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Optimizing Cost and Efficiency for Log Analytics and Search Workloads using Amazon OpenSearch Service
Introduction
- The session covers powerful strategies to reduce cost and improve efficiencies for log analytics and search workloads using Amazon OpenSearch Service.
- The presenters are:
- Haer Bif, Senior Open Source Solutions Architect at AWS
- Kevin Fellis, Principal at the Amazon OpenSearch Service Worldwide Sales Organization
- Pavan Shu, Senior Software Engineering Manager at TRX
What is OpenSearch?
- OpenSearch is an open-source platform, licensed under Apache 2.0, that powers Amazon OpenSearch Service.
- OpenSearch has over 750 million downloads and is ranked among the top 4 search engines.
- AWS offers a managed version of OpenSearch called Amazon OpenSearch Service, which combines the capabilities of OpenSearch with the scalability, security, and reliability of the AWS cloud.
Total Cost of Ownership (TCO) Considerations
- TCO is a shared responsibility between AWS and the customer.
- AWS takes care of aspects like scalability, availability, and compliance, while the customer focuses on computer requirements, storage requirements, usage patterns, and search query types.
- The presenters will dive into strategies to optimize these key factors.
Vector Search and Cost Optimization
- Vector search, or k-nearest neighbor (kNN) search, is a powerful capability of OpenSearch that goes beyond text search.
- Exact kNN search can be computationally expensive, especially for large workflows.
- Approximate kNN algorithms like IVF and HNSW can provide faster search speeds, with a slight compromise in accuracy.
- To reduce the memory footprint and cost of vector workloads, techniques like vector quantization (e.g., scalar, binary, product) can be used to compress the vector dimensions.
- Metrics to measure the efficiency of vector workloads include accuracy, search speed, indexing speed, and memory/compute/storage requirements.
Amazon OpenSearch Serverless
- The serverless architecture of Amazon OpenSearch Serverless decouples compute from storage, allowing independent scaling of indexing and search.
- It offers specialized collection types optimized for time series data, text search, and vector search.
- OpenSearch Serverless automatically scales up and down to handle workload spikes, and can scale up to 500 OCUs (OpenSearch Compute Units) for indexing and search.
Amazon OpenSearch Ingestion Service
- Amazon OpenSearch Ingestion Service is a fully managed, pay-as-you-go data ingestion service powered by the open-source Data Prepper.
- It provides secure and reliable data pipelines, integrates with various data sources, and offers out-of-the-box transformations.
- The service provides up to 38 blueprints to quickly set up ingestion pipelines.
Cost Optimization with Amazon OpenSearch
- The introduction of O1 instances enables a more efficient replication process, where new segments are persisted to Amazon S3 and replicated to the replica shards by fetching from S3.
- This improves indexing throughput by 80% and leads to 30% price-performance improvements.
TRX's Use Case and Cost Optimization Journey
- TRX, a cybersecurity company, faced challenges with their existing Elasticsearch setup, including operational overhead and cost concerns.
- They migrated to Amazon OpenSearch Service and implemented various cost optimization strategies:
- Leveraging OpenSearch Ingestion Service with data compression to reduce ingestion costs
- Utilizing O1 instances to optimize replication and reduce compute requirements
- Implementing a tiered storage strategy based on customer access patterns
- Tuning the OpenSearch Ingestion Service configuration for optimal performance and cost
- TRX achieved over 35% cost savings through these optimization efforts.
Additional Cost Optimization Strategies
- Exploring reserved instances and savings plans to reduce on-demand costs
- Leveraging different instance types (e.g., R, I, C series) based on workload characteristics
- Utilizing storage tiers (hot, warm, cold) and features like Zero ETL to optimize data storage and retrieval costs
- Implementing patterns like OpenSearch Ingestion Service to S3 and leveraging Zero ETL for cost-effective data ingestion and querying