Elasticsearch vs Milvus - Key Differences

Vector databases have become crucial tools for managing complex, high-dimensional data in most AI applications. These databases use indexing and search algorithms to perform semantic searches for applications like natural language processing and recommendation systems.

Elasticsearch and Milvus are two of the most popular vector databases currently. However, it can be difficult to choose between the two. From fast searches and data analysis to complex similarity searches, each platform has its own unique offerings.

This guide highlights the key factors that you must consider before choosing between Elasticsearch vs Milvus.

An Overview of Elasticsearch

Built on Apache Lucene, Elasticsearch is a RESTful search and analytics engine that also functions as a vector database and scalable data store. Optimized for performance across enterprise-level workloads, this platform enables you to perform real-time data searches, indexing, storage, and analysis. With Elasticsearch’s versatile search capabilities, you can efficiently search through structured, semi-structured, and unstructured data.

Key Features of Elasticsearch

• Cross-Cluster Replication: This Elasticsearch feature enables you to replicate indices in remote clusters to a local cluster. It works well for disaster recovery; if the primary node fails, the replicas can take over, ensuring high availability.
• Horizontal Scalability: Elasticsearch supports horizontal scalability, facilitating adding more nodes to the cluster for increased capacity and reliability. This helps in handling an increasing number of user requests.
• Index Lifecycle Management (ILM): With the ILM feature, you can define and automate policies on how long an index should last in the four phases: hot, warm, cold, and delete. It also enables you to define the set of actions on an index in each phase.
• Security: Elasticsearch offers multiple security features to safeguard your data from unauthorized access. Some key features include encryption, role-based and attribute-based access control, and IP filtering.
• Document Store: Elasticsearch, being a NoSQL database, allows you to store and analyze unstructured data; you cannot use SQL to query it.

An Overview of Milvus

Milvus is an open-source vector database that offers a scalable architecture with numerous functionalities to enhance search performance across various applications. It supports methods to effectively store, index, and retrieve vector embeddings, which can be beneficial for you when developing AI-driven applications. This is crucial, especially for applications relying on similarity search and data retrieval based on vector representation.

Key Features of Milvus

Let’s explore the critical features of Milvus that have led to its significant recognition with over 50 million downloads as of August 2024.

• Vector Indexing: Milvus employs indexing techniques such as Inverted Files with Vocabulary Trees (IVF) and Hierarchical Navigable Small World (HNSW). These techniques facilitate efficient data organization and retrieval for optimized similarity searches.
• Open Source: Milvus is an open-source vector database with robust similarity search capabilities on extensive vector datasets. This makes it easily accessible to many AI professionals and developers.
• Deployment Lifecycle Support: Milvus’ deployment options allow you to scale your applications from initial testing to full deployment without having to rewrite the code. It offers three deployment models: Lite, Standalone, and Distributed, each providing deployment capabilities at different operational scales.
• Vector Search: During a similarity search in Milvus, you can run a single vector or hybrid search according to the number of vector fields in your collection. If your collection has one field, you can run the search() method to find similar entities. For more than one field, you can use the hybrid_search() API, which returns relevant matches using the approximate nearest neighbor (ANN) concept.
• Scalability: Milvus supports horizontal scaling, enabling you to scale its computational resources by adding or removing worker nodes depending on the workload.

Elasticsearch vs Milvus: Key Comparison Factors  

Now that you understand the critical aspects differentiating Milvus vs Elasticsearch, here is a detailed comparison demonstrating the decisive factors.

Architecture

Elasticsearch supports two architectural forms: stateful and stateless. In stateful architecture, the system retains the session data and state information across multiple requests; this can lead to scaling complexities. However, shifting to a stateless architecture offers high scalability with horizontal scaling features to manage increased traffic.

The Elastic cloud service has a stateless architecture that makes it compatible with all the major cloud-native services, including AWS, GCP, and Azure. To understand the architecture, you must know about the key components of the Elasticsearch architecture, which include a control plane and a data plane.

• Control Plane: This layer acts as a user interaction interface, providing you with UI and APIs to create, manage, and control access to projects.
• Data Plane: This is the infrastructure layer responsible for handling the data processing and advanced operations for your projects. When you perform any logical functions, such as data querying, indexing, or searching, you engage with the data plane.

In contrast, Milvus has a shared storage massively parallel processing (MPP) architecture, with storage and computing resources independent of one another. The data and the control plane are disaggregated, and its architecture comprises four layers: access layer, coordinator services, worker nodes, and storage. Each layer is independent of the others for better disaster recovery and scalability.

