Query engines: What they are & why they matter

Get to know query engines, their benefits, and how they simplify data management for modern apps.

Query engines are powerful tools for processing and retrieving data efficiently, but not all are designed to meet the speed and real-time demands of modern apps.

In this article, we’ll break down what query engines are, their key benefits, and how they’re used for real-time data and AI apps. By the end, you’ll see why query engines are essential in today’s data-driven world and how the Redis Query Engine stands out in the market.

What is a query engine? 

A query engine is a core component of a database management system that processes and executes user queries to retrieve and manipulate data efficiently. It consists of four key components:

1. Input query parsing 
2. Planning/optimization of the query
3. Query execution to interface with the underlying storage to fetch the data 
4. Formatting and returning results

Examples of query engines include PostgreSQL for relational data, MongoDB for document-based queries, Elasticsearch for search and analytics, and Redis for real-time data retrieval, search, and secondary indexing. These tools are essential for efficiently managing and retrieving data from large datasets.

Benefits of a query engine

• Unlocks actionable insights: Move beyond simple key/value lookups by querying directly, allowing rich analysis through search, filtering, and aggregations on your data.
• Simplifies access to complex data: Query structured data—like JSON or Hash Sets—natively, without relying on additional code or external tools, saving you time and reducing complexity.
• Enables advanced operations: Sort, run full-text searches, and do aggregations right in the database, keeping your application logic simple and clean.
• Simplifies data exploration: Offers a defined syntax for querying and analyzing large, complex datasets with low processing overhead.
• Improves developer productivity: Simplify complex data operations so developers can write intuitive queries and iterate faster without custom processing logic.
• Optimizes resource efficiency: Execute queries directly in the database, reducing application computational overhead, cutting costs, and keeping your app performance high. 

What to look for in a query engine

The right query engine delivers real-time performance, not delays. Systems that scan data on disk or lack efficient indexing are too slow and often return stale results. A real-time query engine runs directly on live data, delivering fast, fresh, and actionable insights at scale. Here’s what to look for:

• Low-latency queries: Delivers instant responses, even for complex queries—crucial for apps that demand immediate insights and seamless user experiences.
• High-speed aggregations:  Enables processing of search query results with grouping, sorting, and transformations, making it easy to perform faceted queries and generate analytic reports. 
• High throughput: Processes large data volumes without compromising performance, making it ideal for real-time personalization, IoT, and other data-intensive environments. Redis inherently delivers high throughput, further enhanced in the Redis Query Engine with multi-threading, known as the Query Performance Factor.
• Scalability: Efficiently handles increasing data sizes and query volume demand. Redis Query Engine scales horizontally via sharding and vertically via multi-threading.
• In-memory data processing: Speeds up query execution by keeping data in memory, eliminating disk latency. Redis is a native in-memory database, delivering the unmatched read/write speed you expect from RAM.
• Real-time analytics: Enables continuous, live monitoring and decision-making based on fresh data. This is where Redis Query Engine shines—powering real-time applications such as analytic dashboards.
• Ease of integration: Seamlessly integrates into applications and DevOps workflows, accelerating time-to-value. Better yet, Redis Query Engine operates directly on your live, real-time data—no additional tools or pipelines needed.

Query engines for real-time data

Query engines designed for real-time data differ significantly from those used for relational databases. In general, real-time queries, like those used in Redis, are expected to return results in less than 100 ms—a goal Redis Query Engine meets in most cases. In contrast, relational database queries often have latencies measured in seconds or even minutes.

Best practices for query engines

Here’s how to get the most out of your query engine:

• Size your query environment properly: Make sure your database is configured to handle the expected load.
  • Create your Redis data model with your query patterns in mind.
  • Make sure the Redis architecture has been sized for the expected load using the sizing calculator.
  • Set up your Redis nodes with sufficient resources (RAM, CPU, network) to support the expected max load.
• Write efficient queries: Optimize your query language to reduce processing time. For Redis Query Engine, a few basic tips:
  • Favor TAG over NUMERIC and TEXT for use cases that only require matching
  • Use DIALECT 2
  • Avoid returning large result sets.  Use CURSOR or LIMIT
  • Avoid projecting all fields, i.e – LOAD *
  • If queries are long-running, enable threading (query performance factor) to reduce contention for the main Redis thread
• Optimize query performance: Monitor and tune your queries for better performance using Redis’s built-in tools and observability features:
  • Command-line tools
    • Use FT.INFO to gather detailed information about your indexes, such as size, document counts, and optimization status.
    • Use FT.PROFILE to analyze query execution plans and identify performance bottlenecks or anomalies.
  • Redis Insight
    • Leverage Redis Insight’s search and query features for a visual, user-friendly way to analyze query performance.
    • Easily inspect indexes, track query latency, and fine-tune queries to ensure optimal execution.
• Monitor CPU, disk, and memory utilization: Keep an eye on resource usage to prevent bottlenecks.
  • Use the INFO and LATENCY DOCTOR commands through the CLI for troubleshooting, or integrate monitoring tools like Prometheus and Grafana for proactive alerting and deeper observability.
• Implement and enforce data governance: Make sure your data meets quality standards and complies with regulations.
• Maintain high data quality: Regularly clean and validate your data to guarantee accuracy.
• Test in a dev/test environment prior to production rollout:
  • Conduct load testing in a test environment with real-world queries and load generated by either memtier_benchmark or a custom load application.

Query engines & AI

Redis Query Engine is particularly powerful for companies building GenAI apps.

GenAI apps are transforming how enterprises use databases. Redis Query Engine, for example, is built to handle the unique demands of AI apps. Its multi-threading capabilities and sub-second response times make it a powerful tool for real-time retrieval-augmented generation (RAG). In fact, the LLM itself will be the bottleneck for latency and throughput—not Redis Query Engine.

Finding nearest neighbors for vector embeddings is a common use case for Redis as a vector database. This operation doesn’t require a fully relational data model. Instead, vectors are stored as fields within Redis hashes or JSON documents, making queries fast, scalable and efficient.