Construction and management method for secondary indexes of key value library

Abstract

The invention relates to a construction and management method for secondary indexes of a key value library and belongs to the technical field of computer data management. The construction and management method for secondary indexes of the key value library mainly aims at performing index on columns in the key value library so as to accelerate range query of the columns. The method includes initializing an index information column family and an index entry column family needed by the secondary indexes of the key value library and setting a comparison type of column names of the column families as a composite column name type; building a batch processing operation list of the secondary indexes to data to be indexed and submitting the completion of an index creation to the key value library; and according to the query requests of a user and with an index name serving as a column key, building a minimum and a maximum of a composite column name with query ranges and obtaining and sending back query results from the index information column family. The method is based on column families of the key value library, indexes are performed by building of the composite column name, most operations of the indexes are column operations, the speed is rapid, and efficient range inquires are facilitated for users.

Description

technical field [0001] The invention relates to a method for constructing and managing an auxiliary index of a key-value database, and belongs to the technical field of computer data management. Background technique [0002] The rapid development of the Internet has brought about an explosive growth of data, which not only increases exponentially in quantity, but also has complex and diverse data types and structures, which cannot be represented by relational two-dimensional tables, making traditional relational databases unable to process and analyze such data. large-scale data. Therefore, the industry and academia began to consider new data models to process massive unstructured data. In 2006, Google published a paper on BigTable, revealing Google's core technology for processing massive data. The data model of the wide table is flexible and changeable, and there is no need to pre-define the columns of the table, and the wide table can dynamically add columns at runtime. ...

AI Technical Summary

Problems solved by technology

This indexing method is simple, but has two major disadvantages. First, the indexed data may not be local but on other machines, which will cause data transmission consumption. Second, data updates and index updates are not atomic, which will cause data inconsistencies