Filter lists can be quite large to store in memory, which is problematic on memory constrained devices. The following steps demonstrate how to generate a smaller filter list by filtering out the least-frequently-used rules on the top N websites (according to Alexa rankings).
This data is made available by the HttpArchive project and is queryable via BigQuery. A short introduction to querying HttpArchive data is available here. Because the output of our query is typically quite large, it's necessary to have a Google Compute Engine account with a storage bucket created to write the resulting table to.
The query to run is:
#standardSQL SELECT pages.url AS origin, requests.url AS request_url, requests.type AS request_type FROM `httparchive.summary_requests.2018_07_15_desktop` AS requests INNER JOIN ( SELECT pageid, url FROM `httparchive.summary_pages.2018_07_15_desktop`) AS pages ON requests.pageid = pages.pageid UNION ALL SELECT pages.url AS origin, requests.url AS request_url, requests.type AS request_type FROM `httparchive.summary_requests.2018_07_15_mobile` AS requests INNER JOIN ( SELECT pageid, url FROM `httparchive.summary_pages.2018_07_15_mobile`) AS pages ON requests.pageid = pages.pageid;
You‘ll need to replace the tables with those of the dates that you’re interested in.
Since the output is too large (>32GB) to display on the page, the results will need to be written to a table in your Google Cloud Project. To do this, press the ‘show options’ button below your query, and press the ‘select table’ button to create a table to write to in your project. You'll also want to check the ‘allow large results’ checkbox.
Now run the query. The results should be available in the table you specified in your project. Find the table on the BigQuery page and export it in JSON format to a bucket that you create in your Google Cloud Storage. Since files in buckets are restricted to 1GB, you'll have to shard the file over many files. Select gzip compression and use
<your_bucket>/site_urls.*.json.gz as your destination.
Once exported, you can download the files from your bucket and extract them into a single file for processing:
ls site_urls.*.gz | xargs gunzip -c > site_urls
Chromium's tools are designed to work with a binary indexed version of filter lists. You can use the
subresource_indexing_tool to convert a text based filter list to an indexed file.
An example using EasyList follows:
1. ninja -C out/Release/ subresource_filter_tools 2. wget https://easylist.to/easylist/easylist.txt 3. out/Release/ruleset_converter --input_format=filter-list --output_format=unindexed-ruleset --input_files=easylist.txt --output_file=easylist_unindexed 4. out/Release/subresource_indexing_tool easylist_unindexed easylist_indexed
1. ninja -C out/Release subresource_filter_tools 2. out/Release/subresource_filter_tool --ruleset=easylist_indexed match_rules --input_file=site_urls > ordered_list.txt 3. head -n 1000 ordered_list.txt | cut -d' ' -f2 > smaller_list.txt
1. grep ^@@ easylist.txt >> smaller_list.txt 2. sort smaller_list.txt | uniq > final_list.txt
The final filterlist has been generated. If you‘d like to convert it to Chromium’s binary indexed format, proceed with the following steps:
1. ninja -C out/Release/ subresource_filter_tools 2. out/Release/ruleset_converter --input_format=filter-list --output_format=unindexed-ruleset --input_files=final_list.txt --output_file=final_list_unindexed 3. out/Release/subresource_indexing_tool final_list_unindexed final_list_indexed