The UK property prices dataset
Projections are a great way to improve the performance of queries that you run frequently. We will demonstrate the power of projections using the UK property dataset, which contains data about prices paid for real-estate property in England and Wales. The data is available since 1995, and the size of the dataset in uncompressed form is about 4 GiB (which will only take about 278 MiB in ClickHouse).
- Source: https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads
- 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.
Create the Table
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)
)
ENGINE = MergeTree
ORDER BY (postcode1, postcode2, addr1, addr2);
Preprocess and Insert the Data
We will use the url function to stream the data into ClickHouse. We need to preprocess some of the incoming data first, which includes:
- splitting the
postcodeto two different columns -postcode1andpostcode2, which is better for storage and queries - converting the
timefield to date as it only contains 00:00 time - ignoring the UUid field because we don't need it for analysis
- transforming
typeanddurationto more readableEnumfields using the transform function - transforming the
is_newfield from a single-character string (Y/N) to a UInt8 field with 0 or 1 - drop the last two columns since they all have the same value (which is 0)
The url function streams the data from the web server into your ClickHouse table. The following command inserts 5 million rows into the uk_price_paid table:
INSERT INTO uk_price_paid
WITH
splitByChar(' ', postcode) AS p
SELECT
toUInt32(price_string) AS price,
parseDateTimeBestEffortUS(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
FROM url(
'http://prod.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv',
'CSV',
'uuid_string String,
price_string String,
time String,
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'
) SETTINGS max_http_get_redirects=10;
Wait for the data to insert - it will take a minute or two depending on the network speed.
Validate the Data
Let's verify it worked by seeing how many rows were inserted:
SELECT count()
FROM uk_price_paid
At the time this query was run, the dataset had 27,450,499 rows. Let's see what the storage size is of the table in ClickHouse:
SELECT formatReadableSize(total_bytes)
FROM system.tables
WHERE name = 'uk_price_paid'
Notice the size of the table is just 221.43 MiB!
Run Some Queries
Let's run some queries to analyze the data:
Query 1. Average Price Per Year
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
The result looks like:
┌─year─┬──price─┬─bar(round(avg(price)), 0, 1000000, 80)─┐
│ 1995 │ 67934 │ █████▍ │
│ 1996 │ 71508 │ █████▋ │
│ 1997 │ 78536 │ ██████▎ │
│ 1998 │ 85441 │ ██████▋ │
│ 1999 │ 96038 │ ███████▋ │
│ 2000 │ 107487 │ ████████▌ │
│ 2001 │ 118888 │ █████████▌ │
│ 2002 │ 137948 │ ███████████ │
│ 2003 │ 155893 │ ████████████▍ │
│ 2004 │ 178888 │ ██████████████▎ │
│ 2005 │ 189359 │ ███████████████▏ │
