What Is Distributed Search?

Turns out the adage “many hands make light work” is as true for servers as it is for any large or difficult manual task. Servers in a web search process, or any search of a large data set, do some heavy lifting—first to identify and index data, and then to search for and present responses. We’ll explore how distributed search uses many individual servers to help search applications and harvest answers from vast plains of data.

What Is Distributed Search?

Distributed search is a way to search large data sets quickly by dividing the search workload among multiple servers. This is unlike a search of your computer’s hard drive, which can easily be indexed and searched by your computer’s CPU. In a distributed search, a query of a very large data set is distributed out to multiple servers, or nodes, to speed up the process. Each node in the system indexes a portion of the data so it can be quickly searched. When a question is posed to the search application, each node performs a search on its local data in parallel with the other nodes in the system. Those local results are then compiled, ranked, and presented to the person who typed the question into the search bar.

A distributed search process might consist of a few servers in a data center or thousands of servers across global regions. In either case, the distributed process provides a fast and efficient search process that would have been impossible on a single server.

A distributed search system can support multiple types of searches, including simple text searches for web content, semantic searches, and the visual searches often used in recommendation engines and natural language processing.

A distributed search is different from a federated search. While both aim to handle large volumes of data, a distributed search is a cohesive system that partitions a single, large data set across multiple nodes, which perform local searches in parallel. In contrast, a federated search queries multiple, independent data sources simultaneously, each of which may have its own indexing and search mechanisms. While distributed search is optimized for scalability and performance, federated search is designed to search across diverse data sources. Both, however, can be achieved in a simplified architecture using a distributed, multimodal database.

Key Takeaways

• Distributed search is a way to speed up searches of very large data sets by splitting up the computations needed to carry out the search between many servers, or nodes.
• Distributed search also results in better fault tolerance because if one server stops working, another node can take on that workload and the search will succeed.
• Distributed search is the most common form of search process in web search engines and the power behind search bars in social media and large retail sites, as well as many corporate applications and municipal sites.

Distributed Search Explained

At its most basic, distributed search is a way to handle searches of large volumes of data by dividing the operation among many servers—speeding the search process while also improving scalability and availability of the system. Making a distributed search work, however, requires many coordinated steps and resources.

These include:

Data partitioning: The first step is to partition the data across nodes, where each node is a server that’s responsible for a subset of the data. Depending on the use case, there are different ways to mete out the data, such as range partitioning, which is commonly used for time-series data—that is, monthly or yearly partitions based on dates—or consistent hashing, which is often used when data needs to be evenly distributed for load balancing.

Indexing: Each node in the distributed architecture must create and maintain an index of the data it holds to allow for fast search and retrieval. Depending on the use case, indexing can be accomplished via a variety of techniques, including inverted indexes for text searches; B-trees for storing and retrieving data in sorted order; and hash tables, which provide fast lookups for exact matches in a data set.

Query distribution: When a search is kicked off, the query is distributed to all, or a subset, of the nodes. A query router ensures that the query reaches all relevant nodes.

Local search: Working in parallel, each node performs the search on its locally indexed data.

Result aggregation: The results from all relevant nodes are collected, merged, and sorted by the query router, sometimes called a query coordinator.

Result presentation: The final, aggregated results are then ranked and presented to the person or application that kicked off the search.

How Does Distributed Search Work?

Distributed search works by letting multiple interconnected nodes collaborate in performing search queries across a vast amount of data. These systems often use specialized algorithms and techniques to optimize the query distribution, load balancing, and result aggregation required to handle queries against massive data sets.

Goals of Distributed Search

Distributed search is designed to deliver the kind of performance, scalability, and flexibility that make it an essential tool for large-scale applications in web search, ecommerce, social media, real-time analytics, and more. The success of these systems is evaluated by their ability to perform the following tasks:

Rapidly search large data sets: A distributed search system uses the compute power of many individual servers working in parallel to quickly respond to questions, even in web-scale search engines.

Deliver responses reliably: Distributed search provides high availability and reliability via its ability to store portions of the data on several servers, allowing it to quickly adjust when a server goes offline by switching the workload to another operational server within the system.

Adaptability to different search types: A distributed search architecture allows the system to handle different types of searches, such as semantic search or text search, by optimizing nodes for different types of data or queries, such as an image search or a map search.

Benefits of Distributed Search

Here’s why distributed search is the most common approach in large systems.

