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| slug | sidebar_label | sidebar_position | description |
|---|---|---|---|
| /ja/getting-started/example-datasets/nyc-taxi | New York Taxi Data | 2 | 2009年以降にニューヨーク市から出発する数十億回のタクシーとハイヤー車両 (Uber, Lyftなど) のデータ |
import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';
New York Taxi Data
ニューヨークのタクシーデータは、2009年以降にニューヨーク市から出発する3億回以上のタクシーとハイヤー車両 (Uber, Lyftなど) のデータで構成されています。このデータセットは以下の方法で入手可能です：
- データをS3またはGCSから直接ClickHouse Cloudに挿入
- 準備済みのパーティションをダウンロード
テーブルtripsを作成する
まず、タクシー乗車用のテーブルを作成します：
CREATE TABLE trips (
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);
オブジェクトストレージからデータを直接ロード
データに慣れるために、小さなサブセットを取り込みましょう。データはオブジェクトストレージのTSVファイルにあり、s3テーブル関数を使用してClickHouse Cloudに簡単にストリーミングできます。
同じデータがS3とGCSの両方に保存されていますので、いずれかのタブを選択してください。
以下のコマンドは、GCSバケットから3つのファイルをtripsテーブルにストリーミングします（{0..2}構文は0、1、および2のワイルドカードです）：
INSERT INTO trips
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'
);
以下のコマンドは、S3バケットから3つのファイルをtripsテーブルにストリーミングします（{0..2}構文は0、1、および2のワイルドカードです）：
INSERT INTO trips
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'
);
サンプルクエリ
挿入された行数を確認してみましょう：
SELECT count()
FROM trips;
各TSVファイルには約100万行あり、3つのファイルで3,000,317行になります。いくつかの行を見てみましょう：
SELECT *
FROM trips
LIMIT 10;
ピックアップおよびドロップオフの日付、地理座標、料金の詳細、ニューヨークの地区などのカラムがあることに注意してください：
┌────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────────────────────────────┐
│ 1200864931 │ 2015-07-01 00:00:13 │ 2015-07-01 00:14:41 │ -73.99046325683594 │ 40.746116638183594 │ -73.97918701171875 │ 40.78467559814453 │ 5 │ 3.54 │ 13.5 │ 0.5 │ 1 │ 0 │ 15.8 │ CSH │ Midtown-Midtown South │ Upper West Side │
│ 1200018648 │ 2015-07-01 00:00:16 │ 2015-07-01 00:02:57 │ -73.78358459472656 │ 40.648677825927734 │ -73.80242919921875 │ 40.64767837524414 │ 1 │ 1.45 │ 6 │ 0.5 │ 0 │ 0 │ 7.3 │ CRE │ Airport │ Airport │
│ 1201452450 │ 2015-07-01 00:00:20 │ 2015-07-01 00:11:07 │ -73.98579406738281 │ 40.72777557373047 │ -74.00482177734375 │ 40.73748779296875 │ 5 │ 1.56 │ 8.5 │ 0.5 │ 1.96 │ 0 │ 11.76 │ CSH │ East Village │ West Village │
│ 1202368372 │ 2015-07-01 00:00:40 │ 2015-07-01 00:05:46 │ -74.00206756591797 │ 40.73833084106445 │ -74.00658416748047 │ 40.74875259399414 │ 2 │ 1 │ 6 │ 0.5 │ 0 │ 0 │ 7.3 │ CRE │ West Village │ Hudson Yards-Chelsea-Flatiron-Union Square │
│ 1200831168 │ 2015-07-01 00:01:06 │ 2015-07-01 00:09:23 │ -73.98748016357422 │ 40.74344253540039 │ -74.00575256347656 │ 40.716793060302734 │ 1 │ 2.3 │ 9 │ 0.5 │ 2 │ 0 │ 12.3 │ CSH │ Hudson Yards-Chelsea-Flatiron-Union Square │ SoHo-TriBeCa-Civic Center-Little Italy │
│ 1201362116 │ 2015-07-01 00:01:07 │ 2015-07-01 00:03:31 │ -73.9926986694336 │ 40.75826644897461 │ -73.98628997802734 │ 40.76075744628906 │ 1 │ 0.6 │ 4 │ 0.5 │ 0 │ 0 │ 5.3 │ CRE │ Clinton │ Midtown-Midtown South │
