What Is a vector database?
A vector database stores and retrieves high-dimensional data, represented as vectors. Unlike traditional databases that handle scalar data, vector databases manage data often encountered in machine learning and artificial intelligence applications. The primary use case involves storing feature vectors—a representation of data points used for making machine learning models smarter by embedding them into a high-dimensional vector space.
Vector databases provide efficient indexing and querying capabilities for vector data, enabling fast similarity searches. They support a range of distance metrics, such as cosine similarity and Euclidean distance, to measure the likeness between vectors. These databases are crucial for applications like recommendation systems, speech recognition, image processing, and natural language processing, where high-dimensional data is prevalent. By maintaining fast retrieval times and scalability, vector databases are integral in modern data architecture.
Modern use cases for vector databases
Vector databases are quickly becoming the foundation for modern AI and machine learning applications. They offer a powerful way to search and analyze complex, high-dimensional data, from images and text to audio. As organizations look to build the next generation of intelligent applications, NetApp Instaclustr provides a suite of fully managed vector database solutions designed for performance, scale, and reliability.
This is part of a series of articles about vector databases
Vector database services on AWS
Amazon Web Services (AWS)–the world’s largest cloud provider–offers multiple data management services, including many that support vector database functionality. These are the primary options:
1. Amazon OpenSearch Service
Amazon OpenSearch Service is a managed solution for search and analytics, derived from the open source OpenSearch project. It handles real-time application monitoring, log analytics, and search functionality at scale. OpenSearch Service can manage high-dimensional data for tasks such as vector search, which is crucial for AI applications like recommendation engines and image recognition. It supports various similarity search algorithms, including cosine similarity and Euclidean distance, making it suitable for machine learning workflows.
2. Amazon Aurora PostgreSQL-Compatible Edition
Amazon Aurora, specifically the PostgreSQL-compatible version, supports vector data storage through PostgreSQL extensions like pgvector. This enables Aurora to handle vector operations necessary for tasks like similarity searches in machine learning applications.
Aurora’s managed environment provides scalability and high availability, making it appropriate for workloads that require both traditional relational data and vector-based processing.
3. Amazon Relational Database Service (Amazon RDS) for PostgreSQL
Amazon RDS for PostgreSQL also supports vector search through the integration of pgvector. This allows RDS to manage high-dimensional vector data, which is essential for machine learning models, recommendation engines, and other applications.
By offering managed database services, RDS helps simplify database operations like backups and scaling, while providing the necessary tools to perform vector-based computations and searches
Multi-cloud options for vector database managed services on AWS
Navigating the world of open source vector databases available in offerings such as PostgreSQL, OpenSearch, ClickHouse and Cassandra becomes much simpler with managed service options. With a managed service, the ability to scale databases effortlessly as data grows, removes the worrying about the underlying hardware or complex configurations. This approach significantly reduces operational burden, eliminating the need for constant maintenance, updates, and troubleshooting. Choosing a managed service that spans multiple cloud providers, enables flexibility, cloud-portability and avoids vendor lock-in for a production-ready environment.
NetApp Instaclustr is one such option for organizations that require augmentation of their teams or fully managed services for open source technologies, including those that provide vector database capabilities. Instaclustr empowers organizations with world-class expertise for many popular open source technologies. Instaclustr includes services and support for pure open source PostgreSQL, OpenSearch, ClickHouse, and Cassandra providing the robust infrastructure needed to handle demanding vector workloads.
Related content: Read our guide to vector similarity search
Vector database services on Instaclustr
1. Instaclustr for PostgreSQL
PostgreSQL is celebrated for its stability, flexibility, and strong community support. With the addition of extensions like pgvector, it transforms into a capable vector database, blending the familiarity of SQL with advanced search capabilities.
Instaclustr for PostgreSQL provides a fully managed solution that makes it easy to deploy and scale delivering the benefits of a robust relational database alongside the tools needed for vector similarity search. This is ideal for applications where vector data is closely tied to structured business data, allowing organizations to run complex queries that combine both.
2. Instaclustr for OpenSearch
When the primary need is lightning-fast search and real-time analytics, OpenSearch is a top contender. Originally designed for text search, its capabilities have expanded to include a powerful k-Nearest Neighbor (k-NN) search feature, making it an excellent choice for vector workloads.
