CN112347108A - Data query method and system suitable for hybrid backend - Google Patents
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Abstract
The invention provides a data query method and a data query system suitable for a hybrid backend, which comprise the following steps: step 1: through API operation instruction, SQL syntax analysis is carried out; step 2: generating an abstract syntax tree through SQL syntax analysis; and step 3: converting the abstract syntax tree into a logical table operation tree, and inquiring and acquiring metadata information of the table and the column through a verifier; and 4, step 4: and creating a query time domain through the configured metadata information, and executing a logic operation tree of the query. The invention can directly provide service for the final user, hide the final data storage information, provide functions of authority control, data access control and the like, and the user can conveniently inquire data only by knowing the structure of the logic table.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a data query method and a data query system suitable for a hybrid backend.
Background
The existing systems such as HIVE, Spark, Flink, Impala and the like do not provide or only provide PushDown operations of filtering and field extraction when the SQL-like statement computation engine is used for processing data. When serving as a TableApi, the external service is provided only by a simpler metadata management mode, and there is no perfect data authority control mechanism. Knowledge of the format and location of the data store is also required to process the data when providing Api.
Patent document CN1858744A (application number: CN200610064845.2) discloses a data query system and a data query method, the data query system includes a plurality of databases, each database is connected with a query terminal, each query terminal and the database are connected with a communication network, wherein the database is provided with a data storage module, a data pointer storage module, and a query request analysis module connected with the data storage module, the data pointer storage module, the query terminal and the communication network. The data query method comprises the steps that the data query terminal sends a data query request to the local database, acquires a data pointer pointing to the database in a different place, and resends the data query request to the database in the different place.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a data query method and a data query system suitable for a hybrid backend.
The data query method applicable to the hybrid backend provided by the invention comprises the following steps:
step 1: through API operation instruction, SQL syntax analysis is carried out;
step 2: generating an abstract syntax tree through SQL syntax analysis;
and step 3: converting the abstract syntax tree into a logical table operation tree, and inquiring and acquiring metadata information of the table and the column through a verifier;
and 4, step 4: and creating a query time domain through the configured metadata information, and executing a logic operation tree of the query.
Preferably, the execution environment for creating the query during the query includes the configuration information of the metadata base and the data processing engine, a query session is created during each query, the query statement is converted into an operation tree, and then the operation tree is handed to the query session for execution.
Preferably, the authority control is carried out on the sub-libraries of the table through the metadata information of the table;
the authority of the column is controlled by the metadata information of the column, and the visibility of the column is configured.
Preferably, the step 4 comprises:
step 4.1: building a database definition statement DDL, and building or modifying a database;
step 4.2: constructing a data query statement DQL and querying data;
step 4.3: and constructing a data operation statement DML and modifying the metadata definition.
Preferably, the step 4.1 comprises: modifying metadata operation, modifying sub-library and sub-table definition, modifying storage back end, modifying data table structure, modifying column metadata information and modifying user authority.
Preferably, the step 4.2 comprises: judging the operation type of the query, and if the operation type is a data query operation, performing data query and returning a result; and if the operation is the metadata query operation, performing the metadata query and returning a result.
Preferably, the step 4.2 comprises:
step 4.2.1: optimizing the query statement, and searching an optimal query path according to the query cost;
step 4.2.2: converting the operation tree into DAG of the physical execution step, processing the DAG by a data processing engine, and printing the execution step into an execution path diagram;
step 4.2.3: converting the DAG into a corresponding execution engine step, and returning an intermediate result set by the execution engine;
step 4.2.4: and executing the query, calling the intermediate result set to return, executing the query calculation task, and returning the calculation result.
Preferably, the nodes in the transformation operation tree, including the target table udf;
the rules include: udf into operation trees, target tables into operation trees, operation trees into target tables, or transformation according to custom rules.
Preferably, the step 4.3 comprises:
judging the operation type, and if the operation type is insertion, executing data insertion operation; if the operation type is updating, executing data updating operation;
the conversion operation tree is used for converting the logic table, including field conversion and storage back-end conversion;
inquiring metadata, modifying the write-in operation of the physical table, and defining according to the metadata;
and executing data writing operation, and writing the data into the physical storage according to the converted format.
