ClickHouse/docs/es/getting-started/example-datasets/nyc-taxi.md

---
machine_translated: true
machine_translated_rev: 72537a2d527c63c07aa5d2361a8829f3895cf2bd
toc_priority: 16
toc_title: Datos de taxis de Nueva York
---

# Datos de taxis de Nueva York {#new-york-taxi-data}

Este conjunto de datos se puede obtener de dos maneras:

-   importación de datos sin procesar
-   descarga de particiones preparadas

## Cómo importar los datos sin procesar {#how-to-import-the-raw-data}

Consulte https://github.com/toddwschneider/nyc-taxi-data y http://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html para obtener la descripción de un conjunto de datos e instrucciones para descargar.

La descarga dará como resultado aproximadamente 227 GB de datos sin comprimir en archivos CSV. La descarga tarda aproximadamente una hora en una conexión de 1 Gbit (la descarga paralela de s3.amazonaws.com recupera al menos la mitad de un canal de 1 Gbit).
Es posible que algunos de los archivos no se descarguen por completo. Verifique los tamaños de archivo y vuelva a descargar cualquiera que parezca dudoso.

Algunos de los archivos pueden contener filas no válidas. Puede arreglarlos de la siguiente manera:

``` bash
sed -E '/(.*,){18,}/d' data/yellow_tripdata_2010-02.csv > data/yellow_tripdata_2010-02.csv_
sed -E '/(.*,){18,}/d' data/yellow_tripdata_2010-03.csv > data/yellow_tripdata_2010-03.csv_
mv data/yellow_tripdata_2010-02.csv_ data/yellow_tripdata_2010-02.csv
mv data/yellow_tripdata_2010-03.csv_ data/yellow_tripdata_2010-03.csv
```

Entonces los datos deben ser preprocesados en PostgreSQL. Esto creará selecciones de puntos en los polígonos (para hacer coincidir los puntos en el mapa con los distritos de la ciudad de Nueva York) y combinará todos los datos en una única tabla plana desnormalizada mediante el uso de una unión. Para hacer esto, deberá instalar PostgreSQL con soporte PostGIS.

Tenga cuidado al correr `initialize_database.sh` y volver a verificar manualmente que todas las tablas se crearon correctamente.

Se tarda entre 20 y 30 minutos en procesar los datos de cada mes en PostgreSQL, por un total de aproximadamente 48 horas.

Puede comprobar el número de filas descargadas de la siguiente manera:

``` bash
$ time psql nyc-taxi-data -c "SELECT count(*) FROM trips;"
## Count
 1298979494
(1 row)

real    7m9.164s
```

(Esto es un poco más de 1.1 mil millones de filas reportadas por Mark Litwintschik en una serie de publicaciones de blog.)

Los datos en PostgreSQL utilizan 370 GB de espacio.

Exportación de los datos de PostgreSQL:

``` sql
COPY
(
    SELECT trips.id,
           trips.vendor_id,
           trips.pickup_datetime,
           trips.dropoff_datetime,
           trips.store_and_fwd_flag,
           trips.rate_code_id,
           trips.pickup_longitude,
           trips.pickup_latitude,
           trips.dropoff_longitude,
           trips.dropoff_latitude,
           trips.passenger_count,
           trips.trip_distance,
           trips.fare_amount,
           trips.extra,
           trips.mta_tax,
           trips.tip_amount,
           trips.tolls_amount,
           trips.ehail_fee,
           trips.improvement_surcharge,
           trips.total_amount,
           trips.payment_type,
           trips.trip_type,
           trips.pickup,
           trips.dropoff,

           cab_types.type cab_type,

           weather.precipitation_tenths_of_mm rain,
           weather.snow_depth_mm,
           weather.snowfall_mm,
           weather.max_temperature_tenths_degrees_celsius max_temp,
           weather.min_temperature_tenths_degrees_celsius min_temp,
           weather.average_wind_speed_tenths_of_meters_per_second wind,

           pick_up.gid pickup_nyct2010_gid,
           pick_up.ctlabel pickup_ctlabel,
           pick_up.borocode pickup_borocode,
           pick_up.boroname pickup_boroname,
           pick_up.ct2010 pickup_ct2010,
           pick_up.boroct2010 pickup_boroct2010,
           pick_up.cdeligibil pickup_cdeligibil,
           pick_up.ntacode pickup_ntacode,
           pick_up.ntaname pickup_ntaname,
           pick_up.puma pickup_puma,

           drop_off.gid dropoff_nyct2010_gid,
           drop_off.ctlabel dropoff_ctlabel,
           drop_off.borocode dropoff_borocode,
           drop_off.boroname dropoff_boroname,
           drop_off.ct2010 dropoff_ct2010,
           drop_off.boroct2010 dropoff_boroct2010,
           drop_off.cdeligibil dropoff_cdeligibil,
           drop_off.ntacode dropoff_ntacode,
           drop_off.ntaname dropoff_ntaname,
           drop_off.puma dropoff_puma
    FROM trips
    LEFT JOIN cab_types
        ON trips.cab_type_id = cab_types.id
    LEFT JOIN central_park_weather_observations_raw weather
        ON weather.date = trips.pickup_datetime::date
    LEFT JOIN nyct2010 pick_up
        ON pick_up.gid = trips.pickup_nyct2010_gid
    LEFT JOIN nyct2010 drop_off
        ON drop_off.gid = trips.dropoff_nyct2010_gid
) TO '/opt/milovidov/nyc-taxi-data/trips.tsv';
```

