AWS OpenSearch Cost – 9 Cost Saving Strategies

AWS OpenSearch, formerly known as Amazon Elasticsearch, is a powerful cloud-based search and analytics platform used by organizations for applications such as full-text search and log and event data analysis. However, without proper optimization, OpenSearch bills can spiral out of control.

In this post, we will help you keep your OpenSearch costs in check using these 9 steps.

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1. Choose the right instance type and size

When choosing an OpenSearch instance, consider factors such as the nature of your workload, the volume of data to store and index, and query performance requirements. By carefully evaluating your needs, you can select an instance type that strikes the right balance between performance and cost.

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2. Utilize reserved instances

Analyze your usage patterns and consider reserving instances if you have a consistent, long-term workload. By committing to a one or three-year term, you can save up to 66% compared to on-demand pricing.

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3. Implement index lifecycle management

Index lifecycle management (ILM) policies automate the management of your data in OpenSearch over time. ILM policies can reduce storage costs by moving less frequently accessed data to less expensive storage tiers or deleting old, unused data.

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4. Optimize data storage

Efficiently managing data storage can significantly reduce costs in OpenSearch. You can optimize data storage in these ways:

• Compress your source data before sending it to OpenSearch to reduce the storage footprint and data transfer costs.
• Enable index-level compression to save storage space and reduces I/O costs.
• Leverage Amazon S3 for infrequently accessed or archived data.

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5. Implement cross-account access

By sharing a single OpenSearch cluster with multiple accounts, you can reduce the overall number of instances and resources required, resulting in significant cost savings. Implementing cross-account access minimizes costs, simplifies administration, and enhances visibility into your OpenSearch deployment.

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6. Configure snapshots and retention policies

Snapshots provide point-in-time backups for your cluster. By adjusting the snapshot frequency and retention period based on your business requirements and data recovery needs, you can save on storage costs. Carefully review and optimize your snapshot and retention policies to ensure efficient use of storage resources and reduce unnecessary expenses.

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7. Optimize index sharding and replication

Optimizing the number of shards and replicas in your OpenSearch indices is crucial for balancing performance, resilience, and cost. Distributing data across multiple shards can improve query performance, but too many shards can increase cluster overhead and costs. Aim to find the right balance based on your workload requirements.

Increasing the number of replicas can enhance search performance and fault tolerance but also increases storage and resource costs. Analyze your needs and determine the appropriate replica count for each index.

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8. Leverage caching

Caching is a powerful tool for improving query performance and reducing resource consumption in OpenSearch. AWS OpenSearch provides several caching mechanisms:

• Cache the results of aggregations to speed up subsequent requests for the same data.
• Cache the results of frequently executed queries to minimize the resources needed to serve them.
• Cache the responses of search requests to reduce load on the cluster.

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9. Optimize data ingestion

Efficient data ingestion practices can significantly reduce costs associated with data transfer and processing. Consider the following best practices for optimizing data ingestion in OpenSearch:

• Group multiple documents into a single request to reduce the overhead of individual indexing operations.
• Services such as Amazon Kinesis Data Firehose or AWS Lambda can help you efficiently ingest data into OpenSearch while providing features such as data transformation, filtering, and buffering.

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Conclusion

Optimizing costs in OpenSearch is an important aspect of maximizing the value of your AWS deployment. By implementing some of the measures listed in this post, such as choosing the right instance type and size, utilizing reserved instances, leveraging index lifecycle management, optimizing data storage, and implementing cross-account access, you can effectively reduce costs in your OpenSearch deployment without compromising performance and functionality. A well-tuned OpenSearch is a cost-effective engine for unlocking insights and propelling you towards data-driven success without financial strain.