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ClickHouse/docs/en/getting-started/example-datasets/uk-price-paid.md

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---
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slug: /en/getting-started/example-datasets/uk-price-paid
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sidebar_label: UK Property Price Paid
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sidebar_position: 1
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title: "UK Property Price Paid"
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---
The dataset contains data about prices paid for real-estate property in England and Wales. The data is available since year 1995.
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The size of the dataset in uncompressed form is about 4 GiB and it will take about 278 MiB in ClickHouse.
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Source: https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads
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Description of the fields: https://www.gov.uk/guidance/about-the-price-paid-data
Contains HM Land Registry data © Crown copyright and database right 2021. This data is licensed under the Open Government Licence v3.0.
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## Download the Dataset {#download-dataset}
Run the command:
```bash
wget http://prod.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv
```
Download will take about 2 minutes with good internet connection.
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## Create the Table {#create-table}
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```sql
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CREATE TABLE uk_price_paid
(
price UInt32,
date Date,
postcode1 LowCardinality(String),
postcode2 LowCardinality(String),
type Enum8('terraced' = 1, 'semi-detached' = 2, 'detached' = 3, 'flat' = 4, 'other' = 0),
is_new UInt8,
duration Enum8('freehold' = 1, 'leasehold' = 2, 'unknown' = 0),
addr1 String,
addr2 String,
street LowCardinality(String),
locality LowCardinality(String),
town LowCardinality(String),
district LowCardinality(String),
county LowCardinality(String),
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category UInt8
) ENGINE = MergeTree ORDER BY (postcode1, postcode2, addr1, addr2);
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```
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## Preprocess and Import Data {#preprocess-import-data}
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We will use `clickhouse-local` tool for data preprocessing and `clickhouse-client` to upload it.
In this example, we define the structure of source data from the CSV file and specify a query to preprocess the data with `clickhouse-local`.
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The preprocessing is:
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- splitting the postcode to two different columns `postcode1` and `postcode2` that is better for storage and queries;
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- ignoring the [UUid](../../sql-reference/data-types/uuid.md) field because we don't need it for analysis;
- transforming `type` and `duration` to more readable Enum fields with function [transform](../../sql-reference/functions/other-functions.md#transform);
- transforming `is_new` and `category` fields from single-character string (`Y`/`N` and `A`/`B`) to [UInt8](../../sql-reference/data-types/int-uint.md#uint8-uint16-uint32-uint64-uint256-int8-int16-int32-int64-int128-int256) field with 0 and 1.
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Preprocessed data is piped directly to `clickhouse-client` to be inserted into ClickHouse table in streaming fashion.
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```bash
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clickhouse-local --input-format CSV --structure '
uuid String,
price UInt32,
time DateTime,
postcode String,
a String,
b String,
c String,
addr1 String,
addr2 String,
street String,
locality String,
town String,
district String,
county String,
d String,
e String
' --query "
WITH splitByChar(' ', postcode) AS p
SELECT
price,
toDate(time) AS date,
p[1] AS postcode1,
p[2] AS postcode2,
transform(a, ['T', 'S', 'D', 'F', 'O'], ['terraced', 'semi-detached', 'detached', 'flat', 'other']) AS type,
b = 'Y' AS is_new,
transform(c, ['F', 'L', 'U'], ['freehold', 'leasehold', 'unknown']) AS duration,
addr1,
addr2,
street,
locality,
town,
district,
county,
d = 'B' AS category
FROM table" --date_time_input_format best_effort < pp-complete.csv | clickhouse-client --query "INSERT INTO uk_price_paid FORMAT TSV"
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```
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It will take about 40 seconds.
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## Validate the Data {#validate-data}
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Query:
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```sql
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SELECT count() FROM uk_price_paid;
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```
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Result:
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```text
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┌──count()─┐
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│ 26321785 │
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└──────────┘
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```
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The size of dataset in ClickHouse is just 278 MiB, check it.