Here’s an overview of each layer of the Milvus architecture:

• Access Layer: This layer serves as the endpoint for the users. Composed of stateless proxies, the access layer validates client requests before returning the final results to the client. The proxy uses load-balancing components like Nginx and NodePort to provide a unified service address.
• Coordinator Service: This layer serves as the system’s brain, assigning tasks to worker nodes. The coordinator service layer performs critical operations, including data management, load balancing, data declaration, cluster topology management, and timestamp generation.
• Worker Nodes: The worker nodes follow the instructions from the coordinator service layer and execute data manipulation language (DML) commands. Due to the separation of computing and storage, these nodes are stateless in nature. When deployed on Kubernetes, the worker nodes facilitate disaster recovery and system scale-out.
• Storage: Responsible for data persistence, the storage layer consists of meta storage, log broker, and object storage. Meta storage stores snapshots of metadata, such as message consumption checkpoints and node status. On the other hand, object storage stores snapshots of index files, logs, and intermediate query results. The log broker functions as a pub-sub system supporting data playback and recovery.

Performance

When it comes to performance, Milvus' exceptional capabilities allow it to boast a 15% improvement in average response time over Elasticsearch. For ANN search, it has a median latency of 2.4 ms, providing it with a significant margin over other vector databases. While considering TP95 (95th percentile response time), Milvus bagged 20% better results than Elasticsearch.

While Elasticsearch might not match Milvus’s performance in vector search, its robust features make it a prominent choice for diverse applications. Its distributed architecture allows you to enhance data storage and retrieval while processing structured, unstructured, and geospatial data. With its advanced analytical tools, you can quickly analyze your data to produce actionable insights.

Scalability

Elasticsearch provides two scaling options: vertical and horizontal scaling. Vertical scaling involves increasing the machine’s capacity to handle more user requests, which is challenging to perform. Elasticsearch achieves horizontal scaling by sharding its index and distributing shards across cluster nodes.

In hindsight, Milvus uses worker nodes, each having its own CPU and memory resources, to manage connections, ingestion, indexing, and searching. With dynamic allocations, Milvus allows you to add or remove nodes from the action group, depending on the workload. This enables you to optimize resources and scale applications while maintaining acceptable latency and throughput.

Use Case

Elasticsearch’s applications span multiple domains, from document processing to data analysis. Its key use cases include fuzzy searches, full-text searches, advanced analytics, auto-completion, and multi-tenancy.

Milvus' critical use case involves processing vector data. It can benefit applications by boosting semantic search capabilities to enhance Gen-AI workflows and real-time processing of large datasets.

Effectively Migrate Data into Elasticsearch or Milvus Using Airbyte

After identifying the vector database that best fits your requirements, the next step is consolidating your data to create a centralized repository and enhance data accessibility. For this purpose, you can use SaaS-based tools like Airbyte to automate data migration from multiple sources to your preferred destination.

Airbyte is a no-code data integration platform that allows you to replicate data from a diverse set of sources into a destination of your preference. It offers 400+ pre-built connectors, enabling you to effortlessly integrate structured, semi-structured, and unstructured data into a central repository.

With Airbyte, you can effortlessly migrate data from various sources into your chosen vector database, whether Elasticsearch or Milvus. However, if the source you are looking for is unavailable, you can also build custom connectors with Airbyte’s Connector Development Kit (CDK).

Here are some of the key features of Airbyte:

• Developer-Friendly Pipelines: PyAirbyte, an open-source Python library, offers you the capability to develop data pipelines using Python. With this library, you can leverage Airbyte connectors to build AI applications.
• Change Data Capture: Airbyte’s CDC feature automatically replicates incremental changes made at the source data into the target system. This feature enables you to keep track of the data updates and maintain consistency between the source and the destination.
• Automatic Chunking and Indexing: Airbyte provides you with automatic chunking and indexing features to transform raw data and store it into popular vector databases. Adding to this feature, you can use pre-built LLM providers to generate vector embeddings.
• Advanced Security: It adheres to prominent security standards and regulations, including GDPR, SOC 2, HIPAA, and ISO 27001, helping protect your data from unauthorized access.
• Orchestrate Data Pipelines: You can effortlessly integrate Airbyte with popular data orchestrators like Prefect, Apache Airflow, Kestra, and Dagster. With this feature, you can streamline your existing data workflows.

Conclusion

Elasticsearch is a highly scalable distributed analytics and search engine that allows you to perform full-text searches. Conversely, Milvus is an effective platform for working on AI applications that require extensive semantic search through vector embeddings.

When choosing a vector database between Elasticsearch vs Milvus, you must consider various factors. The key aspects differentiating these tools include performance, scalability, and the underlying architecture design. However, the most crucial factor is the specific use case.