Optimizing Table Scanning

Overview

Doris, as a high-performance real-time analytics data warehouse, offers a powerful partition pruning feature that can significantly enhance query performance.

Partition pruning is a query optimization technique that intelligently identifies partitions relevant to a query by analyzing its conditions, and scans only the data within these partitions, thereby avoiding unnecessary scans of irrelevant partitions. This approach can greatly reduce I/O operations and computational load, thus accelerating query execution.

Case

Here is a usage case to demonstrate Doris's partition pruning feature.

Suppose we have a sales data table named sales, which is partitioned by date, with each day's data stored in a separate partition. The table structure is defined as follows:

CREATE TABLE sales (
    date DATE,
    product VARCHAR(50),
    amount DECIMAL(10, 2)
)
PARTITION BY RANGE(date) (
    PARTITION p1 VALUES LESS THAN ('2023-01-01'),
    PARTITION p2 VALUES LESS THAN ('2023-02-01'),
    PARTITION p3 VALUES LESS THAN ('2023-03-01'),
    PARTITION p4 VALUES LESS THAN ('2023-04-01')
)
DISTRIBUTED BY HASH(date) BUCKETS 16
PROPERTIES
(
    "replication_num" = "1"
);

Now, we need to query the total sales amount between January 15, 2023 and February 15, 2023. The query statement is as follows:

SELECT SUM(amount) AS total_amount
FROM sales
WHERE date BETWEEN '2023-01-15' AND '2023-02-15';

For the above query, Doris's partition pruning optimization process is as follows:

1. Doris intelligently analyzes the partition column date in the query conditions and identifies the date range of the query as being between '2023-01-15' and '2023-02-15'.
2. By comparing the query conditions with the partition definitions, Doris precisely locates the range of partitions that need to be scanned. In this example, only partitions p2 and p3 need to be scanned, as their date ranges fully cover the query conditions.
3. Doris automatically skips partitions unrelated to the query conditions, such as p1 and p4, avoiding unnecessary data scans and thereby reducing I/O overhead.
4. Finally, Doris performs data scanning and aggregation computations only within partitions p2 and p3, quickly obtaining the query results.

By using the EXPLAIN command, we can view the query execution plan and confirm that Doris's partition pruning optimization has taken effect. In the execution plan, the partition attribute of the OlapScanNode node will display the actually scanned partitions as p2 and p3.

|   0:VOlapScanNode(212)                                                     |
|      TABLE: cir.sales(sales), PREAGGREGATION: ON                           |
|      PREDICATES: (date[#0] >= '2023-01-15') AND (date[#0] <= '2023-02-15') |
|      partitions=2/4 (p2,p3)                                                |

Summary

In summary, Doris's partition pruning feature can intelligently identify the relevance between query conditions and partitions, automatically prune irrelevant partitions, and scan only necessary data, thereby significantly enhancing query performance. Reasonable utilization of the partition pruning feature can help users build efficient real-time analytics systems and easily handle massive data query demands.