CN113806366A - Atlas-based method for realizing multidimensional metadata joint query - Google Patents
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Abstract
The invention discloses a method for realizing multi-dimensional metadata joint query based on Atlas, belonging to the technical field of data query. The method for realizing the multi-dimensional metadata joint query based on Atlas creates the custom Atlas type, performs classified storage according to the custom Atlas type, and performs classified query and joint retrieval according to the content after the classified storage. The Atlas-based method for realizing multidimensional metadata joint query can well realize the expansion of the underlying service without changing the source code of the underlying Atlas service module, greatly reduces the development cost and the failure rate, and has good popularization and application values.
Description
Technical Field
The invention relates to the technical field of data query, and particularly provides a method for realizing multidimensional metadata joint query based on Atlas.
Background
With the advent of the cloud era, it has become increasingly difficult to know which data came from where and how it changed over time, facing a vast and ever-increasing variety of data objects. The Hadoop is adopted, the actual condition of data management must be considered, and metadata and data governance become important parts of an enterprise-level data lake. Atlas is used to manage the aspects of shared metadata, data classification, auditing, security, data protection, etc., and is integrated with Apache range in an effort to be used in data authority control policies. Apache Atlas is a Hadoop data governance and metadata framework that provides a scalable and extensible core base data governance service set, allowing enterprises to efficiently and effectively meet compliance requirements in Hadoop and allow integration with the data ecosystem of the entire enterprise.
However, in actual use, a situation that multi-dimensional metadata joint query is needed is often encountered, the Atlas service does not provide the service content, the Atlas service only provides for creating a single metadata type, does not support multiple types of types for joint query, and cannot solve the problem of cross-type joint query, for example, the metadata information of the subordinate data table of the data table is jointly retrieved according to the metadata information of the data table, and the metadata information of the home database of the data table is reversely queried according to the metadata information of the data table; or, according to the metadata information of the database, scenes such as various metadata information of subordinate data columns of the database are directly queried in a cross-level mode, such situations are common user situations, codes are frequently used for hierarchical query and hierarchical filtering processing in daily processing, great development workload is brought to developers, great development cost is also brought to project managers, and the problem can be solved in a dynamic expansion mode.
Disclosure of Invention
The technical task of the invention is to provide a method for realizing multidimensional metadata joint query based on Atlas, which can well realize the expansion of bottom-layer service without changing the source code of the Atlas service module at the bottom layer and greatly reduce the development cost and the failure rate.
In order to achieve the purpose, the invention provides the following technical scheme:
a method for realizing multi-dimensional metadata joint query based on Atlas is characterized by creating a self-defined Atlas type, performing classified storage according to the self-defined Atlas type, and performing classified query and joint retrieval according to contents after classified storage.
Preferably, the Atlas-based method for implementing the multidimensional metadata joint query comprises the following steps:
s1, deploying Atlas service under Hadoop ecology;
s2, writing custom type information including but not limited to metadata ID, metadata name, metadata type, metadata creation time, metadata modification time and metadata creation user;
s3, calling RestAPI of the custom type of the Apache atlas service to create the type;
s4, calling RESTAPI of the data storage of the Apache atlas service to store the metadata information of the database, the metadata information of the data table and the metadata information of the data column;
s5, calling a corresponding RESTAPI to write the metadata contents of the database, the schema, the data table and the data column collected in the step into the Type and the entity of the defined database, schema, data table and data column respectively;
and S6, calling data of the Apache atlas service to query RESTAPI, inserting corresponding joint retrieval parameters to perform retrieval query, and returning result data.
Preferably, in step S1, Atlas-dependent basic services are deployed, including a service registry Zookeeper, an authority management service Ranger, a distributed file storage system HDFS, a distributed column database Hbase, and a graph database janusgraph.
Preferably, in step S2, the database metadata may extend the metadata database name, the metadata database type, and the metadata database address information.
Preferably, the data table metadata may extend a metadata data table name, a metadata data table Chinese name, a metadata data table description, a metadata data table security level, a metadata data table belonged database ID, a metadata data table belonged database type, a metadata data table belonged data source ID, metadata data table version information, a metadata data table tag value, and whether a metadata data table is primary tag information.
