+1 (201) 720-5410 (USA)

My Cart



Training Mode: Class Room / Live Online


The ArangoDB query language (AQL) can be used to retrieve and modify data that are stored in ArangoDB.

Concepts of ArangoDB

A client application ships an AQL query to the ArangoDB server.The query text contains everything ArangoDB needs to compile the result set

ArangoDB will parse the query, execute it and compile the results. If the query is invalid or cannot be executed, the server will return an error that the client can process and react to. If the query can be executed successfully, the server will return the query results (if any) to the client


ArangoDB is a native multi-model database. Multi-model because ArangoDB provides the capabilities of a graph database, a document database, a key-value store in one C++ core. It’s native, because users can use and freely combine all supported data models and access patterns in a single query.

ArangoDB Structure

Starting with the dataset

The dataset features 43 characters with their name, surname, age, alive status and trait references. The surname and age properties are not always present.

The column traits (resolved) is not part of the actual data used in this tutorial, but included for your convenience.

Import Data with arangoimp

This manual describes the ArangoDB importer arangoimp, which can be used for bulk imports. The most convenient method to import a lot of data into ArangoDB is to use the arangoimp command-line tool. It allows you to import data records from a file into an existing database collection.

First AQL Queries

In its purpose, AQL is similar to the Structured Query Language (SQL). AQL supports reading and modifying collection data, but it doesn't support data-definition operations such as creating and dropping databases, collections and indexes. It is a pure data manipulation language (DML), not a data definition language (DDL) or a data control language (DCL).

Getting Closer to Graph Queries

Thinking about your data as a highly connected set of information is a powerful way to gain insights, solve problems and bring products faster into the hands of your users.

Unlike other databases, relationships take the first priority in graph databases and with ArangoDBs multi-model approach for graphs, documents and key/value pairs you can even switch between models or combine them in a single query.

Importing Edges

ArangoDB comes with a command-line tool utility named arangoimp. This utility can be used for importing JSON-encoded, CSV, and tab-separated files into ArangoDB.

arangoimp needs to be invoked from the command-line once for each import file. The target collection can already exist or can be created by the import run.

Working with Joins - Left / Inner / Right


This is an introduction to ArangoDB's interface for views and how to handle views from the JavaScript shell arangosh. For other languages see the corresponding language API.

Views vs Collections

This is an introduction to ArangoDB's interface for collections and how to handle collections from the JavaScript shell arangosh.For other languages see the corresponding language API.

Graph Basics

This chapter describes the general-graph module. It allows you to define a graph that is spread across several edge and document collections. This allows you to structure your models in line with your domain and group them logically in collections giving you the power to query them in the same graph queries. There is no need to include the referenced collections within the query, this module will handle it for you.

Graph Traversals

ArangoDB provides several ways to query graph data. Very simple operations can be composed with the low-level edge methods edges, inEdges, and outEdges for edge collections. These work on named and anonymous graphs. For more complex operations, ArangoDB provides predefined traversal objects.

Graph Traversal Syntax

First Graph Queries

use a traversal object, we first need to require the traversal module:

var traversal = require("@arangodb/graph/traversal"); var examples = require("@arangodb/graph-examples/example-graph.js"); examples.loadGraph("worldCountry");

Advanced Traversals

Depth vs. Breadth First Search

Uniqueness Options

Advanced Traversal Queries

Excursion: The LET Keyword in AQL

Advanced Graph Queries


Pattern Matching

Error Handling with codes

The errorResponse method provided by controller request contexts has no equivalent in router endpoints.

Manage Data Migrations

Only registered users can write reviews. Please, log in or register

myTectra Stands for Quality Training and provides training using Experienced professionals on the Respective Technologies and who has a good teaching expertise.


myTectra flexible batch schedules enables you to start your class at your convenient date and time.

1. All the enrolled candidates must start the class by any date within 30 days from the date of enrollment.
2. Maximum of 3 Days will be taken by myTectra to schedule the batches from the date of schedule request from the candidate.
3. Individual Focus - Maximum 5 candidates allowed per batches
4. The candidate can request any one of the batch time slot from the below list
Batch Type Time Slab Hours/Day Time Zone Days
Regular Morning 6.30 AM - 9.30 AM Anytime Maximum 2 Hrs IST Monday-to-Friday
Regular Evening 6.30 PM - 9.30 PM Anytime Maximum 2 Hrs IST Monday-to-Friday
Week End 6.30 AM - 9.30 PM Anytime Maximum 3 Hrs IST Saturday,Sunday
Fast Track-Morning 6.30 AM - 9.30 AM Anytime Maximum 2 Hrs IST Monday-to-Saturday
Fast Track-Evening 6.30 PM - 9.30 PM Anytime Maximum 2 Hrs IST Monday-to-Saturday
Customized Batch Customized Timing Customized Hrs IST Customized Days

Note : We can schedule your batch anytime on Saturday and Sunday between 6.30 AM 9.30 PM at your convenient time

Do you have any questions ? please call us on +919019191856 or email to [email protected]