Redis migration success: What you need to know to get it right

There are several reasons why organizations migrate from Redis. Businesses often face cost pressures that prompt them to evaluate whether Redis remains the best choice for their needs. Technical limitations, such as scalability or missing features, can lead decision-makers to explore alternatives. The primary question is whether to migrate off Redis or optimize the existing setup.

Decision-makers, architects, and senior developers typically evaluate these considerations, weighing the benefits of staying with Redis against potential advantages of migration. This exploration requires an objective assessment of current and future business and technical demands.

Common reasons to move away from Redis

Redis poses several challenges that lead teams to explore alternatives. While Redis was widely adopted due to its open-source nature before its license change, teams often face mounting operational expenses, especially when using Redis Enterprise or scaling Redis clusters to handle large workloads. The costs of maintaining high-availability setups, implementing data persistence, and managing clustering across distributed systems quickly add up, making Redis an expensive option for organizations scaling beyond small to medium-sized deployments.

Scalability is another challenge with Redis. Though Redis offers mechanisms such as Redis Cluster, managing large-scale deployments remains complex and resource-intensive. Redis was originally designed as a single-instance in-memory data store. While clustering can distribute data, it introduces additional complexity in terms of cluster management, memory, and CPU overhead. Redis struggles to take advantage of multi-core processors, meaning scaling up requires more instances, which increases both hardware costs and operational complexity. As data grows, Redis’ single-threaded design, while working well enough in smaller environments, doesn’t take advantage of today’s multi-core architectures, often leading to underutilized hardware.

Operational overhead is also a concern. Maintaining high availability and data consistency requires careful handling. In self-managed environments, administrators need to configure and monitor replication, failover processes, and persistence mechanisms, all of which add complexity and increase the chances of downtime. While Redis supports replication and persistence through features such as AOF (Append-Only Files) and snapshots, these mechanisms are not foolproof. For mission-critical workloads, where data loss is unacceptable, Redis' persistence models may fall short, especially as replication can introduce latency and affect consistency in real-time applications.

In addition to scalability and operational challenges, Redis may not have all the features some companies need. For instance, advanced data consistency and complex queries are areas where Redis often falls behind. Redis provides eventual consistency at best, which works well for many caching and session management scenarios. However, for applications that demand strong consistency or transactional guarantees, Redis’ replication mechanisms, designed to offer basic data redundancy, can introduce latency or inconsistency during network partitions, making it more difficult to provide a real-time response. Redis also lacks built-in support for advanced querying (such as joins, aggregations, or full-text search), which limits its use cases to simpler key-value and caching scenarios. This can be a roadblock for applications that need more sophisticated data processing capabilities.

This is particularly true with the Redis Community Edition, which lacks the enterprise-level support and advanced features available in Redis Enterprise. Without enterprise support, organizations may have trouble addressing performance bottlenecks, troubleshooting issues, or receiving critical security updates. As businesses grow and their needs become more complex, they often require richer functionality, such as full-text search, time-series data management, or graph processing, which Redis doesn't natively provide. Organizations may need to evaluate whether their current Redis setup meets their evolving requirements or whether a database with more features would be a better fit. 

Ultimately, businesses must assess the pros and cons of Redis and weigh them against their long-term goals. Considerations such as the total cost of ownership, scalability, operational complexity, and the evolving feature set will guide teams toward a solution that not only meets their current needs but also prepares them for the future.

Redis technical and operational limitations 

While Redis is a useful tool for caching and in-memory data storage, several technical and operational limitations can affect its performance and scalability. Understanding these constraints is important for organizations considering Redis for high-volume, mission-critical applications. Below, we break down some of the biggest limitations to help you evaluate whether Redis is the right fit for your company.

Data volume constraints

Redis, as an in-memory data store, requires all data to reside in RAM for high performance. While Redis Cloud offers managed instances with higher memory capacity, the total amount of data you can handle is limited by the available RAM on your Redis instance. This becomes a challenge when your dataset grows beyond the memory limits of your infrastructure, resulting in additional costs and potential performance degradation.

• Memory overhead: Redis is designed for speed, but storing large datasets can use a lot of memory. If you're managing millions of keys or large objects, the memory overhead per Redis instance increases, putting pressure on both your infrastructure and budget.

• Ephemeral storage: Redis stores data primarily in memory. While Redis provides persistence mechanisms such as RDB snapshots and AOF logs, they are not foolproof. These methods can lead to data loss in the event of a crash or unexpected failure, especially when persistence is disabled for performance reasons.

Replication and high availability

Redis offers basic replication and high availability through Redis replication and Redis Sentinel. However, true fault tolerance with minimal downtime is more complex.

• Replication overhead: Redis replication creates read replicas of a primary node, but the replication process can introduce latency, especially in geographically distributed clusters. Any network disruptions or high-latency conditions between the primary and secondary Redis instances can cause inconsistent or delayed data across your replicas.

• Cluster management: Redis Cluster helps scale Redis horizontally, but managing a Redis Cluster adds additional overhead. When scaling your Redis instances or performing online migrations, managing the partitioning of data and rebalancing the cluster uses a lot of resources and is prone to errors.

Migration complexity

Migrating Redis data, especially in large-scale or mission-critical environments, is complicated to run. 

• Data consistency: During migration or scaling, maintaining data consistency across Redis instances becomes a challenge. Although Redis supports online migration techniques, such as the Migrate command, ensuring consistent data transfers while maintaining availability is tricky and requires planning.

• Instance coordination: When migrating or scaling, coordination between Redis instances is important. Redis’ single-threaded nature means coordinating large datasets across multiple instances can use a lot of resources, and improper synchronization can lead to downtime or data inconsistencies.

