Shulou(Shulou.com)06/01 Report--

Experimental environment:

DB is PostgreSQL version 8.2.15

JDK1.8

Test one

Use JDBC to query a 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 SUM 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 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) 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) mLEFT OUTER JOIN locations loc on m.location_id = loc.id WHERE loc.org_id = $4) explain analyze EXECUTE test (195078) (195078) (195078); dev=# EXECUTE test (195078)

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