> ## Documentation Index > Fetch the complete documentation index at: https://private-7c7dfe99-mintlify-fbfa8bee.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. > 2009년 이후 뉴욕시에서 출발한 수십억 건의 택시 및 차량 호출 서비스(Uber, Lyft 등) 운행 데이터 # 뉴욕 택시 데이터 export const RunnableCode = ({children, run = false, showStats = true}) => { const [results, setResults] = useState(null); const [error, setError] = useState(null); const [loading, setLoading] = useState(false); const [showResults, setShowResults] = useState(false); const [stats, setStats] = useState(null); const [isDark, setIsDark] = useState(false); const [hoveredRow, setHoveredRow] = useState(-1); const codeRef = useRef(null); useEffect(() => { if (typeof window !== "undefined") { const check = () => setIsDark(document.documentElement.classList.contains("dark")); check(); const observer = new MutationObserver(check); observer.observe(document.documentElement, { attributes: true, attributeFilter: ["class"] }); return () => observer.disconnect(); } }, []); useEffect(() => { if (codeRef.current) { const block = codeRef.current.querySelector(".code-block"); if (block) { block.style.marginBottom = "0"; block.style.marginTop = "0"; block.style.borderBottomLeftRadius = "0"; block.style.borderBottomRightRadius = "0"; } } }); const getSqlText = () => { if (!codeRef.current) return ""; const code = codeRef.current.querySelector("code"); return (code || codeRef.current).textContent.trim(); }; const executeQuery = async () => { const sql = getSqlText(); if (!sql) return; setLoading(true); setError(null); setResults(null); setShowResults(true); try { const cleanQuery = sql.replace(/;$/, "").trim(); const params = new URLSearchParams({ query: cleanQuery, default_format: "JSONCompact", result_overflow_mode: "break", read_overflow_mode: "break", allow_experimental_analyzer: "1" }); const res = await fetch(`https://sql-clickhouse.clickhouse.com/?${params.toString()}`, { method: "POST", headers: { Authorization: `Basic ${btoa(`demo:`)}` } }); const text = await res.text(); if (!res.ok) { setError(text || `HTTP ${res.status}`); setLoading(false); return; } const json = JSON.parse(text); setResults(json); setStats(json.statistics || null); } catch (err) { setError(err.message || "쿼리 실행에 실패했습니다"); } setLoading(false); }; useEffect(() => { if (run) executeQuery(); }, []); const formatRows = n => { if (n >= 1e9) return `${(n / 1e9).toFixed(1)}B`; if (n >= 1e6) return `${(n / 1e6).toFixed(1)}M`; if (n >= 1e3) return `${(n / 1e3).toFixed(1)}K`; return String(n); }; const formatBytes = b => { if (b >= 1e9) return `${(b / 1e9).toFixed(2)} GB`; if (b >= 1e6) return `${(b / 1e6).toFixed(2)} MB`; if (b >= 1e3) return `${(b / 1e3).toFixed(2)} KB`; return `${b} B`; }; const isNumericType = type => { return (/^(UInt|Int|Float|Decimal)/).test(type); }; const isHyperlink = value => { return typeof value === "string" && (/^https?:\/\//).test(value); }; const computeColumnExtremes = (meta, data) => { const extremes = {}; for (let i = 0; i < meta.length; i++) { if (isNumericType(meta[i].type)) { let min = Infinity, max = -Infinity; for (const row of data) { const v = Number(row[i]); if (!isNaN(v)) { if (v < min) min = v; if (v > max) max = v; } } if (max > -Infinity) { extremes[i] = { min, max }; } } } return extremes; }; const computeColumnWidths = (meta, data) => { const lengths = meta.map((col, i) => { const headerLen = col.name.length + col.type.length + 1; let maxData = 0; for (const row of data) { const v = row[i]; const len = v === null ? 