OpenSearch is great; it’s like Google for data scientists. But it also comes with a lot of management overhead.
From handling storage infrastructure to maintaining clusters for optimal uptime, many different responsibilities can slow down the day-to-day functioning of your tech department, causing unnecessary friction in the cloud management process.
If you’re an AWS customer, Amazon OpenSearch Service can eliminate much of the operational heavy lifting required with self-managed OpenSearch. Essentially, it can provide you with a fully managed open-source search engine for functions like log analytics, application monitoring, and website search.
With just a few clicks in the AWS console, you can launch a production-ready domain and easily scale cluster resources up or down as your data volumes change. AWS also includes built-in integrations with AWS data services like S3, DynamoDB, and Kinesis, enabling a unified analytics pipeline.
By leveraging OpenSearch’s simple API-driven automation, AWS customers can achieve rapid time-to-value, applying search, logging, and visualization to new and existing data workloads.
What is AWS OpenSearch?
Amazon OpenSearch Service is a managed service from AWS for deploying OpenSearch, an open-source search and analytics engine. It handles provisioning, operating, scaling, and securing OpenSearch clusters with just a few clicks in the AWS console.
Is there a difference between OpenSearch and Elasticsearch?
Amazon Web Services originally launched OpenSearch in 2021 as a branch of Elasticsearch and Kibana. Before this rebranding, Amazon’s managed Elasticsearch service was known as Amazon Elasticsearch Service. With the launch of OpenSearch, Amazon shifted to support the fully open-source OpenSearch project rather than Elasticsearch.
OpenSearch is an open-source project stewarded by AWS, while Elasticsearch is a separate proprietary software developed by Elastic.
Both OpenSearch and Elasticsearch provide the same core search and analytics capabilities since Amazon iterated OpenSearch from an earlier version of the Elasticsearch codebase. Amazon renamed its managed service from Amazon Elasticsearch Service to Amazon OpenSearch Service when it adopted support for the OpenSearch open-source project.
How does Amazon OpenSearch work?
Amazon OpenSearch Service provides a fully managed deployment of OpenSearch, handling infrastructure provisioning, software updates, monitoring, and high availability so you can focus solely on ingesting, exploring, and analyzing data.
With just a few clicks, Amazon OpenSearch Service launches production-ready OpenSearch domains while automating cluster operations, storage management, and scalability.
The service offers direct integrations for streaming data from popular AWS data sources, along with OpenSearch’s powerful search and analytics features, enabling a unified pipeline for deriving value from data on AWS.
The managed service eliminates the heavy lifting required when self-managing OpenSearch, allowing users to simply specify instance and storage requirements through the AWS console. Amazon OpenSearch Service then handles underlying OpenSearch deployment, configuration, and ongoing management, providing a performant, resilient, and secure OpenSearch foundation that can scale on demand.
Deployment and management
Amazon simplifies OpenSearch deployment with just a few clicks in the AWS Management Console. Users specify the desired instance type and storage capacity while the service automatically launches and configures a production-ready OpenSearch domain.
The service architecture scales seamlessly to handle growing data volumes. Domains can scale up to petabytes of data across as many instances as needed, enabling massive search and analytics workloads. Storage auto-scales dynamically based on utilization.
Data indexing
OpenSearch uses Apache Lucene processes under the hood to parse incoming data, extract key information, and index the data for fast search and retrieval. OpenSearch tokenizes, normalizes, and processes documents before indexing them into inverted indices that allow for quick lookups and filtering.
The standardized Lucene-based pipeline means OpenSearch can automatically handle ingestion of structured (JSON, CSV), semi-structured (logs, metrics), and unstructured text data without needing upfront schema definitions.
Search and querying
OpenSearch provides a rich Query DSL to construct complex search queries using a JSON over an HTTP interface. Queries can combine full-text search, term/prefix search, range filters, wildcard patterns, and more using Boolean logic like AND, OR, and NOT.
Additional capabilities like autocomplete, suggestions, and context help users refine searches for more precise results. Sorting, aggregation, and pagination features allow slicing and dicing result sets based on business needs.
Data analysis and visualization
OpenSearch Dashboards allow users to create a variety of charts, graphs, and metric visualizations that help identify trends, outliers, and patterns across indexed data sets. Interactive features like filtering, segmentation, and drill-down provide flexibility to slice and dice data on demand.
Embedded analytics functions in OpenSearch, including Elasticsearch SQL, machine learning, and anomaly detection algorithms, enable statistical analysis, predictions, and alerting. These expand analytical possibilities beyond search and aggregation.