La instantánea de datos se crea a una velocidad de aproximadamente 50 MB por segundo. Al crear la instantánea, PostgreSQL lee desde el disco a una velocidad de aproximadamente 28 MB por segundo.
Esto toma alrededor de 5 horas. El archivo TSV resultante es 590612904969 bytes.

Crear una tabla temporal en ClickHouse:

``` sql
CREATE TABLE trips
(
trip_id                 UInt32,
vendor_id               String,
pickup_datetime         DateTime,
dropoff_datetime        Nullable(DateTime),
store_and_fwd_flag      Nullable(FixedString(1)),
rate_code_id            Nullable(UInt8),
pickup_longitude        Nullable(Float64),
pickup_latitude         Nullable(Float64),
dropoff_longitude       Nullable(Float64),
dropoff_latitude        Nullable(Float64),
passenger_count         Nullable(UInt8),
trip_distance           Nullable(Float64),
fare_amount             Nullable(Float32),
extra                   Nullable(Float32),
mta_tax                 Nullable(Float32),
tip_amount              Nullable(Float32),
tolls_amount            Nullable(Float32),
ehail_fee               Nullable(Float32),
improvement_surcharge   Nullable(Float32),
total_amount            Nullable(Float32),
payment_type            Nullable(String),
trip_type               Nullable(UInt8),
pickup                  Nullable(String),
dropoff                 Nullable(String),
cab_type                Nullable(String),
precipitation           Nullable(UInt8),
snow_depth              Nullable(UInt8),
snowfall                Nullable(UInt8),
max_temperature         Nullable(UInt8),
min_temperature         Nullable(UInt8),
average_wind_speed      Nullable(UInt8),
pickup_nyct2010_gid     Nullable(UInt8),
pickup_ctlabel          Nullable(String),
pickup_borocode         Nullable(UInt8),
pickup_boroname         Nullable(String),
pickup_ct2010           Nullable(String),
pickup_boroct2010       Nullable(String),
pickup_cdeligibil       Nullable(FixedString(1)),
pickup_ntacode          Nullable(String),
pickup_ntaname          Nullable(String),
pickup_puma             Nullable(String),
dropoff_nyct2010_gid    Nullable(UInt8),
dropoff_ctlabel         Nullable(String),
dropoff_borocode        Nullable(UInt8),
dropoff_boroname        Nullable(String),
dropoff_ct2010          Nullable(String),
dropoff_boroct2010      Nullable(String),
dropoff_cdeligibil      Nullable(String),
dropoff_ntacode         Nullable(String),
dropoff_ntaname         Nullable(String),
dropoff_puma            Nullable(String)
) ENGINE = Log;
```

Es necesario para convertir campos a tipos de datos más correctos y, si es posible, para eliminar NULL.

``` bash
$ time clickhouse-client --query="INSERT INTO trips FORMAT TabSeparated" < trips.tsv

real    75m56.214s
```

Los datos se leen a una velocidad de 112-140 Mb / segundo.
La carga de datos en una tabla de tipos de registro en una secuencia tardó 76 minutos.
Los datos de esta tabla utilizan 142 GB.

(Importar datos directamente desde Postgres también es posible usando `COPY ... TO PROGRAM`.)

Unfortunately, all the fields associated with the weather (precipitation…average\_wind\_speed) were filled with NULL. Because of this, we will remove them from the final data set.

Para empezar, crearemos una tabla en un único servidor. Posteriormente haremos la mesa distribuida.

