Querying the Database

Time to retrieve our document using AQL, ArangoDB’s query language. We can directly look up the document we created via the _id, but there are also other options. Click the QUERIES menu entry to bring up the query editor and type the following (adjust the document ID to match your document):

RETURN DOCUMENT("users/9883")

Then click Execute to run the query. The result appears below the query editor:

[
  {
    "_key": "9883",
    "_id": "users/9883",
    "_rev": "9883",
    "age": 32,
    "name": "John Smith"
  }
]

As you can see, the entire document including the system attributes is returned. DOCUMENT() is a function to retrieve a single document or a list of documents of which you know the _keys or _ids. We return the result of the function call as our query result, which is our document inside of the result array (we could have returned more than one result with a different query, but even for a single document as result, we still get an array at the top level).

This type of query is called data access query. No data is created, changed or deleted. There is another type of query called data modification query. Let’s insert a second document using a modification query:

INSERT { name: "Katie Foster", age: 27 } INTO users

The query is pretty self-explanatory: the INSERT keyword tells ArangoDB that we want to insert something. What to insert, a document with two attributes in this case, follows next. The curly braces { } signify documents, or objects. When talking about records in a collection, we call them documents. Encoded as JSON, we call them objects. Objects can also be nested. Here’s an example:

{
  "name": {
    "first": "Katie",
    "last": "Foster"
  }
}

INTO is a mandatory part of every INSERT operation and is followed by the collection name that we want to store the document in. Note that there are no quote marks around the collection name.

If you run above query, there will be an empty array as result because we did not specify what to return using a RETURN keyword. It is optional in modification queries, but mandatory in data access queries. Even with RETURN, the return value can still be an empty array, e.g. if the specified document was not found. Despite the empty result, the above query still created a new user document. You can verify this with the document browser.

Let’s add another user, but return the newly created document this time:

INSERT { name: "James Hendrix", age: 69 } INTO users
RETURN NEW

NEW is a pseudo-variable, which refers to the document created by INSERT. The result of the query will look like this:

[
  {
    "_key": "10074",
    "_id": "users/10074",
    "_rev": "10074",
    "age": 69,
    "name": "James Hendrix"
  }
]

Now that we have 3 users in our collection, how to retrieve them all with a single query? The following does not work:

RETURN DOCUMENT("users/9883")
RETURN DOCUMENT("users/9915")
RETURN DOCUMENT("users/10074")

There can only be a single RETURN statement here and a syntax error is raised if you try to execute it. The DOCUMENT() function offers a secondary signature to specify multiple document handles, so we could do:

RETURN DOCUMENT( ["users/9883", "users/9915", "users/10074"] )

An array with the _ids of all 3 documents is passed to the function. Arrays are denoted by square brackets [ ] and their elements are separated by commas.

But what if we add more users? We would have to change the query to retrieve the newly added users as well. All we want to say with our query is: “For every user in the collection users, return the user document”. We can formulate this with a FOR loop:

FOR user IN users
  RETURN user

It expresses to iterate over every document in users and to use user as variable name, which we can use to refer to the current user document. It could also be called doc, u or ahuacatlguacamole, this is up to you. It is advisable to use a short and self-descriptive name however.

The loop body tells the system to return the value of the variable user, which is a single user document. All user documents are returned this way:

[
  {
    "_key": "9915",
    "_id": "users/9915",
    "_rev": "9915",
    "age": 27,
    "name": "Katie Foster"
  },
  {
    "_key": "9883",
    "_id": "users/9883",
    "_rev": "9883",
    "age": 32,
    "name": "John Smith"
  },
  {
    "_key": "10074",
    "_id": "users/10074",
    "_rev": "10074",
    "age": 69,
    "name": "James Hendrix"
  }
]

You may have noticed that the order of the returned documents is not necessarily the same as they were inserted. There is no order guaranteed unless you explicitly sort them. We can add a SORT operation very easily:

FOR user IN users
  SORT user._key
  RETURN user

This does still not return the desired result: James (10074) is returned before John (9883) and Katie (9915). The reason is that the _key attribute is a string in ArangoDB, and not a number. The individual characters of the strings are compared. 1 is lower than 9 and the result is therefore “correct”. If we wanted to use the numerical value of the _key attributes instead, we could convert the string to a number and use it for sorting. There are some implications however. We are better off sorting something else. How about the age, in descending order?

FOR user IN users
  SORT user.age DESC
  RETURN user

The users will be returned in the following order: James (69), John (32), Katie (27). Instead of DESC for descending order, ASC can be used for ascending order. ASC is the default though and can be omitted.

We might want to limit the result set to a subset of users, based on the age attribute for example. Let’s return users older than 30 only:

FOR user IN users
  FILTER user.age > 30
  SORT user.age
  RETURN user

This will return John and James (in this order). Katie’s age attribute does not fulfill the criterion (greater than 30), she is only 27 and therefore not part of the result set. We can make her age to return her user document again, using a modification query:

UPDATE "9915" WITH { age: 40 } IN users
RETURN NEW

UPDATE allows to partially edit an existing document. There is also REPLACE, which would remove all attributes (except for _key and _id, which remain the same) and only add the specified ones. UPDATE on the other hand only replaces the specified attributes and keeps everything else as-is.