Query:
```sql
SELECT formatReadableSize(total_bytes) FROM system.tables WHERE name = 'uk_price_paid';
```
Result:
```text
┌─formatReadableSize(total_bytes)─┐
│ 278.80 MiB │
└─────────────────────────────────┘
```
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## Run Some Queries {#run-queries}
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### Query 1. Average Price Per Year {#average-price}
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Query:
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```sql
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SELECT toYear(date) AS year, round(avg(price)) AS price, bar(price, 0, 1000000, 80) FROM uk_price_paid GROUP BY year ORDER BY year;
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```
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Result:
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```text
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┌─year─┬──price─┬─bar(round(avg(price)), 0, 1000000, 80)─┐
│ 1995 │ 67932 │ █████▍ │
│ 1996 │ 71505 │ █████▋ │
│ 1997 │ 78532 │ ██████▎ │
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│ 1998 │ 85436 │ ██████▋ │
│ 1999 │ 96037 │ ███████▋ │
│ 2000 │ 107479 │ ████████▌ │
│ 2001 │ 118885 │ █████████▌ │
│ 2002 │ 137941 │ ███████████ │
│ 2003 │ 155889 │ ████████████▍ │
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│ 2004 │ 178885 │ ██████████████▎ │
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│ 2005 │ 189351 │ ███████████████▏ │
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│ 2006 │ 203528 │ ████████████████▎ │
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│ 2007 │ 219378 │ █████████████████▌ │
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│ 2008 │ 217056 │ █████████████████▎ │
│ 2009 │ 213419 │ █████████████████ │
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│ 2010 │ 236109 │ ██████████████████▊ │
│ 2011 │ 232805 │ ██████████████████▌ │
│ 2012 │ 238367 │ ███████████████████ │
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│ 2013 │ 256931 │ ████████████████████▌ │
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│ 2014 │ 279915 │ ██████████████████████▍ │
│ 2015 │ 297266 │ ███████████████████████▋ │
│ 2016 │ 313201 │ █████████████████████████ │
│ 2017 │ 346097 │ ███████████████████████████▋ │
│ 2018 │ 350116 │ ████████████████████████████ │
│ 2019 │ 351013 │ ████████████████████████████ │
│ 2020 │ 369420 │ █████████████████████████████▌ │
│ 2021 │ 386903 │ ██████████████████████████████▊ │
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└──────┴────────┴────────────────────────────────────────┘
```
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### Query 2. Average Price per Year in London {#average-price-london}
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Query:
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```sql
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SELECT toYear(date) AS year, round(avg(price)) AS price, bar(price, 0, 2000000, 100) FROM uk_price_paid WHERE town = 'LONDON' GROUP BY year ORDER BY year;
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```
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Result:
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```text
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┌─year─┬───price─┬─bar(round(avg(price)), 0, 2000000, 100)───────────────┐
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│ 1995 │ 109116 │ █████▍ │
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│ 1996 │ 118667 │ █████▊ │
│ 1997 │ 136518 │ ██████▋ │
│ 1998 │ 152983 │ ███████▋ │
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│ 1999 │ 180637 │ █████████ │
│ 2000 │ 215838 │ ██████████▋ │
│ 2001 │ 232994 │ ███████████▋ │
│ 2002 │ 263670 │ █████████████▏ │
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│ 2003 │ 278394 │ █████████████▊ │
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│ 2004 │ 304666 │ ███████████████▏ │
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│ 2005 │ 322875 │ ████████████████▏ │
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│ 2006 │ 356191 │ █████████████████▋ │
│ 2007 │ 404054 │ ████████████████████▏ │
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│ 2008 │ 420741 │ █████████████████████ │
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│ 2009 │ 427753 │ █████████████████████▍ │
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│ 2010 │ 480306 │ ████████████████████████ │
│ 2011 │ 496274 │ ████████████████████████▋ │
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│ 2012 │ 519442 │ █████████████████████████▊ │
│ 2013 │ 616212 │ ██████████████████████████████▋ │
│ 2014 │ 724154 │ ████████████████████████████████████▏ │
│ 2015 │ 792129 │ ███████████████████████████████████████▌ │
│ 2016 │ 843655 │ ██████████████████████████████████████████▏ │
│ 2017 │ 982642 │ █████████████████████████████████████████████████▏ │
│ 2018 │ 1016835 │ ██████████████████████████████████████████████████▋ │
│ 2019 │ 1042849 │ ████████████████████████████████████████████████████▏ │
│ 2020 │ 1011889 │ ██████████████████████████████████████████████████▌ │
│ 2021 │ 960343 │ ████████████████████████████████████████████████ │
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└──────┴─────────┴───────────────────────────────────────────────────────┘
```
Something happened in 2013. I don't have a clue. Maybe you have a clue what happened in 2020?
