Understanding Isolation Levels in Database Systems

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When working with databases, especially those that support transactions and concurrency control, understanding the concept of isolation levels is crucial. In this article, we will delve into what isolation levels are, how they work, and specifically, how to set the isolation level for modin’s parallel read_sql function.

What are Isolation Levels?

Isolation levels determine how transactions interact with each other when multiple sessions access shared data resources concurrently. Think of it like trying to get a table at a busy restaurant: you want the food to be prepared and served before anyone else gets it, or you might end up waiting forever.

There are several isolation levels in databases, but we will focus on READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, and SERIALIZABLE.

• READ UNCOMMITTED: This level allows transactions to read data that has not been committed by other sessions. It’s like grabbing a table just as it’s being served; you might get food that hasn’t been fully prepared yet.
• READ COMMITTED: Transactions can only see committed data from other sessions. You won’t be able to grab the table until it’s been officially handed over by the chef.
• REPEATABLE READ: This level ensures that if multiple transactions read the same row, they will all get the same values (as long as the transactions are executed in a specific order). Think of this like ordering food with multiple courses; you want to ensure that each course has the same ingredients and is served at the same time.
• SERIALIZABLE: This level guarantees that all transactions behave like a serial execution, meaning every operation happens exactly once, in a sequence. Imagine a restaurant where only one person can order at a time.

Setting Isolation Levels

Most databases allow you to set isolation levels when connecting or specifying queries. Here are some ways to do so:

CLI Interface

For the CLI interface (like cpython), you can use settings like ;TxnIsolation=1; for READ UNCOMMITTED, which would look something like this:

db2 --TxnIsolation=1 --query "SELECT * FROM my_table"

In a Python script, you could use the same parameter:

import pyodbc

# Create a connection string with TxnIsolation set to 1
connection_string = "DRIVER={IBM DB2 Type 4};SERVER=mydb;DATABASE=mydatabase;TXNISOLATION=1"

# Connect and execute the query
with pyodbc.connect(connection_string) as conn:
    cur = conn.cursor()
    cur.execute("SELECT * FROM my_table")

Data Source Definition

For more persistent settings, you can use configuration files like db2dsdriver.cfg or db2cli.ini. For example:

[Connection]
IsolationLevel=UNCOMMITTED

Or,

[Connection]
TxnIsolation=1

SQLAlchemy and Modin

Now that we’ve covered how to set isolation levels, let’s dive into how modin’s parallel read_sql function interacts with this. The key point is that the queries generated by read_sql are wrapped around subqueries for parallel execution.

import modin.pandas as pd
from sqlalchemy import create_engine

# Create a connection to the database with an isolation level set
engine = create_engine("db2://user:password@localhost:50000/mydatabase;TxnIsolation=1")

# Use read_sql to generate queries with the specified isolation level
df = pd.read_sql("SELECT * FROM my_table", engine, params={'isolation_level': 'READ UNCOMMITTED'})

print(df)

In this example, we’re setting the TxnIsolation parameter when creating the database connection. This tells SQLAlchemy (and subsequently modin) to use that isolation level for all queries generated by read_sql.

However, there’s a catch. Because of how subqueries are constructed for parallel execution, statement-level isolation doesn’t apply here without adjusting the implementation.

For those interested in diving deeper into this topic, you can find more information on database isolation levels and their implications for transactions and concurrency control.

Last modified on 2024-06-07