Redis Enterprise


A Foundational Look at Redis Enterprise

Integrated Modules

Redis Enterprise allows you to seamlessly integrate Redis modules into your database. Modules are add-ons which extend Redis to cover most of the popular use cases for any industry. Redis Enterprise comes with the following built-in modules, each trusted, tested and verified to work with Redis Enterprise as well as open-source Redis:

  • RediSearch is a high performance full-text search engine;
  • RedisGraph is a high performance, memory-efficient Graph database;
  • RedisJSON is a native JSON data type;
  • RedisBloom is a Bloom filter data type.

In addition, you can deploy one of the certified modules listed on the Modules Hub, but you should be aware that these modules were certified to work with specific versions of Redis Enterprise and OSS Redis and are not considered production ready. Furthermore, Redis Enterprise allows you to deploy any other module listed on the Modules Hub or even your own custom module, albeit without functional, high availability nor security guarantees.

Enterprise Modules are available in VPC and Software deployments; all other modules are only available in Redis Enterprise Software deployment or open-source Redis for non-production use.

Once you configure a module, Redis Enterprise makes sure the module is available for the entire lifecycle of your database, including during operations like replication, failover, data persistence, backup, shard migration rebalancing and others. Modules that are natively cluster-aware, such as RedisJSON and RedisBloom, will immediately employ the resharding and scaling operations. And for modules that require multi-shard communication in order to function correctly in a cluster configuration (e.g. RediSearch), Redis Enterprise adds a module-specific Coordinator entity this is responsible for all the clustering operations and multi-shard communication in a fully transparent manner.

A Redis Enterprise Cluster Node with Integrated Modules

Integrated Modules Cluster Node Diagram

Enterprise Modules

Enterprise modules, RediSearch, RedisJSON and RedisBloom, inherit all of the scaling, high availability and performance advantages of Redis Enterprise, and are described below.


RediSearch is a very powerful module that delivers secondary indexing and search capabilities not just for data in Redis but data in any database. Distinguishing features include extremely high performance during simultaneous search and indexing of large volumes of data. Read more about RediSearch.


RedisGraph is a Redis module that enables enterprises to process any kind of connected data faster than traditional relational or existing Graph databases. RedisGraph represents the connected data as sparse adjacency matrices instead of the common representation of adjacency lists per data point. The combination of sparse matrices and GraphBLAS, a highly optimized library for sparse matrix operations ,enables RedisGraph to deliver the fastest and most efficient way to store, manage and process graphs. Read more about RedisGraph.


RedisJSON provides a new data type that provides native JSON capabilities and is ideal for fast and efficient manipulation of JSON documents. Like any Redis data type, RedisJSON values are stored in keys that can be accessed with a specialized set of commands. These commands are designed to be intuitive to users coming to Redis from the JSON world and vice versa. Consider this example that shows how to set and get values:> JSON.SET scalar . '"Hello JSON!"'
OK> JSON.SET object . '{"foo": "bar", "ans": 42}'
OK> JSON.GET object
"{\"foo\":\"bar",\"ans\":42}"> JSON.GET object .ans


RedisJSON’s commands come prefixed. Both JSON.SET and JSON.GET expect the key’s name as their first argument. In the first line we set the root (denoted by a period character: “.”) of the key named “scalar” to a string value. Next, a different key named “object” is set with a JSON object (which is first read whole) and then a single sub-element by path.

Under the hood, whenever you call JSON.SET, the module takes the value through a streaming lexer that parses the input JSON and builds a tree data structure the value:

RedisJSON stores the data in binary format in the tree’s nodes, and supports a subset of JSONPath for easy referencing of sub-elements. It boasts an arsenal of atomic commands that are tailored for every JSON value type, including:

  • JSON.STRAPPEND for appending strings,
  • JSON.NUMMULTBY for multiplying numbers and
  • JSON.ARRTRIM for trimming arrays.

