Database

From
Jump to: navigation, search

YouTube ... Quora ...Google search ...Google News ...Bing News


Databases are fundamental to training all sorts of Machine Learning (ML) and artificial intelligence (AI) models. They provide a consistent and reliable way to store data, but their value stems from their data management functionalities. Machine Learning (ML) and other AI techniques provide the means for enhancing these functionalities towards increased scalability and intelligence in managing very large datasets. AI databases are a fast-emerging database approach dedicated to creating better machine-learning and deep-learning models and then train them faster and more efficiently. AI databases integrate artificial intelligence technologies to provide value-added services. Databases play a crucial role in supporting AI/Machine Learning (ML) by providing the means to store, manage, and analyze large datasets, which is essential for training accurate and effective AI models. They also provide the means for enhancing data management functionalities towards increased scalability and intelligence in managing very large datasets.

Vector vs Relational vs Graph

Databases designed to handle specific types of data and queries:

  • Vector Database:
    • Vector databases are specialized for handling high-dimensional vectors and performing similarity searches.
    • A vector database, also known as a similarity search or high-dimensional database, is optimized for storing and retrieving high-dimensional vectors that represent complex data.
    • It is particularly suited for tasks that involve similarity searches, such as recommendation systems, image and audio recognition, and natural language processing tasks.
    • Vector databases use specialized indexing and search algorithms to efficiently perform similarity searches in high-dimensional spaces.
    • Examples: Pinecone, Weaviate, Milvus, Redis, Chroma, Qdrant, Vespa, Marqo
  • Relational Database Management System (RDBMS):
    • Relational databases are good at managing structured tabular data.
    • A relational database stores data in structured tables with predefined schemas, where each table has columns and rows.
    • It uses the Structured Query Language (SQL) to manage and query the data.
    • Relational databases are suitable for structured and tabular data and are widely used in business applications, content management systems, and data warehousing.
    • Examples: MySQL, PostgreSQL, Oracle Database, SQL Server, SQLite, SQL Anywhere, PostgreSQL, Aurora, Db2
  • Graph Database:
    • Graph databases are designed for data with intricate relationships.
    • A graph database is designed to store and manage data in the form of graph structures, consisting of nodes and edges.
    • Nodes represent entities, and edges represent relationships between these entities.
    • Graph databases are excellent at handling data with complex relationships and are used for applications such as social networks, recommendation systems, fraud detection, and knowledge graphs.
    • Examples: Neo4j, Amazon Neptune, JanusGraph.

Vector


A vector database is a specialized type of database designed to store and retrieve vector embeddings for fast retrieval and similarity search. Vector embeddings are high-dimensional representations of data items, such as images, documents, or user profiles, that capture their semantic or contextual meaning. Vector databases are adept at handling complex, high-dimensional data, and are revolutionizing data retrieval and analytics in the business world. The key feature of a vector database is its ability to find similar items. When you query the database with a vector, it can find vectors close to it in the numerical space. The closeness of vectors is usually measured by something called cosine similarity or Euclidean distance. Vector databases are particularly useful in fields such as Natural Language Processing (NLP), computer vision, and other AI applications where vector embeddings are commonly used.

