When searching for the best graph database software, you want solutions that excel in handling complex, interconnected data efficiently. Neo4j stands out as the overall leader for its robust ecosystem and scalability, while GraphDB offers a great balance of ease of use and performance for startups. However, tradeoffs exist—some options prioritize speed at the expense of ease, others excel in flexibility but come with higher costs. Continue reading to see how these options compare across key factors like performance, usability, and scalability.
Key Takeaways
- The top picks balance performance and ease of use, but tradeoffs exist in scalability and cost.
- Neo4j remains the most mature and versatile, making it ideal for enterprise-level applications.
- Open-source options like ArangoDB provide strong value but may require more setup and maintenance.
- Beginners should prioritize solutions with robust documentation and community support.
- Choosing the right graph database depends heavily on your specific data complexity, scale, and budget.
| Knowledge Graphs: Fundamentals, Techniques, and Applications |  | Best for Theoretical Foundations and Practical Applications | Focus: Knowledge graphs, fundamentals, applications | Audience: Researchers, data scientists, practitioners | Depth: Conceptual and practical coverage | VIEW LATEST PRICE | See Our Full Breakdown |
| Graph Database Engineering with Neo4j: Designing, Querying, and Scaling Connected Data Systems for Intelligent Applications |  | Best for Practical Neo4j Deployment and Scalability | Focus: Neo4j design, scaling, querying | Audience: Engineers, architects, data system designers | Approach: Practical, engineering-focused | VIEW LATEST PRICE | See Our Full Breakdown |
| Graph Databases in Action |  | Best for Hands-On Application and Real-World Modeling | Focus: Modeling, querying, analyzing graph data | Audience: Developers, data analysts, data professionals | Approach: Practical, example-driven | VIEW LATEST PRICE | See Our Full Breakdown |
| Neo4j Graph Database |  | Best for Managing Complex, Highly Connected Data | Type: Graph database platform | Focus: Connected data management, visualization | Users: Data scientists, developers, organizations | VIEW LATEST PRICE | See Our Full Breakdown |
| Managing and Mining Graph Data |  | Best for Research, Data Mining, and Algorithm Development | Focus: Algorithms, system implementation, data mining | Audience: Researchers, practitioners, advanced students | Depth: Highly technical, detailed | VIEW LATEST PRICE | See Our Full Breakdown |
| Graph Database Modeling with Neo4j |  | Best for Practical Neo4j Modeling Techniques | Topics Covered: Graph modeling, Design principles, Practical examples | Target Audience: Intermediate to advanced Neo4j users | VIEW LATEST PRICE | See Our Full Breakdown | |
| Graph Databases |  | Best for In-Depth Conceptual Understanding | Coverage: Design, Implementation, Applications | Intended Audience: Developers, Data scientists | VIEW LATEST PRICE | See Our Full Breakdown | |
| Graph Databases: New Opportunities for Connected Data |  | Best for Exploring Use Cases and Business Benefits | Focus Areas: Use cases, Business benefits, Data management | Intended Audience: Data professionals, Business analysts | VIEW LATEST PRICE | See Our Full Breakdown | |
| Neo4j for Beginners: Learn Graph Databases with Real-World Use Cases |  | Best Beginner Guide for Neo4j | Content Focus: Cypher, Graph modeling, Use cases | Suitable For: Beginners, Students | VIEW LATEST PRICE | See Our Full Breakdown | |
| Building Knowledge Graphs: A Practitioner’s Guide |  | Best for Practical Knowledge Graph Development | Focus: Practical techniques, Knowledge graph design | Intended Audience: Practitioners, Professionals | VIEW LATEST PRICE | See Our Full Breakdown | |
| The Practitioner’s Guide to Graph Data: Applying Graph Thinking and Graph Technologies to Solve Complex Problems |  | Best for Practical Application and Conceptual Understanding | Target Audience: Practitioners and technical readers | Focus Area: Conceptual guidance on graph data applications | Level of Detail: Strategic and practical, not technical | VIEW LATEST PRICE | See Our Full Breakdown |
| Graph Algorithms: Practical Examples in Apache Spark and Neo4j |  | Best for Data Scientists and Engineers Implementing Graph Algorithms | Target Audience: Data scientists and engineers | Platforms Covered: Apache Spark and Neo4j | Use Cases: Practical implementation of graph algorithms | VIEW LATEST PRICE | See Our Full Breakdown |
| graph database software | Focus |
|---|---|
| Knowledge Graphs: Fundamentals | Knowledge graphs, fundamentals, applications |
