ArangoDB
Neo4j
OrientDB

Comprehensive comparison for Database technology in Software Development applications

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Quick Comparison

See how they stack up across critical metrics

Best For
Building Complexity
Community Size
Software Development-Specific Adoption
Pricing Model
Performance Score
ArangoDB
Multi-model applications requiring graph, document, and key-value capabilities in a single database with complex relationship queries
Large & Growing
Moderate to High
Open Source with Enterprise Paid Options
8
OrientDB
Graph databases with complex relationships, multi-model applications combining document, graph, and object-oriented data
Large & Growing
Moderate to High
Open Source
7
Neo4j
Graph relationships, social networks, recommendation engines, fraud detection, knowledge graphs, and complex connected data scenarios
Large & Growing
Moderate to High
Free/Paid/Open Source
8
Technology Overview

Deep dive into each technology

ArangoDB is a native multi-model database that combines graph, document, and key-value data models in a single engine with a unified query language (AQL). For software development companies, it eliminates the complexity of maintaining multiple database systems, enabling faster development cycles and reduced operational overhead. Companies like Barclays, Cisco, and Deutsche Telekom leverage ArangoDB for complex relationship modeling, real-time analytics, and fraud detection. In e-commerce contexts, it powers recommendation engines, customer journey mapping, and inventory management systems where relationships between products, users, and transactions require flexible graph traversals alongside traditional document storage.

Pros & Cons

Strengths & Weaknesses

Pros

  • Multi-model architecture supports documents, graphs, and key-value data in one database, eliminating the need for multiple database systems and reducing infrastructure complexity for development teams.
  • Native graph traversal capabilities with AQL enable efficient relationship queries without expensive joins, particularly valuable for building social networks, recommendation engines, and knowledge graphs.
  • ACID transactions across multiple documents and collections ensure data consistency, critical for financial applications, inventory systems, and any software requiring strong data integrity guarantees.
  • Flexible schema design allows rapid iteration during development cycles, enabling teams to evolve data models without costly migrations while maintaining optional schema validation when needed.
  • Horizontal scalability through sharding and clustering supports growth from prototype to production, allowing software companies to scale their database layer as customer demand increases.
  • AQL query language provides SQL-like familiarity while adding graph traversal syntax, reducing learning curve for developers and enabling complex queries across different data models seamlessly.
  • Built-in full-text search and geospatial indexing eliminate dependencies on external search engines like Elasticsearch for many use cases, simplifying architecture and reducing operational overhead.

Cons

  • Smaller community and ecosystem compared to PostgreSQL or MongoDB means fewer third-party tools, limited Stack Overflow answers, and potential difficulty finding experienced developers during hiring.
  • Memory-intensive operations, especially for complex graph traversals, can lead to high resource consumption and increased infrastructure costs when handling large-scale datasets or concurrent queries.
  • Limited managed cloud service options compared to competitors restrict deployment flexibility, with fewer providers offering fully-managed ArangoDB, increasing operational burden on development teams.
  • Performance tuning requires deep understanding of sharding strategies and index optimization, creating a steeper learning curve for teams unfamiliar with distributed database architectures.
  • Enterprise features like SmartGraphs and OneShard databases are locked behind commercial licenses, potentially increasing costs significantly as applications scale beyond open-source edition capabilities.
Use Cases

Real-World Applications

Complex Multi-Model Data Relationship Management

ArangoDB excels when your application needs to handle documents, graphs, and key-value data within a single database. It eliminates the complexity of maintaining multiple database systems while supporting flexible schema designs. This is ideal for applications requiring diverse data models like social networks, recommendation engines, or content management systems.

Graph Traversal and Network Analysis Applications

Choose ArangoDB when your project involves heavy graph operations like finding shortest paths, pattern matching, or analyzing connected data. Its native graph database capabilities with AQL (ArangoDB Query Language) provide efficient traversal algorithms. Perfect for fraud detection, knowledge graphs, logistics routing, and social network analysis.

Real-Time Analytics with Complex Queries

ArangoDB is ideal when you need to perform complex joins, aggregations, and analytical queries on large datasets in real-time. Its query optimizer and indexing capabilities support sophisticated data analysis without sacrificing performance. Suitable for dashboards, business intelligence tools, and applications requiring instant insights from interconnected data.