Because RedisJSON is implemented as a Redis module, you can use it with any Redis client that either supports modules or allows sending raw commands. For example, you can use a RedisJSON-enabled Redis server from your Python code with redis-py, as shown below:


import redis
import json

data = {
   'foo': 'bar',
   'ans': 42

r = redis.StrictRedis()
r.execute_command('JSON.SET', 'object', '.', json.dumps(data))
reply = json.loads(r.execute_command('JSON.GET', 'object'))


Initial benchmarks have demonstrated that RedisJSON is extremely powerful in terms of performance:

The above graphs compare the rate (operations/sec) and average latency of read and write operations performed on a 3.4KB JSON payload with three nested levels. RedisJSON is pitted against two variants that store the data in Strings. Both variants are implemented as Redis server-side Lua scripts with the json.lua variant storing the raw serialized JSON, and msgpack.lua using MessagePack encoding.


What is a Bloom Filter?

A Bloom filter is a probabilistic data structure which provides an efficient way to verify that an entry is not in a set. This makes it especially useful when trying to search for items on expensive-to-access resources, such as over a network or disk. With a large on-disk database, if you are trying to know if the key “foo” exists, it is possible to query the Bloom filter first. The Bloom filter query will return, with certainty, whether it potentially exists (and then the disk lookup can continue) or whether it does not exist, at which point you can forego the expensive disk lookup and simply send a negative reply up the stack.

While it’s possible to use other data structures to perform this, Bloom filters are also especially useful because they occupy very little space per element, typically counted in the number of bits (not bytes!). False negatives are not possible, but a percentage of false positives will exist, which is controllable, but for an initial test of whether a key exists in a set, they provide excellent speed, and most importantly, excellent space efficiency.

Bloom filters are used in a wide variety of applications:

  • ad serving, making sure a user doesn’t see an ad too often;
  • content recommendation systems, ensuring recommendations don’t appear too often,
  • databases, quickly checking if an entry exists in a table before accessing it on disk.

How Bloom filters work

Most of the literature on Bloom filters use highly symbolic and/or mathematical descriptions to describe them. An alternative explanation is below.

A Bloom filter is an array of many bits. When an element is ‘added’ to a bloom filter, the element is hashed. Then bit[hashval % nbits] is set to 1. This looks fairly similar to how buckets in a hash table are mapped. To check if an item is present or not, the hash is computed and the filter checks to see if the corresponding bit is set or not.


This is subject to collisions, of course. If a collision occurs, the filter will return a false positive, indicating that the entry is found. It is important to understand that a Bloom filter will never return a false negative, in other words, claim that something does not exist when it fact it is present.

In order to reduce the risk of collisions, an entry may use more than one bit: the entry is hashed bits_per_element (bpe) times with a different seed for each iteration, resulting in a different hash value. For each hash value, the corresponding hash % nbits bit is set. To check if an entry exists, the candidate key is also hashed bpe times, and if any corresponding bit is unset, then it can be determined with certainty that the item does not exist.

The value of bpe is determined at the time the filter is created. Generally the more bits per element, the lower the likelihood of false positives.


In the example above, all three bits would need to be set in order for the filter to return a positive result.

The accuracy of a Bloom filter affected by the fill ratio, or how many bits in the filter are actually set. When a filter has a vast majority of bits set, the likelihood of any specific lookup returning false is decreased, and likewise the possibility of the filter returning false positives is increased.

Scalable Bloom filters

Typically, Bloom filters must be created with foreknowledge of how many entries they will contain. The bpe number must be fixed, and likewise the width of the bit array is also fixed.

Unlike hash tables, Bloom filters cannot be “rebalanced” because there is no way to know which entries are part of the filter- the filter can determine if a given entry is not present but does not actually store the entries which are present.

In order to allow Bloom filters to ‘scale’ and be able to accommodate more elements than they’ve been designed to, they may be stacked. Once a single Bloom filter reaches capacity, a new one is created on top. Typically the new filter will have greater capacity than the previous one in order to reduce the likelihood of needing to stack yet another filter.