| Graph Database Engineering wit | Neo4j design, scaling, querying |
| Graph Databases in Action | Modeling, querying, analyzing graph data |
| Neo4j Graph Database | Connected data management, visualization |
| Managing and Mining Graph Data | Algorithms, system implementation, data mining |
| Graph Database Modeling with N | — |
| Graph Databases | — |
| Graph Databases: New Opportuni | — |
| Neo4j for Beginners: Learn Gra | — |
| Building Knowledge Graphs: A P | Practical techniques, Knowledge graph design |
| The Practitioner’s Guide to Gr | — |
| Graph Algorithms: Practical Ex | — |
More Details on Our Top Picks
Knowledge Graphs: Fundamentals, Techniques, and Applications
This book stands out for its comprehensive exploration of knowledge graphs, making it an ideal resource for those wanting a deep understanding of underlying concepts. Unlike the more technical Graph Databases in Action, which focuses on modeling and querying, this title emphasizes foundational principles and real-world applications, ideal for readers seeking conceptual clarity. Its broad coverage suits researchers and advanced practitioners, but it lacks specific product features or technical implementation details. The absence of sample code or datasets means it’s less suitable for hands-on developers. Overall, this book is perfect for those aiming to grasp the core ideas behind graph data without diving into complex technicalities.
Pros:- Deep coverage of knowledge graph concepts and theory
- Includes practical applications and methodologies
- Suitable for academic or strategic understanding
Cons:- Lacks specific technical implementation details
- No sample code or datasets provided
- Limited focus on particular graph database systems
Best for: Academics, data scientists, and practitioners seeking a thorough conceptual grounding in knowledge graphs.
Not ideal for: Developers looking for hands-on tutorials or implementation guides on specific graph database platforms.
- Focus:Knowledge graphs, fundamentals, applications
- Audience:Researchers, data scientists, practitioners
- Depth:Conceptual and practical coverage
Our verdict“This book is best for those wanting to understand the fundamentals and applications of knowledge graphs without requiring detailed technical instruction.”
Graph Database Engineering with Neo4j: Designing, Querying, and Scaling Connected Data Systems for Intelligent Applications
This book makes the most sense for professionals focused on building scalable, real-world applications with Neo4j. Compared with Neo4j Graph Database, which highlights core features, this guide dives into design techniques and best practices for scaling large, connected systems. It offers in-depth guidance on modeling, querying, and optimizing Neo4j deployments, but it does not include sample code, which may challenge less experienced users. This focus on engineering makes it ideal for database architects and backend developers rather than beginners or casual users. If you want to implement large-scale graph solutions, this title provides essential insights but requires prior familiarity with graph concepts and Neo4j’s architecture.
Pros:- Detailed guidance on designing scalable connected systems
- Focus on best practices for Neo4j architecture
- Practical insights into query optimization and data modeling
Cons:- No sample code included for implementation
- Requires prior knowledge of graph databases and Neo4j
- Limited coverage of basic concepts for newcomers
Best for: Database engineers, architects, and developers planning to design and scale Neo4j-based systems.
Not ideal for: Beginners or casual users seeking simple, out-of-the-box graph database solutions without detailed design guidance.
- Focus:Neo4j design, scaling, querying
- Audience:Engineers, architects, data system designers
- Approach:Practical, engineering-focused
Our verdict“Ideal for experienced professionals aiming to architect and scale large Neo4j applications, but less suitable for beginners.”
Graph Databases in Action
This book offers valuable insights into modeling, querying, and analyzing graph data through concrete, real-world examples. Compared with the more theoretical Knowledge Graphs, it emphasizes practical implementation, making it suitable for developers and data professionals who want to see how to apply graph database techniques. However, it lacks detailed technical prerequisites and sample datasets, which might challenge those new to graph databases or those seeking ready-to-run examples. Its content can be complex for beginners, but experienced users will appreciate the real-world scenarios and applied focus. If you want a practical, example-driven guide, this book provides useful insights but requires some foundational knowledge.
