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为什么相同的 SQLite 查询在仅获取两倍结果时却慢了 30 倍？

2024-01-05

我一直在努力加快我正在使用的查询大约一周的时间，并在这里提出了几个有关它的问题（运行 sqlite 查询后如何加快获取结果的速度？ https://stackoverflow.com/questions/10412604/how-can-i-speed-up-fetching-the-results-after-running-an-sqlite-query, sqlite.fetchall() 这么慢正常吗？ https://stackoverflow.com/questions/10336492/is-it-normal-that-sqlite-fetchall-is-so-slow, 如何有效地使用 min() 和 max()？ https://stackoverflow.com/questions/10334031/how-to-use-min-and-max-in-an-efficient-way).

借助那里给出的答案的非常有用的帮助，我设法将时间缩短到 sqlite 查询的时间100.95秒数和 fetchall 占用：1485.43。这仍然不够，所以在尝试了一些不同的索引后，我设法将查询时间缩短为0.08一个样本的秒数和全部提取时间降至54.97秒。所以我想我终于设法加快速度了。

然后查询运行下一个样本，取0.58秒，以及 fetchall 花费3952.80秒。对于第三个样本，查询采用1.01几秒钟并花了1970.67秒取所有。

第一个样本获取了 12951 行，第二个样本获取了 24972 行，第三个样本获取了 6470 行。 我很好奇为什么第一个示例获取行的速度如此之快，而它的获取量只有第二个示例的一半左右。

Code (spectrumFeature_inputValues是 (1,)、(2,) 和 (3,)，来自使用的 3 个样本。）：

self.cursor.execute('begin')
self.cursor.execute("EXPLAIN QUERY PLAN "+
                    "SELECT precursor_id, feature_table_id "+
                    "FROM `MSMS_precursor` "+
                    "INNER JOIN `spectrum` ON spectrum.spectrum_id = MSMS_precursor.spectrum_spectrum_id "+
                    "INNER JOIN `feature` ON feature.msrun_msrun_id = spectrum.msrun_msrun_id "+
                    "WHERE spectrum.scan_start_time BETWEEN feature.rtMin AND feature.rtMax "+
                    "AND MSMS_precursor.ion_mz BETWEEN feature.mzMin AND feature.mzMax "+
                    "AND feature.msrun_msrun_id = ?", spectrumFeature_InputValues)
print 'EXPLAIN QUERY PLAN: '
print self.cursor.fetchall()
import time
time0 = time.time()
self.cursor.execute("SELECT precursor_id, feature_table_id "+
                    "FROM `MSMS_precursor` "+
                    "INNER JOIN `spectrum` ON spectrum.spectrum_id = MSMS_precursor.spectrum_spectrum_id "+
                    "INNER JOIN `feature` ON feature.msrun_msrun_id = spectrum.msrun_msrun_id "+
                    "WHERE spectrum.scan_start_time BETWEEN feature.rtMin AND feature.rtMax "+
                    "AND MSMS_precursor.ion_mz BETWEEN feature.mzMin AND feature.mzMax "+
                    "AND feature.msrun_msrun_id = ?", spectrumFeature_InputValues)
print 'query took:',time.time()-time0,'seconds'
time0 = time.time()
precursorFeatureIds = self.cursor.fetchall()
print 'it fetched:',len(precursorFeatureIds),'rows'
print 'fetchall took',time.time()-time0,'seconds'
time0 = time.time()
for precursorAndFeatureID in precursorFeatureIds:
    feature_has_MSMS_precursor_inputValues = (precursorAndFeatureID[0], precursorAndFeatureID[1])
    self.cursor.execute("INSERT INTO `feature_has_MSMS_precursor` VALUES(?,?)", feature_has_MSMS_precursor_inputValues)
print 'inserting took',time.time()-time0,'seconds'
self.connection.commit()

