Joining together

References to other documents

The character data we imported has an attribute traits for each character, which is an array of strings. It does not store character features directly however:

{
    "name": "Ned",
    "surname": "Stark",
    "alive": false,
    "age": 41,
    "traits": ["A","H","C","N","P"]
}

It is rather a list of letters without an apparent meaning. The idea here is that traits is supposed to store documents keys of another collection, which we can use to resolve the letters to labels such as “strong”. The benefit of using another collection for the actual traits is, that we can easily query for all existing traits later on and store labels in multiple languages for instance in a central place. If we would embed traits directly…

{
    "name": "Ned",
    "surname": "Stark",
    "alive": false,
    "age": 41,
    "traits": [
        {
            "de": "stark",
            "en": "strong"
        },
        {
            "de": "einflussreich",
            "en": "powerful"
        },
        {
            "de": "loyal",
            "en": "loyal"
        },
        {
            "de": "rational",
            "en": "rational"
        },
        {
            "de": "mutig",
            "en": "brave"
        }
    ]
}

… it becomes really hard to maintain traits. If you were to rename or translate one of them, you would need to find all other character documents with the same trait and perform the changes there too. If we only refer to a trait in another collection, it is as easy as updating a single document.

Data model comparison

Importing traits

Below you find the traits data. Follow the pattern shown in Create documents to import it:

Create a document collection Traits
Assign the data to a variable in AQL, LET data = [ ... ]
Use a FOR loop to iterate over each array element of the data
INSERT the element INTO Traits

Create Traits collection

[
    { "_key": "A", "en": "strong", "de": "stark" },
    { "_key": "B", "en": "polite", "de": "freundlich" },
    { "_key": "C", "en": "loyal", "de": "loyal" },
    { "_key": "D", "en": "beautiful", "de": "schön" },
    { "_key": "E", "en": "sneaky", "de": "hinterlistig" },
    { "_key": "F", "en": "experienced", "de": "erfahren" },
    { "_key": "G", "en": "corrupt", "de": "korrupt" },
    { "_key": "H", "en": "powerful", "de": "einflussreich" },
    { "_key": "I", "en": "naive", "de": "naiv" },
    { "_key": "J", "en": "unmarried", "de": "unverheiratet" },
    { "_key": "K", "en": "skillful", "de": "geschickt" },
    { "_key": "L", "en": "young", "de": "jung" },
    { "_key": "M", "en": "smart", "de": "klug" },
    { "_key": "N", "en": "rational", "de": "rational" },
    { "_key": "O", "en": "ruthless", "de": "skrupellos" },
    { "_key": "P", "en": "brave", "de": "mutig" },
    { "_key": "Q", "en": "mighty", "de": "mächtig" },
    { "_key": "R", "en": "weak", "de": "schwach" }
]

Resolving traits

Let’s start simple by returning only the traits attribute of each character:

FOR c IN Characters
    RETURN c.traits

[
    ["A","H","C","N","P"],
    ["D","H","C"],
    ...
]

Also see the Fundamentals of Objects / Documents about attribute access.

We can use the traits array together with the DOCUMENT() function to use the elements as document keys and look them up in the Traits collection:

FOR c IN Characters
    RETURN DOCUMENT("Traits", c.traits)

[
  [
    {
      "_key": "A",
      "_id": "Traits/A",
      "_rev": "_V5oRUS2---",
      "en": "strong",
      "de": "stark"
    },
    {
      "_key": "H",
      "_id": "Traits/H",
      "_rev": "_V5oRUS6--E",
      "en": "powerful",
      "de": "einflussreich"
    },
    {
      "_key": "C",
      "_id": "Traits/C",
      "_rev": "_V5oRUS6--_",
      "en": "loyal",
      "de": "loyal"
    },
    {
      "_key": "N",
      "_id": "Traits/N",
      "_rev": "_V5oRUT---D",
      "en": "rational",
      "de": "rational"
    },
    {
      "_key": "P",
      "_id": "Traits/P",
      "_rev": "_V5oRUTC---",
      "en": "brave",
      "de": "mutig"
    }
  ],
  [
    {
      "_key": "D",
      "_id": "Traits/D",
      "_rev": "_V5oRUS6--A",
      "en": "beautiful",
      "de": "schön"
    },
    {
      "_key": "H",
      "_id": "Traits/H",
      "_rev": "_V5oRUS6--E",
      "en": "powerful",
      "de": "einflussreich"
    },
    {
      "_key": "C",
      "_id": "Traits/C",
      "_rev": "_V5oRUS6--_",
      "en": "loyal",
      "de": "loyal"
    }
  ],
  ...
]

The DOCUMENT() function can be used to look up a single or multiple documents via document identifiers. In our example, we pass the collection name from which we want to fetch documents as first argument ("Traits") and an array of document keys (_key attribute) as second argument. In return we get an array of the full trait documents for each character.

This is a bit too much information, so let’s only return English labels using the array expansion notation:

FOR c IN Characters
    RETURN DOCUMENT("Traits", c.traits)[*].en

[
  [
    "strong",
    "powerful",
    "loyal",
    "rational",
    "brave"
  ],
  [
    "beautiful",
    "powerful",
    "loyal"
  ],
  ...
]

Merging characters and traits

Great, we resolved the letters to meaningful traits! But we also need to know to which character they belong. Thus, we need to merge both the character document and the data from the trait documents:

FOR c IN Characters
    RETURN MERGE(c, { traits: DOCUMENT("Traits", c.traits)[*].en } )

[
  {
    "_id": "Characters/2861650",
    "_key": "2861650",
    "_rev": "_V1bzsXa---",
    "age": 41,
    "alive": false,
    "name": "Ned",
    "surname": "Stark",
    "traits": [
      "strong",
      "powerful",
      "loyal",
      "rational",
      "brave"
    ]
  },
  {
    "_id": "Characters/2861653",
    "_key": "2861653",
    "_rev": "_V1bzsXa--B",
    "age": 40,
    "alive": false,
    "name": "Catelyn",
    "surname": "Stark",
    "traits": [
      "beautiful",
      "powerful",
      "loyal"
    ]
  },
  ...
]

The MERGE() functions merges objects together. Because we used an object { traits: ... } which has the same attribute name traits as the original character attribute, the latter got overwritten by the merge operation.

Join another way

The DOCUMENT() function utilizes primary indices to look up documents quickly. It is limited to find documents via their identifiers however. For a use case like in our example it is sufficient to accomplish a simple join.

There is another, more flexible syntax for joins: nested FOR loops over multiple collections, with a FILTER condition to match up attributes. In case of the traits key array, there needs to be a third loop to iterate over the keys:

FOR c IN Characters
  RETURN MERGE(c, {
    traits: (
      FOR key IN c.traits
        FOR t IN Traits
          FILTER t._key == key
          RETURN t.en
    )
  })

For each character, it loops over its traits attribute (e.g. ["D","H","C"]) and for each document reference in this array, it loops over the Traits collections. There is a condition to match the document key with the key reference. The inner FOR loop and the FILTER get transformed to a primary index lookup in this case instead of building up a Cartesian product only to filter away everything but a single match: Document keys within a collection are unique, thus there can only be one match.

Each written-out, English trait is returned and all the traits are then merged with the character document. The result is identical to the query using DOCUMENT(). However, this approach with a nested FOR loop and a FILTER is not limited to primary keys. You can do this with any other attribute as well. For an efficient lookup, make sure you add a hash index for this attribute. If its values are unique, then also set the index option to unique.

❮ Sorting and limiting Graph traversal ❯