CN107291948B - Access method of distributed newSQL database - Google Patents

Access method of distributed newSQL database Download PDF

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CN107291948B
CN107291948B CN201710580791.3A CN201710580791A CN107291948B CN 107291948 B CN107291948 B CN 107291948B CN 201710580791 A CN201710580791 A CN 201710580791A CN 107291948 B CN107291948 B CN 107291948B
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data
index
result
user request
sql statement
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CN107291948A (en
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晋彤
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Yunrun Da Data Service Co.,Ltd.
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Yunrun Da Data Service Co ltd
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Abstract

The invention discloses an access method of a distributed newSQL database system, which comprises the steps of receiving a user request through a JDBC interface; calling a JDBC object method through the JDBC interface to give an SQL statement corresponding to the user request; sending the SQL statement corresponding to the user request to a specified bottom database so as to obtain a result from the bottom database according to the SQL statement of the user request; and returning the result to the user through a JDBC interface. The invention can support the access mode of JDBC interface, simplify ETL process and reduce cost.

Description

Access method of distributed newSQL database
Technical Field
The invention relates to the technical field of big data, in particular to an access method of a distributed newSQL database.
Background
HBase is currently one of the best known distributed NoSQL databases in the Hadoop ecosystem. The design concept is derived from the Bigtable of Google. The HBase main components comprise an HMmaster and an HRegionserver, a table type data model is provided for a user, a main key range is divided into a plurality of regions, the HMmaster is responsible for managing and distributing the regions, and the HRegionserver is responsible for reading and writing region data. But as more and more applications attempt to migrate to hbases: a user needs to access the HBase through API programming, a standard JDBC interface is not supported, the use cost of complex applications is too high, a lot of complex applications cannot use the HBase directly, and the use cost is high.
Disclosure of Invention
The embodiment of the invention aims to provide an access method of a distributed newSQL database, which supports an access mode of a JDBC interface, simplifies an ETL process and reduces cost.
In order to achieve the above object, an embodiment of the present invention provides an access method for a distributed newSQL database, including:
receiving a user request through a JDBC interface;
calling a JDBC object method through the JDBC interface to give an SQL statement corresponding to the user request;
sending the SQL statement corresponding to the user request to a specified bottom database so as to obtain a result from the bottom database according to the SQL statement of the user request;
and returning the result to the user through a JDBC interface.
Further, the obtaining the result from the underlying database according to the SQL statement requested by the user includes:
analyzing the SQL statement corresponding to the user request, and compiling and customizing an execution plan according to the SQL statement;
the plan is executed in parallel to obtain data for consolidation and summarization as a result.
Further, the analyzing the SQL statement corresponding to the user request, and compiling and customizing the execution plan according to the SQL statement includes:
judging whether the SQL statement already exists in the shared cache pool, if so, outputting an execution plan corresponding to the SQL statement;
otherwise, carrying out syntax check and semantic check on the SQL statement, and carrying out view and expression conversion on the SQL statement after the syntax check and the semantic check are passed;
carrying out optimizer selection according to the conversion result;
selecting a data connection mode and a connection sequence according to a selection result of the optimizer;
selecting a search path according to the connection mode and the connection sequence;
and generating an execution plan according to the search path and outputting the execution plan.
Further, the executing the plan in parallel to obtain the data for merging and summarizing as a result includes:
according to the index definition, writing index data into the index table in parallel in an inverted index mode, and accordingly establishing a secondary index; taking the established secondary index as a result; wherein the user request comprises stored data;
or, the index table is firstly inquired according to the index definition and the inquiry condition, and the data table is inquired again through the inquiry result of the index table in parallel; taking as a result the data obtained from the data table; wherein the user request comprises query data;
further, the executing the plan in parallel to obtain the data for merging and summarizing as a result includes:
taking JSON data as a common character string type as a whole and writing the JSON data into a data table of hbase as a field; extracting data in JSON according to the field description, writing the index data into another hbase index table in an inverted index mode, and finishing storage of semi-structured data; returning the index data established as a result; wherein the user request comprises storing JSON data;
or, according to the query condition, the index table is queried in parallel; returning the index ID of the index table to the parallel task executor; inquiring a data table according to the index ID to obtain corresponding JSON data as a result; wherein the user request comprises querying JSON data;
further, the executing the plan in parallel to obtain the data for merging and summarizing as a result includes:
generating an image data format encrypted by an information abstract algorithm for the image data, and writing the encrypted image data into an original data table; writing the encrypted picture data into a picture data table in parallel for independent storage, and taking the picture data written as a result; wherein the user request comprises storing picture data;
or, according to the query condition, querying the original data table to obtain the image data encrypted by the information abstract algorithm; according to the encrypted image data in parallel; and inquiring the picture data table, and acquiring the picture data as a result, wherein the user request comprises the inquired picture data.
Further, the executing the plan in parallel to obtain the data for merging and summarizing as a result includes:
writing fields needing full-text retrieval into a data table of hbase as a common character string type for storage, writing data into a search engine server for indexing according to field description, and taking the established index as a result; wherein the user request comprises a field for obtaining full-text retrieval;
or, according to the query condition, querying a specific index ID in the search engine server, returning the index ID by the search engine server according to the query condition, and querying a data table according to the index ID to obtain query data as a result; wherein the user request comprises a field for querying full text retrieval.
