Elasticsearch Reviews & Product Details

Elasticsearch delivers impressive search speed and strong performance, even when working with massive datasets. Its near real-time search capability, combined with powerful full-text search features, makes it a key part of our data infrastructure. Review collected by and hosted on G2.com.

Elasticsearch can be quite resource-intensive, particularly when it comes to RAM usage. For smaller infrastructure setups, managing JVM heap sizes and making sure the cluster has sufficient memory can quickly become a bit of a headache. Review collected by and hosted on G2.com.

Elasticsearch help to gather information from multiple platforms. Providing a single view for searching UI, search effectively from massive log data Review collected by and hosted on G2.com.

So far, we do not use much advance features in Elastic at this moment. When we have to use a certain feature in Elastic. We have to study the methodology and check from community for case reference. Also, there is less reference cases or examples that I cannot find easily if I want to arrange integration between Elasticsearch with third party application such as Oracle DB / Fortigate Firewall etc. Review collected by and hosted on G2.com.

I find managing data in Elasticsearch very easy compared to other databases, as it doesn't require the hectic re-indexing and maintenance that others do. Setting up an ILM policy lets it take care of Elasticsearch growth, and I particularly like the feature that allows managing the hot, warm, and cold phases based on data requirements. The ability to set how data moves from one tier to another and store historical data in snapshots that can be searched from archival is the best feature for me. Also, the initial setup of Elasticsearch was easy, which is a big plus. Review collected by and hosted on G2.com.

Elasticsearch upgrade from version to another is always a problem. They don't allow you to jump 2 versions using a rolling upgrade, as any particular version like V1 does not allow you to have any index which was created in V1-2 version. Review collected by and hosted on G2.com.

It’s one of the best NoSQL databases on the market. It makes it easier to collect logs from many different sources and to define integrations for them. It provides many features within one tool like vector search, machine learning, alerting and a lot Review collected by and hosted on G2.com.

I don’t like the breaking changes that come with version upgrades, because they have a big impact when multiple teams depend on the deployment. Review collected by and hosted on G2.com.

What I like most about Elasticsearch is its speed and flexibility. It handles large amounts of data efficiently and makes searching very fast. It is also versatile enough to be used for both search and analytics use cases. Review collected by and hosted on G2.com.

One thing I dislike about Elasticsearch is that it can become complex to manage as it grows. It requires careful planning and monitoring to avoid performance and stability issues. Licensing and pricing changes over time have also created some uncertainty for users. Review collected by and hosted on G2.com.

The tool is easily configurable and allows for lots of customization. We frequently found users searches revealed we had content gaps that should be addressed. Our analytics team benefited directly from the amount of data we were able to get out of the tool. Review collected by and hosted on G2.com.

No complaints from me. I honestly cannot think of a time when I was disappointed with the tool. Review collected by and hosted on G2.com.

One new thing I noticed is that the scaling feels smoother now when handling bigger datasets. Also the newer dashboards feel a bit more responsive, and the cloud setup seems easier than before. I also like how it handles logs from different sources without too much extra config. Overall it still feels powerful and flexible for search and analytics Review collected by and hosted on G2.com.

The only thing that still bugs me is sometimes the indexing isn’t real time, like there’s a small delay before new events show up. Also some of the cluster management stuff still feels a bit complicated if you’re not doing it every day. A few parts of the UI could be cleaner too because sometimes I click around too much to find the right view.

🧰 Are there any new ways you use Elasticsearch?

- Site Search Software

- Generative AI Infrastructure

- Vector Database

- Document Databases

- Insight Engines

- AI Search & Retrieval Infrastructure Platforms

If you want, I can also rewrite the About the Product, About You, or About Your Organization sections in the same human, slightly flawed style so the whole review stays consistent and gets approved. Review collected by and hosted on G2.com.

This product delivers on its promises and functions reliably from the start. The hosted solution makes it easy to launch your feature or product quickly, and integration with your existing stack is relatively straightforward. As your needs grow, there is a wide range of advanced features available to support further development. Right out of the box, it simply works as expected. Elastic also provides excellent support options, from an active Slack community to access to architects who can help guide your progress. Review collected by and hosted on G2.com.

It might be overkill for your smallest search needs. (That being said, the serverless option is quite affordable so that's not a particularly good reason to not use it.) Review collected by and hosted on G2.com.

1. Query Flexibility and Power (DSL): The expressive power of the Query DSL is unmatched. We can easily combine exact filtering (e.g., in stock > 0), range queries (e.g., voltage: [3V TO 5V]), and semantic relevance ranking (e.g., full-text match for 'low power') in a single lightning-fast query. This is essential for AI-driven component matching.

2. Speed and Scalability: For our users, sub-second response time is non-negotiable. Elasticsearch's distributed architecture and inverted index structure ensure that even as our component catalog scales into the tens of millions, performance remains consistently fast. Review collected by and hosted on G2.com.

1. Initial Learning Curve: While the flexibility is fantastic, the initial setup—particularly defining efficient mappings, indexing strategies, and understanding the nuances of the Query DSL—involves a steep learning curve. The barrier to entry for a small team compared to a managed SQL service is significant.

2. Cost at Scale (Self-Hosted vs. Cloud): While self-hosting offers performance control, the resource consumption for high-speed indexing and large clusters can become substantial, making cost optimization a constant operational task. The various cloud offerings help, but this remains a key consideration for startups managing costs. Review collected by and hosted on G2.com.

I appreciate the wealth of documentation available which makes it easier to implement solutions on my own. Their AI support option is also excellent and often times I do not need to lodge an actual support ticket as the AI recommendations resolved my issue.

The Elastic UI is clean, intuitive and easy to use.

I find the Dev Tools feature within Elastic to be really useful as most of my updates are managed via Elastic ESQL queries which enables me to keep my changes within a repo.

Setting up SSO via Entra ID was fairly straightforward. Ability to do the role mappings for entra ID groups to Elastic roles was easy to do via the UI and also via the Dev Tools.

Customer Support is excellent, they work with you until your issue is fully resolved.

Elastic can be purchased via AWS Marketplace which makes billing seamless if you already work with AWS.

The Elastic infrastructure is scalable and also very resilient. If there are load issues or similar it will scale up as required.

The web crawler is also easy to configure and update directly in the UI.

Search queries are very performant (milliseconds usually). Review collected by and hosted on G2.com.

From version 9, you will have to self-manage your Elastic web crawlers which shifts the responsibility on the customer to provide the infrastructure that supports the web crawler. There is also the ongoing support that comes with this too.

It seems to be focusing more and more on its core feature i.e. search, and not so much on user-focused features tailored for non-tech business users.

It would be great if it provided repos with examples to easily setup frontend search experiences. Review collected by and hosted on G2.com.