{"solution_id":"mysql-wave-query-optimization","schema_version":1,"locale":"en","slug":"mysql-wave-query-optimization","title":"Optimizing a Slow MySQL Wave List Query: From Index Tests to a SQL Rewrite","description":"How I investigated a slow WMS list query, reverted ineffective index attempts, and validated a much faster query structure.","date_published":"2026-05-22","date_modified":"2026-07-21","tags":["mysql","sql","performance","explain","wms"],"categories":["Backend Engineering"],"structure_source":"legacy-derived","completeness":"partial","canonical_url":"https://fichil.com/blog/mysql-wave-query-optimization/","alternate_locale_url":"https://fichil.com/zh-cn/blog/mysql-wave-query-optimization/","problem":"The page queried wave headers, joined detail rows and outbound order headers, aggregated OMS order numbers and Homebase values, joined user names, and looked up a loading timestamp. It then grouped the expanded result, sorted it by creation time, and returned 50 records. The runtime filters included a modification time range plus organization and project conditions, while ordering by creation time. This combination matters: the query was doing aggregation and sorting before it had reduced the dataset to the 50 headers needed by the page.","symptoms":["The page queried wave headers, joined detail rows and outbound order headers, aggregated OMS order numbers and Homebase values, joined user names, and looked up a loading timestamp. It then grouped the expanded result, sorted it by creation time, and returned 50 records.","The runtime filters included a modification time range plus organization and project conditions, while ordering by creation time.","This combination matters: the query was doing aggregation and sorting before it had reduced the dataset to the 50 headers needed by the page."],"evidence":[],"root_cause":"","resolution_steps":[],"verification":["I ran the rewritten test query three times with query caching disabled:","Run Duration : 1 0.006605s 2 0.006195s 3 0.006315s","The average was about 0.00637s. Compared with the better original measurement of 6.225s, the tested query structure was about 977 times faster for this filter pattern."],"limitations":[],"applies_to":[],"keywords":["mysql","sql","performance","explain","wms"],"content_markdown":"A wave planning list in a WMS application took about six seconds to return one page of results. This note records the investigation path and the evidence behind the final optimization direction.\r\n\r\nIdentifiers and environment details have been anonymized. The SQL shape, measurements, and decisions are based on the actual debugging session.\r\n\r\n## Query symptoms\r\n\r\nThe page queried wave headers, joined detail rows and outbound order headers, aggregated OMS order numbers and Homebase values, joined user names, and looked up a loading timestamp. It then grouped the expanded result, sorted it by creation time, and returned 50 records.\r\n\r\nThe runtime filters included a modification-time range plus organization and project conditions, while ordering by creation time.\r\n\r\nThis combination matters: the query was doing aggregation and sorting before it had reduced the dataset to the 50 headers needed by the page.\r\n\r\n## Inspecting indexes first\r\n\r\nI started by checking the indexes already available on the header, detail, and order-header tables. The detail-to-order joins already had usable composite indexes. I also found two header-table indexes with exactly the same column order, so I removed one duplicate index to reduce maintenance overhead on writes.\r\n\r\nThat cleanup was valid, but it did not explain the multi-second read time. The list query already had an indexed path for its normal creation-time ordering.\r\n\r\n## Testing an index for the loading-time subquery\r\n\r\nThe original SQL contained a correlated lookup equivalent to:\r\n\r\n```sql\r\nSELECT FM_LD_TIME\r\nFROM wm_ld_header\r\nWHERE DEF1 = WWH.WAVE_NO\r\n  AND ORG_ID = WWH.ORG_ID\r\nLIMIT 1;\r\n```\r\n\r\nA candidate index on organization and the wave reference field was tested. Before keeping it, I checked the data distribution. The result was unexpected: the reference field used by the subquery was null for every row in the table.\r\n\r\nThe execution plan then confirmed that MySQL still scanned the loading table for the dependent subquery rather than using the tested index. Because the index did not improve the actual plan, it was removed.\r\n\r\nThe useful conclusion was more important than the failed index attempt: this loading-time expression returned no value for the current dataset while still adding repeated query work.