4 : String(v).length; if (len > maxData) maxData = len; } return Math.max(headerLen, maxData); }); const total = lengths.reduce((s, l) => s + l, 0); return lengths.map(l => `${(l / total * 100).toFixed(1)}%`); }; const copyResultsAsTSV = () => { if (!results || !results.meta || !results.data) return; const header = results.meta.map(col => col.name).join("\t"); const rows = results.data.map(row => row.map(cell => cell === null ? "NULL" : String(cell)).join("\t")); const tsv = [header, ...rows].join("\n"); navigator.clipboard.writeText(tsv); }; const borderColor = isDark ? "rgba(255,255,255,0.15)" : "#e5e7eb"; const bgColor = isDark ? "rgba(255,255,255,0.05)" : "#f9fafb"; const headerBg = isDark ? "#2a2a2a" : "#f3f4f6"; const textColor = isDark ? "#e5e7eb" : "#1f2937"; const mutedColor = isDark ? "#d1d5db" : "#6b7280"; const accentColor = isDark ? "#FAFF69" : "#323232"; const accentTextColor = isDark ? "#000" : "#fff"; const barColor = isDark ? "#35372f" : "#d2d2d2"; const cellBg = isDark ? "#1f201b" : "#ffffff"; const cellBgHover = isDark ? "lch(15.8 0 0)" : "#f0f0f0"; const extremes = results && results.meta && results.data ? computeColumnExtremes(results.meta, results.data) : {}; const colWidths = results && results.meta && results.data ? computeColumnWidths(results.meta, results.data) : []; const getCellBarStyle = (cell, ci, ri) => { if (cell === null) return null; const colMeta = results.meta[ci]; if (!isNumericType(colMeta.type) || !extremes[ci] || results.data.length <= 1 || extremes[ci].max <= 0) return null; const ratio = 100 * Number(cell) / extremes[ci].max; const bg = ri === hoveredRow ? cellBgHover : cellBg; return { background: `linear-gradient(to right, ${barColor} 0%, ${barColor} ${ratio}%, ${bg} ${ratio}%, ${bg} 100%)` }; }; const renderCell = (cell, ci) => { if (cell === null) { return NULL; } const value = String(cell); if (isHyperlink(value)) { return {value} ; } return value; }; return
{}
{children}
{}
{results && } {showStats && stats && {formatRows(stats.rows_read)}행, {formatBytes(stats.bytes_read)} 읽음 ({stats.elapsed.toFixed(3)}s) }
{} {showResults &&
{loading &&
쿼리 실행 중...
} {error &&
{error}
} {results && results.meta && results.data &&
{results.meta.map((col, i) =>
1 ? "right" : "left", backgroundColor: headerBg, borderBottom: `1px solid ${borderColor}`, color: textColor, fontWeight: 600, fontSize: "12px", whiteSpace: "nowrap", overflow: "hidden", textOverflow: "ellipsis" }}> {col.name} {col.type}
)} {results.data.map((row, ri) => row.map((cell, ci) =>
setHoveredRow(ri)} onMouseLeave={() => setHoveredRow(-1)} style={{ padding: "4px 12px", color: textColor, whiteSpace: "nowrap", overflow: "hidden", textOverflow: "ellipsis", textAlign: isNumericType(results.meta[ci].type) && results.meta.length > 1 ? "right" : "left", borderBottom: `1px solid ${borderColor}`, backgroundColor: ri === hoveredRow ? cellBgHover : ri % 2 === 0 ? "transparent" : bgColor, transition: "background-color 0.1s", ...getCellBarStyle(cell, ci, ri) }}> {renderCell(cell, ci)}
))}
} {results && results.data &&
{results.rows}행
}
}
; }; 뉴욕 택시 데이터 샘플은 2009년 이후 뉴욕시에서 출발한 30억 건 이상의 택시 및 차량 호출 서비스(Uber, Lyft 등) 이용 데이터를 포함합니다. 이 시작하기 가이드에서는 300만 행 샘플을 사용합니다. 전체 데이터셋은 다음과 같은 몇 가지 방법으로 구할 수 있습니다: * S3 또는 GCS에서 데이터를 ClickHouse Cloud에 직접 삽입합니다 * 준비된 파티션을 다운로드합니다 * 또는 [sql.clickhouse.com](https://sql.clickhouse.com/?query=U0VMRUNUIGNvdW50KCkgRlJPTSBueWNfdGF4aS50cmlwcw\&chart=eyJ0eXBlIjoibGluZSIsImNvbmZpZyI6eyJ0aXRsZSI6IlRlbXBlcmF0dXJlIGJ5IGNvdW50cnkgYW5kIHllYXIiLCJ4YXhpcyI6InllYXIiLCJ5YXhpcyI6ImNvdW50KCkiLCJzZXJpZXMiOiJDQVNUKHBhc3Nlbmdlcl9jb3VudCwgJ1N0cmluZycpIn19) 데모 환경에서 전체 데이터셋을 쿼리할 수 있습니다. 아래 예시 쿼리는 ClickHouse Cloud의 **운영** 인스턴스에서 실행되었습니다. 자세한 내용은 ["Playground 사양"](/ko/get-started/sample-datasets/playground#specifications)을 참조하십시오.
