Difference between revisions of "DuckDB Cheatsheet"

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(Added link to explanation on using parquet / duckdb)
 
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The OFF database contains a variety of data in different format, such as '''TEXTS, LISTS, STRUCT, DATES''', and even more...
+
The Parquet file dump of the Open Food Facts database contains a variety of data in different format, such as string, list, struct, timestamp,...
  
But no worries, '''DuckDB''' handles any type of data! '''Learn how to use the database by solving the most common use-cases asked by the community:'''
+
In this cheatsheet, we will learn how to use the Parquet dump using DuckDB to perform data analysis, by solving some of the most common use-cases asked by the community.
  
* '''Display product name (for main language) of all products of with category en:butters'''.
+
We assume that you've downloaded the <code>food.parquet</code> file locally, and that you've launched Duckdb CLI. (see [[Reusing Open Food Facts Data#Parquet file hosted on Hugging Face .28beta.29|Reusing Open Food Facts Data]])
  
 +
==== Display product name (for main language) of all products of with category <code>en:butters</code> ====
 
  SELECT
 
  SELECT
 
   code,
 
   code,
 
   unnest(
 
   unnest(
     list_filter (product_name, x -> x.lang = = 'main')
+
     list_filter (product_name, x -> x.lang == 'main')
 
   ) ['text']
 
   ) ['text']
 
  FROM
 
  FROM
Line 15: Line 16:
 
   list_contains (categories_tags, 'en:milks')
 
   list_contains (categories_tags, 'en:milks')
 
  LIMIT
 
  LIMIT
   10;';
+
   10;
┌───────────────┬─────────────────────────────────────────────────────────────────────┐
+
┌───────────────┬─────────────────────────────────────────────────────────────────────┐
│    code      │ unnest(list_filter(product_name, (x -> (x.lang = 'main'))))['text'] │
+
│    code      │ unnest(list_filter(product_name, (x -> (x.lang = 'main'))))['text'] │
│    varchar    │                              varchar                              │
+
│    varchar    │                              varchar                              │
├───────────────┼─────────────────────────────────────────────────────────────────────┤
+
├───────────────┼─────────────────────────────────────────────────────────────────────┤
│ 0011110807625 │ Quality food centers, salted butter                                │
+
│ 0011110807625 │ Quality food centers, salted butter                                │
│ 0011110808998 │ Unsalted Butter                                                    │
+
│ 0011110808998 │ Unsalted Butter                                                    │
│ 0011110842640 │ Ralphs, Salted Sticks, Butter                                      │
+
│ 0011110842640 │ Ralphs, Salted Sticks, Butter                                      │
│ 0011110852878 │ Salted butter                                                      │
+
│ 0011110852878 │ Salted butter                                                      │
│ 0011110854384 │ Qfc, unsalted butter                                                │
+
│ 0011110854384 │ Qfc, unsalted butter                                                │
│ 0011110862600 │ King Soopers City Market, Salted Butter                            │
+
│ 0011110862600 │ King Soopers City Market, Salted Butter                            │
│ 0011110863256 │ Salted Butter                                                      │
+
│ 0011110863256 │ Salted Butter                                                      │
│ 0011110863270 │ Unsalted Butter                                                    │
+
│ 0011110863270 │ Unsalted Butter                                                    │
│ 0011110893017 │ Salted Butter                                                      │
+
│ 0011110893017 │ Salted Butter                                                      │
│ 0011110893055 │ Unsalted butter sticks                                              │
+
│ 0011110893055 │ Unsalted butter sticks                                              │
├───────────────┴─────────────────────────────────────────────────────────────────────┤
+
├───────────────┴─────────────────────────────────────────────────────────────────────┤
│ 10 rows                                                                  2 columns │
+
│ 10 rows                                                                  2 columns │
└─────────────────────────────────────────────────────────────────────────────────────┘
+
└─────────────────────────────────────────────────────────────────────────────────────┘
  
* '''Who are the biggest contributors?'''
+
==== Who are the biggest contributors? ====
 +
''Ex: Top 10 best contributors in OFF''
  
''Ex: Top 10 best contributors in OFF''
 
 
  SELECT creator, count(*) AS count  
 
  SELECT creator, count(*) AS count  
 
  FROM read_parquet('food.parquet')
 
  FROM read_parquet('food.parquet')
Line 60: Line 61:
 
  └──────────────────────────────────────┘
 
  └──────────────────────────────────────┘
  
===== DATE & ARRAY fields =====
+
==== Number of added products per year ====
 
 
* '''Number of added products per year'''
 
 
 
 
  --entry_dates_tags is a list of texts. We take the value at position 3: the year
 
  --entry_dates_tags is a list of texts. We take the value at position 3: the year
 
  SELECT entry_dates_tags[3] AS year, count(*) AS count  
 
  SELECT entry_dates_tags[3] AS year, count(*) AS count  
Line 92: Line 90:
 