和结果：

EXPLAIN QUERY PLAN: 
[(0, 0, 2, u'SCAN TABLE feature (~100000 rows)'), (0, 1, 1, u'SEARCH TABLE spectrum USING INDEX fk_spectrum_scahn_start_time_1 (scan_start_time>? AND scan_start_time<?) (~3125 rows)'), (0, 2, 0, u'SEARCH TABLE MSMS_precursor USING INDEX fk_MSMS_precursor_spectrum_spectrum_id_1 (spectrum_spectrum_id=?) (~5 rows)')]
query took: 0.0754859447479 seconds
it fetched: 12951 rows
fetchall took 54.2855291367 seconds
inserting took 0.602859973907 seconds
It took 54.9704811573 seconds

EXPLAIN QUERY PLAN: 
[(0, 0, 2, u'SCAN TABLE feature (~100000 rows)'), (0, 1, 1, u'SEARCH TABLE spectrum USING INDEX fk_spectrum_scahn_start_time_1 (scan_start_time>? AND scan_start_time<?) (~3125 rows)'), (0, 2, 0, u'SEARCH TABLE MSMS_precursor USING INDEX fk_MSMS_precursor_spectrum_spectrum_id_1 (spectrum_spectrum_id=?) (~5 rows)')]
query took: 0.579694032669 seconds
it fetched: 24972 rows
fetchall took 3950.08093309 seconds
inserting took 2.11575508118 seconds
 It took 3952.80745602 seconds

EXPLAIN QUERY PLAN: 
[(0, 0, 2, u'SCAN TABLE feature (~100000 rows)'), (0, 1, 1, u'SEARCH TABLE spectrum USING INDEX fk_spectrum_scahn_start_time_1 (scan_start_time>? AND scan_start_time<?) (~3125 rows)'), (0, 2, 0, u'SEARCH TABLE MSMS_precursor USING INDEX fk_MSMS_precursor_spectrum_spectrum_id_1 (spectrum_spectrum_id=?) (~5 rows)')]
query took: 1.01185703278 seconds
it fetched: 6470 rows
fetchall took 1970.622962 seconds
inserting took 0.673867940903 seconds
It took 1972.31343699 seconds

SQLite 创建语句：

-- -----------------------------------------------------
-- Table `feature`
-- -----------------------------------------------------
CREATE  TABLE IF NOT EXISTS `feature` (
  `feature_table_id` INT PRIMARY KEY NOT NULL ,
  `feature_id` VARCHAR(40) NOT NULL ,
  `intensity` DOUBLE NOT NULL ,
  `overallquality` DOUBLE NOT NULL ,
  `charge` INT NOT NULL ,
  `content` VARCHAR(45) NOT NULL ,
  `intensity_cutoff` DOUBLE NOT NULL,
  `mzMin` DOUBLE NULL ,
  `mzMax` DOUBLE NULL ,
  `rtMin` DOUBLE NULL ,
  `rtMax` DOUBLE NULL ,
  `msrun_msrun_id` INT NOT NULL ,
  CONSTRAINT `fk_feature_msrun1`
    FOREIGN KEY (`msrun_msrun_id` )
    REFERENCES `msrun` (`msrun_id` )
    ON DELETE NO ACTION
    ON UPDATE NO ACTION);

  CREATE INDEX `fk_mzMin_feature` ON `feature` (`mzMin` ASC); 
  CREATE INDEX `fk_mzMax_feature` ON `feature` (`mzMax` ASC); 
  CREATE INDEX `fk_rtMin_feature` ON `feature` (`rtMin` ASC); 
  CREATE INDEX `fk_rtMax_feature` ON `feature` (`rtMax` ASC);