## trips 테이블 생성
먼저 택시 운행용 테이블을 생성합니다: ```sql theme={null} CREATE DATABASE nyc_taxi; CREATE TABLE nyc_taxi.trips_small ( trip_id UInt32, pickup_datetime DateTime, dropoff_datetime DateTime, pickup_longitude Nullable(Float64), pickup_latitude Nullable(Float64), dropoff_longitude Nullable(Float64), dropoff_latitude Nullable(Float64), passenger_count UInt8, trip_distance Float32, fare_amount Float32, extra Float32, tip_amount Float32, tolls_amount Float32, total_amount Float32, payment_type Enum('CSH' = 1, 'CRE' = 2, 'NOC' = 3, 'DIS' = 4, 'UNK' = 5), pickup_ntaname LowCardinality(String), dropoff_ntaname LowCardinality(String) ) ENGINE = MergeTree PRIMARY KEY (pickup_datetime, dropoff_datetime); ```
## 객체 스토리지에서 데이터를 직접 로드하기
데이터를 익히는 용도로는 일부 부분 집합(300만 행)을 가져와 사용할 수 있습니다. 데이터는 객체 스토리지의 TSV 파일로 제공되며, `s3` 테이블 함수를 사용해 ClickHouse Cloud로 쉽게 스트리밍할 수 있습니다. 동일한 데이터가 S3와 GCS 모두에 저장되어 있으므로 원하는 탭을 선택하십시오. 다음 명령은 S3 버킷에서 파일 3개를 `trips_small` 테이블로 스트리밍합니다(`{0..2}` 구문은 0, 1, 2 값을 나타내는 와일드카드입니다): ```sql theme={null} INSERT INTO nyc_taxi.trips_small SELECT trip_id, pickup_datetime, dropoff_datetime, pickup_longitude, pickup_latitude, dropoff_longitude, dropoff_latitude, passenger_count, trip_distance, fare_amount, extra, tip_amount, tolls_amount, total_amount, payment_type, pickup_ntaname, dropoff_ntaname FROM s3( 'https://datasets-documentation.s3.eu-west-3.amazonaws.com/nyc-taxi/trips_{0..2}.gz', 'TabSeparatedWithNames' ); ``` 다음 명령은 GCS 버킷에서 파일 3개를 `trips` 테이블로 스트리밍합니다(`{0..2}` 구문은 0, 1, 2 값을 나타내는 와일드카드입니다): ```sql theme={null} INSERT INTO nyc_taxi.trips_small SELECT trip_id, pickup_datetime, dropoff_datetime, pickup_longitude, pickup_latitude, dropoff_longitude, dropoff_latitude, passenger_count, trip_distance, fare_amount, extra, tip_amount, tolls_amount, total_amount, payment_type, pickup_ntaname, dropoff_ntaname FROM gcs( 'https://storage.googleapis.com/clickhouse-public-datasets/nyc-taxi/trips_{0..2}.gz', 'TabSeparatedWithNames' ); ```
## 샘플 쿼리
다음 쿼리는 위에서 설명한 샘플을 기준으로 실행됩니다. 아래 쿼리에서 테이블을 `nyc_taxi.trips`로 바꾸면 [sql.clickhouse.com](https://sql.clickhouse.com/?query=U0VMRUNUIGNvdW50KCkgRlJPTSBueWNfdGF4aS50cmlwcw\&chart=eyJ0eXBlIjoibGluZSIsImNvbmZpZyI6eyJ0aXRsZSI6IlRlbXBlcmF0dXJlIGJ5IGNvdW50cnkgYW5kIHllYXIiLCJ4YXhpcyI6InllYXIiLCJ5YXhpcyI6ImNvdW50KCkiLCJzZXJpZXMiOiJDQVNUKHBhc3Nlbmdlcl9jb3VudCwgJ1N0cmluZycpIn19)에서 전체 데이터셋으로 샘플 쿼리를 실행할 수 있습니다. 