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### Query 3. The Most Expensive Neighborhoods {#most-expensive-neighborhoods}
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Query:
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```sql
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SELECT
town,
district,
count() AS c,
round(avg(price)) AS price,
bar(price, 0, 5000000, 100)
FROM uk_price_paid
WHERE date >= '2020-01-01'
GROUP BY
town,
district
HAVING c >= 100
ORDER BY price DESC
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LIMIT 100;
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```
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Result:
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```text
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┌─town─────────────────┬─district───────────────┬────c─┬───price─┬─bar(round(avg(price)), 0, 5000000, 100)────────────────────────────┐
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│ LONDON │ CITY OF WESTMINSTER │ 3606 │ 3280239 │ █████████████████████████████████████████████████████████████████▌ │
│ LONDON │ CITY OF LONDON │ 274 │ 3160502 │ ███████████████████████████████████████████████████████████████▏ │
│ LONDON │ KENSINGTON AND CHELSEA │ 2550 │ 2308478 │ ██████████████████████████████████████████████▏ │
│ LEATHERHEAD │ ELMBRIDGE │ 114 │ 1897407 │ █████████████████████████████████████▊ │
│ LONDON │ CAMDEN │ 3033 │ 1805404 │ ████████████████████████████████████ │
│ VIRGINIA WATER │ RUNNYMEDE │ 156 │ 1753247 │ ███████████████████████████████████ │
│ WINDLESHAM │ SURREY HEATH │ 108 │ 1677613 │ █████████████████████████████████▌ │
│ THORNTON HEATH │ CROYDON │ 546 │ 1671721 │ █████████████████████████████████▍ │
│ BARNET │ ENFIELD │ 124 │ 1505840 │ ██████████████████████████████ │
│ COBHAM │ ELMBRIDGE │ 387 │ 1237250 │ ████████████████████████▋ │
│ LONDON │ ISLINGTON │ 2668 │ 1236980 │ ████████████████████████▋ │
│ OXFORD │ SOUTH OXFORDSHIRE │ 321 │ 1220907 │ ████████████████████████▍ │
│ LONDON │ RICHMOND UPON THAMES │ 704 │ 1215551 │ ████████████████████████▎ │
│ LONDON │ HOUNSLOW │ 671 │ 1207493 │ ████████████████████████▏ │
│ ASCOT │ WINDSOR AND MAIDENHEAD │ 407 │ 1183299 │ ███████████████████████▋ │
│ BEACONSFIELD │ BUCKINGHAMSHIRE │ 330 │ 1175615 │ ███████████████████████▌ │
│ RICHMOND │ RICHMOND UPON THAMES │ 874 │ 1110444 │ ██████████████████████▏ │
│ LONDON │ HAMMERSMITH AND FULHAM │ 3086 │ 1053983 │ █████████████████████ │
│ SURBITON │ ELMBRIDGE │ 100 │ 1011800 │ ████████████████████▏ │
│ RADLETT │ HERTSMERE │ 283 │ 1011712 │ ████████████████████▏ │
│ SALCOMBE │ SOUTH HAMS │ 127 │ 1011624 │ ████████████████████▏ │
│ WEYBRIDGE │ ELMBRIDGE │ 655 │ 1007265 │ ████████████████████▏ │
│ ESHER │ ELMBRIDGE │ 485 │ 986581 │ ███████████████████▋ │
│ LEATHERHEAD │ GUILDFORD │ 202 │ 977320 │ ███████████████████▌ │
│ BURFORD │ WEST OXFORDSHIRE │ 111 │ 966893 │ ███████████████████▎ │
│ BROCKENHURST │ NEW FOREST │ 129 │ 956675 │ ███████████████████▏ │
│ HINDHEAD │ WAVERLEY │ 137 │ 953753 │ ███████████████████ │
│ GERRARDS CROSS │ BUCKINGHAMSHIRE │ 419 │ 951121 │ ███████████████████ │
│ EAST MOLESEY │ ELMBRIDGE │ 192 │ 936769 │ ██████████████████▋ │
│ CHALFONT ST GILES │ BUCKINGHAMSHIRE │ 146 │ 925515 │ ██████████████████▌ │
│ LONDON │ TOWER HAMLETS │ 4388 │ 918304 │ ██████████████████▎ │
│ OLNEY │ MILTON KEYNES │ 235 │ 910646 │ ██████████████████▏ │
│ HENLEY-ON-THAMES │ SOUTH OXFORDSHIRE │ 540 │ 902418 │ ██████████████████ │
│ LONDON │ SOUTHWARK │ 3885 │ 892997 │ █████████████████▋ │
│ KINGSTON UPON THAMES │ KINGSTON UPON THAMES │ 960 │ 885969 │ █████████████████▋ │
│ LONDON │ EALING │ 2658 │ 871755 │ █████████████████▍ │
│ CRANBROOK │ TUNBRIDGE WELLS │ 431 │ 862348 │ █████████████████▏ │
│ LONDON │ MERTON │ 2099 │ 859118 │ █████████████████▏ │
│ BELVEDERE │ BEXLEY │ 346 │ 842423 │ ████████████████▋ │
│ GUILDFORD │ WAVERLEY │ 143 │ 841277 │ ████████████████▋ │
│ HARPENDEN │ ST ALBANS │ 657 │ 841216 │ ████████████████▋ │
│ LONDON │ HACKNEY │ 3307 │ 837090 │ ████████████████▋ │
