import flyingtrain

test_file = 'test.txt'  # the full path of the file

flyingtrain.extract_data(test_file)

the result

(flyingtrain) chuhsuan@ubuntu:~/Desktop$ python
Python 2.7.12 (default, Nov 12 2018, 14:36:49)
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import flyingtrain
>>> flyingtrain.extract_data('test.txt')
"planes": 524
"trains": 150
"cars": 14

"distinct-cars": 3
"distinct-planes": 2
"distinct-trains": 1

Docker solution
Copy the data file to the root folder, assign the file name to test_file in main.py and execute make run. Volume binding can be used like this line in Makefile to avoid copying the file, but it's not implemented here while taking docker as a supplementary solution.

the result of the docker solution

chuhsuan@ubuntu:~/git/flyingtrain$ make run
docker build \
	-t chuhsuanlee/flyingtrain \
	.
Sending build context to Docker daemon  61.44kB
Step 1/5 : FROM python:2
 ---> 3c43a5d4034a
Step 2/5 : WORKDIR /usr/src
 ---> Using cache
 ---> 37e4d0e02609
Step 3/5 : COPY requirements.txt /usr/src/
 ---> Using cache
 ---> 85ae12b2a6f6
Step 4/5 : RUN pip install -r requirements.txt
 ---> Using cache
 ---> 9d33ec10c044
Step 5/5 : ENTRYPOINT ["python", "main.py"]
 ---> Using cache
 ---> e3d261a60154
Successfully built e3d261a60154
Successfully tagged chuhsuanlee/flyingtrain:latest
docker run \
	--rm -v /etc/localtime:/etc/localtime -v /home/chuhsuan/git/flyingtrain:/usr/src \
	chuhsuanlee/flyingtrain
"planes": 524
"trains": 150
"cars": 14

"distinct-cars": 3
"distinct-planes": 2
"distinct-trains": 1

Benchmark

The following command is used in the terminal to show how much time it takes to retrieve the data

python -m timeit -s "import flyingtrain" "flyingtrain.extract_data('test.txt')"

the result

1000 loops, best of 3: 684 usec per loop

which means it takes around 684 usec for executing once

Docker solution
Assign the file name to test_file in benchmark.py and execute make runbenchmark. Again, volume binding is not implemented here, so the file should be put under the root folder.

the result of the docker solution

[0.6676740646362305, 0.6634271144866943, 0.6310489177703857]

which means measuring execution time with 3 repeats counts and each count with 1000 executions. For average it takes 654 usec per execution

Possible optimizations

• First, for benchmarking, the build-in module timeit is used here. There are also some third party packages can be used such as memory_profiler for monitoring memory consumption of a process as well as line-by-line analysis.
• Second, when the record amounts scale up, and the model sets of distinct transports keep increasing, that one can take tons of memory and CPU if we still do it naively by keeping a set of the counts for every model around. There's streaming approximate algorithms for this such as HyperLogLog.
• Last but not least, the format of the datasets. Protocol buffers and recordio, or even Cap'n Proto will be a good try. It's a binary storage format which is faster to parse, and resilient to corruption. (recordio files are checksummed, and can skip damaged section without losing the whole file)