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Procedure for Using Elasticsearch

Updated on 2024-10-26 GMT+08:00
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

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