│ LONDON │ WANDSWORTH │ 6566 │ 832663 │ ████████████████▋ │
│ MAIDENHEAD │ BUCKINGHAMSHIRE │ 123 │ 824299 │ ████████████████▍ │
│ KINGS LANGLEY │ DACORUM │ 145 │ 821331 │ ████████████████▍ │
│ BERKHAMSTED │ DACORUM │ 543 │ 818415 │ ████████████████▎ │
│ GREAT MISSENDEN │ BUCKINGHAMSHIRE │ 226 │ 802807 │ ████████████████ │
│ BILLINGSHURST │ CHICHESTER │ 144 │ 797829 │ ███████████████▊ │
│ WOKING │ GUILDFORD │ 176 │ 793494 │ ███████████████▋ │
│ STOCKBRIDGE │ TEST VALLEY │ 178 │ 793269 │ ███████████████▋ │
│ EPSOM │ REIGATE AND BANSTEAD │ 172 │ 791862 │ ███████████████▋ │
│ TONBRIDGE │ TUNBRIDGE WELLS │ 360 │ 787876 │ ███████████████▋ │
│ TEDDINGTON │ RICHMOND UPON THAMES │ 595 │ 786492 │ ███████████████▋ │
│ TWICKENHAM │ RICHMOND UPON THAMES │ 1155 │ 786193 │ ███████████████▋ │
│ LYNDHURST │ NEW FOREST │ 102 │ 785593 │ ███████████████▋ │
│ LONDON │ LAMBETH │ 5228 │ 774574 │ ███████████████▍ │
│ LONDON │ BARNET │ 3955 │ 773259 │ ███████████████▍ │
│ OXFORD │ VALE OF WHITE HORSE │ 353 │ 772088 │ ███████████████▍ │
│ TONBRIDGE │ MAIDSTONE │ 305 │ 770740 │ ███████████████▍ │
│ LUTTERWORTH │ HARBOROUGH │ 538 │ 768634 │ ███████████████▎ │
│ WOODSTOCK │ WEST OXFORDSHIRE │ 140 │ 766037 │ ███████████████▎ │
│ MIDHURST │ CHICHESTER │ 257 │ 764815 │ ███████████████▎ │
│ MARLOW │ BUCKINGHAMSHIRE │ 327 │ 761876 │ ███████████████▏ │
│ LONDON │ NEWHAM │ 3237 │ 761784 │ ███████████████▏ │
│ ALDERLEY EDGE │ CHESHIRE EAST │ 178 │ 757318 │ ███████████████▏ │
│ LUTON │ CENTRAL BEDFORDSHIRE │ 212 │ 754283 │ ███████████████ │
│ PETWORTH │ CHICHESTER │ 154 │ 754220 │ ███████████████ │
│ ALRESFORD │ WINCHESTER │ 219 │ 752718 │ ███████████████ │
│ POTTERS BAR │ WELWYN HATFIELD │ 174 │ 748465 │ ██████████████▊ │
│ HASLEMERE │ CHICHESTER │ 128 │ 746907 │ ██████████████▊ │
│ TADWORTH │ REIGATE AND BANSTEAD │ 502 │ 743252 │ ██████████████▋ │
│ THAMES DITTON │ ELMBRIDGE │ 244 │ 741913 │ ██████████████▋ │
│ REIGATE │ REIGATE AND BANSTEAD │ 581 │ 738198 │ ██████████████▋ │
│ BOURNE END │ BUCKINGHAMSHIRE │ 138 │ 735190 │ ██████████████▋ │
│ SEVENOAKS │ SEVENOAKS │ 1156 │ 730018 │ ██████████████▌ │
│ OXTED │ TANDRIDGE │ 336 │ 729123 │ ██████████████▌ │
│ INGATESTONE │ BRENTWOOD │ 166 │ 728103 │ ██████████████▌ │
│ LONDON │ BRENT │ 2079 │ 720605 │ ██████████████▍ │
│ LONDON │ HARINGEY │ 3216 │ 717780 │ ██████████████▎ │
│ PURLEY │ CROYDON │ 575 │ 716108 │ ██████████████▎ │
│ WELWYN │ WELWYN HATFIELD │ 222 │ 710603 │ ██████████████▏ │
│ RICKMANSWORTH │ THREE RIVERS │ 798 │ 704571 │ ██████████████ │
│ BANSTEAD │ REIGATE AND BANSTEAD │ 401 │ 701293 │ ██████████████ │
│ CHIGWELL │ EPPING FOREST │ 261 │ 701203 │ ██████████████ │
│ PINNER │ HARROW │ 528 │ 698885 │ █████████████▊ │
│ HASLEMERE │ WAVERLEY │ 280 │ 696659 │ █████████████▊ │
│ SLOUGH │ BUCKINGHAMSHIRE │ 396 │ 694917 │ █████████████▊ │
│ WALTON-ON-THAMES │ ELMBRIDGE │ 946 │ 692395 │ █████████████▋ │
│ READING │ SOUTH OXFORDSHIRE │ 318 │ 691988 │ █████████████▋ │
│ NORTHWOOD │ HILLINGDON │ 271 │ 690643 │ █████████████▋ │
│ FELTHAM │ HOUNSLOW │ 763 │ 688595 │ █████████████▋ │
│ ASHTEAD │ MOLE VALLEY │ 303 │ 687923 │ █████████████▋ │
│ BARNET │ BARNET │ 975 │ 686980 │ █████████████▋ │
│ WOKING │ SURREY HEATH │ 283 │ 686669 │ █████████████▋ │
│ MALMESBURY │ WILTSHIRE │ 323 │ 683324 │ █████████████▋ │
│ AMERSHAM │ BUCKINGHAMSHIRE │ 496 │ 680962 │ █████████████▌ │
│ CHISLEHURST │ BROMLEY │ 430 │ 680209 │ █████████████▌ │
│ HYTHE │ FOLKESTONE AND HYTHE │ 490 │ 676908 │ █████████████▌ │
│ MAYFIELD │ WEALDEN │ 101 │ 676210 │ █████████████▌ │
│ ASCOT │ BRACKNELL FOREST │ 168 │ 676004 │ █████████████▌ │
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└──────────────────────┴────────────────────────┴──────┴─────────┴────────────────────────────────────────────────────────────────────┘
```
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## Let's Speed Up Queries Using Projections {#speedup-with-projections}
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[Projections](../../sql-reference/statements/alter/projection.md) allow to improve queries speed by storing pre-aggregated data.
