PostgreSQL's method of efficiently handling data serialization and deserialization

1. The relationship between data type and serialization

PostgreSQL provides a rich variety of data types. Each data type has its own characteristics and applicable scenarios, and has different impacts on serialization and deserialization.

Basic data types

INTEGER、FLOAT、BOOLEANSerialization of basic data types such as basic data types is relatively simple. The storage form in the database is close to the usual binary representation. When data exchange is performed, they can be directly passed.
Sample code:

CREATE TABLE simple_data (
    id INTEGER,
    price FLOAT,
    is_active BOOLEAN
);

INSERT INTO simple_data (id, price, is_active)
VALUES (1, 45.67, TRUE);

SELECT * FROM simple_data;

String data type

CHAR(n)、VARCHAR(n)andTEXTUsed to store string data. When serializing, you need to pay attention to the encoding and possible truncation or padding of the string.
Sample code:

CREATE TABLE string_data (
    short_char CHAR(5),
    variable_char VARCHAR(50),
    long_text TEXT
);

INSERT INTO string_data (short_char, variable_char, long_text)
VALUES ('abcde', 'This is a longer string', 'This is a very long text that can span multiple lines.');

SELECT * FROM string_data;

Date and time data types

DATE、TIME、TIMESTAMPetc. types are used to process date and time information. When serializing, it is usually based on a specific format (such asISO 8601) Convert.
Sample code:

CREATE TABLE date_time_data (
    event_date DATE,
    start_time TIME,
    creation_timestamp TIMESTAMP
);

INSERT INTO date_time_data (event_date, start_time, creation_timestamp)
VALUES ('2023-09-15', '13:45:00', '2023-09-15 13:45:00');

SELECT * FROM date_time_data;

Array data type

ARRAYTypes allow storing a set of elements of the same data type. When serializing an array, the order and separator of the elements need to be processed.
Sample code:

CREATE TABLE array_data (
    int_array INTEGER[],
    text_array TEXT[]
);

INSERT INTO array_data (int_array, text_array)
VALUES ('{1, 2, 3}', '{"apple", "banana", "cherry"}');

SELECT * FROM array_data;

Composite data type

Can be usedROWType creates a custom composite structure, or useTABLEType to simulate table structure. During serialization, it needs to be processed in the defined field order and data type.
Sample code:

CREATE TYPE person_type AS (
    name VARCHAR(50),
    age INTEGER
);

CREATE TABLE persons (
    data person_type
);

INSERT INTO persons (data)
VALUES (ROW('John Doe', 30));

SELECT * FROM persons;

2. Use conversion functions for serialization and deserialization

PostgreSQL provides a series of built-in functions to help serialize and deserialize data.

Conversion of strings to other data types

TO_CHAR()Functions are used to convert data types such as numeric values, dates, etc. into strings.
TO_NUMBER()Function converts a string to a numeric value.
TO_DATE()Function converts a string to a date.

Sample code:

SELECT TO_CHAR(45.67, '999.99'), TO_NUMBER('123') AS num, TO_DATE('2023-09-15', 'YYYY-MM-DD') AS date;

Conversion of arrays

ARRAY_TO_STRING()Functions convert arrays into strings.
STRING_TO_ARRAY()Function converts strings into arrays.

Sample code:

SELECT ARRAY_TO_STRING('{1, 2, 3}', ',') AS array_to_string, STRING_TO_ARRAY('apple,banana,cherry', ',') AS string_to_array;

Processing of JSON data

JSONBTypes are suitable for storing and processing JSON data.
JSON_BUILD_OBJECT()Functions are used to build JSON objects.
JSON_EXTRACT_PATH()Functions are used to extract fields from JSON data.

Sample code:

CREATE TABLE json_data (
    data JSONB
);

INSERT INTO json_data (data)
VALUES ('{"name": "John", "age": 30}');

SELECT JSON_BUILD_OBJECT('name', 'Jane', 'age', 25) AS built_json, JSON_EXTRACT_PATH(data, 'name') AS extracted_name FROM json_data;

3. Serialization and deserialization in the application

Interaction between programming languages and PostgreSQL drivers

Different programming languages usually have corresponding PostgreSQL drivers, which provide methods to process the conversion between data in a database and programming language data types.

Taking Python as an example, usepsycopg2Library:

import psycopg2

conn = (database="your_database", user="your_user", password="your_password", host="your_host", port="your_port")
cursor = ()

# Execute query("SELECT * FROM your_table")

# Get resultsresults = ()

for row in results:
    # Process each row of data and deserialize it according to the data type    id = row[0]  # Assume that the first column is an integer type    name = row[1]  # Assume that the second column is of string type
# Close the connection()
()

For Java, useJDBC：

import ;
import ;
import ;
import ;
import ;

public class PostgreSQLExample {
    public static void main(String[] args) {
        String url = "jdbc:postgresql://your_host:your_port/your_database";
        String user = "your_user";
        String password = "your_password";

        try (Connection connection = (url, user, password)) {
            String sql = "SELECT * FROM your_table";
            PreparedStatement statement = (sql);
            ResultSet resultSet = ();

            while (()) {
                int id = ("id");  // Assume that the first column is an integer type                String name = ("name");  // Assume that the second column is of string type            }

            ();
            ();
        } catch (SQLException e) {
            ();
        }
    }
}

Custom serialization and deserialization logic

In some cases, it may be necessary to customize the logic of serialization and deserialization based on business needs. For example, if the database stores encrypted data, decryption processing is required on the application side.

