CN111858647B - Method for verifying conversion field type between data sources - Google Patents
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- CN111858647B CN111858647B CN202010770479.2A CN202010770479A CN111858647B CN 111858647 B CN111858647 B CN 111858647B CN 202010770479 A CN202010770479 A CN 202010770479A CN 111858647 B CN111858647 B CN 111858647B
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Abstract
The invention particularly relates to a method for checking conversion field types among data sources. In the method for verifying the conversion field types between the data sources, the fields in the data source base and the fields in the data source target base are mapped mutually in the mapper, and the mapping result is verified; if the data source library and the data source target library belong to the same data source, the mapping result is corresponding to which field type; and a special database is generated to store the verification of the detailed conversion, and a verification table is generated according to the verification result, so that the conversion is more accurate and convenient, and the generation of abnormal data is reduced to the greatest extent. The verification method for converting the field types between the data sources can avoid data transmission failure caused by mutual mismatching of the field mapping types between the data source library and the data source target library, so that the conversion is more accurate and convenient, the generation of abnormal data is reduced to the greatest extent, and the efficiency of rapid migration of a large amount of data is ensured.
Description
Technical Field
The invention relates to the technical field of data aggregation, in particular to a method for verifying conversion field types among data sources.
Background
Data aggregation, otherwise known as ETL, loads data of different business systems into a data warehouse. Data aggregation has various modes, and can be divided into modes such as file transmission, data extraction, content crawling, message pushing and the like according to the transmission mode of data aggregation.
The data aggregation system is software formed by the productized packaging of the distributed data pipeline tool, and mainly comprises functions of cluster monitoring, pipeline design, pipeline management, task monitoring and the like. In a pipeline design interface, in the process of converting different data sources, due to the difference of field types, the types may be mismatched in the transmission process, and abnormal data, messy codes and the like are generated, so that the data conversion does not reach the expected target.
Based on the above problems, the present invention provides a method for verifying the type of a transition field between data sources. The method aims to check whether the field types of different data sources can be converted or not, and further reminds a user of unknown accidents possibly occurring in conversion results.
Disclosure of Invention
In order to make up for the defects of the prior art, the invention provides a simple and efficient method for verifying the conversion field type between the data sources.
The invention is realized by the following technical scheme:
a method for checking the conversion field type between data sources is characterized in that: mapping fields in the data source base and fields in the data source target base in a mapper, and verifying the mapping result;
if the data source library and the data source target library belong to the same data source, the mapping result is corresponding to which field type; meanwhile, considering the byte size factor, a special database is generated to store the verification of the detailed conversion, and the specific steps are as follows:
firstly, establishing a data table for storing data sources and field types;
second, each converted situation is generated by a loop code;
thirdly, judging whether each conversion check is feasible to modify through SQL statements, wherein check results are divided into three types of true, warning and false;
true indicates convertible; warning indicates an alert to the user that anomalous data may be generated; false indicates that conversion is impossible, and prompts a user that abnormal data can be generated certainly;
and a check table is generated according to the check result, so that the conversion is more accurate and convenient, and the generation of abnormal data is reduced to the greatest extent.
Oracle data sources contain field types of NUMBER, INTEREGER, INT, SMALLINT, NUMERIC, DECIMAL, FLOAT, REAL, LONG, CHAR, NCHAR, VARCHAR, VARCHAR2, NVARCHAR2, CLOB, NCLOB, CHARACTER, TIMETAMMP, DATE, BIT, BOOL, BLOB, BFILE, RAW.
When the data source target library is an Oracle database, the numeric field type in the data source target library is mapped to INTEGER, the floating point type is mapped to FLOAT, the character type is mapped to VARCHAR, and the date type is mapped TIMESTAMP; otherwise, the verification of the conversion is false.
MySQL data sources include field types INT, TINYINT, SMALLINT, MEDIUMINT, BIGINT, YEAR, FLOAT, DOUBLE, DECIMAL, VARCHAR, CHAR, TINYTEXT, TEXT, MEDIUMTEXT, LONGTEXT, DATE, DATETIME, TIMESTAMP, TIME, BIT, BOOL, TINYTOB, MEDIUMBLOB, BLOB, LONGBLOB, VARBINARY.