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### Build a Projection {#build-projection}
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Create an aggregate projection by dimensions `toYear(date)`, `district`, `town`:
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```sql
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ALTER TABLE uk_price_paid
ADD PROJECTION projection_by_year_district_town
(
SELECT
toYear(date),
district,
town,
avg(price),
sum(price),
count()
GROUP BY
toYear(date),
district,
town
);
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```
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Populate the projection for existing data (without it projection will be created for only newly inserted data):
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```sql
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ALTER TABLE uk_price_paid
MATERIALIZE PROJECTION projection_by_year_district_town
SETTINGS mutations_sync = 1;
```
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## Test Performance {#test-performance}
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Let's run the same 3 queries.
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### Query 1. Average Price Per Year {#average-price-projections}
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Query:
```sql
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SELECT
toYear(date) AS year,
round(avg(price)) AS price,
bar(price, 0, 1000000, 80)
FROM uk_price_paid
GROUP BY year
ORDER BY year ASC;
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```
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Result:
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```text
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┌─year─┬──price─┬─bar(round(avg(price)), 0, 1000000, 80)─┐
│ 1995 │ 67932 │ █████▍ │
│ 1996 │ 71505 │ █████▋ │
│ 1997 │ 78532 │ ██████▎ │
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│ 1998 │ 85436 │ ██████▋ │
│ 1999 │ 96037 │ ███████▋ │
│ 2000 │ 107479 │ ████████▌ │
│ 2001 │ 118885 │ █████████▌ │
│ 2002 │ 137941 │ ███████████ │
│ 2003 │ 155889 │ ████████████▍ │
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│ 2004 │ 178885 │ ██████████████▎ │
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│ 2005 │ 189351 │ ███████████████▏ │
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│ 2006 │ 203528 │ ████████████████▎ │
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│ 2007 │ 219378 │ █████████████████▌ │
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│ 2008 │ 217056 │ █████████████████▎ │
│ 2009 │ 213419 │ █████████████████ │
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│ 2010 │ 236109 │ ██████████████████▊ │
│ 2011 │ 232805 │ ██████████████████▌ │
│ 2012 │ 238367 │ ███████████████████ │
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│ 2013 │ 256931 │ ████████████████████▌ │
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│ 2014 │ 279915 │ ██████████████████████▍ │
│ 2015 │ 297266 │ ███████████████████████▋ │
│ 2016 │ 313201 │ █████████████████████████ │
│ 2017 │ 346097 │ ███████████████████████████▋ │
│ 2018 │ 350116 │ ████████████████████████████ │
│ 2019 │ 351013 │ ████████████████████████████ │
│ 2020 │ 369420 │ █████████████████████████████▌ │
│ 2021 │ 386903 │ ██████████████████████████████▊ │
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└──────┴────────┴────────────────────────────────────────┘
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```
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### Query 2. Average Price Per Year in London {#average-price-london-projections}
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Query:
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```sql
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SELECT
toYear(date) AS year,
round(avg(price)) AS price,
bar(price, 0, 2000000, 100)
FROM uk_price_paid
WHERE town = 'LONDON'
GROUP BY year
ORDER BY year ASC;
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```
Result:
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```text
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┌─year─┬───price─┬─bar(round(avg(price)), 0, 2000000, 100)───────────────┐
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│ 1995 │ 109116 │ █████▍ │
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│ 1996 │ 118667 │ █████▊ │
│ 1997 │ 136518 │ ██████▋ │
│ 1998 │ 152983 │ ███████▋ │
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│ 1999 │ 180637 │ █████████ │
│ 2000 │ 215838 │ ██████████▋ │
│ 2001 │ 232994 │ ███████████▋ │
│ 2002 │ 263670 │ █████████████▏ │
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│ 2003 │ 278394 │ █████████████▊ │
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│ 2004 │ 304666 │ ███████████████▏ │
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│ 2005 │ 322875 │ ████████████████▏ │
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│ 2006 │ 356191 │ █████████████████▋ │
│ 2007 │ 404054 │ ████████████████████▏ │
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│ 2008 │ 420741 │ █████████████████████ │
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│ 2009 │ 427753 │ █████████████████████▍ │
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│ 2010 │ 480306 │ ████████████████████████ │
│ 2011 │ 496274 │ ████████████████████████▋ │
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│ 2012 │ 519442 │ █████████████████████████▊ │
│ 2013 │ 616212 │ ██████████████████████████████▋ │
│ 2014 │ 724154 │ ████████████████████████████████████▏ │
│ 2015 │ 792129 │ ███████████████████████████████████████▌ │
│ 2016 │ 843655 │ ██████████████████████████████████████████▏ │
│ 2017 │ 982642 │ █████████████████████████████████████████████████▏ │
│ 2018 │ 1016835 │ ██████████████████████████████████████████████████▋ │
│ 2019 │ 1042849 │ ████████████████████████████████████████████████████▏ │
│ 2020 │ 1011889 │ ██████████████████████████████████████████████████▌ │
│ 2021 │ 960343 │ ████████████████████████████████████████████████ │
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└──────┴─────────┴───────────────────────────────────────────────────────┘
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```
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### Query 3. The Most Expensive Neighborhoods {#most-expensive-neighborhoods-projections}
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The condition (date >= '2020-01-01') needs to be modified to match projection dimension (toYear(date) >= 2020).
