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Query method based on regional bitmap indexes in cloud environment

A technology of bitmap index and query method, applied in the field of information, can solve the problems of reducing system throughput, consuming large computing resources, etc., and achieving the effect of avoiding computing resource overhead and good scalability

Inactive Publication Date: 2012-10-10
PEKING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, especially represented by the most commonly used equivalence conditions, since the number of target records in most retrieval tasks is very small, executing the task in parallel in a distributed cluster often causes many data nodes that do not store any target records to also trigger the retrieval process, and will eventually return an empty set
In the case of frequent retrieval tasks, this parallel execution process will consume a large amount of unnecessary computing resources, which will eventually reduce the throughput of the system

Method used

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  • Query method based on regional bitmap indexes in cloud environment
  • Query method based on regional bitmap indexes in cloud environment
  • Query method based on regional bitmap indexes in cloud environment

Examples

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Effect test

example 1

[0051] Example 1: Given a company's employee information table, set the table attribute to A 1 Indicates the gender of the employee, including male male and female female; attribute A 2 Record employee salary, the value is an integer in the range [0, 3000]. First, attribute A 1 The value domain of is divided into two sub-domains, including only male and female respectively; attribute A 2 The value range of is divided into three subfields: [0, 1000], (1000, 2000] and (2000, 3000]. Consider employee 1, whose gender is male and salary is 1300, then the bit string corresponding to employee 1 It is: '10010'. The first two digits of '10' represent attribute A 1 The upper value is male, and the last three digits of '010' indicate that his salary is within the range (1000,2000]. Then consider employee 2, assuming that his gender is female and his salary is 2600, then the bit string corresponding to this employee is: '01001 '. The first two '01' represent attribute A 1 The upper v...

example 2

[0053] Example 2: The assumptions about the employee information table are the same as Example 1. Such as Figure 5 As shown, assume that the table has a total of 7 records on a certain data node. Since B=6, the length of the indicated bitmap on this data node is 6. And since this data node only contains tuples with sort values ​​1, 3, 4, and 5, its indication bitmap should be: 101110.

[0054] Indicates that the bitmap records the presence of a local property value. When a query request arrives at a distributed data node, it first determines whether the data node contains the target tuple by comparing the indicator bitmap, and if not, returns a null value directly without executing the retrieval task. The comparison of the indicated bitmaps is accomplished through the bitwise logical AND operation of the bit strings.

example 3

[0055] Example 3: The assumptions about the employee information table are the same as Example 1. Such as Figure 5 As shown, assume that the table has a total of 7 records on a certain data node. It can be seen from example 3 that the indicated bitmap is 101110. Assuming that the user sends a query for employees whose gender is female and whose salary range is within (1000,2000], the corresponding bit string of the target record can be generated accordingly, and its sorting value is 2. Therefore, the constructed bit string 01000 and the existence of 101110 Perform logical bitwise AND: 01000&101110=0, the result is all zeros, so there is no target tuple on this node, and an empty set can be directly returned as the query result of this node.

[0056] Finally, a bitmap index is established for the locally managed data on each data node.

[0057] Similar to the above-mentioned distributed solution, this method also adopts the way that the data nodes independently manage the i...

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Abstract

The invention provides a query method based on regional bitmap indexes in a cloud environment. The method comprises the following steps of: 1) establishing the regional bitmap indexes; 1.1) performing range division on index attributes on a data table in the cloud environment to generate a global sequencing table of attribute values, wherein the global sequencing table is used for sequencing tuples by using a set rule; 1.2) establishing an indicating bitmap on each data node according to the range division result, wherein the indicating bitmap records the storage condition of local attribute values; 1.3) establishing a local bitmap index on each data node according to the framework of the cloud environment to finish establishment of the regional bitmap indexes; and 2) inputting a query condition, establishing a condition bitmap according to the query condition by a main node, and distributing the condition bitmap to each data node, wherein the condition bitmap covers all probabilities included in the query condition; and concurrently executing retrieval task through each data node, acquiring the query result of each data node by the main node, and returning a union set of the query results of the data nodes to a user. By establishing the regional bitmap indexes, configurable parallel computing resources in the cloud environment can be fully utilized, and quick response can be provided for the data query request using capacity comparison as a condition.

Description

technical field [0001] The invention belongs to the field of information technology, and relates to a distributed bitmap indexing method in a cloud environment and querying data by using the method. Background technique [0002] Cloud computing environment and data management [0003] The rapid development of cloud computing technology provides the possibility for the storage and management of massive data. Compared with the traditional stand-alone computing environment, the cloud environment can effectively use the huge computing resources of distributed clusters to meet the demands of massive data management on computing resources and storage resources, and has excellent characteristics such as easy maintenance, easy expansion and easy management. Faced with the rapid growth of data volume, cloud computing technology can quickly adjust and allocate the required resources to adapt to the crazy expansion of data; at the same time, it can provide elastic, loosely organized s...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 孟必平王腾蛟李红燕高军杨冬青唐世渭
Owner PEKING UNIV
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