│ 2006 │ 203532 │ ████████████████▎ │
│ 2007 │ 219375 │ █████████████████▌ │
│ 2008 │ 217056 │ █████████████████▎ │
│ 2009 │ 213419 │ █████████████████ │
│ 2010 │ 236110 │ ██████████████████▊ │
│ 2011 │ 232805 │ ██████████████████▌ │
│ 2012 │ 238381 │ ███████████████████ │
│ 2013 │ 256927 │ ████████████████████▌ │
│ 2014 │ 280008 │ ██████████████████████▍ │
│ 2015 │ 297263 │ ███████████████████████▋ │
│ 2016 │ 313518 │ █████████████████████████ │
│ 2017 │ 346371 │ ███████████████████████████▋ │
│ 2018 │ 350556 │ ████████████████████████████ │
│ 2019 │ 352184 │ ████████████████████████████▏ │
│ 2020 │ 375808 │ ██████████████████████████████ │
│ 2021 │ 381105 │ ██████████████████████████████▍ │
│ 2022 │ 362572 │ █████████████████████████████ │
└──────┴────────┴────────────────────────────────────────┘
Query 2. Average Price per Year in London
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
The result looks like:
┌─year─┬───price─┬─bar(round(avg(price)), 0, 2000000, 100)───────────────┐
│ 1995 │ 109110 │ █████▍ │
│ 1996 │ 118659 │ █████▊ │
│ 1997 │ 136526 │ ██████▋ │
│ 1998 │ 153002 │ ███████▋ │
│ 1999 │ 180633 │ █████████ │
│ 2000 │ 215849 │ ██████████▋ │
│ 2001 │ 232987 │ ███████████▋ │
│ 2002 │ 263668 │ █████████████▏ │
│ 2003 │ 278424 │ █████████████▊ │
│ 2004 │ 304664 │ ███████████████▏ │
│ 2005 │ 322887 │ ████████████████▏ │
│ 2006 │ 356195 │ █████████████████▋ │
│ 2007 │ 404062 │ ████████████████████▏ │
│ 2008 │ 420741 │ ███████████ ██████████ │
│ 2009 │ 427754 │ █████████████████████▍ │
│ 2010 │ 480322 │ ████████████████████████ │
│ 2011 │ 496278 │ ████████████████████████▋ │
│ 2012 │ 519482 │ █████████████████████████▊ │
│ 2013 │ 616195 │ ██████████████████████████████▋ │
│ 2014 │ 724121 │ ████████████████████████████████████▏ │
│ 2015 │ 792101 │ ███████████████████████████████████████▌ │
│ 2016 │ 843589 │ ██████████████████████████████████████████▏ │
│ 2017 │ 983523 │ █████████████████████████████████████████████████▏ │
│ 2018 │ 1016753 │ ██████████████████████████████████████████████████▋ │
│ 2019 │ 1041673 │ ████████████████████████████████████████████████████ │
│ 2020 │ 1060027 │ █████████████████████████████████████████████████████ │
│ 2021 │ 958249 │ ███████████████████████████████████████████████▊ │
│ 2022 │ 902596 │ █████████████████████████████████████████████▏ │
└──────┴─────────┴───────────────────────────────────────────────────────┘
Something happened to home prices in 2020! But that is probably not a surprise...
Query 3. The Most Expensive Neighborhoods
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
LIMIT 100
The result looks like:
┌─town─────────────────┬─district───────────────┬─────c─┬───price─┬─bar(round(avg(price)), 0, 5000000, 100)─────────────────────────┐
│ LONDON │ CITY OF LONDON │ 578 │ 3149590 │ ██████████████████████████████████████████████████████████████▊ │
│ LONDON │ CITY OF WESTMINSTER │ 7083 │ 2903794 │ ███████████████████████████████████ ███████████████████████ │
