Tencent Cloud TCHouse-D is built based on the Apache Doris kernel used by leading Online Analytical Processing (OLAP) databases and is compatible with the MySQL protocol. It integrates with the cloud big data ecosystem and provides comprehensive cluster management capabilities and a perfect inspection and alarm system, offering customers a simple-to-use, easy-to-maintain, fully managed cloud service to help quickly perform real-time OLAP data analysis. Product Features
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.
High Throughput for Large Queries
The MPP architecture allows the 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 Point Query
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. The concurrency of a 100-node cluster can reach 100,000 queries per second (QPS).
Data Update and Deletion Support
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 ensures data reliability even if some nodes go down. 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.
Rich Data Import Features and Transaction Guarantees
Tencent Cloud TCHouse-D supports multiple import methods, including real-time streaming import and large-volume data import. It also supports direct subscription to and consumption of data in Kafka. It provides import transaction support along with the import label mechanism, ensuring uniqueness and integrity of imported data and atomic consistency.
Efficient Column-oriented Storage Engine and Primary and Secondary Indexes
Tencent Cloud TCHouse-D adopts proprietary column-oriented storage format. 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
You can modify the table structure after data is imported, including adding columns, deleting columns, modifying column types, and changing the column order. These operations will not affect the current database queries and writes.
Ecosystem Support and Compatibility with Peripheral Components
Data from Cloud Object Storage, HDFS, and Kafka can be easily imported into Tencent Cloud TCHouse-D, while Flink or Spark can directly write ETL-processed data into Tencent Cloud TCHouse-D. Users can also directly query data in Tencent Cloud TCHouse-D using Spark. Tencent Cloud TCHouse-D can read data from external sources such as MySQL, PostgreSQL, SQLServer, and Oracle through Java DataBase Connectivity (JDBC). It can also read data from Elasticsearch, providing a powerful distributed SQL query layer.