Microservices Architecture with Diverse Data Needs

Use ArangoDB in microservices environments where different services require different data models but benefit from unified data access. It reduces operational overhead by consolidating document, graph, and search capabilities in one system. This approach simplifies development for platforms with multiple services handling varied data structures and relationships.

Technical Analysis

Performance Benchmarks

Build Time
Runtime Performance
Bundle Size
Memory Usage
Software Development-Specific Metric
ArangoDB
Initial setup: 2-5 minutes for Docker deployment, 10-15 minutes for source compilation
Single-server: 10,000-30,000 reads/sec, 5,000-15,000 writes/sec; Cluster mode: scales linearly with nodes, up to 100,000+ ops/sec
Docker image: ~400MB, Binary installation: ~150MB, Source code: ~50MB
Minimum: 512MB RAM, Recommended: 4GB+ RAM for production, scales with dataset size and concurrent connections (typically 2-8GB for medium workloads)
Graph traversal performance: 1,000-10,000 traversals/sec depending on depth and complexity; AQL query execution: 50-500ms for complex multi-model queries
OrientDB
2-5 minutes for initial setup and schema configuration
10,000-50,000 operations per second for mixed read/write workloads on standard hardware
~60-80 MB for core distribution package
512 MB minimum, 2-4 GB recommended for production workloads
Graph traversal speed: 1-5 million edges per second
Neo4j
2-5 seconds for initial schema setup, ~10-30 seconds for large graph imports (100K+ nodes)
10,000-50,000 traversals per second for complex graph queries (3-5 hops), 100,000+ reads/sec for simple lookups
~250-350 MB for Neo4j Community Edition installation, ~500 MB-1 GB for Enterprise Edition
Minimum 512 MB heap, recommended 4-16 GB heap for production workloads, ~2-4 GB for typical development databases
Graph Traversal Performance - 0.01-5ms per hop depending on relationship density and index usage

Benchmark Context

Neo4j demonstrates superior performance for pure graph traversals and pattern matching queries, particularly excelling in read-heavy workloads with complex relationship navigation—ideal for recommendation engines and fraud detection. ArangoDB offers the most versatile performance profile as a multi-model database, efficiently handling graph, document, and key-value operations within a single query, making it optimal for applications requiring mixed data models. OrientDB provides competitive performance for moderate-scale graph operations with strong document capabilities, though it trails Neo4j in deep traversal scenarios and lacks ArangoDB's query optimization maturity. For write-intensive applications with complex transactions, Neo4j's native graph storage delivers consistent latency, while ArangoDB's flexible indexing strategies provide better performance when combining graph and document queries in a single application.


ArangoDB

ArangoDB is a multi-model database supporting documents, graphs, and key-value pairs. Performance metrics measure query throughput, graph traversal speed, memory efficiency for in-memory operations, and scalability in cluster configurations. Build time includes deployment setup, runtime reflects ACID-compliant transaction processing, and memory usage scales with active dataset and cache requirements.

OrientDB

OrientDB is a multi-model database combining document, graph, and object-oriented capabilities with strong ACID compliance, offering efficient graph traversals and flexible schema design for complex data relationships

Neo4j

Neo4j excels at relationship-heavy queries with native graph storage providing O(1) traversals. Performance scales with proper indexing and memory allocation. Best for connected data patterns with complex relationship queries.