Preferably, the data column metadata may extend metadata data column name, metadata data column chinese name, metadata data column description, metadata data column length, metadata data column precision, metadata data column type, model standard ID mounted on the metadata data column, data dictionary ID mounted on the metadata data column, metadata data column data format, metadata data column security level, ID of the metadata data column belonging data table, metadata data column belonging data source type, and metadata data column belonging data source ID information.
Preferably, in step S3, the calling mode is a POST mode, the corresponding parameters are input to create the corresponding type format, if successful, a success flag is returned and the type format after successful creation is returned, and if unsuccessful, the examination and correction are performed and then the attempt is performed.
Preferably, in step S4, different metadata information, such as database metadata information, data table metadata information, and data column metadata information, is stored at different levels when stored.
Preferably, in step S5, the corresponding metadata content of each type is written into Atlas, the janusgraph data adopted by the Atlas bottom layer is used as a storage engine, Hbase is used as a storage medium, and the Solr service or ElasticSearch is used as a search engine, and the metadata information of each type is subjected to an add-delete-modify-search operation.
Preferably, in step S6, a query statement suitable for the custom Atlas type is assembled according to the incoming query conditions for query. For example, when the information of the subordinate metadata table is retrieved according to the metadata database information, the joint retrieval and query to the target data set can be realized by taking the metadata database information as an incoming parameter and taking the distinguishing information column as a retrieval target type (data table type, corresponding constant value is 'table').
Compared with the prior art, the Atlas-based method for realizing the multidimensional metadata joint query has the following outstanding beneficial effects: the method for realizing multidimensional metadata joint query based on Atlas is based on a mode of carrying out self-defining Apache Atlas type by using an RESTAPI mode provided by the underlying Atlas service, well realizes the extension of the underlying service, can be realized without changing the source code of an underlying Atlas service module, and greatly reduces the development cost and the failure rate. The invention also realizes a storage mechanism of a set of two-dimensional tables by a mode based on the user-defined type, well stores all kinds of metadata information, realizes multi-dimensional joint query according to input query conditions, is suitable for various situations requiring multi-dimensional metadata joint query, and has good popularization and application values.
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FIG. 1 is a flowchart of a method for implementing multidimensional metadata joint query based on Atlas according to the present invention.
Detailed Description
The method for implementing multidimensional metadata joint query based on Atlas according to the present invention will be described in further detail with reference to the accompanying drawings and embodiments.
Examples
The method for realizing the multi-dimensional metadata joint query based on Atlas creates the custom Atlas type, performs classified storage according to the custom Atlas type, and performs classified query and joint retrieval according to the content after the classified storage. Firstly, because the Apache Atlas service supports the mode of self-defining type, the purpose of self-defining type can be realized by calling the REST API interface provided by the Apache Atlas service, so the self-defining type information needs to be written and the corresponding REST API interface needs to be called, wherein the self-defining type information at least comprises a database metadata information column, a data table metadata information column, a data column metadata information column, a column meta _ type for distinguishing metadata attributes and a self name attribute value. Secondly, after the type is customized, a bottom storage REST API (representational State transfer) interface of Apache Atlas can be called to achieve the purpose of storage, wherein during storage, the database only stores various data containing metadata information of the database and meta _ type and name, the storage value of meta _ type is 'database', and the storage value of name is the name value of the database; the data table not only stores various data containing metadata information of the data table, but also needs to query or acquire various metadata information items, meta _ type and name of an upper data base to which the data table belongs, wherein the meta _ type is stored as a table, and the name is stored as a name value of the data table; in addition to storing various items of data containing metadata information of the data table, the data column also needs to query or obtain various items of metadata information items and meta _ type and name of the upper-layer database-level data table to which the data column belongs, where meta _ type is stored as "column" and name is stored as the name value of the data column. Finally, when the type is jointly queried by calling the multidimensional metadata through the interface, the condition to be queried is transmitted, for example, the metadata information of all subordinate data tables is queried according to the database name, namely db _ name is transmitted as the database name, and all the metadata information of the data tables with the type of db _ name as the target database name and the type of table can be queried if the type is 'table'.