│ LONDON │ KENSINGTON AND CHELSEA │ 4986 │ 2333782 │ ██████████████████████████████████████████████▋ │
│ LEATHERHEAD │ ELMBRIDGE │ 203 │ 2071595 │ █████████████████████████████████████████▍ │
│ VIRGINIA WATER │ RUNNYMEDE │ 308 │ 1939465 │ ██████████████████████████████████████▋ │
│ LONDON │ CAMDEN │ 5750 │ 1673687 │ █████████████████████████████████▍ │
│ WINDLESHAM │ SURREY HEATH │ 182 │ 1428358 │ ████████████████████████████▌ │
│ NORTHWOOD │ THREE RIVERS │ 112 │ 1404170 │ ████████████████████████████ │
│ BARNET │ ENFIELD │ 259 │ 1338299 │ ██████████████████████████▋ │
│ LONDON │ ISLINGTON │ 5504 │ 1275520 │ █████████████████████████▌ │
│ LONDON │ RICHMOND UPON THAMES │ 1345 │ 1261935 │ █████████████████████████▏ │
│ COBHAM │ ELMBRIDGE │ 727 │ 1251403 │ █████████████████████████ │
│ BEACONSFIELD │ BUCKINGHAMSHIRE │ 680 │ 1199970 │ ███████████████████████▊ │
│ LONDON │ TOWER HAMLETS │ 10012 │ 1157827 │ ███████████████████████▏ │
│ LONDON │ HOUNSLOW │ 1278 │ 1144389 │ ██████████████████████▊ │
│ BURFORD │ WEST OXFORDSHIRE │ 182 │ 1139393 │ ██████████████████████▋ │
│ RICHMOND │ RICHMOND UPON THAMES │ 1649 │ 1130076 │ ██████████████████████▌ │
│ KINGSTON UPON THAMES │ RICHMOND UPON THAMES │ 147 │ 1126111 │ ██████████████████████▌ │
│ ASCOT │ WINDSOR AND MAIDENHEAD │ 773 │ 1106109 │ ██████████████████████ │
│ LONDON │ HAMMERSMITH AND FULHAM │ 6162 │ 1056198 │ █████████████████████ │
│ RADLETT │ HERTSMERE │ 513 │ 1045758 │ ████████████████████▊ │
│ LEATHERHEAD │ GUILDFORD │ 354 │ 1045175 │ ████████████████████▊ │
│ WEYBRIDGE │ ELMBRIDGE │ 1275 │ 1036702 │ ████████████████████▋ │
│ FARNHAM │ EAST HAMPSHIRE │ 107 │ 1033682 │ ████████████████████▋ │
│ ESHER │ ELMBRIDGE │ 915 │ 1032753 │ ████████████████████▋ │
│ FARNHAM │ HART │ 102 │ 1002692 │ ████████████████████ │
│ GERRARDS CROSS │ BUCKINGHAMSHIRE │ 845 │ 983639 │ ███████████████████▋ │
│ CHALFONT ST GILES │ BUCKINGHAMSHIRE │ 286 │ 973993 │ ███████████████████▍ │
│ SALCOMBE │ SOUTH HAMS │ 215 │ 965724 │ ███████████████████▎ │
│ SURBITON │ ELMBRIDGE │ 181 │ 960346 │ ███████████████████▏ │
│ BROCKENHURST │ NEW FOREST │ 226 │ 951278 │ ███████████████████ │
│ SUTTON COLDFIELD │ LICHFIELD │ 110 │ 930757 │ ██████████████████▌ │
│ EAST MOLESEY │ ELMBRIDGE │ 372 │ 927026 │ ██████████████████▌ │
│ LLANGOLLEN │ WREXHAM │ 127 │ 925681 │ ██████████████████▌ │
│ OXFORD │ SOUTH OXFORDSHIRE │ 638 │ 923830 │ ██████████████████▍ │
│ LONDON │ MERTON │ 4383 │ 923194 │ ██████████████████▍ │
│ GUILDFORD │ WAVERLEY │ 261 │ 905733 │ ██████████████████ │
│ TEDDINGTON │ RICHMOND UPON THAMES │ 1147 │ 894856 │ ███ ██████████████▊ │
│ HARPENDEN │ ST ALBANS │ 1271 │ 893079 │ █████████████████▋ │
│ HENLEY-ON-THAMES │ SOUTH OXFORDSHIRE │ 1042 │ 887557 │ █████████████████▋ │
│ POTTERS BAR │ WELWYN HATFIELD │ 314 │ 863037 │ █████████████████▎ │
│ LONDON │ WANDSWORTH │ 13210 │ 857318 │ █████████████████▏ │
│ BILLINGSHURST │ CHICHESTER │ 255 │ 856508 │ █████████████████▏ │
│ LONDON │ SOUTHWARK │ 7742 │ 843145 │ ████████████████▋ │
│ LONDON │ HACKNEY │ 6656 │ 839716 │ ████████████████▋ │