Compared with the prior art, the method disclosed by the invention receives the user request through the JDBC interface; calling a JDBC object method through the JDBC interface to give an SQL statement corresponding to the user request; sending the SQL statement corresponding to the user request to a specified bottom database so as to obtain a result from the bottom database according to the SQL statement of the user request; the technical scheme of returning the result to the user through the JDBC interface solves the problems that the prior art does not support the standard JDBC interface and the use cost of complex application is too high, and has the advantages of simplifying the ETL process and reducing the cost.
Drawings
Fig. 1 is a schematic flow chart of an access method of a distributed newSQL database in an embodiment of the present invention;
FIG. 2 is a flowchart illustrating the step S3 of the accessing method of the distributed newSQL database according to the embodiment of the invention;
fig. 3 is a flowchart illustrating the step S31 of the access method for the distributed newSQL database according to the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, it is a schematic flow chart of an access method of a distributed newSQL database provided in embodiment 1 of the present invention, including the steps of:
s1, receiving a user request through the JDBC interface;
s2, calling a JDBC object method through the JDBC interface to give an SQL statement corresponding to the user request;
s3, sending the SQL sentence corresponding to the user request to the specified bottom database so as to obtain a result from the bottom database according to the SQL sentence requested by the user;
and S4, returning the result to the user through the JDBC interface.
Existing hbases only provide access interfaces for Java Api, which encapsulates a large number of data manipulation interfaces. But its mode of operation and method is the Hbase specific Api mode. Completely different from the JDBC mode to which people are accustomed. The use cost is high for the common users. According to the embodiment, the user request is accessed in a mode of a standard JDBC/ODBC interface through the process, namely, the access to the distributed newSQL database is realized, the common operation habit of people is kept, the standard JDBC interface is supported, the cleaning, converting and loading processes in the whole ETL process are simplified, and the user cost is reduced.
Further, referring to fig. 2, the step S3 of obtaining the result from the underlying database according to the SQL statement requested by the user includes:
s31, analyzing the SQL statement corresponding to the user request, compiling and customizing the execution plan according to the SQL statement;
s32, executing the plan in parallel to acquire the data for merging and summarizing as a result.
Further, referring to fig. 3, the step S31 of analyzing the SQL statement corresponding to the user request specifically includes:
s311, judging whether the SQL statement already exists in the shared cache pool, if so, outputting an execution plan corresponding to the SQL statement; otherwise, carrying out syntax check and semantic check on the SQL statement, and carrying out view and expression conversion on the SQL statement after the syntax check and the semantic check are passed;
s312, carrying out optimizer selection according to the conversion result;
s313, selecting a data connection mode and a connection sequence according to a selection result of the optimizer;
s314, selecting a search path according to the connection mode and the connection sequence;
and S315, generating an execution plan according to the search path and outputting the execution plan.
Further, step S32 executes the plan in parallel to acquire the data for merging and summarizing as a result, including:
according to the index definition, writing the index data into an index table in parallel in an inverted index mode, and thus establishing a secondary index; taking the established secondary index as a result; wherein the user request comprises stored data;
or, firstly inquiring the index table according to the index definition and the inquiry condition, and parallelly inquiring the data table again through the inquiry result of the index table; taking the data obtained from the data table as a result; wherein the user request comprises query data;
further, for the data requested by the user being JSON data, the step S32 concurrently executes the plan to obtain the data for merging and summarizing as a result may include:
taking JSON data as a common character string type as a whole and writing the JSON data into a data table of hbase as a field; extracting data in JSON according to the field description, writing the index data into another hbase index table in an inverted index mode, and finishing storage of semi-structured data; returning the index data established as a result; the user request comprises storing JSON data;
or, according to the query condition, the index table is queried in parallel; returning the index ID of the index table to the parallel task executor; inquiring a data table according to the index ID to obtain corresponding JSON data as a result; the user request comprises JSON data query;
further, for the data requested by the user as picture data, the step S32 executes the plan in parallel, so that the acquiring the data for merging and summarizing as a result includes:
generating an image data format encrypted by an information abstract algorithm for the image data, and writing the encrypted image data into an original data table; writing the encrypted picture data into a picture data table in parallel for independent storage, and taking the written picture data as a result; wherein the user request comprises storing picture data;
or, according to the query condition, querying the original data table to obtain the image data encrypted by the information abstract algorithm; in parallel, based on the encrypted image data; and inquiring the picture data table, and acquiring the picture data as a result, wherein the user request comprises the inquired picture data.
Further, for the data requested by the user as full-text search data, the step S32 executes the plan in parallel to obtain the data for merging and summarizing as a result, including:
writing fields needing full-text retrieval into a data table of hbase as a common character string type for storage, writing data into a search engine server for indexing according to field description, and taking the established index as a result; the user request comprises a field for acquiring full-text retrieval;
or, according to the query condition, querying a specific index ID in the search engine server, returning the index ID by the search engine server according to the query condition, and querying the data table according to the index ID to obtain query data as a result; wherein the user request comprises a field for querying full text retrieval.
When the method is specifically implemented, firstly, a user request is received through a JDBC interface; then, calling a JDBC object method through a JDBC interface to give an SQL statement corresponding to the user request; then sending the SQL statement corresponding to the user request to a specified underlying database so as to obtain a result from the underlying database according to the SQL statement of the user request; and finally, returning the result to the user through the JDBC interface.
The embodiment solves the problems of high cost, no support for complex query and low availability of the hbase in practical application, keeps the JDBC mode which people are used to, simplifies the ETL process and reduces the user cost.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.

