Updated Jan 24, 2026

5 Tips To Reduce Database Query Latency

Table of Contents

Database query latency can drag your application’s performance down, frustrating users and increasing costs. Here’s how to fix it:

Indexing: Use indexes to speed up data retrieval by targeting columns in WHERE, JOIN, and ORDER BY clauses. Avoid over-indexing to prevent slower write operations.
Efficient Queries: Avoid SELECT *, optimize WHERE conditions for index use, and minimize unnecessary joins or subqueries.
Caching: Store frequently accessed data in memory using tools like Redis or Memcached to reduce database load. Use TTL to keep cached data fresh.
Partitioning: Split large tables into smaller parts or sync data between platforms (horizontal or vertical) to improve query performance on massive datasets.
Query Execution Plans: Analyze execution plans to identify bottlenecks like sequential scans or disk spills. Adjust indexes and query structures accordingly.

Database Query Latency Optimization: 5 Key Techniques Compared

Why Are My Database Queries Running So Slow? - Next LVL Programming

1. Use Database Indexing

Think of database indexes as the index at the back of a book - they act as shortcuts, pointing directly to the rows you need in a table. This saves the database engine from scanning every single row, making data retrieval much faster. Most indexes rely on a B+ tree structure, which is designed for quick, key-based lookups. Setting up proper indexing is a key step toward optimizing your database queries, especially when evaluating database integration options for your application.

Focus on indexing columns that are commonly used in WHERE, JOIN, and ORDER BY clauses - this can lead to noticeable improvements in query performance. For example, a covering index can fetch all the needed columns directly, reducing unnecessary input/output operations.

For simpler queries, single-column indexes often get the job done. However, for queries with multiple conditions, composite indexes are the way to go. When creating composite indexes, arrange the columns strategically: start with equality filters, follow with range filters, and then consider column distinctiveness.

While indexes speed up SELECT operations, they come with a trade-off - they can slow down write operations like INSERT, UPDATE, and DELETE. To avoid unnecessary overhead, keep an eye on how your indexes are being used and remove those that don’t add value.

"A common design mistake is to create many indexes speculatively to 'give the optimizer choices'. The resulting overindexing slows down data modifications and can cause concurrency problems." - Microsoft SQL Server Index Design Guide

2. Write More Efficient Queries

How you structure a query can make or break its performance. For starters, avoid using SELECT *. Instead, specify only the columns you actually need. For example, if you're working with a customer database and only need the ID, name, and email, just request those three fields. Pulling unnecessary columns wastes memory and bandwidth.

Query structure matters just as much as indexing. Using full entity fetching in ORMs (Object-Relational Mappers) can add significant overhead. A benchmark revealed that switching to no-tracking queries reduced execution time from 1,414.7 microseconds to 993.3 microseconds and cut memory usage from 380.11 KB to 232.89 KB. To avoid this overhead, use projections in your ORM - methods like .Select() in EF Core or .values() in Django - to retrieve only the fields you need.

When optimizing WHERE conditions, be mindful of how you write them. Functions on columns, like WHERE YEAR(hire_date) = 2020, prevent indexes from being used effectively. Instead, use range-based conditions, such as WHERE hire_date >= '2020-01-01' AND hire_date < '2021-01-01'. This approach maintains "SARG-ability" (Search ARGument-ability), allowing the query to leverage indexes. Similarly, avoid patterns with leading wildcards in LIKE queries, as these force full table scans.

"The main deciding factor in whether a query runs fast or not is whether it will properly utilize indexes where appropriate."
– Microsoft Documentation

Reduce the use of unnecessary joins and subqueries. Correlated subqueries - those that rely on the outer query - are particularly problematic, as they execute once for every row in the result set. Instead, replace them with standard joins whenever possible. If you're checking for the existence of data, use EXISTS instead of IN. The EXISTS clause stops processing as soon as it finds a match, making it far more efficient. As Mike Payne, a database expert, puts it: "Optimizing these queries is the single most impactful thing you can do to improve the speed and scalability of your database".

