Elasticsearch is a powerful distributed search engine that has, over the years, grown into a more general-purpose NoSQL storage and analytics tool. The recent release of Elasticsearch 7 added many improvements to the way Elasticsearch works. It also formalized support for various applications including machine learning, security information and event management (SIEM), and maps, among others, through a revamped Kibana.

CHAOSSEARCH is building a new standard (a new category) in data analytics. Beyond the cost and complexity of Warehousing, Hadoop, or even Elasticsearch solutions. CHAOSSEARCH is a new kind of big data platform that delivers both search and analytics at a price and simplicity yet experienced. At CHAOS, we are primarily focused on transforming object storage (such as S3) into the first multi-model database, where the user provides read-only access to their S3 storage and CHAOS provides the rest.

Searching in LogDNA is designed to be as intuitive and straightforward as possible. Just type in your search terms, and LogDNA will return your results almost instantaneously. For cases where you need to perform a more advanced search, or where you need greater control over your search results, LogDNA provides a number of features that can help you find exactly what you’re looking for.

As the first part of the three-part series on monitoring Apache Solr, this article explores which Solr metrics are important to monitor and why. The second part of the series covers Solr open source monitoring tools and identify the tools and techniques you need to help you monitor and administer Solr and SolrCloud in production.

As shown in Part 1 Solr Key Metrics to Monitor, the setup, tuning, and operations of Solr require deep insights into the performance metrics such as request rate and latency, JVM memory utilization, garbage collector work time and count and many more. Sematext provides an excellent alternative to other Solr monitoring tools.

Open source software adoption continues to grow. Tools like Kafka and Solr are widely used in small startups, ones that are using cloud ready tools from the start, but also in large enterprises, where legacy software is getting faster by incorporating new tools. In this second part of our Solr monitoring series (see the first part discussing Solr metrics to monitor), we will explore some of the open source tools available to monitor Solr nodes and clusters.