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Query:
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```sql
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SELECT
town,
district,
count() AS c,
round(avg(price)) AS price,
bar(price, 0, 5000000, 100)
FROM uk_price_paid
WHERE toYear(date) >= 2020
GROUP BY
town,
district
HAVING c >= 100
ORDER BY price DESC
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LIMIT 100;
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```
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Result:
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```text
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┌─town─────────────────┬─district───────────────┬────c─┬───price─┬─bar(round(avg(price)), 0, 5000000, 100)────────────────────────────┐
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│ LONDON │ CITY OF WESTMINSTER │ 3606 │ 3280239 │ █████████████████████████████████████████████████████████████████▌ │
│ LONDON │ CITY OF LONDON │ 274 │ 3160502 │ ███████████████████████████████████████████████████████████████▏ │
│ LONDON │ KENSINGTON AND CHELSEA │ 2550 │ 2308478 │ ██████████████████████████████████████████████▏ │
│ LEATHERHEAD │ ELMBRIDGE │ 114 │ 1897407 │ █████████████████████████████████████▊ │
│ LONDON │ CAMDEN │ 3033 │ 1805404 │ ████████████████████████████████████ │
│ VIRGINIA WATER │ RUNNYMEDE │ 156 │ 1753247 │ ███████████████████████████████████ │
│ WINDLESHAM │ SURREY HEATH │ 108 │ 1677613 │ █████████████████████████████████▌ │
│ THORNTON HEATH │ CROYDON │ 546 │ 1671721 │ █████████████████████████████████▍ │
│ BARNET │ ENFIELD │ 124 │ 1505840 │ ██████████████████████████████ │
│ COBHAM │ ELMBRIDGE │ 387 │ 1237250 │ ████████████████████████▋ │
│ LONDON │ ISLINGTON │ 2668 │ 1236980 │ ████████████████████████▋ │
│ OXFORD │ SOUTH OXFORDSHIRE │ 321 │ 1220907 │ ████████████████████████▍ │
│ LONDON │ RICHMOND UPON THAMES │ 704 │ 1215551 │ ████████████████████████▎ │
│ LONDON │ HOUNSLOW │ 671 │ 1207493 │ ████████████████████████▏ │
│ ASCOT │ WINDSOR AND MAIDENHEAD │ 407 │ 1183299 │ ███████████████████████▋ │
│ BEACONSFIELD │ BUCKINGHAMSHIRE │ 330 │ 1175615 │ ███████████████████████▌ │
│ RICHMOND │ RICHMOND UPON THAMES │ 874 │ 1110444 │ ██████████████████████▏ │
│ LONDON │ HAMMERSMITH AND FULHAM │ 3086 │ 1053983 │ █████████████████████ │
│ SURBITON │ ELMBRIDGE │ 100 │ 1011800 │ ████████████████████▏ │
│ RADLETT │ HERTSMERE │ 283 │ 1011712 │ ████████████████████▏ │
│ SALCOMBE │ SOUTH HAMS │ 127 │ 1011624 │ ████████████████████▏ │
│ WEYBRIDGE │ ELMBRIDGE │ 655 │ 1007265 │ ████████████████████▏ │
│ ESHER │ ELMBRIDGE │ 485 │ 986581 │ ███████████████████▋ │
│ LEATHERHEAD │ GUILDFORD │ 202 │ 977320 │ ███████████████████▌ │
│ BURFORD │ WEST OXFORDSHIRE │ 111 │ 966893 │ ███████████████████▎ │
│ BROCKENHURST │ NEW FOREST │ 129 │ 956675 │ ███████████████████▏ │
│ HINDHEAD │ WAVERLEY │ 137 │ 953753 │ ███████████████████ │
│ GERRARDS CROSS │ BUCKINGHAMSHIRE │ 419 │ 951121 │ ███████████████████ │
│ EAST MOLESEY │ ELMBRIDGE │ 192 │ 936769 │ ██████████████████▋ │
│ CHALFONT ST GILES │ BUCKINGHAMSHIRE │ 146 │ 925515 │ ██████████████████▌ │
│ LONDON │ TOWER HAMLETS │ 4388 │ 918304 │ ██████████████████▎ │
│ OLNEY │ MILTON KEYNES │ 235 │ 910646 │ ██████████████████▏ │
