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Using Joins in AQL
The two common scenarios when you want to join documents of collections are:
-
One-to-Many: You may have a collection users and a collection cities. A user lives in a city and you need the city information during a query about the user.
-
Many-To-Many: You may have a collection authors and books. An author can write many books and a book can have many authors. You want to return a list of books with their authors. Therefore you need to join the authors and books.
Unlike many NoSQL databases, ArangoDB does support joins in AQL queries. This is similar to the way traditional relational databases handle this. However, because documents allow for more flexibility, joins are also more flexible. The following sections provide solutions for common questions.
So far we have only dealt with one collection (users) at a time. We also have a collection relations that stores relationships between users. We will now use this extra collection to create a result from two collections.
First of all, we’ll query a few users together with their friends’ ids. For that, we’ll use all relations that have a value of friend in their type attribute. Relationships are established by using the friendOf and thisUser attributes in the relations collection, which point to the userId values in the users collection.
One-To-Many
You have a collection called users. Users live in city and a city is identified by its primary key. In principle you can embedded the city document into the users document and be happy with it.
{
"_id" : "users/2151975421",
"_key" : "2151975421",
"_rev" : "2151975421",
"name" : {
"first" : "John",
"last" : "Doe"
},
"city" : {
"name" : "Metropolis"
}
}
This works well for many use cases. Now assume, that you have additional information about the city, like the number of people living in it. It would be impractical to change each and every user document if this numbers changes. Therefore it is good idea to hold the city information in a separate collection.
arangosh> db.cities.document("cities/2241300989");
{
"population" : 1000,
"name" : "Metropolis",
"_id" : "cities/2241300989",
"_rev" : "2241300989",
"_key" : "2241300989"
}
Now you instead of embedding the city directly in the user document, you can use the key of the city.
arangosh> db.users.document("users/2290649597");
{
"name" : {
"first" : "John",
"last" : "Doe"
},
"city" : "cities/2241300989",
"_id" : "users/2290649597",
"_rev" : "2290649597",
"_key" : "2290649597"
}
We can now join these two collections very easily.
arangosh> db._query(
........>"FOR u IN users " +
........>" FOR c IN cities " +
........>" FILTER u.city == c._id RETURN { user: u, city: c }"
........>).toArray()
[
{
"user" : {
"name" : {
"first" : "John",
"last" : "Doe"
},
"city" : "cities/2241300989",
"_id" : "users/2290649597",
"_rev" : "2290649597",
"_key" : "2290649597"
},
"city" : {
"population" : 1000,
"name" : "Metropolis",
"_id" : "cities/2241300989",
"_rev" : "2241300989",
"_key" : "2241300989"
}
}
]
Unlike SQL there is no special JOIN keyword. The optimizer ensures that the
primary index is used in the above query.
However, very often it is much more convenient for the client of the query if a single document would be returned, where the city information is embedded in the user document - as in the simple example above. With AQL there you do not need to forgo this simplification.
arangosh> db._query(
........>"FOR u IN users " +
........>" FOR c IN cities " +
........>" FILTER u.city == c._id RETURN merge(u, {city: c})"
........>).toArray()
[
{
"_id" : "users/2290649597",
"_key" : "2290649597",
"_rev" : "2290649597",
"name" : {
"first" : "John",
"last" : "Doe"
},
"city" : {
"_id" : "cities/2241300989",
"_key" : "2241300989",
"_rev" : "2241300989",
"population" : 1000,
"name" : "Metropolis"
}
}
]
So you can have both: the convenient representation of the result for your client and the flexibility of joins for your data model.
Many-To-Many
In the relational world you need a third table to model the many-to-many relation. In ArangoDB you have a choice depending on the information you are going to store and the type of questions you are going to ask.
Assume that authors are stored in one collection and books in a second. If all you need is “which are the authors of a book” then you can easily model this as a list attribute in users.
If you want to store more information, for example which author wrote which page in a conference proceeding, or if you also want to know “which books were written by which author”, you can use edge collections. This is very similar to the “join table” from the relational world.