  └──────────────────┘
 
  └──────────────────┘
  
* '''How many images where uploaded in 2024?'''
+
==== How many images where uploaded in 2024? ====
 
 
 
  SELECT
 
  SELECT
 
   SUM(images_filter_count) AS image_count
 
   SUM(images_filter_count) AS image_count
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       'food.parquet'
 
       'food.parquet'
 
   );
 
   );
 
+
┌─────────────┐
┌─────────────┐
+
│ image_count │
│ image_count │
+
│  int128    │
│  int128    │
+
├─────────────┤
├─────────────┤
+
│      716207 │
│      716207 │
+
└─────────────┘
└─────────────┘
+
[[Category:Metrics]]

Latest revision as of 10:17, 26 November 2024

The Parquet file dump of the Open Food Facts database contains a variety of data in different format, such as string, list, struct, timestamp,...

In this cheatsheet, we will learn how to use the Parquet dump using DuckDB to perform data analysis, by solving some of the most common use-cases asked by the community.

We assume that you've downloaded the food.parquet file locally, and that you've launched Duckdb CLI. (see Reusing Open Food Facts Data)

Display product name (for main language) of all products of with category en:butters

SELECT
 code,
 unnest(
   list_filter (product_name, x -> x.lang == 'main')
 ) ['text']
FROM
 'food.parquet'
WHERE
 list_contains (categories_tags, 'en:milks')
LIMIT
 10;
┌───────────────┬─────────────────────────────────────────────────────────────────────┐
│     code      │ unnest(list_filter(product_name, (x -> (x.lang = 'main'))))['text'] │
│    varchar    │                               varchar                               │
├───────────────┼─────────────────────────────────────────────────────────────────────┤
│ 0011110807625 │ Quality food centers, salted butter                                 │
│ 0011110808998 │ Unsalted Butter                                                     │
│ 0011110842640 │ Ralphs, Salted Sticks, Butter                                       │
│ 0011110852878 │ Salted butter                                                       │
│ 0011110854384 │ Qfc, unsalted butter                                                │
│ 0011110862600 │ King Soopers City Market, Salted Butter                             │
│ 0011110863256 │ Salted Butter                                                       │
│ 0011110863270 │ Unsalted Butter                                                     │
│ 0011110893017 │ Salted Butter                                                       │
│ 0011110893055 │ Unsalted butter sticks                                              │
├───────────────┴─────────────────────────────────────────────────────────────────────┤
│ 10 rows                                                                   2 columns │
└─────────────────────────────────────────────────────────────────────────────────────┘

Who are the biggest contributors?

Ex: Top 10 best contributors in OFF

SELECT creator, count(*) AS count 
FROM read_parquet('food.parquet')
GROUP BY creator 
ORDER BY count DESC 
LIMIT 10;
┌────────────────────────────┬─────────┐
│          creator           │  count  │
│          varchar           │  int64  │
├────────────────────────────┼─────────┤
│ kiliweb                    │ 1883982 │
│ foodvisor                  │  208270 │
│ openfoodfacts-contributors │  199459 │
│ usda-ndb-import            │  169554 │
│ org-database-usda          │  134461 │
│ prepperapp                 │  110841 │
│ macrofactor                │   92148 │
│ foodless                   │   87839 │
│ smoothie-app               │   74339 │
│ inf                        │   37999 │
├────────────────────────────┴─────────┤
│ 10 rows                    2 columns │
└──────────────────────────────────────┘

Number of added products per year

--entry_dates_tags is a list of texts. We take the value at position 3: the year
SELECT entry_dates_tags[3] AS year, count(*) AS count 
FROM read_parquet('food.parquet') 
GROUP BY year 
ORDER BY year DESC;
┌─────────┬────────┐
│  year   │ count  │
│ varchar │ int64  │
├─────────┼────────┤
│ 2024    │ 463257 │
│ 2023    │ 361240 │
│ 2022    │ 598326 │
│ 2021    │ 514052 │
│ 2020    │ 466269 │
│ 2019    │ 364272 │
│ 2018    │ 318010 │
│ 2017    │ 279737 │
│ 2016    │  44618 │
│ 2015    │  33968 │
│ 2014    │  12892 │
│ 2013    │   9587 │
│ 2012    │   4267 │
│ 1970    │      3 │
│         │      1 │
├─────────┴────────┤
│     15 rows      │
└──────────────────┘

How many images where uploaded in 2024?

SELECT
  SUM(images_filter_count) AS image_count
FROM
  (
    SELECT
      len (
        list_filter (
          images,
          x -> struct_extract (x, 'uploaded_t') > epoch (TIMESTAMP '2024-01-01 00:00:00')
          AND struct_extract (x, 'uploaded_t') < epoch (TIMESTAMP '2025-01-01 00:00:00')
        )
      ) as images_filter_count
    FROM
      'food.parquet'
  );
┌─────────────┐
│ image_count │
│   int128    │
├─────────────┤
│      716207 │
└─────────────┘