![bitmapist](https://raw.githubusercontent.com/Doist/bitmapist/master/static/bitmapist.png "bitmapist")

[![Build Status](https://travis-ci.org/Doist/bitmapist.svg?branch=master)](https://travis-ci.org/Doist/bitmapist)

# Bitmapist: a powerful analytics library for Redis

Bitmapist makes it possible to implement real-time, highly scalable analytics. The library is very easy to use, enabling you to create reports easily.

Leveraging Redis bitmaps, you can store events for millions of users using a very little amount of memory (megabytes).

> [!TIP]
> Instead of Redis as a backing store, consider using [bitmapist-server](https://github.com/Doist/bitmapist-server).
>
> It is our custom data store that exposes a (partial) Redis-compatible API, fully compatible with Bitmapist. It is 443x more memory efficient for this particular use case, improving scalability and cost-effectiveness.

## Use cases

Bitmapist can answer questions like:

- Has user 123 been online today? This week? This month?
- Has user 123 performed action "X"?
- How many users have been active this month? This hour?
- How many unique users have performed action "X" this week?
- How many % of users that were active last week are still active?
- How many % of users that were active last month are still active this month?
- What users performed action "X"?

Additionally, it can generate cohort graphs that can do following:

- Cohort over user retention
- How many % of users that were active last [days, weeks, months] are still active?
- How many % of users that performed action X also performed action Y (and this over time)
- And a lot of other things!

### Caveat: Avoid large IDs

You should be careful about using large IDs as this will require larger amounts of memory. IDs should be in range `[0, 2^32)`.

## Installation

Can be installed very easily via:

    $ pip install bitmapist

Or, if you use `uv`:

    $ uv add bitmapist

## Usage and examples

Setting things up:

```python
from datetime import datetime, timedelta, timezone
from bitmapist import setup_redis, delete_all_events, mark_event,\
                      MonthEvents, WeekEvents, DayEvents, HourEvents,\
                      BitOpAnd, BitOpOr

now = datetime.now(tz=timezone.utc)
last_month = now - timedelta(days=30)
```

Mark user 123 as active and has played a song:

```python
mark_event('active', 123)
mark_event('song:played', 123)
```

Answer if user 123 has been active this month:

```python
assert 123 in MonthEvents('active', now.year, now.month)
assert 123 in MonthEvents('song:played', now.year, now.month)
assert MonthEvents('active', now.year, now.month).has_events_marked() == True
```

How many users have been active this week?:

```python
iso_year, iso_week, _ = now.isocalendar()
print(len(WeekEvents('active', iso_year, iso_week)))
```

Iterate over all users active this week:

```python
for uid in WeekEvents('active'):
    print(uid)
```

If you're interested in "current events", you can omit extra `now.whatever`
arguments. Events will be populated with current time automatically.

For example, these two calls are equivalent:

```python

MonthEvents('active') == MonthEvents('active', now.year, now.month)

```

Additionally, for the sake of uniformity, you can create an event from
any datetime object with a `from_date` static method.

```python

MonthEvents('active').from_date(now) == MonthEvents('active', now.year, now.month)

```

Get the list of these users (user ids):

```python
iso_year, iso_week, _ = now.isocalendar()
print(list(WeekEvents('active', iso_year, iso_week)))
```

There are special methods `prev` and `next` returning "sibling" events and
allowing you to walk through events in time without any sophisticated
iterators. A `delta` method allows you to "jump" forward or backward for
more than one step. Uniform API allows you to use all types of base events
(from hour to year) with the same code.

```python

current_month = MonthEvents()
prev_month = current_month.prev()
next_month = current_month.next()
year_ago = current_month.delta(-12)

```

Every event object has `period_start` and `period_end` methods to find a
time span of the event. This can be useful for caching values when the caching
of "events in future" is not desirable:

```python

ev = MonthEvent('active', dt)
if ev.period_end() < now:
    cache.set('active_users_<...>', len(ev))

```

As something new tracking hourly is disabled (to save memory!) To enable it as default do::

```python
import bitmapist
bitmapist.TRACK_HOURLY = True
```

Additionally you can supply an extra argument to `mark_event` to bypass the default value::

```python
mark_event('active', 123, track_hourly=False)
```

### Unique events

Sometimes the date of the event makes little or no sense, for example,
to filter out your premium accounts, or in A/B testing. There is a
`UniqueEvents` model for this purpose. The model creates only one
Redis key and doesn't depend on the date.

You can combine unique events with other types of events.

A/B testing example:

```python

active_today = DailyEvents('active')
a = UniqueEvents('signup_form:classic')
b = UniqueEvents('signup_form:new')

print("Active users, signed up with classic form", len(active & a))
print("Active users, signed up with new form", len(active & b))
```

Generic filter example

```python

def premium_up(uid):
    # called when user promoted to premium
    ...
    mark_unique('premium', uid)


def premium_down(uid):
    # called when user loses the premium status
    ...
    unmark_unique('premium', uid)

active_today = DailyEvents('active')
premium = UniqueEvents('premium')

# Add extra Karma for all premium users active today,
# just because today is a special day
for uid in premium & active_today:
    add_extra_karma(uid)
```

To get the best of two worlds you can mark unique event and regular
bitmapist events at the same time.

```python
def premium_up(uid):
    # called when user promoted to premium
    ...
    mark_event('premium', uid, track_unique=True)

```

### Perform bit operations

How many users that have been active last month are still active this month?

```python
active_2_months = BitOpAnd(
    MonthEvents('active', last_month.year, last_month.month),
    MonthEvents('active', now.year, now.month)
)
print(len(active_2_months))

# Is 123 active for 2 months?
assert 123 in active_2_months
```

Alternatively, you can use standard Python syntax for bitwise operations.

```python
last_month_event = MonthEvents('active', last_month.year, last_month.month)
this_month_event = MonthEvents('active', now.year, now.month)
active_two_months = last_month_event & this_month_event
```

Operators `&`, `|`, `^` and `~` supported.

