As companies scale, their systems face increasing amounts of data and traffic, putting application performance at risk. Issues such as slower response times, downtime, and even server crashes become more likely without the right strategies in place.

So, as a growing company, what can you do to ease this strain and get your systems ready to better handle large datasets, large volumes of user requests, and traffic spikes? 

If you’re unsure where to start, this guide walks you through practical advice for improving the performance of data-heavy apps, especially for high-demand and real-time use cases. We’ll show you how to cut down on latency, scale to meet demand, and keep your application reliable and available at all times. 

1. Choose the right cache and database types

• Database cache: A database cache typically stores the most frequently accessed data in RAM, so you don’t have to hit the database on disk every time—making data retrieval way faster.
• Semantic cache: ​​Semantic caching, or prompt caching, stores the prompt given to a Large Language Model (LLM) along with the answer. When similar prompts come up, it pulls the answer straight from the cache instead of going back to the LLM—making things faster and cutting down on model calls.
• Session cache: Session caching stores a user’s session data, like a  shopping cart in e-commerce apps, so that it can be quickly and easily accessed. It’s especially important for web apps where speed and convenience matter.  When users can access their session data instantly, they’re more likely to stay with your app instead of switching to a competitor. That speed can make a real difference to your revenue.
• Vector database: Unlike a traditional relational database, a vector database is designed to efficiently handle and retrieve vector embeddings of complex, unstructured data types like text, documents, images, videos, and audio. This makes them ideal for retrieving embeddings with the most similar representations or meaning through fast vector similarity search. 
• NoSQL database: NoSQL databases, short for “not only SQL,” are non-relational databases that can support schemas flexibly, sometimes across multiple types of data models. Some ideal use cases for NoSQL databases include supporting personalized digital experiences on mobile, storing data for a backend payment processing application, or storing persistent data as a system of record. 
• Event streams: Event streams are a series of chronological events that represent specific actions within a system. Implementing event stream processing (ESP) means events are processed as they happen, rather than waiting for a batch to accumulate. This way, your app can react to events in real time, allowing your team to meet customer demands more quickly. 

All of these methods can be implemented using the Redis Platform, enabling your application to deliver fast, reliable experiences for every user. 

2. Adopt faster data infrastructure

Not all data infrastructure is built the same. The best data infrastructure delivers speedups that are performant at any scale, with sub-millisecond latency and the highest read/write availability in any geographic location.

Hyperscaling your data architecture using a cloud data platform can have a powerful impact on your data processing speeds. It’s built to handle growing data volumes and user traffic without breaking a sweat. Low latency stays intact, even with heavy demand, which means your users get real-time responses—a must-have for apps like e-commerce, gaming, and social media, where speed and responsiveness make all the difference.

Because hyperscale architectures can process large datasets in real time, they can more efficiently deliver highly personalized content, recommendations, and experiences to individual users. Think about how Netflix or Hulu suggest shows based on what you’ve watched—they’re using hyperscaling to make it happen.

Hyperscaling in the cloud is one of the best methods for dramatically improving data infrastructure speeds and app performance. 

3. Use dynamic caching to balance price and performance 

The more gigabytes of your data that move fast, the more responsive your app will be. 

Dynamic caching reduces load on the primary database by storing frequently requested data closer to the app, delivering faster response times. Caching also helps development teams optimize code and algorithms by minimizing redundant computations, use resources more efficiently by reducing database load, and quickly access real-time data to monitor and adjust the codebase.

In-memory caching speeds up data processing by keeping “hot” data in RAM for fast access. Similarly, database query caching stores the results of expensive database queries to avoid redundant queries by serving up the same answers without having to re-query the data. This improves speed while also lowering cost computations.

Redis is designed for caching at scale. Its enterprise-grade functionality ensures that critical apps run fast and reliably, while providing integrations to simplify caching, saving time and money in the process. 

For HackerRank, Redis Cloud’s in-memory performance keeps real-time standings no matter how many developers are taking tests simultaneously. HackerRank not only uses Redis Cloud to build its caching layer but also to build its database for all real-time use cases. For code compilation and execution, HackerRank uses the RedisJSON module to provide live execution status, reducing latency and providing real-time updates to users. 

Cost can be a critical factor for engineering teams when deciding on a caching strategy. Many times, developers feel forced to keep caches small because of cost—but small caches often need special caching strategies and app logic to handle this restriction, which ultimately still creates more cache misses and lowers app performance.

Using a new approach like Redis Flex, which is designed to automatically balance the use of both dynamic random access memory (DRAM) and solid-state drives (SSDs) for hot data, makes caching faster and cheaper than many other approaches currently available on the market. For Redis users, the cost of deployments was reduced by up to 80% using Redis Flex—without changing any code. 

4. Simplify data processing

A modern caching strategy can also simplify data processing to speed up your app. When developing a caching strategy, ensure your cache can meet the requirements of today’s most advanced apps, including: 

• Real-time performance: Speed is one of the most critical components of the user experience. Your customers expect apps to respond instantly, at all times, or they will abandon your apps (and potentially your brand) entirely.
• Flexibility: Modern apps require the ability to adapt to changing requirements and workloads. A flexible caching strategy should support various data types and access patterns, allowing your apps to evolve and scale without significant reconfiguration or downtime. 
• Resilience: Because so much business today depends on connectivity, organizations must ensure that their apps and data are highly resilient. Even small or infrequent cache outages can have a devastating impact on app performance and user experience.
• Cost efficiency: Building responsive modern apps opens new areas of opportunity for businesses. But for these initiatives to be worth it, the cost of these new apps can’t be greater than the value they produce. For this reason, it’s key to prioritize cost efficiency when choosing a database platform and features to support your app.

