enh: optimize performance while scalar subquery as primary key condtion

[dapan1121]

Description

Issue(s)

• Close/close/Fix/fix/Resolve/resolve: Issue Link

Checklist

Please check the items in the checklist if applicable.

• Is the user manual updated?
• Are the test cases passed and automated?
• Is there no significant decrease in test coverage?

[gemini-code-assist]

Summary of Changes

Hello @dapan1121, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on optimizing the performance of database queries that incorporate scalar subqueries within primary key conditions, such as those applied to timestamps (ts) or row identifiers (_c0). The changes involve a series of enhancements to the query planner and executor, allowing the system to intelligently identify, store, and process these specific types of conditions, particularly when they interact with remote data or complex scalar evaluations. The overall goal is to streamline the execution of such queries, leading to improved efficiency and faster data retrieval.

Highlights

• Primary Key Condition Storage: Introduced a new pPrimaryCond field in SScanLogicNode and STableScanPhysiNode to specifically store primary key conditions that are derived from scalar subqueries, enabling more targeted optimization.
• Remote Node Detection in Filters: Enhanced the filterGetTimeRange function to detect and report if a remote node is involved in the time range condition, which is crucial for handling distributed query optimizations.
• Specialized Scalar Subquery Evaluation: Implemented scalarCalculateRemoteConstants and related logic within the scalar calculation framework to efficiently evaluate scalar subqueries that involve remote values, improving performance for such scenarios.
• Integrated Query Execution Optimization: Integrated the new primary key condition handling into the query execution pipeline via initQueryTableDataCond, allowing the system to process scalar subqueries in ts or _c0 conditions more effectively by calculating their time ranges and merging them into the main filter conditions when appropriate.
• Expanded Test Coverage: Added new test cases to validate the correctness and performance of scalar subqueries used with ts and _c0 conditions, including those involving aggregate functions like first() and last().

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[gemini-code-assist]

Code Review

This pull request introduces a performance optimization for scalar subqueries used as primary key conditions. The overall approach is sound, involving separating these conditions during planning and evaluating them at execution time. The changes are well-integrated throughout the codebase. However, I've identified a few issues that should be addressed: a typo in a function name, a high-severity error-masking bug in the new logic, and a potential high-severity race condition involving a global variable. My review includes detailed explanations and suggestions for fixing these issues.

[gemini-code-assist]

This function modifies the global variable gTaskScalarExtra. If scalarCalculateRemoteConstants can be executed by multiple threads concurrently, this will cause a data race, leading to unpredictable behavior. If gTaskScalarExtra is not already thread-local, it should be made thread-local (e.g., using __thread or _Thread_local) or the necessary state should be passed as a parameter to ensure thread safety.

[gemini-code-assist]

There's a typo in the function name. getPrimayTimeRange should be getPrimaryTimeRange. This should be corrected in both the function definition and its call site for better code maintainability.

static int32_t getPrimaryTimeRange(SNode** pPrimaryKeyCond, STimeWindow* pTimeRange, bool* isStrict) {