Indexing & Stores Vector embeddings for Fast Retrieval and Similarity Search


  • Vector Embeddings Generation: Data items are first converted into vectors using feature extraction or embedding techniques. For example, images can be represented as vectors using convolutional neural networks (CNNs), and text documents can be represented as vectors using word embeddings or sentence embeddings.
  • Indexing: The vector embeddings are indexed using specialized data structures and algorithms to enable efficient search and retrieval. Approximate Nearest Neighbor (ANN) algorithms are allowed to return points whose distance from the query is at most times the distance from the query to its nearest points. Proximity graph methods such as Hierarchical Navigable Small World (HNSW) are considered the current state-of-the-art for the approximate nearest neighbors search. Other ANN techniques include K-dimensional trees (KD-trees), locality-sensitive hashing (LSH), and Product Quantization (PQ). Common indexing techniques include K-Nearest Neighbors (KNN) indexes, Hierarchical Navigable Small World (HNSW) indexes, and Inverted File Indexes (IVF). These indexes organize the vectors in a way that allows for fast nearest-neighbor search based on similarity measures.
    • Product Quantization (PQ): a vector indexing technique that is used to compress high-dimensional vectors while preserving their semantic relationships. PQ works by dividing the high-dimensional vector into a number of subvectors, and then quantizing each subvector independently. This means that each subvector is represented by a codebook index, which is a small integer value. The codebook indices for all of the subvectors are then concatenated to form a single codeword, which represents the original high-dimensional vector.
    • Locality-sensitive hashing (LSH): is a vector indexing technique that is used to find approximate nearest neighbors (ANNs) in high-dimensional spaces. LSH works by hashing the vectors into buckets, such that similar vectors are more likely to be hashed into the same bucket. This makes it possible to quickly find approximate nearest neighbors by only searching a small subset of the vectors. There are a number of different LSH algorithms, but they all work on the same basic principle. The first step is to divide the high-dimensional vector into a number of subvectors. Then, each subvector is hashed into a bucket using a hash function. The hash functions are designed to be locality-sensitive, meaning that similar vectors are more likely to be hashed into the same bucket. Once the vectors have been hashed into buckets, it is possible to find approximate nearest neighbors by only searching a small subset of the buckets. For example, if we are looking for the nearest neighbor to a given vector, we can simply search the bucket that the vector was hashed into. If we don't find the nearest neighbor in that bucket, we can then search the neighboring buckets. LSH is a very effective way to find approximate nearest neighbors in high-dimensional spaces. It is fast and scalable, making it suitable for indexing large datasets of vectors. Additionally, LSH is often used in conjunction with other vector indexing techniques, such as product quantization, to further improve the search performance. Some of the disadvantages of using locality-sensitive hashing for vector indexing it can be difficult to tune the hyperparameters for optimal performance and it can be sensitive to the choice of distance metric. Here are some of the advantages of using locality-sensitive hashing for vector indexing:
      • It is very effective at finding approximate nearest neighbors in high-dimensional spaces.
      • It is fast and scalable, making it suitable for indexing large datasets of vectors.
      • It is often used in conjunction with other vector indexing techniques to further improve the search performance.
    • Hierarchical Navigable Small World (HNSW): is a vector indexing technique that is used to find approximate nearest neighbors (ANNs) in high-dimensional spaces. It is based on the idea of constructing a navigable graph, in which similar vectors are connected to each other by edges. The graph is structured in a hierarchical way, with shorter edges at the lower levels and longer edges at the higher levels.
    • K-dimensional trees (KD-trees): are a type of spatial partitioning data structure that can be used to index vectors in high-dimensional spaces. They are a generalization of binary search trees to multiple dimensions. KD-trees work by recursively partitioning the data space into two halves at each node. The partitioning is done along the dimension that has the largest variance in the data. This process continues until all of the data points in a node have the same value along the selected dimension, or until a certain depth is reached. KD-trees are a very efficient way to index vectors in high-dimensional spaces. They are particularly well-suited for applications where the data is spatially distributed, such as image retrieval and natural language processing. Some of the advantages of using KD-trees for vector indexing are they are very efficient for nearest neighbor search and range search, they are relatively easy to implement, and they are scalable to large datasets. Once the KD-tree has been constructed, it can be used to perform a variety of operations on the data, such as:
      • Nearest neighbor search: Finding the data point that is closest to a given query point.
      • Range search: Finding all data points that fall within a given range of the query point.
      • Window search: Finding all data points that fall within a given window around the query point.
  • Similarity Search: To perform similarity search, a query vector is used to find the most similar vectors in the vector database. The query vector represents the desired information or criteria. The similarity between vectors is measured using distance metrics such as cosine similarity or Euclidean distance. The vector database uses algorithms like Approximate Nearest Neighbor (ANN) search to optimize the search process through techniques like hashing, quantization, or graph-based search.
  • Retrieval: The vector database retrieves the vectors that are most similar to the query vector based on the similarity measure. These retrieved vectors can be used for various purposes, such as recommendation systems, content-based search, or clustering.

