Maximize efficiency and reduce costs with Amazon OpenSearch Service (ANT347)

Here is a detailed summary of the video transcription in markdown format:

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

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