CTE查詢性能改進（postgres 9.6）

我在下面有一個查詢需要什麼，但超級慢。它在給定的時間段內返回（例如，在2017-05-01 00:00:00和2017-05-01 01:00:00之間，但可以是1秒到幾天之間的任何時間）第一個和最後一個記錄（例如15秒，但可以是從1秒到幾天的任何時間）。CTE查詢性能改進（postgres 9.6）

這是難以忍受的緩慢。例如，對於一段時間的時間間隔1分鐘2017-05-01 00:00:00至2017-05-01 00:00:01它在我的i7 7700HQ上運行6秒。對於期間爲1的區間2017-05-01 00:00:00至2017-05-01 00:00:05我從未見過結果！數據庫現在有大約6千萬行。在生產中將達到10億美元，每月增加約5000萬美元。

QUERY：

WITH ranges as (
    SELECT dd as start_range, 
      dd + '15 seconds'::interval as end_range, 
      ROW_NUMBER() over() as grp 
    FROM generate_series 
      ('2017-05-01 00:00:00'::timestamp 
      , '2017-05-01 01:00:00'::timestamp 
      , '15 seconds'::interval) dd 
), create_grp as (
    SELECT r.grp, r.start_range, r.end_range, p.* 
    FROM prices p 
    JOIN ranges r 
     ON p.dt >= r.start_range 
     AND p.dt < r.end_range 
    WHERE instrument='EURGBP' 
), minmax as ( 
    SELECT row_number() over (partition by grp 
          order by dt asc) as rn1, 
      row_number() over (partition by grp 
          order by dt desc) as rn2,    
      create_grp.* 
    FROM create_grp 
) 
SELECT *, 
     CASE WHEN rn1 = 1 and rn2 = 1 THEN 'first and last' 
      WHEN rn1 = 1 THEN 'first' 
      WHEN rn2 = 1 THEN 'last' 
     END as row_position 
FROM minmax 
WHERE 
    1 IN (rn1, rn2) 
ORDER BY dt 
;

SCHEMA：

CREATE TABLE public.prices 
(
    uid uuid NOT NULL DEFAULT uuid_generate_v4(), 
    instrument character varying COLLATE pg_catalog."default" NOT NULL, 
    bid double precision NOT NULL, 
    ask double precision NOT NULL, 
    dt timestamp without time zone NOT NULL DEFAULT now(), 
    CONSTRAINT prices_pkey PRIMARY KEY (uid) 
) 
WITH (
    OIDS = FALSE 
) 
TABLESPACE pg_default;

指標：

CREATE INDEX idx_dt_instrument 
    ON public.prices USING btree 
    (dt, instrument COLLATE pg_catalog."default") 
    TABLESPACE pg_default; 

CREATE INDEX idx_dt_instrument_bid_ask 
    ON public.prices USING btree 
    (dt, instrument COLLATE pg_catalog."default", bid, ask) 
    TABLESPACE pg_default; 

CREATE INDEX idx_instrument 
    ON public.prices USING btree 
    (instrument COLLATE pg_catalog."default") 
    TABLESPACE pg_default;

