Amazon Web Services (AWS) OpenSearch is a scalable and open-source search and analytics suite derived from Amazon Elasticsearch Service. It offers enhanced features and compatibility with Elasticsearch APIs.
However, navigating AWS OpenSearch can often feel daunting, even for experienced AWS customers.
To get the most out of the platform, it’s not enough to optimize your searches; you need to ensure you’re leveraging your data’s full potential while managing your OpenSearch clusters.
This article outlines 15 AWS OpenSearch best practices that give you practical, actionable tips to elevate your OpenSearch experience.
From optimizing cluster performance to ensuring high-level security, we’ll cover key practices that can significantly impact your operations. These 15 practices are divided into four categories: data management, performance and scalability, cost optimization, and security.
In managing your Amazon OpenSearch Service, specific strategies for cluster configuration, sharding, replication, and operation restrictions are essential for optimal performance and reliability.
1. Changing default cluster name
By default, your OpenSearch Service domain is assigned a generic cluster name that isn’t easily distinguishable.
You should immediately change the default cluster name to a clear, meaningful identifier. This change not only aids in better organization but also prevents confusion when managing multiple clusters within your infrastructure.
2. Sharding and replication
Understanding and configuring shards and replicas is critical to how your data is distributed and made resilient across the OpenSearch Service.
When creating an index, aim for an optimal shard count that suits your data volume and query demands.
Each index contains primary shards holding your data and replica shards, providing redundancy and increasing search operations’ availability. Be strategic; while more shards provide higher parallel processing, they also consume more resources.
A balanced approach maximizes both performance and fault tolerance.
3. Restrict wildcards in destructive operations
To ensure the integrity of your indices, it’s wise to restrict wildcards (* or ?) in destructive operations, such as deleting indices.
Accidental deletion is a real risk that can result in significant data loss.
By limiting wildcard usage, you enforce more granular control over such operations. You’ll safeguard your data within the Amazon OpenSearch Service domain against unintended bulk deletions.
Performance and scalability
You need to understand the complex dynamics of performance and scalability to maintain an efficient Amazon OpenSearch Service environment. By leveraging best practices, you can ensure your deployment is fine-tuned for optimal performance and the ability to scale effectively as demand grows.
4. Limit script usage
Scripts, particularly those in Painless, can be resource-intensive. You should minimize script usage and optimize your deploy scripts to conserve RAM and boost overall performance.
Heavily scripted operations can increase latency, so judicious use is key for high availability.
5. Careful use of regex in scripts
Be careful when using regular expressions (regex) in scripts, as these can quickly become performance bottlenecks. Poorly constructed regex can lead to significant resource drain, so it’s imperative to employ efficient patterns that don’t overburden the CPU.
6. Dedicated master and coordinating nodes
Deploying dedicated master nodes is recommended for clusters that are critical or have heavy loads. This ensures better throughput and stability during volatility, for instance, in count or data-intensive operations. Similarly, dedicated coordinating nodes can manage request routing, reducing the load on data nodes.
7. Correct configuration of minimum master nodes
To prevent split-brain scenarios and potential data loss, you should correctly configure the minimum number of master-eligible nodes. Typically, this is (number of dedicated master nodes / 2) + 1, ensuring high availability and mitigating failure risks.
8. Autotune for Amazon OpenSearch Service
Autotune is an Amazon OpenSearch Service feature that automatically adjusts resources for improved performance. It can modify Java Virtual Machine (JVM) settings and other performance-related parameters, influencing RAM usage and accelerating operations based on your workload patterns.
9. Use CloudWatch for monitoring and alerting
Consistent, real-time monitoring is pivotal for performance and scaling.
Leverage Amazon CloudWatch to track crucial metrics like CPU utilization, RAM usage, and disk I/O, which will inform you about the health of your cluster.
Set CloudWatch alarms and use Cloudwatch Logs to avoid potential issues and maintain availability. You’ll also better understand query latency and throughput, which are crucial for scaling decisions such as adjusting the number of replicas, shards, and shard sizes across availability zones.
