CobbleDB Achieves Faster AI Search with Cost-Effective Storage Solutions
Discover how CobbleDB revolutionizes AI search storage with its decoupled architecture, achieving lower latency and cost efficiency, setting a new standard for processing and serving web content.
Key Facts
- CobbleDB achieves 5x lower batch-read latency, enhancing user experience and operational efficiency.
- Cost savings of at least 20% versus DynamoDB highlight financial advantages of in-house solutions.
- Decoupled architecture reveals vulnerabilities in managed services, emphasizing need for tailored solutions.
- AI agents accelerated development, showcasing strategic shift towards automation in tech infrastructure.
- Open-sourcing CobbleDB could position the company as a leader in AI-native search technology.
Summary
Summary
CobbleDB, developed by a tech company specializing in AI-native search, addresses the challenges of high latency and rising costs associated with traditional managed databases like DynamoDB. By implementing a decoupled architecture with a custom key-value store, CobbleDB achieved a 5× reduction in batch-read latency and lowered operational costs by at least 20%.
Background
The customer is a tech company focused on AI-native search solutions. Prior to deploying CobbleDB, they relied on managed databases such as DynamoDB for storage, which provided reliable service but lacked the flexibility needed for performance tuning and resulted in escalating costs as their index size and query volume increased.
Challenge
The primary challenge was to overcome the limitations of their existing database system, which was tightly coupled with document processing. This setup made it difficult to manage updates independently from live reads, leading to increased latency and costs as the workload grew.
Solution
The company rebuilt their storage infrastructure around CobbleDB, a key-value hot store optimized for low-latency reads. They separated the durable document state and update delivery from query-time storage, utilizing Pillar for managing document state and Lorry for handling batched updates. This architecture allowed them to tune serving specifically for their workload, improving performance.
Results
After implementing CobbleDB, the median batch-read latency decreased from 31.4 ms to 5.60 ms, with the p90 latency dropping from 56.7 ms to 9.77 ms and the p99 latency from 123 ms to 24.2 ms. Additionally, CobbleDB proved to be at least 20% cheaper than DynamoDB, significantly reducing operational costs associated with storage and read/write capacity.
Key Insights
Decoupling storage responsibilities allows for more efficient management of updates and reads, optimizing performance for specific workloads. Custom solutions can provide significant cost savings and performance improvements over traditional managed services when tailored to unique operational needs.
Customer Testimonial
No direct quote is provided in the source material.
Entities Mentioned
Companies
Products
Technologies
Key Concepts
Definitions
- CobbleDB
- A distributed key-value hot store optimized for serving prepared records at query time.
- Pillar
- A document state and publishing layer that tracks and manages the durable document state.
- Lorry
- A service that converts export records into partitioned batches for CobbleDB.
- AI-native search
- A search approach designed specifically for processing and serving content for AI models.
- RocksDB
- An open-source embedded key-value storage engine optimized for read-heavy workloads.
Use Cases
- →Improving performance of AI search systems
- →Reducing costs associated with data storage and retrieval
- →Optimizing batch processing for large datasets
- →Enhancing query-time retrieval speeds
- →Managing updates independently of live reads
- →Supporting scalable storage for AI workloads
Frequently Asked Questions
What is CobbleDB?
CobbleDB is a specialized database designed for AI-native search, focusing on lower latency and cost. It separates document processing from storage to optimize performance.
How does CobbleDB improve latency?
CobbleDB achieves lower latency by optimizing its architecture for batch reads and allowing for independent tuning of storage and processing components.
What are the cost benefits of using CobbleDB?
CobbleDB is estimated to be at least 20% cheaper than DynamoDB due to its efficient handling of storage and read/write operations, which reduces usage-based charges.
How does the architecture of CobbleDB differ from traditional databases?
CobbleDB uses a decoupled architecture that separates durable document state, update delivery, and query-time serving, allowing for independent optimization of each component.
What role do AI agents play in building CobbleDB?
AI agents assisted in the development of CobbleDB by automating tasks such as code reviews, monitoring system health, and managing project workflows, enabling a small team to work efficiently.