Security and compliance
Amazon OpenSearch Service enables encryption, access control policies, VPC settings, and audit logging to protect indexed data. The service natively integrates OpenSearch with AWS IAM for authentication and access authorization.
The software stays up to date with the latest security patches across versions. Additional options like SAML auth, Cognito, and the Security Analytics plugin expand security functionality.
OpenSearch Service also meets standards like HIPAA eligibility, SOC, PCI DSS, ISO 27001, and FedRAMP, as third-party audits ensure controls satisfy regulatory requirements.
Monitoring and maintenance
Amazon OpenSearch provides granular metrics on factors like CPU utilization, JVM pressure, node status, search and indexing performance, shard statistics, and more. You can view these metrics through Amazon CloudWatch dashboards.
Alerting capabilities based on thresholds allow setting up alarms for critical metrics like cluster health, storage utilization, node availability, etc. This enables proactive notification of issues before they cause performance degradation or outages.
Cost and pricing
OpenSearch Service follows standard AWS pay-as-you-go pricing with no minimum commitments. You pay only for what you provision and consume without any minimum fees or required usage. AWS bases billing on three dimensions: instance hours, storage, and data transfer.
Additionally, features like multi-AZ deployments, EBS volumes, and VPC access do not incur additional charges. You can optimize costs by choosing between on-demand and reserved instances based on workload patterns. AWS designed the service to scale elastically alongside data growth in order to balance performance and costs.
Use cases for OpenSearch
With its strong search, analytics, and visualization capabilities, OpenSearch is a versatile platform for deriving insights from data across various use cases. Users can build custom solutions leveraging OpenSearch’s capabilities for security, scalability, and developer agility across these data-driven workloads.
Whether creating application search engines, analyzing logs, gathering metrics, or visualizing data, OpenSearch empowers users to ingest, process, explore, and act on large volumes of information. Here are a few examples:
Log analytics
AWS OpenSearch can ingest log data from applications, servers, databases, and networks. Powerful log parsing capabilities transform messy, unstructured log data into clean, structured documents suitable for search and analysis.
OpenSearch’s efficient processing of massive volumes of log data allows for identifying trends, patterns, and anomalies that provide visibility into system and application performance, usage metrics, errors, security threats, and more.
Embedded analytics features like aggregations, dashboards, and anomaly detection combined with fast search across structured log data enable complex analysis like finding correlated events across logs or identifying usage trends.
Cloud-native scalability allows collecting, indexing, and analyzing log data at a massive scale without infrastructure bottlenecks. This unlocks granular historical visibility for long-term trend analysis.
By handling the heavy lifting of log data transformation, storage, and analysis, OpenSearch simplifies how businesses build log analytics solutions that derive deeper business insights from machine-generated log data streams.
Application monitoring
The OpenSearch Performance Analyzer collects and visualizes granular application metrics like request rates, response times, and error rates. You can also push custom metrics via StatsD/Graphite integrations.
Ingesting and correlating metrics, logs, and traces in OpenSearch provides a single pane of glass for monitoring application health. Logs offer deeper root cause analysis while metrics show trends.
Real-time dashboards combined with historical data analysis help identify performance degradation or usage pattern shifts quickly. Embedded anomaly detection can automatically trigger alerts on deviations.
OpenSearch enables storing application telemetry affordably at scale while still allowing interactive analysis. Developers can visually troubleshoot issues by pivoting across metrics and logs.
By handling the complexity of aggregating, correlating, storing and visualizing key application telemetry streams, OpenSearch empowers organizations to monitor application health and quickly diagnose issues.
Website search
OpenSearch can significantly enhance website search experiences through customizable relevance tuning, fast query performance, and an extensive set of search features.
Websites leveraging OpenSearch can empower users to quickly find precise, relevant information from extensive content catalogs by offering auto-suggestions, spelling corrections, synonym matching, and query refinements that iteratively improve search accuracy.
Under the hood, OpenSearch provides optimized inverted indices, caching strategies, and distributed search architectures that deliver sub-second response times even at scale, keeping users engaged.
Beyond basic keyword search, website developers can build advanced experiences like faceted navigation, contextual filtering, and sort customizations using OpenSearch’s extensive search API.
By combining relevancy tuning with speed and customization, OpenSearch enables websites to deliver the findability and personalization users expect from modern search applications. Tight integration with visualization dashboards also gives usage metrics and insights to guide search optimization.
