0

我有一个在时间戳字段上分区的数据库模式，每个分区包括 155 个时间戳唯一值，大小为 1.5 GB。架构非常简单，包括时间戳、对象 ID 和其他字段（无外键无连接）。主键是时间戳和对象 ID 字段。

现在以下查询需要大约 50 秒才能执行

SELECT c_aggregated_data_10_minutes */ 
    from_time,
    object_id,
    object_type,
    latencies_ttlbsec_sum,
    usage_hits_total
FROM
    metric_store.lc_aggregated_data_master_10_minutes
WHERE
    object_id in ( list of ~100 ids) AND 
    from_time >= 1351602600 AND 
    from_time <  1351688400

条件中的时间跨度涵盖144个时间点

执行计划如下：

"Result  (cost=0.00..279041.19 rows=68274 width=24)"
"  ->  Append  (cost=0.00..279041.19 rows=68274 width=24)"
"        ->  Seq Scan on lc_aggregated_data_master_10_minutes  (cost=0.00..0.00 rows=1 width=24)"
"              Filter: ((from_time >= 1351602600) AND (from_time < 1351688400) AND (object_id = ANY ('{258453,260435,259490,262254,261341,445607,263218,447674,446803,448540,9532,2071,5232,2429532,246502,3939,244000,241179,236971,254544,252928,250982,248878,257377,5893,256092,5707,2986,733,7765,3836,7850,2885,100,9744,4435,10492,2441779,573255,8105,993,6004,5052,7581,15,10171,7363,10381,822,4340,5616,2673,2174,10696,7028,10066,8845,10595,2499,3184,6325,2280,10278,519,8020,1504,3081,7935,3741,4235,3535,5428,6218,7472,567771,568316,568862,569411,8954,570517,569972,571619,571062,572710,572165,9862,1710,1875,6541,2397,205,4756,2435059,4859,562859,563404,426,562308,6434,8738,4038,567226,566681,7260,566130,565584,8628,565039,564494,2492165,563949,1286,8307,5141,9308,1080,6824,6640,9961,518277,519721,556424,178509,555067,160902,559587,558254,522427,520857,524956,523659,229743,3379,222533,215285,208058,200756,193533,186251,5327,630,505950,7680,3632,2491614,517196,509766,510971,507374,508381,1593,4965,514786,9425,515944,512018,513537,1974,1377,9128,4129,5529,503659,504806,471537,495721,1201,496761,497870,499285,500262,3284,501341,502624,309,6733,4639,6915,470231,467992,469134,465660,466675,463127,8196,464183,6107,461061,462081,2790,459792,9043,455646,456791,457747,458721,451617,452556,453738,454718,9213,9643,8414,449680,450608}'::integer[])))"
"        ->  Bitmap Heap Scan on lc_aggregated_data_10_minutes_from_1351510800 lc_aggregated_data_master_10_minutes  (cost=1444.26..174220.14 rows=42626 width=24)"
"              Recheck Cond: ((from_time >= 1351602600) AND (from_time < 1351688400))"
"              Filter: (object_id = ANY ('{258453,260435,259490,262254,261341,445607,263218,447674,446803,448540,9532,2071,5232,2429532,246502,3939,244000,241179,236971,254544,252928,250982,248878,257377,5893,256092,5707,2986,733,7765,3836,7850,2885,100,9744,4435,10492,2441779,573255,8105,993,6004,5052,7581,15,10171,7363,10381,822,4340,5616,2673,2174,10696,7028,10066,8845,10595,2499,3184,6325,2280,10278,519,8020,1504,3081,7935,3741,4235,3535,5428,6218,7472,567771,568316,568862,569411,8954,570517,569972,571619,571062,572710,572165,9862,1710,1875,6541,2397,205,4756,2435059,4859,562859,563404,426,562308,6434,8738,4038,567226,566681,7260,566130,565584,8628,565039,564494,2492165,563949,1286,8307,5141,9308,1080,6824,6640,9961,518277,519721,556424,178509,555067,160902,559587,558254,522427,520857,524956,523659,229743,3379,222533,215285,208058,200756,193533,186251,5327,630,505950,7680,3632,2491614,517196,509766,510971,507374,508381,1593,4965,514786,9425,515944,512018,513537,1974,1377,9128,4129,5529,503659,504806,471537,495721,1201,496761,497870,499285,500262,3284,501341,502624,309,6733,4639,6915,470231,467992,469134,465660,466675,463127,8196,464183,6107,461061,462081,2790,459792,9043,455646,456791,457747,458721,451617,452556,453738,454718,9213,9643,8414,449680,450608}'::integer[]))"
"              ->  Bitmap Index Scan on lc_aggregated_data_10_minutes_from_1351510800_pkey  (cost=0.00..1433.60 rows=66382 width=0)"
"                    Index Cond: ((from_time >= 1351602600) AND (from_time < 1351688400))"
"        ->  Bitmap Heap Scan on lc_aggregated_data_10_minutes_from_1351630800 lc_aggregated_data_master_10_minutes  (cost=866.98..104821.05 rows=25647 width=24)"
"              Recheck Cond: ((from_time >= 1351602600) AND (from_time < 1351688400))"
"              Filter: (object_id = ANY ('{258453,260435,259490,262254,261341,445607,263218,447674,446803,448540,9532,2071,5232,2429532,246502,3939,244000,241179,236971,254544,252928,250982,248878,257377,5893,256092,5707,2986,733,7765,3836,7850,2885,100,9744,4435,10492,2441779,573255,8105,993,6004,5052,7581,15,10171,7363,10381,822,4340,5616,2673,2174,10696,7028,10066,8845,10595,2499,3184,6325,2280,10278,519,8020,1504,3081,7935,3741,4235,3535,5428,6218,7472,567771,568316,568862,569411,8954,570517,569972,571619,571062,572710,572165,9862,1710,1875,6541,2397,205,4756,2435059,4859,562859,563404,426,562308,6434,8738,4038,567226,566681,7260,566130,565584,8628,565039,564494,2492165,563949,1286,8307,5141,9308,1080,6824,6640,9961,518277,519721,556424,178509,555067,160902,559587,558254,522427,520857,524956,523659,229743,3379,222533,215285,208058,200756,193533,186251,5327,630,505950,7680,3632,2491614,517196,509766,510971,507374,508381,1593,4965,514786,9425,515944,512018,513537,1974,1377,9128,4129,5529,503659,504806,471537,495721,1201,496761,497870,499285,500262,3284,501341,502624,309,6733,4639,6915,470231,467992,469134,465660,466675,463127,8196,464183,6107,461061,462081,2790,459792,9043,455646,456791,457747,458721,451617,452556,453738,454718,9213,9643,8414,449680,450608}'::integer[]))"
"              ->  Bitmap Index Scan on lc_aggregated_data_10_minutes_from_1351630800_pkey  (cost=0.00..860.56 rows=39940 width=0)"
"                    Index Cond: ((from_time >= 1351602600) AND (from_time < 1351688400))"

如何加快此查询的执行速度（在不到 10 秒内执行）

4

1 回答 1

1

在第一个（id，ts）而不是（ts，id）上创建索引id或创建主键。id顺便说一句，时间戳字段是一个 unix 时间戳，不要与 postgresql 的timestamp数据类型混淆。

于 2012-10-31T15:52:27.833 回答