實施例的數據集5秒從2017年5月1日00:00:00盯着：

"uid","instrument","bid","ask","dt" 
"4ecaa607-3733-4aba-9093-abc8f59e1638","EURGBP","0.84331","0.8434","2017-05-01 00:00:00.031" 
"d1a41847-4945-4cf4-a45f-781db977ae07","GBPJPY","143.949005","143.970993","2017-05-01 00:00:00.031" 
"34972c12-899b-404a-b0de-bae0fd3f6733","GBPJPY","143.947998","143.970993","2017-05-01 00:00:00.056" 
"384b8246-3eac-4826-b6d2-d6caaa364f81","GBPUSD","1.29311","1.29323","2017-05-01 00:00:00.066" 
"d879b04d-a4ed-452e-9208-7dfff672e860","GBPJPY","143.947006","143.970993","2017-05-01 00:00:00.067" 
"a9e735ec-30d9-4c9a-a28e-5e5553273372","GBPJPY","143.945999","143.970993","2017-05-01 00:00:00.079" 
"ee40ee5f-d8ac-41ce-9f39-ae50ef15d02d","GBPJPY","143.947006","143.964005","2017-05-01 00:00:00.091" 
"7b605c3b-121f-46c2-a3e4-297d187f0a28","GBPJPY","143.947006","143.968994","2017-05-01 00:00:00.115" 
"ccb307b0-7fa3-4354-8707-1426eded49e8","GBPJPY","143.942001","143.968994","2017-05-01 00:00:00.205" 
"206c339d-bc36-469d-82d1-c7ae74002f44","EURGBP","0.84332","0.8434","2017-05-01 00:00:00.206" 
"bc581318-91c7-4c80-85e0-e06f7236b277","GBPJPY","143.944","143.968994","2017-05-01 00:00:00.206" 
"1fabf850-9045-4beb-81ae-bfa3adada62e","GBPUSD","1.29311","1.29324","2017-05-01 00:00:00.208" 
"06d40f9a-a47e-466d-aebf-97a0154bdc74","GBPJPY","143.942001","143.968994","2017-05-01 00:00:00.209" 
"b3b7fac9-340f-4e3b-8946-7bdecc383191","GBPUSD","1.29311","1.29327","2017-05-01 00:00:00.211" 
"b5c28955-b40f-446f-9f43-9d5e9f145c1b","EURGBP","0.84331","0.8434","2017-05-01 00:00:00.212" 
"192a40d6-8001-42ea-9430-96e800f2d4e8","GBPJPY","143.942993","143.968994","2017-05-01 00:00:00.212" 
"e98dbba7-8231-4fa3-926b-291eb22b0f87","GBPJPY","143.944","143.968994","2017-05-01 00:00:00.215" 
"7c6e952d-d01f-4dac-a1df-6b32168246fd","GBPUSD","1.29311","1.29326","2017-05-01 00:00:00.216" 
"c86ba29f-3edb-4147-ba99-d9dfe594b0ff","GBPUSD","1.29312","1.29327","2017-05-01 00:00:00.243" 
"35ca131e-b714-462d-827f-b5bc593aa3e6","GBPJPY","143.942993","143.968994","2017-05-01 00:00:00.262" 
"91fc6fc0-7af9-4036-8e0e-29d4a3bb3e43","EURGBP","0.8433","0.8434","2017-05-01 00:00:00.283" 
"e71946e0-1859-461a-b3eb-0539584ac4dc","GBPJPY","143.944","143.968994","2017-05-01 00:00:00.296" 
"7321eea8-2610-408b-8dbf-4087f01e8c6e","GBPJPY","143.947998","143.968994","2017-05-01 00:00:00.377" 
"f146716d-cadf-4e2f-9f17-6e7e8c5f2175","GBPUSD","1.29312","1.29327","2017-05-01 00:00:00.38" 
"b3d81295-8cd5-44e7-879c-f15476ffac21","GBPUSD","1.29312","1.29326","2017-05-01 00:00:00.391" 
"c037e27b-a8f4-4ec3-8472-b0f72a58fd33","EURGBP","0.8433","0.8434","2017-05-01 00:00:00.413" 
"dba0f8f5-f218-49ea-8ebf-132f3ecf8910","GBPJPY","143.947998","143.968994","2017-05-01 00:00:00.443" 
"a08449e3-44aa-4fed-b8e9-bf1a6bfc35c5","EURGBP","0.8433","0.84339","2017-05-01 00:00:00.585" 
"a0b7ba20-653f-46db-93e9-d1edd8972dba","GBPUSD","1.29312","1.29326","2017-05-01 00:00:00.588" 
"c2855ce8-8c5b-4de7-a92b-186d928e8f31","GBPJPY","143.947998","143.968994","2017-05-01 00:00:00.591" 
"01b19a70-3ce7-44c7-9abd-9945321fdfd0","EURGBP","0.8433","0.84339","2017-05-01 00:00:00.621" 