│ 1200639419 │ 2015-07-01 00:01:13 │ 2015-07-01 00:03:56 │ -74.00382995605469 │ 40.741981506347656 │ -73.99711608886719 │ 40.742271423339844 │ 1 │ 0.49 │ 4 │ 0.5 │ 0 │ 0 │ 5.3 │ CRE │ Hudson Yards-Chelsea-Flatiron-Union Square │ Hudson Yards-Chelsea-Flatiron-Union Square │
│ 1201181622 │ 2015-07-01 00:01:17 │ 2015-07-01 00:05:12 │ -73.9512710571289 │ 40.78261947631836 │ -73.95230865478516 │ 40.77476119995117 │ 4 │ 0.97 │ 5 │ 0.5 │ 1 │ 0 │ 7.3 │ CSH │ Upper East Side-Carnegie Hill │ Yorkville │
│ 1200978273 │ 2015-07-01 00:01:28 │ 2015-07-01 00:09:46 │ -74.00822448730469 │ 40.72113037109375 │ -74.00422668457031 │ 40.70782470703125 │ 1 │ 1.71 │ 8.5 │ 0.5 │ 1.96 │ 0 │ 11.76 │ CSH │ SoHo-TriBeCa-Civic Center-Little Italy │ Battery Park City-Lower Manhattan │
│ 1203283366 │ 2015-07-01 00:01:47 │ 2015-07-01 00:24:26 │ -73.98199462890625 │ 40.77289962768555 │ -73.91968536376953 │ 40.766082763671875 │ 3 │ 5.26 │ 19.5 │ 0.5 │ 5.2 │ 0 │ 26 │ CSH │ Lincoln Square │ Astoria │
└────────────┴─────────────────────┴─────────────────────┴────────────────────┴────────────────────┴────────────────────┴────────────────────┴─────────────────┴───────────────┴─────────────┴───────┴────────────┴──────────────┴──────────────┴──────────────┴────────────────────────────────────────────┴────────────────────────────────────────────┘
いくつかのクエリを実行してみましょう。まず、ピックアップが最も頻繁に行われる上位10の地区を表示します：
SELECT
pickup_ntaname,
count(*) AS count
FROM trips
GROUP BY pickup_ntaname
ORDER BY count DESC
LIMIT 10;
結果は次のとおりです：
┌─pickup_ntaname─────────────────────────────┬──count─┐
│ Midtown-Midtown South │ 526864 │
│ Hudson Yards-Chelsea-Flatiron-Union Square │ 288797 │
│ West Village │ 210436 │
│ Turtle Bay-East Midtown │ 197111 │
│ Upper East Side-Carnegie Hill │ 184327 │
│ Airport │ 151343 │
│ SoHo-TriBeCa-Civic Center-Little Italy │ 144967 │
│ Murray Hill-Kips Bay │ 138599 │
│ Upper West Side │ 135469 │
│ Clinton │ 130002 │
└────────────────────────────────────────────┴────────┘
次のクエリは、乗客数に基づく平均運賃を示します：
SELECT
passenger_count,
avg(total_amount)
FROM trips
GROUP BY passenger_count;
┌─passenger_count─┬──avg(total_amount)─┐
│ 0 │ 25.226335263065018 │
│ 1 │ 15.961279340656672 │
│ 2 │ 17.146174183960667 │
│ 3 │ 17.65380033178517 │
│ 4 │ 17.248804201047456 │
│ 5 │ 16.353501285179135 │
│ 6 │ 15.995094439202836 │
│ 7 │ 62.077143805367605 │
│ 8 │ 26.120000791549682 │
│ 9 │ 10.300000190734863 │
└─────────────────┴────────────────────┘
次は、乗客数と旅行距離の相関関係です：
SELECT
passenger_count,
toYear(pickup_datetime) AS year,
round(trip_distance) AS distance,
count(*)
FROM trips
