PostgreSQL 使用 PreparedStatement 导致查询慢的分析

实验环境：

DB is PostgreSQL version 8.2.15

JDK1.8

测试一

使用JDBC查询一个SQL：

public static void test1(String url, Properties props){        String sql = "SELECT l.src_ip, l.location_id, "                + "SUM(l.us_bytes) as up_usage, "                + "SUM(l.ds_bytes) as down_usage, "                + "(SUM(l.us_bytes) + SUM(l.ds_bytes) ) as total_usage "                + "FROM unmapped_endpoint_location_hours l "                + "where l.org_id = 195078 "                + "AND date_time >= '2017-04-01 00:00:00.0' AND date_time < '2017-04-08 00:00:00.0' "                + "AND l.location_id in (2638,2640,2654 ) "                + "GROUP BY l.src_ip, l.location_id ";                Connection conn = null;        Statement sta = null;        try {            System.out.println("Start query1:" );            long s_time = System.currentTimeMillis();            conn = DriverManager.getConnection(url, props);            sta = conn.createStatement();            sta.execute(sql);            System.out.println("Using Time: " + (System.currentTimeMillis() - s_time));        } catch (SQLException e) {            e.printStackTrace();        } finally {            if (conn != null) {                try {                    conn.close();                } catch (SQLException e) {                    e.printStackTrace();                }            }            if (sta != null) {                try {                    sta.close();                } catch (SQLException e) {                    e.printStackTrace();                }            }        }    }

结果：

Start query1:
Using Time: 11519 ms

测试二

使用JDBC PreparedStatement 查询相同的SQL：

public static void test2(String url, Properties props){        String sql2 = "SELECT l.src_ip, l.location_id, "                + "SUM(l.us_bytes) as up_usage, "                + "SUM(l.ds_bytes) as down_usage, "                + "(SUM(l.us_bytes) + SUM(l.ds_bytes) ) as total_usage "                + "FROM unmapped_endpoint_location_hours l "                + "where l.org_id = ? "                + "AND date_time >= ? AND date_time < ? "                + "AND l.location_id in (2638,2640,2654 ) "                + "GROUP BY l.src_ip, l.location_id";                Connection conn = null;        PreparedStatement preSta = null;        try {            System.out.println("Start query2:");            long s_time = System.currentTimeMillis();            conn = DriverManager.getConnection(url, props);            preSta = conn.prepareStatement(sql2);            preSta.setString(1, "195078");            preSta.setString(2, "2017-04-01 00:00:00.0");            preSta.setString(3, "2017-04-09 00:00:00.0");            preSta.executeQuery();            System.out.println("Using Time: " + (System.currentTimeMillis() - s_time));        } catch (SQLException e) {            e.printStackTrace();        } finally {            if (conn != null) {                try {                    conn.close();                } catch (SQLException e) {                    e.printStackTrace();                }            }            if (preSta != null) {                try {                    preSta.close();                } catch (SQLException e) {                    e.printStackTrace();                }            }        }    }

结果：

Start query2:
Using Time: 143031 ms

相同的SQL，测试二和测试一结果为什么差别这么大？

测试一的SQL没有使用PreparedStatement 方式，直接给了原始的SQL。测试二的使用了PreparedStatement ，但是在set参数的时候用的都是String。

两者查询速度相差10倍，这是不是很奇怪？

现在来做另一个实验：

测试三

使用JDBC PreparedStatement 查询相同的SQL：

public static void test3(String url, Properties props){        String sql2 = "SELECT l.src_ip, l.location_id, "                + "SUM(l.us_bytes) as up_usage, "                + "SUM(l.ds_bytes) as down_usage, "                + "(SUM(l.us_bytes) + SUM(l.ds_bytes) ) as total_usage "                + "FROM unmapped_endpoint_location_hours l "                + "where l.org_id = ? "                + "AND date_time >= ? AND date_time < ? "                + "AND l.location_id in (2638,2640,2654 ) "                + "GROUP BY l.src_ip, l.location_id";                Connection conn = null;        PreparedStatement preSta = null;        try {            System.out.println("Start query3:");            long s_time = System.currentTimeMillis();            conn = DriverManager.getConnection(url, props);            preSta = conn.prepareStatement(sql2);                        int org_id = 195078;            SimpleDateFormat df = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");            TimeZone.setDefault(TimeZone.getTimeZone("UTC"));            Date d1 = null;            Date d2 = null;            try {                d1 = df.parse("2017-04-01 00:00:00");                d2 = df.parse("2017-04-09 00:00:00");            } catch (ParseException e1) {                e1.printStackTrace();            }            preSta.setInt(1, org_id);            preSta.setTimestamp(2, new java.sql.Timestamp(d1.getTime()));            preSta.setTimestamp(3, new java.sql.Timestamp(d2.getTime()));            preSta.executeQuery();            System.out.println("Using Time: " + (System.currentTimeMillis() - s_time));        } catch (SQLException e) {            e.printStackTrace();        } finally {            if (conn != null) {                try {                    conn.close();                } catch (SQLException e) {                    e.printStackTrace();                }            }            if (preSta != null) {                try {                    preSta.close();                } catch (SQLException e) {                    e.printStackTrace();                }            }        }    }

结果：

Start query3:
Using Time: 16245 ms

测试结果和测试一的结果差不多，为什么？

这次测试同样使用了PreparedStatement，但是在设置参数的时候指定了参数的类型。

explan analyze

查看explan

dev=# explain analyze SELECT count(loc.name) AS totalNumdev-# FROM (SELECT t.src_ip, t.location_id, t.up_usage, t.down_usage, t.total_usagedev(#       FROM (SELECT l.src_ip, l.location_id,dev(#                   SUM(l.us_bytes) as up_usage,dev(#                   SUM(l.ds_bytes) as down_usage,dev(#                   (SUM(l.us_bytes) + SUM(l.ds_bytes) ) as total_usagedev(#             FROM unmapped_endpoint_location_hours ldev(#             where l.org_id = 195078dev(#                   AND date_time >= '2017-04-11 00:00:00.0' AND date_time < '2017-04-20 00:00:00.0'dev(#                   AND l.location_id in (2638,2640)dev(#                   GROUP BY l.src_ip, l.location_id ) tdev(# WHERE t.total_usage > 0.0 ) mdev-# LEFT OUTER JOIN locations loc on m.location_id = loc.id WHERE loc.org_id = 195078;

Time: 15202.518 ms

Prepare Expalin:PREPARE  test(int,text,text,int) asSELECT count(loc.name) AS totalNumFROM (SELECT t.src_ip, t.location_id, t.up_usage, t.down_usage, t.total_usage      FROM (SELECT l.src_ip, l.location_id,                  SUM(l.us_bytes) as up_usage,                  SUM(l.ds_bytes) as down_usage,                  (SUM(l.us_bytes) + SUM(l.ds_bytes) ) as total_usage            FROM unmapped_endpoint_location_hours l            where l.org_id = $1                  AND date_time >= $2 AND date_time < $3                  AND l.location_id in (2638,2640)                  GROUP BY l.src_ip, l.location_id ) tWHERE t.total_usage > 0.0 ) mLEFT OUTER JOIN locations loc on m.location_id = loc.id WHERE loc.org_id = $4;Explain analyze EXECUTE test(195078,'2017-04-11 00:00:00.0','2017-04-20 00:00:00.0',195078);dev=# EXECUTE test(195078,'2017-04-11 00:00:00.0','2017-04-20 00:00:00.0',195078);