GBase data source contains field types INT, TINYINT, SMALLINT, MEDIUMINT, BIGINT, YEAR, FLOAT, DOUBLE, DECIMAL, VARCHAR, CHAR, TINYTEXT, TEXT, MEDIUMTEXT, LONGTEXT, DATE, DATETIME, TIMESTAMP, TIME, BIT, BOOL, TINYTOB, MEDIUMBLOB, BLOB, LONGBLOB, VARBINARY.
When the data source target library is a MySQL database or a GBase database, the numerical field type in the data source library is mapped to INT, floating point type mapping DOUBLE, character type mapping VARCHAR and date type mapping DATETIME in the MySQL database or the GBase database; otherwise, the verification of the conversion is false.
The SqlServer data source includes field types INT, SMALLINT, NUMERIC, FLOAT, ERAL, CHAR, VARCHAR, NCHAR, NVARCHAR, DATETIME, DATETIME2, BIT, BITVATRAYING, BOOLEAN.
When the data source target library is a SqlServer database, the numerical field type in the data source library is mapped to INT, floating point type is mapped to FLOAT, character type is mapped to VARCHAR and date type is mapped to DATETIME in the SqlServer database; otherwise, the verification of the conversion is false.
The postgreSQL data source includes field types of BIGINT, BIGSERIAL, INTEREGER, SMALLINT, SERIAL, DOUBLE, PRECISION, MONEY, NUMERIC, REAL, VARCHAR, CHAR, TEXT, BIT, DATE, TIME, TIMEMETAMP, BOOL, BYTEA.
DB2 data sources contain fields of the type CHAR, VARCHAR, LONGVARCHAR, GRAPHIC, VARGRAPHIC, LONGVARGRAPHIC, TIMETAMMP, DATE, TIME, INTER, SMALLINT, BIGINT, REAL, DOUBLE, FLOAT, NUMERIC, DECIMAL, CLOB, CLDBCLOB, BLOB.
When the data source target library is a PostgreSQL database or a DB2 database, the numeric field type in the data source target library maps INTEGER in the PostgreSQL database or a DB2 database, the floating point type maps DOUBLE, the character type maps VARCHAR and the date type maps TIMESTAMP; otherwise, the verification of the conversion is false.
The hdfs data source includes fields of the type LONG, DOUBLE, STRING, BOOLEAN, DATE, TINYINT, SMALLINT, INT, BIGINT, FLOAT, VARCHAR, CHAR, TIMETAMMP.
When the data source target library is an hdfs database, the numerical field type in the data source library is mapped to INT in the hdfs database, the floating point type is mapped to DOUBLE, the character type is mapped to VARCHAR, and the date type is mapped TIMESTAMP; otherwise, the verification of the conversion is false.
The hbase data source contains field types BOOLEAN, SHORT, INT, LONG, FLOAT, DOUBLE, STRING.
The MongoDB data sources contain field types INT, LONG, DOUBLE, STRING, ARRAY, DATE, BOOLEAN, BYTES.
When the data source target library is an hbase database or a MongoDB database, the numerical field type in the data source library is mapped to INT in the hbase database or the MongoDB database, the floating point type is mapped to DOUBLE, and the character type and the date type are both mapped to STRING; otherwise, the verification of the conversion is false.
The hive data source contains the field types INT, BIGINT, FLOAT, DOUBLE, STRING, TIMETAMP.
When the data source target library is a hive database, the numerical field type in the data source library is mapped to INT, floating point type is mapped to DOUBLE, character type is mapped to STRING, and date type is mapped TIMESTAMP; otherwise, the verification of the conversion is false.
The es data source includes fields of the type ID, IP, DOUBLE, LONG, INTEGER, KEYWORD, TEXT, GEO _ POINT, GEO _ SHAPE, DATE, NESTED, OBJECT, STRING.
