Overview
Robin Hood Hashing is a data structure that maintains an invariant that supports efficient queries, updates, summaries, or indexing. It trades implementation complexity for faster or more compact operations on repeated workloads.
CoreSource checked
Robin Hood Hashing
Robin Hood Hashing organizes data around a query, update, ordering, or approximation invariant.
Quick Facts
Compact metadata for implementation and discovery.
- Category
- Data Structures
- Difficulty
- Intermediate
- Family
- Data Structure
- Tier
- Core
- Status
- Verified
- Review
- Source Checked
- Sources
- 1
- Formulas
- 1
- Relationships
- 2
- Code example
- Ready
- Visualization
- Ready
Core Concepts
invariantquery operationupdate operationstorage layout
Quick facts
Complexity
Runtime and memory behavior at a glance.
Best
O(1)
Average
O(log n)
Worst
O(n)
Space
O(n)
When to Use
- Use Robin Hood Hashing when repeated operations benefit from its invariant or summary structure.
- Use it when operation mix is stable enough to justify maintaining the structure.
When Not to Use
- Avoid when the workload is too small to justify the added invariant maintenance.
- Avoid when updates violate assumptions behind the query guarantees.
What It Does
- Organizes data for efficient access
- Maintains structural invariants
- Supports queries, inserts, deletes, or summaries
Use Cases
- Indexing data
- Caching
- Fast query processing
Inputs
- Items, keys, or graph records
- Queries or updates
Outputs
- Stored structure
- Query result
- Updated structure
Key Formulas (1)
Probe distance
distance = currentindex − idealindex
distance = current_index - ideal_indexSwaps entries so long-probed keys are not unfairly displaced.
Pseudocode
- 1.Initialize the structure and invariants
- 2.Apply insert, query, update, or delete operations
- 3.Rebalance, hash, summarize, or split nodes as needed
- 4.Return the query result or updated structure
Canonical Example
- Input
- operations = insert A, insert B, query A
- Output
- updated structure and query result
- Explanation
- Robin Hood Hashing maintains its invariant while answering the requested operation.
Implementation Notes
- Write invariant checks for debug builds.
- Benchmark the expected query/update mix instead of isolated operations only.
Common Pitfalls
- Breaking the invariant during an update can make later queries silently wrong.
- Ignoring adversarial or skewed input can erase expected performance.
Interactive Mechanism Walkthrough
Graph Frontier & Edge RelaxationShow the structure changing as items are inserted, queried, or removed.
Step 1 of 4: Initialize the structure and invariants
data-structureNotes
Robin Hood Hashing operates in O(log n) time and O(n) space within the Data Structure family. Key governing relation: distance = current_index - ideal_index. Often compared with Hash Table and Binary Search Tree.
Sources (1)
What each reference supports for this entry.
Robin Hood Hashing
PaperOriginal reference for Robin Hood open addressing and probe-length balancing.
Open sourceExplore Next
Good next jumps from the relationship graph, ranked for useful discovery.
Relationships
View full relationship pageVariant ofHash Table
Robin Hood hashing is an open-addressed hash-table variant that balances probe distances during insertion.
Related Algorithms
Tags
Data Structure