A tool like the Redis Query Engine can handle high query volumes, which means it can deliver data quickly even under heavy traffic loads. Redis’ architecture uses a shared-nothing model, which helps avoid the bottlenecks and locking issues associated with multithreaded access to memory. Each operation within the Query Engine must have a low time complexity, so even with a single thread, Redis delivers a high throughput.

Dumb data stores only let you retrieve data using the exact key or name. Using a dumb data store is like being in a crowd of 100 people and having to call out someone’s exact name to get their attention. With the Redis Query Engine, you can perform queries and searches. Using a data query, you can ask all men between the ages of 18 – 25 to step forward or all women with a bachelor’s degree. You don’t have to figure out the list yourself and call out each person’s name one-by-one—you just get to all the people you’re looking for right away. 

Without the Redis Query Engine, app developers have to write all the code themselves in order to perform data queries and searches. The Redis Query Engine does all of this work for them. 

Deutsche Börse uses Redis’ intelligent cache to keep data fast and on time. With its diverse set of data structures and capacity as a fast data ingest solution, Redis boosts the performance of Deutsche Börse’s app framework. Redis also delivers seamless scaling, always-on availability, and automated deployment—all of which are critical in the financial industry. 

Imagine a giant e-commerce platform like Amazon on Black Friday or Cyber Monday—millions of visitors are trying to purchase items at once, putting a huge strain on the system. Redis’ architecture can handle these types of massive traffic spikes and high volumes of requests without sacrificing latency, thanks to its shared-nothing model and low time complexity.

Using a data integration tool is another way to simplify and easily access and manage all of your data. Data integration tools combine data from different sources, like databases, apps, and APIs, into a cohesive view. This reduces data silos and enables more comprehensive analytics. These tools can also automate the movement and transformation of data, which reduces manual labor and saves time. 

The Redis Data Integration (RDI) tool synchronizes data from existing relational databases into Redis in near real time. This allows app read queries to be completely offloaded to Redis, which speeds up slow data and reduces database costs. Data integration tools like RDI allow engineering teams to focus on app innovation instead of on integration and data transformation chores.

5. Streamline development 

Your time is valuable—make it easier for you and your team to increase app performance without a lot of trial and error. Deploying a more complete and mature developer environment can help you spend less time fixing bugs and dealing with system maintenance.

To streamline your development, make sure you have:

1. A complete developer environment where you can build new queries, test queries, and optimize query performance.
2. AI assistance to write queries faster.
3. Official clients to connect your apps to databases so you have guaranteed reliability.
4. A library of integrations with the most popular third-party technologies such as the framework for building AI knowledge assistants, LlamaIndex, to save you time.

The Redis support team helped Ulta Beauty achieve a seamless migration from open source Redis to Redis Cloud. Ulta’s new system made development tasks easier. This let their innovation team focus on bigger projects, like building a digital store of the future and adding new tools to personalize their website.

Using an AI-powered coding assistant can also speed up development by helping engineers write code faster. Redis Copilot can quickly retrieve information from docs, automatically generate code snippets or commands, and rapidly answer questions about data. Using Redis Insight, your team has a rich developer environment to visually work with Redis data across all operating systems and Redis deployments to get a more complete picture of your data sets.

If you’re building a machine learning-driven app, AWS Sagemaker can help you move faster by providing the components needed to build, train, and deploy machine learning models. It takes away many of the complexities involved in building secure, high-performance ML-based apps. By offering a set of purpose-built ML tools, Sagemaker speeds up development so you can spend more time focusing on your app’s performance.

Optimizing app performance with Redis and CSPs

Improving app performance is an ongoing process, especially as your systems scale and user expectations evolve. Following the five key strategies outlined in this guide will help your team continue to optimize app performance as you grow:

1. Choose the right cache and database types: Invest in caching solutions like database, semantic, and session caches, and explore databases like NoSQL and vector databases to meet your app’s specific needs.
2. Adopt faster data infrastructure: Implement hyperscale cloud architectures to handle high demand, ensuring sub-millisecond latency and real-time responsiveness for users.
3. Use dynamic caching: Balance cost and performance by leveraging advanced caching technologies like Redis Flex to increase your cache hits on more data volume.
4. Simplify data processing: Streamline workflows with technologies like the Redis Query Engine and Redis Data Integration (RDI) to reduce bottlenecks and boost developer productivity.
5. Streamline development: Optimize your development environment with tools like Redis Insight, Redis Copilot, and integrations with third-party technologies to save time and reduce complexity.

When implementing these strategies, it’s crucial to consider your application environment. For developers working in the cloud like AWS, lean on the many solutions and services that complement Redis such asAWS Lambda and Amazon SageMaker. They provide the additional logic and tooling needed to support modern, high-performing apps.

Together, Redis and cloud service providers like AWS create a powerful framework for delivering fast, reliable, and scalable applications. Developers can focus on innovation, streamline their workflows, and provide the best possible user experiences—no matter how demanding their applications become.

Book time with a Redis solutions architect today to learn more and get answers to your questions on how to boost your apps’ performance.