Upgrading Elasticsearch: Best Migration Strategies with their pros and cons

At HOW, we are committed to helping our clients optimise their technology infrastructure and maximise their efficiency. Recently, we were approached by a client to upgrade their Elasticsearch infrastructure from an outdated version to the latest release. While we have accepted the challenge, we have not yet implemented the upgrade. However, we recognize the importance of sharing our insights into this process, as upgrading Elasticsearch can be a complex and challenging undertaking.

In this blog post, we will share our research and expertise on the different migration strategies available, provide guidance on how to choose the best approach for your organisation, and highlight some of the potential challenges and pitfalls to be aware of.

Whether you’re planning an upgrade in the near future or just want to learn more about Elasticsearch, this blog post is for you.

Before we dive into the upgrade process, it’s important to note that Elasticsearch upgrades are not always straightforward. The complexity of the upgrade process can vary depending on the version you are upgrading from, the size of your cluster, and the customizations you have made to your Elasticsearch configuration.

Step 1: Check the Compatibility Matrix

The first step in upgrading Elasticsearch is to check the compatibility matrix. Elasticsearch maintains a compatibility matrix that shows which versions of Elasticsearch are compatible with each other. You can find the compatibility matrix on the Elasticsearch website.

Step 2: Test the Upgrade in a Non-Production Environment

Before you upgrade your production Elasticsearch cluster, it is essential to test the upgrade in a non-production environment. This will allow you to identify any issues that may arise during the upgrade process and resolve them before upgrading your production cluster.

Step 3: Upgrade Elasticsearch

Once you have tested the upgrade in a non-production environment and have resolved any issues, you can upgrade your production Elasticsearch cluster. The upgrade process can vary depending on the version you are upgrading from, but generally, the process involves the following steps:

1. Take a backup of your data
2. Stop Elasticsearch
3. Install the new version of Elasticsearch
4. Start Elasticsearch
5. Reindex your data (if necessary)
6. Test the new version of Elasticsearch

Now that we have discussed on a very high level how to upgrade Elasticsearch let’s talk about the different migration strategies you can use.

Strategy 1: In-Place Upgrade

The in-place upgrade strategy involves upgrading Elasticsearch on the same servers that are currently running Elasticsearch. This strategy is suitable for small clusters and environments where downtime is not an issue.

Pros:

• The simplest and quickest migration strategy, as it involves upgrading Elasticsearch on the same servers that are currently running Elasticsearch.
• Suitable for small clusters with minimal customizations and limited downtime requirements.
• Minimal setup and infrastructure requirements.

Cons:

• No fallback plan in case the upgrade fails or causes issues.
• No easy way to roll back to the previous version in case of problems.
• Not suitable for large clusters or environments where downtime is not acceptable.

Strategy 2: Rolling Upgrade

The rolling upgrade strategy involves upgrading Elasticsearch one node at a time while the cluster remains online. This strategy is suitable for larger clusters where downtime is not an option.

Pros:

• Enables upgrading Elasticsearch one node at a time, while the cluster remains online, which ensures that the cluster remains operational throughout the upgrade process.
• Suitable for larger clusters with moderate downtime requirements.
• Provides a fallback plan if an issue occurs during the upgrade.

Cons:

• Complexity is higher than the in-place upgrade, as it requires coordinating the upgrade across multiple nodes.
• The upgrade process takes longer due to the need to upgrade each node individually.
• Not suitable for environments with strict downtime requirements.

Strategy 3: Blue-Green Deployment

The blue-green deployment strategy involves setting up a new Elasticsearch cluster with the latest version and migrating the data from the old cluster to the new one. This strategy is suitable for large clusters and environments where downtime must be minimized.

Pros:

• Provides a completely new Elasticsearch cluster with the latest version, which can be fully tested before switching over.
• Provides a fallback plan by keeping the previous cluster operational until the new one is fully operational.
• Suitable for large clusters with strict downtime requirements.

Cons:

• Requires additional infrastructure and setup, which can be more expensive and time-consuming.
• Can require additional effort to set up and configure data replication between the old and new clusters.
• Complexity is higher than other strategies, as it requires coordinating the migration between two separate clusters.

Ultimately, the migration strategy you choose depends on the size and complexity of your Elasticsearch cluster, your downtime requirements, and your tolerance for risk. While the in-place upgrade strategy may be the quickest and simplest, it may not be suitable for all environments. Similarly, while the blue-green deployment strategy may provide the most flexibility and safety, it may require additional time and resources to set up. By understanding the pros and cons of each migration strategy, you can make an informed decision that best fits your needs.

In conclusion, upgrading Elasticsearch can be a complex process, but it is essential to keep your Elasticsearch cluster up-to-date to take advantage of the latest features and improvements. Before upgrading, be sure to check the compatibility matrix, test the upgrade in a non-production environment, and choose the migration strategy that best fits your environment.