│ LUTTERWORTH │ HARBOROUGH │ 1096 │ 836546 │ ████████████████▋ │
│ KINGSTON UPON THAMES │ KINGSTON UPON THAMES │ 1846 │ 828990 │ ████████████████▌ │
│ LONDON │ EALING │ 5583 │ 820135 │ ████████████████▍ │
│ INGATESTONE │ CHELMSFORD │ 120 │ 815379 │ ████████████████▎ │
│ MARLOW │ BUCKINGHAMSHIRE │ 718 │ 809943 │ ████████████████▏ │
│ EAST GRINSTEAD │ TANDRIDGE │ 105 │ 809461 │ ████████████████▏ │
│ CHIGWELL │ EPPING FOREST │ 484 │ 809338 │ ████████████████▏ │
│ EGHAM │ RUNNYMEDE │ 989 │ 807858 │ █████████ ███████▏ │
│ HASLEMERE │ CHICHESTER │ 223 │ 804173 │ ████████████████ │
│ PETWORTH │ CHICHESTER │ 288 │ 803206 │ ████████████████ │
│ TWICKENHAM │ RICHMOND UPON THAMES │ 2194 │ 802616 │ ████████████████ │
│ WEMBLEY │ BRENT │ 1698 │ 801733 │ ████████████████ │
│ HINDHEAD │ WAVERLEY │ 233 │ 801482 │ ████████████████ │
│ LONDON │ BARNET │ 8083 │ 792066 │ ███████████████▋ │
│ WOKING │ GUILDFORD │ 343 │ 789360 │ ███████████████▋ │
│ STOCKBRIDGE │ TEST VALLEY │ 318 │ 777909 │ ███████████████▌ │
│ BERKHAMSTED │ DACORUM │ 1049 │ 776138 │ ███████████████▌ │
│ MAIDENHEAD │ BUCKINGHAMSHIRE │ 236 │ 775572 │ ███████████████▌ │
│ SOLIHULL │ STRATFORD-ON-AVON │ 142 │ 770727 │ ███████████████▍ │
│ GREAT MISSENDEN │ BUCKINGHAMSHIRE │ 431 │ 764493 │ ███████████████▎ │
│ TADWORTH │ REIGATE AND BANSTEAD │ 920 │ 757511 │ ███████████████▏ │
│ LONDON │ BRENT │ 4124 │ 757194 │ ███████████████▏ │
│ THAMES DITTON │ ELMBRIDGE │ 470 │ 750828 │ ███████████████ │
│ LONDON │ LAMBETH │ 10431 │ 750532 │ ███████████████ │
│ RICKMANSWORTH │ THREE RIVERS │ 1500 │ 747029 │ ██████████████▊ │
│ KINGS LANGLEY │ DACORUM │ 281 │ 746536 │ ██████████████▊ │
│ HARLOW │ EPPING FOREST │ 172 │ 739423 │ ██████████████▋ │
│ TONBRIDGE │ SEVENOAKS │ 103 │ 738740 │ ██████████████▋ │
│ BELVEDERE │ BEXLEY │ 686 │ 736385 │ ██████████████▋ │
│ CRANBROOK │ TUNBRIDGE WELLS │ 769 │ 734328 │ ██████████████▋ │
│ SOLIHULL │ WARWICK │ 116 │ 733286 │ ██████████████▋ │
│ ALDERLEY EDGE │ CHESHIRE EAST │ 357 │ 732882 │ ██████████████▋ │
│ WELWYN │ WELWYN HATFIELD │ 404 │ 730281 │ ██████████████▌ │
│ CHISLEHURST │ BROMLEY │ 870 │ 730279 │ ██████████████▌ │
│ LONDON │ HARINGEY │ 6488 │ 726715 │ ██████████████▌ │
│ AMERSHAM │ BUCKINGHAMSHIRE │ 965 │ 725426 │ ██████████████▌ │
│ SEVENOAKS │ SEVENOAKS │ 2183 │ 725102 │ ██████████████▌ │
│ BOURNE END │ BUCKINGHAMSHIRE │ 269 │ 724595 │ ██████████████▍ │
│ NORTHWOOD │ HILLINGDON │ 568 │ 722436 │ ██████████████▍ │
│ PURFLEET │ THURROCK │ 143 │ 722205 │ ██████████████▍ │
│ SLOUGH │ BUCKINGHAMSHIRE │ 832 │ 721529 │ ██████████████▍ │
│ INGATESTONE │ BRENTWOOD │ 301 │ 718292 │ ██████████████▎ │
│ EPSOM │ REIGATE AND BANSTEAD │ 315 │ 709264 │ ██████████████▏ │
│ ASHTEAD │ MOLE VALLEY │ 524 │ 708646 │ ██████████████▏ │
│ BETCHWORTH │ MOLE VALLEY │ 155 │ 708525 │ ██████████████▏ │
│ OXTED │ TANDRIDGE │ 645 │ 706946 │ ██████████████▏ │
│ READING │ SOUTH OXFORDSHIRE │ 593 │ 705466 │ ██████████████ │
│ FELTHAM │ HOUNSLOW │ 1536 │ 703815 │ ██████████████ │