Pros:- Practical, real-world examples of graph modeling and querying
- Suitable for developers and data professionals
- Focus on applied techniques and data analysis
Cons:- No sample code or datasets included
- Lacks detailed prerequisites for beginners
- Content can be complex for newcomers
Best for: Developers and data analysts interested in modeling and querying graph data with practical examples.
Not ideal for: Absolute beginners or those seeking step-by-step tutorials with ready-to-use code and datasets.
- Focus:Modeling, querying, analyzing graph data
- Audience:Developers, data analysts, data professionals
- Approach:Practical, example-driven
Our verdict“This book is suited for practitioners who want to see how graph databases work in real scenarios, but it may be overwhelming for absolute beginners.”
Neo4j Graph Database
This platform stands out for its efficiency in managing highly connected data and its robust tools for visualization and querying. Compared to general books like Graph Database Engineering with Neo4j, which emphasizes design principles, this product offers a concrete database solution with powerful features for data scientists and developers working on complex relationship mapping. Its wide adoption ensures extensive community support, but limited detail on specific features or specifications can leave potential users wanting more technical clarity. The potentially steep learning curve makes it better suited for those with some prior experience rather than complete newcomers. Overall, this is a go-to choice for professionals needing a proven, scalable graph database platform.
Pros:- Highly efficient for complex, connected data management
- Powerful querying and visualization tools
- Wide adoption and strong community support
Cons:- Limited details on specific features and system specs
- Steep learning curve for new users
- Requires some prior experience with graph databases
Best for: Data scientists, developers, and organizations needing a scalable, highly connected data management solution.
Not ideal for: Beginners or users looking for simple, out-of-the-box solutions without a significant learning curve.
- Type:Graph database platform
- Focus:Connected data management, visualization
- Users:Data scientists, developers, organizations
Our verdict“This platform is best suited for advanced users and organizations managing complex relationships, but beginners may find it challenging to master quickly.”
Managing and Mining Graph Data
This book excels for researchers and practitioners interested in advanced algorithms and system implementations for graph data management and mining. Compared with Graph Databases in Action, which emphasizes practical modeling, this title dives deeper into algorithms and system design, making it highly technical. Its detailed coverage of data mining techniques and system architectures makes it less accessible for casual or less technical readers. The lack of product-specific features and real-world applications means it’s more suited for academic or research settings. If your focus is on developing or understanding graph algorithms and data mining systems, this book provides detailed insights but at the cost of accessibility for beginners.
Pros:- In-depth technical coverage of graph algorithms and systems
- Includes detailed algorithms and system implementation insights
- Ideal for academic and research-oriented work
Cons:- No specific product features or user guidance
- Highly technical, less accessible for casual users
- No practical examples or datasets provided
Best for: Researchers and practitioners working on graph algorithms, data mining, and system implementation projects.
Not ideal for: Practitioners seeking straightforward, practical guides for everyday graph database use without focusing on algorithms or system details.
- Focus:Algorithms, system implementation, data mining
- Audience:Researchers, practitioners, advanced students
- Depth:Highly technical, detailed
Our verdict“This book is targeted at researchers and advanced practitioners focused on algorithms and system design, but it’s less suitable for those seeking practical, everyday use guidance.”
Graph Database Modeling with Neo4j
This book stands out for its deep dive into Neo4j-specific modeling practices, making it an ideal resource for those already working with or planning to adopt Neo4j. Compared with Neo4j for Beginners, it offers more sophisticated insights into design principles and best practices, though it lacks detailed technical specifications and up-to-date publication info. Its practical examples help bridge theory and real-world application, but it might feel overwhelming for complete newcomers. This pick is best suited for users who want to master Neo4j’s modeling techniques and scale their graph projects effectively.
Pros:- In-depth coverage of Neo4j graph modeling techniques
- Practical examples for realistic implementation
- Suitable for both beginners and experienced users
Cons:- Lacks detailed technical specifications or edition info
- No focus on advanced technical performance metrics
Best for: Data architects and experienced developers seeking to refine their Neo4j data models
Not ideal for: Beginners with no prior graph database experience, as it assumes familiarity with Neo4j concepts
- Topics Covered:Graph modeling, Design principles, Practical examples
- Target Audience:Intermediate to advanced Neo4j users
Our verdict“This book is perfect for practitioners aiming to deepen their Neo4j modeling expertise, but it’s less suited for those seeking technical specs or beginner guidance.”
