SQL, Python & More for DuckDB - Page 10

‌

Creating parameterized views with TVFs macros (Table-Valued functions)SQL

Execute this SQL

-- create macro
CREATE OR REPLACE MACRO udf_products_in_year (v_year, v_category)
AS TABLE
SELECT 
	name, 
	category, 
	created_at
FROM products 
WHERE category = v_category
AND year(created_at) = v_year;

-- select using the macro as you would do from a table
SELECT *
FROM udf_products_in_year (2020, 'Home and Garden');

| Copper Light	| Home and Garden	| 2020-04-05 00:00:00.000 |
| Pink Armchair	| Home and Garden	| 2020-06-23 00:00:00.000 |

-- input ddl and data
CREATE TABLE products 
(
	name varchar,
	category varchar,
	created_at timestamp
);

INSERT INTO products
VALUES
('Cream Sofa', 'Home and Garden', '2019-03-14'),
('Copper Light', 'Home and Garden', '2020-04-05'),
('Pink Armchair', 'Home and Garden', '2020-06-23');
Copy code

Octavian Zarzu

Created 04/27/23

‌

Editor's note: DuckDB users often work with files in Parquet format, which has become a standard for representing data in data lakes. While DuckDB lets you work with local Parquet files, you can also use files stored in blob storage such as Amazon AWS S3, Azure Blob Storage and Google Cloud Storage.

Execute this Bash

# Assuming you have the following environment variables defined:
# AWS_ACCESS_KEY_ID
# AWS_SECRET_ACCESS_KEY
# AWS_DEFAULT_REGION
duckdb -c 'LOAD httpfs; SELECT count(*) FROM read_parquet("s3://<bucket>/<prefix>/*.parquet");'
Copy code

Damon

Edited 02/23/24

‌

SUMMARIZESQL

Editor's note: the SUMMARIZE() function allows you to quickly understand your data. If you want to understand a little more about how it works under the hood, see Hamilton's other snippet on building your own SUMMARIZE() capabilities using built-in analytics functions.

Execute this SQL

SUMMARIZE some_tbl;
SUMMARIZE from 'some_file.csv';
--- summary informations about a given table
Copy code

Carlo Piovesan

Edited 02/23/24

‌

Compute a metric for each numeric column and return the values in a long table (requires 0.7.2+)SQL

Editor's note: if your data originates as different types or in a format like CSV, you might want to do so without risking throwing an error for oddly-typed values. You can do so with TRY_CAST(), which will attempt a CAST but return NULL if not possible.

Execute this SQL

with computed as (
    select sum(try_cast(columns(*) as double))
    from read_csv_auto('aapl.csv')
)

select
    -- restore original column names
    trim(list_element(regexp_extract_all(name,'\.(.*?) AS',1),1),'"') as name,
    value
from (pivot_longer computed on *)
Copy code

Ben Ayre

Edited 02/23/24

‌

Convert CSV to Parquet and provide schema to useBash

Editor's note: while there are other snippets showing file conversion, Parth's shows you how to convert from CSV to Parquet files using DuckDB with specification of the entire schema (columns) and compression codec.

Execute this Bash

duckdb -c "COPY (SELECT * FROM read_csv('pageviews-sanitized-20230101-000000.csv', delim=' ', header=False, columns={'domain_code': 'VARCHAR', 'page_title': 'VARCHAR', 'count_views': 'UINTEGER', 'total_response_size': 'UINTEGER'})) TO 'pageviews-sanitized-20230101-000000.parquet' (FORMAT 'PARQUET', CODEC 'zstd')"
Copy code

Parth Patil

Edited 02/23/24

‌

Combine several parquet files into one and compress with zstdBash

Editor's note: another great example of using DuckDB's wide data format support to merge/combine multiple Parquet files. Parth also kindly shows you how to compress the resulting Parquet file with the zstd codec. DuckDB also supports gzip and snappy compression codecs.

Execute this Bash

duckdb -c "COPY (SELECT * FROM '~/Binance_Spot_Data/*.parquet') TO 'binance.parquet' (FORMAT 'PARQUET', CODEC 'zstd')"
Copy code

Parth Patil

Edited 02/23/24

‌

Boxplot quantiles all at onceSQL

Editor's note: with a variety of supported statistical functions, including things like sampling and producing quantiles, DuckDB provides great analytical capabilities not always present in other databases.

Execute this SQL

select 
    unnest([.1,.25,.5,.75,.9]) as quantile,
    unnest(quantile_cont(i, [.1,.25,.5,.75,.9])) as value 
from range(101) t(i);

/* This returns:
| quantile | value |
|     0.10 |    10 |
|     0.25 |    25 |
|     0.50 |    50 |
|     0.75 |    75 |
|     0.90 |    90 |
*/
Copy code

Alex Monahan

Edited 02/23/24

Creating parameterized views with TVFs macros (Table-Valued functions)SQL

Execute this SQL

Remove duplicatesSQL

Execute this SQL

Query Parquet data in S3Bash

Execute this Bash

SUMMARIZESQL

Execute this SQL

Compute a metric for each numeric column and return the values in a long table (requires 0.7.2+)SQL

Execute this SQL

Convert CSV to Parquet and provide schema to useBash

Execute this Bash

Combine several parquet files into one and compress with zstdBash

Execute this Bash

Boxplot quantiles all at onceSQL

Execute this SQL