OLTP concept
Operational processing is called online transaction processing OLTP (On-Line Transaction Processing). The main goal is data processing. It is a daily operation in the database for specific businesses, usually querying and modifying a small number of records.
Users are more concerned about issues such as operation response time, data security, integrity, and the number of concurrently supported users. Traditional relational database system (RDBMS), as the main means of data management, is mainly used for operational processing.
OLAP concepts
Analytical processing is called online analytical processing OLAP (On-Line Analytical Processing), and its main goal is data analysis. Generally, complex multi-dimensional analysis is performed on historical data on certain topics to support management decisions. Data warehouse is a typical example of OLAP system, mainly used for data analysis.
OLTP and OLAP comparison
OLTP is online transaction processing, and OLAP is online analytical processing. There are many big differences in comparison. For details, please refer to the following table:
OLAP engine classification
OLAP is divided into MOLAP (Multi-dimensional OLAP), ROLAP (Relational OLAP) and HOLAP (Hybrid OLAP) according to the data storage format of the memory.
MOLAP, a storage model based on multi-dimensional arrays, is also the original form of OLAP. It is characterized by pre-computing data, exchanging space for efficiency, and both detailed and aggregated data are stored in cubes. But generating cubes requires a lot of time and space. MOLAP can choose Kylin, Druid and other open source products.
ROLAP stores data completely based on the relational model. It does not require pre-computation and can be queried instantly on demand. Both detailed and summary data are stored in relational database fact tables.
HOLAP, hybrid model, detailed data is stored in ROLAP, and aggregate data is stored in MOLAP. This method is relatively flexible and more efficient.
There are several types of open source OLAP engines. It can be seen that Doris has many advantages, such as low operation and maintenance costs, compatibility with Mysql access protocol, etc. In application scenarios of changing dimensions, non-preset dimensions, and fine-grained statistics, the MPP engine driver is used The ROLAP mode can simplify model design, reduce the cost of pre-computation, and support a good real-time interactive experience through powerful real-time computing capabilities.
doris is a ROLAP engine that can meet: flexible multi-dimensional analysis, detail + aggregation, and primary key update.