我有一张超过1000万行的表.大约有50列.该表存储传感器数据/参数.假设我需要查询全天或86,400秒的数据.完成此查询需要大约20秒或更长时间.
原文链接：https://www.f2er.com/postgresql/192098.html我在几个列上添加了单独的索引,例如recordTimestamp(捕获数据时存储),deviceId(传感器的标识),positionValid(GPS地理定位是否有效).然后我添加了一个包含所有三列的复合索引.
以下是我的查询：
t1 = time.time()
conn = engine.connect()
select_statement = select([Datatable]).where(and_(
Datatable.recordTimestamp >= start_date,Datatable.recordTimestamp <= end_date,Datatable.deviceId == device_id,Datatable.positionValid != None,Datatable.recordTimestamp % query_interval == 0))
lol_data = conn.execute(select_statement).fetchall()
conn.close()
t2 = time.time()
time_taken = t2 - t1
print('Select: ' + time_taken)
以下是我的EXPLAIN ANALYZE声明：
EXPLAIN ANALYZE SELECT datatable.id,datatable."createdAt",datatable."analogInput01",datatable."analogInput02",datatable."analogInput03",datatable."analogInput04",datatable."analogInput05",datatable."analogInput06",datatable."analogInput07",datatable."canEngineRpm",datatable."canEngineTemperature",datatable."canFuelConsumedLiters",datatable."canFuelLevel",datatable."canVehicleMileage",datatable."deviceId",datatable."deviceTemperature",datatable."deviceInternalVoltage",datatable."deviceExternalVoltage",datatable."deviceAntennaCut",datatable."deviceEnum",datatable."deviceVehicleMileage",datatable."deviceSimSignal",datatable."deviceSimStatus",datatable."iButton01",datatable."iButton02",datatable."recordSequence",datatable."recordTimestamp",datatable."accelerationAbsolute",datatable."accelerationBrake",datatable."accelerationBump",datatable."accelerationTurn",datatable."accelerationX",datatable."accelerationY",datatable."accelerationZ",datatable."positionAltitude",datatable."positionDirection",datatable."positionSatellites",datatable."positionSpeed",datatable."positionLatitude",datatable."positionLongitude",datatable."positionHdop",datatable."positionMovement",datatable."positionValid",datatable."positionEngine" FROM datatable WHERE datatable."recordTimestamp" >= 1519744521 AND datatable."recordTimestamp" <= 1519745181 AND datatable."deviceId" = '864495033990901' AND datatable."positionValid" IS NOT NULL AND datatable."recordTimestamp" % 1 = 0;
以下是SELECT的EXPLAIN ANALYZE的结果：
Index Scan using "ix_dataTable_recordTimestamp" on dataTable (cost=0.44..599.35 rows=5 width=301) (actual time=0.070..10.487 rows=661 loops=1) Index Cond: (("recordTimestamp" >= 1519744521) AND ("recordTimestamp" <= 1519745181)) Filter: (("positionValid" IS NOT NULL) AND (("deviceId")::text = '864495033990901'::text) AND (("recordTimestamp" % 1) = 0)) Rows Removed by Filter: 6970 Planning time: 0.347 ms Execution time: 10.658 ms
以下是Python计算的时间结果：
Select: 47.98712515830994 JSON: 0.19731807708740234
以下是我的代码分析：
10302 function calls (10235 primitive calls) in 12.612 seconds
Ordered by: cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 12.595 12.595 /Users/afeezaziz/Projects/Bursa/backend/env/lib/python3.6/site-packages/sqlalchemy/engine/base.py:882(execute)
1 0.000 0.000 12.595 12.595 /Users/afeezaziz/Projects/Bursa/backend/env/lib/python3.6/site-packages/sqlalchemy/sql/elements.py:267(_execute_on_connection)
1 0.000 0.000 12.595 12.595 /Users/afeezaziz/Projects/Bursa/backend/env/lib/python3.6/site-packages/sqlalchemy/engine/base.py:1016(_execute_clauseelement)
1 0.000 0.000 12.592 12.592 /Users/afeezaziz/Projects/Bursa/backend/env/lib/python3.6/site-packages/sqlalchemy/engine/base.py:1111(_execute_context)
1 0.000 0.000 12.590 12.590 /Users/afeezaziz/Projects/Bursa/backend/env/lib/python3.6/site-packages/sqlalchemy/engine/default.py:506(do_execute)
1 12.590 12.590 12.590 12.590 {method 'execute' of 'psycopg2.extensions.cursor' objects}
1 0.000 0.000 0.017 0.017 /Users/afeezaziz/Projects/Bursa/backend/env/lib/python3.6/site-packages/sqlalchemy/engine/result.py:1113(fetchall)
1 0.000 0.000 0.017 0.017 /Users/afeezaziz/Projects/Bursa/backend/env/lib/python3.6/site-packages/sqlalchemy/engine/result.py:1080(_fetchall_impl)
1 0.008 0.008 0.017 0.017 {method 'fetchall' of 'psycopg2.extensions.cursor' objects}