Claims (5)

1. An access method of a distributed newSQL database system is characterized by comprising the following steps:
receiving a user request through a JDBC interface;
calling a JDBC object method through the JDBC interface to give an SQL statement corresponding to the user request;
sending the SQL statement corresponding to the user request to a specified bottom database so as to obtain a result from the bottom database according to the SQL statement of the user request; wherein the obtaining a result from the underlying database according to the SQL statement requested by the user comprises:
analyzing the SQL statement corresponding to the user request, and compiling and customizing an execution plan according to the SQL statement; specifically, whether the SQL statement already exists in the shared cache pool is judged, and if yes, an execution plan corresponding to the SQL statement is output; otherwise, carrying out syntax check and semantic check on the SQL statement, and carrying out view and expression conversion on the SQL statement after the syntax check and the semantic check are passed; carrying out optimizer selection according to the conversion result; selecting a data connection mode and a connection sequence according to a selection result of the optimizer; selecting a search path according to the connection mode and the connection sequence; generating an execution plan according to the search path and outputting the execution plan;
executing the execution plan in parallel to obtain data for merging and summarizing as a result;
and returning the result to the user through a JDBC interface.
2. The method of claim 1, wherein said executing said execution plan in parallel to obtain data for consolidation and summarization as a result comprises:
according to the index definition, writing index data into the index table in parallel in an inverted index mode, and accordingly establishing a secondary index; taking the established secondary index as a result; wherein the user request comprises stored data;
or, the index table is firstly inquired according to the index definition and the inquiry condition, and the data table is inquired again through the inquiry result of the index table in parallel; taking as a result the data obtained from the data table; wherein the user request comprises query data.
3. The method of claim 1, wherein said executing said execution plan in parallel to obtain data for consolidation and summarization as a result comprises:
taking JSON data as a common character string type as a whole and writing the JSON data into a data table of hbase as a field; extracting data in JSON according to the field description, writing the index data into another hbase index table in an inverted index mode, and finishing storage of semi-structured data; returning the index data established as a result; wherein the user request comprises storing JSON data;
or, according to the query condition, the index table is queried in parallel; returning the index ID of the index table to the parallel task executor; inquiring a data table according to the index ID to obtain corresponding JSON data as a result; wherein the user request comprises querying JSON data.
4. The method of claim 1, wherein said executing said execution plan in parallel to obtain data for consolidation and summarization as a result comprises:
generating an image data format encrypted by an information abstract algorithm for the image data, and writing the encrypted image data into an original data table; writing the picture data into a picture data table in parallel for independent storage, and taking the picture data written as a result; wherein the user request comprises storing picture data;
or, according to the query condition, querying the original data table to obtain the image data encrypted by the information abstract algorithm; according to the encrypted image data in parallel; and inquiring the picture data table, and acquiring the picture data as a result, wherein the user request comprises the inquired picture data.
5. The method of claim 1, wherein said executing said execution plan in parallel to obtain data for consolidation and summarization as a result comprises:
writing fields needing full-text retrieval into a data table of hbase as a common character string type for storage, extracting index data according to field description, writing the index data into an index table in a search engine server, and taking the established index as a result; wherein the user request comprises a field for obtaining full-text retrieval;
or, according to the query condition, querying a specific index ID in the search engine server, returning the index ID by the search engine server according to the query condition, and querying a data table according to the index ID to obtain query data as a result; wherein the user request comprises a field for querying full text retrieval.
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