"4518aa9d-1f76-428e-ace4-7dcffeb6aa22","GBPJPY","143.947998","143.968994","2017-05-01 00:00:00.796" 
"e4d4bac4-dd02-4da3-b231-20bfa6424412","EURGBP","0.8433","0.84339","2017-05-01 00:00:00.907" 
"e48d3721-3157-4033-bd4f-09baae0f989c","GBPUSD","1.29312","1.29326","2017-05-01 00:00:00.909" 
"64a33b2d-c756-4a0a-823e-075143ae7263","GBPJPY","143.947998","143.968994","2017-05-01 00:00:00.913" 
"e477c47d-efd1-44dd-8058-cac17e08bc5d","GBPUSD","1.29314","1.29327","2017-05-01 00:00:00.914" 
"cf6d5341-f7fd-47bc-89f6-a5448f78fb99","EURGBP","0.84329","0.84339","2017-05-01 00:00:00.943" 
"4caa8bb9-094e-48ca-8a9a-7b2dd23a4fb4","GBPJPY","143.947006","143.968994","2017-05-01 00:00:00.967" 
"274b7f51-3b07-430d-bfc5-a0b1ce62a750","GBPUSD","1.29312","1.29327","2017-05-01 00:00:00.975" 
"2e3c2cd6-2525-46b3-86c5-b88e48bb0138","GBPJPY","143.947998","143.968994","2017-05-01 00:00:01.076" 
"bcb12b90-2795-4789-bfa2-8e9a494eaeb1","GBPUSD","1.29312","1.29326","2017-05-01 00:00:01.076" 
"d63d6037-fa81-47cc-bd62-4655850c0f80","GBPUSD","1.29312","1.29327","2017-05-01 00:00:01.077" 
"6dbf8d8e-37c8-4537-80b5-c9219f4356b1","EURGBP","0.8433","0.84339","2017-05-01 00:00:01.079" 
"7e3b6eaf-22e1-4f87-a7c1-226d3ee76146","EURGBP","0.84329","0.84339","2017-05-01 00:00:01.08" 
"63e451c5-b8c6-4b57-ac9e-2171bc1dfbfa","GBPJPY","143.947998","143.968994","2017-05-01 00:00:01.121" 
"866316e7-90a2-4c80-9a38-b3a062837415","GBPJPY","143.949005","143.968994","2017-05-01 00:00:01.143" 
"fb11e963-cd36-4cfc-89b3-1bba3b8595b5","GBPUSD","1.29312","1.29327","2017-05-01 00:00:01.156" 
"ad57e34f-5cbe-4b79-8579-b2c77c83b50b","EURGBP","0.84329","0.84339","2017-05-01 00:00:01.249" 
"46b1840e-e424-41c2-8c71-691b201183ab","GBPJPY","143.949005","143.968994","2017-05-01 00:00:01.259" 
"c5fa5e09-46df-4ea4-8640-9cf18e1f8fcc","GBPUSD","1.29312","1.29327","2017-05-01 00:00:01.265" 
"926d092d-601e-43ec-b398-3300b0345cfd","GBPJPY","143.949997","143.968994","2017-05-01 00:00:01.267" 
"4ddfbc84-20a1-4281-86c2-f0a4e77d0152","EURGBP","0.84329","0.84339","2017-05-01 00:00:01.305" 
"e75af139-51a2-4a0a-acc8-e0adfbe5472a","GBPUSD","1.29313","1.29327","2017-05-01 00:00:01.346" 
"408bca2d-2d57-471b-b6a7-819a3257741f","GBPJPY","143.951004","143.968994","2017-05-01 00:00:01.348" 
"53c6f444-bd76-4a28-a370-7af98d5fa9ec","GBPUSD","1.29313","1.29326","2017-05-01 00:00:01.359" 
"cd002ef4-925e-469b-8f2f-848cadd5943f","EURGBP","0.84329","0.84339","2017-05-01 00:00:01.443" 
"13587e44-b694-4ce9-a626-e592d28c507d","EURGBP","0.8433","0.84339","2017-05-01 00:00:01.45" 
"aabbacaf-3f09-4313-b992-e8b8d91df7ad","GBPJPY","143.951004","143.968994","2017-05-01 00:00:01.461" 
"6b2111ef-c285-4482-b93c-238f27522ca3","GBPUSD","1.29313","1.29326","2017-05-01 00:00:01.477" 
"19507f29-149c-4fdf-a1fa-aac312ab8479","EURGBP","0.8433","0.84338","2017-05-01 00:00:01.506" 
"916cc759-a536-449d-b825-8203ebea9bf8","GBPJPY","143.949997","143.968994","2017-05-01 00:00:01.649" 
"1fdd2b35-fd44-4dbb-81df-b98514af7004","GBPUSD","1.29313","1.29326","2017-05-01 00:00:01.649" 
"3e9fc214-3cc6-4991-9eb3-2f5a5e557e96","GBPUSD","1.29312","1.29326","2017-05-01 00:00:01.65" 
