我有以下查询:
WITH CTE
AS (
SELECT CASE
WHEN shareClassdata.valueDate IS NULL
THEN NULL
ELSE performanceData.valueDate
END AS valueDate
,CASE
WHEN shareClassdata.benchmarkTypeName IS NULL
THEN NULL
ELSE performanceData.benchmarkTypeName
END AS benchmarkTypeName
,CASE
WHEN shareClassdata.NAVLocal IS NULL
THEN NULL
ELSE performanceData.NAVLocal
END AS NAVLocal
FROM getPerformances(2, 12045, 0, 308, 31) AS performanceData
LEFT JOIN (
SELECT *
FROM getPerformances(2, 12045, 0, 308, 31)
) shareClassdata ON shareClassdata.shareClassGroupId = performanceData.shareClassGroupId
AND shareClassdata.currencyId = performanceData.currencyId
AND shareClassdata.financialStructureGroupId = performanceData.financialStructureGroupId
AND shareClassdata.valueDate = performanceData.valueDate
AND shareClassdata.benchmarkTypeName = 'Fund'
WHERE performanceData.shareClassGroupId = 22050
AND performanceData.valueDate <= '2017-06-30 00:00:00.000'
AND (
isnull(performanceData.valueDate, '') <> ''
AND (
performanceData.benchmarkTypeName = 'Benchmark'
OR performanceData.benchmarkTypeName = 'Fund'
)
)
)
SELECT valueDate
,benchmarkTypeName
,NAVLocal
FROM CTE AS a
WHERE valueDate IS NOT NULL
AND benchmarkTypeName IS NOT NULL
ORDER BY benchmarkTypeName ASC
,valueDate ASC
给出以下结果:
+------------+-------------------+---------------+
| valueDate | benchmarkTypeName | NAVLocal |
| 2016-11-30 | Benchmark | 3005.96900000 |
| 2016-12-01 | Benchmark | 2994.49800000 |
| 2016-12-02 | Benchmark | 2981.91900000 |
| 2016-12-05 | Benchmark | 2981.43800000 |
| 2016-12-07 | Benchmark | 3020.05600000 |
| 2016-12-09 | Benchmark | 3110.80600000 |
| 2016-12-12 | Benchmark | 3086.44800000 |
| 2016-12-13 | Benchmark | 3097.51400000 |
| 2016-12-14 | Benchmark | 3069.05100000 |
| 2016-12-15 | Benchmark | 3151.47600000 |
| 2016-12-16 | Benchmark | 3147.68300000 |
| 2016-12-19 | Benchmark | 3145.64400000 |
| 2016-12-20 | Benchmark | 3175.35200000 |
| 2016-12-21 | Benchmark | 3150.92500000 |
| 2016-12-22 | Benchmark | 3138.26200000 |
| 2016-12-23 | Benchmark | 3140.90700000 |
| 2016-12-28 | Benchmark | 3142.34100000 |
| 2016-12-29 | Benchmark | 3112.91600000 |
| 2016-12-30 | Benchmark | 3081.54600000 |
| 2017-01-03 | Benchmark | 3156.68300000 |
| 2017-01-04 | Benchmark | 3147.51700000 |
| 2017-01-05 | Benchmark | 3108.02700000 |
| 2017-01-09 | Benchmark | 3121.82400000 |
| 2017-01-10 | Benchmark | 3111.07500000 |
| 2017-01-11 | Benchmark | 3156.88600000 |
| 2017-01-12 | Benchmark | 3095.46800000 |
| 2017-01-13 | Benchmark | 3109.50500000 |
| 2017-01-16 | Benchmark | 3109.50500000 |
| 2017-01-17 | Benchmark | 3080.41900000 |
| 2017-01-18 | Benchmark | 3086.20700000 |
| 2017-01-19 | Benchmark | 3098.86800000 |
| 2017-01-20 | Benchmark | 3089.86100000 |
| 2017-01-23 | Benchmark | 3062.10800000 |
| 2017-01-24 | Benchmark | 3079.78000000 |
| 2017-01-25 | Benchmark | 3108.25500000 |
| 2017-01-26 | Benchmark | 3127.41500000 |
| 2017-01-27 | Benchmark | 3114.02100000 |
| 2017-01-30 | Benchmark | 3099.60200000 |
| 2017-01-31 | Benchmark | 3063.61700000 |
| 2016-11-30 | Fund | 280.77300000 |
| 2016-12-01 | Fund | 279.07500000 |
| 2016-12-02 | Fund | 278.43100000 |
| 2016-12-05 | Fund | 279.70400000 |
| 2016-12-07 | Fund | 284.04200000 |
| 2016-12-09 | Fund | 290.47600000 |
| 2016-12-12 | Fund | 289.81900000 |
