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By: Abhin K

An Overview of Performance Optimization in Odoo 16

Technical Odoo 16

* Database Optimization

Database optimization is crucial for enhancing the performance and responsiveness of Odoo 16, an open-source ERP system. You can significantly improve the overall user experience by fine-tuning queries and streamlining data access. Here are some essential points and examples of bad queries transformed into optimized ones:

1. Indexing:

Bad Query: SELECT * FROM product WHERE name = 'Widget'
The absence of an index on the 'name' column results in a slow query, especially for large datasets.
Optimized Query: CREATE INDEX ON product (name); ? then use the above query, the database automatically searches for the index.
By adding an index on the 'name' column, the query execution time improves dramatically.

2. Join Optimization:

Bad Query: SELECT * FROM sale_order JOIN sale_order_line ON sale_order.id = sale_order_line.order_id WHERE sale_order.state = 'confirmed'
This query performs a costly join operation without specifying indexes, leading to reduced performance.
Optimized Query: SELECT * FROM sale_order INNER JOIN sale_order_line ON sale_order.id = sale_order_line.order_id WHERE sale_order.state = 'confirmed'
By using INNER JOIN and indexing the 'state' column, the query executes faster.

3. Avoiding SELECT * :

Bad Query: SELECT * FROM res_partner WHERE category_id = 5
Retrieving all columns with SELECT * wastes resources and can slow down the query.
Optimized Query: SELECT id, name, email FROM res_partner WHERE category_id = 5
Select only the necessary columns, reducing the query's processing time.

4. Limiting Results:

Bad Query: SELECT * FROM stock_move WHERE date < '2023-01-01'
Fetching an extensive range of data without any limit can strain the database.
Optimized Query: SELECT * FROM stock_move WHERE date < '2023-01-01' LIMIT 1000
Limit the results using 'LIMIT' to prevent excessive data retrieval.

5. Batch Processing:

Bad Query: Updating thousands of records with individual queries.
Running multiple queries for each record can lead to performance degradation.
Optimized Query: UPDATE product SET stock_quantity = stock_quantity - 10 WHERE category = 'Electronics'
Perform bulk updates to minimize the number of queries and enhance performance.

6. Caching:

Bad Query: Frequent repetitive requests to fetch static data.
Redundant requests for static data can overload the database.
Optimized Query: Implementing caching mechanisms (e.g., Redis) for static data retrieval.
Caching reduces database load and speeds up data access.

7. Periodic Database Maintenance:

Bad Query: Neglecting regular database maintenance tasks.
Accumulation of unnecessary data and fragmentation can slow down database performance.
Optimized Query: Perform regular maintenance tasks like vacuuming and reindexing.
Keep the database optimized and running smoothly.
By following these database optimization techniques and employing optimized queries, you can achieve a significant performance boost in your Odoo system, leading to a more efficient and responsive ERP experience for your users.

* Lazy Evaluation

> Memory Efficiency: Lazy evaluation allows you to generate values on-the-fly, which is particularly beneficial when working with large datasets, as it avoids loading everything into memory at once.
> Performance Optimization: By deferring computations until they are needed, you can optimize the performance of your code, especially for scenarios where not all values are required.
> Infinite Sequences: Lazy evaluation enables the handling of infinite sequences since the next value is generated only when required, and the computation can continue indefinitely.
> Although odoo discourages the use of generators.
sale_order_id = self.env['sale.order'].search([], limit=10)
        # list comprehension
        data = [rec.amount_total for rec in sale_order_id]
        print(data, 'total',sum(data), 'list comprehension')
        # >> [10307.0, 5287.0, 7391.0, 6945.0, 2855.5, 18069.75, 
        # 6620.0, 5984.0, 15908.0, 5622.0] total 84989.25 list comprehension
        # generator method
        data = (rec.amount_total for rec in sale_order_id)
        print(data, 'total',sum(data), 'generator')
        # >> <generator object FormForm.test_func.<locals>.<genexpr> at 0x7f428140bb30> \
        # total 84989.25 generator

* Minimize I/O Operation

To improve performance and minimize input/output (I/O) operations in Python, you can follow these guidelines:

1. Batch Processing:

   - Process data in batches rather than one item at a time.
   - Group data and perform operations on the entire batch at once.
   - Use generator functions to yield data in chunks instead of loading all data into memory at once.

2. Buffered I/O:

   - Use buffered I/O when reading from or writing to files to reduce the number of system calls.
   - Use the `open()` function with the `buffering` parameter set to a positive integer to enable buffering.
# Writing to a file with buffering enabled (buffer size: 8192 bytes)
with open('output.txt', 'w', buffering=8192) as f:
    for i in range(1000000):
        f.write(f'This is line {i}\n')
# Reading from a file with buffering enabled (buffer size: 4096 bytes)
with open('input.txt', 'r', buffering=4096) as f:
    for line in f:

3. Context Managers:

   - Use context managers (`with` statement) when opening files or network connections to ensure proper resource management and automatic cleanup.

4. Compressed File Formats:

   - Use compressed file formats (e.g., gzip, zip) for large data files to reduce file size and I/O time.
   - Python has built-in modules for working with compressed files, such as `gzip`, `zipfile,` and `shuti.l`

5. Binary I/O:

   - Use binary I/O (`rb` for read, `wb` for write) instead of text I/O (`r` for read, `w` for write) for non-text data to avoid unnecessary encoding/decoding overhead.

6. Avoid Redundant I/O Operations:

   - Cache data in memory or use data structures like dictionaries to avoid redundant read operations from files or databases.
   - Avoid repetitive writes to files when the same data can be written once and reused.

7. Asynchronous I/O (asyncio):

   - Use asynchronous I/O with the `asyncio` module for concurrent I/O operations, which can improve performance in I/O-bound tasks.
import asyncio
# Define an asynchronous function to simulate an I/O operation
async def fetch_data(url):
    # Simulate some I/O delay using asyncio.sleep()
    await asyncio.sleep(1)
    return f"Data from {url}"
# Define a main asynchronous function to run multiple I/O operations concurrently
async def main():
    urls = ['https://example.com', 'https://api.example.com', 'https://google.com']
    # Create a list of tasks that represent the I/O operations
    tasks = [fetch_data(url) for url in urls]
    # Execute tasks concurrently using asyncio.gather()
    results = await asyncio.gather(*tasks)
    # Process the results
    for result in results:
# Run the main asynchronous function
if __name__ == "__main__":

8. Use Libraries Optimized for Performance:

   - Choose third-party libraries that are optimized for performance and minimize I/O operations.
   - For example, use `numpy` for numerical computations, which is highly optimized for array operations.

9. Database Optimization:

    - Optimize database queries to retrieve only the required data instead of fetching unnecessary data.
    - Use indexes and caching mechanisms to speed up data retrieval.

10. Data Streaming:

    - When working with large datasets, consider streaming data instead of loading the entire dataset into memory.
    - Use Python's generator functions to stream data from files or databases.

11. Profile and Optimize:

    - Use profiling tools (e.g., `cProfile`, `line_profiler`) to identify bottlenecks in your code.
    - Focus on optimizing the parts of the code that consume the most I/O resources.
import cProfile
import pstats
# Define your code or function to be profiled
def my_function():
    for i in range(1000000):
        _ = i * i
# Run the code with cProfile
if __name__ == "__main__":
    profiler = cProfile.Profile()
    stats = pstats.Stats(profiler)
    stats.sort_stats('time')  # Sort the results by time spent in each function
By following these guidelines, you can minimize I/O operations and improve the overall performance of your Python applications, especially when dealing with large datasets or I/O-bound tasks.

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