Offerings

Pinecone

Pinecone is a cloud-based vector database designed for machine learning applications. It provides a specialized infrastructure for indexing and searching high-dimensional vectors efficiently and accurately. Pinecone is not an open-source platform and is developed and maintained by the Pinecone company. Pinecone offers multiple proprietary algorithms and components that work together to enable efficient vector indexing and search. These components include:

  • Vector Index: At the core of Pinecone is a highly specialized software component called the vector index. It efficiently indexes high-dimensional vectors and allows for fast and accurate interaction with them[1]. This index is optimized for similarity search and retrieval of vectors.
  • Container Distribution Platform: Pinecone utilizes a container distribution platform that enables horizontal scaling and can handle any workload. This platform ensures that Pinecone can handle large-scale deployments and efficiently distribute the computational load across multiple containers[1].
  • Cloud Management System: Pinecone incorporates a cloud management system that provides the necessary infrastructure and resources for running the vector database in a cloud environment. This system allows for easy deployment, management, and monitoring of the Pinecone infrastructure[1].

Key concepts:

  • Vector search - Unlike traditional search methods that revolve around keywords, it is done by indexing and searching through ML-generated representations of data — vector embeddings — to find items most similar to the query.
  • Vector embeddings - Vector embeddings, or “vectors,” are sets of floating-point numbers that represent objects. They are generated by embedding models trained to capture the semantic similarity of objects in a given set.

Weaviate | SeMI

Weaviate is an open source vector search engine that uses machine learning models to output vectors, also known as embeddings. Here are some of the unique capabilities of Weaviate:

  • Vector-native search: Weaviate is a vector-native search database, which means that data is stored as vectors, enabling semantic search. This combination of data storage is unique and enables fast, filtered, and semantic search from end-to-end.
  • Filtered vector search: Weaviate provides powerful filtered vector search capabilities, meaning that candidates in a "fuzzy" vector search can be eliminated based on individual properties. Thanks to Weaviate's efficient pre-filtering mechanism, the recall can be kept high, even when filters are very restrictive. Additionally, the process is efficient and has minimal overhead compared to an unfiltered vector search.
  • Scalability: Weaviate is scalable, and the GraphQL API allows users to query data efficiently.
  • Schema support: Weaviate supports ontology, RDF-like definitions in its schema, and it runs out of the box.
  • Cross-references: Creating cross-references does not affect object vectors in either direction. Where data objects have relationships with each other, they can be represented in Weaviate with cross-references.
  • Graph functionalities: Although Weaviate's primary focus is on searching, it also has graph functionalities on top of the vector-search focus.
  • Containerized: Weaviate comes containerized, making it easy to run everywhere.


Milvus | Zilliz

Milvus is an open-source cloud-native vector database designed for managing massive quantities of both structured and unstructured data. Here are some of the unique capabilities of Milvus vector database:

  • Versatile query types:: Milvus supports versatile query types such as vector similarity search with various similarity functions, attribute filtering, and multi-vector query processing.
  • Multiple Approximate Nearest Neighbor (ANN) algorithms:: Milvus allows indexing data with multiple ANN algorithms, enabling users to compare their performance for their specific use case.
  • Hardware efficiency:: Milvus is hardware efficient and provides advanced indexing algorithms, achieving a 10x performance boost in retrieval speed.
  • Highly available and scalable:: Milvus has been battle-tested by over a thousand enterprise users in a variety of use cases. With extensive isolation of individual system components, Milvus is highly resilient and reliable. The distributed and high-throughput nature of Milvus makes it a natural fit for serving large scale vector data.