Achieving cost efficiency in AWS OpenSearch is pivotal as your data grows and your infrastructure scales. Careful consideration of instance types and the use of cost optimization tools can lead to significant savings.
10. Latest generation instance types
Selecting the latest generation instance types can improve resource utilization and cost efficiency when using AWS OpenSearch.
Your choice should align with your workload’s specific requirements:
- m5 and r5 instances: Ideal for balanced memory and compute-optimized tasks, offering a mix of CPU power and memory suitable for general-purpose workloads.
- i3 instances: Best for high I/O-intensive applications, which demand low-latency, high-throughput workloads.
- c5 instances: Favored for compute-intensive applications, delivering high CPU performance for processes that require heavy calculations.
Be aware that, often, newer instances can deliver higher performance at a lower cost than older generation instances at comparable pricing models. Scaling your instance counts to match your demand will optimize costs without compromising service delivery.
11. Using a cost optimization tool
Incorporate a cost optimization tool designed for AWS services to automate saving processes and manage costs effectively.
For example, you can leverage ProsperOps’ Automated Discount Management for OpenSearch to streamline your cost optimization efforts. Through this offering, ProsperOps will automatically manage Reserved Instances (RIs) for OpenSearch by building an RI ladder over time to balance savings and risk.
A cost optimization tool simplifies the process of:
- Analyzing spending trends: Understand where the costs come from and identify underutilized resources.
- Implementing pricing models: Opt for pricing models that work best for your infrastructure costs, like Reserved Instances or Savings Plans, which offer savings over on-demand pricing.
Remember that these tools help customers use their resources more effectively, ensuring you only pay for the throughput and instance types you need. Regularly review your strategy to ensure it adapts to your business’s data growth and operational changes.
By prioritizing the latest generation instance types and employing cost optimization tools, you can maintain cost efficiency while managing a robust AWS OpenSearch service.
Security is paramount when managing your AWS OpenSearch Service to protect your data and ensure access is controlled and monitored. Implementing the following best practices helps to maintain high availability and prevent data loss or exposure.
12. Enabling fine-grained access control
To safeguard your OpenSearch clusters, fine-grained access control is essential.
You’re able to establish precise user permissions for your OpenSearch data. Use AWS Identity and Access Management (IAM) to define roles and responsibilities, ensuring that users only have access to the resources relevant to their duties.
13. Restrictive access policy
Employ a restrictive access policy to limit access to your OpenSearch environment. By default, deny all incoming traffic and explicitly allow only necessary communication. This minimizes the risk of unauthorized access or accidental exposure of sensitive information.
14. Schedule time for updates
Regularly schedule updates and upgrades to your OpenSearch Service to patch any vulnerabilities and ensure your systems are up to date. Expect minimal downtime by using dedicated master nodes, which also help maintain cluster stability during updates.
15. Enable encryption at rest and node-to-node encryption
Protect your data by enabling encryption at rest for your EBS volume size to secure disk space against unauthorized access. Also, activating node-to-node encryption ensures that data in transit between nodes in your cluster is not intercepted, thus maintaining the integrity and privacy of your search engine’s traffic.
Optimize your AWS costs easily with ProsperOps
Get the most from AWS OpenSearch features, including visualization capabilities with OpenSearch Dashboards and Kibana. Optimize your costs with ProsperOps and craft a cost-effective AWS environment by selecting the right instance types and scaling appropriately.
ProsperOps makes AWS cost optimization simpler with hands-off:
- Pricing optimization: Reduce your AWS costs through automated tools that ensure you use the most cost-effective solutions.
- Resource allocation & budget management: Align your spending with company goals, ensuring every dollar spent drives value.
- Long-term savings: Commit to smarter resource use for sustained reductions in cloud expenses.
Ready to learn more about how ProsperOps can help your business optimize AWS costs? Book a demo today to see what our solutions can do firsthand.