OpenSearch vs Elasticsearch Which One Is Better to Use?
In this article, we will review OpenSearch vs Elasticsearch further in detail and help you pick the appropriate one based on your requirements. OpenSearch and Elasticsearch are two open-source search and analytics engines that have notable differences, but one common lineage. The histories and distinctions of the two are contrasted in this piece with an emphasis on their usual applications.
An Overview of Elasticsearch Past
Since 2010, many people have learned, and used, Elasticsearch, the popular search engine for search & log analytics. It is based on the Apache Lucene project as well as Apache Solr (parent of the Apache Lucene project). It can be used to perform both basic search capabilities to highly intricate data analysis and be succinctly as it is designed to be extremely scalable.
Kibana and Elasticsearch are parts of the Elastic Stack that offer a tremendous number of functions including enterprise search, full-text search, real-time analytics, and geographic searches. It was completely open source under the Apache License before rival Amazon began developing its project in early 2021. Sometimes, Elasticsearch is deployed on Elastic Cloud or self-managed.
An Overview of Elasticsearch OpenSearch
Amazon has been supporting the OpenSearch search engine since January 2021, that being a descendant of Elasticsearch. The codebase, before the fork event (when the projects started to mostly diverge), was almost identical. The main feature of OpenSearch is its open and community-driven development. Unlike Elasticsearch, which is owned by Elastic NV, OpenSearch is being managed by an open-source community-driven foundation, with funding from big companies like Microsoft.
That everyone can help OpenSearch grow. The codebases of both software products are available for public perusal, yet it is easier to contribute code and change the OpenSearch direction than it is to do the same with Elasticsearch. It’s used by the Amazon OpenSearch Service, formerly the Amazon Elasticsearch Service.
We’ll look at Codebase as an indication of which of the two is better for your needs as well as Features.
Comparison of OpenSearch vs Elasticsearch
When version 7.10.2 was the most recent release, the OpenSearch project split off from the Elasticsearch source. After that, a lot of work was done on the OpenSearch codebase to rename the project and clean up all non-Apache-licensed code (namely, X-Pack functionality). To accurately evaluate the work completed on both, we counted contributions made to the master/main branch from April 22, 2021, which was OpenSearch’s first release candidate after the fork a few months earlier.
As a result, OpenSearch has produced fewer releases than Elasticsearch, both significantly and significantly.
Although the number of commits is not a direct indicator of the software’s performance or code quality, it is obvious that the Elasticsearch project is seeing more core work, which undoubtedly translates to improved performance, more features, staying up-to-date with the most recent versions of Lucene features and dependencies, and so on—especially when the difference is that large.
In April 2023, the previously stated statistics were gathered.
Comparing the Features of OpenSearch vs Elasticsearch
The two technologies have precisely the same fundamental search, analytics, and dashboard functions. After all, an extremely developed version of Elasticsearch was the source of OpenSearch’s fork. From the standpoint of functionalities, it makes little difference which search engine you choose for the typical use cases.
Any features that were included in Elastic’s X-Pack (free or paid) and any features introduced after the fork will have different features between the projects.
Important elements that are a bit more than fundamental are those that will ultimately be included in both.
- Both use the Data Streams API, while Elasticsearch just announced time-series data streams that aren’t currently available in OpenSearch.
- The transformation of Index Lifecycle Management into Index OpenSearch’s State Management
- Both offer some alerting functionality, however we advise using ElastAlert2 rather than any integrated alerting system.
- Both allow for cross-cluster replication, an Elasticsearch Premium tier (not free) capability.
Elasticsearch still has several specialized basic features, such as shape and geohex grid aggregations, as at the time this article was written.
While Elastic Cloud is constantly up to date with the current version of Elasticsearch, Amazon’s managed OpenSearch offering is often two to three versions behind. Additionally, some OpenSearch capabilities are exclusive to the managed service Amazon OpenSearch Service.
The stack of vertical solutions accessible for different use cases (e.g. APM, SIEM, and more) is where the majority of the significant variations may be found. The main distinctions between Elasticsearch and OpenSearch are shown here.
Safety
The topic of Elasticsearch and OpenSearch security features is quite large and there are a lot of features and issues to talk about. These include authorization (allowing a user to authenticate), role-based access control (RBAC) (allowing a user to select the services or individuals s/he wants to access), user impersonation (allowing a user to assume another’s access rights), audit logging, encryption in trauma and at rest, etc.
Elasticsearch security included in the X-Pack Basic license is restricted to the user directory using Elasticsearch. From version 7.0 they are free to all users. In order to authenticate via LDAP, OpenID, SAML, and others, it requires a paid license. The same goes for other security features such as field and document level security, IP filtering, etc.
Just as free, with the same security features and restrictions, OpenSearch is available. Security module created fully in the open has all the members of Active Directory and LDAP, SAML, OpenID, and Access Control features like masking and field level security, audit logs, encryption support, etc.
The security is the same between Elasticsearch and OpenSearch, as OpenSearch offers better security because it is an open-source build module that gives you all of that for free.