DROP TABLE IF EXISTS `spectrum`;
-- -----------------------------------------------------
-- Table `spectrum`
-- -----------------------------------------------------
CREATE  TABLE IF NOT EXISTS `spectrum` (
  `spectrum_id` INT PRIMARY KEY NOT NULL ,
  `spectrum_index` INT NOT NULL ,
  `ms_level` INT NOT NULL ,
  `base_peak_mz` DOUBLE NOT NULL ,
  `base_peak_intensity` DOUBLE NOT NULL ,
  `total_ion_current` DOUBLE NOT NULL ,
  `lowest_observes_mz` DOUBLE NOT NULL ,
  `highest_observed_mz` DOUBLE NOT NULL ,
  `scan_start_time` DOUBLE NOT NULL ,
  `ion_injection_time` DOUBLE,
  `binary_data_mz` BLOB NOT NULL,
  `binary_data_rt` BLOB NOT NULL,
  `msrun_msrun_id` INT NOT NULL ,
  CONSTRAINT `fk_spectrum_msrun1`
    FOREIGN KEY (`msrun_msrun_id` )
    REFERENCES `msrun` (`msrun_id` )
    ON DELETE NO ACTION
    ON UPDATE NO ACTION);

CREATE INDEX `fk_spectrum_spectrum_id_1` ON  `spectrum` (`spectrum_id` ASC);
CREATE INDEX `fk_spectrum_scahn_start_time_1` ON  `spectrum` (`scan_start_time` ASC);

DROP TABLE IF EXISTS `feature_has_MSMS_precursor`;
-- -----------------------------------------------------
-- Table `spectrum_has_feature`
-- -----------------------------------------------------
CREATE  TABLE IF NOT EXISTS `feature_has_MSMS_precursor` (
  `MSMS_precursor_precursor_id` INT NOT NULL ,
  `feature_feature_table_id` INT NOT NULL ,
  CONSTRAINT `fk_spectrum_has_feature_spectrum1`
    FOREIGN KEY (`MSMS_precursor_precursor_id` )
    REFERENCES `MSMS_precursor` (`precursor_id` )
    ON DELETE NO ACTION
    ON UPDATE NO ACTION,
  CONSTRAINT `fk_spectrum_has_feature_feature1`
    FOREIGN KEY (`feature_feature_table_id` )
    REFERENCES `feature` (`feature_table_id` )
    ON DELETE NO ACTION
    ON UPDATE NO ACTION);

  CREATE INDEX `fk_feature_has_MSMS_precursor_feature1` ON `feature_has_MSMS_precursor` (`feature_feature_table_id` ASC);
  CREATE INDEX `fk_feature_has_MSMS_precursor_precursor1` ON `feature_has_MSMS_precursor` (`MSMS_precursor_precursor_id` ASC);

正如你所看到的，我已经制作了索引mz and rt频谱和特征中的值，因为我认为大部分时间都花在将这些数字进行比较上。

那么为什么第一个样本比第二个和第三个样本快得多呢？查询时间与 fetchall 时间有何关系？最重要的是，有什么办法可以加快速度吗？

更新1：

与同事交谈后，这可能是因为将点与二维维度（rtMin、rtMax、mzaMin、mzMax）进行比较将花费 n^2 时间。这roughly对应于第二次 fetchall 花费的时间略多于 60^2 秒（大约是第一次 fetchall 花费的时间），并且它检索的行数略少于两倍。但这并不能回答我的任何问题。

更新2：

我按照评论中的建议尝试使用 R*tree 。我做了一个新表：

CREATE VIRTUAL TABLE convexhull_edges USING rtree(
   feature_feature_table_id,             
   rtMin, rtMax,      
   mzMin, mzMax,       
);

