QUANTRIUM DEEP DIVE

Demystifying Python’s Garbage Collector: Cleaning Up the Mess

A Closer Look at the Garbage Collector

Abstract

This article explores Python’s Garbage Collector (GC), a key feature for automated memory management. It discusses the GC’s design principles, including reference counting and its role in handling cyclic references. It also explores the Generational GC approach, which optimizes memory management by categorizing objects into generations based on their lifespan. Finally, it highlights the importance of GC optimization in real-world applications with frequent object generation and disposal, and shows how it can improve memory management and reduce performance overhead.

Introduction

Python, a popular programming language known for its simplicity and flexibility, offers a significant advantage: automatic memory management.

Garbage collection is a technique for automatic memory management. It tracks objects in memory and deallocates them when they are no longer needed. This frees up memory for other programs to use and prevents memory leaks.

Python’s garbage collector (GC) is important because it frees programmers from having to manually manage memory. This article will explore how the GC works and its role in memory management.

What is Python’s Garbage Collector?

In Python, the Garbage Collector is a built-in feature with it’s primary role to identify and remove objects that are no longer referenced and freeing up memory for other data. This process helps prevent memory leaks and keeps Python applications running efficiently.

Garbage Collector Design

Python uses reference counting to track how many references an object has. When the reference count reaches zero, the object is deallocated. This scheme covers most cases, but it doesn’t handle reference cycles.

Cyclic reference cycles are situations where objects reference each other, preventing reference counts from reaching zero.

For instance, consider a container object, container, holding a reference to itself. Even when you remove your reference to it, the reference count never reaches zero due to its internal reference.

Python’s garbage collector handles reference cycles by using a cyclic garbage collector. A cyclic garbage collector is a type of garbage collector that can detect and break reference cycles.

Memory Layout and Object Structure

Following is the representation of the normal C structure which supports a regular Python object:

Python Garbage Collector: Object Memory Regular Layout

Memory layout in Python is altered to accommodate garbage collection. Two extra fields are added to the memory layout of objects: _gc_next and _gc_prev.

Python Garbage Collector: Object Memory Altered Layout

These fields are used to maintain doubly linked lists of all objects tracked by the garbage collector. These lists partition objects into generations and support efficient operations for moving, adding, and removing objects during collection.

The Reference Counting Approach

The fundamental mechanism behind Python’s GC is reference counting. Every Python object has an associated reference count. When an object’s reference count drops to zero, it means there are no references to that object, making it eligible for removal.

Consider this example:

a = [1, 2, 3]
b = a

In this case, both a and b reference the same list. If we assign a new value to a or b, the reference count decreases by 1. When both a and b are reassigned to different values, the reference count drops to zero, and the list can be removed by the GC.

The Cyclic Reference Problem

While reference counting is effective for most situations, it has limitations, particularly with circular references. Circular references occur when two or more objects reference each other, preventing their reference counts from dropping to zero.

For instance:

python
class Node:
 def __init__(self):
 self.next = None

node1 = Node()
node2 = Node()

node1.next = node2
node2.next = node1

In this example, node1 and node2 reference each other, and their reference counts never reach zero. This is where Python’s Garbage Collector comes into play.

The Generational GC Approach

To address the cyclic reference problem, Python utilizes a generational GC approach. It is one of the algorithm which used to optimize memory management. In this approach, objects are divided into three generations: Young, Middle-Aged, and Old.

• Young objects are newly created and are considered short-lived.
• Middle-Aged objects have survived a few Garbage Collection cycles.
• Old objects are long-lived and have survived multiple GC cycles.

The GC gives more attention to Young objects since they are likely to become unreachable sooner. As objects survive more GC cycles, they are promoted to older generations.

Let’s take a deeper dive into Generational GC approach:

The Generational Hypothesis

The generational GC approach is based on the generational hypothesis, which assumes that most objects die young. In other words, newly created objects are more likely to become garbage soon, while objects that have survived multiple garbage collection cycles are more likely to remain in use for a longer time.

Generational Segregation

In Python, memory is divided into multiple generations, typically three: Generation 0 (young), Generation 1 (middle-aged) and Generation 2(old). Objects start in Generation 0, and if they survive a garbage collection cycle, they are promoted to Generation 1 and when the same object survive next garbage collection cycle, it is promoted to Generation 2.