When the data source target library is an es database, the numeric field type in the data source library is mapped to the INTEREGER, the floating point type is mapped to the DOUBLE, the character type is mapped to the STRING, and the DATE type is mapped to the DATE; otherwise, the verification of the conversion is false.
The ftp data source contains field types of STRING, LONG, DOUBLE, BOOLEAN, DATE.
When the data source target library is an ftp database, the numerical field type in the data source library is mapped to LONG in the ftp database, the floating point type is mapped to DOUBLE, and the character type and the date type are both mapped to STRING; otherwise, the verification of the conversion is false.
The TXT data source contains a field of type STRING.
The kafka data source contains a field type of STRING.
When the data source target library is the TXT database or the kafka database, the field types in the data source library all map the STRING in the TXT database or the kafka database.
The beneficial effects of the invention are: the verification method for converting the field types between the data sources can avoid data transmission failure caused by mutual mismatching of the field mapping types between the data source library and the data source target library, so that the conversion is more accurate and convenient, the generation of abnormal data is reduced to the greatest extent, and the efficiency of rapid migration of a large amount of data is ensured.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart illustrating conversion of field types between data sources according to the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the embodiment of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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.
The source library of the data source passes through a converter (for replacing and intercepting fields, etc.) or is processed by a filter (Like, equal, etc.), and if the number of the source libraries is more than 1, the source libraries pass through an aggregator and then reach a mapper; if the types of the field mappings are not matched with each other, data transmission failure is possible, so that a checking function is added for reminding a user, and a data source target library is connected behind the mapper.
The method for verifying the conversion field type between the data sources comprises the steps of mapping fields in a data source library and fields in a data source target library in a mapper, and verifying mapping results;
if the data source library and the data source target library belong to the same data source, the mapping result is corresponding to which field type; meanwhile, considering the byte size factor, a special database is generated to store the verification of the detailed conversion, and the specific steps are as follows:
firstly, establishing a data table for storing data sources and field types;
second, each converted situation is generated by a loop code;
thirdly, judging whether each conversion check is feasible to modify through SQL statements, wherein check results are divided into three types of true, warning and false;
true indicates convertible; warning indicates an alert to the user that anomalous data may be generated; false indicates that conversion is impossible, and prompts a user that abnormal data can be generated certainly;
and a check table is generated according to the check result, so that the conversion is more accurate and convenient, and the generation of abnormal data is reduced to the greatest extent.
Oracle data sources contain field types of NUMBER, INTEREGER, INT, SMALLINT, NUMERIC, DECIMAL, FLOAT, REAL, LONG, CHAR, NCHAR, VARCHAR, VARCHAR2, NVARCHAR2, CLOB, NCLOB, CHARACTER, TIMEST, BIT, BOOL, BLOB, BFILE, RAW.
When the data source target library is an Oracle database, the numeric field type in the data source target library is mapped to INTEGER, the floating point type is mapped to FLOAT, the character type is mapped to VARCHAR, and the date type is mapped TIMESTAMP; otherwise, the verification of the conversion is false.
MySQL data sources contain field types INT, TINYINT, SMALLINT, MEDIUMINT, BIGINT, YEAR, FLOAT, DOUBLE, DECIMAL, VARCHAR, CHAR, TINYTEXT, TEXT, MEDIUMTEXT, LONGTEXT, DATE, DATETIME, TIMETAMP, TIME, BIT, BOOL, TINYTBLOB, MEDIUMBLOB, BLOB, LONGBLOB, VARBINARY.
GBase data sources contain field types INT, TINYINT, SMALLINT, MEDIUMINT, BIGINT, YEAR, FLOAT, DOUBLE, DECIMAL, VARCHAR, CHAR, TINYTEXT, TEXT, MEDIUMTEXT, LONGTEXT, DATE, DATETIME, TIMESTAMP, TIME, BIT, BOOL, TINYTBLOB, MEDIUMBLOB, BLOB, LONGBLOB, VARBINARY.
