Elasticsearch’s aggressive, twice monthly update schedule can easily lead to an organization becoming overwhelmed. Even a dedicated team can find itself falling behind on its update schedule — and new updates may introduce issues that must then be mitigated. Elasticsearch is steadily becoming a more useful and comprehensive platform, but there are a few major challenges that their upgrade cycle may introduce. Here are the core issues that make Elasticsearch difficult to upgrade.
1. Rapid product development
Elasticsearch has gone from a highly-focused text search engine to a much broader database technology. In doing so, they’ve created both incredible benefits and challenges for adopters. Adopters are able to take advantage of a new, robust ecosystem, which is designed to provide auxiliary database services. At the same time, to take advantage of these new features, adopters must be willing to continually upgrade their solution.
Going from one version to the next version may introduce minimal changes. Going from one version to four versions later can be overwhelmingly complex. Organizations may fall behind on their upgrade paths. When this occurs, it becomes prohibitively more difficult for the organization to complete an upgrade. Even given the time, organizations may be wary of upgrading their solutions due to the fear of potentially breaking their application.
As Elasticsearch continues to evolve, companies are going to need to know how to best implement its new features into their workflow and processes. This is where organizations may need help. It isn’t a matter of just upgrading the Elasticsearch product; companies need to know how to best utilize this product for improved efficiency and better business operations. This is where a professional DBaaS provider can help.
2. Aggressive deprecating features
Due to the aggressive development of Elasticsearch, it isn’t uncommon for features to disappear entirely. Elasticsearch is attempting to alter its product platform, which means it will strip out features that it feels are deprecated, or will remove features that it feels may not move in the same direction as the company. If something isn’t core to Elasticsearch’s goals, it’s likely that the feature will be cut.
As features are removed and evolve, organizations need to find a way to compensate. They must either find a comparative feature within Elasticsearch or support Elasticsearch through the use of a different solution.
Features may not entirely go away, but may be changed so dramatically that the organization will need to adapt to it, both in terms of workflow and how data is stored and analyzed. A significant investment in training could simply be lost; hundreds of hours could go into training staff on a feature that would ultimately be removed. This doesn’t mean that Elasticsearch is not a comprehensive, useful utility, but it does mean that employees may need to be regularly retrained in addition to the system itself being updated.
Some major features of Elasticsearch can change from version to version, such as the way that documents or data is stored. It is not an option to simply stop updating the platform; many updates also include security and safety measures, designed to make Elasticsearch less vulnerable to attack.
3. New technology working itself out
Elasticsearch provides a variety of valuable features such as third-party integration and visualization. Through its data analytics solution, Kibana, Elasticsearch can provide charts, graphs, and other visual representations of data. This technology is useful for companies that rely upon their business data first and foremost. Integration can pull in information from other applications and services, while Kibana can aid in sorting and analyzing it. As an open source solution, there are a number of additional technologies that can be connected to Elasticsearch. However, they may not have as much support as a commercial solution.
These new technologies may be associated with road bumps in terms of integration. It can take days or even weeks to complete a particularly large Elasticsearch migration, which could leave a business without its data or operating on a partial system for that amount of time. Some companies may find that all of their integrated and customized software no longer works when they complete their upgrade, which will lead to a sequence of new technology that has to be tested and developed.
Elasticsearch can be integrated into a number of open source features, but not all of these integrations may be fully supported or well-integrated. With the current rate of innovation, it becomes necessary for companies to sometimes modify their own code in order to preserve their ecosystem. The core benefit to this is that Elasticsearch is able to provide scalable and stable architecture. This architecture can be supported through a third-party.
Learn more about Upgrading Elasticsearch on our blog.
Finding Elasticsearch Upgrade Expertise & Assistance
Even with a dedicated staff, upgrading Elasticsearch can become prohibitively time-consuming. As Elasticsearch becomes more complex, companies may find themselves needing to outsource their Elasticsearch updates and integration. Having a resource may be critical to ongoing success and feature maximization.
For more information, consult with the experts at ObjectRocket.