Community & Long-term Support

Community Size
GitHub Stars
NPM Downloads
Stack Overflow Questions
Job Postings
Major Companies Using It
Active Maintainers
Release Frequency
ArangoDB
Estimated 50,000+ developers globally using ArangoDB across various applications
5.0
Approximately 25,000-30,000 weekly downloads for arangojs (official JavaScript driver)
Approximately 3,800 questions tagged with 'arangodb'
200-300 job openings globally mentioning ArangoDB as a skill requirement
Companies include Cisco (network analytics), Barclays (financial services), Verizon (telecommunications), Bosch (IoT data management), and various startups in graph analytics and multi-model database applications
Primarily maintained by ArangoDB Inc. (commercial company founded in 2015) with core team of 20+ engineers, plus community contributors. Open-source Apache 2.0 licensed with enterprise version available
Major releases approximately every 6-8 months, with minor releases and patches monthly. Current stable version is 3.12.x series as of early 2025
OrientDB
Estimated 50,000-100,000 developers globally have used OrientDB, with active community of approximately 5,000-10,000 developers
4.7
Approximately 15,000-20,000 weekly downloads across OrientDB npm packages
Approximately 3,500-4,000 questions tagged with OrientDB
50-100 job postings globally mentioning OrientDB as a skill requirement
Sky UK (media/broadcasting), Cisco (networking), Comcast (telecommunications), Deutsche Telekom (telecommunications), and various financial services companies use OrientDB for graph-based data management, network topology, and relationship-heavy applications
Primarily maintained by ArcadeData (the company founded by OrientDB creator) and community contributors. The project transitioned from OrientDB Ltd to community-driven development with commercial support from ArcadeData
Major releases occur 1-2 times per year, with minor releases and patches more frequently (quarterly). Note: Development activity has slowed compared to peak years 2014-2018
Neo4j
Over 500,000 developers and data professionals worldwide using Neo4j
5.0
Neo4j JavaScript driver averages 400,000+ weekly downloads on npm
Over 25,000 questions tagged with 'neo4j' on Stack Overflow
Approximately 3,500-4,000 job postings globally mentioning Neo4j skills
NASA, eBay, Walmart, UBS, Cisco, Microsoft, Adobe, Airbnb, LinkedIn, and Comcast use Neo4j for knowledge graphs, fraud detection, recommendation engines, network management, and master data management
Maintained by Neo4j Inc. (commercial company founded 2007) with dedicated engineering teams, plus active open-source community contributors. Neo4j Community Edition is open-source under GPLv3
Major versions released approximately every 12-18 months, with minor releases and patches every 2-3 months. Neo4j 5.x series active as of 2025

Software Development Community Insights

Neo4j maintains the largest and most mature graph database community with extensive enterprise adoption, comprehensive documentation, and a robust ecosystem of drivers, tools, and integrations—though its proprietary nature limits some community contributions. ArangoDB shows steady growth with an active open-source community, particularly strong among developers building multi-model applications, and benefits from transparent development practices and responsive maintainers. OrientDB has experienced declining momentum since 2019, with slower release cycles and reduced community engagement, though it retains a loyal user base in specific verticals. For software development teams, Neo4j offers the safest bet for long-term support and talent availability, while ArangoDB presents an increasingly attractive option for teams valuing flexibility and modern development practices. The graph database market continues expanding rapidly, with all three databases seeing increased adoption in knowledge graphs, identity management, and network analysis use cases.

Pricing & Licensing

Cost Analysis

License Type
Core Technology Cost
Enterprise Features
Support Options
Estimated TCO for Software Development
ArangoDB
Apache License 2.0
Free (open source)
ArangoDB Enterprise Edition includes additional features like SmartGraphs, OneShard, Enhanced Security, and Datacenter-to-Datacenter Replication. Pricing is custom based on deployment size and requirements, typically starting from $5,000-$10,000+ annually for production environments
Free community support via GitHub, Stack Overflow, and community Slack. Paid support options include Professional Support (starting ~$10,000/year) and Enterprise Support (starting ~$25,000/year) with SLAs, dedicated support engineers, and 24/7 coverage
For a medium-scale Software Development application handling 100K transactions/month: Infrastructure costs approximately $200-$800/month for cloud hosting (3-node cluster on AWS/GCP/Azure with 8-16GB RAM per node), plus optional Enterprise license ($400-$800/month amortized) and support costs ($800-$2,000/month). Total estimated TCO: $1,400-$3,600/month for production-grade deployment
OrientDB
Apache License 2.0
Free (open source)
Enterprise Edition available with additional features like advanced security, query profiler, distributed clustering, and teleporter tool - pricing requires contact with vendor, typically starts at $3,000-$10,000+ per server/year
Free community support via GitHub, Stack Overflow, and Discord; Paid support starts at $3,000-$5,000 per year for basic plans; Enterprise support with SLA ranges from $10,000-$50,000+ annually depending on scale
$500-$2,000 per month including cloud infrastructure (2-4 servers, 16-32GB RAM total, storage, networking), monitoring tools, and backup strategies. Does not include enterprise license or paid support costs which would add $250-$4,000+ monthly if required
Neo4j
GPLv3 (Community Edition) / Commercial (Enterprise Edition)
Free for Community Edition; Enterprise Edition requires commercial license starting at $36,000/year for production use
Enterprise features (advanced security, clustering, hot backups, monitoring) require paid Enterprise Edition license; Community Edition has limited features
Free community forums and documentation for Community Edition; Paid support starts at $3,000-5,000/month for Enterprise with SLA; Premium support available at higher tiers
$500-2,000/month for Community Edition on cloud infrastructure (compute, storage, bandwidth); $5,000-10,000/month for Enterprise Edition including licensing, infrastructure, and basic support for medium-scale software development application

