OLAP (On-Line Analysis Processing) Online Analysis Processing is a **** enjoy multi-dimensional information rapid analysis technology; OLAP uses multi-dimensional database technology to enable users to observe data from different perspectives; OLAP is used to support the complexity of the analytical operations, focusing on decision-making support for managers can meet the analysts quickly, Flexibility to carry out the requirements of the complex query of large data volume, and in an intuitive, easy to understand the form of query results to assist in decision-making.

(1) Variables (metrics)

Variables are data metrics, the actual meaning of the data, i.e., describing what the data "is". Like the number of people in the example.

(2) Dimension

A dimension describes a set of attributes related to a business topic, and a single attribute or set of attributes can form a dimension. For example, education, ethnicity, and gender in the example are all dimensions.

(3) Hierarchy of dimensions

A dimension can often have multiple hierarchies, for example, the time dimension is divided into levels such as year, quarter, month, and day, and the regional dimension can be the level of the country, region, province, and city. The hierarchy here indicates the degree of data refinement, corresponding to the conceptual hierarchy. The drill-down operation described later is a mapping from low-level concepts to high-level concepts. Concept hierarchies can be achieved by discretizing and grouping the data, in addition to determining the full and partial order relationships of the concepts.

(4) Membership of dimensions

If a dimension is multilevel, the values of different levels constitute a dimension member. Some of the dimensional hierarchies can also be members of dimensions, for example, "quarter of a year", "quarter of a month", etc. can be members of the time dimension.

(5) Multidimensional arrays

Multidimensional arrays are represented as combinations of dimensions and measures. A multidimensional array can be represented as (dimension 1, dimension 2, ......, dimension n, variable), e.g. (department, grade, ethnicity, gender, number of people) to form a multidimensional array.

(6) Data cells (cells)

Multidimensional array of values. The value of a variable is uniquely determined when each dimension in a multidimensional array has a defined value. A data unit can be represented as (Dimension 1 member, Dimension 2 member, ......, Dimension N member, value of the variable), e.g. (Ministry of Personnel and Education, Skills, Hui, Male, 1) represents a data unit that indicates that there is 1 Hui male in the Ministry of Personnel and Education's grade of Skills.

(7) facts

Facts are different dimensions in a certain value under the measure, for example, the above Ministry of Personnel Education grade is the skill of the Hui males have 1 person is expressed in the department, grade, ethnicity, gender, four dimensions on the number of enterprises in the fact of the measure and in the number of people in the fact of the fact of the inclusion of departmental dimensions of the Ministry of Personnel Education, a dimension of this Level, if all dimensions of the number of people facts are taken into account, it constitutes a multidimensional analysis cube about the number of people.

Compared to OLAP, electronic data tables do not have the multidimensionality, hierarchy, dimensionality calculations, and separation of structure and view that characterize OLAP.

1. Fast. End-users have high demand for fast system response. Surveys show that users become impatient if they don't get a response within 30 seconds. Therefore, OLAP platforms use a variety of techniques to improve response speed, such as specialized data storage formats, extensive preprocessing, and special hardware design, etc. By reducing the dynamic computation of online analysis and processing, and by storing the data required by the OLAP granularity in advance, the main means to obtain an increase in the response speed of the OLAP, but in spite of this, the slow response to the query is still a frequently mentioned problem in OLAP products.

2. Analyzable. Users can apply the OLAP platform to analyze data, as well as use other external analytical tools, such as spreadsheets, which essentially provide analytics to users in an intuitive way.

3. *** Enjoyment. The perception that OLAP is read-only and requires only simple security management has led to many of today's OLAP products having a number of issues with security *** enjoyment. So when multiple users access the OLAP server, the system is locked at the appropriate granularity.

4. Multi-dimensional. Dimension is the core concept of OLAP, and multidimensionality is a key attribute of OLAP, which complements exactly the multidimensional data organization of a data warehouse. In order to enable users to view data from multiple dimensions and data granularity, and to understand the information embedded in the data, the system needs to provide multi-dimensional analysis of the data, including a variety of operations such as slicing, rotating, and drilling down

Categorized by Processing Mode

Categorized by Storage Mode

Since ROLAP models multidimensional data with relational tables, it is more complex to access than MOLAP. MOLAP can use multi-dimensional query language directly to the user query into the form of MDDB can handle, but multi-dimensional data storage of large amounts of data will be due to data sparse and waste a lot of storage space; therefore, many OLAP service providers are using hybrid OLAP technology.

Limited space, on the OLAP aspects of the content is introduced to this, you are interested in continuing to understand the OLAP engine Mondrian. the back will share more devops and DBA content, interested friends can pay attention to!