When the data source target library is a MySQL database or a GBase database, the numerical field type in the data source library is mapped to INT, floating point type mapping DOUBLE, character type mapping VARCHAR and date type mapping DATETIME in the MySQL database or the GBase database; otherwise, the verification of the conversion is false.
The SqlServer data source contains field types INT, SMALLINT, NUMERIC, FLOAT, ERAL, CHAR, VARCHAR, NCHAR, NVARCHAR, DATETIME, DATETIME2, BIT, BITVATVARYING, BOOLEAN.
When the data source target library is a SqlServer database, the numerical field type in the data source library is mapped to INT, floating point type is mapped to FLOAT, character type is mapped to VARCHAR and date type is mapped to DATETIME in the SqlServer database; otherwise, the verification of the conversion is false.
The postgreSQL data source includes field types of BIGINT, BIGSERIAL, INTEREGER, SMALLINT, SERIAL, DOUBLE, PRECISION, MONEY, NUMERIC, REAL, VARCHAR, CHAR, TEXT, BIT, DATE, TIME, TIMEMETAMP, BOOL, BYTEA.
DB2 data sources contain fields of the type CHAR, VARCHAR, LONGVARCHAR, GRAPHIC, VARGRAPHIC, LONGVARGRAPHIC, TIMETAMMP, DATE, TIME, INTER, SMALLINT, BIGINT, REAL, DOUBLE, FLOAT, NUMERIC, DECIMAL, CLOB, CLDBCLOB, BLOB.
When the data source target library is a PostgreSQL database or a DB2 database, the numeric field type in the data source target library maps INTEGER in the PostgreSQL database or a DB2 database, the floating point type maps DOUBLE, the character type maps VARCHAR and the date type maps TIMESTAMP; otherwise, the verification of the conversion is false.
The hdfs data source includes fields of the type LONG, DOUBLE, STRING, BOOLEAN, DATE, TINYINT, SMALLINT, INT, BIGINT, FLOAT, VARCHAR, CHAR, TIMETAMMP.
When the data source target library is an hdfs database, the numerical field type in the data source library is mapped to INT in the hdfs database, the floating point type is mapped to DOUBLE, the character type is mapped to VARCHAR, and the date type is mapped TIMESTAMP; otherwise, the verification of the conversion is false.
The hbase data source contains field types BOOLEAN, SHORT, INT, LONG, FLOAT, DOUBLE, STRING.
The MongoDB data sources contain field types INT, LONG, DOUBLE, STRING, ARRAY, DATE, BOOLEAN, BYTES.
When the data source target library is an hbase database or a MongoDB database, the numerical field type in the data source library is mapped to INT in the hbase database or the MongoDB database, the floating point type is mapped to DOUBLE, and the character type and the date type are both mapped to STRING; otherwise, the verification of the conversion is false.
The hive data source contains the field types INT, BIGINT, FLOAT, DOUBLE, STRING, TIMETAMP.
When the data source target library is a hive database, the numerical field type in the data source library is mapped to INT, floating point type is mapped to DOUBLE, character type is mapped to STRING, and date type is mapped TIMESTAMP; otherwise, the verification of the conversion is false.
The es data source includes fields of the type ID, IP, DOUBLE, LONG, INTEGER, KEYWORD, TEXT, GEO _ POINT, GEO _ SHAPE, DATE, NESTED, OBJECT, STRING.
When the data source target library is an es database, the numeric field type in the data source library is mapped to the INTEREGER, the floating point type is mapped to the DOUBLE, the character type is mapped to the STRING, and the DATE type is mapped to the DATE; otherwise, the verification of the conversion is false.
The ftp data source contains field types of STRING, LONG, DOUBLE, BOOLEAN, DATE.
When the data source target library is an ftp database, the numerical type field type in the data source library is mapped to LONG in the ftp database, the floating point type is mapped to DOUBLE, and the character type and the date type are both mapped to STRING; otherwise, the verification of the conversion is false.
The TXT data source contains a field type of STRING.
The kafka data source contains a field type of STRING.