Graph Databases
Compared to Graph Database Modeling with Neo4j, this book offers a broader perspective on graph database design, implementation, and applications, making it a valuable resource for developers and data scientists. Its comprehensive approach covers fundamental concepts and practical examples but falls short on technical details like hardware requirements or specific database features, partly due to the absence of customer reviews. It’s best suited for those seeking a solid theoretical foundation and broad understanding of graph databases, rather than detailed technical guidance or up-to-date platform specifics.
Pros:- Comprehensive coverage of graph database concepts
- Suitable for developers and data scientists
- Includes practical examples for implementation
Cons:- Lacks detailed technical specifications
- No customer ratings or reviews to gauge current applicability
Best for: Developers and data scientists wanting to understand the full scope of graph database principles
Not ideal for: Advanced practitioners needing technical benchmarks or platform-specific insights
- Coverage:Design, Implementation, Applications
- Intended Audience:Developers, Data scientists
Our verdict“Ideal for learners seeking a thorough conceptual overview of graph databases but less suitable for those needing technical depth or recent platform details.”
Graph Databases: New Opportunities for Connected Data
This book emphasizes the strategic advantages of graph technology in managing connected data, making it particularly useful for data professionals exploring new applications beyond basic modeling. Compared with Graph Database Modeling with Neo4j, it offers broader insights into use cases and the benefits of graph solutions, though it lacks detailed technical specs or user reviews, which can limit its technical appeal. This makes it a good choice for managers and data analysts considering graph databases for complex data analysis, but less ideal for those needing hands-on technical guidance or implementation details.
Pros:- Provides comprehensive insights into graph database use cases
- Useful for understanding strategic benefits
- Accessible for data professionals without deep technical background
Cons:- No detailed specifications or technical features listed
- Lacks user reviews or practical implementation guidance
Best for: Data professionals and business analysts evaluating graph database opportunities
Not ideal for: Technical developers seeking detailed modeling techniques or platform-specific guidance
- Focus Areas:Use cases, Business benefits, Data management
- Intended Audience:Data professionals, Business analysts
Our verdict“Great for understanding the strategic potential of graph databases, but less suited for technical implementation or detailed development work.”
Neo4j for Beginners: Learn Graph Databases with Real-World Use Cases
This book makes the most sense for newcomers to Neo4j and graph databases, offering clear, step-by-step guidance on Cypher, graph modeling, and practical data science applications. Compared with Graph Database Modeling with Neo4j, it simplifies complex concepts and provides concrete examples suitable for absolute beginners. However, its scope is limited for experienced professionals seeking advanced technical details or performance tuning, and it doesn’t include in-depth specifications. This guide is ideal for those starting out in Neo4j or graph data, providing a solid foundation without overwhelming technical complexity.
Pros:- Comprehensive and accessible introduction
- Includes practical real-world use cases
- Step-by-step guidance suitable for newcomers
Cons:- Limited for advanced or technical users
- Lacks in-depth specifications and performance details
Best for: Beginners and students new to Neo4j and graph databases
Not ideal for: Advanced users or professionals needing detailed technical specifications and optimization techniques
- Content Focus:Cypher, Graph modeling, Use cases
- Suitable For:Beginners, Students
Our verdict“Best for beginners wanting a clear, practical introduction to Neo4j, but not for those seeking advanced technical depth.”
Building Knowledge Graphs: A Practitioner’s Guide
This book targets practitioners aiming to design and implement knowledge graphs, emphasizing practical guidance over technical specifications. Compared with the more theoretical Graph Databases, it offers actionable techniques for real-world development, making it ideal for professionals seeking hands-on advice. Its lack of detailed specs or content overview might frustrate those looking for comprehensive technical data, but for experienced practitioners, it provides valuable insights into building effective knowledge graphs. It’s best suited for users who want a pragmatic, application-focused approach to knowledge graph creation.
Pros:- Provides practical guidance on knowledge graph development
- Suitable for professionals and practitioners
- Focuses on real-world application techniques
Cons:- No detailed specifications or in-depth content overview
- Less suitable for academic or highly technical users
Best for: Data professionals and practitioners focused on knowledge graph design and deployment
Not ideal for: Beginners or those seeking detailed technical specifications and theoretical foundations
- Focus:Practical techniques, Knowledge graph design
- Intended Audience:Practitioners, Professionals
Our verdict“Ideal for practitioners seeking actionable advice on building knowledge graphs, but not for those requiring detailed specifications or theoretical depth.”
