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+ Import CSV to DuckDB
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=== Looking for a selection of products? ===
 
=== Looking for a selection of products? ===
Then use the advanced search. The Open Food Facts advanced search feature allows to download selections of the data. See: https://world.openfoodfacts.org/cgi/search.pl
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Then use '''the advanced search'''. The Open Food Facts advanced search feature allows to download selections of the data. See: https://world.openfoodfacts.org/cgi/search.pl
    
When your search is done, you will be able to download the selection in '''CSV or Excel format''', just give a try!
 
When your search is done, you will be able to download the selection in '''CSV or Excel format''', just give a try!
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Do notice you can download up to 10,000 results only. If you need to download more results you have to use some other methods described below.
    
=== Looking for the whole database? ===
 
=== Looking for the whole database? ===
 
The whole database can be downloaded at https://world.openfoodfacts.org/data
 
The whole database can be downloaded at https://world.openfoodfacts.org/data
   −
It's very big. Open Food Facts hosts more than 1,400,000 products (as of July 2020). So you will probably need skills to reuse the data.
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It's very big. Open Food Facts hosts more than 2,800,000 products (as of April 2023). So you will probably need skills to reuse the data.
    
You'll be able to find here different kinds of data.
 
You'll be able to find here different kinds of data.
    
==== The MongoDB daily export ====
 
==== The MongoDB daily export ====
It represents the most complete data; it's very big and you have to know how to deal with MongoDB. It's very big! More than 9GB uncompressed.
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It represents the most complete data; it's very big and you have to know how to deal with MongoDB. It's very big! More than 30GB uncompressed.
    
==== The JSONL daily export ====
 
==== The JSONL daily export ====
While still undocumented, there is a daily export of the whole database in [https://jsonlines.org/ JSONL format] (sometimes called LDJSON or NDJSON) where each line is a JSON object. It represents the same data as the MongoDB export. The file is 2,7GB (2020-09), compressed with gzip. It takes more than 14GB uncompressed.
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There is a daily export of the whole database in [https://jsonlines.org/ JSONL format] (sometimes called LDJSON or NDJSON) where each line is a JSON object. It represents the same data as the MongoDB export. The file is 4,8GB (2022-10), compressed with gzip. It takes more than 14GB uncompressed.
    
You can find it at https://static.openfoodfacts.org/data/openfoodfacts-products.jsonl.gz
 
You can find it at https://static.openfoodfacts.org/data/openfoodfacts-products.jsonl.gz
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==== csvkit tips ====
 
==== csvkit tips ====
 
[https://csvkit.readthedocs.io/en/latest/ csvkit] is a very efficient tool to manipulate huge amounts of CSV data. Here are some useful tips to manipulate Open Food Facts CSV export.
 
[https://csvkit.readthedocs.io/en/latest/ csvkit] is a very efficient tool to manipulate huge amounts of CSV data. Here are some useful tips to manipulate Open Food Facts CSV export.
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''Converting whole Open Food Facts "CSV" export to regular CSV''. Open Food Facts export use tabs as separator: it should be called TSV (tab separated values) instead of CSV (comma separated values). <code>csvkit</code> can convert TSV file into CSV very easily:
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<code>$ csvclean -t en.openfoodfacts.org.products.csv > myCSV.csv</code>
    
''Selecting 2 column''s. Selecting two or three columns can be useful for some usages. Extracting two columns produce a smaller CSV file which can be opened by common softwares such as Libre Office or Excel. The following command creates a CSV file (brands.csv) containing two columns from Open Food Facts (code and brands). (It generally takes more than 2 minutes, depending on your computer.)
 
''Selecting 2 column''s. Selecting two or three columns can be useful for some usages. Extracting two columns produce a smaller CSV file which can be opened by common softwares such as Libre Office or Excel. The following command creates a CSV file (brands.csv) containing two columns from Open Food Facts (code and brands). (It generally takes more than 2 minutes, depending on your computer.)
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* product countries
 
* product countries
 
* full categories hierarchy imported from the <code>categories.txt</code> taxonomy ([https://github.com/openfoodfacts/openfoodfacts-server/tree/master/taxonomies see])
 