When the data source target library is the TXT database or the kafka database, the field types in the data source library all map the STRING in the TXT database or the kafka database.
The above-described embodiment is only one specific embodiment 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 verifying a conversion field type between data sources is characterized in that: mapping fields in the data source base and fields in the data source target base in a mapper, and checking mapping results;
if the data source library and the data source target library belong to the same data source, the mapping result is corresponding to which field type; meanwhile, considering the byte size factor, a special database is generated to store the verification of the detailed conversion, and the specific steps are as follows:
firstly, establishing a data table for storing data sources and field types;
second, each converted situation is generated by a loop code;
thirdly, judging whether each conversion check is feasible to modify through SQL statements, wherein check results are divided into three types of true, warning and false;
true indicates convertible; warning indicates an alert to the user that anomalous data may be generated; false indicates that conversion is impossible, and prompts a user that abnormal data can be generated;
and generating a check table according to the check result.
2. A method for checking a type of a transition field between data sources according to claim 1, wherein: when the data source target library is an Oracle database, the numeric field type in the data source target library is mapped to INTEGER, the floating point type is mapped to FLOAT, the character type is mapped to VARCHAR, and the date type is mapped TIMESTAMP; otherwise, the verification of the conversion is false.
3. A method for checking a type of a transition field between data sources according to claim 1, wherein: when the data source target library is a MySQL database or a GBase database, the numerical field type in the data source library is mapped to INT, floating point type mapping DOUBLE, character type mapping VARCHAR and date type mapping DATETIME in the MySQL database or the GBase database; otherwise, the verification of the conversion is false.
4. A method for checking a type of a transition field between data sources according to claim 1, wherein: when the data source target library is a SqlServer database, the numerical field type in the data source library is mapped to INT, floating point type is mapped to FLOAT, character type is mapped to VARCHAR and date type is mapped to DATETIME in the SqlServer database; otherwise, the verification of the conversion is false.
5. A method for checking a type of a transition field between data sources according to claim 1, wherein: when the data source target library is a PostgreSQL database or a DB2 database, the numeric field type in the data source target library maps INTEGER in the PostgreSQL database or a DB2 database, the floating point type maps DOUBLE, the character type maps VARCHAR and the date type maps TIMESTAMP; otherwise, the verification of the conversion is false.
6. The method for checking a converted field type between data sources according to claim 1, wherein: when the data source target library is an hdfs database, the numerical field type in the data source library is mapped to INT in the hdfs database, the floating point type is mapped to DOUBLE, the character type is mapped to VARCHAR, and the date type is mapped TIMESTAMP; otherwise, the verification of the conversion is false.
7. A method for checking a type of a transition field between data sources according to claim 1, wherein: when the data source target library is an hbase database or a MongoDB database, the numerical field type in the data source library is mapped to INT in the hbase database or the MongoDB database, the floating point type is mapped to DOUBLE, and the character type and the date type are both mapped to STRING; otherwise, the verification of the conversion is false.
8. A method for checking a type of a transition field between data sources according to claim 1, wherein: when the data source target library is a hive database, the numerical field type in the data source library is mapped to INT, floating point type is mapped to DOUBLE, character type is mapped to STRING, and date type is mapped TIMESTAMP; otherwise, the verification of the conversion is false.
9. A method for checking a type of a transition field between data sources according to claim 1, wherein: when the data source target library is an es database, the numeric field type in the data source library is mapped to the INTEREGER, the floating point type is mapped to the DOUBLE, the character type is mapped to the STRING, and the DATE type is mapped to the DATE; otherwise, the verification of the conversion is false.
10. A method for checking a type of a transition field between data sources according to claim 1, wherein: when the data source target library is an ftp database, the numerical field type in the data source library is mapped to LONG in the ftp database, the floating point type is mapped to DOUBLE, and the character type and the date type are both mapped to STRING; otherwise, the converted verification is false; when the data source target library is the TXT database or the kafka database, the field types in the data source library all map the STRING in the TXT database or the kafka database.
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