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358 lines
28 KiB
Markdown
358 lines
28 KiB
Markdown
---
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slug: /en/getting-started/example-datasets/nyc-taxi
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sidebar_label: New York Taxi Data
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sidebar_position: 2
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description: Data for billions of taxi and for-hire vehicle (Uber, Lyft, etc.) trips originating in New York City since 2009
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---
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# New York Taxi Data
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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:
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- insert the data directly into ClickHouse Cloud from S3 or GCS
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- download prepared partitions
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## Create the table trips
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Start by creating a table for the taxi rides:
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```sql
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CREATE TABLE trips (
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trip_id UInt32,
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pickup_datetime DateTime,
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dropoff_datetime DateTime,
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pickup_longitude Nullable(Float64),
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pickup_latitude Nullable(Float64),
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dropoff_longitude Nullable(Float64),
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dropoff_latitude Nullable(Float64),
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passenger_count UInt8,
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trip_distance Float32,
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fare_amount Float32,
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extra Float32,
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tip_amount Float32,
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tolls_amount Float32,
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total_amount Float32,
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payment_type Enum('CSH' = 1, 'CRE' = 2, 'NOC' = 3, 'DIS' = 4, 'UNK' = 5),
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pickup_ntaname LowCardinality(String),
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dropoff_ntaname LowCardinality(String)
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)
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ENGINE = MergeTree
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PRIMARY KEY (pickup_datetime, dropoff_datetime);
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```
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## Load the Data directly from Object Storage
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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
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ClickHouse Cloud using the `s3` table function.
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The same data is stored in both S3 and GCS; choose either tab.
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<Tabs groupId="storageVendor">
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<TabItem value="gcs" label="GCS" default>
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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):
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```sql
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INSERT INTO trips
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SELECT
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trip_id,
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pickup_datetime,
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dropoff_datetime,
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pickup_longitude,
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pickup_latitude,
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dropoff_longitude,
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dropoff_latitude,
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passenger_count,
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trip_distance,
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fare_amount,
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extra,
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tip_amount,
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tolls_amount,
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total_amount,
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payment_type,
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pickup_ntaname,
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dropoff_ntaname
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FROM gcs(
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'https://storage.googleapis.com/clickhouse-public-datasets/nyc-taxi/trips_{0..2}.gz',
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'TabSeparatedWithNames'
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);
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```
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</TabItem>
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<TabItem value="s3" label="S3">
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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):
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```sql
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INSERT INTO trips
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SELECT
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trip_id,
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pickup_datetime,
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dropoff_datetime,
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pickup_longitude,
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pickup_latitude,
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dropoff_longitude,
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dropoff_latitude,
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passenger_count,
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trip_distance,
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fare_amount,
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extra,
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tip_amount,
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tolls_amount,
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total_amount,
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payment_type,
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pickup_ntaname,
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dropoff_ntaname
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FROM s3(
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'https://datasets-documentation.s3.eu-west-3.amazonaws.com/nyc-taxi/trips_{0..2}.gz',
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'TabSeparatedWithNames'
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);
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```
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</TabItem>
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</Tabs>
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## Sample Queries
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Let's see how many rows were inserted:
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```sql
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SELECT count()
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FROM trips;
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```
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Each TSV file has about 1M rows, and the three files have 3,000,317 rows. Let's look at a few rows:
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```sql
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SELECT *
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FROM trips
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LIMIT 10;
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```
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Notice there are columns for the pickup and dropoff dates, geo coordinates, fare details, New York neighborhoods, and more:
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```response
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┌────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────────────────────────────┐
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│ 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 │
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│ 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 │
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│ 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 │
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│ 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 │
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│ 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 │
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│ 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 │
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│ 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 │
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│ 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 │
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│ 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 │
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│ 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 │
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└────────────┴─────────────────────┴─────────────────────┴────────────────────┴────────────────────┴────────────────────┴────────────────────┴─────────────────┴───────────────┴─────────────┴───────┴────────────┴──────────────┴──────────────┴──────────────┴────────────────────────────────────────────┴────────────────────────────────────────────┘
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```
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Let's run a few queries. This query shows us the top 10 neighborhoods that have the most frequent pickups:
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``` sql
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SELECT
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pickup_ntaname,
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count(*) AS count
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FROM trips
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GROUP BY pickup_ntaname
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ORDER BY count DESC
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LIMIT 10;
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```
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The result is:
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```response
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┌─pickup_ntaname─────────────────────────────┬──count─┐
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│ Midtown-Midtown South │ 526864 │
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│ Hudson Yards-Chelsea-Flatiron-Union Square │ 288797 │
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│ West Village │ 210436 │
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│ Turtle Bay-East Midtown │ 197111 │
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│ Upper East Side-Carnegie Hill │ 184327 │
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│ Airport │ 151343 │