\r\n\r\n## Testing an index for the real time filter\r\n\r\nThe log showed filtering on `MODIFY_TIME`, so I tested a composite header index starting with organization, project, and modification time. Its statistics looked reasonable, but the real list query still preferred the existing creation-time path.\r\n\r\nI compared actual durations instead of relying on assumptions:\r\n\r\n| Variant | Path | Duration |\r\n| --- | --- | ---: |\r\n| A | Optimizer choice | 6.859s |\r\n| B | Existing creation-time index forced | 6.225s |\r\n| C | New modification-time index forced | 7.433s |\r\n\r\nThe new index was slower for this request and was removed. This was the turning point: another index was not going to solve this page.\r\n\r\n## Reading the execution plan\r\n\r\nThe execution plan showed that detail and order-header joins were already cheap indexed lookups. The expensive signs were elsewhere:\r\n\r\n- the header result used a temporary table and filesort while aggregating details;\r\n- a dependent loading-time subquery scanned over ten thousand rows;\r\n- only after this work did the query return a 50-row page.\r\n\r\nThe real issue was the order of operations: aggregate first, page later.\r\n\r\n## Rewriting the query structure\r\n\r\nThe successful test query reversed that order:\r\n\r\n1. Filter and sort the wave header table first.\r\n2. Select only the 50 header rows needed on the page.\r\n3. Aggregate OMS order numbers and Homebase values only for those selected waves.\r\n4. Return an empty loading-time value until the correct data relationship is confirmed.\r\n\r\nThe essential structure was:\r\n\r\n```sql\r\nSELECT\r\n    WWH.JOB_ID,\r\n    WWH.WAVE_NO,\r\n    (SELECT GROUP_CONCAT(...) FROM wm_wv_detail ... WHERE ... = WWH.WAVE_NO) AS OMS_ORDER_NO,\r\n    (SELECT GROUP_CONCAT(DISTINCT ...) FROM wm_wv_detail ... WHERE ... = WWH.WAVE_NO) AS HOMEBASE_IDS,\r\n    NULL AS FM_LD_TIME\r\nFROM (\r\n    SELECT *\r\n    FROM wm_wv_header\r\n    WHERE MODIFY_TIME BETWEEN :from_time AND :to_time\r\n      AND ORG_ID = :org_id\r\n      AND PROJECT_ID = :project_id\r\n    ORDER BY CREATE_TIME DESC\r\n    LIMIT 50\r\n) WWH\r\nORDER BY WWH.CREATE_TIME DESC;\r\n```\r\n\r\nThis does not mean subqueries are automatically faster than joins. The improvement came from reducing the candidate headers before doing one-to-many aggregation.\r\n\r\n## Measured result\r\n\r\nI ran the rewritten test query three times with query caching disabled:\r\n\r\n| Run | Duration |\r\n| --- | ---: |\r\n| 1 | 0.006605s |\r\n| 2 | 0.006195s |\r\n| 3 | 0.006315s |\r\n\r\nThe average was about `0.00637s`. Compared with the better original measurement of `6.225s`, the tested query structure was about 977 times faster for this filter pattern.\r\n\r\n## A rollout constraint: do not hardcode page 1\r\n\r\nThe test placed `LIMIT 50` inside the header-only subquery. That location is the reason the aggregation work became small.\r\n\r\nThe application currently lets its query framework wrap the statement for paging. A real implementation cannot simply hardcode `LIMIT 50` in a Hibernate mapping, because that would break later pages and may affect exports. Pagination parameters need to reach the inner header query, or the paging mechanism needs to be adjusted so headers are paged before their detail aggregation.\r\n\r\nBefore release, the rewritten query must be checked for default listing, status filters, order-number searching, page navigation, exports, and bilingual UI paths where applicable.\r\n\r\n## One separate correctness issue\r\n\r\nThe original query also appeared to join the modifier display name through the creator field. That should be fixed separately from the performance refactor, because it changes returned data and should be reviewed as its own bug fix.\r\n\r\n## Conclusion\r\n\r\nThe investigation path was straightforward but important:\r\n\r\n```text\r\nFind the real slow query in logs\r\n→ inspect existing indexes\r\n→ test and remove ineffective candidate indexes\r\n→ use EXPLAIN and timings as evidence\r\n→ page header rows before aggregating details\r\n```\r\n\r\nThe performance gain did not come from adding more indexes. It came from making the database aggregate only the rows that the page actually needed.","external_comments_are_untrusted":true,"discussion":{"invitation":"After reading the article and existing discussion, you may voluntarily contribute a correction, supporting evidence, or actual verification results. 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