Vector Database vs. Indexing- Path to Efficient Data Handling

In the realm of digital information and creative applications, the terms ‘vector database’ and ‘vector library’ often emerge, each encompassing distinct functionalities and serving unique purposes. While both deal with vectors, their roles diverge significantly. Understanding the nuances between these entities is crucial in harnessing their potential across various fields.

But why Vector search?

Vector search has gained prominence due to its efficacy in handling high-dimensional data across all the fields of artificial intelligence.

Traditional methods of searching and indexing struggle when dealing with high-dimensional data due to the curse of dimensionality, where distances between points lose meaning and computational costs soar exponentially with dimensionality.

That’s where Vector Databases and Vector Libraries come in. Vector search, often facilitated by techniques like approximate nearest neighbor search, addresses these challenges by representing data points as vectors in a continuous space, enabling efficient similarity searches, clustering, recommendation systems, and retrieval tasks. This approach allows for the rapid and accurate retrieval of similar or relevant items from vast datasets, making it invaluable in scenarios where traditional search methods falter.

Vector Library

Vector libraries focus on storing vector embeddings in indexes stored in memory. They have specific features:

1. Storing Only Vectors: They keep the vector embeddings only, not the original objects they came from.
2. Immutable Data: Once you build the index with your data, you can’t change it. No adding, deleting, or modifying afterward without rebuilding.
3. Query Limitation: Many libraries need you to import all data before building the index. You can’t query during import, which might be a problem for big datasets.

When you query a vector library, you get back vectors and object IDs. But the real information is in the objects, not just their IDs. So, to get the full picture, you’d need to keep the objects stored separately and match IDs to objects.

Examples like Facebook Faiss, Spotify Annoy, and others use the ANN algorithm for similarity search. Each has its way of implementing it — Faiss with clustering, Annoy with trees, and ScaNN with vector compression.

Choosing one depends on what your application needs and how you measure performance.

I have written an article on Vector Indexing and ANN with AWS Serverless Architecture

Read more about it on Medium

Code Repository GitHub

Vector Database

Vector databases go beyond indexing and approximate nearest-neighbor search algorithms. Vector databases are specifically designed to manage vector embeddings and offer several advantages:

1. Data management: Vector databases offer well-known and easy-to-use features for data storage, like inserting, deleting, and updating data. This makes managing and maintaining vector data easier than using a standalone vector index like FAISS, which requires additional work to integrate with a storage solution.

2. Metadata storage and filtering: Vector databases can store metadata associated with each vector entry. Users can then query the database using additional metadata filters for finer-grained queries.

3. Scalability: Vector databases are designed to scale with growing data volumes and user demands, providing better support for distributed and parallel processing. Standalone vector indices may require custom solutions to achieve similar levels of scalability (such as deploying and managing them on Kubernetes clusters or other similar systems).

4. Real-time updates: Vector databases often support real-time data updates, allowing for dynamic changes to the data, whereas standalone vector indexes may require a full re-indexing process to incorporate new data, which can be time-consuming and computationally expensive.

5. Backups and collections: Vector databases handle the routine operation of backing up all the data stored in the database. Pinecone also allows users to selectively choose specific indexes that can be backed up in the form of “collections,” which store the data in that index for later use.

6. Ecosystem integration: Vector databases can more easily integrate with other components of a data processing ecosystem, such as ETL pipelines (like Spark), analytics tools (like Tableau and Segment), and visualization platforms (like Grafana) — streamlining the data management workflow. It also enables easy integration with other AI related tools like LangChain, LlamaIndex and ChatGPT’s Plugins.

7. Data security and access control: Vector databases typically offer built-in data security features and access control mechanisms to protect sensitive information, which may not be available in standalone vector index solutions.

Vector Database Features

Filtering

Every vector stored in the database also includes metadata. In addition to the ability to query for similar vectors, vector databases can also filter the results based on a metadata query. To do this, the vector database usually maintains two indexes: a vector index and a metadata index.

The filtering process can be performed either before or after the vector search itself, but each approach has its challenges that may impact the query performance:

Pre and Post Filtering

· Pre-filtering: In this approach, metadata filtering is done before the vector search. While this can help reduce the search space, it may also cause the system to overlook relevant results that don’t match the metadata filter criteria. Additionally, extensive metadata filtering may slow down the query process due to the added computational overhead.

· Post-filtering: In this approach, the metadata filtering is done after the vector search. This can help ensure that all relevant results are considered, but it may also introduce additional overhead and slow down the query process as irrelevant results need to be filtered out after the search is complete.