Instaclustr for OpenSearch delivers a fully managed, production-ready cluster optimized for high-performance vector search. It’s ideal for applications that need to sift through millions of vectors in milliseconds, such as semantic search engines, product recommendation systems, and log analysis.
3. Instaclustr for ClickHouse
For applications dealing with massive datasets and requiring extreme analytical performance, ClickHouse is a phenomenal choice. This open source columnar database is built to process analytical queries at incredible speeds, and its vector search capabilities make it a strong option for large-scale AI workloads.
Instaclustr for ClickHouse provides a managed environment that harnesses this power without the administrative overhead. Its columnar storage format is highly efficient for storing and querying large volumes of numerical data, including vector embeddings. This makes it a great fit for use cases like large-scale anomaly detection, real-time analytics on streaming data, and complex business intelligence.
4. Instaclustr for Cassandra: Distributed scale and high availability
Apache Cassandra® is a master of distributed data management, renowned for its fault tolerance and linear scalability. When paired with vector search capabilities, it becomes an unstoppable force for global-scale applications that require constant uptime and low-latency performance.
Instaclustr for Cassandra offers a battle-tested, fully managed Cassandra solution that is ready for vector data needs. Integrating vector search functionality, enables the creation of AI applications on a database designed for massive scale and resilience. This is perfect for systems that need to serve vector searches across multiple geographic regions with no single point of failure.
Related content: Read our guide to vector database use cases
Best practices for getting started with vector databases
Choose the right vector database
Selecting the appropriate vector database depends on your specific use case. Start by assessing the nature of your data and the types of queries you expect to run. If you require high-dimensional vector search and analytics with strong integration capabilities, Amazon OpenSearch Service is an option due to its support for various distance metrics and integration with AWS services like AWS Lambda and Amazon S3. If your workload involves relational data with vector capabilities, Amazon Aurora PostgreSQL-Compatible Edition or Amazon RDS for PostgreSQL with pgvector extensions offer features and compatibility with existing PostgreSQL tools and extensions, which can reduce the learning curve and development time.
When choosing a vector database, consider factors such as query performance, scalability, integration with your existing systems, and the ease of managing and maintaining the database. Evaluate the cost implications of each service, keeping in mind the potential need for scaling and the volume of data you will be handling.
Accelerate production with managed vector databases
The rise of AI and machine learning has made vector databases an essential component of the modern technology stack. Choosing the right one depends on specific use cases, whether the requirement is the relational strength of PostgreSQL, the search power of OpenSearch, the analytical speed of ClickHouse, or the distributed scale of Cassandra. Choosing the right service provider depends on the importance of flexibility to move from one cloud to another avoiding vendor lock-in or the convenience of an integrated offering.
Set up your AWS environment
Setting up your AWS environment involves the following main steps:
- Begin by creating an AWS account and navigating to the AWS Management Console, which provides a centralized interface for accessing and managing AWS services.
- Configure a virtual private cloud (VPC) to establish a secure network environment for your vector database. This involves setting up subnets, route tables, and internet gateways to control traffic flow and ensure secure communication between your database and other AWS services.
- Implement identity and access management (IAM) roles and policies to enforce granular access controls. Define roles for different user groups and services, ensuring that only authorized entities can access and modify your vector database.
- Use AWS CloudFormation templates to automate the setup and deployment of your infrastructure, which ensures consistency and reduces the risk of manual configuration errors. CloudFormation allows you to define your infrastructure as code, making it easier to manage and replicate across different environments.
- Set up monitoring and logging using AWS CloudWatch and AWS CloudTrail to track the performance and security of your environment. CloudWatch provides metrics and logs that help you understand the health and performance of your services, while CloudTrail records API calls, enabling you to monitor and audit activities in your AWS account.
- Ensure your environment is scalable by leveraging auto-scaling features of the various database services.
Provision the vector database
Provisioning your vector database involves selecting the appropriate instance types and configurations to match your workload requirements. For managed services like Amazon RDS or Amazon MemoryDB, choose instance types based on the expected load, performance needs, and budget constraints. Consider factors such as CPU, memory, storage capacity, and network performance. Configure replication and backup settings to ensure data durability and high availability. For example, enable Multi-AZ (availability zone) deployments to provide automatic failover in case of an infrastructure failure.
