sys.monitoring

Execution Event Monitoring with sys.monitoring

Tool Identifiers

Imagine your computer as a stage, where different programs are like actors performing. Each actor needs a unique name tag, like "Actor A" or "Actor B," so we know who's doing what. Similarly, in Python's sys.monitoring, tools monitoring the program's execution need their own unique names, called "tool identifiers." These identifiers are numbers from 0 to 5.

Events

Think of events as special moments during a program's performance, like when a character enters the stage or exits. Sys.monitoring lets tools listen for specific events, like "character entered the stage" or "function was called."

Callbacks

When a tool wants to monitor an event, it can register a "callback," which is like a special function that gets called whenever that event happens. It's like saying, "Hey, when the character enters the stage, call my function!"

How it Works

  1. Choose a Tool Identifier: Each tool chooses a unique tool identifier (number from 0 to 5).

  2. Register Events: Tools tell the sys.monitoring system which events they want to monitor and the callback function to use.

  3. Activate Monitoring: The sys.monitoring system starts listening for the specified events.

  4. Callback Execution: When an event occurs, the tool's callback function is called with information about the event.

Real-World Applications

Sys.monitoring is useful for tools that want to track and analyze program execution:

  • Performance Analysis Tools: Can track how long functions take to execute, helping developers identify inefficiencies.

  • Profilers: Can monitor memory and CPU usage, identifying areas where the program can be optimized.

  • Debugging Tools: Can help identify the exact point in the program where errors or crashes occur.

Code Example

import sys

# Choose our tool identifier
TOOL_ID = 1

# Define our callback function
def callback(event):
    # Print information about the event
    print(f"Event occurred: {event.name}")

# Register the callback for the "function_called" event
sys.monitoring.register_event(TOOL_ID, "function_called", callback)

# Activate monitoring
sys.monitoring.enable()

# Now, when any function is called, our callback will be executed!

Function: use_tool_id

Simplified Explanation:

Imagine you have a toolkit with 6 different tools (numbered 0 to 5). You need to use one of the tools. This function is like telling the toolkit, "I want to use this specific tool (tool_id) and give it a name (name)."

Detailed Explanation:

  • tool_id: This is the number of the tool you want to use. It can be 0, 1, 2, 3, 4, or 5.

  • name: This is the name you want to give to the tool. It can be any string, like "hammer" or "wrench."

Usage:

You must call this function before you can use the tool identified by tool_id. If you try to use a tool without first calling this function, you will get an error.

Example:

import sys.monitoring as monitoring

# Use tool number 3 and give it the name "screwdriver"
monitoring.use_tool_id(3, "screwdriver")

# Now you can use the screwdriver tool
monitoring.use_tool("screwdriver")

Real-World Applications:

This function is used in monitoring and profiling tools to track which tools are being used and how often. This information can be used to identify performance bottlenecks and improve efficiency.

Potential Applications:

  • Profiling code to see which functions are called most often

  • Debugging memory leaks

  • Tracking CPU and memory usage

free_tool_id Function

  • Purpose: Releases a tool ID after it's no longer needed by a tool.

  • How it Works:

    Imagine you have a toolbox with different tools. Each tool has a specific ID. When you're done using a tool, you should put it back in the toolbox. This is what free_tool_id does for tool IDs.

  • Why it's Important:

    Keeping track of unused tool IDs helps the system run smoothly and efficiently. It's like organizing your toolbox.

  • Code Example:

# Assume you have a tool with ID 123
free_tool_id(123)

Note:

Be aware that free_tool_id doesn't remove any events or callback functions associated with the tool ID. It simply frees up the ID for reuse.

notify_detached() method in sys.monitoring module

The notify_detached() method is used to notify the VM that the particular tool_id is no longer in use. This can be useful for cleaning up resources or preventing the VM from being charged for unused services.

Syntax

def notify_detached(tool_id: str)

Parameters

  • tool_id (str): The ID of the tool that is no longer in use.

Example

The following example shows how to use the notify_detached() method to notify the VM that the "my-tool" tool is no longer in use:

import google.cloud.monitoring_v3 as monitoring_v3

client = monitoring_v3.MetricServiceClient()
client.notify_detached(tool_id="my-tool")

Real-world applications

The notify_detached() method can be used in a variety of real-world applications, such as:

  • Cleaning up resources when a tool is no longer in use.

  • Preventing the VM from being charged for unused services.

  • Tracking the usage of tools to identify which tools are most popular.

Getting Tool Names

Imagine you have a bunch of tools in a toolbox, each with a number. You can use the get_tool() function to find out what each tool is called, like "Screwdriver" or "Hammer."

tool_id = 2
tool_name = get_tool(tool_id)
print(tool_name)  # Output: "Profiler"

Monitoring Events

Think of events as things that happen in your program, like when a function starts or a loop finishes. You can tell the monitoring system to watch out for certain events, like:

  • Local events: These are specific to what's happening in your code, like when a function starts or ends.

  • Ancillary events: These are related to local events, like when an exception is raised or a function returns.

  • Other events: These aren't tied to specific code locations, like when an exception is handled.

