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

---
slug: /en/getting-started/example-datasets/nyc-taxi
sidebar_label: New York Taxi Data
sidebar_position: 2
description: Data for billions of taxi and for-hire vehicle (Uber, Lyft, etc.) trips originating in New York City since 2009
---

import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';

# New York Taxi Data

The New York taxi data consists of 3+ billion taxi and for-hire vehicle (Uber, Lyft, etc.) trips originating in New York City since 2009. The dataset can be obtained in a couple of ways:

- insert the data directly into ClickHouse Cloud from S3 or GCS
- download prepared partitions

## Create the table trips

Start by creating a table for the taxi rides:

```sql
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);
```

## Load the Data directly from Object Storage

Let's grab a small subset of the data for getting familiar with it. The data is in TSV files in object storage, which is easily streamed into
ClickHouse Cloud using the `s3` table function. 

The same data is stored in both S3 and GCS; choose either tab.

<Tabs groupId="storageVendor">
<TabItem value="gcs" label="GCS" default>

The following command streams three files from a GCS bucket into the `trips` table (the `{0..2}` syntax is a wildcard for the values 0, 1, and 2):

```sql
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://storage.googleapis.com/clickhouse-public-datasets/nyc-taxi/trips_{0..2}.gz',
    'TabSeparatedWithNames'
);
```

</TabItem>
<TabItem value="s3" label="S3">

The following command streams three files from an S3 bucket into the `trips` table (the `{0..2}` syntax is a wildcard for the values 0, 1, and 2):

```sql
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'
);
```

</TabItem>
</Tabs>

## Sample Queries

Let's see how many rows were inserted:

```sql
SELECT count()
FROM trips;
```

Each TSV file has about 1M rows, and the three files have 3,000,317 rows. Let's look at a few rows:

```sql
SELECT *
FROM trips
LIMIT 10;
```

Notice there are columns for the pickup and dropoff dates, geo coordinates, fare details, New York neighborhoods, and more:

```response
┌────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                                    │
└────────────┴─────────────────────┴─────────────────────┴────────────────────┴────────────────────┴────────────────────┴────────────────────┴─────────────────┴───────────────┴─────────────┴───────┴────────────┴──────────────┴──────────────┴──────────────┴────────────────────────────────────────────┴────────────────────────────────────────────┘
```

Let's run a few queries. This query shows us the top 10 neighborhoods that have the most frequent pickups:

``` sql
SELECT
   pickup_ntaname,
   count(*) AS count
FROM trips
GROUP BY pickup_ntaname
ORDER BY count DESC
LIMIT 10;
```

The result is:

```response
┌─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 │
└────────────────────────────────────────────┴────────┘
```

This query shows the average fare based on the number of passengers:

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

```response
┌─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 │
└─────────────────┴────────────────────┘
```

Here's a correlation between the number of passengers and the distance of the trip:

``` sql
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;
```

The first part of the result is:

```response
┌─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 │
```

## Download of Prepared Partitions {#download-of-prepared-partitions}

:::note
The following steps provide information about the original dataset, and a method for loading prepared partitions into a self-managed ClickHouse server environment.
:::

See https://github.com/toddwschneider/nyc-taxi-data and http://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html for the description of a dataset and instructions for downloading.

Downloading will result in about 227 GB of uncompressed data in CSV files. The download takes about an hour over a 1 Gbit connection (parallel downloading from s3.amazonaws.com recovers at least half of a 1 Gbit channel).
Some of the files might not download fully. Check the file sizes and re-download any that seem doubtful.

``` bash
$ curl -O https://datasets.clickhouse.com/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
If you will run the queries described below, you have to use the full table name, `datasets.trips_mergetree`.
:::

## Results on Single Server {#results-on-single-server}

Q1:

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

0.490 seconds.

Q2:

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

1.224 seconds.

Q3:

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

2.104 seconds.

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 seconds.

The following server was used:

Two Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 16 physical cores total, 128 GiB RAM, 8x6 TB HD on hardware RAID-5

Execution time is the best of three runs. But starting from the second run, queries read data from the file system cache. No further caching occurs: the data is read out and processed in each run.

Creating a table on three servers:

On each server:

``` 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, 
```

On the source server:

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

The following query redistributes data:

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

This takes 2454 seconds.

On three servers:

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

No surprises here, since the queries are scaled linearly.

We also have the results from a cluster of 140 servers:

Q1: 0.028 sec.
Q2: 0.043 sec.
Q3: 0.051 sec.
Q4: 0.072 sec.

In this case, the query processing time is determined above all by network latency.
We ran queries using a client located in a different datacenter than where the cluster was located, which added about 20 ms of latency.

## Summary {#summary}

| servers | 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 |