When considering Redis for larger or more complex use cases, it is important to evaluate the technical limitations related to data volume, schema flexibility, replication, and migration. While Redis provides high-performance data access, operational challenges related to memory management, data consistency, and high availability can limit its effectiveness for certain workloads. By understanding these constraints, system architects and DBAs can make more informed decisions about whether Redis can meet their long-term needs or if an alternative solution may be more suitable.

To learn more about these technologies, check out our Redis vs. Aerospike comparison.

Evaluating alternative solutions or target databases

Migrating from Redis means you need to understand your application's unique requirements and how different solutions meet them. Depending on your workload, different database solutions have advantages, such as improved data consistency, enhanced scalability, and high availability.

For applications that demand strong data consistency and transactional integrity, some databases excel at providing robust and reliable data management. 

When scalability is a primary concern, particularly for large-scale or globally distributed workloads, some solutions support horizontal scalability, adapting efficiently as data volumes grow. These systems are designed to handle large amounts of data across multiple nodes while maintaining speed and flexibility, ensuring smooth performance even as demand increases.

While Redis offers in-memory processing, other platforms provide additional benefits designed to handle workloads that require not just in-memory speed but also advanced data consistency, complex querying, or scalability across distributed environments. Whether you're processing real-time analytics, managing high throughput, or supporting read-write operations, these platforms offer improved data processing infrastructure to meet these needs.

When selecting a database solution, it’s important to make sure the features match your operational requirements, considering factors such as cost, scalability, and ease of management. Evaluate them thoroughly so the chosen solution can handle your data migration smoothly while reducing complexity and downtime.

Preparing for migration 

Migrating from Redis requires planning and execution to reduce disruption to your services. Before diving into the technical steps, you need to address several high-level considerations and prerequisites. These steps are critical to avoid costly mistakes and to ensure that the migration is as smooth as possible. Here’s a checklist to help guide your team through the preparation phases.

1. Plan for downtime and service availability

Migration often requires some level of downtime. Whether you’re moving data from Redis Cloud or a self-managed Redis instance, it’s important to understand the effect on service availability and to plan accordingly.

• Determine service windows: Schedule migrations during low-traffic periods to minimize disruption to users.

• High availability: If your application requires high availability during migration, explore tools that make online migrations easier, such as Redis replication or using Redis Enterprise software features to reduce downtime.

2. Ensure data consistency

One of the biggest challenges during a migration is keeping data consistent across Redis instances. This is especially important for mission-critical applications where data loss or corruption cannot be tolerated.

• Backup first: Back up your Redis data before starting the migration process, either with Redis' built-in RDB or AOF persistence methods or with third-party backup solutions.

• Verify data integrity: Post-migration, it’s essential to validate that all data has been transferred accurately. Use Redis’ redis-cli commands to verify the data and check for consistency.

3. Understand Redis Streams and other special data structures

Redis Streams, along with other complex data types such as hashes and sorted sets, may require specific handling during migration. If you rely on these data structures, make sure you understand how the migration tool you’re using handles them. 

• Custom migration tool: Some advanced data types may require custom migration strategies. Check whether your migration tool supports these data structures or whether additional steps will be needed.

4. Consider Docker and containerization

If your Redis instances are running within Docker containers, you need to figure out how these containers will be migrated. Containers add an extra layer of complexity, especially when it comes to persistent data storage.

• Volume management: Back up Redis data volumes and handle them appropriately during the migration.

• Container orchestration: If you're using tools such as Kubernetes for container orchestration, plan your migration process to ensure the smooth transition of both your application and Redis containerized instances.

5. Review Redis version and release notes

Each version of Redis has unique characteristics and potential changes that may affect your migration process. Before initiating the migration, make sure you are working with the correct version of Redis and check the release notes for any breaking changes, deprecations, or new features that could affect your migration.

• Make sure your versions are compatible: If you're migrating from an older Redis version, make sure the destination Redis instance is compatible with your data and configurations.

• Review release notes: Redis frequently releases updates with new features and fixes. Make sure your migration plan aligns with any recent changes to Redis functionality.

6. Prepare your Redis clients and interfaces

Migration also involves updating your Redis clients so they are compatible with the new Redis instance. Make sure your client code supports any changes in the Redis API or command set that may have been introduced.

• Client compatibility: Test your Redis client code with the new Redis instance before starting the full migration process.

• Redis CLI tools: Use Redis CLI tools to monitor, configure, and manage your Redis instance during the migration. These tools verify data transfers, troubleshoot,  and perform manual interventions if necessary.

Understanding potential costs, risks, and downtime

Don’t overlook financial and operational considerations. Make sure you figure out the costs associated with licensing for alternative platforms, acquiring or training the appropriate staff, and ongoing management. Evaluating these aspects helps the migration align with budgetary constraints and organizational goals.

Risks during migration can include data loss, incomplete migrations, and prolonged downtime, all of which can disrupt business operations. Ensuring data integrity throughout the migration process is crucial to maintaining system reliability. Using strategies such as incremental data migration and comprehensive testing can help mitigate such risks. 

Downtime is another critical concern because it can affect both user experience and revenue. By planning the migration during low-traffic periods and setting up redundancy to keep the systems and data available, organizations can reduce the impact of downtime.

To address these challenges, it's vital to implement contingency plans and regularly update stakeholders on migration progress. Expect the unexpected. 

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Read our comprehensive Redis migration guide

Explore our detailed Redis migration guide to move from planning to execution. This guide covers step-by-step instructions, what tools to use, and code snippets, equipping teams to manage the transition smoothly. Whether you're dealing with data consistency challenges, selecting a target database, or ensuring a smooth migration of Redis data, this guide tells you what you need to know. Click below to access the full guide and get started on your migration planning.