I’ll give a short overview of Redis native datastructures, what can they be used for, what can be stored inside, how to use it. In the previous part, I described the basic native datastructures.

You can find links to the related video recordings and printable materials at the end of this post.



Internally, this structure uses a sorted set to store the items and uses a geo-hash calculated from the coordinates as a score. The difference between two geo-hashes is proportional to the physical distance between the coordinates used to compute these geo-hashes. So, this is not really a new native basic datastructure, but a derivated datastructure. It has the same properties as the sorted set with additionnal commands to calculate distances between points or retrieving items by distance. Redis unnderstands the international metric system and the weird imperial system.

Geo-indices can be used to retrieve proximity items, to compute distances, routes.


This data structure is derived from strings. It is not really a new datastructure, but a set of commands to manipulate a string at the bit level. It can address every individual bit of a 512MB string, to get, set or clear it. It can count the number of set bits. It also has commands to execute bit operations between several bitmaps such as AND, OR, XOR.

This structure is convenient to implement quick filters, categorized counters or analytic. It is easy to get the list of male customers, maried, without children, accepting emails that were interested in a specified product, for example.


This one is also a set of commands to manipulate numeric fields of any arbitrary length stored at fixed positions in a string structure. This is a huge storage saver. If you need to store values in the 0-8 range, you will need only 3 bits per value.

It can be used to store timeseries, sequential things, configuration and settings, for example.


This structure is a counter of unique values. When you need to count thousands of unique values, you usually need to also store the counted values to count them only once. Hyperloglog only stores the counter in a 12KB record. It will never use more than 12KB. The tradeoff is that it will return a counter with a 1% accuracy.

If you want to count unique visitors on your web site, using the source IP addresses. Hyperloglog will be able to tell you that you had 1000000 unique IP addresses with a 1% accuracy, but it will only use 12KB of RAM instead of 4MB to store 1M IP addresses.


This one can be seen as a log. You can add new entries, they will be timestamped and stored, but you can not delete or alter an existing entry. You can limit the size of the log. An entry contains fields with field name and field values. Ok, so, you’ve got a log.

Then, you can execute a range query such as “get all records between yesterday and today”. You can subscribe to a stream to receive each new record in real-time, you can subscribe to a stream in the past to resume a lost connection and you can ask the stream to distribute the records to a consumer group to distribute the load, with acknowledgments. Each entries do not need to store field names if they are the same as the previous entry.

It can be quite convenient to implement data synchronization on a weak link, data ingestion from IoT devices, event logging, or a multi-user chat channel.

Infinity of others : Modules

Then, you can extend Redis and add any kind of datastructure or feature, using modules, but I’ll talk about modules later.

Materials and Links

Link Description
Video Video Presentation with pictures


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