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Converting Files from Parquet to CSV or JSON

You can use clickhouse-local to convert files between any of the input and output formats that ClickHouse supports (which is over 70 different formats!). In this article, we are convert a Parquet file in S3 into a CSV and JSON file. Let’s start at the beginning. ClickHouse has a collection of table functions that read from files, databases and other resoures and converts the data to a table. To demonstrate, suppose we have a Parquet file in S3. We will use the s3 table function to read it (ClickHouse knows it’s a Parquet file based on the filename). But first, let’s download the clickhouse binary: 
curl https://clickhouse.com/ | sh

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Accessing the data using a table function

Let’s verify we can read the file by using DESCRIBE on the resulting table that the s3 table function creates: 
./clickhouse local -q "DESCRIBE s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/house_parquet/house_0.parquet')"
This particular file contains home prices of properties sold in the United Kingdom. The response looks like: 
price	Nullable(Int64)
date	Nullable(UInt16)
postcode1	Nullable(String)
postcode2	Nullable(String)
type	Nullable(String)
is_new	Nullable(UInt8)
duration	Nullable(String)
addr1	Nullable(String)
addr2	Nullable(String)
street	Nullable(String)
locality	Nullable(String)
town	Nullable(String)
district	Nullable(String)
county	Nullable(String)
You can run any query you want on the data. For example, let’s see which towns have the highest average price of homes: 
./clickhouse local -q "SELECT
   town,
   avg(price) AS avg_price
FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/house_parquet/house_0.parquet')
GROUP BY town
ORDER BY avg_price DESC
LIMIT 10"
The response looks like: 
GATWICK	16818750
CHALFONT ST GILES	938090.0985915493
VIRGINIA WATER	789301.1320224719
COBHAM	699874.7111622555
BEACONSFIELD	677247.5483146068
ESHER	616004.6888297872
KESTON	607585.8597560975
GERRARDS CROSS	566330.2959086584
ASCOT	551491.2975753123
WEYBRIDGE	548974.828692494

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Convert the Parquet file to a CSV

You can send the result of any SQL query to a file. Let’s grab all the columns from our Parquet file in S3 and send the output to a new CSV file. Because the output file ends in .csv, ClickHouse knows to use the CSV output format: 
./clickhouse local -q "SELECT *
FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/house_parquet/house_0.parquet')
INTO OUTFILE 'house_prices.csv'"
Let’s verify it worked: 
$ tail house_prices.csv
70000,10508,"YO8","9XN","detached",0,"freehold","7","","POPPY CLOSE","SELBY","SELBY","SELBY","NORTH YORKSHIRE"
130000,14274,"YO8","9XP","detached",0,"freehold","10","","HEATHER CLOSE","","SELBY","SELBY","NORTH YORKSHIRE"
150000,18180,"YO8","9XP","detached",0,"freehold","11","","HEATHER CLOSE","","SELBY","SELBY","NORTH YORKSHIRE"
157000,18088,"YO8","9XP","detached",0,"freehold","12","","HEATHER CLOSE","","SELBY","SELBY","NORTH YORKSHIRE"
134000,17333,"YO8","9XP","semi-detached",0,"freehold","16","","HEATHER CLOSE","","SELBY","SELBY","NORTH YORKSHIRE"
250000,13405,"YO8","9YA","detached",0,"freehold","6","","YORKDALE COURT","HAMBLETON","SELBY","SELBY","NORTH YORKSHIRE"
59500,11166,"YO8","9YB","semi-detached",0,"freehold","4","","YORKDALE DRIVE","HAMBLETON","SELBY","SELBY","NORTH YORKSHIRE"
142500,17648,"YO8","9YB","semi-detached",0,"freehold","4A","","YORKDALE DRIVE","HAMBLETON","SELBY","SELBY","NORTH YORKSHIRE"
230000,15125,"YO8","9YD","detached",0,"freehold","1","","ONE ACRE GARTH","HAMBLETON","SELBY","SELBY","NORTH YORKSHIRE"
250000,15950,"YO8","9YD","detached",0,"freehold","3","","ONE ACRE GARTH","HAMBLETON","SELBY","SELBY","NORTH YORKSHIRE"