"5da9a29b-0d8a-42b1-98b6-f89dd2893c77","EURGBP","0.8433","0.84338","2017-05-01 00:00:01.651" 
"b87f85e3-fba5-4556-8ca5-52625d978d53","EURGBP","0.84329","0.84338","2017-05-01 00:00:01.652" 
"75b7624c-b90c-40d4-a7fc-09df7da9f659","GBPJPY","143.949997","143.968994","2017-05-01 00:00:01.715" 
"b076534f-b893-4b55-9d9a-e3bac6e77ab2","GBPUSD","1.29312","1.29325","2017-05-01 00:00:01.732" 
"6464da85-9eb5-4548-bd5e-3331a69f2121","EURGBP","0.84329","0.84338","2017-05-01 00:00:01.817" 
"f9937464-e36a-4c57-a212-2f32943307d3","EURGBP","0.8433","0.84338","2017-05-01 00:00:01.83" 
"1c7848e5-c101-4a50-87fe-1f5e980dbb95","GBPJPY","143.949005","143.968994","2017-05-01 00:00:01.83" 
"30ad5bfc-f968-4b49-9b22-80c12e801f37","GBPUSD","1.29312","1.29325","2017-05-01 00:00:01.847" 
"94eb4b51-4901-4e56-873a-05f93ef35e64","EURGBP","0.8433","0.84338","2017-05-01 00:00:02.007" 
"97ef7e05-0a2f-4d70-80d1-1d3c748ea70f","GBPJPY","143.949005","143.968994","2017-05-01 00:00:02.007" 
"af9274d9-02d6-43fc-9336-c3f512e4bd78","GBPUSD","1.29312","1.29325","2017-05-01 00:00:02.008" 
"78c12ba8-992a-469e-bdda-bef004596aa1","GBPUSD","1.29311","1.29325","2017-05-01 00:00:02.062" 
"4e1e31a9-bfdc-4f56-b8e1-0c7e83852fe0","GBPUSD","1.29311","1.29324","2017-05-01 00:00:02.071" 
"e4f1c459-f75b-4005-8dd1-00c572ded4f9","GBPUSD","1.29311","1.29324","2017-05-01 00:00:02.203" 
"2c8b38ce-50de-43e7-ba63-b6d1d7a7f76c","GBPJPY","143.947998","143.968994","2017-05-01 00:00:02.244" 
"438f4439-a259-421f-8f45-a0dc88d80e1d","GBPJPY","143.949997","143.968994","2017-05-01 00:00:02.257" 
"f12b7e18-84f0-4686-9588-0ecfab8f8bf8","GBPUSD","1.29311","1.29324","2017-05-01 00:00:02.301" 
"f335ee32-cd5e-4cd2-aabe-06c14985778f","GBPJPY","143.949997","143.968994","2017-05-01 00:00:02.394" 
"3c55786e-f9b8-424b-9c32-0b4b432a553f","EURGBP","0.8433","0.84338","2017-05-01 00:00:02.404" 
"53c3e013-b016-41bb-a3ed-48158640831a","GBPUSD","1.29311","1.29324","2017-05-01 00:00:02.417" 
"a944e2b6-3700-4ecd-994d-cb5ab97cf5ad","EURGBP","0.8433","0.84339","2017-05-01 00:00:02.508" 
"ab32781d-1502-4047-b320-c3f4cda389fd","GBPJPY","143.949997","143.968994","2017-05-01 00:00:02.605" 
"24759950-e095-4bb7-be3d-fda725423589","EURGBP","0.84329","0.84339","2017-05-01 00:00:02.625" 
"cd7940d9-8a30-4a12-9dc9-a2c710cbb982","GBPUSD","1.29311","1.29325","2017-05-01 00:00:02.649" 
"233e0a3d-eeae-403f-b512-2add055d6735","GBPJPY","143.949997","143.968994","2017-05-01 00:00:02.761" 
"58c7c33c-75b2-45bd-a50c-615d9eaaec07","EURGBP","0.84329","0.84339","2017-05-01 00:00:02.763" 
"675b2847-b0f9-44f1-ab75-d6ddafbddfd5","GBPUSD","1.29311","1.29325","2017-05-01 00:00:02.766" 
"d2946714-e84c-4a36-9819-0cca4f2dd197","EURGBP","0.8433","0.84339","2017-05-01 00:00:02.81" 
"5322418b-4c45-4771-b591-cc8fea167424","GBPJPY","143.949997","143.968994","2017-05-01 00:00:02.832" 
"9a1c572e-28b9-4d16-b196-ccef3e48f602","EURGBP","0.84331","0.84339","2017-05-01 00:00:02.929" 
"a4377169-5400-4dd6-81f0-8b455d178b65","EURGBP","0.8433","0.84339","2017-05-01 00:00:02.951" 
"37cdddb0-db5e-4574-bf4d-45fb8d52c6be","GBPUSD","1.29311","1.29325","2017-05-01 00:00:02.951" 
