Concepts: OLAP, ROLAP, molap, holap

OLAP (on-line analysis processing)It is a kind of software technology that enables analysts, administrators, or executors to quickly, consistently, and interactively access information from multiple perspectives to gain a deeper understanding of data.

OLAP is designed to meet decision-making support or specific query and report requirements in multi-dimensional environments. Its core technology is the concept of "dimension.
Dimension is a high-level classification from the perspective of observing the objective world. Dimensions generally contain hierarchical relationships, which are sometimes quite complex. By defining multiple important attributes of an object into multiple dimensions, you can compare data in different dimensions. Therefore, OLAP is also a collection of multidimensional data analysis tools.
OLAP basic multidimensional analysis operations include drilling (roll up and drill down), slice, dice, rotation, drill SS, and drill through.
* Drilling is used to change the dimension hierarchy and the granularity of analysis. It includes roll up and drill down ). Roll up summarizes low-level detailed data to high-level summary data in one dimension, or reduces the dimension, it goes from summarized data to detailed data to observe or add new dimensions.
* Slice and slice are concerned with the distribution of measurement data on the remaining dimension after a set value is selected on some dimensions. If there are only two remaining dimensions, the slice is used; if there are three, the slice is used.
* Rotation is to change the direction of a dimension, that is, to reschedule the placement of a dimension in a table (for example, row-column swaps ).

OLAP has multiple implementation methods. Different data storage methods can be divided into ROLAP, molap, and holap.

ROLAP indicates the relational database-based OLAP implementation (Relational OLAP ).With relational databases as the core, multidimensional data is represented and stored in a relational structure. ROLAP divides the multidimensional structure of a multi-dimensional database into two types of tables: fact tables used to store data and dimension keywords, and dimension tables, that is, at least one table is used for each dimension to store the description information of dimension levels, member categories, and other dimensions. A dimension table is associated with a fact table by the primary keyword and the external keyword to form a "star mode ". For complex hierarchical dimensions, to avoid occupying too much storage space for redundant data, you can use multiple tables to describe this star mode extension called "Snowflake mode ". The feature is to keep detailed data in fact tables of relational databases, and the aggregated data is also stored in relational databases. This method has the lowest query efficiency and is not recommended.

Molap indicates the implementation of OLAP based on multi-dimensional data organization ).Taking multi-dimensional data as the core, that is, molap uses multi-dimensional arrays to store data. Multi-dimensional data will form a "cube" structure in storage, in molap, "Rotate", "cut", and "slice" of "cube" are the main technologies used to generate multidimensional data reports. The feature is that the detailed data and aggregated data are stored in the cube. Therefore, the query efficiency is high, but a large amount of time and space are required when the cube is generated.

Holap indicates the OLAP implementation (Hybrid OLAP) based on the hybrid data organization ).For example, the lower layer is relational and the higher layer is multi-dimensional matrix. This method provides better flexibility. The feature is to keep detailed data in fact tables of relational databases, but the aggregated data is stored in the cube. During aggregation, it takes more time than ROLAP, and the query efficiency is higher than ROLAP, but lower than molap.