The synergy between Ansible, a powerhouse of infrastructure automation, and Redis, an ultra-high-performance in-memory data structure store, represents a critical evolution in how modern DevOps engineers manage state and performance at scale. In the standard operational flow of Ansible, the "Gather Facts" phase is a foundational step where the control node retrieves a comprehensive set of variables from target hosts. These variables, known as facts, encompass essential system data such as operating system versions, IP addresses, and attached filesystems, all of which are stored in the ansible_facts dictionary. While this process is indispensable for dynamic playbooks, the default behavior of caching these facts in volatile memory means they are purged upon the completion of a playbook run. This necessitates a re-execution of the gathering process every time a playbook is launched, introducing a latency that, while often lasting only a few seconds, becomes a significant bottleneck in massive environments.
To mitigate this inefficiency, Ansible implements cache plugins. While the default memory plugin is ephemeral, Redis serves as a persistent, high-speed alternative. By offloading gathered facts to a Redis instance, Ansible can maintain a stateful cache across multiple disparate runs, drastically reducing the computational overhead on the control node and minimizing the network traffic between the control node and the managed hosts. Beyond simple fact caching, Redis plays a systemic role in the Red Hat Ansible Automation Platform (AAP) 2.5, serving as the backbone for queuing, session management, and event-driven orchestration. The transition from local memory to a distributed Redis architecture allows for horizontal scalability, where data is partitioned across nodes to ensure stability and reliability through replication and sharding.
The Mechanics of Ansible Fact Caching with Redis
The process of gathering facts is computationally expensive. When a control node initiates a playbook, it must query the target system for its current state. In large-scale deployments, fetching this data repeatedly consumes significant memory and CPU cycles on the control node. By integrating Redis, the system shifts from a stateless execution model to a stateful one.
Technical Implementation of Fact Caching
To transition from ephemeral memory caching to persistent Redis caching, the administrator must configure the Ansible environment to recognize the Redis plugin. This can be achieved through two primary methods: environment variables or the ansible.cfg configuration file.
Using the environment variable method, the user executes:
export ANSIBLE_CACHE_PLUGIN=redis
Alternatively, for a permanent configuration, the ansible.cfg file is modified as follows:
ini
[defaults]
fact_caching=redis
fact_caching_timeout = 7200
fact_caching_connection = localhost:6379:0
The technical parameters defined in the configuration are as follows:
fact_caching: Specifies the active plugin. Only one plugin can be active at a time.fact_caching_timeout: Defines the lifespan of the cached data in seconds. While the default is 86400 seconds (24 hours), it can be set to 0 to ensure data never expires, though this is generally discouraged in production environments where system states change.fact_caching_connection: Defines the connection string in the formathost:port:db. For example,localhost:6379:0points to a local Redis instance on the default port using database 0.
Impact on System Performance
The real-world consequence of this implementation is a marked reduction in playbook execution time. By avoiding the repeated "Gather Facts" phase, the control node avoids the overhead of SSH connections and the execution of setup modules on every target host for every run. This is particularly beneficial in CI/CD pipelines where playbooks may be triggered frequently.
Contextual Integration within the Workflow
The use of Redis for fact caching connects directly to the broader goal of reducing "noise" in automation. When integrated, the ansible_facts dictionary remains available for use in templates and conditionals without the penalty of a full system scan, allowing for faster iterations and more responsive infrastructure-as-code deployments.
Redis in the Ansible Automation Platform (AAP) 2.5
In the context of the Red Hat Ansible Automation Platform 2.5, Redis is not merely a cache for facts but a centralized caching and queueing system essential for the stability of the entire ecosystem. It operates as an in-memory NoSQL key-value store, serving as both an application cache and a lightweight message broker.
Distribution of Redis Instances
The architecture of AAP 2.5 differentiates between centralized and component-specific Redis instances to ensure isolation and performance.
| Component | Data Types Cached | Redis Instance Type |
|---|---|---|
| Platform Gateway | Settings, Session Information, JSON Web Tokens | Centralized Redis |
| Event-Driven Ansible Server | Event queues | Centralized Redis |
| Automation Controller | Internal state and queues | Dedicated Instance |
| Automation Hub | Internal state and caches | Dedicated Instance |
The technical design ensures that data from the platform gateway and Event-Driven Ansible are strictly partitioned. This means that neither service can access the other's data, providing a layer of security and preventing data corruption between the gateway's session management and the event-driven server's queueing system.
High Availability and Deployment Models
To achieve High Availability (HA), Redis is deployed in a way that ensures no single point of failure. A Redis HA compatible deployment typically requires 6 Virtual Machines (VMs).