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Convert the Parquet file to a JSON

To convert the Parquet file to JSON, simply change the extension on the output filename: 
./clickhouse local -q "SELECT *
FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/house_parquet/house_0.parquet')
INTO OUTFILE 'house_prices.ndjson'"
Let’s verify it worked: 
 $ tail house_prices.ndjson
{"price":"70000","date":10508,"postcode1":"YO8","postcode2":"9XN","type":"detached","is_new":0,"duration":"freehold","addr1":"7","addr2":"","street":"POPPY CLOSE","locality":"SELBY","town":"SELBY","district":"SELBY","county":"NORTH YORKSHIRE"}
{"price":"130000","date":14274,"postcode1":"YO8","postcode2":"9XP","type":"detached","is_new":0,"duration":"freehold","addr1":"10","addr2":"","street":"HEATHER CLOSE","locality":"","town":"SELBY","district":"SELBY","county":"NORTH YORKSHIRE"}
{"price":"150000","date":18180,"postcode1":"YO8","postcode2":"9XP","type":"detached","is_new":0,"duration":"freehold","addr1":"11","addr2":"","street":"HEATHER CLOSE","locality":"","town":"SELBY","district":"SELBY","county":"NORTH YORKSHIRE"}
{"price":"157000","date":18088,"postcode1":"YO8","postcode2":"9XP","type":"detached","is_new":0,"duration":"freehold","addr1":"12","addr2":"","street":"HEATHER CLOSE","locality":"","town":"SELBY","district":"SELBY","county":"NORTH YORKSHIRE"}
{"price":"134000","date":17333,"postcode1":"YO8","postcode2":"9XP","type":"semi-detached","is_new":0,"duration":"freehold","addr1":"16","addr2":"","street":"HEATHER CLOSE","locality":"","town":"SELBY","district":"SELBY","county":"NORTH YORKSHIRE"}
{"price":"250000","date":13405,"postcode1":"YO8","postcode2":"9YA","type":"detached","is_new":0,"duration":"freehold","addr1":"6","addr2":"","street":"YORKDALE COURT","locality":"HAMBLETON","town":"SELBY","district":"SELBY","county":"NORTH YORKSHIRE"}
{"price":"59500","date":11166,"postcode1":"YO8","postcode2":"9YB","type":"semi-detached","is_new":0,"duration":"freehold","addr1":"4","addr2":"","street":"YORKDALE DRIVE","locality":"HAMBLETON","town":"SELBY","district":"SELBY","county":"NORTH YORKSHIRE"}
{"price":"142500","date":17648,"postcode1":"YO8","postcode2":"9YB","type":"semi-detached","is_new":0,"duration":"freehold","addr1":"4A","addr2":"","street":"YORKDALE DRIVE","locality":"HAMBLETON","town":"SELBY","district":"SELBY","county":"NORTH YORKSHIRE"}
{"price":"230000","date":15125,"postcode1":"YO8","postcode2":"9YD","type":"detached","is_new":0,"duration":"freehold","addr1":"1","addr2":"","street":"ONE ACRE GARTH","locality":"HAMBLETON","town":"SELBY","district":"SELBY","county":"NORTH YORKSHIRE"}
{"price":"250000","date":15950,"postcode1":"YO8","postcode2":"9YD","type":"detached","is_new":0,"duration":"freehold","addr1":"3","addr2":"","street":"ONE ACRE GARTH","locality":"HAMBLETON","town":"SELBY","district":"SELBY","county":"NORTH YORKSHIRE"}

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Convert CSV to Parquet

It works both ways - we can easily read in the new CSV file and output it into a Parquet file. The local file house_prices.csv can be read in ClickHouse using the file table function, and ClickHouse outputs the file in Parquet format based on the filename ending in .parquet (or we could have added the FORMAT Parquet clause): 
./clickhouse local -q "SELECT *
FROM file('house_prices.csv')
INTO OUTFILE 'house_prices.parquet'"
As we mentioned above, you can use any of the ClickHouse input and output formats along with clickhouse local to easily convert files into different formats.