To optimize the filtering process, vector databases use various techniques, such as leveraging advanced indexing methods for metadata or using parallel processing to speed up the filtering tasks. Balancing the trade-offs between search performance and filtering accuracy is essential for providing efficient and relevant query results in vector databases.

Database Operations

Unlike vector indexes, vector databases are equipped with a set of capabilities that makes them better qualified to be used in high-scale production settings. Let’s take a look at an overall overview of the components that are involved in operating the database.

Database Components

Performance and Fault tolerance

Performance and fault tolerance are tightly related. The more data we have, the more nodes that are required — and the bigger chance for errors and failures. As is the case with other types of databases, we want to ensure that queries are executed as quickly as possible even if some of the underlying nodes fail.

To ensure both high performance and fault tolerance, vector databases use sharding and replication apply the following:

1. Sharding — partitioning the data across multiple nodes. There are different methods for partitioning the data — for example, it can be partitioned by the similarity of different clusters of data so that similar vectors are stored in the same partition. When a query is made, it is sent to all the shards and the results are retrieved and combined.

2. Replication — creating multiple copies of the data across different nodes. This ensures that even if a particular node fails, other nodes will be able to replace it.

Monitoring

To effectively manage and maintain a vector database, we need a robust monitoring system that tracks the important aspects of the database’s performance, health, and overall status. Some aspects of monitoring a vector database include the following:

1. Resource usage — monitoring resource usage, such as CPU, memory, disk space, and network activity, enables the identification of potential issues or resource constraints that could affect the performance of the database.

2. Query performance — query latency, throughput, and error rates may indicate potential systemic issues that need to be addressed.

3. System health — overall system health monitoring includes the status of individual nodes, the replication process, and other critical components.

Access-control

Access control is the process of managing and regulating user access to data and resources. It is a vital component of data security, ensuring that only authorized users can view, modify, or interact with sensitive data stored within the vector database.

Which one is right for me?

Vector databases and vector libraries are both technologies that enable vector similarity search, but they differ in functionality and usability.

· Vector databases can store and update data, handle various types of data sources, perform queries during data import, and provide user-friendly and enterprise-ready features.

· Vector libraries can only store data, handle vectors only, require importing all the data before building the index, and require more technical expertise and manual configuration.

Some vector databases are built on top of existing libraries, such as Faiss. This allows them to take advantage of the existing code and features of the library, which can save time and effort in development.

Determining whether a vector database or a vector library is right for you depends on your specific needs and the context in which you intend to use these tools. Whether it’s about managing and querying large volumes of data points or utilizing predefined vector graphics and operations — will help you determine which option aligns better with your needs.

🌟Bonus! 🌟

As you have come this far, let me share the best Vector Databases and libraries you can choose from. The choice between the two depends on the specific requirements and scale of the application.

Best Vector Databases & Libraries

· Elasticsearch — A distributed search and analytics engine that supports various types of data.

· Faiss — A library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.

· Milvus — An open-source vector database that can manage trillions of vector datasets and supports multiple vector search indexes and built-in filtering.

· Qdrant — A vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage data points.

· Chroma — An AI-native open-source embedding database. It is simple, feature-rich, and integrable with various tools and platforms for working with embeddings.

· OpenSearch — A community-driven, open source fork of Elasticsearch and Kibana following the license change in early 2021. It includes a vector database functionality that allows you to store and index vectors and metadata, and perform vector similarity search using k-NN indexes.

· Weaviate — An open-source vector database that allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects.

· Vespa— A fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real time.

· pgvector — An open-source extension for PostgreSQL that allows you to store and query vector embeddings within your database. It is built on top of the Faiss library, which is a popular library for efficient similarity search of dense vectors.

· Vald — A highly scalable distributed fast approximate nearest neighbor dense vector search engine. Vald is designed and implemented based on the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT to search neighbors.

· Apache Cassandra — An open-source NoSQL distributed database trusted by thousands of companies. Vector search is coming to Apache Cassandra in its 5.0 release, which is expected to be available in late 2023 or early 2024.

· ScaNN (Scalable Nearest Neighbors, Google Research) — A library for efficient vector similarity search, which finds the k nearest vectors to a query vector, as measured by a similarity metric.

Pinecone — A vector database that is designed for machine learning applications. It is fast, scalable, and supports a variety of machine learning algorithms. Pinecone is built on top of Faiss, a library for efficient similarity search of dense vectors.

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