Compute
Elastic Cloud Server
Huawei Cloud Flexus
Bare Metal Server
Auto Scaling
Image Management Service
Dedicated Host
FunctionGraph
Cloud Phone Host
Huawei Cloud EulerOS
Networking
Virtual Private Cloud
Elastic IP
Elastic Load Balance
NAT Gateway
Direct Connect
Virtual Private Network
VPC Endpoint
Cloud Connect
Enterprise Router
Enterprise Switch
Global Accelerator
Management & Governance
Cloud Eye
Identity and Access Management
Cloud Trace Service
Resource Formation Service
Tag Management Service
Log Tank Service
Config
OneAccess
Resource Access Manager
Simple Message Notification
Application Performance Management
Application Operations Management
Organizations
Optimization Advisor
IAM Identity Center
Cloud Operations Center
Resource Governance Center
Migration
Server Migration Service
Object Storage Migration Service
Cloud Data Migration
Migration Center
Cloud Ecosystem
KooGallery
Partner Center
User Support
My Account
Billing Center
Cost Center
Resource Center
Enterprise Management
Service Tickets
HUAWEI CLOUD (International) FAQs
ICP Filing
Support Plans
My Credentials
Customer Operation Capabilities
Partner Support Plans
Professional Services
Analytics
MapReduce Service
Data Lake Insight
CloudTable Service
Cloud Search Service
Data Lake Visualization
Data Ingestion Service
GaussDB(DWS)
DataArts Studio
Data Lake Factory
DataArts Lake Formation
IoT
IoT Device Access
Others
Product Pricing Details
System Permissions
Console Quick Start
Common FAQs
Instructions for Associating with a HUAWEI CLOUD Partner
Message Center
Security & Compliance
Security Technologies and Applications
Web Application Firewall
Host Security Service
Cloud Firewall
SecMaster
Anti-DDoS Service
Data Encryption Workshop
Database Security Service
Cloud Bastion Host
Data Security Center
Cloud Certificate Manager
Edge Security
Situation Awareness
Managed Threat Detection
Blockchain
Blockchain Service
Web3 Node Engine Service
Media Services
Media Processing Center
Video On Demand
Live
SparkRTC
MetaStudio
Storage
Object Storage Service
Elastic Volume Service
Cloud Backup and Recovery
Storage Disaster Recovery Service
Scalable File Service Turbo
Scalable File Service
Volume Backup Service
Cloud Server Backup Service
Data Express Service
Dedicated Distributed Storage Service
Containers
Cloud Container Engine
Software Repository for Container
Application Service Mesh
Ubiquitous Cloud Native Service
Cloud Container Instance
Databases
Relational Database Service
Document Database Service
Data Admin Service
Data Replication Service
GeminiDB
GaussDB
Distributed Database Middleware
Database and Application Migration UGO
TaurusDB
Middleware
Distributed Cache Service
API Gateway
Distributed Message Service for Kafka
Distributed Message Service for RabbitMQ
Distributed Message Service for RocketMQ
Cloud Service Engine
Multi-Site High Availability Service
EventGrid
Dedicated Cloud
Dedicated Computing Cluster
Business Applications
Workspace
ROMA Connect
Message & SMS
Domain Name Service
Edge Data Center Management
Meeting
AI
Face Recognition Service
Graph Engine Service
Content Moderation
Image Recognition
Optical Character Recognition
ModelArts
ImageSearch
Conversational Bot Service
Speech Interaction Service
Huawei HiLens
Video Intelligent Analysis Service
Developer Tools
SDK Developer Guide
API Request Signing Guide
Terraform
Koo Command Line Interface
Content Delivery & Edge Computing
Content Delivery Network
Intelligent EdgeFabric
CloudPond
Intelligent EdgeCloud
Solutions
SAP Cloud
High Performance Computing
Developer Services
ServiceStage
CodeArts
CodeArts PerfTest
CodeArts Req
CodeArts Pipeline
CodeArts Build
CodeArts Deploy
CodeArts Artifact
CodeArts TestPlan
CodeArts Check
CodeArts Repo
Cloud Application Engine
MacroVerse aPaaS
KooMessage
KooPhone
KooDrive
Help Center/ Cloud Search Service/ Best Practices/ Using Elasticsearch to Accelerate Query and Analysis for Relational Databases

Using Elasticsearch to Accelerate Query and Analysis for Relational Databases

Updated on 2025-01-06 GMT+08:00

This section explains how to synchronize data from a MySQL database to an Elasticsearch cluster in CSS to enable full-text search, ad hoc queries, and statistical analysis of the database.

