Want to bring more performance, speed, and scalability to your website? Or scale your sites for real-time services or message passing? Learn how, and get practical real-world tips in this exploration of Redis, part of a series on choosing the right data storage.
Steven Edouard and I (Rami Sayar) show you how to get up and running with Redis, a powerful key-value cache and store. In this tutorial series, you can check out a number of practical and advanced use cases for Redis as cache, queue, and publish/subscribe (pub/sub) tool, look at NoSQL and data structures, see how to create list sets and sorted sets in the cache, and much more. You can watch the course online on Microsoft Virtual Academy.
Level: Beginner to Intermediate.
By the end of this installment, you will:
- Learn about advanced data structures in Redis
- Learn how to use hashes to store objects and other data models
- Learn about sets and sorted sets.
- Learn about bitmaps and hyperloglogs.
Redis is often called a data structure store because it builds on top of the concept of key-value pairs to provide more advanced data structures like lists, hashes, sets, etc. We saw in the previous installment that Redis supports lists as a data type for value and includes several specialized commands for dealing with lists.
As we saw in the first installment, we can decompose objects and complex data modules into a series of key-value pairs by using several indices in keys. Furthermore, you may have noticed that the tediousness of having to do execute multiple commands to make the jump. Luckily, Redis supports hashes which are the subject of this section.
Hashes are collections of key-value pairs. A hash serves as a map to a series of string keys and their associated string values. Thus, they are the perfect representation of objects in your key-value store.
The commands to get & set hashes follow a very similar pattern to what we have seen before. To create a hash, you can use the
HSET command with a key for your hash and followed by a key-value pair (with
HSETNX to set only if it doesn’t already exist). To get a value in the hash, you can use the
HGET command with the key for your hash and the key for the value. You can also GET & set multiple key-value pairs in your hash with the
HMSET command respectively. You can also use
HGETALL to get all the key-value pairs in your hash.
Here is an example with the same data structure as in Part 1.
You can use
HINCRBYFLOAT to increment a string integer or float in a hash respectively. You can use
HLEN to get the number of keys in a hash. You can use
HVALS to get all the keys or values in a hash respectively.
To delete a field in a hash, you can use the command
HDEL with the key of the hash. To check if a field in a hash already exists, you can use
Redis sets are unordered collections of Strings. They are similar to lists but have the desirable property of ensuring every member is unique. It is possible to add the same element multiple times without needing to check if it already exists as if it does, nothing will happen. You can add, remove and test the existence of members in constant time.
Sets allow you to do interesting operations with other sets such as unions, intersections and differences. If you are familiar with Set Theory, we are applying the same mathematical operations. You can perform these operations directly on the database as opposed to in the application code.
The commands to create sets are a little different than the previous pattern. To add a member to a set you have to use the
SADD command, to remove a member the
SREM command, to check if a member already exists the
SISMEMBERcommand and to get the members of a set the
To get the difference between multiple sets you can use the
SDIFF command. To get the difference and store the results in a new set, you can use the
SDIFFSTORE. To find out if a multiple sets intersect, you can use the
SINTER command, the equivalent command but also stores the results is
You can also get a random member from the set by using
SRANDMEMBER. To get a member and also remove it from the set, you can use
Read more here.
Redis sorted sets are similar to regular sets with one major difference; members of the set are given a rank that determines their position in the set. Immediately, you can use Redis to handle situations like leaderboards in your games or task priority queues that other databases cannot.
In sorted sets, members are automatically sorted by their rank and although the members must be unique, their rank not. Since members are ordered immediately as they are added to the set, the performance for adding, removing or updating members is faster of O(log(n)). You can retrieve elements by position or rank.
The commands to create sorted sets are a little different than the previous pattern but similar to set commands. To add a member to a sorted set you have to use the
ZADD command along with a score, to remove a member the
ZREMcommand, to check if a member already exists or get the score the
ZSCORE command and to get the members of a set the
You can also run operations like unions and intersections.
Sorted sets have the unique ability to be able to retrieve and remove elements based on their lexicographical ordering or their rank or their score. You can read more about it here.
Bitmaps are not data types in Redis so much as they are bit operations you can run on a string. You can count the number of bits set to 1, perform AND, OR, XOR and NOT operations, and find the first bit of a specific value. To set & get bits, you can use the
GETBIT commands respectively, both allow you to offset the bit you are operating on. To perform a bit-wise operation, you can use the
BITOP NOT commands with a result destination key set as the first parameter and all other keys containing values following, e.g.
BITOP AND destkey srckey1 ... srckeyN.
BITCOUNT counts the number of bits set to 1.
BITPOS finds the first bit with a value of 0 or 1.
The support for bitmaps and the subsequent operations you can perform on bits are typically not found in databases.
HyperLogLogs are an interesting data structure, designed to count unique elements. Counting unique element is incredibly important for analytics e.g. web analytics, etc. The magic of HyperLogLogs is that you do not need to keep a copy of all the members to ensure you do not count members multiple times. HyperLogLogs will give you an estimate along with a standard error. You can thus use HyperLogLogs as a running estimate of your analytics in a cache while offloading the true calculations to another system or database to be performed over time.
The commands to use with HyperLogLogs are fairly straightforward. You use
PFADD to count new elements and you use
PFCOUNT to retrieve the current approximation of unique elements. That’s it.
P.S. Currently, the standard error estimation is close to 1%.