Deep Dive into the Apache Doris Optimizer: Unveiling the Magic with Examples

Apache Doris’ impressive speed relies heavily on its intelligent optimizer. Let’s dissect its workings and see how it optimizes queries with real-world examples.

Cost-Based Optimization in Action:

Imagine you run a retail store and want to analyze monthly sales data across different product categories and locations. You fire off a query like this:

SQL

SELECT category, city, SUM(sales) AS total_sales
FROM sales_data
WHERE purchase_date >= '2024-03-01'
GROUP BY category, city;

The Doris Optimizer swings into action. It considers various execution plans:

1. Scan All Data: Simply scan the entire sales_data table for rows matching the WHERE clause, then group and aggregate by category and city. This might be fast for small datasets but inefficient for massive tables.
2. Partition Pruning: Doris stores data in partitions (e.g., by date). The optimizer knows you only care about data since March 1st. It can efficiently identify and scan only the relevant partitions, significantly reducing data to process.
3. Column Pruning: The query only needs category, city, and sales columns. The optimizer tells Doris to only retrieve these specific columns, saving bandwidth and processing power.

By estimating the cost of each plan (data scanned, processing required), the optimizer chooses the most efficient one – likely option 2 with partition pruning.

Advanced Techniques for Enhanced Performance:

Let’s expand the query to include joins:

SQL

SELECT p.product_name, o.order_date, SUM(s.sales) AS total_sales
FROM sales_data s
JOIN orders o ON s.order_id = o.id
JOIN products p ON s.product_id = p.id
WHERE o.order_date >= '2024-03-01'
GROUP BY p.product_name, o.order_date;

Here, the optimizer goes a step further:

• Join Reordering: It considers different join orders (e.g., sales joined with orders first, or vice versa). If the sales table is much larger, joining it with the smaller orders table first might be more efficient.
• Materialized Views: Imagine you frequently analyze daily sales by product. The optimizer might leverage a pre-computed materialized view containing daily sales aggregates, significantly speeding up query execution.

Nereids: The Next-Gen Optimizer for Even More Power

The future of Doris optimization is bright with Nereids. It combines the strengths of cost-based and rule-based optimization, allowing it to tackle a wider range of queries effectively. Additionally, Nereids can explore a much broader spectrum of join orders, potentially leading to even faster execution plans. Imagine Nereids intelligently choosing the most efficient join order based on the size and distribution of the tables involved.

Optimizing Your Queries:

By understanding these concepts, you can write more efficient queries. Utilize the EXPLAIN PLAN feature to see the chosen execution plan and identify potential bottlenecks. Let’s say EXPLAIN PLAN reveals the optimizer chose a less-than-ideal join order. You can rewrite the query to nudge it towards a more efficient plan.

Remember, the Apache Doris Optimizer is constantly learning and evolving. As you provide more statistics about your data (e.g., table sizes, column distributions), the optimizer can make even more informed decisions, ensuring your queries run at peak performance.