This capability is a part of Redis Stack Top-K

Top-K is a probabilistic data structure that allows you to find the most frequent items in a data stream.

Top K is a probabilistic data structure in Redis Stack used to estimate the K highest-rank elements from a stream.

"Highest-rank" in this case means "elements with a highest number or score attached to them", where the score can be a count of how many times the element has appeared in the stream - thus making the data structure perfect for finding the elements with the highest frequency in a stream. One very common application is detecting network anomalies and DDoS attacks where Top K can answer the question: Is there a sudden increase in the flux of requests to the same address or from the same IP?

There is, indeed, some overlap with the functionality of Count-Min Sketch, but the two data structures have their differences and should be applied for different use cases.

The Redis Stack implementation of Top-K is based on the HeavyKeepers algorithm presented by Junzhi Gong et al. It discards some older approaches like "count-all" and "admit-all-count-some" in favour of a "count-with-exponential-decay" strategy which is biased against mouse (small) flows and has a limited impact on elephant (large) flows. This ensures high accuracy with shorter execution times than previous probabilistic algorithms allowed, while keeping memory utilization to a fraction of what is typically required by a Sorted Set and also having the additional benefit of being able to get real time notifications when elements are added or removed from the Top K list.

Use cases

Leader boards (gaming)

This application answers this question: Who are the K players with the highest score?

Data flow is the incoming game scores. Flow id is the user id, and the value is the score. A separate sorted set is kept, storing the top K users' ids and their scores. Every time a user scores points, they're added to the Top K list.

  • If the result is nil, the user is already in the Top K. Update the sorted set with the user's new score.
  • if the result is an id of another player, the player you just added (id1) took over the player who got returned (id2). Remove player id2 from the sorted set and add player id1.

Trending hashtags (social media platforms, news distribution networks)

This application answers these questions:

  • What are the K hashtags people have mentioned the most in the last X hours?
  • What are the K news with highest read/view count today?

Data flow is the incoming social media posts from which you parse out the different hashtags.

The TOPK.LIST command has a time complexity of O(K) so if K is small, there is no need to keep a separate set or sorted set of all the hashtags. You can query directly from the Top K itself.


  • Initialize a Top-K with specific parameters
> TOPK.RESERVE my-topk 50 2000 7 0.925
  • Add elements to the Top-K

Multiple items can be added at once. If an item enters the Top-K list, the item which is expelled is returned. This allows dynamic heavy-hitter detection of items being entered or expelled from Top-K list.*

> TOPK.ADD my-topk foo bar 42
  • Return list of the top K items
> TOPK.LIST my-topk 
  • Check whether an item is one of Top-K items
> TOPK.QUERY my-topk 42


Choosing the size for a Top K sketch is relatively easy, because the only two parameters you need to set are a direct function of the number of elements (K) you want to keep in your list.

> TOPK.RESERVE key k width depth decay_constant

If you start by knowing your desired k you can easily derive the width and depth:

width = k*log(k)
depth =  log(k)  # but a minimum of 5

For the decay_constant you can use the value 0.9 which has been found as optimal in many cases, but you can experiment with different values and find what works best for your use case.


Insertion in a top-k has time complexity of O(K + depth) ≈ O(K) and lookup has time complexity of O(K), where K is the number of top elements to be kept in the list and depth is the number of hash functions used.

Academic sources


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