The Hidden Cost: How Composite Indexes Impact Write Performance (3 Key Areas)

Indexes are database superheroes for reading data. They help your queries find information in massive tables at lightning speed. Composite indexes, which span multiple columns, are particularly useful for complex filters and sorts. But while they make reads faster, indexes come with a trade-off: they can impact write operations (INSERT, UPDATE, DELETE).

For databases with heavy write workloads – think logging systems, IoT data ingestion platforms, or high-volume transaction processing – understanding this impact is crucial. The very indexes that accelerate your reports might be slowing down your data intake. This is the composite index write performance paradox.

It’s not always obvious, but there’s a hidden cost associated with every index, and that cost is paid during writes. Let’s uncover the 3 key areas where composite indexes specifically influence the performance of your INSERT, UPDATE, and DELETE statements.

Understanding this helps you find the right balance between read and write optimization.

The Indexing Balancing Act: Reads vs. Writes

Database indexing is fundamentally a trade-off.

• More Indexes / Wider Indexes = Faster Reads (Often): Indexes provide more paths for the database to find data, reducing the need for full table scans.
• More Indexes / Wider Indexes = Slower Writes: Every time data is changed in the base table, the corresponding indexes must also be updated to remain accurate. This adds overhead to write operations.

The goal of performance tuning is rarely to maximize reads or writes in isolation, but to find the optimal balance that meets the overall requirements of your application.

How Composite Indexes Affect Write Operations

When you perform a data modification (DML) statement:

• INSERT: The new row is added to the base table. For every index on that table (including composite ones), the database must insert a new entry into the index structure, ensuring it’s placed in the correct sorted order based on the indexed columns.
• UPDATE: If the update modifies one or more columns that are part of any index on the table, the database typically has to delete the old entry from that index and insert a new entry reflecting the changed values. If non-indexed columns are updated, the base table row is modified, but indexes are unaffected.
• DELETE: The row is removed from the base table. For every index on that table, the corresponding entry must be removed.

A composite index, involving multiple columns, can make these steps more complex and resource-intensive than updating a simple single-column index.

The Hidden Cost: 3 Key Areas Where Composite Indexes Impact Write Performance

Let’s look at the specific ways composite indexes add overhead during writes:

Area 1: Increased Disk I/O

Disk Input/Output (I/O) is often a major bottleneck in database systems. Every time the database needs to read from or write to storage, it incurs I/O costs.

• How Composite Indexes Contribute: When you write data, the database needs to write to the base table and to the disk blocks/pages that store each index structure.
• The Impact: More indexes mean more places to write data on disk for every DML operation. Wider composite indexes mean potentially writing more data per index entry. On write-heavy systems, this translates directly to more disk activity, which can become a performance bottleneck if the storage system can’t keep up.

Illustrative Example: Inserting one row into a table with no indexes requires 1 write operation (to the table). Inserting the same row into a table with 5 indexes (single or composite) requires 1 write to the table + 5 writes to the index structures = 6 write operations.

Area 2: CPU Overhead for Index Maintenance

Databases use CPU resources to manage and maintain their index structures, primarily B-trees for most composite indexes.

• How Composite Indexes Contribute: When inserting or updating indexed columns, the database must traverse the index B-tree to find the correct insertion/update point. This involves reading index pages into memory and potentially balancing the tree structure (splitting pages, merging pages) to keep it efficient.
• The Impact: More indexes and wider keys in composite indexes mean more complex tree traversals and potentially more structural maintenance work. This consumes CPU cycles that could otherwise be used for processing queries or other tasks. In highly concurrent write environments, this can also lead to increased latch contention (short-term locks on memory structures), slowing down parallel write operations.

Area 3: Increased Write Amplification and Cache Pressure

Write amplification is a phenomenon where a small logical write (e.g., changing a single value) results in a larger amount of physical data being written.

• How Composite Indexes Contribute: Changing a value in the base table might require updating one or more index entries. An index page typically contains multiple index entries. Updating just one entry on a page often requires reading the entire page into memory, modifying it, and writing the entire modified page back to disk. This “amplifies” the write.
• The Impact: More and wider indexes increase write amplification. This not only means more disk writes (Area 1) but also puts more pressure on the database’s cache. Index pages being read and written for DML operations push out other useful data (like frequently read table data or other index pages) from the cache, potentially leading to more frequent disk reads for subsequent queries.

When Does This Impact Matter Most? (Write-Heavy Workloads)

The impact of composite indexes on write performance is most pronounced in workloads characterized by a high volume of INSERT, UPDATE, and/or DELETE operations relative to SELECT queries. Examples include:

• Logging and Auditing Tables: Constantly inserting new records.
• IoT Data Ingestion: Receiving and inserting data points from many devices.
• Financial Transaction Systems: High volume of new transactions (inserts) and status updates.
• Real-time Data Feeds: Continuously adding new data.

In these scenarios, excessive or poorly designed composite indexes can become a significant bottleneck, leading to higher latency for write operations and potentially impacting the stability and scalability of the system.

Strategies to Minimize Composite Index Write Overhead

You don’t have to abandon composite indexes entirely on write-heavy tables. Here are strategies to mitigate their impact:

• Be Selective: Only create composite indexes that are essential for optimizing your most critical read queries. Each index must justify its write overhead.
• Keep Indexes Narrow: Include only the necessary columns in the composite index key. If certain columns are only needed in the SELECT list, consider using “included columns” if your database supports them (like SQL Server) instead of adding them to the index key.
• Match Index Order to Query Filters: Ensure the leftmost columns of your composite index align with the primary filtering columns in your queries. A composite index that isn’t used effectively for reads still imposes write overhead.
• Partitioning: For very large tables (especially time-series), partitioning can help. Indexes can be created per partition, making them smaller and faster to update when new data is added to the latest partition.
• Monitor Performance: Continuously monitor both read and write performance metrics. Use database tools to identify slow DML statements and analyze their impact.
• Review and Drop Unused Indexes: Periodically check which indexes are actually being used by your workload and drop those that are obsolete.

• General Database Performance Tuning Concepts (Oracle Docs – applicable principles)

• Understanding Indexing Overhead (MariaDB Docs – relevant to MySQL)

Finding the Right Balance: Testing and Monitoring

Optimizing for both reads and writes is an ongoing process. There’s no one-size-fits-all solution. The key is to:

1. Understand your workload (read vs. write ratio, query patterns).
2. Apply indexing strategies based on that understanding.
3. Test the impact of your changes on both read and write performance.
4. Monitor your database metrics continuously.

Use tools like slow query logs and EXPLAIN ANALYZE (or equivalent) not just for SELECT statements, but also to understand what happens during slow INSERT, UPDATE, or DELETE operations.

Conclusion

Composite indexes are invaluable for accelerating multi-column queries, but it’s vital to be aware of their hidden cost on write performance. Increased disk I/O, CPU overhead, and write amplification are the 3 Key Areas where indexes add overhead to your INSERT, UPDATE, and DELETE operations.

On write-heavy workloads, neglecting this impact can lead to significant performance bottlenecks. By being selective with the indexes you create, keeping them narrow, matching them to actual query needs, and continuously monitoring both read and write performance, you can effectively manage composite index write performance.

Don’t let indexes designed for reads cripple your writes. Find the right balance for your specific workload!

How do you manage the impact of composite indexes on your write-heavy databases? Share your strategies in the comments below!