MySQL Enhancing Performance without Cache

I am using MySQL version 5.5.14 to run the following query from a table of 5 Million rows:

SELECT P.ID, P.Type, P.Name, P.cty
     , X(P.latlng) as 'lat', Y(P.latlng) as 'lng'
     , P.cur, P.ak, P.tn, P.St, P.Tm, P.flA, P.ldA, P.flN
     , P.lv, P.bd, P.bt, P.nb
     , P.ak * E.usD as 'usP' 
FROM PIG P 
  INNER JOIN EEL E 
    ON E.cur = P.cur 
WHERE act='1' 
  AND flA >= '1615' 
  AND ldA >= '0' 
  AND yr >= (YEAR(NOW()) - 100) 
  AND lv >= '0' 
  AND bd >= '3' 
  AND bt >= '2' 
  AND nb <= '5' 
  AND cDate >= NOW() 
  AND MBRContains(LineString( Point(-65.6583, -87.8906)
                            , Point(65.6583, 87.8906)
                            ), latlng) 
  AND Type = 'g' 
  AND tn = 'l' 
  AND St + Tm - YEAR(NOW()) >= '30' 
HAVING usP BETWEEN 300/2 AND 300 LIMIT 100;

The table definitions are:

CREATE TABLE `PIG` (
  `ID` int(10) unsigned NOT NULL AUTO_INCREMENT,
  `Email` char(50) NOT NULL,
  `Type` char(1) NOT NULL,
  `Name` char(25) DEFAULT NULL,
  `cty` char(2) DEFAULT NULL,
  `latlng` point NOT NULL,
  `tn` char(1) NOT NULL DEFAULT 'l',
  `St` smallint(4) unsigned NOT NULL DEFAULT '0',
  `Tm` smallint(3) unsigned NOT NULL DEFAULT '0',
  `yr` smallint(4) unsigned NOT NULL DEFAULT '0',
  `flA` mediumint(6) unsigned NOT NULL DEFAULT '0',
  `ldA` mediumint(6) unsigned NOT NULL DEFAULT '0',
  `flN` smallint(3) unsigned NOT NULL DEFAULT '1',
  `lv` smallint(3) unsigned NOT NULL DEFAULT '0'开发者_如何学运维,
  `bd` tinyint(2) unsigned NOT NULL DEFAULT '0',
  `bt` tinyint(2) unsigned NOT NULL DEFAULT '0',
  `nb` tinyint(1) unsigned NOT NULL DEFAULT '9',
  `cur` char(3) DEFAULT NULL,
  `ak` int(10) unsigned NOT NULL DEFAULT '0',
  `Des` tinytext,
  `pDate` datetime DEFAULT NULL,
  `cDate` date DEFAULT NULL,
  `act` tinyint(1) unsigned NOT NULL DEFAULT '0',
  `bid` tinyint(3) unsigned NOT NULL DEFAULT '0',
  `ab` tinyint(3) unsigned NOT NULL DEFAULT '0',
  PRIMARY KEY (`ID`),
  KEY `id_ca` (`cty`,`ak`),
  SPATIAL KEY `id_latlng` (`latlng`)
) ENGINE=MyISAM AUTO_INCREMENT=5000001 DEFAULT CHARSET=latin1

And:

CREATE TABLE `EEL` (
  `cur` char(3) NOT NULL,
  `usD` decimal(11,10) NOT NULL,
  PRIMARY KEY (`cur`)
) ENGINE=MyISAM DEFAULT CHARSET=latin1

The following shows the query execution plan:

*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: P
         type: range
possible_keys: id_latlng
          key: id_latlng
      key_len: 34
          ref: NULL
         rows: 742873
        Extra: Using where
*************************** 2. row ***************************
           id: 1
  select_type: SIMPLE
        table: E
         type: eq_ref
possible_keys: PRIMARY
          key: PRIMARY
      key_len: 3
          ref: BS.P.cur
         rows: 1
        Extra: 

This query does not use query cache due to the presence of NOW() function. From my previous posting, I discovered that other forms of cache exist to speed up the query from initial time of 300s down to less than 2s. My question is: "how does one improve the above query time, knowing that the cache won't be of much use since the search criteria for latlng is constantly changing?" Note that a spatial index on latlng has already been built for optimisation purpose.

Cheers, Ben

---

Good indexes are the ones with high selectivity. Your conditions are mostly range conditions and this poses a limit on the fields that can be used in a composite index.

Possible indexes to investigate (composed from those fields that have an equality check with the addition in the end, of one field with a range check):

(act, Type, tn, flA)

(act, Type, tn, cDate)

(act, Type, tn, nb)

To check selectivity without creating indexes, you could use:

SELECT COUNT(*)
FROM PIG P 
WHERE act='1' 
  AND Type = 'g' 
  AND tn = 'l' 
  AND flA >= '1615'

and

SELECT COUNT(*)
FROM PIG P 
WHERE act='1' 
  AND Type = 'g' 
  AND tn = 'l' 
  AND cDate >= NOW() 

and

SELECT COUNT(*)
FROM PIG P 
WHERE act='1' 
  AND Type = 'g' 
  AND tn = 'l' 
  AND nb <= '5' 

and compare the output with the 742873 you have from the spatial index.