As shown in FIG. 1, the method for implementing multidimensional metadata joint query based on Atlas includes the following steps:
s1, deploying Atlas service under Hadoop ecology.
Deployment Atlas depends on basic services, including a service registry Zookeeper, an authority management service Ranger, a distributed file storage system HDFS, a distributed column database Hbase, and a database janusgraph. After all the dependent services are started normally, the Atlas service is started, and whether the Atlas service is started normally is verified. Calling a custom type interface provided by Apache Atlas service to create a custom type, wherein the custom type further comprises database metadata information, data table metadata information, data column metadata information, shared metadata information and a distinguishing information column; further comprising creating a column containing all metadata information for each type of metadata and some for distinguishing the metadata type.
S2, writing custom type information including but not limited to metadata ID, metadata name, metadata type, metadata creation time, metadata modification time, metadata creation user.
1) The database metadata may extend information such as a metadata database name, a metadata database type, a metadata database address, and the like.
2) The data table metadata can expand metadata data table name, metadata data table Chinese name, metadata data table description, metadata data table security level, metadata data table belonged database ID, metadata data table belonged database type, metadata data table belonged data source ID, metadata data table version information, metadata data table tag value, metadata data table whether main table mark exists or not and other information.
3) The data column metadata can expand information such as metadata data column name, metadata data column Chinese name, metadata data column description, metadata data column length, metadata data column precision, metadata data column type, model standard ID mounted by the metadata data column, data dictionary ID mounted by the metadata data column, metadata data column data format, metadata data column security level, ID of a data table to which the metadata data column belongs, metadata column data source type, and metadata data source ID.
S3, calling RestAPI of the custom type of Apache atlas service to create the type.
The calling method is a POST mode, corresponding parameters are transmitted to create corresponding types, if the corresponding parameters are successful, a success mark is returned, the type format after the corresponding parameters are successfully created is returned, and if the corresponding parameters are unsuccessful, the calling method tries again after the examination and the correction are carried out according to the steps.
The data is stored hierarchically based on the custom Atlas type, different metadata information such as database metadata information, data table metadata information and data column metadata information can be stored according to different hierarchies when being stored, the database metadata information and a distinguishing information column need to be stored when the database type is stored, the corresponding database metadata information, the own data table metadata information and a distinguishing information column need to be stored when the data table type is stored, and the corresponding database metadata information, the metadata information of the corresponding data table, the own data column metadata information and a distinguishing information column need to be stored when the data column type is stored.
S4, calling the RESTAPI of the data storage of the Apache atlas service to store the metadata information of the database, the metadata information of the data table and the metadata information of the data column.
Different metadata information such as database metadata information, data table metadata information and data column metadata information can be stored according to different levels when being stored, and the method further comprises the steps of storing the database metadata information and a distinguishing information column when the database type is stored, storing the corresponding database metadata information, the own data table metadata information and the distinguishing information column when the data table type is stored, and storing the corresponding database metadata information, the metadata information of the corresponding data table, the own data column metadata information and the distinguishing information column when the data column type is stored.
And assembling a query statement suitable for the custom Atlas type according to the input query condition for query, and if subordinate metadata table information is retrieved according to the metadata database information, performing joint retrieval and query on a target data set by taking the metadata database information as input parameters and taking the distinguishing information as a retrieval target type (data table type, corresponding constant value is 'table').
And S5, calling the corresponding RESTAPI to write the metadata contents of the database, the schema, the data table and the data column collected in the step into the Type and the entity of the defined database, schema, data table and data column respectively.
Writing corresponding various metadata contents into Atlas, using janusgraph data adopted by the Atlas bottom layer as a storage engine, using Hbase as a storage medium, using Solr service or elastic search as a retrieval engine, and performing operations such as increasing, deleting, modifying and searching on various metadata information in the Atlas.
And S6, calling data of the Apache atlas service to query RESTAPI, inserting corresponding joint retrieval parameters to perform retrieval query, and returning result data.
