Explanation:
Elasticsearch is a distributed, full-text search and analytics engine that is built on top of the Apache Lucene search library. It is classified as a NoSQL document-oriented database, which means that it stores data in a flexible, schema-free format that allows for dynamic and fast indexing, searching, and querying of large amounts of structured and unstructured data.
In addition to text-based search, Elasticsearch also supports structured search and filtering of numeric, geospatial, and date-based data, and it provides features for real-time data analysis and visualization through its integration with the Kibana data visualization platform. Elasticsearch is designed to be highly scalable, fault-tolerant, and resilient to node failures, making it well-suited for use cases such as log analysis, e-commerce search, and application performance monitoring.
Explanation:
Kibana is a data visualization and exploration tool that is used with Elasticsearch. It provides a user-friendly interface for exploring and analyzing data stored in Elasticsearch indexes, allowing users to create custom visualizations, dashboards, and reports.
Kibana is a valuable tool for data analysts, developers, and business users who need to make sense of large amounts of data stored in Elasticsearch indexes.
Explanation:
In Elasticsearch, it is recommended to have a minimum of three master-eligible nodes in a cluster to ensure high availability and prevent a split-brain scenario where multiple nodes think they are the master.
Having at least three master-eligible nodes allows for a majority-based quorum system where a minimum of two nodes must agree on who the master is. This prevents a split-brain scenario where two nodes each think they are the master and start making conflicting changes to the cluster state.
Explanation:
Elastic Beats is a family of lightweight data shippers that are used for collecting, shipping, and processing data from a variety of sources into Elasticsearch. One of the Beats, called "Filebeat," is specifically designed for shipping data from log files to Elasticsearch or Logstash for further processing and analysis. Filebeat is a lightweight, open-source log shipper that is easy to set up and configure. It can be installed on a wide range of operating systems and can be used to monitor log files on local disks, network file systems, or remote servers. Filebeat can also be used to enrich log data with metadata or custom fields and can be configured to handle a wide range of log formats, including JSON, CSV, Apache logs, and more. Overall, Filebeat is a valuable tool for organizations that need to collect and analyze log data from a variety of sources.
Explanation:
Filebeat can send data to a variety of outputs, including Logstash, Elasticsearch, Kafka, Redis, and others. When you configure Filebeat, you can specify one or more outputs where you want to send the data. For example, if you want to send the data directly to Elasticsearch, you can specify the Elasticsearch output in the Filebeat configuration file. Alternatively, if you want to send the data to Logstash, you can specify the Logstash output. You can even send the data to multiple outputs simultaneously by specifying multiple output configurations in the Filebeat configuration file. This flexibility makes Filebeat a versatile tool for shipping data from log files to a variety of destinations for further processing and analysis.
Explanation:
The Input, Filter, and Output stages form the core of a Logstash pipeline, allowing users to collect, process, and send data from various sources to multiple destinations.
Explanation:
There are three types of nodes that are commonly used in a cluster:
1. Master-eligible nodes: These nodes participate in the election process to select the master node and handle cluster-wide changes such as creating or deleting indices.
2. Data nodes: These nodes store the data and perform data-related operations such as indexing, search, and retrieval.
3. Coordinating nodes: These nodes are also called client nodes and are responsible for routing requests from clients to the appropriate data nodes. They help to distribute search and indexing load across the cluster, and also handle aggregations and sorting.
In some cases, coordinating nodes may also perform additional functions such as transforming data before indexing or caching search results to improve performance. It's important to note that all nodes in an Elasticsearch cluster can serve as a coordinating nodes, but it's recommended to have dedicated coordinating nodes to avoid overloading data nodes with search and indexing requests.
