RisingWave Query Optimization Inlining Scalar Subqueries For Improved Performance

Hey guys, let's dive into a fascinating discussion about query optimization in RisingWave! Specifically, we're going to explore how adding parentheses to an expression in the select list to create a simple scalar subquery (without its own FROM clause) can sometimes lead to a more complex execution plan than necessary. The goal? To figure out how to make RisingWave smarter about these situations and boost query performance.

The Problem: Unnecessary Complexity with Scalar Subqueries

So, what's the deal? It seems that when we wrap a simple expression in parentheses within a SELECT statement, turning it into a scalar subquery, the query planner might overcomplicate things. Let's break this down with some examples.

Example 1: The Simplest Case

First, let's look at the most basic query:

explain select 1;

The query plan for this is straightforward:

BatchValues { rows: [[1:Int32]] }

Simple enough, right? Now, let's add those parentheses to make it a scalar subquery:

explain select ( select 1 );

Suddenly, the plan explodes in complexity:

BatchNestedLoopJoin { type: LeftOuter, predicate: true }
 ├─BatchValues { rows: [[]] }
 └─BatchValues { rows: [[1:Int32]] }

Whoa! What happened? Instead of just returning the value 1, we now have a BatchNestedLoopJoin. That's definitely not ideal. The core issue here is that the query planner isn't recognizing the simplicity of the subquery and is treating it like a full-blown join operation. We need to find a way to tell RisingWave, "Hey, this is just a simple value – no need to go overboard!"

Example 2: A More Realistic Scenario

Let's consider a more practical example with a table t containing columns a and b:

explain select a + b from t;

The query plan here is quite reasonable:

BatchExchange { order: [], dist: Single }
 └─BatchProject { exprs: [(t.a + t.b) as $expr1] }
   └─BatchScan { table: t, columns: [a, b] }

We're scanning the table, projecting the sum of a and b, and then exchanging the data. Makes sense. But watch what happens when we introduce the scalar subquery:

explain select (select a + b) from t;

Boom! Complexity overload:

BatchExchange { order: [], dist: Single }
 └─BatchHashJoin { type: LeftOuter, predicate: t.a IS NOT DISTINCT FROM t.a AND t.b IS NOT DISTINCT FROM t.b }
   ├─BatchExchange { order: [], dist: HashShard(t.a, t.b) }
   │ └─BatchScan { table: t, columns: [a, b] }
   └─BatchProject { exprs: [t.a, t.b, (t.a + t.b) as $expr1] }
     └─BatchHashAgg { group_key: [t.a, t.b], aggs: [] }
       └─BatchExchange { order: [], dist: HashShard(t.a, t.b) }
         └─BatchScan { table: t, columns: [a, b] }

We've gone from a simple scan and projection to a full-blown BatchHashJoin with aggregations and exchanges! This is a huge performance killer. The scalar subquery, which should just calculate a + b for each row, is instead being treated as a complex join operation, leading to unnecessary overhead.

Why Does This Happen?

The underlying reason for this behavior likely lies in how the query planner interprets the parentheses. It sees a subquery and, by default, creates a plan that can handle more complex scenarios. However, in the case of simple scalar subqueries without a FROM clause, this complexity is unwarranted.

The Solution: Inlining Simple Scalar Subqueries

So, what's the fix? The key idea is to recognize these simple scalar subqueries and inline them directly into the select list. In other words, instead of treating (select a + b) as a separate subquery, we should treat it as just another expression to be evaluated.

Recognizing the Pattern

The first step is to identify the specific pattern we're dealing with: a subquery in the select list that:

• Doesn't have its own FROM clause.
• Consists of a simple expression (like a + b or 1).

Once we've identified this pattern, we can apply our optimization.

Inlining the Subquery

Inlining the subquery means replacing the subquery expression with its actual value. In our select (select a + b) from t example, we would effectively rewrite the query (internally, within the query planner) as select a + b from t. This allows the query planner to use its standard optimization techniques for expressions, avoiding the unnecessary join and aggregation.

The Benefits of Inlining

Inlining simple scalar subqueries offers several benefits:

• Reduced Plan Complexity: As we've seen, it eliminates unnecessary join operations and aggregations, leading to simpler and more efficient query plans.
• Improved Performance: Simpler plans translate to faster execution times. By avoiding the overhead of joins and aggregations, we can significantly improve query performance.
• Better Resource Utilization: Less complex plans consume fewer resources, such as CPU and memory, allowing RisingWave to handle more concurrent queries.

Implementation Considerations

Implementing this optimization requires careful consideration. We need to ensure that:

• Correctness is Preserved: The inlining process must not change the semantics of the query. We need to be absolutely sure that the inlined expression produces the same result as the original subquery.
• Edge Cases are Handled: There might be edge cases or special scenarios where inlining is not appropriate. We need to identify and handle these cases correctly.
• Performance is Actually Improved: We need to benchmark and verify that the inlining optimization actually improves performance in real-world scenarios.

Conclusion: A Step Towards Smarter Query Optimization

Guys, inlining simple scalar subqueries is a promising optimization technique that can significantly improve query performance in RisingWave. By recognizing the pattern of unnecessary complexity and applying the inlining transformation, we can create simpler, more efficient query plans. This ultimately leads to faster query execution and better resource utilization. It's a step towards making RisingWave even smarter and more performant!

This is an ongoing effort, and feedback from the community is incredibly valuable. Let's continue to explore ways to optimize RisingWave and make it the best streaming database out there!

RisingWave Query Optimization Improve Performance by Inlining Scalar Subqueries

Repair Input Keyword

Can parentheses in select lists complicate query plans? Should RisingWave inline simple scalar subqueries? How does scalar subquery inlining improve query performance?