Observability
The OpenSearch Observability plugin provides unified data collection, storage, analysis, and visualization for key telemetry sources like metrics, logs, and traces. It ingests monitoring data from systems and applications, leveraging open standards like OpenTelemetry as well as log formats like JSON and CSV.
Once ingested, OpenSearch’s search, analytics, and visualization engines allow exploring and analyzing the observability data to gain insights into system health, performance issues, usage patterns, etc. Users can search logs for specific errors, analyze metrics for trends, correlate traces with logs, identify anomalies, and more.
Customizable dashboards combined with embedded analytics like aggregations, statistics, and machine learning algorithms enable proactive monitoring. Alerting capabilities allow configuring thresholds and alarms for critical metrics.
OpenSearch allows users to store observability data cost-efficiently for historical analysis while its scalability handles high-volume, high-velocity monitoring streams. Its flexibility to analyze structured, unstructured, and semi-structured data provides a unified observability pipeline.
Security analytics
The OpenSearch Security Analytics plugin provides security teams with a security information and event management (SIEM) solution for detecting potential threats by analyzing security logs and event data.
OpenSearch includes out-of-the-box detectors that AWS has configured to identify common cybersecurity issues based on log data analysis. Rules and machine learning models evaluate events and trigger alerts for suspicious anomalies.
Once threats are detected, OpenSearch allows further investigation through features like fast search across historical logs, visualizations for exploring event timelines, and aggregations to identify impacted assets.
The platform provides the scalability to store security telemetry data over long periods while its analytics features enable complex historical analysis to establish patterns and evaluate risks. It plays an integral role in security analytics by providing sophisticated data ingestion, analysis, and visualization tools that security teams can leverage to gain better visibility.
Data enrichment and preprocessing
Amazon OpenSearch’s Data Prepper allows collecting, parsing, and normalizing various data streams like metrics, logs, and traces into a common schema suitable for downstream analytics. This handles all the heavy lifting of data transformation.
It enriches raw data by adding metadata, aggregations, and derivations that make the data more contextual for analysis. GeoIP lookup, threat intelligence feeds, and custom lookup enrichment add valuable signals.
Data Prepper’s filtering and sampling capabilities allow selectively capturing subsets of high-value data from scale while its text parsing handles the ingestion of unstructured data.
Together, these enrichment, normalization, and preprocessing capabilities empower the efficient derivation of actionable intelligence from both real-time and historical data to guide better decision-making.
Personalization and recommendations
The newly launched Amazon Personalize plugin for OpenSearch allows re-ranking search results personalized to each user based on their interests, context, and past interactions. It leverages deep learning algorithms to go beyond keyword search relevancy.
Fine-grained controls allow tuning the level of personalization per search query, while comparison dashboards simplify evaluating improvements from the personalized rankings.
Over time, Amazon Personalize self-improves recommendations as it ingests more usage data. Tight integration with OpenSearch eliminates the need for customers to build and maintain their own ML models for personalization.
Through integrations like Amazon Personalize, OpenSearch provides an effective platform for adding personalization and tailored recommendations into applications like search, e-commerce, and more to boost relevance for end users.
Setting up Amazon OpenSearch for success
Self-managed OpenSearch deployments demand extensive efforts for security, resilience, scaling, and monitoring—which divert focus from deriving value from data.
While you can solve many of these challenges by incorporating Amazon’s fully managed OpenSearch Service, managing costs remains a significant challenge, especially when workloads fluctuate or lack predictability. This is where solutions like ProsperOps can provide major value through automated rate optimization.
ProsperOps’ automated discount management assesses historical workload patterns and demand variability. It then calculates the optimal blend of 1-year and 3-year RIs to purchase to reduce costs while avoiding over-committing budget. Then, as utilization stabilizes, ProsperOps gradually shifts the RI mix toward more 3-year RIs to drive higher discounts.
Maximize AWS savings with ProsperOps
Through its Automated Discount Management capability, ProsperOps optimizes Reserved Instance usage to maximize OpenSearch savings while balancing commitment risks. By automatically constructing the right blend of 1-year and 3-year RIs aligned to actual workloads, ProsperOps can drive discounts of up to 72%.
ProsperOps also simplifies governance across centralized teams owning budgets and decentralized engineers owning instances. Its unified visibility and automation empower organizations to reduce OpenSearch costs, gain flexibility, and focus engineering resources on deriving insights vs manual forecasting.
Ready to experience the power of automated OpenSearch rate optimization firsthand? Sign up for a ProsperOps demo today!