삽입된 행 수를 확인해 보겠습니다: ```sql theme={null} SELECT count() FROM nyc_taxi.trips_small; ``` 각 TSV 파일에는 약 100만 개의 행이 있으며, 세 파일을 합치면 총 3,000,317개의 행이 있습니다. 몇 개의 행을 살펴보겠습니다: ```sql theme={null} SELECT * FROM nyc_taxi.trips_small LIMIT 10; ``` 픽업 및 하차 날짜, 지리 좌표, 요금 세부 정보, 뉴욕 지역명 등을 위한 컬럼이 있는 것을 확인할 수 있습니다. 몇 가지 쿼리를 실행해 보겠습니다. 다음 쿼리는 픽업이 가장 자주 발생한 상위 10개 지역을 보여줍니다: ```sql theme={null} SELECT pickup_ntaname, count(*) AS count FROM nyc_taxi.trips_small WHERE pickup_ntaname != '' GROUP BY pickup_ntaname ORDER BY count DESC LIMIT 10; ``` 이 쿼리는 승객 수에 따른 평균 요금을 보여줍니다: ```sql runnable view='chart' chart_config='eyJ0eXBlIjoiYmFyIiwiY29uZmlnIjp7InhheGlzIjoicGFzc2VuZ2VyX2NvdW50IiwieWF4aXMiOiJhdmcodG90YWxfYW1vdW50KSIsInRpdGxlIjoiQXZlcmFnZSBmYXJlIGJ5IHBhc3NlbmdlciBjb3VudCJ9fQ' theme={null} SELECT passenger_count, avg(total_amount) FROM nyc_taxi.trips_small WHERE passenger_count < 10 GROUP BY passenger_count; ``` 다음은 승객 수와 이동 거리 사이의 상관관계입니다: ```sql runnable chart_config='eyJ0eXBlIjoiaG9yaXpvbnRhbCBiYXIiLCJjb25maWciOnsieGF4aXMiOiJwYXNzZW5nZXJfY291bnQiLCJ5YXhpcyI6ImRpc3RhbmNlIiwic2VyaWVzIjoiY291bnRyeSIsInRpdGxlIjoiQXZnIGZhcmUgYnkgcGFzc2VuZ2VyIGNvdW50In19' theme={null} SELECT passenger_count, avg(trip_distance) AS distance, count() AS c FROM nyc_taxi.trips_small GROUP BY passenger_count ORDER BY passenger_count ASC ```
## 준비된 파티션 다운로드
다음 단계에서는 원본 데이터셋에 대한 정보와 준비된 파티션을 자가 관리형 ClickHouse 서버 환경에 로드하는 방법을 안내합니다. 데이터셋 설명과 다운로드 지침은 [https://github.com/toddwschneider/nyc-taxi-data](https://github.com/toddwschneider/nyc-taxi-data) 및 [http://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html를](http://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html를) 참조하십시오. 다운로드하면 CSV 파일로 약 227 GB의 비압축 데이터가 생성됩니다. 1 Gbit 연결에서는 다운로드에 약 1시간이 걸립니다(`s3.amazonaws.com`에서 병렬 다운로드를 수행하면 1 Gbit 채널의 최소 절반 이상을 활용할 수 있습니다). 일부 파일은 완전히 다운로드되지 않을 수 있습니다. 파일 크기를 확인하고 의심스러운 파일은 다시 다운로드하십시오. ```bash theme={null} $ curl -O https://datasets.clickhouse.com/trips_mergetree/partitions/trips_mergetree.tar # 체크섬 검증 $ md5sum trips_mergetree.tar # 체크섬은 다음 값과 일치해야 합니다: f3b8d469b41d9a82da064ded7245d12c $ tar xvf trips_mergetree.tar -C /var/lib/clickhouse # ClickHouse 데이터 디렉터리 경로 $ # 압축 해제된 데이터의 권한을 확인하고, 필요 시 수정하십시오 $ sudo service clickhouse-server restart $ clickhouse-client --query "select count(*) from datasets.trips_mergetree" ``` 아래에서 설명하는 쿼리를 실행하려면 전체 테이블 이름인 `datasets.trips_mergetree`를 사용해야 합니다.