For Amazon OpenSearch Service, configure your cluster size, shard settings, and index configurations to optimize both storage and search performance. Define the number of shards and replicas based on your data size and query throughput requirements. Utilize automated scaling features where available to handle varying workloads efficiently. For instance, OpenSearch Service can automatically scale your cluster based on usage patterns, ensuring optimal performance during peak times and cost savings during off-peak periods.
Data preparation and ingestion
Data preparation and ingestion are critical steps to ensure your vector database performs efficiently and accurately. Start by cleaning and preprocessing your data to remove any inconsistencies, errors, or irrelevant information. This step is crucial for improving the quality and accuracy of your vector representations. For text data, use embedding models like Word2Vec, BERT, or FastText to generate vector representations. These models convert text into high-dimensional vectors that capture semantic meaning and relationships between words.
For images, employ convolutional neural networks (CNNs) to create feature vectors. Pre-trained models like ResNet, Inception, or VGG can be used to extract meaningful features from images, which are then converted into vectors. Ensure your data is normalized and scaled appropriately to enhance search performance and accuracy. Use AWS Data Pipeline or AWS Glue for automated data preparation and transformation tasks. These services provide scalable and flexible solutions for processing large datasets, enabling you to define workflows for extracting, transforming, and loading (ETL) data into your vector database.
Ingest the prepared data into your vector database using bulk upload features or streaming data services like Amazon Kinesis. Bulk uploads are suitable for initial data ingestion, while streaming services are ideal for real-time data processing and ingestion. For example, Amazon Kinesis Data Streams can capture and process streaming data from various sources, enabling you to continuously update your vector database with new data points. Monitor the ingestion process to ensure data integrity and handle any errors or inconsistencies that may arise.
Optimize search and retrieval
Optimizing search and retrieval involves configuring your vector database to use the most appropriate distance metrics and indexing methods. Choose distance metrics such as cosine similarity or Euclidean distance based on your application’s requirements. Cosine similarity is often used for text data, where the angle between vectors represents similarity, while Euclidean distance is suitable for scenarios where the magnitude of differences is important.
Implement efficient indexing techniques like HNSW (hierarchical navigable small world) or IVF (inverted file index) to speed up search operations. These indexing methods are designed to handle high-dimensional data efficiently, reducing the time and computational resources required for similarity searches. Monitor query performance using AWS CloudWatch and adjust your configurations as needed to maintain optimal performance. Use AWS CloudWatch Logs to analyze query patterns and identify potential bottlenecks.
Utilize caching mechanisms where applicable to reduce latency for frequent queries. AWS services like Amazon ElastiCache can be used to cache query results, significantly improving response times for repeated searches. Implement query optimization techniques such as query batching, pagination, and pre-computed indices to enhance performance further. Regularly review and fine-tune your indexing and search configurations based on usage patterns and performance data.
Ensure reliability and performance
Ensuring reliability and performance involves continuous monitoring and maintenance of your vector database. Use multi-AZ deployments to enhance availability and failover capabilities. Multi-AZ deployments replicate your data across multiple availability zones, providing automatic failover in case of an infrastructure failure. This ensures that your database remains accessible and operational even during outages.
Implement automated backups and regularly test your disaster recovery procedures. AWS services like Amazon RDS and Amazon OpenSearch Service offer automated backup features, allowing you to define backup schedules and retention policies. Regularly test your backup and recovery processes to ensure that you can quickly restore data in case of an incident. Monitor system performance and resource utilization using AWS CloudWatch, and set up alarms for any critical metrics, such as high CPU usage, memory consumption, or disk I/O.
Regularly review and optimize your database configurations based on usage patterns and performance data. This includes adjusting instance sizes, scaling configurations, and indexing strategies to ensure optimal performance. Keep your database software and extensions up-to-date to benefit from the latest performance improvements, security patches, and new features. Use AWS Systems Manager to automate patch management and configuration updates, ensuring your environment remains secure and compliant with best practices.
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