Elasticsearch Reviews & Product Details

It helps us in very ease way It doesn't just search text; it can aggregate complex log data, metrics, and security events in real time to create live visual dashboards in Kibana. Review collected by and hosted on G2.com.

It is a massive resource hog. It relies heavily on the Java Virtual Machine (JVM) heap space and file system cache, requiring substantial RAM and CPU to run efficiently, which quickly drives up infrastructure costs. Review collected by and hosted on G2.com.

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.

I like that it makes it easy to monitor applications, logs, and system performance in one place. The dashboards are helpful and make troubleshooting issues much easier. Review collected by and hosted on G2.com.

The main downside is that it can take some time to understand and set up properly. Some features and configurations can feel a little complex for new users. Review collected by and hosted on G2.com.

Traces logsI don't need to build machine learning models from scratch; I can use its built-in vector search to easily power AI chatbots or recommendation engines. Review collected by and hosted on G2.com.

Managing a multi-node production cluster is difficult. Dealing with shard allocation, node crashes, split-brain scenarios, and optimizing performance during data spikes often requires a dedicated DevOps engineer or Elasticsearch expert. 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.

What I like best about Elasticsearch is its incredible speed and near real-time search capabilities. Even when dealing with massive datasets containing millions of rows, it returns highly relevant search results almost instantly. Review collected by and hosted on G2.com.

The most common downsides of Elasticsearch include high memory consumption, a steep learning curve, and complex cluster management. Review collected by and hosted on G2.com.

I use Elasticsearch to build search products for websites, and I appreciate the fast and highly customizable search experience it provides. I like that it solves problems related to indexing and search speed, and its ability to heavily customize the search experience while incorporating AI is very beneficial. I find the supportive community around Elasticsearch really valuable. There's lots of support when building with it, and the good documentation makes things easier. The technical support is accessible if I need more help. I also enjoy the regular events like ElasticON, which are free and allow people to learn how to use the products better. Additionally, the initial setup was really easy thanks to the great documentation. Review collected by and hosted on G2.com.

Sometimes, the Elastic Cloud 'PaaS' experience is a little more hands-on than we'd expect. We have to really dig into areas we don't expect to investigate/fix things. We expected it to be managed by Elastic but it's not totally hands off. Review collected by and hosted on G2.com.

We use Elastic for semantic search to fetch data for our LLM, which we then use to generate financial reports. Review collected by and hosted on G2.com.

All is good We use Elastic for semantic search to fetch data for our LLM, which we then use to generate financial reports. 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.