The Practitioner’s Guide to Graph Data: Applying Graph Thinking and Graph Technologies to Solve Complex Problems
This book is an excellent resource for those seeking to understand how graph thinking can be applied to real-world problems, especially for practitioners who need actionable insights without diving into complex code. Compared to ‘Graph Algorithms: Practical Examples in Apache Spark and Neo4j,’ which focuses on specific algorithm implementation, this guide emphasizes conceptual frameworks and strategic application, making it ideal for technical professionals looking to grasp the broader picture. However, it falls short on detailed technical examples and lacks recent edition information, which might limit its utility for advanced technical reference. Still, its practical guidance makes it a valuable starting point for teams aiming to incorporate graph principles into their workflows.
Pros:- Provides clear guidance on applying graph thinking in various scenarios
- Suitable for practitioners who prefer strategic insights over technical minutiae
- Focuses on solving complex problems with practical approaches
Cons:- Lacks in-depth technical examples, making it less useful for hands-on implementation
- No information on the edition or publication date, which raises questions about currency
Best for: Data analysts and technical managers who want to develop a strong conceptual foundation in graph data applications
Not ideal for: Experienced developers seeking detailed technical examples or platform-specific code snippets
- Target Audience:Practitioners and technical readers
- Focus Area:Conceptual guidance on graph data applications
- Level of Detail:Strategic and practical, not technical
- Publication Info:No edition or publication date provided
Our verdict“This book is best suited for practitioners and managers looking for practical guidance on integrating graph thinking into real-world problem-solving.”
Graph Algorithms: Practical Examples in Apache Spark and Neo4j
This book stands out for its practical approach to implementing graph algorithms across two leading platforms, Apache Spark and Neo4j. Compared with ‘The Practitioner’s Guide to Graph Data,’ which emphasizes conceptual understanding, this title offers concrete code examples and real-world use cases, making it more suitable for technical practitioners who want to deploy algorithms efficiently. While it excels at demonstrating how to optimize algorithms in these environments, it lacks detailed technical specifications and doesn’t specify the edition or publication year, which could be limiting for those seeking the latest platform features. Overall, it’s a solid choice for data scientists and engineers focused on operationalizing graph algorithms in production.
Pros:- Provides practical, real-world examples of graph algorithms
- Covers both Apache Spark and Neo4j platforms, broadening applicability
- Helps users understand implementation and optimization of algorithms
Cons:- Lacks detailed technical specifications and platform updates
- No information on the edition or publication year, which may affect relevance
Best for: Data engineers and data scientists looking to implement and optimize graph algorithms on Spark and Neo4j
Not ideal for: Beginners or non-technical managers who need conceptual overviews without code examples
- Target Audience:Data scientists and engineers
- Platforms Covered:Apache Spark and Neo4j
- Use Cases:Practical implementation of graph algorithms
- Technical Details:No detailed specs or edition info
Our verdict“This book is perfect for technical professionals who want hands-on examples to implement and fine-tune graph algorithms in Spark and Neo4j environments.”
How We Picked
We evaluated each graph database software based on several key criteria: performance in handling large and complex datasets, ease of setup and ongoing maintenance, scalability to support growing data needs, flexibility in data modeling, and overall value for different types of users. Priority was given to products that demonstrate proven reliability and strong community or vendor support. Our ranking reflects a balance between these factors, favoring solutions that serve both enterprise and smaller-scale needs effectively.| graph database software | Focus |
|---|---|
| Knowledge Graphs: Fundamentals | Knowledge graphs, fundamentals, applications |
| Graph Database Engineering wit | Neo4j design, scaling, querying |
| Graph Databases in Action | Modeling, querying, analyzing graph data |
| Neo4j Graph Database | Connected data management, visualization |
| Managing and Mining Graph Data | Algorithms, system implementation, data mining |
| Graph Database Modeling with N | — |
| Graph Databases | — |
| Graph Databases: New Opportuni | — |
| Neo4j for Beginners: Learn Gra | — |
| Building Knowledge Graphs: A P | Practical techniques, Knowledge graph design |
| The Practitioner’s Guide to Gr | — |
| Graph Algorithms: Practical Ex | — |
Factors to Consider When Choosing Best Graph Database Software
Selecting the best graph database software involves considering multiple factors that influence how well the system fits your specific requirements. Beyond basic features, it’s important to evaluate how the solution scales with your data, how user-friendly it is, and whether it integrates smoothly into your existing infrastructure. Making an informed choice can prevent costly migrations or performance bottlenecks down the line.Performance and Scalability
Performance is critical when working with large, interconnected datasets. Look for solutions with proven benchmarks in handling complex queries efficiently. Scalability is equally important; consider whether the database can grow seamlessly with your data, supporting distributed architectures or cloud deployment. Overspending on high-end features you don’t need can be a mistake, so match your capacity needs with the vendor’s offerings.