GROUP BY passenger_count, year, distance
ORDER BY year, count(*) DESC;
結果の最初の部分は次のとおりです：
┌─passenger_count─┬─year─┬─distance─┬─count()─┐
│ 1 │ 2015 │ 1 │ 748644 │
│ 1 │ 2015 │ 2 │ 521602 │
│ 1 │ 2015 │ 3 │ 225077 │
│ 2 │ 2015 │ 1 │ 144990 │
│ 1 │ 2015 │ 4 │ 134782 │
│ 1 │ 2015 │ 0 │ 127284 │
│ 2 │ 2015 │ 2 │ 106411 │
│ 1 │ 2015 │ 5 │ 72725 │
│ 5 │ 2015 │ 1 │ 59343 │
│ 1 │ 2015 │ 6 │ 53447 │
│ 2 │ 2015 │ 3 │ 48019 │
│ 3 │ 2015 │ 1 │ 44865 │
│ 6 │ 2015 │ 1 │ 39409 │
準備済みパーティションのダウンロード
:::note 以下のステップは、元のデータセットに関する情報と、準備済みパーティションをセルフマネージドのClickHouseサーバー環境にロードする方法を示しています。 :::
データセットの説明とダウンロード手順については、https://github.com/toddwschneider/nyc-taxi-data および http://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html を参照してください。
ダウンロードすると、CSVファイルで約227GBの未圧縮データになります。1 Gbit接続で約1時間かかります（s3.amazonaws.comからの並列ダウンロードにより、少なくとも1 Gbitチャンネルの半分が回復されます）。 一部のファイルは完全にダウンロードされないことがあります。ファイルサイズを確認し、問題があると思われるものを再ダウンロードしてください。
$ curl -O https://datasets.clickhouse.com/trips_mergetree/partitions/trips_mergetree.tar
# Checksumを確認
$ md5sum trips_mergetree.tar
# Checksumは次と等しいはずです: f3b8d469b41d9a82da064ded7245d12c
$ tar xvf trips_mergetree.tar -C /var/lib/clickhouse # ClickHouseのデータディレクトリへのパス
$ # 解凍したデータの権限を確認し、必要に応じて修正
$ sudo service clickhouse-server restart
$ clickhouse-client --query "select count(*) from datasets.trips_mergetree"
:::info
以下に記載されたクエリを実行する場合、フルテーブル名datasets.trips_mergetreeを使用する必要があります。
:::
シングルサーバーでの結果
Q1:
SELECT cab_type, count(*) FROM trips_mergetree GROUP BY cab_type;
0.490秒。
Q2:
SELECT passenger_count, avg(total_amount) FROM trips_mergetree GROUP BY passenger_count;
1.224秒。
Q3:
SELECT passenger_count, toYear(pickup_date) AS year, count(*) FROM trips_mergetree GROUP BY passenger_count, year;
2.104秒。
Q4:
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;
3.593秒。
使用したサーバー：
2つのIntel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz、合計16物理コア、128 GiB RAM、8x6 TB HD on hardware RAID-5
実行時間は3回のランの中で最も短いものです。しかし、2回目以降のランからは、データがファイルシステムキャッシュから読み込まれます。これ以上のキャッシュは発生しません：データは各ランで読み込まれ処理されます。
3台のサーバーでテーブルを作成：
各サーバーで：
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);
ソースサーバーで：
CREATE TABLE trips_mergetree_x3 AS trips_mergetree_third ENGINE = Distributed(perftest, default, trips_mergetree_third, rand());
以下のクエリデータを再分配します：
INSERT INTO trips_mergetree_x3 SELECT * FROM trips_mergetree;
この処理には2454秒かかります。
3サーバーの場合：
Q1: 0.212秒。 Q2: 0.438秒。 Q3: 0.733秒。 Q4: 1.241秒。
ここに驚きはありません、クエリは線形にスケールしています。
また、140台のサーバークラスタからの結果もあります：
Q1: 0.028秒。 Q2: 0.043秒。 Q3: 0.051秒。 Q4: 0.072秒。
この場合、クエリ処理時間は主にネットワーク遅延によって決定されます。 クエリはクラスターが位置するデータセンターとは異なる場所にあるクライアントを使用して実行されました。このため、約20 msの遅延が追加されました。
まとめ
| サーバー数 | 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 |