## 단일 서버 결과
Q1: ```sql theme={null} SELECT cab_type, count(*) FROM trips_mergetree GROUP BY cab_type; ``` 0.490초. Q2: ```sql theme={null} SELECT passenger_count, avg(total_amount) FROM trips_mergetree GROUP BY passenger_count; ``` 1.224초. Q3: ```sql theme={null} SELECT passenger_count, toYear(pickup_date) AS year, count(*) FROM trips_mergetree GROUP BY passenger_count, year; ``` 2.104초. Q4: ```sql theme={null} SELECT passenger_count, toYear(pickup_date) AS year, round(trip_distance) AS distance, count(*) FROM trips_mergetree GROUP BY passenger_count, year, distance ORDER BY year, count(*) DESC; ``` 3.593초. 다음 서버를 사용했습니다: Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz 2개, 총 16개의 물리 코어, 128 GiB RAM, 하드웨어 RAID-5의 6 TB HD 8개 실행 시간은 3회 실행한 결과 중 가장 좋은 값입니다. 다만 2회차 실행부터는 쿼리가 파일 시스템 캐시에서 데이터를 읽습니다. 추가적인 캐싱은 발생하지 않으며, 각 실행마다 데이터를 읽어 처리합니다. 3대의 서버에 테이블 생성: 각 서버에서: ```sql theme={null} CREATE TABLE default.trips_mergetree_third ( trip_id UInt32, vendor_id Enum8('1' = 1, '2' = 2, 'CMT' = 3, 'VTS' = 4, 'DDS' = 5, 'B02512' = 10, 'B02598' = 11, 'B02617' = 12, 'B02682' = 13, 'B02764' = 14), pickup_date Date, pickup_datetime DateTime, dropoff_date Date, dropoff_datetime DateTime, store_and_fwd_flag UInt8, rate_code_id UInt8, pickup_longitude Float64, pickup_latitude Float64, dropoff_longitude Float64, dropoff_latitude Float64, passenger_count UInt8, trip_distance Float64, fare_amount Float32, extra Float32, mta_tax Float32, tip_amount Float32, tolls_amount Float32, ehail_fee Float32, improvement_surcharge Float32, total_amount Float32, payment_type_ Enum8('UNK' = 0, 'CSH' = 1, 'CRE' = 2, 'NOC' = 3, 'DIS' = 4), trip_type UInt8, pickup FixedString(25), dropoff FixedString(25), cab_type Enum8('yellow' = 1, 'green' = 2, 'uber' = 3), pickup_nyct2010_gid UInt8, pickup_ctlabel Float32, pickup_borocode UInt8, pickup_boroname Enum8('' = 0, 'Manhattan' = 1, 'Bronx' = 2, 'Brooklyn' = 3, 'Queens' = 4, 'Staten Island' = 5), pickup_ct2010 FixedString(6), pickup_boroct2010 FixedString(7), pickup_cdeligibil Enum8(' ' = 0, 'E' = 1, 'I' = 2), pickup_ntacode FixedString(4), pickup_ntaname Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195), pickup_puma UInt16, dropoff_nyct2010_gid UInt8, dropoff_ctlabel Float32, dropoff_borocode UInt8, dropoff_boroname Enum8('' = 0, 'Manhattan' = 1, 'Bronx' = 2, 'Brooklyn' = 3, 'Queens' = 4, 'Staten Island' = 5), dropoff_ct2010 FixedString(6), dropoff_boroct2010 FixedString(7), dropoff_cdeligibil Enum8(' ' = 0, 'E' = 1, 'I' = 2), dropoff_ntacode FixedString(4), dropoff_ntaname Enum16('' = 0, 'Airport' = 1, 'Allerton-Pelham Gardens' = 2, 'Annadale-Huguenot-Prince\'s Bay-Eltingville' = 3, 'Arden Heights' = 4, 'Astoria' = 5, 'Auburndale' = 6, 'Baisley Park' = 7, 'Bath