gpdb

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Enhancement

In Greenplum, limiting the memory usage of queries is achieved by setting a fixed statement_mem, with spill operations safeguarding the normal execution of queries. Over time, this coarse-grained approach to memory allocation has been recognized as problematic. Specifically, when system concurrency is high, setting the statement_mem too large might trigger the vmem_protect_limit (virtual memory protection limit). Conversely, setting it too low means that in a scenario where memory is allocated evenly, some operators processing larger volumes of data, such as aggregation operations, cannot fully utilize the available memory, thereby affecting overall performance. The strategy of simply dividing memory usage evenly based on memory-intensive operators is now considered outdated. A more reasonable approach seems to be setting a memory pool to limit queries, allowing each operator to use memory based on its needs. This should be a priority development requirement and at least put on the schedule for consideration.

What did you mean?

SELECT p.category, SUM(s.amount) AS total_sales
FROM sales AS s
JOIN products AS p ON s.product_id = p.product_id
WHERE s.date >= '2021-01-01' AND s.date <= '2021-12-31'
GROUP BY p.category
ORDER BY total_sales DESC;

For example, this query uses a richer set of memory during execution, as memory must be allocated for join operations, grouping, and sorting. In a high-concurrency environment, if statement_mem is not set appropriately:

A too high statement_mem might trigger system-level memory usage constraints, such as vmem_protect_limit, leading to query failures or system stability issues. A too low statement_mem can result in multiple operators (especially sorting and aggregation) using insufficient memory resources, increasing the frequency of disk spills and execution time. The Need for Dynamic Memory Management With such queries, the mechanism for dynamic memory management becomes critically important. It ensures that:

Memory-intensive operations like JOIN operators can dynamically obtain more memory when needed to process large datasets, reducing disk I/O operations and improving performance. Sorting and aggregation operators can dynamically allocate memory based on the volume of data, avoiding unnecessary disk spills and maintaining efficiency. In systems with high concurrency, memory resources can be managed effectively to prevent query failures caused by memory exhaustion. To achieve this, introducing the concept of a memory pool that dynamically allocates memory based on the actual demands of each query and the memory intensity of its operators can be considered. Such a memory pool would ensure the efficient use of resources and optimize overall query performance.

To achieve this, introducing the concept of a memory pool that dynamically allocates memory based on the actual demands of each query and the memory intensity of its operators can be considered. Such a memory pool would ensure the efficient use of resources and optimize overall query performance.

Still unsure what's being proposed. Can you take some concrete example queries running concurrently and then explain what you are proposing in detail (considering aspects like some query has already started in system and allocated x amount, and next query starts execution and needs Y amount and all scenarios).

Memory is dynamically allocated in GPDB and based on concurrency setting can be dynamically configured on-fly for queries to use less or more. Have you looked into and played with Resource Group Memory Configurations. Please can you compared and contract with what's being proposed with it.