Artificial Intelligence

Artificial intelligence (AI) applications like recommendation engines, search, and natural language processing rely heavily on vector similarity searches. This requires efficiently storing and querying large pools of vector data. Choosing the right database for these AI workloads is critical for performance.

In this post, we compare two popular options - vector databases and Elasticsearch - analyzing their relative strengths and weaknesses for AI vector data.

Overview of Vector Storage Needs for AI

Before diving into databases, let's look at what vector storage entails for AI:

High dimensionality - Vectors can have hundreds or thousands of dimensions. This high dimensionality makes indexing tricky.

Approximate similarity - AI applications look for vectors similar but not identical to queries. Exact matches aren't needed.

Fast similarity search - Finding similar vectors across billions of vectors must be fast. Slow searches degrade user experience.

Point queries and aggregations - AI apps need to fetch individual vectors by ID and run analytics across vector pools.

High write throughput - New vectors are continuously added as users interact with AI apps. Writes must keep up with reads.

High availability - Vector stores need redundancy and failover as they are critical to apps. Downtime is unacceptable.

These requirements make vector storage and querying challenging compared to traditional databases optimized for transactions and exact matching. Next we'll see how well Elasticsearch and vector databases fit the bill.

Elasticsearch for Vectors

Elasticsearch is a popular open source search and analytics engine. It powers searches at companies like Netflix, eBay, and Facebook.

Though not purpose-built for vectors, Elasticsearch has added support for AI workloads:

How Vectors Are Stored

  • Vectors stored as dense float arrays in documents. Special vector datatype added.
  • Allows indexing vectors with 100s or 1000s of dimensions.
  • Multiple vectors per document possible.

Similarity Search

  • Supports approximate k-NN search for finding nearest vectors.
  • Uses space partitioning trees and pruning heuristics for efficient neighbor search over billions of vectors.
  • Custom similarity metrics like L1, L2, etc. can be used.

Scalability and Resilience

  • Elasticsearch built to scale out. Linear scalability with number of nodes.
  • Sharding of vector data across nodes. Fault tolerance via replicas.
  • Elasticsearch Service on cloud platforms provides managed service with high availability.

Other Features

  • Supports point lookups, aggregations, joins etc like a traditional database.
  • Real-time search and analytics as documents are added.
  • Integrations with libraries like NumPy, Pandas, SciPy for data analysis.
  • Visualization plugins like Kibana for exploring data.

When To Use Elasticsearch?

Elasticsearch works well if you:

  • Want to use same database for search, analytics and machine learning.
  • Have small to medium vector data size - <50 million vectors per index.
  • Like Elasticsearch SQL support for easier querying.
  • Require real-time sync of vector data to apps.
  • Want to leverage other Elasticsearch functionality like logging, monitoring etc.

However, Elasticsearch starts running into limits at scale:

  • Hundreds of vector dimensions supported but can get slow. Works best for <1000 dimensions.
  • Similarity search slows down significantly beyond 50 million vectors per index.
  • Hard to optimize for just fast vector search while supporting other query types.
  • Eventual consistency only. Data not immediately available to search after writes.
  • Lack of globalnearest neighbors search across shards.

Next, let's see how well optimized vector databases fare for massive vector workloads.

Vector Databases

Vector databases are designed exclusively for storing and querying large vector datasets. Leading options include Weaviate, Pinecone, Milvus and Siren. Here's how they stack up for core requirements:

How Vectors Are Stored

  • Data stored in a vector space optimized for cosine similarity.
  • Specialized data structures like HNSW graphs for multi-dimensional vectors.
  • Better compression of float vectors using techniques like Quantization.

Similarity Search

  • Support for ultra fast nearest neighbor search even for billions of vectors.
  • Advanced indexing structure optimizations like HNSW for O(log N) search complexity.
  • SIMD and multi-threading used to parallelize search across many cores and servers.

Scalability and Resilience

  • Distributed architecture for horizontal scalability to billions of vectors across clusters.
  • Sharding and replication of vectors across nodes for redundancy.
  • Some like Milvus offer Kubernetes integration for container orchestration.

Other Features

  • ANN libraries integration for ML training and inference.
  • Strict consistency and high ingest rates for real-time apps.
  • Role based access control, encryption and auditing for security.
  • Monitoring, load balancing and autoscaling capabilities.

When To Use Vector Databases?