"1258c886-b797-43ed-9886-323daac83dde","GBPUSD","1.29311","1.29325","2017-05-01 00:00:02.999" 
"453406c3-5902-4dab-b39c-2e0b85739767","EURGBP","0.8433","0.84339","2017-05-01 00:00:03.005" 
"95699a6d-8412-4f2f-9dd4-86a1d3eed57d","GBPUSD","1.29312","1.29325","2017-05-01 00:00:03.129" 
"2268fcc4-104e-434b-8237-bce765bda084","EURGBP","0.8433","0.84339","2017-05-01 00:00:03.131" 
"ba771b59-b04c-4683-8ceb-8e9b8451bcc7","GBPUSD","1.29312","1.29325","2017-05-01 00:00:03.17" 
"00541afa-79a6-4d09-8c6e-4d59e8d15bb2","GBPJPY","143.947006","143.968994","2017-05-01 00:00:03.216" 
"b4dd6e15-0e25-4da1-b210-021b91e4245a","GBPUSD","1.29312","1.29325","2017-05-01 00:00:03.323" 
"79b11513-97c5-4146-a4c8-7daf7d8ffe6f","GBPJPY","143.947006","143.966995","2017-05-01 00:00:03.324" 
"c5283502-eb09-45d3-b92b-d5625de9e9fc","GBPJPY","143.947006","143.966003","2017-05-01 00:00:03.411" 
"67715855-9171-4ae7-b205-5ba017d328f2","EURGBP","0.8433","0.84339","2017-05-01 00:00:03.501" 
"00b7647e-7d53-407d-9704-78a8712ac580","GBPJPY","143.947006","143.966003","2017-05-01 00:00:03.525" 
"b41b5483-5fb2-4c57-9892-0d02e5e6a823","GBPJPY","143.947998","143.966003","2017-05-01 00:00:03.549" 
"881712b2-ff9a-4065-ad6a-9caee03283e3","GBPJPY","143.947998","143.964996","2017-05-01 00:00:03.56" 
"7248bc13-5a9f-4fba-97a1-fe041e975c38","EURGBP","0.8433","0.84339","2017-05-01 00:00:03.581" 
"0379b4bb-7691-48e8-a668-2388f6e5d510","GBPJPY","143.947998","143.964996","2017-05-01 00:00:03.672" 
"2715fe9a-eb6f-445d-b7d0-850293bb5b2e","GBPJPY","143.947006","143.964996","2017-05-01 00:00:03.698" 
"52ce7685-0964-46e4-ab8f-282e68bdb73d","GBPJPY","143.947006","143.964005","2017-05-01 00:00:03.711" 
"6b31dee4-1aec-4d10-9b37-0c58bae7e449","GBPJPY","143.947006","143.964005","2017-05-01 00:00:03.848" 
"7e61571a-ea8b-44cb-9481-ae31ba8b86c4","GBPUSD","1.29312","1.29325","2017-05-01 00:00:03.868" 
"89e316cb-e01d-4f01-9dcd-a380e94ed4e8","GBPJPY","143.947006","143.964005","2017-05-01 00:00:03.943" 
"966318e4-a6b5-4c1b-82e1-fbbf0029ce02","GBPUSD","1.29312","1.29325","2017-05-01 00:00:04.096" 
"8fc7b075-7f5e-40f5-9879-55604f77a3c5","EURGBP","0.8433","0.84339","2017-05-01 00:00:04.227" 
"23ab8b66-337e-4c4e-b73a-f31101716565","GBPJPY","143.947006","143.964005","2017-05-01 00:00:04.227" 
"eb7469be-8a40-4498-af85-7b2c42630149","GBPJPY","143.947006","143.964005","2017-05-01 00:00:04.288" 
"f945a821-9790-4760-beea-1b54bb1c2a85","EURGBP","0.8433","0.84339","2017-05-01 00:00:04.406" 
"978c6d4c-22f9-4218-b447-940a3d3c436e","EURGBP","0.8433","0.84339","2017-05-01 00:00:04.505" 
"2ace7d77-b228-491c-9cf0-610b8e48cdf7","GBPJPY","143.947006","143.966003","2017-05-01 00:00:04.786" 
"0f89d5dd-52f2-44e9-aa54-cbb423cd7416","GBPJPY","143.947006","143.964996","2017-05-01 00:00:04.787" 
"db7748a8-317b-4c62-a1f2-d6679430b343","GBPUSD","1.29312","1.29325","2017-05-01 00:00:04.895" 
"ae29728e-773b-42a9-9f1e-199ba996731a","EURGBP","0.8433","0.84339","2017-05-01 00:00:04.986" 
"19ffa8e4-63c5-4f47-90d5-771dd5a839ba","GBPJPY","143.947006","143.962997","2017-05-01 00:00:04.986"

