Filtering data within a GROQ query

When dealing with multiple sets of similar data, it's not uncommon to encounter overlapping filters. Let's say you need to pull up all your articles, then categorize them into two subsets: English and French. You might think you need three separate queries - one for each category. However, they end up looking almost identical, with only minor differences.

1{
2  ...
3  "existingData": *[_type== "article"] { __i18n_lang }),
4  // These queries are the same as the "existingData" but with more specific filtering
5  "en": *[_type== "article" && __i18n_lang == "en"] { __i18n_lang },
6  "fr": *[_type== "article" && __i18n_lang == "fr"] { __i18n_lang },
7  ...
8}

This is where GROQ comes in with a more efficient solution for filtering existing data within a query. By designing a query that captures all three categories, you streamline the process. This singular, comprehensive query maintains the common elements, then employs specific filters for each subset.

Typically, a query uses the format *[ FILTER ] to pinpoint specific documents, where * indicates all documents. But GROQ allows us to replace the * with an existing item, existingData[ FILTER ]. This format sifts through an existing data set instead of all documents.

1{
2  // A single common query
3  "existingData": *[_type== "article"] { __i18n_lang },
4} | {
5  // filter the "existingData" with more specific filters as needed
6  "en": existingData[__i18n_lang == "en"],
7  "fr": existingData[__i18n_lang == "fr"]
8}

The advantages? The streamlined approach not only simplifies the structure of the query, enhancing its readability, but it's also likely to speed up the query execution. By simplifying the process, you're cutting down on computational complexity.