| 2016-12-13 | Fund | 292.15500000 |
| 2016-12-14 | Fund | 290.95000000 |
| 2016-12-15 | Fund | 291.62200000 |
| 2016-12-16 | Fund | 292.25700000 |
| 2016-12-19 | Fund | 293.07300000 |
| 2016-12-20 | Fund | 294.23700000 |
| 2016-12-21 | Fund | 293.81300000 |
| 2016-12-22 | Fund | 292.81400000 |
| 2016-12-23 | Fund | 293.08400000 |
| 2016-12-28 | Fund | 294.89500000 |
| 2016-12-29 | Fund | 294.22000000 |
| 2016-12-30 | Fund | 295.24100000 |
| 2017-01-03 | Fund | 296.37400000 |
| 2017-01-04 | Fund | 294.59900000 |
| 2017-01-05 | Fund | 295.30700000 |
| 2017-01-09 | Fund | 294.18400000 |
| 2017-01-10 | Fund | 294.42100000 |
| 2017-01-11 | Fund | 294.96700000 |
| 2017-01-12 | Fund | 293.68800000 |
| 2017-01-13 | Fund | 295.94300000 |
| 2017-01-16 | Fund | 294.73900000 |
| 2017-01-17 | Fund | 294.24300000 |
| 2017-01-18 | Fund | 295.48600000 |
| 2017-01-19 | Fund | 294.83300000 |
| 2017-01-20 | Fund | 294.29800000 |
| 2017-01-23 | Fund | 293.80800000 |
| 2017-01-24 | Fund | 294.64100000 |
| 2017-01-25 | Fund | 296.76600000 |
| 2017-01-26 | Fund | 297.37800000 |
| 2017-01-27 | Fund | 297.26900000 |
| 2017-01-30 | Fund | 294.67800000 |
| 2017-01-31 | Fund | 292.99700000 |
+------------+-------------------+---------------+
我需要将第三列重新设置为 100,因此我编写了以下内容,它们完美地工作:
WITH CTE
AS (
SELECT CASE
WHEN shareClassdata.valueDate IS NULL
THEN NULL
ELSE performanceData.valueDate
END AS valueDate
,CASE
WHEN shareClassdata.benchmarkTypeName IS NULL
THEN NULL
ELSE performanceData.benchmarkTypeName
END AS benchmarkTypeName
,CASE
WHEN shareClassdata.NAVLocal IS NULL
THEN NULL
ELSE performanceData.NAVLocal
END AS NAVLocal
FROM getPerformances(2, 12045, 0, 308, 31) AS performanceData
LEFT JOIN (
SELECT *
FROM getPerformances(2, 12045, 0, 308, 31)
) shareClassdata ON shareClassdata.shareClassGroupId = performanceData.shareClassGroupId
AND shareClassdata.currencyId = performanceData.currencyId
AND shareClassdata.financialStructureGroupId = performanceData.financialStructureGroupId
AND shareClassdata.valueDate = performanceData.valueDate
AND shareClassdata.benchmarkTypeName = 'Fund'
WHERE performanceData.shareClassGroupId = 22050
AND performanceData.valueDate <= '2017-06-30 00:00:00.000'
AND (
isnull(performanceData.valueDate, '') <> ''
AND (
performanceData.benchmarkTypeName = 'Benchmark'
OR performanceData.benchmarkTypeName = 'Fund'
)
)
)
SELECT valueDate
,benchmarkTypeName
,(
sum(a.NAVLocal) / (
SELECT TOP 1 b.NAVLocal
FROM CTE AS b
WHERE b.NAVLocal != 0
AND a.benchmarkTypeName = b.benchmarkTypeName
ORDER BY b.valueDate ASC
)
) * 100 AS NAVLocal
FROM CTE AS a
WHERE valueDate IS NOT NULL
AND benchmarkTypeName IS NOT NULL
GROUP BY valueDate
,benchmarkTypeName
ORDER BY benchmarkTypeName ASC
,valueDate ASC
它给出了以下结果(注意:NAVLocal 列只是为了向您显示旧值与先前值):
+------------+-------------------+----------+------------------+
| valueDate | benchmarkTypeName | NAVLocal | NAVLocal rebased |
| 2016-11-30 | Benchmark | 3005.969 | 100 |
| 2016-12-01 | Benchmark | 2994.498 | 99.6183 |
| 2016-12-02 | Benchmark | 2981.919 | 99.1999 |
| 2016-12-05 | Benchmark | 2981.438 | 99.1839 |
| 2016-12-07 | Benchmark | 3020.056 | 100.4686 |
| 2016-12-09 | Benchmark | 3110.806 | 103.4876 |
| 2016-12-12 | Benchmark | 3086.448 | 102.6773 |
| 2016-12-13 | Benchmark | 3097.514 | 103.0454 |
| 2016-12-14 | Benchmark | 3069.051 | 102.0985 |