You'll see best results with vector databases if:

  • You have over 50 million vectors currently or expect massive growth.
  • Your vectors have very high dimensionality - 1000s of dimensions.
  • Blazing fast similarity search is critical for your app's user experience.
  • You need ms latency for real-time apps.
  • Your primary workload is ANN search, not analytics.

The high scalability and fast retrieval make them ideal for large scale vector workloads common in AI applications.

Comparing Benchmarks

Benchmarks can help quantify the performance differences between Elasticsearch and vector databases for AI apps.

A 2022 benchmark test by Siren on a 1.5 billion vector dataset with 128 dimensions compared Milvus and Elasticsearch.

It measured throughput and latency for key operations. Here are some highlights:

Another benchmark by Pinecone on 512 dimensional vector data showed:

The results demonstrate 10-30x better performance with vector databases compared to Elasticsearch for core AI workloads. The performance gap widens with scale.

However, Elasticsearch provides more general purpose search and analytics capabilities. The optimal choice depends on your specific feature and performance needs.

Common Use Cases

Looking at common AI applications can further illustrate when to choose vector databases vs Elasticsearch.

Recommendation Engines

Recommendation engines like on Netflix and Amazon rely on fast similarity matching between user vectors, item vectors and context. Key needs:

  • Billions of user and item vectors require high scalability.
  • Latency sensitive - recommendations shown in real-time as users browse.
  • Mostly similarity search queries with some aggregations.

Verdict: Vector databases are better suited for large scale low latency recommendations.

Search Relevance

Semantic search in e-commerce apps needs to match user search vectors to product or document vectors. Requirements:

  • 10s of millions of product/document vectors.
  • Query latency impacts user experience.
  • Requires search, analytics, logging.

Verdict: Elasticsearch reasonable for mid-size product catalogs. Vector databases better for 100s of millions of items.

Chatbots

Smart assistants like Alexa use vector search over knowledge graphs with 100s of millions of text embeddings. Needs:

  • Low latency critical for conversational response.
  • Constantly increasing knowledge base.
  • Requires joins, analytics over knowledge graph.

Verdict: Vector databases preferred for scale, speed and ANN focus. Elasticsearch helpful for knowledge graph analytics.

As you can see, vector databases tend to be a better fit as the scale and real-time search needs grow. But Elasticsearch has advantages in supporting broader analytics workloads.

Conclusion

In this post, we looked at how well vector databases and Elasticsearch are suited for AI applications in terms of core requirements like scale, search speed, high dimensionality and real-time capabilities.

While both options now support AI workloads, benchmarks demonstrate vector databases have significant advantages for large vector datasets:

  • Up to 30x faster similarity search latency
  • 10-20x higher throughput
  • Ability to scale to billions of vectors with 1000s of dimensions

Vector databases shine for ultra fast similarity search on massive vector datasets powering real-time AI apps. Their focused vector store architecture outperforms Elasticsearch optimized for general search workloads.

However, if you have broader analytics needs or smaller datasets, Elasticsearch can be a good option offering other benefits like SQL support, knowledge graph management and real-time sync.

Choosing the right vector store is key to getting optimal speed and scalability. As AI pervades more apps, high performance vector databases purpose built for machine learning workloads are becoming crucial.


What are the key differences between Elasticsearch and vector databases?

The fundamental difference is that Elasticsearch is optimized for general purpose search and analytics while vector databases are purpose-built for large scale vector similarity workloads common in AI applications.

Elasticsearch uses inverted indexes for term matching across documents and supports a wide range of query types. Vector databases use specialized data structures like HNSW graphs for efficient approximate nearest neighbor search in high dimensional vector spaces.

Other key differences:

  • Data model: Documents vs vectors
  • Query types: Full text search, aggregations vs ANN, point lookups
  • Performance: Higher latency at scale for ANN in Elasticsearch vs optimized vector retrieval in vector databases.
  • Scalability: Harder to scale Elasticsearch for billions of vectors and high dimensions compared to native support in vector databases.

When should I consider Elasticsearch over vector databases?

Use cases where Elasticsearch may be preferable:

  • You need broader capabilities beyond just high performing vector search like full text search, graph algorithms, analytics etc.
  • Your dataset is small to medium sized, less than 50 million vectors. Performance is still decent.
  • You need real-time sync of vector updates to applications. Vector databases favor consistency over real-time.
  • Your vectors have lower dimensionality, less than 500 dimensions.
  • You want to leverage other Elasticsearch functionality like logging, monitoring, security features.
  • Your team has more expertise using Elasticsearch's SQL while vector databases use proprietary APIs.