3. Cache Frequently Used Queries

Caching is like giving your application a memory boost. Instead of repeatedly querying the database, frequently accessed data is stored in memory, cutting down on the time it takes to retrieve information. This avoids the delays that come with disk access, which - even at its best - can take double-digit milliseconds.

Two popular tools for caching are Redis and Memcached. Redis stands out for its ability to handle complex data structures and its option for disk persistence. On the other hand, Memcached is simpler and lighter, designed purely for high-speed caching. To give you an idea of their power, a single in-memory cache node can process hundreds of thousands of requests per second.

The most common caching method is cache-aside, also called lazy loading. Here’s how it works: the application checks the cache first. If the data isn’t there (a "miss"), it queries the database, retrieves the data, and then updates the cache. This method is particularly effective in read-heavy scenarios where data is read at least 10 times more often than it’s written. By combining this strategy with earlier query optimization techniques, you can significantly reduce the strain on your database.

To prevent outdated data from lingering, always set a TTL (time-to-live) for your cached data. If you’re working with Redis, consider using Hashes to store database rows. This approach allows you to update individual fields without needing to process an entire JSON blob. Additionally, keep an eye on your cache hit ratio - a low ratio means your cache isn’t being used effectively, which wastes memory without easing the database load.

"The speed and throughput of your database can be the most impactful factor for overall application performance." – AWS

4. Partition Large Datasets

When tables grow into the millions of rows, even the best-indexed queries can start to slow down. Partitioning offers a way to tackle this by breaking large tables into smaller, more manageable pieces - called partitions - while still treating them as a single logical table. This allows the database engine to use partition elimination, which skips over irrelevant partitions during a query, significantly cutting down the amount of data it needs to scan. The key is to choose the right method for splitting your data to ensure efficient scanning.

There are two primary ways to partition data: horizontal partitioning and vertical partitioning.

Horizontal partitioning divides the table by rows, often based on a specific column like a date or region. For instance, you could split a sales table into monthly chunks. This method works particularly well for time-series data or scenarios where queries frequently filter by a specific range.
Vertical partitioning, on the other hand, separates columns. It’s ideal for wide tables with many fields, especially if only a few columns are regularly accessed. For example, you could offload large BLOBs or rarely used fields into separate tables.

Here’s a real-world example: partitioning a 5,000,000-row Airtable database orders table by month reduced query time from 23ms to just 1ms. Modern database engines like SQL Server can handle up to 15,000 partitions per table. However, it’s important not to overdo it - over-partitioning can lead to increased memory usage and hurt performance if queries end up scanning multiple partitions.

To make partitioning work effectively, pick a column that’s frequently used in WHERE clauses. This ensures the database can take full advantage of partition elimination. Additionally, align your indexes with the partitioning scheme to improve maintenance tasks. Partitioning is especially well-suited for OLAP workloads that involve large scans, rather than OLTP systems where queries typically fetch single rows.

Partitioning Type	Method	Best For
Horizontal	Divides rows (e.g., by date or ID range)	Large datasets with range-based queries
Vertical	Splits columns (e.g., separating BLOBs from frequently accessed fields)	Wide tables where only a few columns are regularly queried

5. Review Query Execution Plans

Once you've tackled indexing and query refactoring, diving into execution plans can provide deeper insights into query performance. Even well-optimized queries might hit unexpected bottlenecks, and execution plans help uncover how the database processes a query. They detail things like index usage, join methods, and sorting operations.

In PostgreSQL, tools like EXPLAIN and EXPLAIN ANALYZE are invaluable. EXPLAIN provides estimated costs, while EXPLAIN ANALYZE adds actual performance metrics, such as row counts and execution times. By comparing these, you can spot discrepancies that might point to outdated statistics or suboptimal indexing. Similarly, SQL Server's actual execution plans in Management Studio offer comparable insights. These tools help identify inefficiencies that might not be obvious through other optimization techniques.

