Redis bitmap powered analytics in Go
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bitesized is a library that uses redis's bit operations to store and calculate analytics. It comes with a http server that can be used as an stand alone api (not implemented yet).


It started when I saw a blog post about using redis bitmaps to store user event data. It sounded pretty neat and simple, not to mention fun, to implement.

This project started as simple wrapper around bit operations, but has since taken a life of its own. I'm currently in the process of adding functionality that's a level higher, ie provide user analytics, not just store data.


go get


Initialize client:

package main

import (

func main() {
  redisuri := "localhost:6379"
  client, err := bitesized.NewClient(redisuri)

Optionally, set intervals you want to track; by default these intervals are tracked: all, daily, weekly and monthly:

client.Intervals = []Interval{
  bitesized.All, bitesized.Hour, bitesized.Day, bitesized.Week, bitesized.Month, bitesized.Year

Optionally, set prefix to use for ALL keys; defaults to bitesized:

client.KeyPrefix = "bitesized"

Track an event that an user did:

err = client.TrackEvent("dodge rock", "indianajones", time.Now())

If indianajones above is a new user, user-counter key is incremented and value stored in user-list key. That id is used as bit offset for events.

This approach, as opposed to be checksum, enables us to take advantage of all offsets in a key in the beginning. However, as time goes on and old users generate less and less events, bit offsets will be wasted. In future sparse bitmaps maybe used to reduce wasting of bits such as here:

Get count of users who did an event on particular interval:

count, err = client.CountEvent("dodge rock", time.Now(), bitesized.Hour)

Check if user did an event for particular interval:

didEvent, err := client.DidEvent("dodge rock", "indianajones", time.Now(), bitesized.Hour)

Get retention for specified interval:

from := time.Date(2015, time.January, 1, 0, 0, 0, 0, time.UTC)
till := time.Date(2015, time.January, 3, 0, 0, 0, 0, time.UTC)

// this defines how many days of retention to return for each day
// for example if your interval contains 20 days, but want to look
// back only 10 days for each day in your interval
numOfDaysToLookBack := 10

rs, err := client.Retention("dodge rock", from, till, bitesized.Day, numOfDaysToLookBack)

This returns a result like below. The keys are sorted asc by time:

    { "day:2015-01-01": [ 30, 15, 3 ] },
    { "day:2015-01-02": [ 50, 10 ] },
    { "day:2015-01-03": [ 67 ] }

Get retention for specified interval in percentages:

rs, err := client.RetentionPercent("dodge rock", from, till, bitesized.Day, 10)

This returns a result like below. The keys are sorted asc by time. The first entry is total number

    { "day:2015-01-01": [ 30, .5, .01 ] },
    { "day:2015-01-02": [ 50, .05 ] },
    { "day:2015-01-03": [ 67 ] }

Get list of events:

// * returns all events
events, err := client.GetEvents("*")

// dodge* returns events with dodge prefix
events, err := client.GetEvents("dodge*")

Check if user was seen before:

isUserNew, err := client.IsUserNew("indianajones")

Do a bitwise operation on key/keys:

count, err := client.Operation(bitesized.AND, "dodge rock", "dodge nazis")

Following operations are support:

  • AND
  • OR
  • XOR
  • NOT (only accepts 1 arg)

Get list of users who did an event on particular time/interval:

// returns list of users who did 'dodge rock' event in the last hour
users, err := client.EventUsers("dodge rock", time.Now(), Hour)

Untrack ALL events and ALL intervals for user. Note, the user isn't deleted from user-list hash. If new event is tracked for the user, it'll use the same bit offset as before.

err = client.RemoveUser("indianajones")

Untrack an event for user. This will only untrack the client specified intervals.

err = client.UntrackEvent("dodge rock", "indianajones", time.Now())
// returns list of users who did 'dodge rock' event in the last hour
users, err := client.EventUsers("dodge rock", time.Now(), Hour)


  • Make threadsafe by using redis conn pooling
  • Write blog post explaning bitmaps and this library
  • Retention starting with an event, then comeback as diff. event(s)
  • Cohorts: users who did this event, also did
  • List of events sorted DESC/ASC by user count
  • Http server
  • List of users who didn't do an event metric
  • Identify user with properties
  • Option to return user with identified properties for metrics
  • Total count of users metric
  • Add method to undo an event
  • Move to lua scripts wherever possible