Android SQLite在复杂查询中的表现

时间：2012-11-15 16:15:00

标签： java android sqlite

假设我有这种查询

String sql = "SELECT s.team_id, s.team_name, s.gp, s.w, s.t, s.l, s.go, s.ga, s.score, s.p FROM "
           + "(SELECT team_id, team_name, SUM (gp) gp, SUM (w) w, SUM (t) t, SUM (l) l, SUM (GO) go, SUM (GA) ga, SUM (GO)- SUM (GA) score, SUM (2*w+t) p FROM "
           + "(SELECT t._id team_id, t.name team_name, COUNT(CASE WHEN score_home IS NOT NULL THEN 1 END) gp, COUNT (CASE WHEN score_home > score_away THEN 1 END) w,"
           + " COUNT (CASE WHEN score_home = score_away THEN 1 END) t, COUNT (CASE WHEN score_home < score_away THEN 1 END) l,"
           + " SUM (score_home) go, SUM (score_away) ga"
           + " FROM team_table t LEFT OUTER JOIN match_table m ON m.team_home = t._id"
           + " WHERE t.tournament_id = ? GROUP BY t._id, t.name"
           + " UNION ALL"
           + " SELECT t._id team_id, t.name team_name, COUNT(CASE WHEN score_away IS NOT NULL THEN 1 END) gp, COUNT (CASE WHEN score_home < score_away THEN 1 END) w,"
           + " COUNT (CASE WHEN score_home = score_away THEN 1 END) t, COUNT (CASE WHEN score_home > score_away THEN 1 END) l,"
           + " SUM (score_away) go, SUM (score_home) ga"
           + " FROM team_table t LEFT OUTER JOIN match_table m ON m.team_away = t._id"
           + " WHERE t.tournament_id = ? GROUP BY t._id, t.name)"
           + " GROUP BY team_id, team_name) s"
           + " ORDER BY s.p DESC, s.score DESC, s.go ASC";

然后像这样使用

Cursor cursor = database.rawQuery(sql, args);

cursor.moveToFirst();
while (!cursor.isAfterLast()) {
    TeamStats stat = new TeamStats();

    stat.setTeamId(cursor.getLong(0));
    stat.setTeamName(cursor.getString(1));
    stat.setGamesPlayed(cursor.getInt(2));
    stat.setWins(cursor.getInt(3));
    stat.setTies(cursor.getInt(4));
    stat.setLoses(cursor.getInt(5));
    stat.setGoalsOwn(cursor.getInt(6));
    stat.setGoalsAgaist(cursor.getInt(7));
    stat.setScore(cursor.getInt(8));
    stat.setPoints(cursor.getInt(9));

    stats.add(stat);
    cursor.moveToNext();
}
cursor.close();

因此它从许多表中选择值，执行某些操作等。正如您所看到的，查询非常复杂（非常难以调试），并且性能似乎不如我预期的那么好。我的问题是：

  1. 我可以使用某种准备好的声明改善表现吗？
  2. 执行更简单的查询并使用一些自定义代码手动处理它们会更快吗？

5 个答案:

答案 0 :(得分：6)

如果我是你，我会将你的sqlite数据库复制到主机，然后尝试在某些SQLite GUI中手动执行它，同时用你拥有的实际变量值替换绑定变量（?）。对于Windows上的GUI，我非常喜欢SQLite Expert Personal，而且在Linux sqliteman上非常好。

在调试SQL时（在命令行或GUI中），请务必通过在EXPLAIN和/或EXPLAIN QUERY PLAN下运行SQL语句来分析它们。注意表扫描。您应该尝试通过添加索引来消除昂贵的扫描。但不要索引所有内容 - 它可能会使事情变得更糟。 通常，使用复合（多列）索引可以大大提高性能。请注意，在任何给定的表上，SQLite不能仅使用一个索引（在运行给定的SQL语句时） - 因此，请明智地选择索引。 （另请参阅Query Planning中的基本说明。）

为了解决您对Java与SQLite中数据处理的担忧 - 我认为完全优化（使用适当的索引等）SQLite查询针对关系数据将（几乎）总是会比在Java中手动处理这些数据更快。在您的情况下尤其如此 - 您的所有数据基本上都是关系型的。

一个小小的注意事项：使用Java的Android APK可能比SQLite默认访问更多内存 - 您可能希望使用setMaxSqlCacheSize()（相当于PRAGMA cache_size来增加数据库的SQLite缓存大小）。 Android默认值为10（最多100），尝试增加它，看看你的查询是否有任何区别。请注意，此设置的桌面SQLite默认值要高得多 - 2000。

答案 1 :(得分：2)