Embedded Lists
If you only want to store the authors of a book, you can embed them as list in the book document. There is no need for a separate collection.
arangosh> db.authors.toArray()
[
{
"_id" : "authors/2661190141",
"_key" : "2661190141",
"_rev" : "2661190141",
"name" : {
"first" : "Maxima",
"last" : "Musterfrau"
}
},
{
"_id" : "authors/2658437629",
"_key" : "2658437629",
"_rev" : "2658437629",
"name" : {
"first" : "John",
"last" : "Doe"
}
}
]
You can query books
arangosh> db._query("FOR b IN books RETURN b").toArray();
[
{
"_id" : "books/2681506301",
"_key" : "2681506301",
"_rev" : "2681506301",
"title" : "The beauty of JOINS",
"authors" : [
"authors/2661190141",
"authors/2658437629"
]
}
]
and join the authors in a very similar manner given in the one-to-many section.
arangosh> db._query(
........>"FOR b IN books " +
........>" LET a = (FOR x IN b.authors " +
........>" FOR a IN authors FILTER x == a._id RETURN a) " +
........>" RETURN { book: b, authors: a }"
........>).toArray();
[
{
"book" : {
"title" : "The beauty of JOINS",
"authors" : [
"authors/2661190141",
"authors/2658437629"
],
"_id" : "books/2681506301",
"_rev" : "2681506301",
"_key" : "2681506301"
},
"authors" : [
{
"name" : {
"first" : "Maxima",
"last" : "Musterfrau"
},
"_id" : "authors/2661190141",
"_rev" : "2661190141",
"_key" : "2661190141"
},
{
"name" : {
"first" : "John",
"last" : "Doe"
},
"_id" : "authors/2658437629",
"_rev" : "2658437629",
"_key" : "2658437629"
}
]
}
]
… or embed the authors directly:
arangosh> db._query(
........>"FOR b IN books LET a = (" +
........>" FOR x IN b.authors " +
........>" FOR a IN authors FILTER x == a._id RETURN a)" +
........>" RETURN merge(b, { authors: a })"
........>).toArray();
[
{
"_id" : "books/2681506301",
"_key" : "2681506301",
"_rev" : "2681506301",
"title" : "The beauty of JOINS",
"authors" : [
{
"_id" : "authors/2661190141",
"_key" : "2661190141",
"_rev" : "2661190141",
"name" : {
"first" : "Maxima",
"last" : "Musterfrau"
}
},
{
"_id" : "authors/2658437629",
"_key" : "2658437629",
"_rev" : "2658437629",
"name" : {
"first" : "John",
"last" : "Doe"
}
}
]
}
]
Using Edge Collections
If you also want to query which books are written by a given author, embedding authors in the book document is possible, but it is more efficient to use a edge collections for speed.
Or you are publishing a proceeding, then you want to store the pages the author has written as well. This information can be stored in the edge document.
First create the users
arangosh> db._create("authors");
[ArangoCollection 2926807549, "authors" (type document, status loaded)]
arangosh> db.authors.save({ name: { first: "John", last: "Doe" } })
{
"error" : false,
"_id" : "authors/2935261693",
"_rev" : "2935261693",
"_key" : "2935261693"
}
arangosh> db.authors.save({ name: { first: "Maxima", last: "Musterfrau" } })
{
"error" : false,
"_id" : "authors/2938210813",
"_rev" : "2938210813",
"_key" : "2938210813"
}
Now create the books without any author information.
arangosh> db._create("books");
[ArangoCollection 2928380413, "books" (type document, status loaded)]
arangosh> db.books.save({ title: "The beauty of JOINS" });
{
"error" : false,
"_id" : "books/2980088317",
"_rev" : "2980088317",
"_key" : "2980088317"
}
An edge collection is now used to link authors and books.
