kbfreder/cc-pyspark

Process Common Crawl data with Python and Spark

Common Crawl PySpark Examples

This project provides examples how to process the Common Crawl dataset with Apache Spark and Python:

• count HTML tags in Common Crawl's raw response data (WARC files)
• count web server names in Common Crawl's metadata (WAT files or WARC files)
• list host names and corresponding IP addresses (WAT files or WARC files)
• word count (term and document frequency) in Common Crawl's extracted text (WET files)
• extract links from WAT files and construct the (host-level) web graph – for further details about the web graphs see the project cc-webgraph
• work with the columnar URL index (cf. cc-index-table):
  • run a SQL query and export the result as a table
  • select WARC records by a SQL query, parse the HTML, extract the text and count words

Setup

To develop and test locally, you will need to install

• Spark, see the detailed instructions, and
• all required Python modules by running

pip install -r requirements.txt

Compatibility and Requirements

Tested with Spark 2.1.0 – 2.4.3 in combination with Python 2.7 or 3.5 and 3.6.

Get Sample Data

To develop locally, you'll need at least three data files – one for each format used in at least one of the examples. They can be fetched from the following links:

• warc.gz
• wat.gz
• wet.gz

Alternatively, running get-data.sh will download the sample data. It also writes input files containing

• sample input as file:// URLs
• all input of one monthly crawl as s3:// URLs

Note that the sample data is from an older crawl (CC-MAIN-2017-13 run in March 2017). If you want to use more recent data, please visit the Common Crawl site.

Running locally

First, point the environment variable SPARK_HOME to your Spark installation. Then submit a job via

$SPARK_HOME/bin/spark-submit ./server_count.py \
	--num_output_partitions 1 --log_level WARN \
	./input/test_warc.txt servernames

This will count web server names sent in HTTP response headers for the sample WARC input and store the resulting counts in the SparkSQL table "servernames" in your warehouse location defined by spark.sql.warehouse.dir (usually in your working directory as ./spark-warehouse/servernames).

The output table can be accessed via SparkSQL, e.g.,

$SPARK_HOME/bin/pyspark
>>> df = sqlContext.read.parquet("spark-warehouse/servernames")
>>> for row in df.sort(df.val.desc()).take(10): print(row)
... 
Row(key=u'Apache', val=9396)
Row(key=u'nginx', val=4339)
Row(key=u'Microsoft-IIS/7.5', val=3635)
Row(key=u'(no server in HTTP header)', val=3188)
Row(key=u'cloudflare-nginx', val=2743)
Row(key=u'Microsoft-IIS/8.5', val=1459)
Row(key=u'Microsoft-IIS/6.0', val=1324)
Row(key=u'GSE', val=886)
Row(key=u'Apache/2.2.15 (CentOS)', val=827)
Row(key=u'Apache-Coyote/1.1', val=790)

See also

• running the Spark shell and submitting Spark jobs
• PySpark SQL API

Running in Spark cluster over large amounts of data

As the Common Crawl dataset lives in the Amazon Public Datasets program, you can access and process it on Amazon AWS without incurring any transfer costs. The only cost that you incur is the cost of the machines running your Spark cluster.

1. spinning up the Spark cluster: AWS EMR contains a ready-to-use Spark installation but you'll find multiple descriptions on the web how to deploy Spark on a cheap cluster of AWS spot instances. See also launching Spark on a cluster.

2. choose appropriate cluster-specific settings when submitting jobs and also check for relevant command-line options (e.g., --num_input_partitions or --num_output_partitions, see below)

3. don't forget to deploy all dependencies in the cluster, see advanced dependency management

4. also the the file sparkcc.py needs to be deployed or added as argument --py-files sparkcc.py to spark-submit. Note: some of the examples require further Python files as dependencies.

Command-line options

All examples show the available command-line options if called with the parameter --help or -h, e.g.

$SPARK_HOME/bin/spark-submit ./server_count.py --help

Overwriting Spark configuration properties

There are many Spark configuration properties which allow to tune the job execution or output, see for example see tuning Spark or EMR Spark memory tuning.

It's possible to overwrite Spark properties when submitting the job:

$SPARK_HOME/bin/spark-submit \
    --conf spark.sql.warehouse.dir=myWareHouseDir \
    ... (other Spark options, flags, config properties) \
    ./server_count.py \
	... (program-specific options)

Credits

Examples are originally ported from Stephen Merity's cc-mrjob with the following changes and upgrades:

• based on Apache Spark (instead of mrjob)
• boto3 supporting multi-part download of data from S3
• warcio a Python 2 and Python 3 compatible module to access WARC files

Further inspirations are taken from

• cosr-back written by Sylvain Zimmer for Common Search. You definitely should have a look at it if you need a more sophisticated WARC processor (including a HTML parser for example).
• Mark Litwintschik's blog post Analysing Petabytes of Websites

License

MIT License, as per LICENSE