* full categories hierarchy imported from the <code>categories.txt</code> taxonomy ([https://github.com/openfoodfacts/openfoodfacts-server/tree/master/taxonomies see])
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==== Import CSV to DuckDB ====
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[https://duckdb.org/ DuckDB] is very close to SQLite, except it has higher performances: database size is 3 times lighter, and requests performs 5-10 times better.
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# Discard invalid characters
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<nowiki>#</nowiki> duckdb doesn't like invalid UTF8. It did not want to read some parquet file as such, with the following error:
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<nowiki>#</nowiki> Error: near line 1: Invalid Input Error: Invalid string encoding found in Parquet file: value "........."
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<nowiki>#</nowiki> (occuring namely on this product: <nowiki>https://world.openfoodfacts.org/product/9900109008673?rev=4</nowiki> )
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<nowiki>#</nowiki> The issue, and its solution below, seems to be well-known: <nowiki>https://til.simonwillison.net/linux/iconv</nowiki>
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iconv -f utf-8 -t utf-8 -c en.openfoodfacts.org.products.csv -o en.openfoodfacts.org.products.converted.csv
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<nowiki>#</nowiki> Create duckdb database and import data
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duckdb products.db <<EOF
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CREATE TABLE products AS
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<nowiki> </nowiki>   SELECT * FROM read_csv_auto('en.openfoodfacts.org.products.converted.csv', quote=<nowiki>''</nowiki>, sample_size=3000000, delim='\t');
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EOF
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<nowiki>#</nowiki> Then you can try a SQL request
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duckdb products.db -csv <<EOF
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SELECT * FROM products
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<nowiki> </nowiki> WHERE completeness > 0.99 -- products with a good level of completeness
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<nowiki> </nowiki> ORDER BY last_modified_datetime LIMIT 10;
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EOF
    
==== Python ====
 
==== Python ====
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Beware, each line is a product and some variable need to be unnest: tidyverser::unnest_wider()
 
Beware, each line is a product and some variable need to be unnest: tidyverser::unnest_wider()
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=== JSONL delta exports ===
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Every day, Open Food Facts exports all the products created during the last 24 hours. The documentation of this export can be found in the /data page.
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If you don't have MongoDB and just want to use these delta exports to build an up-to-date database, you can merge each export with the help of <code>[https://stedolan.github.io/jq/manual/v1.6/ jq]</code> tool.
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$ gunzip products_1638076899_1638162314.json.gz # will decompress the file
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$ wc -l products_1638076899_1638162314.json # will count the number of products in this export (in JSONL each line is a JSON object)
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$ jq -c '. + .' 2021-11-30.json products_1638162314_1638248379.json > 2021-12-01.json # merge the delta with previous complete data
    
=== JSONL export ===
 
=== JSONL export ===
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Filtering barcodes which are different from a code containing 1 to 13 digits:
 
Filtering barcodes which are different from a code containing 1 to 13 digits:
  zcat openfoodfacts-products.jsonl.gz | jq -r '. | select(.code|test("^[0-9]{1,13}$") | not) | .code' > ean_gt_13.csv
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  $ zcat openfoodfacts-products.jsonl.gz | jq -r '. | select(.code|test("^[0-9]{1,13}$") | not) | .code' > ean_gt_13.csv
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Some part of the data are arrays, you must aggregate them using <code>join</code> for CSV export. For example, to export each product and its states in CSV:
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$ zcat openfoodfacts-products.jsonl.gz | jq -r '[.code,(.states_tags|join(","))] | @csv'
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Selecting products with quality issues and exporting the barcode and the issues in CSV:
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$ zcat openfoodfacts-products.jsonl.gz | jq -r '. | select(.data_quality_errors_tags[]? != "")' | jq -r '[.code,(.data_quality_errors_tags|join(","))] | @csv'
 
These operations can be quite long (more than 10 minutes depending on your computer and your selection).
 