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│ SoHo-TriBeCa-Civic Center-Little Italy │ 144967 │
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│ Murray Hill-Kips Bay │ 138599 │
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│ Upper West Side │ 135469 │
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│ Clinton │ 130002 │
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└────────────────────────────────────────────┴────────┘
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```
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This query shows the average fare based on the number of passengers:
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``` sql
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SELECT
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passenger_count,
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avg(total_amount)
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FROM trips
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GROUP BY passenger_count;
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```
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```response
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┌─passenger_count─┬──avg(total_amount)─┐
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│ 0 │ 25.226335263065018 │
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│ 1 │ 15.961279340656672 │
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│ 2 │ 17.146174183960667 │
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│ 3 │ 17.65380033178517 │
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│ 4 │ 17.248804201047456 │
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│ 5 │ 16.353501285179135 │
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│ 6 │ 15.995094439202836 │
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│ 7 │ 62.077143805367605 │
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│ 8 │ 26.120000791549682 │
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│ 9 │ 10.300000190734863 │
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└─────────────────┴────────────────────┘
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```
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Here's a correlation between the number of passengers and the distance of the trip:
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``` sql
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SELECT
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passenger_count,
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toYear(pickup_datetime) AS year,
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round(trip_distance) AS distance,
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count(*)
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FROM trips
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GROUP BY passenger_count, year, distance
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ORDER BY year, count(*) DESC;
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```
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The first part of the result is:
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```response
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┌─passenger_count─┬─year─┬─distance─┬─count()─┐
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│ 1 │ 2015 │ 1 │ 748644 │
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│ 1 │ 2015 │ 2 │ 521602 │
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│ 1 │ 2015 │ 3 │ 225077 │
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│ 2 │ 2015 │ 1 │ 144990 │
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│ 1 │ 2015 │ 4 │ 134782 │
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│ 1 │ 2015 │ 0 │ 127284 │
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│ 2 │ 2015 │ 2 │ 106411 │
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│ 1 │ 2015 │ 5 │ 72725 │
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│ 5 │ 2015 │ 1 │ 59343 │
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│ 1 │ 2015 │ 6 │ 53447 │
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│ 2 │ 2015 │ 3 │ 48019 │
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│ 3 │ 2015 │ 1 │ 44865 │
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│ 6 │ 2015 │ 1 │ 39409 │
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```
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## Download of Prepared Partitions {#download-of-prepared-partitions}
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:::note
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The following steps provide information about the original dataset, and a method for loading prepared partitions into a self-managed ClickHouse server environment.
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:::
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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.
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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).
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Some of the files might not download fully. Check the file sizes and re-download any that seem doubtful.
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``` bash
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$ curl -O https://datasets.clickhouse.com/trips_mergetree/partitions/trips_mergetree.tar
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# Validate the checksum
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$ md5sum trips_mergetree.tar
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# Checksum should be equal to: f3b8d469b41d9a82da064ded7245d12c
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$ tar xvf trips_mergetree.tar -C /var/lib/clickhouse # path to ClickHouse data directory
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$ # check permissions of unpacked data, fix if required
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$ sudo service clickhouse-server restart
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$ clickhouse-client --query "select count(*) from datasets.trips_mergetree"
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```
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:::info
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If you will run the queries described below, you have to use the full table name, `datasets.trips_mergetree`.
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:::
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## Results on Single Server {#results-on-single-server}
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Q1:
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``` sql
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SELECT cab_type, count(*) FROM trips_mergetree GROUP BY cab_type;
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```
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0.490 seconds.
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Q2:
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``` sql
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SELECT passenger_count, avg(total_amount) FROM trips_mergetree GROUP BY passenger_count;
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```
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1.224 seconds.
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Q3:
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``` sql
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SELECT passenger_count, toYear(pickup_date) AS year, count(*) FROM trips_mergetree GROUP BY passenger_count, year;
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```
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2.104 seconds.
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Q4:
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``` sql
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SELECT passenger_count, toYear(pickup_date) AS year, round(trip_distance) AS distance, count(*)
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FROM trips_mergetree
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GROUP BY passenger_count, year, distance
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ORDER BY year, count(*) DESC;
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```
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3.593 seconds.
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The following server was used:
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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
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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.
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Creating a table on three servers:
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On each server:
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``` sql
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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);
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```
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On the source server:
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``` sql
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CREATE TABLE trips_mergetree_x3 AS trips_mergetree_third ENGINE = Distributed(perftest, default, trips_mergetree_third, rand());
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```
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The following query redistributes data:
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``` sql
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INSERT INTO trips_mergetree_x3 SELECT * FROM trips_mergetree;
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```
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This takes 2454 seconds.
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On three servers:
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Q1: 0.212 seconds.
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Q2: 0.438 seconds.
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Q3: 0.733 seconds.
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Q4: 1.241 seconds.
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No surprises here, since the queries are scaled linearly.
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We also have the results from a cluster of 140 servers:
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Q1: 0.028 sec.
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Q2: 0.043 sec.
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Q3: 0.051 sec.
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Q4: 0.072 sec.
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In this case, the query processing time is determined above all by network latency.
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We ran queries using a client located in a different datacenter than where the cluster was located, which added about 20 ms of latency.
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## Summary {#summary}
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| servers | Q1 | Q2 | Q3 | Q4 |
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|---------|-------|-------|-------|-------|
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| 1, E5-2650v2 | 0.490 | 1.224 | 2.104 | 3.593 |
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| 3, E5-2650v2 | 0.212 | 0.438 | 0.733 | 1.241 |
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| 1, AWS c5n.4xlarge | 0.249 | 1.279 | 1.738 | 3.527 |
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| 1, AWS c5n.9xlarge | 0.130 | 0.584 | 0.777 | 1.811 |
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| 3, AWS c5n.9xlarge | 0.057 | 0.231 | 0.285 | 0.641 |
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| 140, E5-2650v2 | 0.028 | 0.043 | 0.051 | 0.072 |
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