Comparison Item | HBase | ClickHouse | Doris |
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Application Scenario | - Real-time data storage and processing: HBase is a distributed, column-oriented open-source data warehouse. It is designed to handle massive amounts of data, both real-time and non-real-time, and is well-suited for scenarios requiring high-concurrency read and write operations.
- Big data query and analysis: HBase integrates with big data processing frameworks like Hadoop and Spark to deliver fast data access, making it suitable for big data analysis scenarios.
- Data warehouse: HBase functions as a component of a data warehousing solution. It stores structured or semi-structured data and supports real-time data query and analysis.
| - Real-time data analysis
This component applies to data analysis scenarios that require low latency and quick response. - Log and monitoring analysis: This component is used for log aggregation and monitoring data analysis systems.
- Real-time user behavior analytics: Data of user behaviors (such as clicks, browsing, and purchase behaviors) can be analyzed in real time, which is used for product recommendation, user preference analysis, and ad placement optimization.
- Massive data processing
Data warehouse: TB-level or even PB-level data is supported. It is usually used to build an enterprise data platform to support enterprise decision-making and data insight by aggregating data sources and analyzing historical data. - OLAP
This component delivers excellent performance and can handle complex analytical queries like aggregation, filtering, and sorting. It improves complex query execution efficiency through column-based storage, data compression, and query optimization.
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Supported Capability | - Mass data storage
- It can store massive structured, semi-structured, and unstructured data. A single HBase table can accommodate tens of billions of rows and millions of columns, and data can be inserted horizontally and vertically. Therefore, HBase exhibits high elasticity and capacity.
- HBase can implement large-scale data storage by deploying inexpensive server clusters with high scalability.
- Fast random query
It is suitable for processing large-scale datasets, supports fast random access, and supports real-time data update and insertion. - NoSQL query
HBase is suitable for querying and analyzing data using non-SQL statements.
| - Data query and analysis of ultra-large wide tables
It supports data query and analysis of a large wide table with thousands of columns, delivering subsecond-level performance. - Reads > Writes
Partial columns in a large wide table can be read. Reading one column at a time can meet requirements. Data is integrated in append or update batches. - Distributed scalability
It adopts a distributed architecture and supports flexible horizontal scaling.
| - Real-time data import, query, and analysis
Data can be imported from various data sources such as Kafka, Hadoop, and MySQL in real time, and can be queried and analyzed in real time. - High-performance online multi-table join query and analysis
It supports high-concurrency multi-table join query and analysis on PB-level massive data. With subsecond-level performance, it is applicable to real-time analysis and service reports. - Distributed scalability
It adopts a distributed architecture and supports flexible horizontal scaling. - Flexible data models
Multiple data models and data types are supported to meet requirements of different service scenarios.
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Not Recommended Capability | - No support for OLTP capability
The ACID capability is not supported. RDS capabilities are unavailable in terms of atomicity, consistency, and real-time performance of point query transactions. - No support for SQL statement capability
There is no support for using SQL statements to import or query data.
| - Not applicable to scenarios where a small amount of data is imported and updated frequently
There is no support for full update or deletion operations. Existing data cannot be modified or deleted at a high frequency and low latency. Only batch deletion or modification of data is supported. - Not good at sorting out unstructured data
It is effective in querying and analyzing structured data based on SQL statements, but not good at processing semi-structured or unstructured data. - Weak multi-table join operation capability
| - WeakOLTP capability
The ACID capability is not supported. RDS capabilities are unavailable in terms of atomicity, consistency, and real-time performance of point query transactions. - Not applicable to scenarios where a small amount of data is imported and updated frequently
There is no support for full update or deletion operations. Existing data cannot be modified or deleted at a high frequency and low latency. Only batch deletion or modification of data is supported. - Weak query and analysis capabilities for large wide tables
It is suitable for query and analysis of small- and medium-sized data warehouses. The query and analysis performance of ultra-wide tables with more than 1000 columns is weak. - Not good at sorting out unstructured data
It is effective in querying and analyzing structured data based on SQL statements, but not good at processing semi-structured or unstructured data.
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