并将我的查询更改为：

self.cursor.execute("SELECT precursor_id, feature_table_id "+
                    "FROM `MSMS_precursor` "+
                    "INNER JOIN `spectrum` ON spectrum.spectrum_id = MSMS_precursor.spectrum_spectrum_id "+
                    "INNER JOIN `feature` ON feature.msrun_msrun_id = spectrum.msrun_msrun_id "+
                    "INNER JOIN `convexhull_edges` ON convexhull_edges.feature_feature_table_id = feature.feature_table_id "
                    "WHERE spectrum.scan_start_time BETWEEN convexhull_edges.rtMin AND convexhull_edges.rtMax "+
                    "AND MSMS_precursor.ion_mz BETWEEN convexhull_edges.mzMin AND convexhull_edges.mzMax "+
                    "AND feature.msrun_msrun_id = ?", spectrumFeature_InputValues)

这给出了以下结果：

EXPLAIN QUERY PLAN: 
[(0, 0, 3, u'SCAN TABLE convexhull_edges VIRTUAL TABLE INDEX 2: (~0 rows)'), (0, 1, 2, u'SEARCH TABLE feature USING INDEX sqlite_autoindex_feature_1 (feature_table_id=?) (~1 rows)'), (0, 2, 1, u'SEARCH TABLE spectrum USING INDEX fk_spectrum_scahn_start_time_1 (scan_start_time>? AND scan_start_time<?) (~3125 rows)'), (0, 3, 0, u'SEARCH TABLE MSMS_precursor USING INDEX fk_MSMS_precursor_spectrum_spectrum_id_1 (spectrum_spectrum_id=?) (~5 rows)')]
query took: 0.0572800636292 seconds
it fetched: 13140 rows
fetchall took 34.4445540905 seconds

EXPLAIN QUERY PLAN: 
[(0, 0, 3, u'SCAN TABLE convexhull_edges VIRTUAL TABLE INDEX 2: (~0 rows)'), (0, 1, 2, u'SEARCH TABLE feature USING INDEX sqlite_autoindex_feature_1 (feature_table_id=?) (~1 rows)'), (0, 2, 1, u'SEARCH TABLE spectrum USING INDEX fk_spectrum_scahn_start_time_1 (scan_start_time>? AND scan_start_time<?) (~3125 rows)'), (0, 3, 0, u'SEARCH TABLE MSMS_precursor USING INDEX fk_MSMS_precursor_spectrum_spectrum_id_1 (spectrum_spectrum_id=?) (~5 rows)')]
query took: 0.819370031357 seconds
it fetched: 25402 rows
fetchall took 3625.72873998 seconds

EXPLAIN QUERY PLAN: 
[(0, 0, 3, u'SCAN TABLE convexhull_edges VIRTUAL TABLE INDEX 2: (~0 rows)'), (0, 1, 2, u'SEARCH TABLE feature USING INDEX sqlite_autoindex_feature_1 (feature_table_id=?) (~1 rows)'), (0, 2, 1, u'SEARCH TABLE spectrum USING INDEX fk_spectrum_scahn_start_time_1 (scan_start_time>? AND scan_start_time<?) (~3125 rows)'), (0, 3, 0, u'SEARCH TABLE MSMS_precursor USING INDEX fk_MSMS_precursor_spectrum_spectrum_id_1 (spectrum_spectrum_id=?) (~5 rows)')]
query took: 0.878498077393 seconds
it fetched: 6761 rows
fetchall took 1419.34246588 seconds
inserting took 0.340960025787 seconds
It took 1420.56637716 seconds

所以比我以前的方法快一点，但仍然不够快。接下来我将尝试 web_bod 的解决方案。

Update 3

使用 web_bod 的解决方案我得到了以下时间：

EXPLAIN QUERY PLAN: 
[(0, 0, 2, u'SCAN TABLE feature (~100000 rows)'), (0, 1, 1, u'SEARCH TABLE spectrum USING INDEX fk_spectrum_scahn_start_time_1 (scan_start_time>? AND scan_start_time<?) (~3125 rows)'), (0, 2, 0, u'SEARCH TABLE MSMS_precursor USING INDEX fk_MSMS_precursor_spectrum_spectrum_id_1 (spectrum_spectrum_id=?) (~5 rows)')]
query took: 0.0521960258484 seconds
it fetched: 13052 rows
fetchall took 90.5810132027 seconds