Beach' = 8, 'Battery Park City-Lower Manhattan' = 9, 'Bay Ridge' = 10, 'Bayside-Bayside Hills' = 11, 'Bedford' = 12, 'Bedford Park-Fordham North' = 13, 'Bellerose' = 14, 'Belmont' = 15, 'Bensonhurst East' = 16, 'Bensonhurst West' = 17, 'Borough Park' = 18, 'Breezy Point-Belle Harbor-Rockaway Park-Broad Channel' = 19, 'Briarwood-Jamaica Hills' = 20, 'Brighton Beach' = 21, 'Bronxdale' = 22, 'Brooklyn Heights-Cobble Hill' = 23, 'Brownsville' = 24, 'Bushwick North' = 25, 'Bushwick South' = 26, 'Cambria Heights' = 27, 'Canarsie' = 28, 'Carroll Gardens-Columbia Street-Red Hook' = 29, 'Central Harlem North-Polo Grounds' = 30, 'Central Harlem South' = 31, 'Charleston-Richmond Valley-Tottenville' = 32, 'Chinatown' = 33, 'Claremont-Bathgate' = 34, 'Clinton' = 35, 'Clinton Hill' = 36, 'Co-op City' = 37, 'College Point' = 38, 'Corona' = 39, 'Crotona Park East' = 40, 'Crown Heights North' = 41, 'Crown Heights South' = 42, 'Cypress Hills-City Line' = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, 'East New York (Pennsylvania Ave)' = 54, 'East Tremont' = 55, 'East Village' = 56, 'East Williamsburg' = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, 'Georgetown-Marine Park-Bergen Beach-Mill Basin' = 71, 'Glen Oaks-Floral Park-New Hyde Park' = 72, 'Glendale' = 73, 'Gramercy' = 74, 'Grasmere-Arrochar-Ft. Wadsworth' = 75, 'Gravesend' = 76, 'Great Kills' = 77, 'Greenpoint' = 78, 'Grymes Hill-Clifton-Fox Hills' = 79, 'Hamilton Heights' = 80, 'Hammels-Arverne-Edgemere' = 81, 'Highbridge' = 82, 'Hollis' = 83, 'Homecrest' = 84, 'Hudson Yards-Chelsea-Flatiron-Union Square' = 85, 'Hunters Point-Sunnyside-West Maspeth' = 86, 'Hunts Point' = 87, 'Jackson Heights' = 88, 'Jamaica' = 89, 'Jamaica Estates-Holliswood' = 90, 'Kensington-Ocean Parkway' = 91, 'Kew Gardens' = 92, 'Kew Gardens Hills' = 93, 'Kingsbridge Heights' = 94, 'Laurelton' = 95, 'Lenox Hill-Roosevelt Island' = 96, 'Lincoln Square' = 97, 'Lindenwood-Howard Beach' = 98, 'Longwood' = 99, 'Lower East Side' = 100, 'Madison' = 101, 'Manhattanville' = 102, 'Marble Hill-Inwood' = 103, 'Mariner\'s Harbor-Arlington-Port Ivory-Graniteville' = 104, 'Maspeth' = 105, 'Melrose South-Mott Haven North' = 106, 'Middle Village' = 107, 'Midtown-Midtown South' = 108, 'Midwood' = 109, 'Morningside Heights' = 110, 'Morrisania-Melrose' = 111, 'Mott Haven-Port Morris' = 112, 'Mount Hope' = 113, 'Murray Hill' = 114, 'Murray Hill-Kips Bay' = 115, 'New Brighton-Silver Lake' = 116, 'New Dorp-Midland Beach' = 117, 'New Springville-Bloomfield-Travis' = 118, 'North Corona' = 119, 'North Riverdale-Fieldston-Riverdale' = 120, 'North Side-South Side' = 121, 'Norwood' = 122, 'Oakland Gardens' = 123, 'Oakwood-Oakwood Beach' = 124, 'Ocean Hill' = 125, 'Ocean Parkway