│ HENLEY-ON-THAMES │ SOUTH OXFORDSHIRE │ 540 │ 902418 │ ██████████████████ │
│ LONDON │ SOUTHWARK │ 3885 │ 892997 │ █████████████████▋ │
│ KINGSTON UPON THAMES │ KINGSTON UPON THAMES │ 960 │ 885969 │ █████████████████▋ │
│ LONDON │ EALING │ 2658 │ 871755 │ █████████████████▍ │
│ CRANBROOK │ TUNBRIDGE WELLS │ 431 │ 862348 │ █████████████████▏ │
│ LONDON │ MERTON │ 2099 │ 859118 │ █████████████████▏ │
│ BELVEDERE │ BEXLEY │ 346 │ 842423 │ ████████████████▋ │
│ GUILDFORD │ WAVERLEY │ 143 │ 841277 │ ████████████████▋ │
│ HARPENDEN │ ST ALBANS │ 657 │ 841216 │ ████████████████▋ │
│ LONDON │ HACKNEY │ 3307 │ 837090 │ ████████████████▋ │
│ LONDON │ WANDSWORTH │ 6566 │ 832663 │ ████████████████▋ │
│ MAIDENHEAD │ BUCKINGHAMSHIRE │ 123 │ 824299 │ ████████████████▍ │
│ KINGS LANGLEY │ DACORUM │ 145 │ 821331 │ ████████████████▍ │
│ BERKHAMSTED │ DACORUM │ 543 │ 818415 │ ████████████████▎ │
│ GREAT MISSENDEN │ BUCKINGHAMSHIRE │ 226 │ 802807 │ ████████████████ │
│ BILLINGSHURST │ CHICHESTER │ 144 │ 797829 │ ███████████████▊ │
│ WOKING │ GUILDFORD │ 176 │ 793494 │ ███████████████▋ │
│ STOCKBRIDGE │ TEST VALLEY │ 178 │ 793269 │ ███████████████▋ │
│ EPSOM │ REIGATE AND BANSTEAD │ 172 │ 791862 │ ███████████████▋ │
│ TONBRIDGE │ TUNBRIDGE WELLS │ 360 │ 787876 │ ███████████████▋ │
│ TEDDINGTON │ RICHMOND UPON THAMES │ 595 │ 786492 │ ███████████████▋ │
│ TWICKENHAM │ RICHMOND UPON THAMES │ 1155 │ 786193 │ ███████████████▋ │
│ LYNDHURST │ NEW FOREST │ 102 │ 785593 │ ███████████████▋ │
│ LONDON │ LAMBETH │ 5228 │ 774574 │ ███████████████▍ │
│ LONDON │ BARNET │ 3955 │ 773259 │ ███████████████▍ │
│ OXFORD │ VALE OF WHITE HORSE │ 353 │ 772088 │ ███████████████▍ │
│ TONBRIDGE │ MAIDSTONE │ 305 │ 770740 │ ███████████████▍ │
│ LUTTERWORTH │ HARBOROUGH │ 538 │ 768634 │ ███████████████▎ │
│ WOODSTOCK │ WEST OXFORDSHIRE │ 140 │ 766037 │ ███████████████▎ │
│ MIDHURST │ CHICHESTER │ 257 │ 764815 │ ███████████████▎ │
│ MARLOW │ BUCKINGHAMSHIRE │ 327 │ 761876 │ ███████████████▏ │
│ LONDON │ NEWHAM │ 3237 │ 761784 │ ███████████████▏ │
│ ALDERLEY EDGE │ CHESHIRE EAST │ 178 │ 757318 │ ███████████████▏ │
│ LUTON │ CENTRAL BEDFORDSHIRE │ 212 │ 754283 │ ███████████████ │
│ PETWORTH │ CHICHESTER │ 154 │ 754220 │ ███████████████ │
│ ALRESFORD │ WINCHESTER │ 219 │ 752718 │ ███████████████ │
│ POTTERS BAR │ WELWYN HATFIELD │ 174 │ 748465 │ ██████████████▊ │
│ HASLEMERE │ CHICHESTER │ 128 │ 746907 │ ██████████████▊ │
│ TADWORTH │ REIGATE AND BANSTEAD │ 502 │ 743252 │ ██████████████▋ │
│ THAMES DITTON │ ELMBRIDGE │ 244 │ 741913 │ ██████████████▋ │
│ REIGATE │ REIGATE AND BANSTEAD │ 581 │ 738198 │ ██████████████▋ │
│ BOURNE END │ BUCKINGHAMSHIRE │ 138 │ 735190 │ ██████████████▋ │
│ SEVENOAKS │ SEVENOAKS │ 1156 │ 730018 │ ██████████████▌ │
│ OXTED │ TANDRIDGE │ 336 │ 729123 │ ██████████████▌ │
│ INGATESTONE │ BRENTWOOD │ 166 │ 728103 │ ██████████████▌ │
│ LONDON │ BRENT │ 2079 │ 720605 │ ██████████████▍ │
│ LONDON │ HARINGEY │ 3216 │ 717780 │ ██████████████▎ │
│ PURLEY │ CROYDON │ 575 │ 716108 │ ██████████████▎ │
│ WELWYN │ WELWYN HATFIELD │ 222 │ 710603 │ ██████████████▏ │
│ RICKMANSWORTH │ THREE RIVERS │ 798 │ 704571 │ ██████████████ │
│ BANSTEAD │ REIGATE AND BANSTEAD │ 401 │ 701293 │ ██████████████ │
│ CHIGWELL │ EPPING FOREST │ 261 │ 701203 │ ██████████████ │