Turning Events On and Off

To watch for an event, you need to turn it on. You can do this for all events globally, or just for a particular part of your code.

Real-World Applications

Monitoring events can be useful for:

  • Debugging: Finding out what's happening in your code when it's misbehaving.

  • Profiling: Measuring how long different parts of your code take to run.

  • Optimizing: Improving the speed and efficiency of your code.

Function: get_events

Purpose: To get all the active events for a specific tool.

Parameters:

  • tool_id: The ID of the tool for which you want to get the events.

Return Value:

  • An integer representing all the active events.

Simplified Explanation:

Imagine you have a toolbox with different tools. Each tool can fire events when something happens. For example, a screwdriver could fire an event when it's being used to tighten a screw.

This function allows you to ask a specific tool for all the events it has recorded. It will give you a number that represents all those events.

Real-World Example:

You can use this function to monitor the usage of different tools in a factory. For instance, you could track how often a particular screwdriver is being used. This information can help you improve efficiency and plan maintenance schedules.

Improved Code Snippet:

from google.cloud.monitoring_v3 import MetricServiceClient

client = MetricServiceClient()
project_name = f"projects/{your-project-id}"
filter_ = (
    f"metric.type = \"custom.googleapis.com/logging/log_entry\" AND "
    f"resource.type = \"global\" AND "
    f"resource.labels.log_name = \"my_logging_resource\""
)
results = client.list_time_series(name=project_name, filter=filter_)

for result in results:
    print(result.metric)
    print(result.resource)
    print(result.points)

This code snippet shows how to use the get_events method to retrieve all the active events for a specific tool. The client is created and the project name is set. The filter_ variable is used to specify the type of events to retrieve. The results are then printed to the screen.

Potential Applications:

  • Monitoring tool usage: Track how often specific tools are being used to improve efficiency and plan maintenance schedules.

  • Troubleshooting: Identify issues with tools by analyzing event logs.

  • Security: Detect suspicious activity by monitoring event logs for unauthorized access attempts.

set_events Function

Purpose: Activates specific events for a tool.

Parameters:

  • tool_id: Integer ID of the tool for which events should be activated.

  • event_set: Integer that represents the set of events to be activated.

Usage:

import sys.monitoring

tool_id = 1  # Can be any integer representing the tool.
event_set = sys.monitoring.TOOL_EVENT_ALLOC | sys.monitoring.TOOL_EVENT_FREE  # Combine event constants to activate multiple events.

sys.monitoring.set_events(tool_id, event_set)

Explanation:

  • When you create a tool, you assign it an ID.

  • Each event has a corresponding constant.

  • To activate multiple events, combine their constants with the bitwise OR operator |.

  • This function tells the monitoring system to track the specified events for the specified tool.

Real-World Application:

  • Monitoring memory usage by tracking memory allocation and deallocation events.

  • Profiling code execution by tracking function calls and returns.

  • Debugging performance issues by identifying bottlenecks.

Per Code Object Events

Purpose: Controls events on a per-code object basis.

Concept:

  • Each code object (function) can have its own set of active events.

  • This allows you to track different events for different parts of your code.

Usage:

import sys.monitoring

def my_function():
    # Code that you want to monitor
    
# Enable allocation and free events for "my_function"
event_set = sys.monitoring.TOOL_EVENT_ALLOC | sys.monitoring.TOOL_EVENT_FREE
code_object = my_function.__code__  # Get the code object of the function
sys.monitoring.set_events_for_code(code_object, event_set)

my_function()  # Call the function

Explanation:

  • Get the code object of the function.

  • Set the active events for the code object.

  • Call the function to trigger the monitored events.

Real-World Application:

  • Profiling performance of specific functions.

  • Debugging issues in specific parts of your code.

get_local_events Function

Definition:

def get_local_events(tool_id: int, code: CodeType, /) -> int

Purpose:

The get_local_events() function retrieves all the local events (errors, warnings, and exceptions) associated with a specific tool and codetype.

Parameters:

  • tool_id: The ID of the tool to retrieve events for.

  • code: The type of events to retrieve. Can be one of the following:

    • CodeType.ERROR: Errors

    • CodeType.WARNING: Warnings

    • CodeType.EXCEPTION: Exceptions

Returns:

  • int: The count of local events found for the given tool and codetype.

Example:

import sys.monitoring

# Get the tool ID for the current process
tool_id = sys.monitoring.get_current_tool_id()

# Get the count of error events for the process
error_count = sys.monitoring.get_local_events(tool_id, sys.monitoring.CodeType.ERROR)

# Get the count of warning events for the process
warning_count = sys.monitoring.get_local_events(tool_id, sys.monitoring.CodeType.WARNING)

# Get the count of exception events for the process
exception_count = sys.monitoring.get_local_events(tool_id, sys.monitoring.CodeType.EXCEPTION)

print(f"Error count: {error_count}, Warning count: {warning_count}, Exception count: {exception_count}")

Applications in the Real World:

  • Monitoring and debugging software applications

  • Analyzing error and exception logs

  • Identifying performance bottlenecks and potential issues

Setting Local Events

Local events allow you to trigger specific events for a particular part of your code, while global events apply to the entire program.

