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ArangoDB

Master ArangoDB: native multi-model database, graph, document, and key-value in one platform with AQL.

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What is ArangoDB?

ArangoDB is a native multi-model database that combines documents, graphs, and key-value storage in a single database engine. Unlike other databases that bolt on different data models, ArangoDB was built from the ground up to handle multiple data models efficiently with a unified query language (AQL) that can work across all models in a single query.

With features like ACID transactions, horizontal scaling, and SmartGraphs for distributed graph processing, ArangoDB eliminates the need for multiple database systems and the complexity of keeping data synchronized across different databases. It's particularly powerful for applications that need to handle complex relationships alongside traditional document storage.

ArangoDB Performance Calculator

17,160
Operations/sec
35ms
Avg Query Latency
4GB
Total Storage
$1050
Monthly Cost

Memory: 48GB total cluster

Per Node: 333,333 docs

Replication: Configurable (default 2)

Native Multi-Model Architecture

Document Store

Schema-flexible JSON documents with secondary indexes and full-text search.

• JSON documents
• Secondary indexes
• Full-text search
• Geo-spatial queries
• Schema validation

Graph Database

Native graph storage with efficient traversals and pattern matching.

• Vertices and edges
• Graph traversals
• Pattern matching
• SmartGraphs clustering
• Social network analysis

Key-Value Store

High-performance key-value access with consistent hashing.

• Simple key-value pairs
• Consistent hashing
• High-speed lookups
• TTL support
• Atomic operations

AQL - Unified Query Language

Document Query

Find users by age range and city:

AQL Document Query
FOR user IN users
  FILTER user.age >= 25 AND user.age <= 35
  FILTER user.city == "San Francisco"
  SORT user.created_at DESC
  LIMIT 10
  RETURN {
    name: user.name,
    email: user.email,
    age: user.age
  }

Graph Traversal

Find friends of friends with graph traversal:

AQL Graph Traversal
FOR vertex, edge, path IN 2..2 OUTBOUND "users/alice" 
  GRAPH "social_network"
  FILTER vertex._id != "users/alice"
  RETURN DISTINCT vertex.name

Multi-Model Join

Join document collections with graph relationships:

AQL Multi-Model Query
FOR user IN users
  FILTER user.city == "New York"
  FOR friend IN 1..1 OUTBOUND user GRAPH "friendships"
  FOR order IN orders
    FILTER order.user_id == friend._key
    FILTER order.total > 100
  COLLECT user_name = user.name INTO friend_orders
  RETURN {
    user: user_name,
    high_spending_friends: LENGTH(friend_orders)
  }

Real-World ArangoDB Implementations

Barclays

Uses ArangoDB for fraud detection combining transaction data with relationship graphs.

  • • Real-time fraud pattern detection
  • • Multi-model queries across transactions and relationships
  • • Complex traversals for money laundering detection
  • • Sub-second response times for critical decisions

Cisco

Leverages ArangoDB for network topology management and IT asset relationships.

  • • Network device relationship modeling
  • • IT asset dependency tracking
  • • Configuration management database (CMDB)
  • • Impact analysis for network changes

Adidas

Powers recommendation systems combining product catalogs with user behavior graphs.

  • • Product recommendation engine
  • • Customer journey analysis
  • • Inventory and catalog management
  • • Real-time personalization at scale

Deutsche Bank

Utilizes ArangoDB for regulatory compliance and risk management analytics.

  • • Regulatory reporting and compliance
  • • Risk portfolio analysis
  • • Client relationship mapping
  • • Complex financial product modeling

Enterprise Features

Clustering & Scaling

  • • Automatic sharding with configurable keys
  • • SmartGraphs for co-located graph data
  • • OneShard databases for optimal performance
  • • DC2DC replication for disaster recovery
  • • Satellite collections for reference data
  • • Automatic failover and self-healing

Security & Compliance

  • • Encryption at rest and in transit
  • • Fine-grained access control
  • • LDAP and Active Directory integration
  • • Audit logging and compliance reporting
  • • Hot backup and point-in-time recovery
  • • SOC 2 Type II certification

ArangoDB Best Practices

✅ Do

  • • Design shard keys based on query patterns
  • • Use SmartGraphs for distributed graph workloads
  • • Leverage AQL's multi-model capabilities in single queries
  • • Implement proper indexing strategies for all models
  • • Use OneShard for datasets under 100GB
  • • Monitor query performance with built-in profiler

❌ Don't

  • • Create hot shards with uneven data distribution
  • • Use ArangoDB for simple key-value only workloads
  • • Ignore transaction boundaries in multi-collection operations
  • • Create overly complex graph traversals without optimization
  • • Mix transactional and analytical workloads without tuning
  • • Skip capacity planning for multi-model workloads
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