And assembling a query statement suitable for the custom Atlas type according to the input query condition for query, and if subordinate metadata table information is retrieved according to the metadata database information, performing joint retrieval and query on a target data set by taking the metadata database information as input parameters and taking the distinguishing information as a retrieval target type (data table type, corresponding constant value is 'table').
The above-described embodiments are merely preferred embodiments of the present invention, and general changes and substitutions by those skilled in the art within the technical scope of the present invention are included in the protection scope of the present invention.
Claims (10)
1. A method for realizing multidimensional metadata joint query based on Atlas is characterized in that: the method creates a self-defined Atlas type, performs classified storage according to the self-defined Atlas type, and performs classified query and combined retrieval according to the contents after the classified storage.
2. The Atlas-based method for implementing multidimensional metadata federated queries as recited in claim 1, wherein: the method comprises the following steps:
s1, deploying Atlas service under Hadoop ecology;
s2, writing custom type information including but not limited to metadata ID, metadata name, metadata type, metadata creation time, metadata modification time and metadata creation user;
s3, calling RestAPI of the custom type of the Apache atlas service to create the type;
s4, calling RESTAPI of the data storage of the Apache atlas service to store the metadata information of the database, the metadata information of the data table and the metadata information of the data column;
s5, calling a corresponding RESTAPI to write the metadata contents of the database, the schema, the data table and the data column collected in the step into the Type and the entity of the defined database, schema, data table and data column respectively;
and S6, calling data of the Apache atlas service to query RESTAPI, inserting corresponding joint retrieval parameters to perform retrieval query, and returning result data.
3. The Atlas-based method for implementing multidimensional metadata federated queries as recited in claim 2, wherein: in step S1, Atlas-dependent basic services are deployed, including a service registry Zookeeper, an authority management service Ranger, a distributed file storage system HDFS, a distributed column database Hbase, and a database janusgraph.
4. The Atlas-based method for implementing multidimensional metadata federated queries as recited in claim 3, wherein: in step S2, the database metadata may extend the metadata database name, the metadata database type, and the metadata database address information.
5. The Atlas-based method for implementing multidimensional metadata federated queries as recited in claim 4, wherein: the data table metadata can expand metadata data table name, metadata data table Chinese name, metadata data table description, metadata data table security level, metadata data table belonged database ID, metadata data table belonged database type, metadata data table belonged data source ID, metadata data table version information, metadata data table tag value and whether the metadata data table is primary tag information.
6. The Atlas-based method for implementing multidimensional metadata federated queries as recited in claim 5, wherein: the data column metadata can expand metadata data column names, metadata data column Chinese names, metadata data column descriptions, metadata data column lengths, metadata data column precisions, metadata data column types, model standard IDs mounted by metadata data columns, data dictionary IDs mounted by metadata data columns, metadata data column data formats, metadata data column security levels, IDs of data tables to which metadata data columns belong, data source types to which metadata data columns belong and data source ID information to which metadata data columns belong.
7. The Atlas-based method for implementing multidimensional metadata federated queries as recited in claim 6, wherein: in step S3, the calling mode is a POST mode, the corresponding parameters are transferred to create the corresponding type format, if successful, a successful flag is returned and the type format after successful creation is returned, and if unsuccessful, the examination and correction are performed before the attempt is performed.
8. The Atlas-based method for implementing multidimensional metadata federated queries as recited in claim 7, wherein: in step S4, different metadata information, such as database metadata information, data table metadata information, and data column metadata information, are stored according to different hierarchies when stored.
9. The Atlas-based method for implementing multidimensional metadata federated queries as recited in claim 8, wherein: in step S5, writing the corresponding metadata content into Atlas, using janusgraph data adopted by the Atlas bottom layer as a storage engine, Hbase as a storage medium, and Solr service or ElasticSearch as a search engine, and performing an add/delete modify operation on the metadata information.
10. The Atlas-based method for implementing multidimensional metadata federated queries as recited in claim 9, wherein: in step S6, a query statement suitable for the custom Atlas type is assembled according to the incoming query conditions for query.
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