Redis

Redis is a popular in-memory data structure store that can be used as a vector database. Here are some of the unique capabilities of Redis vector database:

  • Scalability: Redis vector database is scalable and can handle large amounts of vector data, such as tensors, matrices, and numerical arrays, allowing for storage and analysis of such data.
  • High performance: Redis vector database provides lightning-fast query response times by leveraging the speed and scalability of Redis, an in-memory data store.
  • Indexing and search: Redis vector database includes built-in indexing and search capabilities, enabling quick searching for vectors like images, texts, or audio files based on specific criteria or finding similar vectors.
  • Distance calculation: Redis vector database supports various distance measures, enabling the comparison of vectors and performing complex analytical operations.
  • Operations on vector data: Redis vector database provides various operations for working with vector data.
  • Vector similarity search: Redis vector database has a vector similarity search capability, which is part of RediSearch 2.4 and is available on Docker, Redis Stack, and Redis Enterprise Cloud’s free and fixed subscriptions.
  • Real-time data processing: Redis Enterprise 7.2 introduces scalable search to its vector database capabilities, delivering even higher queries per second, making it a powerful tool for real-time data processing.
  • Flexibility: Redis vector database can be deployed anywhere, on any cloud platform, on-premises, or in a multi-cloud or hybrid cloud architecture.


Chroma DB

Chroma is an open-source vector database designed specifically for storing and searching high-dimensional vector data, with a focus on audio-related use cases. Here are some of the unique capabilities of Chroma vector database:

  • Audio-specific: Chroma is specifically optimized for audio data, making it an ideal solution for applications such as music recommendation systems, audio search engines, and other audio-related use cases.
  • Vector store: The vector store is the main component of Chroma, responsible for storing and indexing the high-dimensional vectors. It is designed to scale horizontally across multiple nodes as data grows.
  • Indexing: Chroma uses an Approximate Nearest Neighbor (ANN) index to speed up search and retrieval of vectors. It supports multiple ANN algorithms, including Hierarchical Navigable Small World (HNSW), IVFADC, and IVFPQ, allowing for fast and efficient searches.
  • Open-source: Chroma is an open-source project, which means that its code is freely available for users to access, modify, and contribute.
  • Embeddings made easy: Chroma provides a simple and feature-rich API for working with embeddings. It offers tools for search, filtering, and other solutions, making it easy to use embeddings in applications.
  • Scalability: Chroma is designed to be efficient, scalable, and flexible, allowing for the storage and search of large amounts of high-dimensional vector data.
  • AI-native: Chroma is AI-native, meaning that it is built with the needs of AI applications in mind. It provides valuable resources for various sectors, offering improved similarity search, matching, and query performance.


Qdrant

Qdrant is an open-source vector database and vector similarity search engine that provides unique capabilities for storing and searching high-dimensional vectors. Here are some of its unique capabilities:

  • High-dimensional vector search: Qdrant specializes in searching for the nearest high-dimensional vectors, making it suitable for applications that involve complex data types such as images, videos, and natural language text.
  • Flexible filtering: Qdrant allows you to apply arbitrary business logic on top of a similarity search by using Qdrant filters. This enables you to define specific conditions and constraints for your search queries, such as finding similar clothes cheaper than $20 or searching for a similar artwork published in the last year.
  • Image similarity search: Qdrant's vector database includes the capability to find similar images, detect duplicates, or even find a picture based on a text description. This feature is particularly useful for applications that require image-based search and recommendation systems.
  • Easy integration with Neural Networks: Qdrant provides a convenient API to store, search, and manage vectors with an additional payload. This makes it useful for applications that involve neural network or semantic-based matching, faceted search, and other AI-related use cases.
  • Stand-alone operation: Qdrant operates independently without reliance on external databases or orchestration controllers, simplifying configuration and deployment.


Vespa | Yahoo!

The capabilities of Vespa vector database include:

  • Broad range of query capabilities: Vespa provides a wide range of query capabilities, including vector search (ANN), lexical search, and search in structured data. It supports querying by nearest neighbors, approximate or exact, with various distance metrics.
  • Powerful computation engine: Vespa has a powerful computation engine that offers great support for modern machine-learned models. This makes it suitable for applications that require complex computations and machine learning algorithms.
  • Hands-off operability: Vespa offers hands-off operability, which means that it automates tasks involved in data and node management, system configuration, and application development. It automatically distributes data over available nodes in the cluster and redistributes content in the background when nodes are added or removed, without impacting query or write traffic.
  • Performance and scalability: Vespa is known for its unbeatable performance and scalability. It can handle large amounts of data and provides efficient query processing even in distributed environments.
  • Data management and application development support: Vespa provides support for data management tasks, node management, system configuration, and application development. It offers a high-level specification of the system, known as the application package, to configure Vespa instances and detailed configuration options.