Collection Frequency

The garbage collector in Python is designed to collect Generation 0 more frequently than Generation 1 or Generation 2 because of the generational hypothesis. This reduces the overhead of collecting objects that are likely to become garbage shortly.

Collection Algorithm

Generational GC in Python uses two primary algorithms for collection:

1. Young Generation Collection: The collector frequently checks Generation 0 for objects that are no longer referenced. This is often done using techniques like reference counting or more sophisticated methods like the tri-color algorithm.
2. Old Generation Collection: Generation 1 and Generation 2 are collected less frequently because older objects are assumed to have a longer lifespan. When a collection does occur in Generation 1 or Generation 2, it’s often more expensive as it involves traversing more objects.

Garbage Collector Modules

Python provides modules like gc that allow you to interact with and configure the garbage collector. You can manually trigger a collection using gc.collect(), and you can adjust the collection thresholds and other settings.

Tuning and Optimization

Python’s generational garbage collection can be tuned and optimized for specific applications. You can adjust the collection frequency and thresholds using the gc module, but you should be cautious and only do this when necessary.

To determine the appropriate timing for execution, the collector maintains a record of object allocations and deallocations since the previous collection. When the difference between allocations and deallocations surpasses the predefined threshold_0 value, the garbage collection process is triggered.

Initially, the collector focuses on inspecting objects in Generation 0. However, if Generation 0 has undergone more examinations than Generation 1 since the last check, the collector extends its inspection to include Generation 1. These configurable thresholds are accessible for review and adjustment via the gc.get_threshold() function.

>>> import gc
>>> gc.get_threshold()
(700, 10, 10)

The Generation 2 is bit more complex. Unlike the customizable thresholds for Generation 0 and Generation 1, the garbage collector (GC) will only initiate a full collection of the oldest generation when a specific condition is met:

the ratio of pending long-lived objects to the total long-lived objects exceeds a fixed threshold (hard-set at 25%).

This is because while partial collections (which target younger and mid-level generations) consistently inspect a roughly consistent number of objects based on the set thresholds, the cost of a full collection is directly related to the total count of long-lived objects, which can potentially be extremely high.

In practical terms, frequently performing full collections after every fixed number of object creations can severely degrade performance in scenarios where many long-lived objects are created and stored (e.g., constructing a substantial list of objects tracked by the GC can lead to quadratic performance issues instead of the expected linear performance).

Instead, using the mentioned ratio provides a more balanced approach:

each full garbage collection becomes more expensive as the total object count increases, but the frequency of these expensive operations decreases, resulting in an overall performance that scales linearly with the number of objects (meaning that as the number of objects grows, the impact of each full garbage collection is lessened).

Manual Garbage Collection

While Python’s GC system is automatic and efficient, developers can also manually trigger Garbage Collection using the gc module. This is useful in cases where memory needs to be managed more explicitly.

To manually run Garbage Collection, you can use the gc.collect() method. Here’s an example:

import gc

# …your code…

gc.collect() # Explicitly run Garbage Collection

A Real World Example of GC Optimization

Let’s consider a web application that experiences high traffic and continuously generates short-lived objects, such as request and response objects, user sessions, and database query results. In such a scenario, frequent garbage collection can lead to performance overhead.

To optimize performance in this case, the GC configuration can be adjusted:

Generational GC Configuration

You can tune the generational GC approach to control how often it runs in different generations of objects. For example, you can increase the collection frequency of Generation 0 to collect short-lived objects more aggressively, reducing the overhead of checking and cleaning them. This ensures that the most commonly generated objects are quickly collected, minimizing memory fragmentation.

Threshold Adjustments

You can adjust the garbage collection thresholds for both the number of allocations and the frequency of examinations between generations. For short-lived objects, you can lower these thresholds to trigger more frequent but quicker collections.

Manual Collection

If you know when certain objects are no longer needed, you can manually trigger the GC at the right time using the gc.collect() function. For example, you might want to run manual collection after processing a user's request and releasing associated resources.

Disabling or Reducing GC

In extreme cases, if your application can manage memory explicitly, you can temporarily disable or reduce the impact of the GC with the gc.disable() function. Use this with caution and only for specific, short-lived scenarios.

You can configure the GC to manage the frequent creation and disposal of short-lived objects efficiently, reducing the chances of performance problems due to garbage collection.