放緩是由「WHERE工具='EURGBP'」造成的。只要我刪除它，查詢就會飛行。但是，我必須通過「樂器」進行篩選。

explain輸出：

Sort (cost=2356660289.95..2356711257.16 rows=20386886 width=144) 
    Sort Key: minmax.dt 
    CTE ranges 
    -> WindowAgg (cost=0.00..25.00 rows=1000 width=24) 
      -> Function Scan on generate_series dd (cost=0.00..10.00 rows=1000 width=8) 
    CTE create_grp 
    -> Nested Loop (cost=344299.48..460976348.77 rows=2043798111 width=71) 
      Join Filter: ((p.dt >= r.start_range) AND (p.dt < r.end_range)) 
      -> Bitmap Heap Scan on prices p (cost=344299.48..1121763.77 rows=18394183 width=47) 
       Recheck Cond: ((instrument)::text = 'EURGBP'::text) 
       -> Bitmap Index Scan on idx_instrument (cost=0.00..339700.94 rows=18394183 width=0) 
         Index Cond: ((instrument)::text = 'EURGBP'::text) 
      -> CTE Scan on ranges r (cost=0.00..20.00 rows=1000 width=24) 
    CTE minmax 
    -> WindowAgg (cost=1796644092.85..1837520055.07 rows=2043798111 width=112) 
      -> Sort (cost=1796644092.85..1801753588.13 rows=2043798111 width=104) 
       Sort Key: create_grp.grp, create_grp.dt DESC 
       -> WindowAgg (cost=880857947.67..921733909.89 rows=2043798111 width=104) 
         -> Sort (cost=880857947.67..885967442.95 rows=2043798111 width=96) 
          Sort Key: create_grp.grp, create_grp.dt 
          -> CTE Scan on create_grp (cost=0.00..40875962.22 rows=2043798111 width=96) 
    -> CTE Scan on minmax (cost=0.00..51298821.64 rows=20386886 width=144) 
     Filter: ((1 = rn1) OR (1 = rn2))

任何建議，以優化它是非常非常歡迎。

來源

2017-09-23 uzla

「EXPLAIN」顯示'WHERE'子句的索引正在被使用嗎？ –

您已經被告知對原始問題的反應：由於CTE和generate_series（）的查詢速度較慢，這兩者都缺少要加入的索引和/或可用的統計信息。 IOW：實現它們。（'WHERE工具='EURGBP''條件是**最強的**提示，導致規劃人員打開主意） – wildplasser

我是新來的整個SQL的東西。我可以在這裏以某種方式使用臨時表嗎？「物化」意味着什麼？我如何實現它們？ – uzla

CREATE TEMP TABLE ranges 
     (start_range timestamp NOT NULL 
     , end_range timestamp NOT NULL 
     , grp INTEGER NOT NULL UNIQUE 
     , PRIMARY KEY (start_range) 
     ); 
INSERT INTO ranges(start_range, end_range, grp) 
SELECT dd as start_range, 
      dd + '1 seconds'::interval as end_range, 
      ROW_NUMBER() over() as grp 
    FROM generate_series 
      ('2017-05-01 00:00:00'::timestamp 
      , '2017-05-01 01:00:00'::timestamp 
      --, '15 seconds'::interval) dd 
      , '1 seconds'::interval) dd 
     ; 