EXPLAIN QUERY PLAN: 
[(0, 0, 2, u'SCAN TABLE feature (~100000 rows)'), (0, 1, 1, u'SEARCH TABLE spectrum  USING INDEX fk_spectrum_scahn_start_time_1 (scan_start_time>? AND scan_start_time<?) (~3125 rows)'), (0, 2, 0, u'SEARCH TABLE MSMS_precursor USING INDEX fk_MSMS_precursor_spectrum_spectrum_id_1 (spectrum_spectrum_id=?) (~5 rows)')]
query took: 0.278959989548 seconds
it fetched: 25195 rows
fetchall took 4310.6012361 seconds

遗憾的是，第三个由于重新启动而没有完成。所以这比我的第一个解决方案快一点，但比使用 R*Tree 慢

Update 4

在处理一个速度非常慢的不同查询时，我发现它正在进入不间断的睡眠状态（请参阅这个问题 https://stackoverflow.com/questions/10854817/is-uninteruptable-sleep-the-cause-of-my-python-program-being-really-slow-and-if）。因此，我在运行此查询时检查了 top，它在 R 和 D 状态之间切换，将 CPU 使用率从 100% 降低到 50%。这可能就是为什么它在提供的所有解决方案下运行如此缓慢的原因。

Update 5

我迁移到 MySQL 但得到了相同的结果。


执行时间与每个表中的行数成几何比例，而不是算术比例，例如

3 tables with 10 rows each => 1,000 comparision

3 tables with 10, 10 and 40 rows => 4,000 comparisons

3 tables with 20 rows each => 8,000 comparisons

您可能可以重构查询以避免某些连接/游标 - 您何时需要答案？

你能做这样的事情吗：

SELECT precursor_id, feature_table_id 
FROM MSMS_precursor
INNER JOIN 

    (
        SELECT mzMin, mzMax, rtMin, rtMax, spectrum_id, feature_table_id, msrun_msrun_id

        FROM spectrum
        INNER JOIN 

           (select feature_table_id, mzMin, mzMax, rtMin, rtMax, msrun_msrun_id
            from feature
            where feature.msrun_msrun_id = 'value'
           ) subquery 

        ON subquery.msrun_msrun_id = spectrum.msrun_msrun_id
        WHERE 
            spectrum.scan_start_time BETWEEN subquery.rtMin AND subquery.rtMax 
    ) subquery

    ON subquery.spectrum_id = MSMS_precursor.spectrum_spectrum_id 

WHERE 
    MSMS_precursor.ion_mz BETWEEN subquery.mzMin AND subquery.mzMax

使用子查询可以减少表之间的比较次数 - 您可以快速过滤掉不需要的特征，然后过滤掉不相关的光谱，然后再搜索合适的前体。

我不使用 SQLite - 但原则应该仍然适用。

更新：修复了 SQL 中的错误

Notes:

您不必担心 AND，您只会得到：

  • feature.msrun_msrun_id = 'value' 的功能
  • 这些特征的光谱以及其中spectrum.scan_start_time BETWEEN 子查询.rtMin AND 子查询.rtMax
  • 这些光谱的前体，其中 MSMS_precursor.ion_mz BETWEEN subquery.mzMin AND 子查询.mzMax

5 月 18 日更新：

这就是索引！！！您在搜索字段上有索引，但在参与连接的字段上没有索引 - 外键索引确实可以提高性能：

CREATE INDEX `fk_msrun_msrun_id_feature` ON `feature` (`msrun_msrun_id` ASC); 
CREATE INDEX `fk_spectrum_spectrum_id_feature` ON `feature` (`msrun_msrun_id` ASC); 
CREATE INDEX `fk_spectrum_spectrum_id_MSMS_precursor` ON `MSMS_precursor` (`spectrum_spectrum_id` ASC);
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