│ PINNER │ HARROW │ 528 │ 698885 │ █████████████▊ │
│ HASLEMERE │ WAVERLEY │ 280 │ 696659 │ █████████████▊ │
│ SLOUGH │ BUCKINGHAMSHIRE │ 396 │ 694917 │ █████████████▊ │
│ WALTON-ON-THAMES │ ELMBRIDGE │ 946 │ 692395 │ █████████████▋ │
│ READING │ SOUTH OXFORDSHIRE │ 318 │ 691988 │ █████████████▋ │
│ NORTHWOOD │ HILLINGDON │ 271 │ 690643 │ █████████████▋ │
│ FELTHAM │ HOUNSLOW │ 763 │ 688595 │ █████████████▋ │
│ ASHTEAD │ MOLE VALLEY │ 303 │ 687923 │ █████████████▋ │
│ BARNET │ BARNET │ 975 │ 686980 │ █████████████▋ │
│ WOKING │ SURREY HEATH │ 283 │ 686669 │ █████████████▋ │
│ MALMESBURY │ WILTSHIRE │ 323 │ 683324 │ █████████████▋ │
│ AMERSHAM │ BUCKINGHAMSHIRE │ 496 │ 680962 │ █████████████▌ │
│ CHISLEHURST │ BROMLEY │ 430 │ 680209 │ █████████████▌ │
│ HYTHE │ FOLKESTONE AND HYTHE │ 490 │ 676908 │ █████████████▌ │
│ MAYFIELD │ WEALDEN │ 101 │ 676210 │ █████████████▌ │
│ ASCOT │ BRACKNELL FOREST │ 168 │ 676004 │ █████████████▌ │
Update uk-price-paid.md added example with a projection
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└──────────────────────┴────────────────────────┴──────┴─────────┴────────────────────────────────────────────────────────────────────┘
```
Add anchors to all the headings
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### Summary {#summary}
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Update uk-price-paid.md added example with a projection
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All 3 queries work much faster and read fewer rows.
Update uk-price-paid.md
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```text
Query 1
no projection: 27 rows in set. Elapsed: 0.158 sec. Processed 26.32 million rows, 157.93 MB (166.57 million rows/s., 999.39 MB/s.)
projection: 27 rows in set. Elapsed: 0.007 sec. Processed 105.96 thousand rows, 3.33 MB (14.58 million rows/s., 458.13 MB/s.)
Query 2
no projection: 27 rows in set. Elapsed: 0.163 sec. Processed 26.32 million rows, 80.01 MB (161.75 million rows/s., 491.64 MB/s.)
projection: 27 rows in set. Elapsed: 0.008 sec. Processed 105.96 thousand rows, 3.67 MB (13.29 million rows/s., 459.89 MB/s.)
Query 3
no projection: 100 rows in set. Elapsed: 0.069 sec. Processed 26.32 million rows, 62.47 MB (382.13 million rows/s., 906.93 MB/s.)
projection: 100 rows in set. Elapsed: 0.029 sec. Processed 8.08 thousand rows, 511.08 KB (276.06 thousand rows/s., 17.47 MB/s.)
Update uk-price-paid.md added example with a projection
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```
Update uk-price-paid.md
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Add anchors to all the headings
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### Test It in Playground {#playground}
Update uk-price-paid.md
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Change Playground URL in the docs
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The dataset is also available in the [Online Playground](https://play.clickhouse.com/play?user=play#U0VMRUNUIHRvd24sIGRpc3RyaWN0LCBjb3VudCgpIEFTIGMsIHJvdW5kKGF2ZyhwcmljZSkpIEFTIHByaWNlLCBiYXIocHJpY2UsIDAsIDUwMDAwMDAsIDEwMCkgRlJPTSB1a19wcmljZV9wYWlkIFdIRVJFIGRhdGUgPj0gJzIwMjAtMDEtMDEnIEdST1VQIEJZIHRvd24sIGRpc3RyaWN0IEhBVklORyBjID49IDEwMCBPUkRFUiBCWSBwcmljZSBERVNDIExJTUlUIDEwMA==).
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