Scaling Elasticsearch for enterprise use cases can be challenging, especially when dealing with massive datasets, complex joins, and the need for real-time performance. At Siren, we’ve worked with organizations facing these exact challenges, and we’ve developed Siren Federate as a powerful Elasticsearch plugin to help overcome them. In this blog, I’ll walk you through how Siren Federate enables scalable relational search and graph search—helping businesses maximize their Elasticsearch deployments.
The Scaling Challenge in Elasticsearch
Elasticsearch is incredibly powerful, but it wasn’t originally designed for relational joins. Built-in join methods, such as parent-child or nested joins, shift complexity to index-time operations, creating challenges when datasets grow or more indices are involved. Organizations often struggle with:
- Extensive denormalization and precomputed relationships: Traditional solutions rely heavily on denormalization and precomputed joins, introducing significant modeling complexity, higher maintenance overhead, and reduced flexibility—especially as the number of indices grows.
- Expensive reindexing: Frequent updates become costly and degrade overall system performance.
- Slow queries: Query performance significantly declines as data volume and join complexity across multiple indices increase.
- Data consistency problems: Duplicate entries, partial results, or missing data cause frustration and unreliable insights.
Siren Federate, A Scalable Elasticsearch Join Technology
Siren Federate enhances Elasticsearch with advanced distributed join capabilities, enabling scalable data correlation without costly compromises. It addresses key limitations of traditional Elasticsearch joins, such as extensive denormalization, slow queries, expensive reindexing, and data consistency issues.
1. Real-time Relational Search Across Multiple Indices
With Siren Federate, you can query multiple indices in real-time, eliminating the need for extensive data merging or denormalization. Key benefits include:
- Relational search at scale: Efficiently perform joins across billions of records without precomputing relationships.
- No Reindexing Required: Dynamic joins eliminate the need for frequent, costly index updates, reducing infrastructure load.
- Faster response times: Optimized distributed query execution ensures sub-second search performance.
2. Graph Search for Deep Investigations
Siren Federate combines powerful graph-based search capabilities with Elasticsearch’s advanced search capabilities, allowing analysts to explore multi-modal knowledge graphs intuitively and rapidly:
- Integrated Multi-Modal Search: Seamlessly perform graph-based searches that span text, structured data, and multimedia sources.
- Real-time Link Analysis: Instantly discover and visualize hidden connections in structured and unstructured data without precomputed relationships.
- Enhanced Investigation Efficiency: Quickly identify patterns, relationships, and anomalies, facilitating effective cyber threat intelligence, fraud detection, and financial crime analysis.
3. Optimizing Elasticsearch Cluster Performance
Scaling Elasticsearch requires more than just additional hardware. Siren Federate optimizes performance by:
- Reducing query load: Offloading intensive join operations onto a distributed architecture, significantly decreasing overall query load.
- Lower Resource Consumption: Streamlined queries minimize CPU and memory utilization, enabling better resource allocation.
- Improved Reliability and Consistency: Ensuring complete, accurate results without duplication or missing data, thus improving user satisfaction and trust in analytical outcomes.
Real-World Impact: Apollo’s Success Story
One of our customers, Apollo, faced significant challenges scaling Elasticsearch due to a growing enterprise user base. Their initial approach—what they called the “Fake Join”—failed to scale, leading to slow query times, incorrect results, and high operational costs.
By implementing Siren Federate, Apollo:
- Reduced search times from 5-7 seconds to just 1.2 seconds.
- Eliminated 30+ monthly support tickets related to search failures.
- Enabled 50% more search results, unlocking 400K additional contacts per query.
- Successfully migrated 100% of their join traffic to Siren Federate.
Getting Started with Siren Federate
If you’re struggling to scale Elasticsearch and need a more powerful approach to relational search, Siren Federate can help. Here’s how you can get started:
- Evaluate your search architecture: Identify where joins and distributed search operations are causing bottlenecks.
- Install the Siren Federate plugin: Seamlessly integrate it into your Elasticsearch cluster.
- Optimize your queries: Use federated search techniques to reduce load and improve response times.
- Monitor and scale: Continuously measure performance improvements and adjust configurations as needed.
Scaling Elasticsearch for enterprise search and investigative applications requires innovative solutions. Siren Federate combines advanced Elasticsearch capabilities with powerful relational and graph search methods—unlocking new levels of performance, scalability, and efficiency.
If you’re looking to optimize your Elasticsearch deployment and overcome scaling challenges, Siren Federate is the solution you need. Get in touch today or take a look at a recent Use Case.