Scenario

Using Elasticsearch to accelerate relational databases can overcome some limitations of relational databases and enable more efficient and intelligent data processing and analysis. It is typically used in the following scenarios:
  • E-commerce platform: Quickly search for commodities, provide personalized recommendations, and monitor user behavior and transaction data in real time.
  • Content management system: Efficiently retrieve a large number of documents and contents, supporting complex queries and data analysis.
  • Financial services: Monitor transaction data in real time for risk analysis and fraud detection.
  • Social media analysis: Perform sentiment analysis, trend analysis, and influence evaluation on user-generated content.
  • Customer relationship management: Quickly search for customer information, analyze customer behavior, and provide customized services.
  • Log and event monitoring: Collect and analyze a large amount of log data to monitor system status and security events in real time.
  • Healthcare records: Quickly retrieve and analyze patient records to support clinical decision-making and research.

Overview

Figure 1 Architecture of the Elasticsearch-accelerated relational database solution
  1. Service data is stored in the MySQL database.
  2. DRS synchronizes data from MySQL databases to the CSS Elasticsearch cluster in real time.
  3. Users perform full-text search, Ad Hoc query, and statistics analysis in the Elasticsearch cluster.

Advantages

The advantages of using Elasticsearch to accelerate relational databases are as follows:
  • Improved full-text search capability: Elasticsearch is a search engine that provides powerful full-text search functions. Relational databases are usually not good at full-text retrieval, but Elasticsearch can effectively solve this problem.
  • High-concurrency Ad Hoc query: Elasticsearch is designed to process a large number of concurrent query requests, especially in Ad Hoc query scenarios. It can provide fast response to meet query requirements in high-concurrency environments.
  • Real-time data synchronization: Data Replication Service (DRS) synchronizes data from the MySQL database to Elasticsearch in real time, ensuring data consistency and real-time performance.
  • Simplified data migration and index creation: In an Elasticsearch cluster, you can create indexes that match the MySQL database table structure, simplifying data migration and index management.
  • Flexible query language: Elasticsearch provides flexible query languages to support complex query construction, such as range queries, fuzzy queries, and aggregation queries. This may require more complex SQL statements in relational databases.
  • Statistical analysis: The aggregation function of Elasticsearch can quickly collect and analyze data, such as age distribution statistics, which may require more computing resources and time in relational databases.
  • Security and stability: You can configure security-mode Elasticsearch clusters and MySQL databases and use SSL connections between them to ensure data transmission security and system stability.
  • Easy monitoring and maintenance: Elasticsearch provides various monitoring tools and APIs to facilitate system maintenance and performance monitoring.
  • Scalability: The Elasticsearch cluster can be horizontally expanded based on service requirements. More nodes can be added to process larger data volumes and query loads.

These advantages make the Elasticsearch cluster an effective supplement to relational databases in processing full-text retrieval and high-concurrency Ad Hoc queries.

Prerequisites

  • A MySQL database and an Elasticsearch cluster in security mode are available, and they are in the same VPC and security group.
  • Data to be synchronized exists in the MySQL database.

    This document uses the following table structure and initial data as an example.

    1. Create a student information table in MySQL.
      CREATE TABLE `student` (
        `dsc` varchar(100) COLLATE utf8mb4_general_ci DEFAULT NULL,
        `age` smallint unsigned DEFAULT NULL,
        `name` varchar(32) COLLATE utf8mb4_general_ci NOT NULL,
        `id` int unsigned NOT NULL,
        PRIMARY KEY (`id`)
      ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_general_ci;
    2. Insert the initial data of three students into the MySQL database.
      INSERT INTO student (id,name,age,dsc)
      VALUES 
      ('1','Jack Ma Yun','50','Jack Ma Yun is a business magnate, investor and philanthropist.'),
      ('2','will smith','22','also known by his stage name the Fresh Prince, is an actor, rapper, and producer.'),
      ('3','James Francis Cameron','68','the director of avatar');
  • An index has been created in the Elasticsearch cluster and matches the table indexes in the MySQL database.
    Run the following command to create an index for the Elasticsearch cluster:
    PUT student
    {
      "settings": {
        "number_of_replicas": 0,
        "number_of_shards": 3
    	},
      "mappings": {
        "properties": {
          "id": {
            "type": "keyword"
    		},
          "name": {
            "type": "short"
    		},
          "age": {
            "type": "short"
    		},
          "desc": {
            "type": "text"
    		}
        }
      }
    }

    Configure number_of_shards and number_of_replicas as needed.

Procedure

  1. Use DRS to synchronize MySQL data to CSS in real time. For details, see From MySQL to CSS/ES.

    In this example, configure the parameters by following the suggestions in Table 1.
    Table 1 Synchronization parameters

    Module

    Parameter

    Suggestion

    Create Synchronization Instance > Synchronize Instance Details

    Network Type

    Select VPC.

    Source DB Instance

    Select the RDS for MySQL instance to be synchronized, that is, the MySQL database that stores service data.