Cost Comparison Summary

Neo4j's pricing model starts with a free Community Edition but requires commercial licensing (Enterprise Edition) for production deployments needing clustering, advanced security, or support—costs typically range from $36,000 to $200,000+ annually depending on scale and features. ArangoDB offers a fully-featured open-source version under Apache 2.0 license with optional enterprise support starting around $15,000 annually, making it significantly more cost-effective for startups and mid-market companies while maintaining production-grade capabilities. OrientDB provides a free Community Edition with commercial licenses for enterprise features, though pricing is less transparent and generally positioned between the other two. For software development teams, ArangoDB delivers the best cost-to-value ratio when you need multiple data models, while Neo4j's higher costs are justified only when graph performance is business-critical. Cloud-managed options (Neo4j Aura, ArangoDB Oasis) shift to consumption-based pricing, where ArangoDB typically costs 30-40% less for equivalent workloads.

Industry-Specific Analysis

Software Development

  • Metric 1: Query Response Time

    Average time to execute complex queries (SELECT, JOIN, aggregations)
    Target: <100ms for simple queries, <500ms for complex analytical queries
  • Metric 2: Database Connection Pool Efficiency

    Percentage of successful connection acquisitions without timeout
    Optimal connection pool utilization rate (70-85% to avoid bottlenecks)
  • Metric 3: Schema Migration Success Rate

    Percentage of zero-downtime migrations completed successfully
    Rollback capability and average rollback time for failed migrations
  • Metric 4: Data Integrity Compliance Score

    Adherence to ACID properties (Atomicity, Consistency, Isolation, Durability)
    Foreign key constraint validation rate and referential integrity maintenance
  • Metric 5: Backup and Recovery Time Objective (RTO)

    Time required to restore database from backup to operational state
    Recovery Point Objective (RPO): Maximum acceptable data loss measured in time
  • Metric 6: Index Optimization Impact

    Query performance improvement after index creation/optimization
    Storage overhead vs. read performance trade-off ratio
  • Metric 7: Concurrent Transaction Throughput

    Number of simultaneous transactions processed per second without deadlocks
    Deadlock occurrence rate and average resolution time

Code Comparison

Sample Implementation

const { Database, aql } = require('arangojs');

/**
 * Production-ready User Authentication Service using ArangoDB
 * Demonstrates: user registration, login, session management, and graph relationships
 */

class UserAuthService {
  constructor(config) {
    this.db = new Database({
      url: config.url || 'http://localhost:8529',
      databaseName: config.dbName || 'auth_db',
      auth: { username: config.username, password: config.password }
    });
    this.usersCollection = null;
    this.sessionsCollection = null;
    this.userRolesGraph = null;
  }

  async initialize() {
    try {
      // Create collections if they don't exist
      const collections = await this.db.listCollections();
      const collectionNames = collections.map(c => c.name);

      if (!collectionNames.includes('users')) {
        this.usersCollection = await this.db.createCollection('users');
        await this.usersCollection.ensureIndex({
          type: 'persistent',
          fields: ['email'],
          unique: true
        });
      } else {
        this.usersCollection = this.db.collection('users');
      }

      if (!collectionNames.includes('sessions')) {
        this.sessionsCollection = await this.db.createCollection('sessions');
        await this.sessionsCollection.ensureIndex({
          type: 'ttl',
          fields: ['createdAt'],
          expireAfter: 86400 // 24 hours
        });
      } else {
        this.sessionsCollection = this.db.collection('sessions');
      }

      console.log('UserAuthService initialized successfully');
    } catch (error) {
      console.error('Initialization error:', error.message);
      throw error;
    }
  }