首先，我对SQLite了解不多，但我认为它的行为或多或少与MS SQL-Server类似。

大多数情况下，像这样的简单查询的性能问题通常与缺少索引的情况有关，从而导致全表扫描而不是部分表扫描或表搜索。如果你在team_table.tournament_id上没有索引，那么SQLite必须扫描整个表来执行“t.tournament_id =？”操作。 match_table.team_home和match_table.team_away也会发生同样的事情：缺少的索引将导致m.team_home和m.team_away上的连接操作进行全表扫描。

对于其他人，您可以通过两种方式简化查询。第一种方法是删除外部子查询，并在Order by中使用表达式或列顺序;也就是说，您可以将“ORDER BY sp DESC，s.score DESC，s.go ASC”替换为“ORDER BY SUM（2 * w + t）DESC，SUM（GO） - SUM（GA）DESC，SUM（ GO）ASC“并摆脱子查询。

第二种方法是通过在m.team_home和m.team_away上同时执行左连接操作来用单个查询替换UNION：

... FROM team_table t LEFT OUTER JOIN match_table m ON（m.team_home = t._id或m.team_away = t._id）...

之后，很容易改变你的Case语句，以正确计算t._id等于m.team_home或m.team_away的各种分数。这样，您不仅可以删除UNION，还可以删除第二个子查询。

最后，你必须看看左连接的使用;因为我不确定是否真的需要使用常规的内部连接。

之后，您应该最终得到一个简单的连接查询，其中包含Group By和Order By，没有子查询或联合，​​并且可能没有任何左连接。但是，此时，Order By中的表达式可能会变得有点复杂，因此您必须决定是以这种方式保留它们，放回子查询或使用列排序（我最后一个喜欢的选择）。

如果没有Union，查询的执行速度至少要快两倍，但最终要获得良好的性能，最终的要求是拥有所有正确的索引;否则，如果sql server需要执行多次全表扫描，性能将永远不会好。

答案 2 :(得分：1)

我个人建议您在Android上尽可能简化查询和数据库结构，并通过代码进行重大处理。

一个原因是，由于复杂的数据库结构与需要处理应用程序的不同版本的升级和降级以及不丢失数据的需求相结合，很快就会失控。我现在倾向于以一种NoSQL方式设置和处理数据。

另一个原因是因为SQLite缺少实际任务中需要的许多功能，并且最终还是会通过代码处理数据。例如，没有触发功能，因此找到最近的项目，可能会变得复杂;）

private String getRelitiveDistanceQuery( double lng, double lat, int max){
    return "SELECT *, " +
    // NOTE: this long query was done because there are no trig functions in SQLite so this is an series expansion of some of the functions
    "((3.14159265358979/2-( ((("+Double.toString(lat)+"*0.0174532925199433)-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/6+("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/120-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/5040)*((`lat`*0.0174532925199433)-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/6+(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/120-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/5040)+(1-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/2+("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/24-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/720)*(1-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/2+(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/24-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/720)*(1-(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)/2+(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)/24-(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)/720))+1/6*((("+Double.toString(lat)+"*0.0174532925199433)-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/6+("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/120-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/5040)*((`lat`*0.0174532925199433)-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/6+(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/120-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/5040)+(1-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/2+("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/24-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/720)*(1-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/2+(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/24-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/720)*(1-(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)/2+(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)/24-(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)/720))*((("+Double.toString(lat)+"*0.0174532925199433)-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/6+("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/120-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/5040)*((`lat`*0.0174532925199433)-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/6+(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/120-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/5040)+(1-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/2+("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/24-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/720)*(1-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/2+(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/24-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/720)*(1-(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)/2+(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)/24-(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)/720))*((("+Double.toString(lat)+"*0.0174532925199433)-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/6+("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/120-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/5040)*((`lat`*0.0174532925199433)-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/6+(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/120-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/5040)+(1-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/2+("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/24-("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)*("+Double.toString(lat)+"*0.0174532925199433)/720)*(1-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/2+(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/24-(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)*(`lat`*0.0174532925199433)/720)*(1-(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)/2+(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)/24-(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)*(("+Double.toString(lng)+" -`lng`)*0.0174532925199433)/720)) ))) AS relDistance " +
    "FROM `"+TABLE_ITEMS+"` ORDER BY relDistance ASC LIMIT "+Integer.toString(max);
}

我编写了一个perl脚本来生成这个代码，它扩展了trig函数，它实际上工作得很好，但它无法管理，我不推荐它。

答案 3 :(得分：0)

不完全是关于快速查询的答案，但是：您可以尝试使用其他帮助程序表并通过在实际数据表上定义触发器来填充它们。通过这种方式，您可以随时掌握大部分聚合数据，并且查询会更简单。

答案 4 :(得分：0)

如果您使用预备语句，那么它对您有益，因为 1.准备好的陈述更加安全 2. sql注入很难 他们并不那么复杂 4.维护很容易

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