In a stackable (scalable) Bloom filter, checking for membership now involves inspecting each layer for presence. Adding new items now involves checking that the Bloom filter does not exist beforehand, and adding the value to the current filter. Hashes still only need to be computed once, however.

When creating a Bloom filter—even a scalable one—it’s important to have an approximation of how many items it is expected to contain. A filter with an initial layer that can only contain a small number of elements will degrade performance significantly because it will take more layers to reach a larger capacity.


RedisBloom in Action

Here are a few examples of how RedisBloom works:> BF.ADD bloom mark
1) (integer) 1> BF.ADD bloom redis
1) (integer) 1> BF.EXISTS bloom mark
(integer) 1> BF.EXISTS bloom redis
(integer) 1> BF.EXISTS bloom nonexist
(integer) 0> BF.EXISTS bloom que?
(integer) 0> BF.MADD bloom elem1 elem2 elem3
1) (integer) 1
2) (integer) 1
3) (integer) 1> BF.MEXISTS bloom elem1 elem2 elem3
1) (integer) 1
2) (integer) 1
3) (integer) 1


You can also create a custom Bloom filter. The BF.ADD command creates a new Bloom filter suitable for a smaller number of items. This consumes less memory but may be less ideal for large filters:> BF.RESERVE largebloom 0.0001 1000000
OK> BF.ADD largebloom mark
1) (integer) 1


Benchmarks, Speed and Implementation

RedisBloom uses a modified version of libloom, with some additional enhancements:

  • We discovered that when testing for presence, libbloom would continue checking for each bit even when an unset bit was discovered. Returning after the first unset bit was found to increase performance.
  • Decoupling the hash calculation API from the lookup APIs meant that RedisBloom only needed to compute the hash and feed it into the filter code.
  • The table below shows a benchmark of setting a single Bloom filter with an initial capacity of 10,000, an error rate of 0.01 and a scaling factor of 2. Tests were conducted using a single-client thread, and with pipelining to avoid network/scheduling interference as much as possible:
Implementation Add Check
Lua 29k/s 25k/s
bloomd 250k/s 200k/s
RedisBloom 400k/s 440k/s

Creating a Custom Module

What’s in a Module


Basically, modules contain command handlers—these are C functions with the following signature:


int MyCommand(RedisModuleCtx *ctx, RedisModuleString **argv, int argc)


As can be seen from the signature, the function returns an integer, either OK or ERR. Usually, returning OK (even if returning an error to the user) is fine.


The command handler accepts a RedisModuleCtx* object. This object is opaque to the module developer, but internally it contains the calling client’s state, and even internal memory management, which we’ll get to later. Next it receives argv and argc, which are basically the arguments the user has passed to the command being called. The first argument is the name of the call itself, and the rest are simply parsed arguments from the Redis protocol. Notice that they are received as RedisModuleString objects, which again, are opaque. They can be converted to normal C strings with zero copy if manipulation is needed.


To activate the module’s commands, the standard entry point for a module is a function called int RedisModule_OnLoad(RedisModuleCtx *ctx). This function tells Redis which commands are in the module and maps them to their handler.

Writing a Module

In this short tutorial we’ll focus on a very simple example of a module that implements a new Redis command: HGETSET <key> <element> <new value>. HGETSET is a combination of HGET and HSET that allows you to retrieve the current value in a HASH object and set a new value in its place, atomically. This is pretty basic, and could also be done with a simple transaction or a LUA script, but HGETSET has the advantage of being really simple.


  1. Let’s start with a bare command handler:


int HGetSetCommand(RedisModuleCtx *ctx, RedisModuleString **argv, int argc) {




Again, this currently does nothing, it just returns the OK code. So let’s give it some substance.


  1. Validate the arguments

Remember, our command is HGETSET <key> <element> <new value>, meaning it will always have four arguments in argv. So let’s make sure this is indeed what happens:


/* HGETSET <key> <element> <new value> */

int HGetSetCommand(RedisModuleCtx *ctx, RedisModuleString **argv, int argc) {

if (argc != 4) {

return RedisModule_WrongArity(ctx);






RedisModule_WrongArity will return a standard error to the client in the form of:

(error) ERR wrong number of arguments for ‘get’ command.