  async registerUser(email, passwordHash, profile) {
    try {
      const user = {
        email: email.toLowerCase(),
        passwordHash,
        profile: profile || {},
        createdAt: Date.now(),
        isActive: true,
        failedLoginAttempts: 0
      };

      const result = await this.usersCollection.save(user);
      return { userId: result._key, email: user.email };
    } catch (error) {
      if (error.errorNum === 1210) {
        throw new Error('User with this email already exists');
      }
      throw error;
    }
  }

  async authenticateUser(email, passwordHash) {
    try {
      const query = aql`
        FOR user IN ${this.usersCollection}
        FILTER user.email == ${email.toLowerCase()}
        FILTER user.isActive == true
        RETURN user
      `;

      const cursor = await this.db.query(query);
      const users = await cursor.all();

      if (users.length === 0) {
        throw new Error('Invalid credentials');
      }

      const user = users[0];

      if (user.failedLoginAttempts >= 5) {
        throw new Error('Account locked due to too many failed attempts');
      }

      if (user.passwordHash !== passwordHash) {
        await this.db.query(aql`
          UPDATE ${user._key} WITH {
            failedLoginAttempts: ${user.failedLoginAttempts + 1}
          } IN ${this.usersCollection}
        `);
        throw new Error('Invalid credentials');
      }

      // Reset failed attempts on successful login
      await this.db.query(aql`
        UPDATE ${user._key} WITH {
          failedLoginAttempts: 0,
          lastLoginAt: ${Date.now()}
        } IN ${this.usersCollection}
      `);

      return { userId: user._key, email: user.email };
    } catch (error) {
      console.error('Authentication error:', error.message);
      throw error;
    }
  }

  async createSession(userId, metadata = {}) {
    try {
      const sessionToken = this.generateToken();
      const session = {
        userId,
        token: sessionToken,
        metadata,
        createdAt: Date.now()
      };

      await this.sessionsCollection.save(session);
      return sessionToken;
    } catch (error) {
      console.error('Session creation error:', error.message);
      throw error;
    }
  }

  async validateSession(token) {
    try {
      const query = aql`
        FOR session IN ${this.sessionsCollection}
        FILTER session.token == ${token}
        FOR user IN ${this.usersCollection}
        FILTER user._key == session.userId
        FILTER user.isActive == true
        RETURN { user, session }
      `;

      const cursor = await this.db.query(query);
      const results = await cursor.all();

      if (results.length === 0) {
        return null;
      }

      return results[0];
    } catch (error) {
      console.error('Session validation error:', error.message);
      return null;
    }
  }

  generateToken() {
    return require('crypto').randomBytes(32).toString('hex');
  }
}

module.exports = UserAuthService;

Side-by-Side Comparison

TaskBuilding a social network feature with friend connections, activity feeds, and content recommendations

ArangoDB

Building a microservices dependency tracker that maps service relationships, API endpoints, deployment history, and impact analysis for a distributed software system

OrientDB

Building a code dependency analyzer that tracks relationships between modules, functions, and developers, supporting queries like 'find all modules affected by a change', 'identify circular dependencies', and 'trace code ownership paths'

Neo4j

Building a code dependency analyzer that tracks relationships between modules, functions, classes, and identifies circular dependencies with impact analysis for Software Development

Analysis

For consumer-facing social applications requiring complex friend-of-friend queries and real-time recommendation algorithms, Neo4j's Cypher query language and native graph processing deliver unmatched performance and developer productivity. ArangoDB becomes the superior choice when your social features are part of a larger application requiring user profiles, content storage, and analytics—eliminating the need for multiple databases and simplifying your architecture. OrientDB suits smaller-scale social features within existing applications where budget constraints are significant and query complexity remains moderate. Enterprise B2B applications with relationship mapping (organizational hierarchies, account relationships) benefit most from Neo4j's mature security model and enterprise tooling, while startups building multi-faceted products should seriously consider ArangoDB's multi-model approach to reduce operational complexity. If your social graph will scale beyond 10 million nodes with deep traversals, Neo4j's performance advantages become decisive.