Snapshots that can be searched
For many use cases, it is a game changer to be able to offer an “offline” search experience, which drastically lowers the hardware needed to run Elasticsearch clusters with older, less often accessible data.
OpenSearch only recently published this functionality, which is currently classified as experimental, whereas Elasticsearch has had it deployed and widely used for some time. Importantly, though, Elasticsearch’s Searchable Snapshots functionality is entirely free, whereas Elasticsearch requires a premium (Enterprise) license to utilize.
Managed services like Ultrawarm on Amazon OpenSearch Service and “Searchable Snapshots” or “search on frozen tier” in Elastic Cloud offer this functionality.
Learning Machines
It is not purposefully built to do that workloads so we do not recommend using Elasticsearch or OpenSearch for the purposes of ML and AI workloads. It can be useful at times, but you need to pay.
Elasticsearch and OpenSearch are best conceptualized as serving layer engines. You should configure the data in a way that data can be served simply from it whether or not machine learning will be used. For example, with Vector Search you can use the vector fields and kNN/ANN algorithms to locate related articles.
Another way to increase scoring capabilities is to use recording techniques, as did the LTR plugin.
For the sort of problems where Machine Learning is a good fit, both Elasticsearch and OpenSearch have solutions called (or “apps”) for this and, while they’re useful in certain circumstances (like the built-in SIEM in the Elastic Stack), we think they’re not quite ready for larger scale, everyday use.
Consumption of Data
Elasticsearch implemented a version check in all of the auxiliary software tools that are shipped as part of the Elastic Stack at the time of the fork. The Elasticsearch cluster is being checked by Logstash, Beats, and client libraries for JavaScript, Java, and other languages to make sure it is Elasticsearch and not OpenSearch. You would have to investigate alternatives because you are unable to utilize contemporary Logstash or Beats with OpenSearch, which causes a major divergence in these project elements.
That need is intended to be met by the OpenSearch project’s Data Prepper technology.
As an alternative, specific connectors are available for a variety of data streaming systems, including Flink sink for usage with multiple sources, Kafka Connect for Kafka, and so forth.
Libraries for Clients
On both accounts of simplicity of use and client library maturity, the two are very different.
Elasticsearch provides client libraries for almost every possible platform for software development, from Ruby, Javascript,.NET, Java, Python to many more. An HTTP REST API, there are many different APIs with significantly different flavors and a decent client library does help with syntactic sugar and makes building and maintaining code that interacts with it less painful.
Since the fork, the majority of client libraries have been giving failures when trying to connect to OpenSearch clusters. Taking time like that most humans have, things will change, even the core, APIs are eventually going to change whether it’s tech or people. Hence, the client libraries of OpenSearch need to be created and modified.
Unfortunately, OpenSearch has a serious weakness here. They are sparsely available, lacking, and even include gaps and flaws in the documentation, we attempted to use a few of them, but they were lacking, deficient, and in some cases obvious because the documentation lacked gaps and flaws. Often, but not always, they are almost always nigh useless, but not entirely so. Sometimes working with the simplest of HTTP client libraries directly is easier than working with OpenSearch’s client libraries.
Limitations and Licensing
Naturally, we cannot compare the two in a piece without addressing the crucial issue—the licensing scheme. Previously, Elasticsearch was made available under the relatively liberal Apache license. This is also the license that OpenSearch currently uses; however, Elasticsearch is also available under a different, less lenient license that many people believe does not qualify as open source.
The team and I like to be very technical, and this is the true value we can offer. Neither of us is a lawyer. However, we are frequently questioned about whether performing X is legal or would violate Elastic’s license.
The main idea is that Elasticsearch APIs cannot be served as managed services under the new license. All you need to do is utilize Elasticsearch as your application’s backend. However, there are many ambiguities, including providing certain APIs that may be interpreted as Elasticsearch APIs, integrating Elasticsearch into a broader solution that is advertised as a single unit, and so on. Many of our clients choose OpenSearch and utilize its basic capabilities and more because they want to have no risk, especially if they don’t want anything unique from Elasticsearch.
Assistance and Records
Since OpenSearch is open source, there’s no formal support. Hardware and software management will be taken care of by openSearch managed services such as Aiven, Amazon OpenSearch Service, and others but not how you use it.
There is support from Elastic Co, the company behind Elasticsearch, through its managed solution on Elastic cloud or its regular subscription licenses. But, once more, that help will be limited and may not always give you the most piece-by-piece guidance on how to employ the technology to reach your goals every day.
Conclusion
Lastly, both OpenSearch vs Elasticsearch support solid search and data analytics however, each with its own power and online community. Organizations wanting flexibility and transparency turn to OpenSearch, a species of OpenSearch because of its open-source nature and focus on security and community-driven features. On the other hand, Elasticsearch is still an excellent choice because it has a very extensive ecosystem and some very advanced features that can be added to dump data into Elasticsearch.
However, your case, the budget, and compliance requirements are the key factors in your final decision. If you conducted a trial with both platforms then you can look at which fits your team’s needs the best. The right tool can embrace a patient’s data search and analysis, providing better insights and informed decision-making. Empower your organization’s data strategy by choosing wisely.
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