Getting started

Overview

This beginner's guide will make you familiar with ArangoDB. We will cover how to

  • install and run a local ArangoDB server
  • use the web interface to interact with it
  • store example data in the database
  • query the database to retrieve the data again
  • edit and remove existing data

Installation

Head to arangodb.com/download, select your operating system and download ArangoDB. You may also follow the instructions on how to install with a package manager, if available.

If you installed a binary package under Linux, the server is automatically started.

If you installed ArangoDB using homebrew under MacOS X, start the server by running /usr/local/sbin/arangod.

If you installed ArangoDB under Windows as a service, the server is automatically started. Otherwise, run the arangod.exe located in the installation folder's bin directory. You may have to run it as administrator to grant it write permissions to C:\Program Files.

For more in-depth information on how to install ArangoDB, as well as available startup parameters, installation in a cluster and so on, see Installing.

Securing the installation

The default installation contains one database _system and a user named root.

Debian based packages and the Windows installer will ask for a password during the installation process. Red-Hat based packages will set a random password. For all other installation packages you need to execute

shell> arango-secure-installation

This will asked for a root password and sets this password.

Web interface

The server itself (arangod) speaks HTTP / REST, but you can use the graphical web interface to keep it simple. There's also arangosh, a synchronous shell for interaction with the server. If you're a developer, you might prefer the shell over the GUI. It does not provide features like syntax highlighting however.

When you start using ArangoDB in your project, you will likely use an official or community-made driver written in the same language as your project. Drivers implement a programming interface that should feel natural for that programming language, and do all the talking to the server. Therefore, you can most certainly ignore the HTTP API unless you want to write a driver yourself or explicitly want to use the raw interface.

To get familiar with the database system you can even put drivers aside and use the web interface (code name Aardvark) for basic interaction. The web interface will become available shortly after you started arangod. You can access it in your browser at http://localhost:8529 - if not, please see Troubleshooting.

By default, authentication is enabled. The default user is root. Depending on the installation method used, the installation process either prompted for the root password or the default root password is empty (see above).

Aardvark Login Form

Next you will be asked which database to use. Every server instance comes with a _system database. Select this database to continue.

select database

You should then be presented the dashboard with server statistics like this:

Aardvark Dashboard Request Statistics

For a more detailed description of the interface, see Web Interface.

Databases, collections and documents

Databases are sets of collections. Collections store records, which are referred to as documents. Collections are the equivalent of tables in RDBMS, and documents can be thought of as rows in a table. The difference is that you don't define what columns (or rather attributes) there will be in advance. Every document in any collection can have arbitrary attribute keys and values. Documents in a single collection will likely have a similar structure in practice however, but the database system itself does not impose it and will operate stable and fast no matter how your data looks like.

Read more in the data-model concepts chapter.

For now, you can stick with the default _system database and use the web interface to create collections and documents. Start by clicking the COLLECTIONS menu entry, then the Add Collection tile. Give it a name, e.g. users, leave the other settings unchanged (we want it to be a document collection) and Save it. A new tile labeled users should show up, which you can click to open.

There will be No documents yet. Click the green circle with the white plus on the right-hand side to create a first document in this collection. A dialog will ask you for a _key. You can leave the field blank and click Create to let the database system assign an automatically generated (unique) key. Note that the _key property is immutable, which means you can not change it once the document is created. What you can use as document key is described in the naming conventions.

An automatically generated key could be "9883" (_key is always a string!), and the document _id would be "users/9883" in that case. Aside from a few system attributes, there is nothing in this document yet. Let's add a custom attribute by clicking the icon to the left of (empty object), then Append. Two input fields will become available, FIELD (attribute key) and VALUE (attribute value). Type name as key and your name as value. Append another attribute, name it age and set it to your age. Click Save to persist the changes. If you click on Collection: users at the top on the right-hand side of the ArangoDB logo, the document browser will show the documents in the users collection and you will see the document you just created in the list.

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 RESULT, 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 interchangable 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.

How to continue

There is a lot more to discover in AQL and much more functionality that ArangoDB offers. Continue reading the other chapters and experiment with a test database to foster your knowledge.

If you want to write more AQL queries right now, have a look here:

  • Data Queries: data access and modification queries
  • High-level operations: detailed descriptions of FOR, FILTER and more operations not shown in this introduction
  • Functions: a reference of all provided functions

ArangoDB programs

The ArangoDB package comes with the following programs:

  • arangod: The ArangoDB database daemon. This server program is intended to run as a daemon process and to serve the various clients connection to the server via TCP / HTTP.

  • arangosh: The ArangoDB shell. A client that implements a read-eval-print loop (REPL) and provides functions to access and administrate the ArangoDB server.

  • arangoimp: A bulk importer for the ArangoDB server. It supports JSON and CSV.

  • arangodump: A tool to create backups of an ArangoDB database in JSON format.

  • arangorestore: A tool to load data of a backup back into an ArangoDB database.

  • arango-dfdb: A datafile debugger for ArangoDB. It is primarily intended to be used during development of ArangoDB.

  • arangobench: A benchmark and test tool. It can be used for performance and server function testing.