The data query system suitable for the hybrid backend provided by the invention comprises:
module M1: through API operation instruction, SQL syntax analysis is carried out;
module M2: generating an abstract syntax tree through SQL syntax analysis;
module M3: converting the abstract syntax tree into a logical table operation tree, and inquiring and acquiring metadata information of the table and the column through a verifier;
module M4: and creating a query time domain through the configured metadata information, and executing a logic operation tree of the query.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention can directly provide service for the final user, hide the final data storage information, provide functions of authority control, data access control and the like, and the user can conveniently inquire data only by knowing the structure of the logic table;
2. the invention can support various metadata back ends such as catalogs, HIVE or self-contained OLAP metadata warehouse through flexible metadata management, and supports the simultaneous query of tables in different metadata warehouses, the OLAP metadata warehouse supports the mapping of configuration fields, segments by rows or columns, and the back end stores the information such as databases, authorities and the like;
3. the flexible execution engine supports various mainstream stream batch processing frameworks; search statements that can support different formats and syntax; the existing storage structure is not modified, data migration is not carried out, and a set of flexible data query platform or data warehouse is built only by configuring metadata information; the data query middleware can be simply constructed, and the operations of optimization, database division, table division, authority control and the like can be performed on the existing database or query engine.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Example (b):
referring to fig. 1, the data query method applicable to the hybrid backend provided by the present invention includes:
step 1: the API can realize syntax tree conversion of different types of query APIs by performing SQL syntax analysis or other syntax analysis through API operation instructions, such as Mongobb pipeline and the like. The execution environment of the query (including metadata repository, custom method repository, configuration information of the data processing engine) is first created at the time of the query, and a query session is created for each query. Then the query statement is converted into an operation tree and then submitted to a query session for execution.
Step 2: and generating an abstract syntax tree through syntax analysis, wherein the process and the previous step are processed in the same program, and different query APIs need to simultaneously realize the conversion of the two processes into an operation tree of the execution step.
And step 3: the abstract syntax tree is converted into a logical table operation tree, and the process queries metadata information by using a verifier to obtain the metadata information of the table and the column and the authority information of the user. And the authority control can be performed on the sub-base of the table through the metadata information of the table. The authority of the columns can be controlled through the metadata information of the columns, the visibility of the columns can be configured, whether the visibility is included in query or not, the FieldFamily attribute can be configured on the metadata of the other columns, and the physical storage back ends of the columns can be configured according to the attribute. With these metadata definitions, a large wide table containing a large number of rows and columns can be supported in theory.
And 4, step 4: executing a logic operation tree, creating a query session through configured metadata and other information, and executing the queried logic operation tree;
wherein, the step 4 comprises the following steps:
step 4.1: DDL, database definition statement, creating or modifying database;
step 4.2: DQL, data query statement, query data;
step 4.3: DML, data operation statement, modification metadata definition;
wherein, the step 4.1 comprises the following steps:
step 4.1.1: modifying metadata operation, modifying sub-library and sub-table definition, modifying storage back end, modifying data table structure, modifying column metadata information, and modifying user authority;
wherein, the step 4.2 comprises the following steps:
step 4.2.1: judging whether the operation type of the query is a data query or a metadata information query;
step 4.2.2: judging as data query operation, performing data query and returning a result;
step 4.2.3: judging as a metadata query operation, performing metadata query and returning a result;
wherein, the step 4.2.2 comprises the following steps:
step 4.2.2.1: the nodes in the operation tree are transformed, such as the target table, udf, etc.
According to the rules as follows:
translating certain udf into operation trees, such as udf based on template definition, placeholders as parameters of the method;
-translating certain target tables into operation trees, such as views;
-replacing the operation tree with a target table, such as a materialized view;
-transforming according to custom rules;
step 4.2.2.2: and applying an optimization rule, optimizing the query statement, and searching for an optimal query path according to the query cost. In the step, query statements which can be executed by the back end are automatically merged, and as many operations as possible are pushed down to the database end for execution. In this step, if some olap systems such as clickhouse, etc. support querying other data backend such as files and mysql, etc. at the same time, the target database may be converted into the query statement of the system by configuration. For operations which are not supported by other databases, the system can be handed to a data processing engine for execution, and the default data processing engine is a stand-alone engine and can be configured as spark, flash and the like;
step 4.2.2.3: converting the operation tree into DAG of the physical execution step, processing the DAG by a data processing engine, and printing the execution step into an execution path diagram;
step 4.2.2.4: the DAG is converted to a corresponding execution engine step, which returns an intermediate result set. If the query is simple (namely the query statement can be completely pushed down to a single database query), the query is directly executed, otherwise, the data processing engine queries each database according to the pushed-down query statement and then carries out merging processing;
step 4.2.2.5: executing query, calling the intermediate result set to return, executing query calculation task, and returning calculation result;
wherein, the step 4.3 comprises the following steps:
step 4.3.1: judging the operation type, namely updating or inquiring;
step 4.3.2: insert, performing a data insertion operation;
step 4.3.3: update, which executes data updating operation;
wherein, the step 4.3.2 comprises the following steps:
step 4.3.2.1: and converting the operation tree, and converting the logic table, including field conversion, storage back-end conversion and the like. Inquiring metadata, modifying the writing operation into the writing operation of a physical table, wherein one logic table may correspond to different physical tables and the field names may be different and are defined according to the metadata;
step 4.3.2.2: and executing data writing operation, and writing the data into the physical storage according to the converted format.
