B-tree
A B-tree is a balanced multiway search tree that maintains ordered keys and supports efficient searching, insertion, deletion, and sequential access.
Just Use Postgres!
A B-tree index is a balanced tree-based database index that supports ordered searches with logarithmic lookup complexity.
- How it is used
- Optimizes PostgreSQL equality, range, ordering, filtering, and aggregation queries through sorted indexed access.
- How deep it goes
- Explains tree structure, linked leaves, logarithmic lookup, execution plans, and practical SQL, without theoretical or implementation-level detail.
- Examples
- Demonstrates player scores, leaderboards, JSON pizza fields, and smartwatch heart-rate timestamps.
- What you can do after
- Apply it via PostgreSQL SQL and EXPLAIN to create indexes and verify sequential scans become index-assisted plans.
100 SQL Server Mistakes and How to Avoid Them
A B-tree is a balanced, multi-level tree structure whose pages contain keys and pointers for ordered data access.
- How it is used
- Explains how clustered and nonclustered indexes organize pages for seeks, scans, joins, filters, and aggregations.
- How deep it goes
- Defines root, intermediate, and leaf levels, then relates leaf contents to seeks, scans, fragmentation, and page splits.
- Examples
- Illustrates clustered seeks and scans with a B-tree diagram and contrasts a heap diagram with an ordered clustered-index diagram.
- What you can do after
- Evaluate/understand why key width, index type, and page condition affect read I/O and index-maintenance choices.
Read how 100 SQL Server Mistakes and How to Avoid Them explains B-tree →
PostgreSQL Mistakes and How to Avoid Them
A B-tree index is an ordered database index structure that efficiently supports equality, ordering, and compatible prefix-pattern lookups.
- How it is used
- Uses B-tree indexes to accelerate equality, ordering, and compatible prefix-pattern queries in PostgreSQL.
- How deep it goes
- Demonstrates expression indexes and EXPLAIN plans, but defers the B-tree algorithm’s internal mechanics.
- Examples
- Uses ArXiv JSONB metadata, title-prefix searches, substring searches, oversized abstracts, and DOI equality lookups.
- What you can do after
- Diagnose/debug with it by comparing predicates against index expressions and reading EXPLAIN ANALYZE output.
Read how PostgreSQL Mistakes and How to Avoid Them explains B-tree →
Grokking Relational Database Design
A B-tree is a self-balancing tree data structure that maintains sorted data and supports search, sequential access, insertion, and deletion in logarithmic time.
- How it is used
- Speeds database searches, filters, and sorted retrieval by maintaining additional structures over column values.
- How deep it goes
- Defines balancing, ordering, supported operations, and logarithmic performance, then explains index construction without detailing tree internals.
- Examples
- Uses movie-rating searches, ordering queries, release-date indexes, and combined rating-and-date indexes.
- What you can do after
- Operate/configure it by creating database indexes that support frequently searched or sorted columns.
Read how Grokking Relational Database Design explains B-tree →
Related concepts
- indexingkind of · Indexing is the organization or use of positions, keys, or lookup structures to identify and retrieve selected data or elements.
- Mapalternative to · A map is an associative data structure that stores key-value pairs and supports retrieving values by key.
- multikey indexincludes · A multikey index is a MongoDB index that creates index entries for the elements of an array field to support efficient queries on array contents.
- unique constraintproduced by · A data-model rule requiring designated values or combinations of values to distinguish the records or entities to which the rule applies.
- dateuses · A date is a calendar value identifying a particular day.
- composite indexused by · A composite index is a database index whose key contains multiple fields or columns, ordered according to the index definition.
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