Skip to main content

Inverted Index using efficient Redis set

Project description

Redis-index: Inverted Index using efficient Redis set

Redis-index helps to delegate part of the work from database to cache. It is useful for highload projects, with complex serach logic underneath the hood.

Build Status codecov License Code style: black PyPI


Suppose you have to implement a service that will fetch data for a given set of filters.

GET /api/companies?region=US&currency=USD&search_ids=233,816,266,...

Filters may require a significant costs for the database: each of them involves joining multiple tables. By writing a solution on raw SQL, we have a risk of stumbling into database performance.

Such "heavy" queries can be precalculated, and put into redis SET. We can intersect the resulting SETs with each other, thereby greatly simplifying our SQL.

search_ids = {233, 816, 266, ...}
us_companies_ids = {266, 112, 643, ...}
usd_companies_ids = {816, 54, 8395, ...}

filtered_ids = search_ids & us_companies_ids & usd_companies_ids  # intersection
"SELECT * from companies whrere id in {filtered_ids}"

But getting such precalculated SETS from Redis to Python memory could be another bottleneck: filters can be really large, and we don't want to transfer a lot of data between servers.

The solution is intersect these SETs directly in redis. This is exactly what redis-index library does.


Use pip to install redis-index.

pip install redis-index


  1. Declare your filters. They must inherit BaseFilter class.
from redis_index import BaseFilter

class RegionFilter(BaseFilter):

    def get_ids(self, region, **kwargs) -> List[int]:
        get_ids should return a precalculated list of ints.
        with psycopg2.connect(...) as conn:
            with conn.cursor() as cursor:
                cursor.execute('SELECT id FROM companies WREHE region = %s', (region, ))
                return cursor.fetchall()

class CurrencyFilter(BaseFilter):

    def get_ids(self, currency, **kwargs):
        with psycopg2.connect(...) as conn:
            with conn.cursor() as cursor:
                cursor.execute('SELECT id FROM companies WREHE currency = %s', (currency, ))
                return cursor.fetchall()
  1. Initialize Filtering object
from redis_index import RedisFiltering
from hot_redis import HotClient

redis_clent = HotClient(host="localhost", port=6379)
filtering = RedisFiltering(redis_clent)
  1. Now you can use filtering as a singleton in your project. Simply call filter() method with specific filters, and your search_ids
company_ids = request.GET["company_ids"]  # input list
result = filtering.filter(search_ids, [RegionFilter("US"), CurrencyFilter("USD")])

The result will be a list, that contains only ids, that are both satisfying RegionFilter and CurrencyFilter.

How to warm the cache?

You can warm up the cache in various ways, for example, using the cron command

*/5  *   *   *   *   python warm_filters

Inside such a command, you can use specific method warm_filters

result = filtering.filter(search_ids, [RegionFilter("US"), CurrencyFilter("USD")])

Or directly RedisIndex class

for _filter in [RegionFilter("US"), CurrencyFilter("USD")]:
    filter_index = RedisIndex(_filter, redis_client)

Statsd integration

Redis-index optionally supports statsd-integration.

Redis-Index performance

Redis-Index by filters

Code of Conduct

Everyone interacting in the project's codebases, issue trackers, chat rooms, and mailing lists is expected to follow the PyPA Code of Conduct.


[0.1.11] - 2019-11-08


  • Added code for initial release

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 redis-index, version 0.1.11
Filename, size File type Python version Upload date Hashes
Filename, size redis_index-0.1.11-py3-none-any.whl (6.7 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size redis-index-0.1.11.tar.gz (6.1 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page