arangosh> db._createEdgeCollection("written");
[ArangoCollection 2931132925, "written" (type edge, status loaded)]
arangosh> db.written.save("authors/2935261693",
........>"books/2980088317",
........>{ pages: "1-10" })
{
"error" : false,
"_id" : "written/3006237181",
"_rev" : "3006237181",
"_key" : "3006237181"
}
arangosh> db.written.save("authors/2938210813",
........>"books/2980088317",
........>{ pages: "11-20" })
{
"error" : false,
"_id" : "written/3012856317",
"_rev" : "3012856317",
"_key" : "3012856317"
}
In order to get all books with their authors you can use a graph traversal
arangosh> db._query(
...> "FOR b IN books " +
...> "LET authorsByBook = ( " +
...> " FOR author, writtenBy IN INBOUND b written " +
...> " RETURN { " +
...> " vertex: author, " +
...> " edge: writtenBy " +
...> " } " +
...> ") " +
...> "RETURN { " +
...> " book: b, " +
...> " authors: authorsByBook " +
...> "} "
...> ).toArray();
[
{
"book" : {
"_key" : "2980088317",
"_id" : "books/2980088317",
"_rev" : "2980088317",
"title" : "The beauty of JOINS"
},
"authors" : [
{
"vertex" : {
"_key" : "2935261693",
"_id" : "authors/2935261693",
"_rev" : "2935261693",
"name" : {
"first" : "John",
"last" : "Doe"
}
},
"edge" : {
"_key" : "2935261693",
"_id" : "written/2935261693",
"_from" : "authors/2935261693",
"_to" : "books/2980088317",
"_rev" : "3006237181",
"pages" : "1-10"
}
},
{
"vertex" : {
"_key" : "2938210813",
"_id" : "authors/2938210813",
"_rev" : "2938210813",
"name" : {
"first" : "Maxima",
"last" : "Musterfrau"
}
},
"edge" : {
"_key" : "6833274",
"_id" : "written/6833274",
"_from" : "authors/2938210813",
"_to" : "books/2980088317",
"_rev" : "3012856317",
"pages" : "11-20"
}
}
]
}
]
Or if you want only the information stored in the vertices.
arangosh> db._query(
...> "FOR b IN books " +
...> "LET authorsByBook = ( " +
...> " FOR author IN INBOUND b written " +
...> " OPTIONS { " +
...> " order: 'bfs', " +
...> " uniqueVertices: 'global' " +
...> " } " +
...> " RETURN author " +
...> ") " +
...> "RETURN { " +
...> " book: b, " +
...> " authors: authorsByBook " +
...> "} "
...> ).toArray();
[
{
"book" : {
"_key" : "2980088317",
"_id" : "books/2980088317",
"_rev" : "2980088317",
"title" : "The beauty of JOINS"
},
"authors" : [
{
"_key" : "2938210813",
"_id" : "authors/2938210813",
"_rev" : "2938210813",
"name" : {
"first" : "Maxima",
"last" : "Musterfrau"
}
},
{
"_key" : "2935261693",
"_id" : "authors/2935261693",
"_rev" : "2935261693",
"name" : {
"first" : "John",
"last" : "Doe"
}
}
]
}
]
Or again embed the authors directly into the book document.
arangosh> db._query(
...> "FOR b IN books " +
...> "LET authors = ( " +
...> " FOR author IN INBOUND b written " +
...> " OPTIONS { " +
...> " order: 'bfs', " +
...> " uniqueVertices: 'global' " +
...> " } " +
...> " RETURN author " +
...> ") " +
...> "RETURN MERGE(b, {authors: authors}) "
...> ).toArray();
[
{
"_id" : "books/2980088317",
"_key" : "2980088317",
"_rev" : "2980088317",
"title" : "The beauty of JOINS",
"authors" : [
{
"_key" : "2938210813",
"_id" : "authors/2938210813",
"_rev" : "2938210813",
"name" : {
"first" : "Maxima",
"last" : "Musterfrau"
}
},
{
"_key" : "2935261693",
"_id" : "authors/2935261693",
"_rev" : "2935261693",
"name" : {
"first" : "John",
"last" : "Doe"
}
}
]
}
]
If you need the authors and their books, simply reverse the direction.
> db._query(
...> "FOR a IN authors " +
...> "LET booksByAuthor = ( " +
...> " FOR b IN OUTBOUND a written " +
...> " OPTIONS { " +
...> " order: 'bfs', " +
...> " uniqueVertices: 'global' " +
...> " } " +
...> " RETURN b" +
...> ") " +
...> "RETURN MERGE(a, {books: booksByAuthor}) "
...> ).toArray();
[
{
"_id" : "authors/2935261693",
"_key" : "2935261693",
"_rev" : "2935261693",
"name" : {
"first" : "John",
"last" : "Doe"
},
"books" : [
{
"_key" : "2980088317",
"_id" : "books/2980088317",
"_rev" : "2980088317",
"title" : "The beauty of JOINS"
}
]
},
{
"_id" : "authors/2938210813",
"_key" : "2938210813",
"_rev" : "2938210813",
"name" : {
"first" : "Maxima",
"last" : "Musterfrau"
},
"books" : [
{
"_key" : "2980088317",
"_id" : "books/2980088317",
"_rev" : "2980088317",
"title" : "The beauty of JOINS"
}
]
}
]
More examples
Join tuples
We will start with a SQL-ish result set and return each tuple (user name, friends userId) separately. The AQL query to generate such result is:
We iterate over the collection users. Only the ‘active’ users will be examined. For each of these users we will search for up to 4 friends. We locate friends by comparing the userId of our current user with the friendOf attribute of the relations document. For each of those relations found we return the users name and the userId of the friend.