When analyzing an execution plan, pay attention to patterns like "Sequential Scan" on large tables. This often suggests that adding an index could improve performance. Also, look for filter conditions that discard most rows after scanning, as these might benefit from converting to an "Index Cond" operation. Another red flag is sort or hash operations spilling to disk, which can significantly increase query latency. Comparing CPU time to elapsed time can also reveal whether your query is constrained by CPU usage or waiting on I/O operations.

If a single operator, like "Sort" or "Hash Join", accounts for 90% of the query cost, it’s a clear target for optimization. You can also experiment with temporarily disabling certain planner options to test alternative join strategies and see if they perform better in practice. Keep an eye out for warnings about implicit data type conversions, as these can force the engine to process each row individually, undermining index efficiency. Reviewing these details allows you to fine-tune your query optimization efforts.

Conclusion

Reducing database query latency is all about improving speed and ensuring scalability. Techniques like indexing, writing efficient queries, caching, partitioning, and reviewing execution plans can turn sluggish 30-second queries into lightning-fast, sub-second responses.

But the benefits go beyond just speed. Streamlined queries mean fewer server resources are consumed, which can lower monthly costs and ensure a smoother experience as your user base grows. Efficient queries also help reduce server strain and avoid hitting API rate limits, such as Airtable's restriction of 5 requests per second. Small tweaks now can save you from major headaches down the road.

Adalo is a no-code app builder for database-driven web, iOS, and Android apps published to the Apple App Store and Google Play Store. If you're working with the platform, it simplifies these optimizations through its visual interface and integrated backend. For apps with fewer than 5,000 records, Adalo's built-in database delivers zero API latency with fast performance. Need to scale or work collaboratively? You can connect to external databases like Airtable, PostgreSQL, or MS SQL Server using External Collections, available on the Professional plan starting at $52–$65 per month. This flexibility allows you to begin with a simple setup and scale as needed without overhauling your app.

To get started, focus on profiling your slowest queries with tools like EXPLAIN and tackle the most pressing bottlenecks first. Whether it’s adding an index or setting up a caching layer, every improvement builds on the last. As Mike Payne from Paessler wisely notes:

"You can't optimize what you can't see. Database monitoring shines a light on exactly where performance problems reside."

Once you’ve identified the trouble spots, the fixes are often straightforward and deliver immediate results.

FAQ

Question	Answer
Can I easily optimize database queries in my no-code app?	Yes, with Adalo's No Code App Builder, you can easily optimize database performance through its visual interface and integrated backend. For apps with fewer than 5,000 records, Adalo's built-in database delivers zero API latency, and you can connect to external databases like PostgreSQL or Airtable for larger datasets using External Collections.
Why choose Adalo over other App Builder solutions?	Adalo is a no-code app builder for database-driven web, iOS, and Android apps published to the Apple App Store and Google Play Store. This direct app store publishing capability is crucial because getting your app in front of users through the Apple App Store and Google Play Store is often the hardest part of launching a new app or business—Adalo handles this seamlessly, giving you a major distribution and marketing advantage from day one.
What is the most impactful way to reduce database query latency?	Proper database indexing is often the most impactful first step, as indexes act as shortcuts that point directly to needed rows instead of scanning entire tables. Focus on indexing columns commonly used in WHERE, JOIN, and ORDER BY clauses for the best performance gains.
When should I use caching versus partitioning for large datasets?	Use caching when you have frequently accessed data that doesn't change often—tools like Redis or Memcached can handle hundreds of thousands of requests per second. Use partitioning when your tables grow into millions of rows and queries filter by specific ranges like dates, as it allows the database to skip irrelevant data entirely.
How do I identify which queries are causing performance problems?	Use query execution plan tools like EXPLAIN in PostgreSQL or actual execution plans in SQL Server to see exactly how the database processes your queries. Look for warning signs like sequential scans on large tables, sort operations spilling to disk, or operators consuming 90% of query cost—these are clear targets for optimization.
*Why should I avoid using SELECT in my database queries?**	Using SELECT * retrieves all columns from a table, wasting memory and bandwidth when you only need specific fields. Specifying only the columns you need can significantly reduce execution time and memory usage—benchmarks show switching to targeted queries can cut memory consumption by nearly 40%.