Partition Pruning

SUMMARY

Partition pruning is a query optimization technique that improves performance by automatically eliminating irrelevant partitions from consideration during query execution. In time-series databases, this is especially powerful as it allows the system to skip entire time ranges that aren't relevant to the query's time window.

How partition pruning works

When a database receives a query with time-range conditions, the query optimizer evaluates which partitions could contain relevant data. For time-series data, partitions typically represent specific time intervals (e.g., days, months, or years). The optimizer uses the query's time predicates to determine which partitions need to be scanned.

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Benefits in time-series databases

Time-series databases benefit significantly from partition pruning because:

Data is naturally partitioned by time
Queries typically target specific time ranges
Historical data remains unchanged

For example, if querying last week's data from a database with monthly partitions, the system only needs to scan one partition instead of all historical data.

# Conceptual example of partition pruning
query_time_range = "2024-01-01 to 2024-01-07"
available_partitions = [
    "2023-12", "2024-01", "2024-02"
]
# Optimizer only selects "2024-01" partition

Next generation time-series database

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Implementation considerations

Partition key selection

The effectiveness of partition pruning depends heavily on how data is partitioned. For time-series data, common strategies include:

Fixed-time intervals (days, months)
Dynamic ranges based on data volume
Hybrid approaches combining time with other dimensions

Query optimization

Modern query optimizers use partition pruning in conjunction with other techniques:

Cost-based optimizer decisions
Predicate pushdown to partition boundaries
Integration with materialized views

Here's an example showing how QuestDB leverages partition pruning:

SELECT * 
FROM trades_OHLC_15m 
WHERE timestamp BETWEEN '2024-01-01' AND '2024-01-07'

The optimizer automatically identifies and scans only the January 2024 partition, ignoring all others.

Performance impact

Partition pruning can dramatically improve query performance by:

Reducing I/O operations
Decreasing memory usage
Enabling parallel processing of relevant partitions
Minimizing CPU overhead

The impact is particularly noticeable when:

Working with large datasets
Running queries with precise time ranges
Dealing with high-cardinality time-series data

Best practices

To maximize the benefits of partition pruning:

Align partition boundaries with common query patterns
Use appropriate partition sizes for your data velocity
Monitor partition statistics and adjust strategies
Ensure queries include partition key predicates
Regularly maintain partition metadata