Marqo

Marqo is an end-to-end vector search engine and database that enables users to store and query unstructured data such as text, images, and code using vectors; providing a single API for vector generation, storage, and retrieval. Marqo is designed to support multimodal vector search, allowing users to search for similar vectors across different types of data.

Marqo's vector search engine works by generating vectors for unstructured data such as text, images, and code, and then storing these vectors in a database. The vectors are generated using deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) that are trained on large amounts of data. When a user queries the database, Marqo's search engine generates a vector for the query and compares it to the vectors in the database using similarity metrics such as cosine similarity. The search engine returns the most similar vectors to the query, allowing users to find similar data across different types of data. Marqo's search engine is designed to support multimodal vector search, which means that users can search for similar vectors across different types of data such as text, images, and code. This is achieved by generating vectors for each type of data and storing them in the same database.

Marqo's vector search engine uses continuous learning technology to improve the relevance of search results based on user engagement. This allows the search engine to adapt to changing user needs and preferences. Marqo's vector search engine continuously improves by learning from user engagement. Its technology is a new form of vector search that improves based on user engagement; the search engine can learn from user behavior such as clicks, "add to cart" actions, and other engagement metrics to improve the relevance of search results. Marqo's continuous learning technology is designed to automatically improve the search engine based on user engagement, allowing it to adapt to changing user needs and preferences.

  • End-to-end vector search engine:: Marqo is described as an end-to-end vector search engine, providing everything required to integrate vector search into an application through a single API.
  • Multi-modal vector search:: Marqo is designed to handle multi-modal data, including text, images, and code. It allows users to store and query unstructured data through a single interface.
  • Fluid and schemaless data storage:: Marqo offers a fluid and schemaless approach to data storage, allowing for flexible adaptation to different data storage needs.
  • Versatile search capabilities:: Marqo provides features such as semantic text search, end-to-end image search functionality, horizontal scalability for fast search times, custom model integration, search highlighting, and powerful query domain-specific language (DSL)-based filtering.
  • Open-source: Marqo is an open-source vector search engine, which means that its code is freely available for users to access, modify, and contribute.

Relational

In-database Machine Learning

In-database machine learning refers to the ability to build and train Machine Learning (ML) models directly within a database, using the data that already resides there. This approach eliminates the need to move data out of the database and into a separate analytics engine, which can save time and reduce costs providing a simpler, faster, and more efficient way to build and train Machine Learning (ML) models by leveraging the data that already resides within your database.

Some of the benefits of in-database machine learning include:

  • Simplicity: Since you're starting with tools and data you're already familiar with, it's easier for you and your employees to get started with Machine Learning (ML).
  • Speed: With algorithms in the database that ensure minimized data movement, you can build and train models faster, which saves time and costs.
  • Ease of deployment: Models built in the database are easier to deploy and operationalize, allowing you to see results faster.

There are several databases that support in-database machine learning:

  • Amazon Redshift: is a managed, petabyte-scale data warehouse service designed to make it simple and cost-effective to analyze all of your data using your existing business intelligence tools. Amazon Redshift ML is designed to make it easy for SQL users to create, train, and deploy Machine Learning (ML) models using SQL commands.
  • BlazingSQL: is a GPU-accelerated SQL engine built on top of the RAPIDS ecosystem; it exists as an open-source project and a paid service. RAPIDS is a suite of open source software libraries and APIs, incubated by Nvidia, that uses CUDA and is based on the Apache Arrow columnar memory format.
  • Brytlyt: is a GPU database and analytics platform that provides real-time insights on large and streaming datasets. It uses patent-pending IP and the power of GPUs to deliver results up to 1,000x faster than legacy systems.
  • Google Cloud BigQuery: is a fully managed, cloud-native data warehouse that enables super-fast SQL queries using the processing power of Google's infrastructure.
  • IBM Db2 Warehouse: is a software-defined data warehouse for private and virtual clouds that support Docker container technology. It provides scalable, elastic, and flexible deployment options for analytics workloads.
  • Microsoft SQL Server: is a relational database management system developed by Microsoft. It supports in-database machine learning through its Machine Learning (ML) Services component, which allows you to run R and Python scripts within the database.
  • Oracle Database: is a multi-model database management system produced and marketed by Oracle Corporation. It supports in-database machine learning through its Oracle Machine Learning (ML) component, which allows you to build and deploy Machine Learning (ML) models within the database.

