Skip to main content

Scrapy pipeline to store chunked items into AWS S3 bucket

Project description

Scrapy S3 Pipeline

PyPI version

Scrapy pipeline to store items into S3 bucket with JSONLines format. Unlike built-in FeedExporter, the pipeline has the following features:

  • The pipeline upload items to S3 by chunk while crawler is running.
  • Support GZip compression.

The pipeline aims to run crawler and scraper in different processes, e.g. run crawler process with Scrapy in AWS Fargate and run scraper process with lxml in AWS Lambda.


  • Python 3.4+ (Tested in 3.7)
  • Scrapy 1.1+ (Tested in 1.6)
  • boto3


$ pip3 install scrapy-s3pipeline

Getting started

  1. Install Scrapy S3 Pipeline with pip.

    $ pip3 install scrapy-s3pipeline
  2. Add 's3pipeline.S3Pipeline' to ITEM_PIPELINES setting in your Scrapy project.

        's3pipeline.S3Pipeline': 100,  # Add this line.
  3. Add S3PIPELINE_URL setting. You need to change my-bucket to your bucket name.

    S3PIPELINE_URL = 's3://my-bucket/{name}/{time}/items.{chunk:07d}.jl.gz'
  4. Setup AWS credentials via AWS CLI's aws configure command. Alternatively, use Scrapy's AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY settings.

  5. Run your spider. You will see items in your bucket after 100 items are crawled or the spider is closed.



S3 Bucket URL to store items.

e.g.: s3://my-bucket/{name}/{time}/items.{chunk:07d}.jl.gz

The following replacement fields are supported in S3PIPELINE_URL.

  • {chunk} - gets replaced by a start index of items in current chunk, e.g. '0', '100', '200',....
  • {time} - gets replaced by a timestamp when the spider is started.

You can also use other spider fields, e.g. {name}. You can use format string syntax here, e.g. {chunk:07d}.


Default: 100

Max count of items in a single chunk.


Default: True if S3PIPELINE_URL ends with .gz; otherwise False.

If True, uploaded files will be compressed with Gzip.

Page item

For convinience, Scrapy S3 Pipeline provides s3pipeline.Page item class to store entire HTTP body. It has url, body and crawled_at fields.

This make it easy to store entire HTTP body and run scraper in other process. It's friendly to server-less architecture which run scraper in AWS Lambda.

Example usage of Page:

from datetime import datetime, timezone

import scrapy
from s3pipeline import Page

# ...

class YourSpider(scrapy.Spider):

    # ...

    def parse(self, response):
        # You can create Page instance just one line.
        yield Page.from_response(response)

        # Or, you can fill item fields manually.
        item = Page()
        item['url'] = response.url
        item['body'] = response.text
        item['crawled_at'] =
        yield item

Note: Page's body is omitted when printed to logs to improve readbility of logs.



$ python3 test


$ python3 bdist_wheel sdist
$ twine upload dist/*

Project details

Download files

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

Files for scrapy-s3pipeline, version 0.3.0
Filename, size & hash File type Python version Upload date
scrapy_s3pipeline-0.3.0-py3-none-any.whl (6.0 kB) View hashes Wheel py3
scrapy-s3pipeline-0.3.0.tar.gz (4.7 kB) View hashes Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page