Database Indexing Simulator - Learn How Indexes Speed Up SQL Queries
Database Indexing
See how indexes speed up queries by avoiding full table scans
users table10 rows
| id | name | age | city | |
|---|---|---|---|---|
| 1 | alice@example.com | Alice Johnson | 28 | New York |
| 2 | bob@example.com | Bob Smith | 35 | Los Angeles |
| 3 | carol@example.com | Carol White | 42 | Chicago |
| 4 | david@example.com | David Brown | 31 | Houston |
| 5 | emma@example.com | Emma Davis | 28 | Phoenix |
| 6 | frank@example.com | Frank Miller | 45 | New York |
| 7 | grace@example.com | Grace Lee | 29 | San Diego |
| 8 | henry@example.com | Henry Wilson | 38 | Dallas |
| 9 | ivy@example.com | Ivy Taylor | 33 | Chicago |
| 10 | jack@example.com | Jack Anderson | 28 | Austin |
Index ManagerIndexes
Add indexes to columns to speed up queries on those columns.
email
age
city
Select QueryQuery
SELECT * FROM users WHERE email = 'emma@example.com'0
Queries Run
0ms
Total Time
0
Index Hits
How Database Indexes Work
Without Index (Full Table Scan)
The database must check every row in the table to find matches. Slow for large tables!
With Index (Index Seek)
The index acts like a book's index - the database jumps directly to matching rows. Much faster!
When to Create an Index:
- • Columns used frequently in WHERE clauses
- • Columns used in JOIN conditions
- • Columns used for sorting (ORDER BY)
- • High-cardinality columns (many unique values)
Understanding Database Indexes
How Indexes Work
B-Tree Structure: Most common index type. Like a book's index - sorted for fast lookup.
Index Seek: With an index, the database jumps directly to matching rows (O(log n)).
Full Table Scan: Without an index, every row must be checked (O(n)).
Index Types
Single-Column: Index on one column. Great for WHERE clauses on that column.
Composite: Index on multiple columns. Order matters! (A, B) ≠ (B, A).
Unique: Enforces uniqueness and provides fast lookups (emails, usernames).
💡 When to Index
✅ Good for Indexing
- • Columns in WHERE clauses
- • JOIN columns (foreign keys)
- • ORDER BY / GROUP BY columns
- • High cardinality (many unique values)
❌ Avoid Indexing
- • Small tables (<1000 rows)
- • Low cardinality (few unique values)
- • Frequently updated columns
- • Columns rarely used in queries
⚠️ Index Trade-offs
- • Storage: Indexes use additional disk space
- • Write Performance: INSERTs/UPDATEs are slower (index must be updated)
- • Maintenance: Indexes can become fragmented over time
- • Over-indexing: Too many indexes can hurt more than help