Taking Python as an example, customize the process of encryption fields:

import psycopg2
from  import AES

class CustomSerializer:
    def __init__(self, key):
         = (key, AES.MODE_ECB)

    def serialize(self, value):
        # Encryption logic        padded_value = (value)
        encrypted_value = (padded_value)
        return encrypted_value

    def deserialize(self, encrypted_value):
        # Decryption logic        decrypted_value = (encrypted_value)
        unpadded_value = (decrypted_value)
        return unpadded_value

    def pad(self, value):
        block_size = 16
        padding_length = block_size - len(value) % block_size
        padding = bytes([padding_length] * padding_length)
        return value + padding

    def unpad(self, value):
        padding_length = value[-1]
        return value[:-padding_length]

conn = (database="your_database", user="your_user", password="your_password", host="your_host", port="your_port")
cursor = ()

serializer = CustomSerializer(b'your_secret_key')

("SELECT encrypted_column FROM your_table")
results = ()

for row in results:
    encrypted_value = row[0]
    decrypted_value = (encrypted_value)
    # Process the decrypted data
()
()

4. Performance considerations and optimization

Select the right data type

For data columns that store a large number of duplicate values, useENUMType instead ofVARCHARSave storage space.
For fixed-length strings, useCHAR(n)Can improve query performance.

Use of indexes

Creating indexes on columns that are often used for querying, joining, or sorting can speed up data retrieval.
But too many indexes can affect the performance of data insertion, update and deletion, and need to be carefully weighed.

Batch operation

Performing batch inserts, updates, and delete operations instead of operating multiple rows of data one by one can reduce the number of interactions with the database and improve performance.

Sample code (using Pythonpsycopg2Do batch insert):

import psycopg2
import random

conn = (database="your_database", user="your_user", password="your_password", host="your_host", port="your_port")
cursor = ()

data = [((1, 100), f'Name {i}') for i in range(1000)]

# Batch Insert("INSERT INTO your_table (id, name) VALUES (%s, %s)", data)

()
()
()

Avoid unnecessary type conversion

When querying and manipulating data, try to avoid unnecessary data type conversions, as this may lead to additional performance overhead.

5. Solutions in complex scenarios

Process hierarchical data

If you need to process data with complex hierarchies (such as tree structures or nested objects), you can consider usingJSONBTypes or using recursive queries to implement.

Sample code (using recursive query to process tree structure data):

CREATE TABLE tree_nodes (
    id INTEGER,
    parent_id INTEGER,
    name VARCHAR(50)
);

INSERT INTO tree_nodes (id, parent_id, name)
VALUES (1, NULL, 'Root'),
       (2, 1, 'Child 1'),
       (3, 1, 'Child 2'),
       (4, 2, 'Grandchild 1'),
       (5, 3, 'Grandchild 2');

-- Recursive query to obtain the complete tree structure
WITH RECURSIVE tree AS (
    SELECT id, parent_id, name, 1 AS level
    FROM tree_nodes
    WHERE parent_id IS NULL
    UNION ALL
    SELECT , t.parent_id, ,  + 1 AS level
    FROM tree_nodes t
    JOIN tree ON t.parent_id = 
)
SELECT * FROM tree;

Process large object data

For large binary data (such as images, files), you can useBYTEAType orLO(Large Object) type.

Sample code (storing and retrieving binary data):

CREATE TABLE large_objects (
    id SERIAL PRIMARY KEY,
    data BYTEA
);

-- Insert binary data
INSERT INTO large_objects (data)
VALUES (DECODE('hex_data', 'hex'));  -- Convert hexadecimal data to binary

-- Retrieve binary data
SELECT data FROM large_objects;

Data partition

When the amount of data in the table is very large, data partitioning can be performed and the data is distributed in multiple physical partitions according to some rules to improve query performance.

Sample code (partition based on date):

CREATE TABLE sales (
    sale_id SERIAL PRIMARY KEY,
    sale_date DATE,
    amount DECIMAL(10, 2)
)
PARTITION BY RANGE (sale_date);

CREATE TABLE sales_2023_q1 PARTITION OF sales
FOR VALUES FROM ('2023-01-01') TO ('2023-03-31');

CREATE TABLE sales_2023_q2 PARTITION OF sales
FOR VALUES FROM ('2023-04-01') TO ('2023-06-30');

-- Insert sample data
INSERT INTO sales (sale_date, amount)
VALUES ('2023-02-15', 100.00), ('2023-05-20', 200.00);

6. Summary

Effectively handling the serialization and deserialization of data in PostgreSQL requires comprehensive consideration of the selection of data types, the use of conversion functions, the interaction with the application, performance optimization, and solutions for complex scenarios. By rationally utilizing the functions and features provided by PostgreSQL and customizing development according to actual business needs, data can be ensured to be efficient, accurate and consistent during storage and processing. At the same time, continuous attention to performance bottlenecks and optimization and adjustment can maintain good system performance when processing large-scale and complex data.

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PostgreSQL's method of efficiently handling data serialization and deserialization

1. The relationship between data type and serialization

Basic data types

String data type

Date and time data types

Array data type

Composite data type

2. Use conversion functions for serialization and deserialization

Conversion of strings to other data types

Conversion of arrays

Processing of JSON data

3. Serialization and deserialization in the application

Interaction between programming languages ​​and PostgreSQL drivers

Custom serialization and deserialization logic

4. Performance considerations and optimization

Select the right data type

Use of indexes

Batch operation

Avoid unnecessary type conversion

5. Solutions in complex scenarios

Process hierarchical data

Process large object data

Data partition

6. Summary

Interaction between programming languages and PostgreSQL drivers