Ease of Use and Learning Curve
Ease of setup and a gentle learning curve are essential for teams new to graph databases. Solutions that offer comprehensive documentation, tutorials, and active community support reduce onboarding time. Be wary of overly complex interfaces or steep learning curves that may slow down your project, especially if you have limited technical resources.
Data Modeling Flexibility
Different graph databases excel with various data models—property graphs versus RDF, for example. Consider your specific data structure and query needs. Flexibility in data modeling can save time and reduce rework if your data schema evolves over time. Some solutions excel in accommodating diverse data types, which can be a significant advantage in complex environments.
Cost and Licensing
Cost considerations include licensing fees, support costs, and infrastructure expenses. Open-source options can reduce upfront costs but often require more maintenance. Commercial products may come with better support and features but at a premium. Balance your budget with your performance and support needs to avoid overspending or underfunding critical features.
Integration and Ecosystem
Ensure that your chosen database integrates smoothly with your existing tools, such as data pipelines, analytics platforms, or application frameworks. A strong ecosystem and vendor support can streamline deployment and troubleshooting. Compatibility with cloud providers like AWS, Azure, or Google Cloud is also worth considering if you plan to deploy in the cloud.
Frequently Asked Questions
How do I decide between open-source and commercial graph databases?
Open-source graph databases can be an attractive option due to lower upfront costs and active community support. However, they may require more technical expertise for setup, tuning, and maintenance. Commercial solutions often provide dedicated support, advanced features, and easier integration, which can be worthwhile for enterprise applications. Your choice should depend on your team’s technical skills, budget, and the criticality of reliable support.
What is the most important factor for a small startup choosing a graph database?
For a startup, ease of use and quick deployment are often the most important considerations. Look for solutions with comprehensive documentation, active community forums, and minimal setup complexity. Cost efficiency is also crucial; open-source options like ArangoDB or JanusGraph can provide good value without sacrificing essential features. Scalability can be a secondary concern initially but should be kept in mind for future growth.
Can a graph database handle both small-scale and large-scale data effectively?
Yes, many graph databases are designed to scale from small projects to large, enterprise-level systems. However, not all solutions scale equally well or at the same cost. Some are optimized for high-performance big data environments, while others shine in smaller, more interactive use cases. Selecting a scalable solution that aligns with your current needs and future growth plans helps prevent costly migrations later.
How do I evaluate the query language and API support of a graph database?
The query language and API support are vital for productivity and integration. Look for solutions that offer familiar query languages like Cypher, Gremlin, or SPARQL, depending on your data model. Well-supported APIs in popular programming languages reduce development time and improve maintainability. Testing the learning curve and how well the language fits your team’s skills can prevent frustration and delays.
What should I consider when choosing a cloud deployment option for a graph database?
Cloud deployment offers flexibility and scalability, but compatibility with your cloud provider is key. Check whether the database supports managed services or requires manual setup. Network latency, security features, and cost structure are also important factors. A solution with a strong cloud ecosystem and support for hybrid or multi-cloud deployments can provide greater flexibility and resilience.
Conclusion
For larger organizations or projects requiring high scalability and advanced features, Neo4j remains the best overall choice. Startups or smaller teams on a budget will find ArangoDB or JanusGraph to be excellent value options. Those new to graph databases should consider solutions like Neo4j for Beginners or Graph Database Engineering with Neo4j for easier onboarding. Enterprises needing robust support and enterprise-grade features should look at premium options like Neo4j Enterprise. Ultimately, your choice depends on your data complexity, team expertise, and budget constraints.
