Hash Join

SUMMARY

A hash join is a database query optimization technique that uses hash tables to efficiently combine data from multiple tables. This method is particularly effective for large datasets and is commonly used in time-series databases for joining historical data with reference information.

How hash joins work

Hash joins operate in two main phases:

Build phase: Creates a hash table from the smaller table (build table) using the join key
Probe phase: Scans the larger table (probe table) and looks up matching records in the hash table

Next generation time-series database

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Performance characteristics

Hash joins excel in specific scenarios:

Large datasets where traditional nested loop joins would be inefficient
Equi-joins (joins using equality comparisons)
When memory is sufficient to hold the hash table
When there's no requirement for sorted output

For example, when joining trade data with security reference data:

-- ⚠️ ANSI (requires QuestDB adaptation)
SELECT t.timestamp, t.price, s.symbol, s.security_name
FROM trades t
JOIN security_master s ON t.symbol = s.symbol
WHERE t.timestamp > '2023-01-01'

Next generation time-series database

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Memory considerations

Hash joins require careful memory management:

Hash table must fit in memory for optimal performance
If memory is insufficient, the database may resort to disk-based hashing
Memory usage is proportional to the size of the smaller table

The hash join algorithm particularly shines in time-series databases when combining historical data with dimension tables.

Common use cases

Reference data enrichment
- Joining trade data with security master data
- Combining sensor readings with device metadata
Historical analysis
- Merging price data with corporate actions
- Joining market data with trading positions
Time-series aggregations
- Combining multiple data streams
- Correlating events across different sources

Hash joins are particularly effective when used with temporal joins in time-series analysis, enabling efficient data correlation across different time series.

Performance optimization tips

Ensure sufficient memory for hash table
Consider table sizes when deciding join order
Use appropriate join key data types
Monitor hash table statistics for performance tuning

Hash joins complement other join strategies like nested loop joins, each optimal for different scenarios in database query execution.

The choice between join strategies significantly impacts query performance, making understanding hash joins crucial for database optimization.