    Synchronization Instance Subnet

    Select the subnet where the synchronization instance is located. You are advised to select the subnet where the database instance and the Elasticsearch cluster are located.

    Configure Source and Destination Databases > Destination Database

    VPC

    Select the same VPC as the Elasticsearch cluster.

    Subnet

    Select the same subnet as the Elasticsearch cluster.

    IP Address or Domain Name

    Enter the IP address of the Elasticsearch cluster. For details, see Obtaining the IP address of a CSS cluster.

    Database Username

    Enter the administrator username (admin) and password of the Elasticsearch cluster.

    Database Password

    Enter the administrator password of the Elasticsearch cluster.

    Encryption Certificate

    Select the security certificate of the Elasticsearch cluster. If SSL Connection is not enabled, you do not need to select any certificate. For details, see Obtaining the security certificate of a CSS cluster.

    Set Synchronization Task

    Flow Control

    Select No.

    Synchronization Object Type

    Deselect Table structure, because the indexes matching MySQL tables have been created in the Elasticsearch cluster.

    Synchronization Object

    Select Tables. Select the database and table name corresponding to the Elasticsearch cluster.

    NOTE:

    Ensure the type name in the configuration item is the same as the index name, that is, _doc.

    Process Data

    -

    Click Next.

    After the synchronization task is started, wait until the Status of the task changes from Full synchronization to Incremental, indicating real-time synchronization has started.

  2. Check the synchronization status of the database.

    1. Verify full data synchronization.

      Run the following command in Kibana of the Elasticsearch cluster to check whether full data has been synchronized to CSS:

      GET student/_search
    2. Insert new data in the source cluster and check whether the data is synchronized to Elasticsearch.
      For example, insert a record whose id is 4 in the source cluster.
      INSERT INTO student (id,name,age,dsc)
      VALUES 
      ('4','Bill Gates','50','Gates III is a business magnate, software developer, investor, author, and philanthropist.')

      Run the following command in Kibana of the Elasticsearch cluster to check whether new data has been synchronized to CSS:

      GET student/_search
    3. Update data in the source cluster and check whether the data is synchronized to Elasticsearch.
      For example, in the record whose id is 4, change the value of age from 50 to 55.
      UPDATE student set age='55' WHERE id=4;

      Run the following command in Kibana of the Elasticsearch cluster to check whether the data is updated in CSS:

      GET student/_search
    4. Delete data from the source cluster and check whether the data is deleted synchronously from Elasticsearch.
      For example, delete the record whose id is 4.
      DELETE FROM student WHERE id=4;

      Run the following command in Kibana of the Elasticsearch cluster to check whether the data is deleted synchronously from CSS:

      GET student/_search

  3. Verify the full-text search capability of the database.

    For example, run the following command to query the data that contains avatar in dsc in the Elasticsearch cluster:

    GET student/_search
    {
      "query": {
        "match": {
          "dsc": "avatar"
        }
      }
    }

  4. Verify the ad hoc query capability of the database.

    For example, query philanthropist whose age is greater than 40 in the Elasticsearch cluster.

    GET student/_search
    {
      "query": {
        "bool": {
          "must": [
            {
              "match": {
                "dsc": "philanthropist"
              }
            },
            {
              "range": {
                "age": {
                  "gte": 40
                }
              }
            }
          ]
        }
      }
    }

  5. Verify the statistical analysis capability of the database.

    For example, collect statistics on the age distributions of all users in the Elasticsearch cluster.

    GET student/_search
    {
      "size": 0, 
      "query": {
        "match_all": {}
      },
      "aggs": {
        "age_count": {
          "terms": {
            "field": "age",
            "size": 10
          }
        }
      }
    }

Other Operations

  • Obtaining the IP address of a CSS cluster
    1. In the navigation pane on the left, choose Clusters.
    2. In the cluster list, obtain the IP address of a CSS cluster from the Private Network Address column. Generally, the IP address format is <host>:<port> or <host>:<port>,<host>:<port>.

      If the cluster has only one node, the IP address and port number of only one node are displayed, for example, 10.62.179.32:9200. If the cluster has multiple nodes, the IP addresses and port numbers of all nodes are displayed, for example, 10.62.179.32:9200,10.62.179.33:9200.

  • Obtaining the security certificate of a CSS cluster
    1. Log in to the CSS management console.
    2. In the navigation pane, choose Clusters. The cluster list is displayed.
    3. Click a cluster name to go to the cluster details page.
    4. In the Configuration area, click Download Certificate next to HTTPS Access.
      Figure 2 Download Certificate

We use cookies to improve our site and your experience. By continuing to browse our site you accept our cookie policy. Find out more

Feedback

Feedback

Feedback

0/500

Selected Content

Submit selected content with the feedback