Work with nested bit operations (imagine what you can do with this ;-))!

```python
active_2_months = BitOpAnd(
    BitOpAnd(
        MonthEvents('active', last_month.year, last_month.month),
        MonthEvents('active', now.year, now.month)
    ),
    MonthEvents('active', now.year, now.month)
)
print(len(active_2_months))
assert 123 in active_2_months

# Delete the temporary AND operation
active_2_months.delete()
```

### Deleting

If you want to permanently remove marked events for any time period you can use the `delete()` method:

```python
last_month_event = MonthEvents('active', last_month.year, last_month.month)
last_month_event.delete()
```

If you want to remove all bitmapist events use:

```python
bitmapist.delete_all_events()
```

When using Bit Operations (ie `BitOpAnd`) you can (and probably should) delete the results unless you want them cached. There are different ways to go about this:

```python
active_2_months = BitOpAnd(
    MonthEvents('active', last_month.year, last_month.month),
    MonthEvents('active', now.year, now.month)
)
# Delete the temporary AND operation
active_2_months.delete()

# delete all bit operations created in runtime up to this point
bitmapist.delete_runtime_bitop_keys()

# delete all bit operations (slow if you have many millions of keys in Redis)
bitmapist.delete_temporary_bitop_keys()
```

## Cohorts

With bitmapist cohort you can get a form and a table rendering of the data you keep in bitmapist. If this sounds confusing [please look at Mixpanel](https://mixpanel.com/retention/).

Here's a simple example of how to generate a form and a rendering of the data you have inside bitmapist:

```python
from bitmapist import cohort

html_form = cohort.render_html_form(
    action_url='/_Cohort',
    selections1=[ ('Are Active', 'user:active'), ],
    selections2=[ ('Task completed', 'task:complete'), ]
)
print(html_form)

dates_data = cohort.get_dates_data(select1='user:active',
                                   select2='task:complete',
                                   time_group='days')

html_data = cohort.render_html_data(dates_data,
                                    time_group='days')

print(html_data)

# All the arguments should come from the FORM element (html_form)
# but to make things more clear I have filled them in directly
```

This will render something similar to this:

![bitmapist cohort screenshot](https://raw.githubusercontent.com/Doist/bitmapist/master/static/cohort_screenshot.png "bitmapist cohort screenshot")

## References

If you want to read more about bitmaps please read following:

- http://blog.getspool.com/2011/11/29/fast-easy-realtime-metrics-using-redis-bitmaps/
- http://redis.io/commands/setbit
- http://en.wikipedia.org/wiki/Bit_array
- http://www.slideshare.net/crashlytics/crashlytics-on-redis-analytics

## Contributing

Please see our guide [here](./CONTRIBUTING.md)

## Local Development

We use `uv` for dependency management & packaging. Please see [here for setup instructions](https://docs.astral.sh/uv/getting-started/).

Once you have `uv` installed, you can run the following to install the dependencies in a virtual environment:

```bash
uv sync
```

## Testing

### Quick Start with Docker (Recommended)

The easiest way to run tests locally is with Docker:

```bash
# Start both backend servers
docker compose up -d

# Run tests
uv run pytest

# Stop servers when done
docker compose down
```

This runs tests against both Redis and bitmapist-server backends automatically.

### Alternative: Native Binaries

To run tests with native binaries, you'll need at least one backend server installed:

**Redis:**
- Install `redis-server` using your package manager
- Ensure it's in your `PATH`, or set `BITMAPIST_REDIS_SERVER_PATH`

**Bitmapist-server:**
- Download from the [releases page](https://github.com/Doist/bitmapist-server/releases)
- Ensure it's in your PATH, or set `BITMAPIST_SERVER_PATH`

Then run:
```bash
uv run pytest
```

The test suite auto-detects available backends and runs accordingly:
- **Docker containers running?** Uses them
- **Native binaries available?** Starts them automatically
- **Nothing available?** Shows error

### Configuration

#### Environment Variables

Customize backend locations and ports if needed:

```bash
# Backend binary paths (optional - auto-detected from PATH by default)
export BITMAPIST_REDIS_SERVER_PATH=/custom/path/to/redis-server
export BITMAPIST_SERVER_PATH=/custom/path/to/bitmapist-server

# Backend ports (optional - defaults shown)
export BITMAPIST_REDIS_PORT=6399
export BITMAPIST_SERVER_PORT=6400
```

#### Testing Specific Backends

```bash
# Test only Redis
uv run pytest -k redis

# Test only bitmapist-server
uv run pytest -k bitmapist-server
```

## Releasing new versions

1. Bump version in `pyproject.toml` (or use `uv version`)
   ```sh
   uv version --bump minor
   ```
1. Update the CHANGELOG
1. Commit the changes with a commit message "Version X.X.X"
   ```sh
   git commit -m "Version $(uv version --short)"
   ```
1. Tag the current commit with `vX.X.X`
   ```sh
   git tag -a -m "Release $(uv version --short)" "v$(uv version --short)"
   ```
1. Create a new release on GitHub named `vX.X.X`
1. GitHub Actions will publish the new version to PyPI for you

## Legal

Copyright: 2012 by Doist Ltd.

License: BSD-3-Clause