Horizontal lists
Note that in the above result, a user can be returned multiple times. This is the SQL way of returning data. If this is not desired, the friends’ ids of each user can be returned in a horizontal list. This will return each user at most once.
The AQL query for doing so is:
FOR u IN users
FILTER u.active == true LIMIT 0, 4
RETURN {
"user" : u.name,
"friendIds" : (
FOR f IN relations
FILTER f.friendOf == u.userId && f.type == "friend"
RETURN f.thisUser
)
}
[
{
"user" : "Abigail",
"friendIds" : [
108,
102,
106
]
},
{
"user" : "Fred",
"friendIds" : [
209
]
},
{
"user" : "Mary",
"friendIds" : [
207,
104
]
},
{
"user" : "Mariah",
"friendIds" : [
203,
205
]
}
]
In this query we are still iterating over the users in the users collection and for each matching user we are executing a subquery to create the matching list of related users.
Self joins
To not only return friend ids but also the names of friends, we could “join” the users collection once more (something like a “self join”):
FOR u IN users
FILTER u.active == true
LIMIT 0, 4
RETURN {
"user" : u.name,
"friendIds" : (
FOR f IN relations
FILTER f.friendOf == u.userId && f.type == "friend"
FOR u2 IN users
FILTER f.thisUser == u2.useId
RETURN u2.name
)
}
[
{
"user" : "Abigail",
"friendIds" : [
"Jim",
"Jacob",
"Daniel"
]
},
{
"user" : "Fred",
"friendIds" : [
"Mariah"
]
},
{
"user" : "Mary",
"friendIds" : [
"Isabella",
"Michael"
]
},
{
"user" : "Mariah",
"friendIds" : [
"Madison",
"Eva"
]
}
]
This query will then again in term fetch the clear text name of the friend from the users collection. So here we iterate the users collection, and for each hit the relations collection, and for each hit once more the users collection.
Outer joins
Lets find the lonely people in our database - those without friends.
FOR user IN users
LET friendList = (
FOR f IN relations
FILTER f.friendOf == u.userId
RETURN 1
)
FILTER LENGTH(friendList) == 0
RETURN { "user" : user.name }
[
{
"user" : "Abigail"
},
{
"user" : "Fred"
}
]
So, for each user we pick the list of their friends and count them. The ones where count equals zero are the lonely people. Using RETURN 1 in the subquery saves even more precious CPU cycles and gives the optimizer more alternatives.
Index usage
Especially on joins you should make sure indexes can be used to speed up your query. Please note that sparse indexes don’t qualify for joins:
In joins you typically would also want to join documents not containing the property you join with. However sparse indexes don’t contain references to documents that don’t contain the indexed attributes - thus they would be missing from the join operation. For that reason you should provide non-sparse indexes.
Pitfalls
Since we’re free of schemata, there is by default no way to tell the format of the documents. So, if your documents don’t contain an attribute, it defaults to null. We can however check our data for accuracy like this:
RETURN LENGTH(FOR u IN users FILTER u.userId == null RETURN 1)
[
10000
]
RETURN LENGTH(FOR f IN relations FILTER f.friendOf == null RETURN 1)
[
10000
]
So if the above queries return 10k matches each, the result of the Join tuples query will become 100,000,000 items larger and use much memory plus computation time. So it is generally a good idea to revalidate that the criteria for your join conditions exist.
Using indexes on the properties can speed up the operation significantly. You can use the explain helper to revalidate your query actually uses them.
If you work with joins on edge collections you would typically aggregate over the internal fields _id, _from and _to (where _id equals userId, _from friendOf and _to would be thisUser in our examples). ArangoDB implicitly creates indexes on them.