Procedure for Using Elasticsearch

**Table 1** Procedure for using an Elasticsearch cluster
Category	Operation		Details
Use	Planning a cluster	Before creating an Elasticsearch cluster, develop a plan for it, such as whether to deploy the cluster across multiple AZs to improve availability; the node quantity and specifications; the cluster version and security mode; and index sharding, in order to ensure the desired performance and reliability.	Elasticsearch Cluster Planning Suggestions
	Creating a cluster	Create an Elasticsearch cluster based on the plan.	Creating an Elasticsearch Cluster
	Accessing a cluster	There are many ways to access an Elasticsearch cluster, such as Kibana, Cerebro, open-source APIs, Java, Python, and Go clients, as well as multiple network configurations over an intranet and the public network. You can select the most appropriate access method based on the programming language you prefer as well as your network environment.	Elasticsearch Cluster Access Methods
	Importing data	There are many ways to import data to an Elasticsearch cluster, including Logstash, open-source Elasticsearch APIs, Cloud Data Migration (CDM), and Data Replication Service (DRS), with support for different data sources and formats, as well as real-time synchronization for relational databases. You can select the best way for yourself based on your use case and the characteristics of your data.	Different Ways to Import Data to an Elasticsearch Cluster
	Searching for data	With CSS, you are advised to use DSL for data search in Elasticsearch clusters. You may also use SQL.	Using DSL to Search for Data in Elasticsearch Using SQL to Search for Data in Elasticsearch
	Enhancing the cluster's search capability	On top of the open-source version, CSS's Elasticsearch clusters offer a range of enhanced features, including vector search, storage-compute decoupling, flow control, large query isolation, aggregation enhancement, read/write splitting, switchover between hot and cold data storage classes, and index recycle bin. These features help you meet performance and cost optimization requirements for different use cases, while enhancing the service's cluster stability and search capability.	Search Enhancement Features for Elasticsearch Clusters
O&M	Backup and restoration	Snapshots can be created to back up the data of an Elasticsearch cluster, so that data can be quickly restored in the case of accidental data loss or in case historical data is needed, improving cluster data availability.	Creating a Snapshot to Back Up the Data of an Elasticsearch Cluster Restoring the Data of an Elasticsearch Cluster Using a Snapshot
	Scaling a cluster	CSS provides flexible scale-out and scale-in options, using which you can add or reduce nodes (either randomly or with specified nodes), add node types, and increase or reduce node specifications. This allows you to dynamically adjust cluster resources to meet changing demand and optimize costs.	Scaling Out an Elasticsearch Cluster Scaling In an Elasticsearch Cluster
	Upgrade	Elasticsearch clusters support same-version upgrade, cross-version upgrade, and cross-engine upgrade. Same-version upgrade means to upgrade the kernel patches to fix problems or optimize performance. Cross-version upgrade means to upgrade the cluster version to enhance functionality or incorporate versions. Cross-engine upgrade means to upgrade an Elasticsearch cluster to an OpenSearch cluster.	Upgrading the Version of an Elasticsearch Cluster
	Managing clusters	CSS provides comprehensive cluster management functions. Users can view cluster information, authorize cluster access, change the cluster's security mode, manage tags, replace nodes, bind clusters with enterprise projects, switches AZs, and configure custom word dictionaries for Elasticsearch clusters. They help users efficiently manage Elasticsearch clusters and ensure cluster security, high availability, and optimized performance.	Viewing Elasticsearch Cluster Information Creating Users for an Elasticsearch Cluster and Granting Cluster Access Setting Tags for an Elasticsearch Cluster Configuring Default Parameters in the .yml Configuration File of an Elasticsearch Cluster Binding an Elasticsearch Cluster to an Enterprise Project Replacing Specified Nodes for an Elasticsearch Cluster Changing the Security Mode of an Elasticsearch Cluster Switching AZs for an Elasticsearch Cluster Configuring and Using Custom Word Dictionaries for an Elasticsearch Cluster Switching Between Simplified and Traditional Chinese for Data Search in an Elasticsearch Cluster Restarting an Elasticsearch Cluster Deleting an Elasticsearch Cluster
	Managing cluster index policies	The Index State Management (ISM) plug-in of Elasticsearch can be used to create and manage index lifecycle policies. These policies help automate index rollovers and deletions, helping optimize cluster performance and cut storage costs.	Creating and Managing Index Policies for an Elasticsearch Cluster
	Monitoring and log management	CSS provides comprehensive monitoring and log management functions. Users can configure and check monitoring metrics for clusters and nodes, configure alarm rules, and back up and view logs. Intelligent O&M tools help users efficiently monitor, analyze, and maintain Elasticsearch clusters and ensure cluster stability and performance.	Elasticsearch Cluster Monitoring Metrics Configuring Elasticsearch Cluster Monitoring Setting Alarm Alerting via SMN for an Elasticsearch Cluster Intelligent Risk Detection for Elasticsearch Clusters Querying and Managing Elasticsearch Cluster Logs
	Audit logs	Cloud Trace Service (CTS) can be used to log mission-critical operations related to Elasticsearch clusters. Used for auditing and accountability purposes, these log records are retained for seven days on the management console.	Viewing Elasticsearch Cluster Audit Logs