Hidden Costs of Blindly Creating Database Indexes

Indexes are a powerful tool in relational databases, improving query performance and enabling efficient data retrieval. However, indexes are not without cost, and creating them without understanding their impact can lead to unintended consequences. This blog will explore the hidden costs of database indexes, their internal mechanics, and how to use them wisely.

What Are Indexes?

In simple terms, a database index is a data structure that helps speed up data retrieval. Think of it as the "index" pages at the back of a dictionary: instead of flipping through every page to find a word, you can jump straight to the relevant page using the index.

In databases, an index serves a similar purpose. Instead of scanning all rows in a table (a full table scan), the database uses the index to quickly locate rows matching a query condition.

Internal Mechanics of Indexes

From a disk layout perspective, most database indexes use a structure called a B-tree or its variations. Here’s a high-level explanation:

  • B-tree structure:

    • A B-tree index is like a sorted hierarchy of pointers.
    • At the top level, you have a "root" node pointing to other nodes.
    • As you go deeper, nodes point to more specific data blocks.
    • At the lowest level, leaf nodes contain references (e.g., row IDs) to the actual table data.

  • How it works:

    • When you query a column with an index, the database navigates the B-tree structure instead of scanning the entire table.
    • This significantly reduces the amount of data read from disk, especially for large tables.

The Downsides of Blind Index Creation

While indexes can greatly enhance read performance, they come with trade-offs.

1. Write Performance Degradation

Every time you insert, update, or delete a row in a table, the database must:

  • Modify the table’s data.
  • Update all relevant indexes to reflect the change.

This additional work can slow down write operations significantly, especially for tables with multiple indexes. For example:

  • If you update a column that has an index, the database must find and modify the index entry.
  • For high-frequency writes, such as in logging or transactional systems, the overhead can become a bottleneck.

Additionally, updating both the data files and the corresponding index files often requires a lock to maintain consistency. This lock ensures that no other operations can modify or read the affected rows while the update is in progress. In high-concurrency environments, these locks can lead to contention, further degrading performance.

2. Increased Storage Requirements

Indexes consume disk space. For large tables, the size of the index can rival or even exceed the size of the table itself. Blindly adding indexes can:

  • Lead to excessive storage costs.
  • Increase backup and restore times.
  • Put strain on disk I/O during maintenance tasks like index rebuilding.

3. Query Optimization Pitfalls

More indexes don’t always mean better performance. In fact:

  • The query planner must evaluate all available indexes to decide which one to use. With too many indexes, this process can slow down query planning.
  • Poorly designed indexes can confuse the query planner, leading to suboptimal execution plans.

Practical Analogy: The Dictionary Index

Imagine you have a dictionary with the following "index pages":

  1. Words sorted alphabetically.
  2. Words sorted by length.
  3. Words sorted by syllable count.

Each index is helpful for specific tasks, but maintaining them all is burdensome:

  • If you add a new word, you must update all three indexes.
  • Searching for a word by alphabetical order is fast, but searching by syllable count might not always be efficient.
  • The additional pages make the dictionary bulkier and harder to manage.

Similarly, in databases, adding unnecessary indexes can make writes slower, consume extra disk space, and complicate query planning.

How to Use Indexes Wisely

1. Understand Your Workload

  • Use indexes where they will have the most impact, such as frequently queried columns.
  • Avoid adding indexes to columns rarely used in WHERE, JOIN, or ORDER BY clauses.

2. Monitor Index Usage

  • Use tools like PostgreSQL’s pg_stat_user_indexes or MySQL’s SHOW INDEX to check index usage.
  • Drop unused or rarely used indexes to reduce maintenance overhead.

3. Design Multi-Column Indexes Thoughtfully

  • For queries involving multiple columns, a single multi-column index is often better than multiple single-column indexes.
  • Ensure the order of columns in the index matches the query patterns.

4. Balance Reads and Writes

  • If your application is write-heavy, minimize the number of indexes to reduce write latency.
  • For read-heavy applications, prioritize indexes that optimize the most expensive queries.

5. Regularly Review Indexes

  • Periodically audit your indexes to ensure they still align with your workload.
  • Use query execution plans (EXPLAIN or EXPLAIN ANALYZE) to verify if indexes are being used effectively.

Conclusion

Indexes are a double-edged sword: they can significantly enhance query performance but come with costs that developers must manage carefully. Blindly creating indexes without understanding their impact can lead to slower write performance, increased storage costs, and unnecessary maintenance.

By understanding how indexes work internally and applying them thoughtfully, you can achieve a balanced database that performs well for both reads and writes.

Start small, measure performance, and iteratively refine your indexing strategy to meet your application’s needs.

2025-01-11
Ken W.

Programmer. Generalist.