The power of a database often lies not just in how it stores data, but in how efficiently it retrieves it. At the heart of that efficiency is indexing. Whether you're building a customer-facing app or a machine learning pipeline, the right indexing strategy can make the difference between real-time insights and frustrating delays. For AI databases in particular, indexing plays a critical role in how fast models can be trained, features can be retrieved and real-time inference can be executed.
In this post, we’ll explore core database indexing techniques, how they work and why they matter for AI applications.
What is a database index?
A database index is a data structure that improves the speed of data retrieval operations on a table at the cost of additional storage and write overhead.
Indexes are especially useful when dealing with large datasets, where full table scans become prohibitively expensive. A full table scan, or table scan, occurs when the database engine must read every row in a table to find the desired data, which is slow and resource intensive. Indexes help avoid these costly operations by enabling quick data retrieval. In traditional transactional systems, indexes might be used to speed up queries involving customer names, order dates or inventory counts. In AI systems, they often enable fast access to features, embeddings or labels needed during training or inference.
When an index is created, it maps search key values to the corresponding data locations using index entries. Each index entry contains a search key, which is the attribute or set of attributes used for sorting and retrieving data and a search key value, which points to the specific row containing the requested data. This structure enables quick data retrieval by allowing the database to efficiently find and return the relevant information.
Speed vs. scale: Choosing the right index
Several types of indexes are used in relational and AI-optimized databases. Each serves different purposes and suits different types of queries. The indexing process involves creating and maintaining various indexing methods, like B-Tree, hash and bitmap indexes, to organize and access underlying data efficiently and improve data retrieval performance.
B-Tree indexes. This is the most common type of index used in relational databases. It maintains a balanced tree structure, allowing logarithmic-time lookups for equality and range queries. B-Trees are ideal for filtering by numeric fields, timestamps or IDs, making them a good fit for structured AI feature stores.
Hash indexes. Hash indexes use a hash function to map keys to locations, making them extremely fast for exact-match lookups. Hash tables serve as the underlying data structure for these indexes, enabling rapid access by mapping keys directly to data locations. However, they are not useful for range queries or sorting. These indexes are helpful when you need to quickly retrieve vector IDs or token hashes, which are common in AI pipelines.
Bitmap indexes. Bitmap indexes represent column values as bitmaps and are particularly effective in read-heavy environments with low-cardinality columns (like yes/no flags or category fields). In AI pre-processing, these can be useful when filtering categorical data across many rows.
Full-text indexes. These indexes are designed to search for keywords or phrases within unstructured text. AI systems that incorporate natural language search or language models benefit from full-text indexes during feature extraction or document retrieval tasks. Full-text indexes also operate on the data file to efficiently locate and retrieve relevant text data.
Vector indexes. Vector indexes, like HNSW (Hierarchical Navigable Small World) or IVFFlat (Inverted File Index with Flat quantization), are used for similarity search on high-dimensional vectors. These are critical for applications involving image search, recommendation engines or Large Language Models (LLMs). Unlike traditional indexes, they approximate nearest neighbors rather than finding exact matches, enabling real-time vector search on massive datasets.
Why indexing and efficient data retrieval matter for AI databases
AI applications often rely on large volumes of both structured and unstructured data. As a result, the workload on a database is no longer just transactional or analytical. Modern database systems rely on various index types, like B-Tree, hash, bitmap and full-text indexes, to support the diverse and demanding requirements of AI workloads. AI workloads include real-time inference, model retraining, feature extraction and vector similarity search — all of which require highly optimized data access patterns.
Without appropriate indexing, feature stores slow down, vector searches become inefficient and model-serving APIs suffer latency. Indexing is what allows an AI database to serve both batch and real-time queries without compromise.
Here’s how indexing supports different parts of an AI workflow:
Training. Fast access to historical features and labels using B-Tree or bitmap indexes.
Inference. Real-time retrieval of user or context-specific data via hash or B-Tree indexes.
Similarity search. Approximate nearest neighbor indexing for embedding-based matching.
Pipeline orchestration. Efficient filtering and joins on metadata for data versioning and model lineage.
To ensure efficient AI workflow execution, it is crucial to properly index the database by selecting the appropriate index types for each use case.
The challenge of hybrid AI workloads and query performance
Modern AI systems demand more than just fast reads. They require a database that can handle hybrid workloads: ingesting data at high speed, transforming it on the fly and serving AI queries without delay. Indexing is key to this performance but traditional databases often force trade-offs. You get fast ingestion or fast reads, but rarely both. When deleting data, the database must also update or remove associated index entries to keep indexes in sync with the underlying data, which can impact write performance.
Moreover, not every database supports vector indexing natively. Many bolt on external vector stores or require manual configuration to bridge structured and unstructured search. This adds complexity and slows down innovation.
Database indexing best practices
Effective database indexing is key to optimizing query performance and ensuring efficient data management, especially as your datasets grow. Here are some best practices to help you get the most out of your database indexing strategy:
Avoid over-indexing. While indexes can greatly improve data retrieval, too many indexes can slow down write operations like inserts, updates and deletes. Each index must be updated whenever data changes, so regularly review your indexes and remove unused indexes to keep write performance high and resource utilization low.
Identify frequently used queries. Start by analyzing your workload to determine which queries are run most often. Focus on indexing the columns that appear in WHERE, JOIN and ORDER BY clauses of these high-traffic queries to maximize the impact on query speed and efficiency.
Maintain indexes regularly. Over time as data is inserted, updated or deleted, indexes can become fragmented, reducing their efficiency. Schedule regular index maintenance, like rebuilding or reorganizing indexes, to ensure they continue to provide rapid data retrieval and optimal query performance.
Consider data distribution. Not all columns benefit equally from indexing. Indexes on columns with very few distinct values (low cardinality) may not provide significant performance gains and can even waste resources. Focus on indexing columns with a high degree of selectivity to maximize the benefits.
Index join columns. If your queries frequently join tables on specific columns, make sure those columns are indexed. This can dramatically speed up join operations and reduce the need for full table scans.
By following these best practices, you can create indexes that strike the right balance between efficient data retrieval and optimal resource utilization, ensuring your database remains responsive and scalable as your data grows.
Bringing it all together with SingleStore and database indexes
SingleStore is built to meet the needs of modern AI applications. It combines traditional indexing techniques like B-Tree and hash indexes with support for real-time ingestion and vector search in a single unified platform. SingleStore supports covering indexes, which include all the columns needed to satisfy specific queries without accessing the main table. Covering indexes are built on indexed columns and can significantly improve query performance. Additionally, SingleStore allows the creation of primary indexes, as well as sparse and dense indexes, to optimize different data access patterns. Unlike specialized vector databases that only support similarity search or OLTP systems that struggle with analytics, SingleStore enables hybrid AI workloads without compromise.
Whether you’re powering real-time recommendations, building an AI feature store, or performing vector search across billions of rows, indexing matters — and SingleStore makes it easy to do it all in one place.
