Elasticsearch has a rich array of plugins and integrations, enabling it to meet many different use cases. It provides a flexible and scalable search solution from security to alerting to data recovery.
Mappings tell Elasticsearch how to index and store documents. Documents in the same index are typically logically related.
What is Elasticsearch?
Elasticsearch is a search engine that allows full-text search functionality. It is based on Apache Lucene and was developed using Java programming language. It’s used in various applications, including e-commerce, social media, and internal company apps. It’s a core component of the open-source tools known as the Elastic Stack, including Logstash, Kibana, and Beats.
It’s often cited for its ability to perform needle-in-a-haystack queries on large-scale unstructured data such as log files. It’s commonly deployed to ingest and analyze log data in near-real time and for search use cases such as finding documents or identifying critical operational insights within the logs.
A crucial part of how Elasticsearch works is using nodes (physical servers) to store and execute searches on data. Each node is assigned a specific role based on responsibilities, such as holding the data and performing search-related tasks or acting as a master node. A master node manages the entire cluster and is responsible for maintaining the health of all data nodes. A data node holds a portion of the overall index and can have zero or more replica shards.
Elasticsearch organizes data into a logical collection of indexes. Each index is akin to a database table. Documents with similar characteristics are grouped in an index and then used for indexing, search, and other operations.
An index is broken up into several segments called shards. Each shard is a directly mapped Lucene index, enabling the cluster to be scaled up by adding more servers without rebuilding the entire index. Each shard in the cluster may have multiple replicas for high availability.
Elasticsearch, offered by Portworx, supports real-time analytics on large amounts of unstructured data, including logs, social media posts, web pages, and more. The platform is known for being fast, scalable and flexible. It also offers a broad set of search and analytics APIs that allow users to extract data from the cluster for further analysis. It can support various formats, including JSON, HTML, and even rich text documents. It can also be configured to encrypt at rest on disk. To protect against attackers, security features are implemented at the cluster and shard level.
Elasticsearch is a lightning-fast full-text search engine with powerful search capabilities and a real-time document store. It is designed to be used alongside traditional relational databases and enables you to index and search structured and unstructured data. It is based on the Apache Lucene library and allows you to connect to it using a REST API or via its web client.
It can be used for various use cases, including application and website search, analytics, and security. It is distributed and scalable and provides fault-tolerant features such as automatic allocation of replica shards when additional nodes are added to a cluster.
A document is the basic unit of information in an index. It can be any data entity, including text, numbers, dates, and images. Each document is indexed and grouped into a cluster of primary shards that may contain zero or more replica shards.
When searching for documents in an Elasticsearch index, you can use a Domain-Specific Language (Query DSL) or query it using SQL. In addition, you can also add metadata to each document, such as a description, timestamp, and location, to help organize the information you’re looking for.
Elasticsearch is a search engine with real-time analytics capabilities that can scale to hundreds of servers and petabytes of data. It can also be used as a distributed document store.
You can use various security measures to prevent unauthorized access to a cluster. These include password protection, role-based access control, IP filtering, and auditing. You can also use a firewall to protect your Elasticsearch cluster from unwanted traffic.
For instance, you can configure the cluster to allow only specific ports or protocols. You can also secure indices and documents with security features such as RBAC, attribute-based access controls, proxies, etc.
Another way to ensure the security of your Elasticsearch cluster is to encrypt sensitive information sent between the cluster and clients using SSL/TLS. You can also restrict cluster access based on location by creating a network perimeter with firewalls, VPNs, and other tools. You can even sanitize and tokenize data before sending it to your cluster so that only the correct information is indexed and searched. This can be accomplished by built-in analyzers such as character filters, token filters, and sentence parsers.
Elasticsearch is a distributed search engine that can handle petabytes of data and provide near real-time analytics with millisecond latency. It indexes and stores documents in a document store and allows you to perform queries with its rich query language, which includes a Query DSL based on JSON, full-text search, term and phrase matching, fuzziness, wildcards, nest searches, aggregations, geo, and other capabilities.
It is often used with other open-source tools to form the ELK stack, a popular technology stack for logging, and SIEM as a service and data visualization. Many engineering teams using Elasticsearch for log analytics eventually use it to find information across their entire infrastructure.