South' = 126, 'Old Astoria' = 127, 'Old Town-Dongan Hills-South Beach' = 128, 'Ozone Park' = 129, 'Park Slope-Gowanus' = 130, 'Parkchester' = 131, 'Pelham Bay-Country Club-City Island' = 132, 'Pelham Parkway' = 133, 'Pomonok-Flushing Heights-Hillcrest' = 134, 'Port Richmond' = 135, 'Prospect Heights' = 136, 'Prospect Lefferts Gardens-Wingate' = 137, 'Queens Village' = 138, 'Queensboro Hill' = 139, 'Queensbridge-Ravenswood-Long Island City' = 140, 'Rego Park' = 141, 'Richmond Hill' = 142, 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner' = 152, 'Soundview-Castle Hill-Clason Point-Harding Park' = 153, 'South Jamaica' = 154, 'South Ozone Park' = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park West' = 166, 'Todt Hill-Emerson Hill-Heartland Village-Lighthouse Hill' = 167, 'Turtle Bay-East Midtown' = 168, 'University Heights-Morris Heights' = 169, 'Upper East Side-Carnegie Hill' = 170, 'Upper West Side' = 171, 'Van Cortlandt Village' = 172, 'Van Nest-Morris Park-Westchester Square' = 173, 'Washington Heights North' = 174, 'Washington Heights South' = 175, 'West Brighton' = 176, 'West Concourse' = 177, 'West Farms-Bronx River' = 178, 'West New Brighton-New Brighton-St. George' = 179, 'West Village' = 180, 'Westchester-Unionport' = 181, 'Westerleigh' = 182, 'Whitestone' = 183, 'Williamsbridge-Olinville' = 184, 'Williamsburg' = 185, 'Windsor Terrace' = 186, 'Woodhaven' = 187, 'Woodlawn-Wakefield' = 188, 'Woodside' = 189, 'Yorkville' = 190, 'park-cemetery-etc-Bronx' = 191, 'park-cemetery-etc-Brooklyn' = 192, 'park-cemetery-etc-Manhattan' = 193, 'park-cemetery-etc-Queens' = 194, 'park-cemetery-etc-Staten Island' = 195), dropoff_puma UInt16) ENGINE = MergeTree(pickup_date, pickup_datetime, 8192); ``` 소스 서버에서: ```sql theme={null} CREATE TABLE trips_mergetree_x3 AS trips_mergetree_third ENGINE = Distributed(perftest, default, trips_mergetree_third, rand()); ``` 다음 쿼리는 데이터를 재분배합니다: ```sql theme={null} INSERT INTO trips_mergetree_x3 SELECT * FROM trips_mergetree; ``` 이는 2454초가 소요됩니다. 서버 3대에서는 다음과 같습니다: Q1: 0.212초. Q2: 0.438초. Q3: 0.733초. Q4: 1.241초. 쿼리가 선형적으로 확장되므로 이는 예상 가능한 결과입니다. 서버 140대로 구성된 클러스터의 결과도 있습니다: Q1: 0.028초. Q2: 0.043초. Q3: 0.051초. Q4: 0.072초. 이 경우 쿼리 처리 시간은 무엇보다도 네트워크 지연 시간에 의해 결정됩니다. 클러스터가 위치한 데이터 센터와 다른 데이터 센터에 있는 클라이언트로 쿼리를 실행했으며, 이로 인해 약 20 ms의 지연 시간이 추가되었습니다.
## 요약
| 서버 수 | Q1 | Q2 | Q3 | Q4 | | ------------------ | ----- | ----- | ----- | ----- | | 1, E5-2650v2 | 0.490 | 1.224 | 2.104 | 3.593 | | 3, E5-2650v2 | 0.212 | 0.438 | 0.733 | 1.241 | | 1, AWS c5n.4xlarge | 0.249 | 1.279 | 1.738 | 3.527 | | 1, AWS c5n.9xlarge | 0.130 | 0.584 | 0.777 | 1.811 | | 3, AWS c5n.9xlarge | 0.057 | 0.231 | 0.285 | 0.641 | | 140, E5-2650v2 | 0.028 | 0.043 | 0.051 | 0.072 |