VACUUM ANALYZE ranges; 

-- EXPLAIN ANALYZE 
SELECT *, 
     CASE WHEN rn1 = 1 and rn2 = 1 THEN 'first and last' 
      WHEN rn1 = 1 THEN 'first' 
      WHEN rn2 = 1 THEN 'last' 
     END as row_position 
FROM (
    SELECT r.grp, p.uid, p.instrument, r.start_range , p.dt AS dt 
      , p.bid, p.ask 
     , row_number() over (partition by r.grp order by p.dt asc) as rn1 
     , row_number() over (partition by r.grp order by p.dt desc) as rn2 
    FROM prices p 
    JOIN ranges r ON p.dt >= r.start_range AND p.dt < r.end_range 
    WHERE p.instrument='EURGBP' 
     ) zzzz 
WHERE (rn1=1 OR rn2=1) 
-- ORDER BY instrument,grp, dt 
     ;

而且，你實際上並不需要的行數，因爲你只需要開始一個窗口/結束：

-- EXPLAIN ANALYZE 
SELECT *, 
     CASE WHEN (prev IS NULL AND next IS NULL) THEN 'first and last' 
      WHEN prev IS NULL THEN 'first' 
      WHEN next IS NULL THEN 'last' 
     END as row_position 
FROM (
    SELECT r.grp, p.uid, p.instrument, r.start_range , p.dt AS dt, p.bid, p.ask 
     , lag(uid) over (www) as prev 
     , lead(uid) over (www) as next 
    FROM prices p 
    JOIN ranges r ON p.dt >= r.start_range AND p.dt < r.end_range 
    WHERE p.instrument='EURGBP' 
     WINDOW www AS (partition by r.grp order by p.dt) 
     -- ORDER BY p.instrument,r.grp, p.dt 
     ) qqqq 
WHERE (prev IS NULL OR next IS NULL) 
-- ORDER BY instrument,grp, dt 
     ;

來源

2017-09-23 12:36:04 wildplasser

感謝您的重構。但是，你的方法甚至比原來慢。我不知道爲什麼。同一時間段（1秒）和相同間隔（1秒）的原始查詢在10秒內運行。你在12。我猜60米的數據庫大小很重要。 – uzla

調整事項。基數很重要。索引很重要。 – wildplasser

感謝所有幫助和提示！顯然，如果我將WHERE子句從create_gpr移到minimax，查詢變得非常快。我什麼也沒有解釋。但它的工作。那就是：

WITH ranges as (
    SELECT dd as start_range, 
      dd + '1 seconds'::interval as end_range, 
      ROW_NUMBER() over() as grp 
    FROM generate_series 
      ('2017-05-01 00:00:00'::timestamp 
      , '2017-05-01 00:01:00'::timestamp 
      , '1 seconds'::interval) dd 
), create_grp as (
    SELECT r.grp, r.start_range, r.end_range, p.* 
    FROM prices p 
    JOIN ranges r 
     ON p.dt >= r.start_range 
    AND p.dt < r.end_range 
-- WHERE need to be moved out of here, which has no indexes as being temporary 
-- WHERE instrument='EURGBP' 
), minmax as ( 
    SELECT row_number() over (partition by grp 
          order by dt asc) as rn1, 
      row_number() over (partition by grp 
          order by dt desc) as rn2,    
      create_grp.* 
    FROM create_grp 
-- Here WHERE goes! It does use index here for some reason. And the query flies. 
    WHERE instrument='EURGBP' 
) 
SELECT *, 
     CASE WHEN rn1 = 1 and rn2 = 1 THEN 'first and last' 
      WHEN rn1 = 1 THEN 'first' 
      WHEN rn2 = 1 THEN 'last' 
     END as row_position 
FROM minmax 
WHERE 
    1 IN (rn1, rn2) 
ORDER BY dt 
;

這個查詢（1米分鐘範圍）運行在84毫秒，舊的沒有完成運行（我放棄了10分鐘後）。舊查詢需要10秒才能運行1秒的範圍。

感謝大家的幫助！

來源

2017-09-23 14:45:01 uzla

CTE查詢性能改進（postgres 9.6）

回答

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