ElasticSearch is a search engine based on the Lucene library. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.
Official clients are available in Java, .NET (C#), PHP, Python, Apache Groovy, Ruby, and many other languages. According to the DB-Engines ranking, Elasticsearch is the most popular enterprise search engine followed by Apache Solr, also based on Lucene.
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Frequently Asked Questions
ElasticSearch’s system requirements depend on your specific needs and desired performance. Here’s a breakdown of the key factors to consider:
CPU: While ElasticSearch can run on a single core, at least 2-4 cores are recommended for non-trivial queries. For larger setups and complex workloads, consider CPUs like Intel Xeon Platinum or AMD EPYC processors.
RAM: Minimum 4GB is recommended, but the actual requirement depends on query complexity and data volume. Heavier workloads might require 16GB or more.
Storage: ElasticSearch utilizes SSDs for optimal performance. The actual disk space depends on your data volume, but at least 256GB is recommended for initial setups.
Operating System: ElasticSearch supports Linux distributions like Ubuntu, CentOS, Red Hat, and Debian.
Disk Format: NVMe drives are optimal, but SATA SSDs are also workable.
Dependencies: ElasticSearch requires specific libraries and tools like C++, zlib, and Poco. The installation process usually takes care of installing these dependencies.
For more detailed information, refer to the ElasticSearch documentation.
ElasticSearch shines in numerous scenarios requiring fast and sophisticated searching and analysis of large, often unstructured, data sets. Some of the most common use cases include full-text search, real-time analytics and monitoring, geospatial analysis, e-commerce product recommendations, fraud detection, and social media analytics.
Here are some notable alternatives to ElasticSearch:
Apache Solr, Amazon CloudSearch, Algolia, Splunk, MeiliSearch, Sphinx, Coveo, SearchBlox, Elastic Cloud (formerly Bonsai), Azure Cognitive Search.
While both ElasticSearch and database management systems (DBMS) deal with data, they diverge significantly in purpose, functionality, and architecture. Here’s a breakdown of some of their key differences:
Elasticsearch: Elasticsearch is designed for handling and indexing unstructured or semi-structured data, particularly text-based data. It excels at full-text search, ranking, and relevance scoring.
Traditional DBMS: Traditional databases, such as relational databases, are structured and excel at storing structured data with predefined schemas. They use tables, rows, and columns to organize and relate data.
Elasticsearch: Elasticsearch is schema-less, meaning you can add fields to documents on-the-fly without a predefined schema. This flexibility is suitable for scenarios where data structures can evolve or change frequently.
Traditional DBMS: Traditional databases have a fixed schema, requiring predefined tables and data types. Any changes to the schema typically involve altering the database structure, which can be a more rigid process.
ElasticSearch: Employs powerful and specialized query languages like Lucene Query Syntax for full-text search, filtering, and aggregation.
DBMS: Utilizes structured query languages (SQL) for specific data retrieval and manipulation based on defined relationships and schema.
Here’s why Kamatera stands out as a compelling option for ElasticSearch hosting:
Cutting-edge hardware: Kamatera leverages Intel Xeon Platinum processors and NVMe SSD storage, guaranteeing exceptional performance for your ElasticSearch queries and data analysis.
Global network reach: With 18 strategically located data centers across four continents, Kamatera provides low-latency access to your ElasticSearch cluster. This minimizes lag and ensures consistent performance for geographically distributed teams.
Unmatched Scalability: Kamatera’s infrastructure seamlessly scales on-demand to accommodate your growing data volume and increasing query complexity. You can easily add or remove nodes in your ElasticSearch cluster without downtime or performance bottlenecks.
Robust Security: Kamatera prioritizes security by implementing data encryption, access control mechanisms, vulnerability scanning, and compliance with industry standards like PCI DSS and SOC 2. This ensures that your ElasticSearch cluster and sensitive data are protected from unauthorized access and potential security threats.
24/7 Support: Kamatera’s dedicated support team is available 24/7 to assist you with any questions or issues you may encounter.