Skip to main content

Python binding port for JSONSki

Project description

GitHub Static Badge

JSONSki_python

JSONSki_python is the Python binding port for JSONSki

JSONSki is a streaming JSONPath processor with fast-forward functionality. During the streaming, it can automatically fast-forward over certain JSON substructures that are irrelavent to the query evaluation, without parsing them in detail. To make the fast-forward efficient, JSONSki features a highly bit-parallel solution that intensively utilizes bitwise and SIMD operations that are prevelent on modern CPUs to implement the fast-forward APIs.

Github

You can check out the github from here - https://github.com/AutomataLab/JSONSki_python

Installation

pip install JSONSki

Quick Start

import jsonski as jski
print(jski.loadSingleRecord("$.features[150].actor.login", "datasets/test.json"))
  • We interface the following method:
jski.loadSingleRecord(args1, args2)    //args1 - String(query) and args2 - String(file_location)

Requirements

Hardware requirements

  • CPUs: 64-bit ALU instructions, 256-bit SIMD instruction set, and the carry-less multiplication instruction (pclmulqdq)
  • Operating System: Linux, MacOs (Intel Chips only)
  • C++ Compiler: g++ (7.4.0 or higher)

Software requirements

Before starting to use JSONSki-API you need to assure you have the following prerequisites:

Getting Started with Querying using JSONSki

JSONPath

JSONPath is the basic query language of JSON data. It refers to substructures of JSON data in a similar way as XPath queries are used for XML data. For the details of JSONPath syntax, please refer to Stefan Goessner's article.

JSONSki Queries Operators

Operator Description
$ root object
. child object
[] child array
* wildcard, all objects or array members
[index] array index
[start:end] array slice operator

Path Examples

Consider a piece of geo-referenced tweet in JSON

{
    "coordinates": [
        40.74118764, -73.9998279
    ],
    "user": {
        "id": 6253282
    },
    "place": {
        "name": "Manhattan",
        "bounding_box": {
            "type": "Ploygon",
            "pos": [
                [-74.026675, 40.683935],
                ......
            ]
        }
    }
}
JsonPath Result
$.coordinates[*] all coordinates
$.place.name place name
$.place.bounding_box.pos[0] first position of the bounding box in place
$.place.bounding_box.pos[0:2] first two positions of the bounding box in place

APIs

Records Loading

JSONSki API: Simplifying Data Handling

JSONski is a powerful and user-friendly API designed to streamline data handling and processing tasks, particularly when dealing with JSON-based data. It offers a range of functions to manipulate and manage data, making complex operations effortless. Two key functions within the JSONski API are jski.loadSingleRecord and jski.loadRecords:

  • loadSingleRecord(args1, args2) //args1 - String(query) and args2 - String(file_location): loads the whole input file as one single record (allow newlines in strings and other legal places).

  • loadRecords(args1, args2) //args1 - String(query) and args2 - String(file_location): loads multiple records from the input file (all newlines are treated as delimiters; no newlines (except for \n and \r in JSON strings) are allowed within a record);.

Performance Comparison with Python Parsing

Below is an example usage of JSONSki pip package.

#JSONSki
import jsonski as jski
import time

start_time = time.time()
print(jski.loadSingleRecord("$[*].entities.urls[*].url","./JSONSki/dataset/twitter_sample_large_record.json"))
end_time = time.time()
elapsed_time = end_time - start_time
print("Elapsed jsonski time:", elapsed_time, "seconds")


#Python`s inbuilt JSON parser
import json as j

start_time = time.time()
def parse_json_file(file_path):
    with open(file_path, 'r') as file:
        json_data = j.load(file)
        return json_data
json_file_path = './JSONSki/dataset/twitter_sample_large_record.json'
print(parse_json_file(json_file_path))
end_time = time.time()
elapsed_time = end_time - start_time
print("Elapsed default_python_json time:", elapsed_time, "seconds")
  • Note: The code snippet above benchmarks performance for JSONSki parsing VS Python in-built parsing.

Publication

[1] Lin Jiang and Zhijia Zhao. JSONSki: Streaming Semi-structured Data with Bit-Parallel Fast-Forwarding. In Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2022.

@inproceedings{jsonski,
  title={JSONSki: Streaming Semi-structured Data with Bit-Parallel Fast-Forwarding},
  author={Lin Jiang and Zhijia Zhao},
  booktitle={Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)},
  year={2022}
}

Performance

image

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

JSONSki-1.0.1.tar.gz (237.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

JSONSki-1.0.1-cp39-cp39-macosx_13_0_universal2.whl (73.2 kB view details)

Uploaded CPython 3.9macOS 13.0+ universal2 (ARM64, x86-64)

File details

Details for the file JSONSki-1.0.1.tar.gz.

File metadata

  • Download URL: JSONSki-1.0.1.tar.gz
  • Upload date:
  • Size: 237.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for JSONSki-1.0.1.tar.gz
Algorithm Hash digest
SHA256 78f06002aee2f56b632c4d59e20c4340a63974da838ae5abc335c49c85a2a75d
MD5 53be03d0a94a0482517bdad83a93d51b
BLAKE2b-256 5428bc680cf65d1e07b1f270ad6856e94608d41717c240066289b916761705e2

See more details on using hashes here.

File details

Details for the file JSONSki-1.0.1-cp39-cp39-macosx_13_0_universal2.whl.

File metadata

File hashes

Hashes for JSONSki-1.0.1-cp39-cp39-macosx_13_0_universal2.whl
Algorithm Hash digest
SHA256 801df0eaa271f1ac42ebfc96631b27ac63172ea0c39b9ffa246f505a12e846f7
MD5 023ea25dea6a948de5bb502278f503c0
BLAKE2b-256 5bd65cafdad8110daf36647ffe70b1484c9d17563816d11f38d59f7ac1b561fb

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page