Crear y rellenar una tabla de resumen:

``` sql
CREATE TABLE trips_mergetree
ENGINE = MergeTree(pickup_date, pickup_datetime, 8192)
AS SELECT

trip_id,
CAST(vendor_id AS Enum8('1' = 1, '2' = 2, 'CMT' = 3, 'VTS' = 4, 'DDS' = 5, 'B02512' = 10, 'B02598' = 11, 'B02617' = 12, 'B02682' = 13, 'B02764' = 14)) AS vendor_id,
toDate(pickup_datetime) AS pickup_date,
ifNull(pickup_datetime, toDateTime(0)) AS pickup_datetime,
toDate(dropoff_datetime) AS dropoff_date,
ifNull(dropoff_datetime, toDateTime(0)) AS dropoff_datetime,
assumeNotNull(store_and_fwd_flag) IN ('Y', '1', '2') AS store_and_fwd_flag,
assumeNotNull(rate_code_id) AS rate_code_id,
assumeNotNull(pickup_longitude) AS pickup_longitude,
assumeNotNull(pickup_latitude) AS pickup_latitude,
assumeNotNull(dropoff_longitude) AS dropoff_longitude,
assumeNotNull(dropoff_latitude) AS dropoff_latitude,
assumeNotNull(passenger_count) AS passenger_count,
assumeNotNull(trip_distance) AS trip_distance,
assumeNotNull(fare_amount) AS fare_amount,
assumeNotNull(extra) AS extra,
assumeNotNull(mta_tax) AS mta_tax,
assumeNotNull(tip_amount) AS tip_amount,
assumeNotNull(tolls_amount) AS tolls_amount,
assumeNotNull(ehail_fee) AS ehail_fee,
assumeNotNull(improvement_surcharge) AS improvement_surcharge,
assumeNotNull(total_amount) AS total_amount,
CAST((assumeNotNull(payment_type) AS pt) IN ('CSH', 'CASH', 'Cash', 'CAS', 'Cas', '1') ? 'CSH' : (pt IN ('CRD', 'Credit', 'Cre', 'CRE', 'CREDIT', '2') ? 'CRE' : (pt IN ('NOC', 'No Charge', 'No', '3') ? 'NOC' : (pt IN ('DIS', 'Dispute', 'Dis', '4') ? 'DIS' : 'UNK'))) AS Enum8('CSH' = 1, 'CRE' = 2, 'UNK' = 0, 'NOC' = 3, 'DIS' = 4)) AS payment_type_,
assumeNotNull(trip_type) AS trip_type,
ifNull(toFixedString(unhex(pickup), 25), toFixedString('', 25)) AS pickup,
ifNull(toFixedString(unhex(dropoff), 25), toFixedString('', 25)) AS dropoff,
CAST(assumeNotNull(cab_type) AS Enum8('yellow' = 1, 'green' = 2, 'uber' = 3)) AS cab_type,

assumeNotNull(pickup_nyct2010_gid) AS pickup_nyct2010_gid,
toFloat32(ifNull(pickup_ctlabel, '0')) AS pickup_ctlabel,
assumeNotNull(pickup_borocode) AS pickup_borocode,
CAST(assumeNotNull(pickup_boroname) AS Enum8('Manhattan' = 1, 'Queens' = 4, 'Brooklyn' = 3, '' = 0, 'Bronx' = 2, 'Staten Island' = 5)) AS pickup_boroname,
toFixedString(ifNull(pickup_ct2010, '000000'), 6) AS pickup_ct2010,
toFixedString(ifNull(pickup_boroct2010, '0000000'), 7) AS pickup_boroct2010,
CAST(assumeNotNull(ifNull(pickup_cdeligibil, ' ')) AS Enum8(' ' = 0, 'E' = 1, 'I' = 2)) AS pickup_cdeligibil,
toFixedString(ifNull(pickup_ntacode, '0000'), 4) AS pickup_ntacode,

CAST(assumeNotNull(pickup_ntaname) AS 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, 'S

toUInt16(ifNull(pickup_puma, '0')) AS pickup_puma,

assumeNotNull(dropoff_nyct2010_gid) AS dropoff_nyct2010_gid,
toFloat32(ifNull(dropoff_ctlabel, '0')) AS dropoff_ctlabel,
assumeNotNull(dropoff_borocode) AS dropoff_borocode,
CAST(assumeNotNull(dropoff_boroname) AS Enum8('Manhattan' = 1, 'Queens' = 4, 'Brooklyn' = 3, '' = 0, 'Bronx' = 2, 'Staten Island' = 5)) AS dropoff_boroname,
toFixedString(ifNull(dropoff_ct2010, '000000'), 6) AS dropoff_ct2010,
toFixedString(ifNull(dropoff_boroct2010, '0000000'), 7) AS dropoff_boroct2010,
CAST(assumeNotNull(ifNull(dropoff_cdeligibil, ' ')) AS Enum8(' ' = 0, 'E' = 1, 'I' = 2)) AS dropoff_cdeligibil,
toFixedString(ifNull(dropoff_ntacode, '0000'), 4) AS dropoff_ntacode,

CAST(assumeNotNull(dropoff_ntaname) AS 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, '

toUInt16(ifNull(dropoff_puma, '0')) AS dropoff_puma

FROM trips
```