The data query system suitable for the hybrid backend provided by the invention comprises:
module M1: through API operation instruction, SQL syntax analysis is carried out;
module M2: generating an abstract syntax tree through SQL syntax analysis;
module M3: converting the abstract syntax tree into a logical table operation tree, and inquiring and acquiring metadata information of the table and the column through a verifier;
module M4: and creating a query time domain through the configured metadata information, and executing a logic operation tree of the query.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A data query method suitable for a hybrid backend, comprising:
step 1: through API operation instruction, SQL syntax analysis is carried out;
step 2: generating an abstract syntax tree through SQL syntax analysis;
and step 3: converting the abstract syntax tree into a logical table operation tree, and inquiring and acquiring metadata information of the table and the column through a verifier;
and 4, step 4: and creating a query time domain through the configured metadata information, and executing a logic operation tree of the query.
2. The data query method applicable to the hybrid backend according to claim 1, wherein the execution environment of the query is created during the query, and includes configuration information of the metadata database and the data processing engine, and a query session is created during each query, and the query statement is converted into the operation tree and then executed by the query session.
3. The data query method applicable to the hybrid backend according to claim 1, wherein authority control is performed on the sub-banks of the table through metadata information of the table;
the authority of the column is controlled by the metadata information of the column, and the visibility of the column is configured.
4. The data query method applicable to the hybrid backend according to claim 1, wherein the step 4 comprises:
step 4.1: building a database definition statement DDL, and building or modifying a database;
step 4.2: constructing a data query statement DQL and querying data;
step 4.3: and constructing a data operation statement DML and modifying the metadata definition.
5. The data query method applicable to the hybrid backend according to claim 4, wherein the step 4.1 comprises: modifying metadata operation, modifying sub-library and sub-table definition, modifying storage back end, modifying data table structure, modifying column metadata information and modifying user authority.
6. The data query method applicable to the hybrid backend according to claim 4, wherein the step 4.2 comprises: judging the operation type of the query, and if the operation type is a data query operation, performing data query and returning a result; and if the operation is the metadata query operation, performing the metadata query and returning a result.
7. The data query method applicable to the hybrid backend according to claim 6, wherein the step 4.2 comprises:
step 4.2.1: optimizing the query statement, and searching an optimal query path according to the query cost;
step 4.2.2: converting the operation tree into DAG of the physical execution step, processing the DAG by a data processing engine, and printing the execution step into an execution path diagram;
step 4.2.3: converting the DAG into a corresponding execution engine step, and returning an intermediate result set by the execution engine;
step 4.2.4: and executing the query, calling the intermediate result set to return, executing the query calculation task, and returning the calculation result.
8. The data query method for the hybrid backend according to claim 1, wherein nodes in the transformation operation tree, including the target table udf;
the rules include: udf into operation trees, target tables into operation trees, operation trees into target tables, or transformation according to custom rules.
9. The data query method applicable to the hybrid backend according to claim 4, wherein the step 4.3 comprises:
judging the operation type, and if the operation type is insertion, executing data insertion operation; if the operation type is updating, executing data updating operation;
the conversion operation tree is used for converting the logic table, including field conversion and storage back-end conversion;
inquiring metadata, modifying the write-in operation of the physical table, and defining according to the metadata;
and executing data writing operation, and writing the data into the physical storage according to the converted format.
10. A data query system adapted for use in a hybrid backend, comprising:
module M1: through API operation instruction, SQL syntax analysis is carried out;
module M2: generating an abstract syntax tree through SQL syntax analysis;
module M3: converting the abstract syntax tree into a logical table operation tree, and inquiring and acquiring metadata information of the table and the column through a verifier;
module M4: and creating a query time domain through the configured metadata information, and executing a logic operation tree of the query.
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Application publication date: 20210209 |