The UPDATE keyword is followed by the document key (or a document / object with a _key attribute) to identify what to modify. The attributes to update are written as object after the WITH keyword. IN denotes in which collection to perform this operation in, just like INTO (both keywords are actually interchangeable here). The full document with the changes applied is returned if we use the NEW pseudo-variable:

[
  {
    "_key": "9915",
    "_id": "users/9915",
    "_rev": "12864",
    "age": 40,
    "name": "Katie Foster"
  }
]

If we used REPLACE instead, the name attribute would be gone. With UPDATE, the attribute is kept (the same would apply to additional attributes if we had them).

Let us run our FILTER query again, but only return the user names this time:

FOR user IN users
  FILTER user.age > 30
  SORT user.age
  RETURN user.name

This will return the names of all 3 users:

[
  "John Smith",
  "Katie Foster",
  "James Hendrix"
]

It is called a projection if only a subset of attributes is returned. Another kind of projection is to change the structure of the results:

FOR user IN users
  RETURN { userName: user.name, age: user.age }

The query defines the output format for every user document. The user name is returned as userName instead of name, the age keeps the attribute key in this example:

[
  {
    "userName": "James Hendrix",
    "age": 69
  },
  {
    "userName": "John Smith",
    "age": 32
  },
  {
    "userName": "Katie Foster",
    "age": 40
  }
]

It is also possible to compute new values:

FOR user IN users
  RETURN CONCAT(user.name, "'s age is ", user.age)

CONCAT() is a function that can join elements together to a string. We use it here to return a statement for every user. As you can see, the result set does not always have to be an array of objects:

[
  "James Hendrix's age is 69",
  "John Smith's age is 32",
  "Katie Foster's age is 40"
]

Now let’s do something crazy: for every document in the users collection, iterate over all user documents again and return user pairs, e.g. John and Katie. We can use a loop inside a loop for this to get the cross product (every possible combination of all user records, 3 * 3 = 9). We don’t want pairings like John + John however, so let’s eliminate them with a filter condition:

FOR user1 IN users
  FOR user2 IN users
    FILTER user1 != user2
    RETURN [user1.name, user2.name]

We get 6 pairings. Pairs like James + John and John + James are basically redundant, but fair enough:

[
  [ "James Hendrix", "John Smith" ],
  [ "James Hendrix", "Katie Foster" ],
  [ "John Smith", "James Hendrix" ],
  [ "John Smith", "Katie Foster" ],
  [ "Katie Foster", "James Hendrix" ],
  [ "Katie Foster", "John Smith" ]
]

We could calculate the sum of both ages and compute something new this way:

FOR user1 IN users
  FOR user2 IN users
    FILTER user1 != user2
    RETURN {
        pair: [user1.name, user2.name],
        sumOfAges: user1.age + user2.age
    }

We introduce a new attribute sumOfAges and add up both ages for the value:

[
  {
    "pair": [ "James Hendrix", "John Smith" ],
    "sumOfAges": 101
  },
  {
    "pair": [ "James Hendrix", "Katie Foster" ],
    "sumOfAges": 109
  },
  {
    "pair": [ "John Smith", "James Hendrix" ],
    "sumOfAges": 101
  },
  {
    "pair": [ "John Smith", "Katie Foster" ],
    "sumOfAges": 72
  },
  {
    "pair": [ "Katie Foster", "James Hendrix" ],
    "sumOfAges": 109
  },
  {
    "pair": [ "Katie Foster", "John Smith" ],
    "sumOfAges": 72
  }
]

If we wanted to post-filter on the new attribute to only return pairs with a sum less than 100, we should define a variable to temporarily store the sum, so that we can use it in a FILTER statement as well as in the RETURN statement:

FOR user1 IN users
  FOR user2 IN users
    FILTER user1 != user2
    LET sumOfAges = user1.age + user2.age
    FILTER sumOfAges < 100
    RETURN {
        pair: [user1.name, user2.name],
        sumOfAges: sumOfAges
    }

The LET keyword is followed by the designated variable name (sumOfAges), then there’s a = symbol and the value or an expression to define what value the variable is supposed to have. We re-use our expression to calculate the sum here. We then have another FILTER to skip the unwanted pairings and make use of the variable we declared before. We return a projection with an array of the user names and the calculated age, for which we use the variable again:

[
  {
    "pair": [ "John Smith", "Katie Foster" ],
    "sumOfAges": 72
  },
  {
    "pair": [ "Katie Foster", "John Smith" ],
    "sumOfAges": 72
  }
]

Pro tip: when defining objects, if the desired attribute key and the variable to use for the attribute value are the same, you can use a shorthand notation: { sumOfAges } instead of { sumOfAges: sumOfAges }.

Finally, let’s delete one of the user documents:

REMOVE "9883" IN users

It deletes the user John (_key: "9883"). We could also remove documents in a loop (same goes for INSERT, UPDATE and REPLACE):

FOR user IN users
    FILTER user.age >= 30
    REMOVE user IN users

The query deletes all users whose age is greater than or equal to 30.