These operations can be quite long (more than 10 minutes depending on your computer and your selection).
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=== MongoDB dump ===
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The [https://world.openfoodfacts.org/data MongoDB dump] needs to be reused with MongoDB. It allows building a full replication of the Open Food Facts database and use MongoDB for selecting, filtering and exporting data. Using MongoDB allows faster manipulations compared to the other methods.
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First, you '''need a running MongoDB installation'''. Open Food Facts is using MongoDB 4.4. It has been reported that prior version should not work for Open Food Facts dump.
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You can see [https://gist.github.com/CharlesNepote/13198c2ed336fc64cb674d63876e8d99 here a quick tutorial on how to install MongoDB on Debian 10 or Debian 11].
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==== Import Open Food Facts MongoDB dump into MongoDB ====
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<pre>
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# Download and decompress the dump
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wget https://static.openfoodfacts.org/data/openfoodfacts-mongodbdump.tar.gz
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tar -xzf openfoodfacts-mongodbdump.tar.gz
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# Restore all the database. mongorestore recreates indexes recorded by mongodump.
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mongorestore --drop ./dump
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# => 2254885 document(s) restored successfully. 0 document(s) failed to restore.
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</pre>
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==== Play with the database ====
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<pre>
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# Display 5 first products in JSON format, using pagination
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# https://www.codementor.io/@arpitbhayani/fast-and-efficient-pagination-in-mongodb-9095flbqr
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mongo off --eval 'db.products.find().limit(5).pretty().shellPrint()' --quiet
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# Combined with JQ (JSON tool) to provide colors
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# JQ has to installed separatly. See https://stedolan.github.io/jq/
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mongo off --eval 'db.products.find().limit(5).pretty().shellPrint()' --quiet | jq .
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# Combined with JQ (JSON tool) to provide colors and compact output (each JSON object on a single line (aka JSONL format))
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mongo off --eval 'db.products.find().limit(5).pretty().shellPrint()' --quiet | jq . -c
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# Get products from Germany; return fields "code" and "counties_tags"; limit to 2 products
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mongo off --eval 'db.products.find({countries_tags: "en:germany"}, {code: 1, countries_tags: 1}).limit(2).pretty().shellPrint()' --quiet
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# get the data from one field without _id
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mongo off --eval 'db.products.find({countries_tags: "en:germany"}, {_id: 0, countries_tags: 1}).limit(2).pretty().shellPrint()' --quiet
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</pre>
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==== Export the database ====
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<pre>
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# Exports
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# See: https://www.mongodb.com/docs/database-tools/mongoexport/
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# 1. The "aggregate" way
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mongo off --eval 'db.products.aggregate([{$match: {product_name: "Coke"}},{$out: "result"}])'
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mongoexport --db off --collection result --fields code,product_name --type=csv --out result.csv
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# 2. the -q,--query option way
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# Export 5 first german products
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mongoexport -d off -c products --type=csv --fields code,countries_tags -q '{"countries_tags": "en:germany"}}' --out report.csv --limit 5
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# Export to STDIN in CSV format; notice option --quiet
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mongoexport -d off -c products --type=csv --fields code,countries_tags -q '{"countries_tags": "en:germany"}' --limit 5 --quiet
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# How long to export all German products?
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time mongoexport -d off -c products --type=csv --fields code,countries_tags -q '{"countries_tags": "en:germany"}' --out report.csv
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# real 0m10.135s
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# Specify the fields in a file containing the line-separated list of fields to export (--fieldFile option)
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# Official csv export fields are coming from @export_fields variable in /lib/ProductOpener/Config_off.pm
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mongoexport -d off -c products --type=csv --fieldFile official_csv_export_fields.txt -q '{"countries_tags": "en:germany"}' --limit 5 --quiet
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</pre>
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==== List all fields used in the database ====
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<pre>
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# Open Food Facts database contains hundreds of fields.
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# An easy way to list them all is to use "variety" Schema Analyzer:
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# https://github.com/variety/variety
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# 1. Install "variety"
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git clone https://github.com/variety/variety.git
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# 2. Use it
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cd ./variety
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# Analyzing can be very long (hours). You can restrict the analysis to a small number
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time mongo off --eval "var collection = 'products', limit = 1000" variety.js > off_schema_1000.txt
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# (17 s)
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time mongo off --eval "var collection = 'products', limit = 10000" variety.js > off_schema_10000.txt
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# (3 minutes)
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time mongo off --eval "var collection = 'products', limit = 100000" variety.js > off_schema_100000.txt
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# (75 minutes)
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time mongo off --eval "var collection = 'products'" variety.js > off_schema_all.txt
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# (more than two days)
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</pre>
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=== CSV export via SQL (beta) ===
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We are testing a new kind of tool to provide the data: every day an SQL database is fed by the regular daily CSV export, and published online thanks to Datasette tool.
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The tool, called ''[[Mirabelle]]'', can be found here: http://mirabelle.openfoodfacts.org/
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The whole CSV export can be found here: http://mirabelle.openfoodfacts.org/products/all
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* The tool supports simple queries with a form, and also facet navigation.
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* For those who know SQL language, it allows rich and complex queries.
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What's different with [https://world.openfoodfacts.org/cgi/search.pl Open Food Facts advanced search]?
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* It's possible to export selections with more than 10,000 products (eg. big queries by countries).
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* It's possible to build queries by date.
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* It allows richer queries with OR, AND, NOT, REGEXP, etc.
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* It is possible to restrict the number of fields displayed and exported.
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* It is possible to order results by any field.
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==== Example ====
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'''1 -- Build your query (or ask someone to build it for you)'''
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Eg. all German products that have been scanned at least one time.
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-- Products from Germany that have been scanned at least one time
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select code, product_name from [all]
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where countries_en like "%germany%" and unique_scans_n is not null
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order by unique_scans_n desc
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-- the limit here displays 20 results; the link "CSV without limit" below allow you to download all the data without limit
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limit 20
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https://mirabelle.openfoodfacts.org/products?sql=--+Products+from+Germany+that+have+been+scanned+at+least+one+time%0D%0Aselect+code%2C+product_name+from+%5Ball%5D%0D%0Awhere+countries_en+like+%22%25germany%25%22+and+unique_scans_n+is+not+null%0D%0Aorder+by+unique_scans_n+desc%0D%0A--+the+limit+here+displays+20+results%3B+the+link+%22CSV+without+limit%22+below+allows+to+download+all+the+data+without+limit%0D%0Alimit+20
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'''2 -- Click on the link "CSV without limit"'''
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Maybe you have to wait several seconds. It will download a product.csv file.
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==== Tips ====
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* Several fields -- such as <code>countries_en</code>, <code>categories_en</code>, etc. -- contain multiple values. To query a particular value you have to use the operator <code>like</code> and use percents like this: <code>like %italy%</code>.

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