  1. Activate AutoMemory

One of the great features of the Redis Modules API is automatic resource and memory management. While the module author can allocate and free memory independently, calling RedisModule_AutoMemory allows you to automate the creation of Redis resources and allocate Redis strings, keys and responses during the handler’s lifecycle.




  1. Perform a Redis call

Now we’ll run the first of two Redis calls, HGET. We pass argv[1] and argv[2], the key and element, as the arguments. We use the generic RedisModule_Call command, which simply allows the module developer to call any existing Redis commands, much like a LUA script:


RedisModuleCallReply *rep = RedisModule_Call(ctx, “HGET”, “ss”, argv[1], argv[2]);

// And let’s make sure it’s not an error

if (RedisModule_CallReplyType(rep) == REDISMODULE_REPLY_ERROR) {

return RedisModule_ReplyWithCallReply(ctx, srep);



Notice that RedisModule_Call’s third argument, “ss,” denotes how Redis should treat the passed variadic arguments to the function. “ss” means “two RedisModuleString objects.” Other specifiers are “c” for a c-string, “d” for double, “l” for long, and “b” for a c-buffer (a string followed by its length).

Now let’s perform the second Redis call, HSET:


RedisModuleCallReply *srep = RedisModule_Call(ctx, “HSET”, “sss”, argv[1], argv[2], argv[3]);

if (RedisModule_CallReplyType(srep) == REDISMODULE_REPLY_ERROR) {

return RedisModule_ReplyWithCallReply(ctx, srep);


Using HSET is similar to the HGET command, except that we pass three arguments to it.


  1. Return the results

In this simple case, we just need to return the result of HGET, or the value before we changed it. This is done using a simple function, RedisModule_ReplyWithCallReply, which forwards the reply object to the client:


RedisModule_ReplyWithCallReply(ctx, rep);




And that’s it! Our command handler is ready; we just need to register our module and command handler properly.


  1. Initialize the module

The entry point for all Redis Modules is a function called RedisModule_OnLoad, which the developer has to implement. It registers and initializes the module, and registers its commands with Redis so that they can be called. Initializing our module works like so:


int RedisModule_OnLoad(RedisModuleCtx *ctx) {

// Register the module itself – it’s called ‘example’ and has an API

version of 1

if (RedisModule_Init(ctx, “example”, 1, REDISMODULE_APIVER_1) == REDISMODULE_ERR) {



// register our command – it is a write command, with one key at argv[1]

if (RedisModule_CreateCommand(ctx, “HGETSET”, HGetSetCommand, “write”, 1, 1, 1) == REDISMODULE_ERR) {





And that’s about it! Our module is done.


  1. A Word on module building

All that’s left is to compile our module. I won’t go into the specifics of creating a makefile for it, but what you need to know is that Redis Modules require no special linking. Once you’ve included the redismodule.h file in your module’s files and implemented the entry point function, that’s all Redis needs to load your module. Any other linking is up to you. The commands needed to compile our basic module with gcc are:


On Linux:

$ gcc -fPIC -std=gnu99 -c -o module.o module.c

$ ld -o module.o -shared -Bsymbolic -lc


$ gcc -dynamic -fno-common -std=gnu99 -c -o module.o module.c

$ ld -o module.o -bundle -undefined dynamic_lookup -lc


  1. Loading our module

Once you’ve built your module, you need to load it. Assuming you’ve downloaded Redis from its latest stable build (which supports Modules), you just run it with the loadmodule command line argument:


redis-server –loadmodule /path/to/


Redis is now running and has loaded our module. We can simply connect with redis-cli and run our commands!


Getting the source

The full source code detailed here can be found as part of RedisModuleSDK, which includes a Module project template, makefile and a utility library (with functions automating some of the more boring stuff around writing modules that are not included in the original API). You do not have to use it, but feel free to. Our module is done.

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