Elasticsearch Elasticsearch Capacity Planning: A Comprehensive Guide

By Opster Team

Updated: Nov 14, 2023

| 3 min read


Elasticsearch capacity planning is a crucial aspect of managing and scaling Elasticsearch clusters. It involves estimating the resources required to handle the expected workload, such as storage, memory, and CPU, while maintaining optimal performance and ensuring high availability. In this article, we will discuss the key factors to consider when planning for Elasticsearch capacity and provide guidelines for making informed decisions. If you want to learn about capacity planning, check out these guides.

Factors to Consider in Elasticsearch Capacity Planning

  1. Existing and Future Data Volume and Retention Period

The first step in capacity planning is to estimate the volume of data that your Elasticsearch cluster will need to store. This includes both the primary data (documents) and the additional overhead introduced by replicas, indices, and shards. Consider the following:

  • The number of documents and their average size
  • The number of indices and shards
  • The number of replicas for each index
  • The desired retention period for the data
  1. Query and Indexing Load

Understanding the query and indexing load on your Elasticsearch cluster is essential for capacity planning. Consider the following factors:

  • The rate of incoming data (indexing rate)
  • The complexity of queries and aggregations
  • The number of concurrent users or applications accessing the cluster
  • The required response time for queries
  1. Hardware Resources

Elasticsearch relies on hardware resources such as CPU, memory, and storage to function efficiently. When planning for capacity, consider the following:

  • The amount of memory required for caching, indexing, and querying
  • The CPU capacity needed to handle query and indexing load
  • The storage capacity required for data, replicas, and indices
  • The network bandwidth needed for data transfer between nodes and clients
  1. High Availability and Fault Tolerance

To ensure high availability and fault tolerance, Elasticsearch uses replicas and distributes shards across multiple nodes. When planning for capacity, consider the following:

  • The number of replicas needed for each index
  • The number of nodes required to distribute shards and replicas
  • The impact of node failures on cluster performance and availability

Guidelines for Elasticsearch Capacity Planning

1. Estimate Data Volume and Retention Period

To estimate the data volume, multiply the number of documents by their average size and the number of primary and replica shard. Then, add the overhead introduced by indices and shards. For example, if you have 1 billion documents with an average size of 1 KB and two replicas, the total data volume would be approximately 3 TB (1 billion * 1 KB * 3).

Next, determine the desired retention period for your data. If you need to store data for 30 days, you would take 90 TB of storage capacity (3 TB * 30 days). Since your storage space should not be filled above 70% (conservative value) to allow the segment merging to happen, you would require approximately 130 TB of storage space (90 TB * 100% / 70%).

2. Assess Query and Indexing Load

Monitor the query and indexing load on your Elasticsearch cluster to determine the required resources. Use tools like Elasticsearch monitoring APIs and Kibana dashboards to analyze the following metrics:

  • Indexing rate (documents per second)
  • Query latency (average and percentile)
  • CPU and memory utilization

Based on these metrics, you can estimate the resources needed to handle the expected workload.

3. Allocate Hardware Resources

Allocate hardware resources based on the estimated data volume, query and indexing load, and high availability requirements. Consider the following recommendations:

  • Memory: Allocate at least 50% of the total system memory to Elasticsearch heap (up to ~30 GB). The remaining memory should be reserved for the operating system and file system cache.
  • CPU: Allocate enough CPU capacity to handle the query and indexing load. Monitor CPU utilization and adjust the number of cores as needed.
  • Storage: Use SSDs for better performance and IOPS. Ensure that the storage capacity is sufficient to accommodate the data volume and retention period.
  • Network: Use high-speed network connections (10 Gbps or higher) to minimize latency and ensure adequate bandwidth for data transfer.

4. Plan for High Availability and Fault Tolerance

Design your Elasticsearch cluster with high availability and fault tolerance in mind. Distribute shards and replicas across multiple nodes and availability zones to minimize the impact of node failures. Consider using dedicated master nodes and separate data nodes to improve cluster stability.


Elasticsearch capacity planning is an essential aspect of managing and scaling Elasticsearch clusters. By considering factors such as data volume, query and indexing load, hardware resources, and high availability requirements, you can make informed decisions and ensure optimal performance and reliability for your Elasticsearch deployment.

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