- Product Introduction
- Purchase Guide
- Getting Started
- Operation Guide
- Development Guide
- Design of Data Table
- Importing Data
- Import Overview
- Import Methods
- Synchronizing Data with JDBC
- Synchronizing Data Through External Tables
- Real-time or Batch Writing of MySQL Data
- Importing Data from Kafka
- Importing with DataX
- Importing Doris from Logstash
- Data Transformation, Column Mapping and Filtering
- Importing Strict Mode
- Importing Data in JSON Format
- Exporting Data
- Basic Feature
- Query Optimization
- Ecological Expansion Feature
- API Documentation
- History
- Introduction
- API Category
- Making API Requests
- Cluster Operation APIs
- ModifyClusterConfigs
- ModifyInstanceKeyValConfigs
- ModifyNodeStatus
- RestartClusterForConfigs
- RestartClusterForNode
- CreateInstanceNew
- DestroyInstance
- ModifySecurityGroups
- ReduceInstance
- ResizeDisk
- ScaleOutInstance
- ScaleUpInstance
- DescribeClusterConfigsHistory
- ModifyInstance
- DescribeCreateTablesDDL
- DescribeInstanceOperationHistory
- Database and Table APIs
- Cluster Information Viewing APIs
- Hot-Cold Data Layering APIs
- Database and Operation Audit APIs
- CancelBackupJob
- CreateBackUpSchedule
- DeleteBackUpData
- DescribeBackUpJob
- DescribeBackUpJobDetail
- DescribeBackUpTables
- DescribeBackUpTaskDetail
- DescribeRestoreTaskDetail
- DescribeBackUpSchedules
- RecoverBackUpJob
- DescribeInstanceOperations
- DescribeDatabaseAuditDownload
- DescribeDatabaseAuditRecords
- DescribeSlowQueryRecords
- DescribeSlowQueryRecordsDownload
- DescribeQueryAnalyse
- User and Permission APIs
- Resource Group Management APIs
- Data Types
- Error Codes
- Cloud Ecosystem
- Practical Tutorial
- Performance Testing
- FAQs
- Contact Us
- Glossary
- Product Policy
- Product Introduction
- Purchase Guide
- Getting Started
- Operation Guide
- Development Guide
- Design of Data Table
- Importing Data
- Import Overview
- Import Methods
- Synchronizing Data with JDBC
- Synchronizing Data Through External Tables
- Real-time or Batch Writing of MySQL Data
- Importing Data from Kafka
- Importing with DataX
- Importing Doris from Logstash
- Data Transformation, Column Mapping and Filtering
- Importing Strict Mode
- Importing Data in JSON Format
- Exporting Data
- Basic Feature
- Query Optimization
- Ecological Expansion Feature
- API Documentation
- History
- Introduction
- API Category
- Making API Requests
- Cluster Operation APIs
- ModifyClusterConfigs
- ModifyInstanceKeyValConfigs
- ModifyNodeStatus
- RestartClusterForConfigs
- RestartClusterForNode
- CreateInstanceNew
- DestroyInstance
- ModifySecurityGroups
- ReduceInstance
- ResizeDisk
- ScaleOutInstance
- ScaleUpInstance
- DescribeClusterConfigsHistory
- ModifyInstance
- DescribeCreateTablesDDL
- DescribeInstanceOperationHistory
- Database and Table APIs
- Cluster Information Viewing APIs
- Hot-Cold Data Layering APIs
- Database and Operation Audit APIs
- CancelBackupJob
- CreateBackUpSchedule
- DeleteBackUpData
- DescribeBackUpJob
- DescribeBackUpJobDetail
- DescribeBackUpTables
- DescribeBackUpTaskDetail
- DescribeRestoreTaskDetail
- DescribeBackUpSchedules
- RecoverBackUpJob
- DescribeInstanceOperations
- DescribeDatabaseAuditDownload
- DescribeDatabaseAuditRecords
- DescribeSlowQueryRecords
- DescribeSlowQueryRecordsDownload
- DescribeQueryAnalyse
- User and Permission APIs
- Resource Group Management APIs
- Data Types
- Error Codes
- Cloud Ecosystem
- Practical Tutorial
- Performance Testing
- FAQs
- Contact Us
- Glossary
- Product Policy
Cloud Hosting
Tencent Cloud TCHouse-D seamlessly integrates with cloud-based virtual hosts, cloud disks, Cloud Object Storage, and cloud-based MySQL instances, achieving the elasticity, scalability, security, high availability, and reliability of cloud-native data warehouses. For example, data can be stored in cloud disks and Cloud Object Storage systems, and metadata can be stored in cloud-based MySQL instances.
OPS and Monitoring
Tencent Cloud TCHouse-D comes with a comprehensive set of cluster management features, reducing the need for laborious manual operations. Nearly all OPS tasks, including cluster scaling, up/downgrading, and parameter configuration, can be accomplished through console operations. Moreover, Tencent Cloud TCHouse-D offers visual monitoring features to peek into cluster operations, aiding businesses to be aware of cluster status timely.
Security and Reliability
Tencent Cloud TCHouse-D allows users to separately deploy clusters and supports isolation by Virtual Private Cloud (VPC). This enhances data access security. It also supports a data replica mechanism to ensure seamless service disaster recovery and failover.
MySQL Protocol Compatibility
A connection API compatible with the MySQL protocol is provided. This enables users to directly use MySQL-related libraries or tools, eliminating the need to deploy new client libraries or tools separately. The API provides easy compatibility with upper-layer applications. This also lower the learning curve and make it easy for users to get started.
High Throughput for Large Queries
The Massively Parallel Processing (MPP) architecture allows concurrent execution of queries on multiple nodes in a distributed manner, making full use of the overall compute resources of clusters and improving the throughput for large queries.
High Concurrency for Small Queries
Using technologies such as partition pruning, pre-aggregation, predicate pushdown, vectorized execution, and asynchronous RPC, Tencent Cloud TCHouse-D supports high-concurrency point query scenarios.
Data Update
Tencent Cloud TCHouse-D supports deleting and updating data by primary key. It conveniently synchronizes real-time updated data from transactional databases like MySQL.
High Availability and Reliability
Data and metadata are stored in three-replica storage by default (this feature requires at least 3 backend (BE) nodes). This ensures data reliability even if some nodes go down. In addition, Tencent Cloud TCHouse-D automatically checks and repairs corrupted data while routing requests to healthy nodes, ensuring data availability around the clock.
Horizontal Scaling and Data Balancing
Both frontend (FE) and backend (BE) nodes can be scaled horizontally. Users can flexibly scale nodes based on computation and storage needs. When BE nodes are scaled, Tencent Cloud TCHouse-D automatically balances data shards based on the load on the nodes, eliminating the need for manual intervention.
Pre-aggregation Engine
Tencent Cloud TCHouse-D supports storing pre-aggregated data results using a rollup table, improving query efficiency in some aggregation scenarios.
Efficient Column-oriented Storage Engine
The column-oriented storage format is used to enhance the query efficiency in the Online Analytical Processing (OLAP) field. This format is coupled with various encoding methods, such as dictionary encoding and Run-Length Encoding (RLE), to provide a high data compression ratio that helps save storage space. Moreover, various query acceleration technologies like smart min/max index, sparse index, Bloom filter, and bitmap inverted index are used to boost query efficiency.
Online Table Structure Modification
Tencent Cloud TCHouse-D allows users to modify the table structure after data is imported, including adding columns, deleting columns, changing column types, and changing the column order. These operations will not affect existing query and write operations on the database.
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