• Availability. Beyond improved performance, high availability and fault tolerance are critical goals for many distributed systems. A distributed search system will succeed in delivering results even if one or more nodes fail.
• Flexibility. Distributed search lets an organization optimize different nodes for specific types of data or of queries. This specialization enables many types of fast searches—for example, an elastic search across text, a semantic search across vector data, or a search across documents and relational data that takes advantage of retrieval-augmented generation, or RAG. In a distributed search architecture, all this can happen behind a single search bar.
• Performance: Nobody wants to wait for search results. Engineers know that distributing a search term across many servers is the way to avoid this. Distributed search boosts performance by spreading the search load to servers that manage their portions of the search operation in parallel.
• Scalability: The chief goal of a distributed search is to provide search capabilities across a vast amount of data. The distribution of work across many compute resources allows that simple search bar to handle growing data volumes and increasing user demands by simply adding more nodes. This architecture, for example, allows OpenSearch, an open source distributed search and analytics engine, to scale from a limit of 250 data nodes up to 750 nodes.

Distributed Search Challenges

Distributed search remains popular despite the challenges it poses because it has proven its value in many use cases, from large consumer search engines to more targeted searches on corporate websites. Still, engineers need to address some core challenges that include the following:

• Complexity: Managing a distributed system is complex versus individual servers and gets more so as the data volume grows. It’s best handled by distributed databases that possess sophisticated coordination and error handling mechanisms.
• Consistency: Keeping all nodes in a distributed search process up to date with consistent data can be challenging, especially in highly dynamic environments that promise near-real-time search data. Depending on the use case, the need for strong consistency can hinder search performance, while a less perfectly synchronous system that offers “eventual consistency,” such as using a document database, can deliver faster large-scale searches.
• Potential latency: It can take time to distribute a query, run the query on multiple machines, and aggregate the results. While the alternatives to a distributed setup are going to have a much larger issue with latency, these systems must still be continuously tuned and monitored to retain optimal performance.

Distributed Search Use Cases

Distributed search use cases share several common characteristics and requirements that make this approach particularly advantageous for certain scenarios. Think large, perhaps geographically dispersed, data volumes and many concurrent users that demand snappy performance.

Distributed search has proven to be the right choice for these use cases, and more.

• Enabling AI workflows: Distributed search architectures are a cornerstone of AI inference processes. It drives better vector search outcomes for connecting AI models and AI agents to corporate data stores and helps the composite AI system distribute data for each model to work on.
• Ecommerce platforms: Online retailers use distributed search to help customers peruse their vast product catalogs and quickly pinpoint products. Think of distributed search next time you’re on Amazon, eBay, or other large retail sites.
• Enterprise search: Large enterprises likewise use distributed search to create internal search engines for documents, emails, and databases. These systems might also include RAG and vector search for more versatile semantic searches of large document stores, further improving access to internal information.
• Log analysis and monitoring: IT teams depend on applications that take advantage of distributed search for log management and monitoring systems. This allows them to quickly search through and analyze log data from multiple applications and other IT sources for troubleshooting, security, and compliance.
• Real-time applications: You’ll find distributed search in applications that require real-time data processing, such as financial trading platforms, inventory management, and real-time analytics.
• Scientific research: Distributed search is helpful in a variety of technical fields, such as this genomics use case, as well as astronomy, climate science, and many others, allowing researchers to manage and analyze large, ever-evolving data sets.
• Social media platforms: Popular social media platforms use distributed search processes to quickly index and search user-generated content, allowing users to quickly find interesting profiles, posts, videos, and comments on their vast sites.
• Web search engines: An obvious example is the large consumer web search engines that made search popular. These sites use distributed search to index and return the vast quantities of data on the internet so they can provide millions of users with fast and accurate search results.

Let Oracle Simplify Your Globally Distributed Search Platform

The best way to simplify a distributed search architecture is with a multimodal distributed database. Oracle AI Database provides native management of vector, JSON, text, and relational data, among others, so you can index and search different data types in one simple database architecture. And because Oracle offers a fully automated, globally distributed cloud database, you can easily bring distributed search to your business-critical, cloud-scale applications and open source projects.

Try Oracle AI Database for free.

There’s a reason distributed search continues to grow in popularity—especially as techniques such as vector search and RAG come into play. As multimodal AI and AI agents gain momentum in the enterprise, distributed systems, including search, will ensure applications can operate with the speed, accuracy, and fault tolerance today’s businesses demand.

Distributed Search FAQ

What is the difference between distributed search and federated search?

Both distributed search and federated search aim to support searches in large volumes of data. The difference is that distributed search partitions a single, large data set across multiple nodes that can be searched in parallel. A federated search, on the other hand, queries many independent data sources, where each might have its own indexing and search mechanisms—allowing for search across diverse data sources.