When are vector databases the better choice than Elasticsearch?

Scenarios where vector databases shine:

  • You have over 50-100 million vectors now or expect massive growth in future.
  • Your vectors have very high dimensionality - 1000s of dimensions.
  • Ultra low latency vector search is critical for your use case.
  • Throughput needs to scale to billions of vectors.
  • ANN search is the dominant query type, not broader analytics.
  • You want enterprise capabilities like high availability, access control, encryption.

How much faster is ANN search in vector databases vs Elasticsearch?

Benchmarks show 10-30x faster search latency and 10-20x higher throughput in vector databases compared to Elasticsearch:

  • Pinecone sees 99th percentile latency of 7ms vs 1600ms in Elasticsearch per independent benchmark.
  • Milvus handles 2.4 ms median latency for ANN search compared to 34 ms in Elasticsearch as per Siren's benchmark.

The gap widens with scale as Elasticsearch degrades more handling hundreds of millions of vectors while native vector databases maintain millisecond latency.

Can I use both Elasticsearch and a vector database together?

Yes, you can use Elasticsearch and vector databases together in a complementary fashion:

  • Use vector database for ultra fast, high scale ANN search for real-time serving.
  • Use Elasticsearch for ingesting and analyzing vector metadata - logging, monitoring, joins with other data.
  • Use vector database for personalized search relevance and Elasticsearch for general keyword based search.

Blending them allows exploiting Elasticsearch's analytics capabilities while offloading performance critical ANN to optimized vector databases.

How do I migrate vector data from Elasticsearch to a vector database?

Migrating vectors from Elasticsearch to a vector database involves:

  1. Use the _mget API to bulk export batches of vectors from Elasticsearch.
  2. Transform the JSON into native vector database import format like Pinecone's LogKV.
  3. Load the vector batches into the target vector database using bulk import.
  4. Redirect application API calls to new database and cut over.

For zero downtime migration, you can temporarily dual write to both systems during the transition.

Can I create a hybrid architecture using Elasticsearch and vector databases in same application?

Yes, you can architect a hybrid system that stores some vectors in Elasticsearch and others in a vector database:

  • Store high value, latency sensitive vectors needing heavy ANN workload in vector database.
  • Use Elasticsearch for archival/stale vectors requiring lower query performance.
  • Or shard vectors across them based on use case - relevance vs recommendations etc.
  • Abstract vectors storage from apps using a unified vector service interface interacting with multiple backends.

How do I choose between the various vector database options like Pinecone vs Milvus?

Some criteria for evaluating vector database options:

  • Ease of use and integrations with your tech stack.
  • Performance and scalability benchmarks on your dataset.
  • Latency and throughput SLA guarantees.
  • High availability, failover offered for production needs.
  • Ability to scale out with additional servers or cloud resources.
  • Monitoring, optimization and security capabilities.
  • Commercial support options.

Can I use Elasticsearch and vector databases as a database for ML model training?

Neither Elasticsearch nor vector databases are suited for direct ML model training:

  • They act as a serving layer for inference, not a training platform.
  • Lack capabilities like native support for tensors, automatic differentiation etc.
  • Don't provide distributed training optimizations needed for deep learning.

You should use optimized ML frameworks like PyTorch, TensorFlow for training and then deploy trained models to Elasticsearch or vector databases for inference.

How do I optimize performance for vector similarity workloads in Elasticsearch?

Some tips for improving vector search performance in Elasticsearch:

  • Reduce number of shards to increase vectors per shard. But beware of node out of memory errors.
  • Disable replicas to remove overhead of replication during searches.
  • Use machine instances with large memory for Java heap space.
  • Limit vector dimensions to few hundred dimensions for faster indexing.
  • Optimize queries to avoid expensive operations like sorting.
  • Use binary vector encoding over text for better compression.
  • Upgrade to latest Elasticsearch version for vector performance fixes.

However, fundamental limitations still exist in scaling to billions of vectors at low latency. So consider vector databases for your most demanding workloads.

Rasheed Rabata

Is a solution and ROI-driven CTO, consultant, and system integrator with experience in deploying data integrations, Data Hubs, Master Data Management, Data Quality, and Data Warehousing solutions. He has a passion for solving complex data problems. His career experience showcases his drive to deliver software and timely solutions for business needs.

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