Solve the problem of data type mismatch during PostgreSQL data migration

1. Understand common data type mismatch

In data migration, the following are some common data type mismatch:

1. Integer type difference

The source database may be usedINT(32-bit), and the target PostgreSQL database may be more suitable for useBIGINT(64 bits) or vice versa.

2. Floating point number type difference

For example, source useFLOAT, while PostgreSQL may be more inclined to useDOUBLE PRECISIONfor higher accuracy.

3. Character type difference

Sources may use fixed-length character types (e.g.CHAR(n)), while PostgreSQL usually uses variable-length character types (such asVARCHAR(n)）。

4. Date and time type differences

Different database systems may have different date and time types and formats.

2. General strategies to solve data type mismatch

1. Data conversion

Convert data types before data migration or during data loading. PostgreSQL provides a wealth of functions to perform data type conversion.

2. Adjust the database table structure

If possible, modify the structure of the target PostgreSQL database table to suit the type of source data.

3. Data cleaning and preprocessing

Before data migration, source data is cleaned and preprocessed to meet the data type requirements of the target database.

3. Data type conversion function in PostgreSQL

PostgreSQL provides numerous built-in functions for data type conversion. Here are some commonly used type conversion functions:

1. Numerical type conversion

CAST(value AS target_type): Used to convert a value to the specified data type.
- Example: Convert a string to an integerSELECT CAST('123' AS INT);
::Operator: A concise type conversion method.
- Example: Convert floating point numbers to integersSELECT 123.45::INT;

2. Character type conversion

TO_CHAR(value, format): Convert numerical, date/time values into formatted strings.
- Example: Convert date to string in a specific formatSELECT TO_CHAR(CURRENT_DATE, 'YYYY-MM-DD');
TO_NUMBER(string, format): Convert a string to a numeric type.
- Example: Convert a numeric value in the form of a string to a floating point numberSELECT TO_NUMBER('123.45', '999.99');

3. Date/time type conversion

TO_DATE(string, format): Convert a string to a date type.
- Example:SELECT TO_DATE('2023-07-15', 'YYYY-MM-DD');

4. Adjust the table structure to adapt to the data type

In PostgreSQL, you can useALTER TABLEStatement to modify the table structure. For example:

-- Add new columns
ALTER TABLE table_name ADD column_name data_type;

-- Modify the column's data type
ALTER TABLE table_name ALTER COLUMN column_name TYPE new_data_type;

However, be careful when modifying table structures, especially when there is already a large amount of data, which may lead to long execution time and potential data consistency problems.

V. Examples of data cleaning and preprocessing

Assume that the date field in the data obtained from the source database is'YYYYMMDD'storage in string format, while PostgreSQL expects a standard date format'YYYY-MM-DD'. We can preprocess before data migration:

import pandas as pd

data = {'date_str': ['20230715', '20230716', '20230717']}
df = (data)

# Data cleaning and preprocessingdf['date'] = pd.to_datetime(df['date_str'], format='%Y%m%d').('%Y-%m-%d')

# Output preprocessed dataprint(df)

In the above Python code, usepandasThe library converts the date string in the source data to the correct date format.

6. Actual data migration examples

Suppose we are migrating data from a MySQL database to a PostgreSQL database, there is a field in the source table source_table that is the FLOAT type, and in the PostgreSQL target_table we want to define it as the DOUBLE PRECISION type.

First, extract data from MySQL:

SELECT amount FROM source_table;

Then, type conversion when inserting the data into PostgreSQL:

INSERT INTO target_table (amount)
SELECT CAST(amount AS DOUBLE PRECISION) FROM source_data;

Or, if the data volume is large, you can use tools such aspgloader, It can automatically handle some common data type conversion problems and provide more efficient data migration performance.

7. Handle complex data type mismatch

Sometimes, data type mismatch can be more complicated, such as a field in source data containing multiple types of values (such as a mix of strings and integers). In this case, more meticulous data cleaning and processing logic may be required.

Assume a source fielddataIntegers that may contain integers or strings, we can handle them in PostgreSQL as follows:

CREATE TABLE temp_data (
    data TEXT
);

-- Insert source data
INSERT INTO temp_data (data) VALUES ('123'), ('abc'), ('456');

-- Process and insert to the target table
INSERT INTO target_table (data)
SELECT CASE 
            WHEN data ~ '^\d+$' THEN CAST(data AS INT)
            ELSE NULL
        END
FROM temp_data;

In the above example, first insert the data into a temporary table and then passCASE WHENThe expression is processed and converted according to the format of the data, converting valid integers into integer types and inserting them into the target table, and inserting them for data that does not conform to the integer format.NULLValue.

8. Data verification and testing

After completing data migration and type conversion, be sure to perform data verification and testing to ensure the accuracy and completeness of the data.

Verification can be performed by:

1. Data sampling inspection

Randomly select some of the migrated data, compare it with the source data, and check the accuracy and consistency of the data value.

2. Perform queries and statistics

Perform various queries and statistical operations in the PostgreSQL database to verify that the logical relationships and business rules of the data are properly retained.

3. Check constraints and indexes

Ensure constraints defined on the target table (e.g.NOT NULL、UNIQUE、FOREIGN KEY) and indexes work properly, without problems caused by data type conversion.

-- Check if a column has a non-null value
SELECT COUNT(*) FROM target_table WHERE column_name IS NULL;

-- Verify uniqueness constraints
SELECT column_name, COUNT(*) FROM target_table GROUP BY column_name HAVING COUNT(*) &gt; 1;

9. Error handling and rollback strategies

During data migration, errors may be encountered due to mismatch in data types. To deal with this situation, error handling and rollback strategies need to be developed.

In scripts that perform data migration, you can useTRY-CATCHBlocks to capture errors and determine whether to perform data repair, skip error records or completely rollback data migration operations based on the type and severity of the error.

BEGIN;
    TRY
        -- Data migration and conversion operations
        INSERT INTO target_table (...) VALUES (...);
    CATCH
        -- Error handling logic
        RAISE NOTICE 'An error occurred: %', SQLERRM;
        ROLLBACK;
    END;
COMMIT;

The above strategies and examples allow you to deal with data type mismatch during PostgreSQL data migration. But each data migration project has its own unique challenges, requiring flexible application of these methods based on circumstances and sufficient testing and verification to ensure the success of data migration.

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