Making Your Decision

Choose ArangoDB If:

  • Data structure complexity: Use relational databases (PostgreSQL, MySQL) for structured data with complex relationships and ACID compliance needs; use NoSQL (MongoDB, DynamoDB) for flexible schemas, rapid iteration, or document-oriented data
  • Scale and performance requirements: Choose distributed databases (Cassandra, ScaleDB) for massive horizontal scaling and high write throughput; use in-memory databases (Redis, Memcached) for sub-millisecond latency; select traditional RDBMS for moderate scale with complex queries
  • Query patterns and access methods: Opt for SQL databases when complex joins, aggregations, and ad-hoc queries are essential; choose key-value stores (Redis, DynamoDB) for simple lookups; use graph databases (Neo4j) for highly connected data with relationship-heavy queries
  • Consistency vs availability trade-offs: Select strongly consistent databases (PostgreSQL, MySQL) for financial transactions and critical data integrity; choose eventually consistent systems (Cassandra, DynamoDB) for high availability in distributed systems where temporary inconsistency is acceptable
  • Team expertise and operational overhead: Favor managed cloud services (RDS, Aurora, Atlas) when minimizing DevOps burden; choose self-hosted solutions (PostgreSQL, MySQL) for maximum control and cost optimization; consider team's existing SQL vs NoSQL proficiency and learning curve impact on delivery timelines

Choose Neo4j If:

  • Data structure complexity and relationships: Choose relational databases (PostgreSQL, MySQL) for complex joins and strict schemas; NoSQL (MongoDB, DynamoDB) for flexible, document-based or key-value data
  • Scale and performance requirements: Choose distributed databases (Cassandra, DynamoDB) for massive horizontal scaling and high write throughput; traditional RDBMS for moderate scale with ACID guarantees
  • Query patterns and access methods: Choose SQL databases for complex analytical queries and reporting; NoSQL for simple lookups, time-series data, or graph relationships (Neo4j)
  • Consistency vs availability trade-offs: Choose PostgreSQL or MySQL for strong consistency and transactional integrity; eventual consistency databases (Cassandra, DynamoDB) for high availability in distributed systems
  • Team expertise and operational overhead: Choose managed cloud services (RDS, Aurora, Atlas) to reduce ops burden; self-hosted solutions when team has deep database expertise or specific compliance requirements

Choose OrientDB If:

  • Data structure complexity: Choose SQL databases (PostgreSQL, MySQL) for structured data with complex relationships and ACID compliance needs; choose NoSQL (MongoDB, Cassandra) for flexible schemas, unstructured data, or rapidly evolving data models
  • Scale and performance requirements: Choose NoSQL databases like Cassandra or DynamoDB for horizontal scaling across distributed systems with massive write throughput; choose SQL databases with read replicas for moderate scale with complex query needs
  • Query complexity and reporting: Choose SQL databases (PostgreSQL, MySQL) when you need complex joins, aggregations, and ad-hoc analytical queries; choose NoSQL when queries are primarily key-based lookups or simple range queries
  • Consistency vs availability trade-offs: Choose SQL databases (PostgreSQL with strong consistency) for financial transactions, inventory systems, or any scenario requiring immediate consistency; choose eventually consistent NoSQL (Cassandra, DynamoDB) for high availability in distributed systems where brief inconsistency is acceptable
  • Development team expertise and ecosystem: Choose databases matching your team's existing skills and the maturity of tooling, ORMs, and community support in your tech stack; PostgreSQL offers the most versatile middle ground with JSON support, full-text search, and both SQL and NoSQL capabilities

Our Recommendation for Software Development Database Projects

Neo4j remains the gold standard for applications where graph relationships are the primary data model, offering unparalleled query performance, the most mature ecosystem, and proven scalability at enterprise scale. Choose Neo4j when graph traversals are your core use case, you need enterprise support, and your budget accommodates commercial licensing. ArangoDB emerges as the most pragmatic choice for modern software development teams building full-featured applications that need graph capabilities alongside document and key-value storage—its multi-model architecture eliminates architectural complexity and reduces operational overhead while delivering competitive graph performance. OrientDB should only be considered for legacy system integration or scenarios with extreme budget constraints where its limitations are acceptable trade-offs. Bottom line: Start with Neo4j if graphs are your primary data structure and performance is critical; choose ArangoDB if you're building a complete application requiring multiple data models and want to minimize database sprawl; avoid OrientDB for new projects unless specific constraints make it necessary. For most modern software development teams, ArangoDB offers the best balance of capabilities, flexibility, and total cost of ownership.

Frequently Asked Questions

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