- RPM Deployments: Redis can be colocated on any AAP component VM, with the exception of the automation controller, execution nodes, or the PostgreSQL database.
- Containerized Deployments: Redis can be colocated on any AAP component VM, except for execution nodes or the PostgreSQL database.
The use of in-memory storage rather than SSDs or HDDs ensures that the delivery of tokens, session data, and event queues happens with minimal latency. To protect this data, communication is secured using Transport Layer Security (TLS) encryption and rigorous authentication.
Scaling Redis with Clustering and Sharding
When a single Redis instance is insufficient for the load, a Redis Cluster provides the mechanism for horizontal scaling. Unlike Redis Sentinel, which focuses on high availability for a single dataset, a cluster shards data across multiple nodes.
The Architecture of a Redis Cluster
A minimum viable Redis Cluster requires six nodes. This configuration consists of three master nodes and three replica nodes. The technical process of sharding involves the distribution of 16,384 hash slots across the available master nodes. When a key is stored, the cluster automatically determines which node should handle that specific slot, ensuring an even distribution of data.
Automation of Cluster Deployment
While a cluster can be created manually using the redis-cli --cluster create command, the industry standard is to automate this via Ansible. Using specialized roles, such as those provided by the community, the deployment of a six-node cluster becomes a repeatable process.
For those utilizing the davidwittman.redis role, the installation is initiated via:
ansible-galaxy install davidwittman.redis
The role supports multiple architectures:
- Single Redis node
- Master-Slave Replication
- Redis Sentinel
- Full Redis Cluster
A basic implementation for a single node can be defined in a playbook as follows:
```yaml
- hosts: redis01.example.com
vars:- redis_bind: 127.0.0.1
roles: - davidwittman.redis
```
- redis_bind: 127.0.0.1
To execute this without a formal inventory file, the user can pass the hostname with a trailing comma:
ansible-playbook -i redis01.example.com, redis.yml
Practical Deployment and Troubleshooting
Implementing Redis for Ansible requires a precise sequence of installation and configuration steps. Whether using a containerized approach for testing or a native package manager for production, the underlying requirements remain consistent.
Containerized Rapid Deployment
For developers testing the integration on platforms like MacOS (e.g., Mojave 10.14.5) targeting CentOS hosts, Docker provides the fastest route to a functional Redis environment.
The command to launch a Redis container is:
docker run -d -p 6379:6379 --name ansibleredis redis
This maps the internal Redis port 6379 to the host machine, allowing the Ansible control node to communicate with the containerized store.
Native Installation on Linux
For production environments, specifically on RHEL or CentOS, Redis and its corresponding Python client must be installed to allow the Ansible control node to interface with the store.
The installation is performed using the DNF package manager:
sudo dnf install -y redis python3-redis
Following installation, the service must be enabled and started to ensure it persists across reboots:
sudo systemctl enable --now redis
Performance Benchmarking
To verify the efficacy of the Redis cache, administrators are encouraged to benchmark the "Gather Facts" phase. This is done by using the time command with the setup module.
The baseline measurement is taken using:
time ansible localhost -m setup
After configuring the ANSIBLE_CACHE_PLUGIN=redis and the fact_caching_timeout, the same command is executed. The delta in execution time represents the efficiency gain provided by the Redis layer.
Conclusion: A Detailed Analysis of the Redis-Ansible Synergy
The integration of Redis into an Ansible workflow is not merely a performance optimization but a strategic architectural decision that impacts the scalability and reliability of automation frameworks. By shifting from the default ephemeral memory plugin to a persistent Redis backend, organizations can eliminate the repetitive latency associated with fact gathering. This transition transforms the Ansible control node from a processor that must constantly rediscover its environment into an intelligent orchestrator that leverages a stateful knowledge base.
In the context of the Ansible Automation Platform 2.5, the role of Redis expands from a simple cache to a critical piece of infrastructure. The use of partitioned Redis instances for the Platform Gateway and Event-Driven Ansible ensures that high-velocity event queues do not interfere with session management. Furthermore, the movement toward clustered Redis architectures allows for the distribution of 16,384 hash slots, providing a mathematical guarantee of load balancing across master and replica nodes.
The reliance on in-memory storage, secured by TLS, addresses the inherent conflict between speed and security. While the use of local disks or SSDs would introduce I/O wait times that could bottleneck the Automation Controller, Redis operates at the speed of RAM, ensuring that JSON Web Tokens and session data are retrieved in microseconds. Ultimately, the ability to automate the deployment of these complex Redis architectures using Ansible itself—via roles like davidwittman.redis—creates a recursive loop of efficiency, where the tool used for automation is used to build the very infrastructure that accelerates its own performance.