我写了一个查询来查找3月至4月美国10个最繁忙的机场。它产生所需的输出,但是我想尝试进一步优化它。
是否有任何适用于查询的HiveQL特定优化? 是GROUPING SETS适用在这里吗?我是Hive的新手,现在这是我提出的最短的查询。
GROUPING SETS
SELECT airports.airport, COUNT(Flights.FlightsNum) AS Total_Flights FROM ( SELECT Origin AS Airport, FlightsNum FROM flights_stats WHERE (Cancelled = 0 AND Month IN (3,4)) UNION ALL SELECT Dest AS Airport, FlightsNum FROM flights_stats WHERE (Cancelled = 0 AND Month IN (3,4)) ) Flights INNER JOIN airports ON (Flights.Airport = airports.iata AND airports.country = 'USA') GROUP BY airports.airport ORDER BY Total_Flights DESC LIMIT 10;
表列如下:
飞机场
|iata|airport|city|state|country|
Flights_stats
|originAirport|destAirport|FlightsNum|Cancelled|Month|
按机场(内部联接)过滤,并在UNION ALL之前进行聚合,以减少传递到最终聚合简化程序的数据集。具有UNION ALL的UNION ALL子查询应该比UNION ALL之后具有更大数据集的Join并行且运行速度更快。
SELECT f.airport, SUM(cnt) AS Total_Flights FROM ( SELECT a.airport, COUNT(*) as cnt FROM flights_stats f INNER JOIN airports a ON f.Origin=a.iata AND a.country='USA' WHERE Cancelled = 0 AND Month IN (3,4) GROUP BY a.airport UNION ALL SELECT a.airport, COUNT(*) as cnt FROM flights_stats f INNER JOIN airports a ON f.Dest=a.iata AND a.country='USA' WHERE Cancelled = 0 AND Month IN (3,4) GROUP BY a.airport ) f GROUP BY f.airport ORDER BY Total_Flights DESC LIMIT 10 ;
调整mapjoin并启用并行执行:
set hive.exec.parallel=true; set hive.auto.convert.join=true; --this enables map-join set hive.mapjoin.smalltable.filesize=25000000; --size of table to fit in memory