│ TUNBRIDGE WELLS │ WEALDEN │ 207 │ 703296 │ ██████████████ │
│ LEWES │ WEALDEN │ 116 │ 701349 │ ██████████████ │
│ OXFORD │ OXFORD │ 3656 │ 700813 │ ██████████████ │
│ MAYFIELD │ WEALDEN │ 177 │ 698158 │ █████████████▊ │
│ PINNER │ HARROW │ 997 │ 697876 │ █████████████▊ │
│ LECHLADE │ COTSWOLD │ 155 │ 696262 │ ████ █████████▊ │
│ WALTON-ON-THAMES │ ELMBRIDGE │ 1850 │ 690102 │ █████████████▋ │
└──────────────────────┴────────────────────────┴───────┴─────────┴─────────────────────────────────────────────────────────────────┘
Let's Speed Up Queries Using Projections
Projections allow you to improve query speeds by storing pre-aggregated data in whatever format you want. In this example, we create a projection that keeps track of the average price, total price, and count of properties grouped by the year, district and town. At query time, ClickHouse will use your projection if it thinks the projection can improve the performance of the query (you don't have to do anything special to use the projection - ClickHouse decides for you when the projection will be useful).
Build a Projection
Let's create an aggregate projection by the dimensions toYear(date), district, and town:
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
)
Populate the projection for existing data. (Without materializing it, the projection will be created for only newly inserted data):
ALTER TABLE uk_price_paid
MATERIALIZE PROJECTION projection_by_year_district_town
SETTINGS mutations_sync = 1
Test Performance
Let's run the same 3 queries again:
Query 1. Average Price Per Year
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
The result is the same, but the performance is better!
No projection: 28 rows in set. Elapsed: 1.775 sec. Processed 27.45 million rows, 164.70 MB (15.47 million rows/s., 92.79 MB/s.)
With projection: 28 rows in set. Elapsed: 0.665 sec. Processed 87.51 thousand rows, 3.21 MB (131.51 thousand rows/s., 4.82 MB/s.)
Query 2. Average Price Per Year in London
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
Same result, but notice the improvement in query performance:
No projection: 28 rows in set. Elapsed: 0.720 sec. Processed 27.45 million rows, 46.61 MB (38.13 million rows/s., 64.74 MB/s.)
With projection: 28 rows in set. Elapsed: 0.015 sec. Processed 87.51 thousand rows, 3.51 MB (5.74 million rows/s., 230.24 MB/s.)
Query 3. The Most Expensive Neighborhoods
The condition (date >= '2020-01-01') needs to be modified so that it matches the projection dimension (toYear(date) >= 2020):
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
LIMIT 100
Again, the result is the same but notice the improvement in query performance:
No projection: 100 rows in set. Elapsed: 0.928 sec. Processed 27.45 million rows, 103.80 MB (29.56 million rows/s., 111.80 MB/s.)
With projection: 100 rows in set. Elapsed: 0.336 sec. Processed 17.32 thousand rows, 1.23 MB (51.61 thousand rows/s., 3.65 MB/s.)
Test it in the Playground
The dataset is also available in the Online Playground.