Esto toma 3030 segundos a una velocidad de aproximadamente 428,000 filas por segundo.
Para cargarlo más rápido, puede crear la tabla con el `Log` motor en lugar de `MergeTree`. En este caso, la descarga funciona más rápido que 200 segundos.

La tabla utiliza 126 GB de espacio en disco.

``` sql
SELECT formatReadableSize(sum(bytes)) FROM system.parts WHERE table = 'trips_mergetree' AND active
```

``` text
┌─formatReadableSize(sum(bytes))─┐
│ 126.18 GiB                     │
└────────────────────────────────┘
```

Entre otras cosas, puede ejecutar la consulta OPTIMIZE en MergeTree. Pero no es necesario ya que todo estará bien sin él.

## Descarga de Prepared Partitions {#download-of-prepared-partitions}

``` bash
$ curl -O https://clickhouse-datasets.s3.yandex.net/trips_mergetree/partitions/trips_mergetree.tar
$ tar xvf trips_mergetree.tar -C /var/lib/clickhouse # path to ClickHouse data directory
$ # check permissions of unpacked data, fix if required
$ sudo service clickhouse-server restart
$ clickhouse-client --query "select count(*) from datasets.trips_mergetree"
```

!!! info "INFO"
    Si va a ejecutar las consultas que se describen a continuación, debe usar el nombre completo de la tabla, `datasets.trips_mergetree`.

## Resultados en un solo servidor {#results-on-single-server}

Q1:

``` sql
SELECT cab_type, count(*) FROM trips_mergetree GROUP BY cab_type
```

0.490 segundos.

Q2:

``` sql
SELECT passenger_count, avg(total_amount) FROM trips_mergetree GROUP BY passenger_count
```

1.224 segundos.

Q3:

``` sql
SELECT passenger_count, toYear(pickup_date) AS year, count(*) FROM trips_mergetree GROUP BY passenger_count, year
```

2.104 segundos.

Q4:

``` sql
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 segundos.

Se utilizó el siguiente servidor:

Dos CPU Intel (R) Xeon (R) E5-2650 v2 @ 2.60GHz, 16 núcleos físicos en total, 128 GiB RAM, 8x6 TB HD en hardware RAID-5

El tiempo de ejecución es el mejor de tres carreras. Pero a partir de la segunda ejecución, las consultas leen datos de la memoria caché del sistema de archivos. No se produce más almacenamiento en caché: los datos se leen y procesan en cada ejecución.

Creación de una tabla en tres servidores:

En cada servidor:

``` sql
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, 
```

En el servidor de origen:

``` sql
CREATE TABLE trips_mergetree_x3 AS trips_mergetree_third ENGINE = Distributed(perftest, default, trips_mergetree_third, rand())
```

La siguiente consulta redistribuye los datos:

``` sql
INSERT INTO trips_mergetree_x3 SELECT * FROM trips_mergetree
```

Esto tarda 2454 segundos.

En tres servidores:

Q1: 0.212 segundos.
Q2: 0.438 segundos.
Q3: 0.733 segundos.
Q4: 1.241 segundos.

No hay sorpresas aquí, ya que las consultas se escalan linealmente.

También tenemos los resultados de un clúster de 140 servidores:

Q1: 0,028 seg.
Q2: 0,043 seg.
Q3: 0,051 seg.
Q4: 0,072 seg.

En este caso, el tiempo de procesamiento de la consulta está determinado sobre todo por la latencia de la red.
Ejecutamos consultas utilizando un cliente ubicado en un centro de datos de Yandex en Finlandia en un clúster en Rusia, que agregó aproximadamente 20 ms de latencia.

## Resumen {#summary}

| servidor | Q1    | Q2    | Q3    | Q4    |
|----------|-------|-------|-------|-------|
| 1        | 0.490 | 1.224 | 2.104 | 3.593 |
| 3        | 0.212 | 0.438 | 0.733 | 1.241 |
| 140      | 0.028 | 0.043 | 0.051 | 0.072 |

[Artículo Original](https://clickhouse.tech/docs/en/getting_started/example_datasets/nyc_taxi/) <!--hide-->