Search

coralogix

Elasticsearch Release: Roundup of Changes in 7.9.2

The latest Elasticsearch release version was made available on September 24, 2020 and contains several bug fixes and new features from the previous minor version released this past August. This article highlights some of the crucial bug fixes and enhancements made, discusses issues common to upgrading to this new minor version and introduces some of the new features released with 7.9 and its subsequent patches. A complete list of release notes can be found on the elastic website.

coralogix

Elasticsearch Vulnerability: How to Remediate the most recent Issues

An Elastic Security Advisory (ESA) is a notice from Elastic to its users of a new Elasticsearch vulnerability. The vendor assigns both a CVE and an ESA identifier to each advisory along with a summary and remediation details. When Elastic receives an issue, they evaluate it and, if the vendor decides it is a vulnerability, work to fix it before releasing a remediation in a timeframe that matches the severity.

coralogix

Improve Elasticsearch Query Performance with Profiling and Slow Logs

If our end users end up too long for a query to return results due to Elasticsearch query performance issues, it can often lead to frustration. Slow queries can affect the search performance of an ecommerce site or a Business Intelligence dashboard – either way, this could lead to negative business consequences. So it’s important to know how to monitor the speed of search queries, diagnose and debug to improve search performance.

stackify

Is Elasticsearch the Ultimate Scalable Search Engine?

For enterprise applications and startups to scale, they need to manage large volumes of data in real-time. Customers must have the ability to search for any product or service from your database within seconds. When you manage a relational database, data is spread across multiple tables. So, customers may experience lag during search and data retrieval. However, this is different with Elasticsearch and other NoSQL databases.

egnyte

The Journey to 7X Search Performance Improvement

Egnyte is used by our customers as a unified platform to govern and secure billions of files everywhere. As the amount of data stored is huge, customers want to search their dataset by metadata attributes like name, user, comments, custom metadata, and many more, including the possibility to find files by content keywords. Taking all of that into consideration, we needed to provide a solution that is able to find relevant content in a fast and accurate way.

coralogix

Elasticsearch Autocomplete with Search-as-you-type

You may have noticed how on sites like Google you get suggestions as you type. With every letter you add, the suggestions are improved, predicting the query that you want to search for. Achieving Elasticsearch autocomplete functionality is facilitated by the search_as_you_type field datatype. This datatype makes what was previously a very challenging effort remarkably easy.

elastic

Add flexibility to your data science with inference pipeline aggregations

Elastic 7.6 introduced the inference processor for performing inference on documents as they are ingested through an ingest pipeline. Ingest pipelines are incredibly powerful and flexible but they are designed to work at ingest. So what happens if your data is already ingested? Introducing the new Elasticsearch inference pipeline aggregation, which lets you apply new inference models on data that's already been indexed.

elastic

Getting started with Elastic Workplace Search on Elastic Cloud

Chances are you already spent a big part of your day looking for a document, an email, or an answer that lies deep within a Google Slides presentation. Thankfully, you landed in the right place. With Workplace Search, finding the right information across all your cloud and on-premises data platforms is now easier than ever, and it’s a few clicks closer than you expect.

elastic

The Go client for Elasticsearch: Working with data

In our previous two blogs, we provided an overview of the architecture and design of the Elasticsearch Go client and explored how to configure and customize the client. In doing so, we pointed to a number of examples available in the GitHub repository. The goal of these examples is to provide executable "scripts" for common operations, so it's a good idea to look there whenever you're trying to solve a specific problem with the client.

elastic

Elastic Workplace Search and Gmail: Unified search across all your content

As work from home has ballooned in 2020, virtual methods for communicating with colleagues have become more critical than ever. Same goes for all the useful productivity and collaboration tools at our disposal. The emerging downside is the difficulty of finding needed information among so many tools. Compounding the problem is the tendency for info to get siloed off by department.