| 2016-12-15 | Benchmark | 3151.476 | 104.8406 |
| 2016-12-16 | Benchmark | 3147.683 | 104.7144 |
| 2016-12-19 | Benchmark | 3145.644 | 104.6465 |
| 2016-12-20 | Benchmark | 3175.352 | 105.6348 |
| 2016-12-21 | Benchmark | 3150.925 | 104.8222 |
| 2016-12-22 | Benchmark | 3138.262 | 104.401 |
| 2016-12-23 | Benchmark | 3140.907 | 104.489 |
| 2016-12-28 | Benchmark | 3142.341 | 104.5367 |
| 2016-12-29 | Benchmark | 3112.916 | 103.5578 |
| 2016-12-30 | Benchmark | 3081.546 | 102.5142 |
| 2017-01-03 | Benchmark | 3156.683 | 105.0138 |
| 2017-01-04 | Benchmark | 3147.517 | 104.7088 |
| 2017-01-05 | Benchmark | 3108.027 | 103.3951 |
| 2017-01-09 | Benchmark | 3121.824 | 103.8541 |
| 2017-01-10 | Benchmark | 3111.075 | 103.4965 |
| 2017-01-11 | Benchmark | 3156.886 | 105.0205 |
| 2017-01-12 | Benchmark | 3095.468 | 102.9773 |
| 2017-01-13 | Benchmark | 3109.505 | 103.4443 |
| 2017-01-16 | Benchmark | 3109.505 | 103.4443 |
| 2017-01-17 | Benchmark | 3080.419 | 102.4767 |
| 2017-01-18 | Benchmark | 3086.207 | 102.6692 |
| 2017-01-19 | Benchmark | 3098.868 | 103.0904 |
| 2017-01-20 | Benchmark | 3089.861 | 102.7908 |
| 2017-01-23 | Benchmark | 3062.108 | 101.8675 |
| 2017-01-24 | Benchmark | 3079.78 | 102.4554 |
| 2017-01-25 | Benchmark | 3108.255 | 103.4027 |
| 2017-01-26 | Benchmark | 3127.415 | 104.0401 |
| 2017-01-27 | Benchmark | 3114.021 | 103.5945 |
| 2017-01-30 | Benchmark | 3099.602 | 103.1149 |
| 2017-01-31 | Benchmark | 3063.617 | 101.9177 |
| 2016-11-30 | Fund | 280.773 | 100 |
| 2016-12-01 | Fund | 279.075 | 99.3952 |
| 2016-12-02 | Fund | 278.431 | 99.1658 |
| 2016-12-05 | Fund | 279.704 | 99.6192 |
| 2016-12-07 | Fund | 284.042 | 101.1642 |
| 2016-12-09 | Fund | 290.476 | 103.4558 |
| 2016-12-12 | Fund | 289.819 | 103.2218 |
| 2016-12-13 | Fund | 292.155 | 104.0538 |
| 2016-12-14 | Fund | 290.95 | 103.6246 |
| 2016-12-15 | Fund | 291.622 | 103.8639 |
| 2016-12-16 | Fund | 292.257 | 104.0901 |
| 2016-12-19 | Fund | 293.073 | 104.3807 |
| 2016-12-20 | Fund | 294.237 | 104.7953 |
| 2016-12-21 | Fund | 293.813 | 104.6443 |
| 2016-12-22 | Fund | 292.814 | 104.2885 |
| 2016-12-23 | Fund | 293.084 | 104.3846 |
| 2016-12-28 | Fund | 294.895 | 105.0296 |
| 2016-12-29 | Fund | 294.22 | 104.7892 |
| 2016-12-30 | Fund | 295.241 | 105.1529 |
| 2017-01-03 | Fund | 296.374 | 105.5564 |
| 2017-01-04 | Fund | 294.599 | 104.9242 |
| 2017-01-05 | Fund | 295.307 | 105.1764 |
| 2017-01-09 | Fund | 294.184 | 104.7764 |
| 2017-01-10 | Fund | 294.421 | 104.8608 |
| 2017-01-11 | Fund | 294.967 | 105.0553 |
| 2017-01-12 | Fund | 293.688 | 104.5998 |
| 2017-01-13 | Fund | 295.943 | 105.4029 |
| 2017-01-16 | Fund | 294.739 | 104.9741 |
| 2017-01-17 | Fund | 294.243 | 104.7974 |
| 2017-01-18 | Fund | 295.486 | 105.2401 |
| 2017-01-19 | Fund | 294.833 | 105.0076 |
| 2017-01-20 | Fund | 294.298 | 104.817 |
| 2017-01-23 | Fund | 293.808 | 104.6425 |
| 2017-01-24 | Fund | 294.641 | 104.9392 |
| 2017-01-25 | Fund | 296.766 | 105.696 |
| 2017-01-26 | Fund | 297.378 | 105.914 |
| 2017-01-27 | Fund | 297.269 | 105.8752 |
| 2017-01-30 | Fund | 294.678 | 104.9523 |
| 2017-01-31 | Fund | 292.997 | 104.3536 |
+------------+-------------------+----------+------------------+
但是,查询在大型数据集上运行非常非常缓慢。有没有更好的方法来执行这个计算?
关于变基数字的提醒:
对于每个系列的基准,结果按起息日升序排序。最重要的值是每个系列的最旧记录。该系列的每个NAV值除以最旧的NAV,乘以100
谢谢