Supporting AI

These databases provide various features and capabilities that can be leveraged to support AI implementations. They offer scalability, availability, and improved accuracy of predictions and actions, making them suitable for handling massive amounts of data and providing high availability for AI applications. Databases like MySQL, Apache Cassandra, PostgreSQL, and Couchbase can support AI implementations in various ways. Here are some examples:

  • MySQL: HeatWave is a fully managed service for the MySQL database from Oracle and has built-in support for machine learning (HeatWave ML). HeatWave ML fully automates the process to train a model, generate inferences, and invoke explanations, all without extracting data or model out of the database. The user can use familiar SQL interfaces to invoke all the machine learning capabilities.
  • Apache Cassandra: is a powerful and scalable distributed database solution that has emerged as a go-to choice for many AI applications including Uber, Netflix, and Priceline. It provides a foundation for two of the most important data management categories — features and events — for real-time AI, enabling the delivery of highly accurate insights based on the right data at the right time.
  • PostgreSQL: Flexible Server and Azure Cosmos DB for PostgreSQL have now introduced support for the pgvector extension. With the pgvector extension, customers can now store embeddings in PostgreSQL databases which are vectors created by generative AI models that represent the semantic meaning of textual data allowing efficient similarity searches.
  • Couchbase: is a document-focused engagement database that is also open-source and distributed. While I couldn't find any specific information about Couchbase's AI support, it does offer enterprise-grade support services to help users understand or troubleshoot Couchbase products.

Database Support for AI Algorithms

Databases support AI algorithms by providing a consistent and reliable way to store and manage data, which is essential for training accurate and effective AI models. Lately, database companies have been adding artificial intelligence routines into databases so the users can explore the power of these smarter, more sophisticated algorithms on their own data stored in the database. The AI algorithms are also finding a home below the surface, where the AI routines help optimize internal tasks like re-indexing or query planning. These new features are often billed as adding automation because they relieve the user of housekeeping work. Developers are encouraged to let them do their work and forget about them. There’s much more interest, though, in AI routines that are open to users. These machine learning algorithms can classify data and make smarter decisions that evolve and adapt over time. They can unlock new use cases and enhance the flexibility of existing algorithms. In summary, databases support AI algorithms by providing a consistent and reliable way to store and manage data, which is essential for training accurate and effective AI models. They also provide the means for enhancing data management functionalities towards increased scalability and intelligence in managing very large datasets. I hope this discussion helps you understand the role of databases in supporting AI algorithms and their role in the development of AI applications.

Examples

There are several database startups that are highlighting their direct support of machine learning and other AI routines. Here are some examples:

  • SingleStore: offers fast analytics for tracking incoming telemetry in real-time. This data can also be scored according to various AI models as it is ingested.
  • MindsDB: adds machine learning routines to standard databases like MongoDB, MariaDB, PostgreSQL, or Microsoft SQL. It extends SQL to include features for learning from the data already in the database to make predictions and classify objects.
  • BlazingSQL: is a GPU-accelerated SQL engine built on the RAPIDS ecosystem. It allows you to ETL raw data directly into GPU memory as a GPU DataFrame, and then execute relational algebra on that data, returning results directly to a GPU DataFrame.
  • Brytlyt: is a GPU database and analytics platform that provides real-time insights on large and streaming datasets. It uses patent-pending IP and the power of GPUs to deliver results up to 1,000x faster than legacy systems.
Retrieved from "https://primo.ai/index.php?title=Database&oldid=37594"