CTEクエリのパフォーマンスの改善（postgres 9.6）

Question

私は以下のクエリが必要ですが、スローは遅いです。指定された期間内に返されます（たとえば、2017-05-01 00:00:00と2017-05-01 01:00:00の間ではありますが、1秒から数日の間の何秒でもかまいません）。最初と最後のレコード（例として15秒ですが、1秒から数日の間であれば何でも構いません）。

これはめったに遅くありません。たとえば、期間2017-05-01 00:00:00〜2017-05-01 00:00:01の1秒間隔の場合、i7 7700HQで6秒間実行されます。 2017-05-01 00:00:00〜2017-05-01 00:00:05私は結果を見たことがない！データベースには現在約60億行があります。生産量は10億となり、毎月約50百万が追加されます。

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; 

INDEXES：

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; 

2017年5月1日0時00分00秒から見つめ5秒間実施例のデー​​タセット：

"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" 

減速は「WHERE instrument = 'EURGBP'」によって引き起こされます。私はそれを削除するとすぐにクエリが飛ぶ。しかし、私は '楽器'でフィルタリングする必要があります。

は、OUTPUTをEXPLAIN：

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)) 

それを最適化するための任意の提案は非常に非常に歓迎されています。

Answer 1

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 
     ; 

---

そして、あなただけのウィンドウの開始/終了をしたいので、あなたが実際に、行番号を必要としない：助けをすべてに

---

-- 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 
     ; 

Answer 2

感謝をし、ヒント！明らかに、WHERE句をcreate_gprからminimaxに移動すると、クエリは非常に高速になります。私はそれについて何の説明もありません。しかし、それは動作します。ここでは、次のとおりです。

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 
; 

このクエリ（1分の範囲では）84ミリ秒で、古いものは（私は10分後にあきらめた）を実行し終えたことはありません実行されます。古いクエリは、1秒の範囲で実行するのに10秒かかりました。

皆様のおかげです。