Function: set_local_events

The set_local_events function is used to activate local events for a specific code portion.

Arguments:

  • tool_id: The ID of the monitoring tool that should listen for the events.

  • code: The code object for which the events should be activated.

  • event_set: A bitmask representing the events that should be activated.

Example:

import sys

def my_function():
    pass

# Set the events to trigger for the `my_function` function.
event_set = sys.monitoring.EventSet.CALL
sys.monitoring.set_local_events(0, my_function, event_set)

Event Sets

Event sets are used to group related events. For example, the EventSet.CALL group includes events related to function calls.

Available event sets:

  • EventSet.CALL: Function calls, returns, and exceptions.

  • EventSet.LINE: Line-by-line code execution.

  • EventSet.COND: Conditional statements (e.g., if-else).

  • EventSet.LOOP: Loop statements (e.g., while, for).

  • EventSet.BRANCH: Branch statements (e.g., break, continue).

Using Local Events

Local events are triggered when the corresponding code portion is executed. They can be used for debugging, profiling, or other performance monitoring tasks.

To use local events:

  1. Import the sys.monitoring module.

  2. Identify the code for which you want to trigger events.

  3. Create an event set to group the events.

  4. Use the set_local_events function to activate the events.

  5. Register callback functions to handle the events (see the sys.monitoring.add_callback function).

Disabling Events

The sys.monitoring.DISABLE constant can be returned from a callback function to disable events for the current code location.

Example:

import sys

def my_callback(event, code, frame, arg):
    # Disable events for the current code location.
    return sys.monitoring.DISABLE

# Register the callback function.
sys.monitoring.add_callback(my_callback)

Potential applications:

  • Disabling events for high-performance profiling.

  • Reducing overhead when debugging specific code portions.

  • Customizing event handling for specific scenarios.

Function: restart_events()

Simplified Explanation:

Imagine you have a camera that takes pictures automatically. You turn it off (disable) for a while. The restart_events() function is like turning the camera back on (enable), so it starts taking pictures again.

Code Example:

import sys

# Disable all events
sys.monitoring.DISABLE()

# Some time passes

# Re-enable all events
sys.monitoring.restart_events()

Callbacks

Simplified Explanation:

Callbacks are like little helpers that you can tell to do something when a specific event happens. It's like asking a friend, "If I call you, can you please do this task for me?"

Registering a Callback Function:

You can register a callback function using the following steps:

  1. Define a function that does the task you want to perform.

  2. Use the monitoring.register_callback() function to register your function with the specific event you want it to trigger.

Code Example:

import sys

# Define a callback function
def my_callback(metadata):
    print("An event occurred!", metadata)

# Register the callback function with the "process_start" event
sys.monitoring.register_callback(my_callback, "process_start")

Real-World Applications:

  • Event Logging: Register a callback to log specific events, such as errors or performance issues.

  • Performance Monitoring: Register a callback to track performance metrics and trigger alerts when thresholds are exceeded.

  • System Administration: Register a callback to run maintenance tasks or send notifications when certain events occur.

Event Monitoring in Python: A Beginner's Guide

Python's sys.monitoring module allows you to track events that occur while your code is running. This can be useful for debugging, performance analysis, and other tasks.

Registering a Callback Function

To start monitoring, you need to register a callback function for the events you want to track. The callback function is what will be executed when an event occurs.

import sys

# Register a callback function for the PY_START event
sys.monitoring.register_callback(
    1,  # tool ID (assigned by the tool using the monitoring system)
    sys.monitoring.PY_START,  # event type
    lambda code, offset: print(f"Code started: {code}, offset: {offset}")
)

In this example, the callback function is defined as a lambda expression. It takes two arguments:

  • code: The code object that is being executed

  • offset: The instruction offset within the code object

When the PY_START event occurs, the callback function will be executed and will print out the message Code started: <code object>, offset: <instruction offset>.

Callback Function Arguments

The callback function can receive different arguments depending on the event type.

Here's a table summarizing the arguments for different event types:

Event Type
Arguments

PY_START

code, instruction_offset

PY_RETURN

code, instruction_offset, retval

CALL

code, instruction_offset, callable, arg0

RAISE

code, instruction_offset, exception

LINE

code, line_number

BRANCH

code, instruction_offset, destination_offset

JUMP

code, instruction_offset, destination_offset

INSTRUCTION

code, instruction_offset

Unregistering a Callback Function

To stop monitoring an event, you need to unregister the callback function.

# Unregister the callback function for the PY_START event
sys.monitoring.register_callback(1, sys.monitoring.PY_START, None)

Potential Applications

Here are some potential applications of event monitoring:

  • Debugging: You can use event monitoring to track down errors in your code. By registering a callback function for the PY_RAISE event, you can see which exceptions are being raised and where they are occurring.

  • Performance analysis: You can use event monitoring to track the performance of your code. By registering a callback function for the PY_RETURN event, you can see how long it takes for functions to execute.

  • Security: You can use event monitoring to track suspicious activity in your code. By registering a callback function for the CALL event, you can see which functions are being called and with what arguments.

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