Loading remote parquet files

Editor's note: DuckDB supports lambda functions for easy transformation. It also supports reading data over http/https [in addition from blob stores]. Simon's snippet shows you how to use both of those capabilities to dynamically generate filenames and query them when globbing is unsupported.

Load a single parquet fileSQL

-- Query a single remote parquet file 
-- Query the January data from NY taxi data set
SELECT tpep_pickup_datetime, total_amount
FROM read_parquet('https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2023-01.parquet')
LIMIT 10;

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Dynamically iterate over remote parquet files - lambda function and list_transformSQL

-- Dynamically create the expected filenames to load
-- eg, the files names `2023-01.parquet` to `2023-06.parquet`
-- a lambda function can use the result of the generate_series(1, 6) 
-- and the format command can the `{:02d}` format specifier 
-- to left-pad filenames with 0's

SELECT filename, 
min(tpep_pickup_datetime), 
max(tpep_pickup_datetime), 
avg(total_amount)
FROM read_parquet(
    list_transform(
        generate_series(1, 6),
        n -> format('https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2023-{:02d}.parquet', n) 
    ), filename=true
)
GROUP BY filename
ORDER BY filename;

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Simon Aubury

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Sort by WeekdaysSQL

Execute this SQL

-- duckdb has only weekday-name not number
-- create enum in the right order
create type Weekday as enum ('Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday');

-- cast the string to enum for sorting
select count(*), cast(dayname(CreationDate) as Weekday) as day
from posts where posttypeid = 1 and tags like '%>sql<%'
group by all
order by day asc;
-- doing the cast in order by causes a bug

-- ┌──────────────┬───────────┐
-- │ count_star() │    day    │
-- │    int64     │  weekday  │
-- ├──────────────┼───────────┤
-- │       103937 │ Monday    │
-- │       115575 │ Tuesday   │
-- │       119825 │ Wednesday │
-- │       119514 │ Thursday  │
-- │       103445 │ Friday    │
-- │        47139 │ Saturday  │
-- │        47390 │ Sunday    │
-- └──────────────┴───────────┘

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Michael Hunger

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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');

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Octavian Zarzu

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Aggregate rows into a sorted list.SQL

Execute this SQL

-- list and array_agg take in their own ORDER BY clause, so that you
-- can sort the aggregate. The statements order by cannot be used 
-- as the columns that are used for sorting then need to be a grouping
-- key and cannot be used in the aggregate

SELECT name, 
       -- Order the aggregated list by another column from line_items  
       list(item_name ORDER BY pos ASC) items 
FROM orders 
JOIN line_items ON order_id = id 
GROUP BY ALL
-- Order by grouping keys is ofc possible
ORDER BY name;

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Michael Simons

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Allow unsigned extensions (own extensions or untrusted third-parties)Bash

Editor's note: While DuckDB has 20+ official extensions, it also allows you to write your own extensions. In order load extensions from unofficial repositories, you'll need to let DuckDB know that it's safe to do so. This can be done using the allow_unsigned_extensions flag or -unsigned for the CLI.

Execute this Bash

duckdb -unsigned

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Mehdi Ouazza

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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 |
*/

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Alex Monahan

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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')"

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Parth Patil

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