[WIP] Update local fork with latest upstream changes

[Copilot]

Coding agent has begun work on Updating Local Fork with Upstream Changes and will replace this description as work progresses.

See problem context

e_config(kube_config)

This is the definitive point where the kubeconfig path is first set or adjusted in your 
AI:  file.
User: how to get current time in python 
AI: To get the current time in Python, you can use the built-in `datetime` module:

```python
from datetime import datetime

# Get current local time
current_time = datetime.now()
print("Current local time:", current_time)

# Get current UTC time
current_utc_time = datetime.utcnow()
print("Current UTC time:", current_utc_time)

# Format the current time as a string
formatted_time = current_time.strftime("%Y-%m-%d %H:%M:%S")
print("Formatted time:", formatted_time)

Explanation:

• datetime.now() returns the current local date and time.
• datetime.utcnow() returns the current UTC date and time.
• strftime() formats the datetime object into a readable string.

Example Output:

Current local time: 2025-03-29 14:30:15.123456
Current UTC time: 2025-03-29 18:30:15.123456
Formatted time: 2025-03-29 14:30:15

In your provided code, you have a function call
AI: . If you want to implement a similar helper function, you can define it as follows:

def utctimenow():
    return datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S")

Then you can use it directly:

print("Current UTC time:", utctimenow())

User: how to end watch_thread.join() with time
AI: To end the
AI: with a timeout, you can pass a timeout parameter to the
AI: method. This allows the main thread to wait for the specified time, and if the thread hasn't finished by then, the main thread continues execution.

Here's how you can modify your existing code:

# Existing code:
stop_event.set()
print("stop event set", datetime.now())

# Replace this line:
watch_thread.join()

# With this line (e.g., timeout after 30 seconds):
watch_thread.join(timeout=30)

# Check if thread is still alive after timeout
if watch_thread.is_alive():
    print("Watch thread did not finish within timeout.")
else:
    print("Watch thread joined successfully.")

Explanation:

AI: waits up to 30 seconds for the thread to finish.

• After the timeout, the main thread continues execution regardless of whether the watch thread has completed.
• You can check if the thread is still running using
  AI: and handle accordingly.

This approach ensures your main thread doesn't block indefinitely if the watch thread doesn't terminate promptly.
User: how to stop python thread with keyboard input
AI: To stop a Python thread gracefully using keyboard input (e.g., pressing Ctrl+C), you can use a
AI: object and handle the KeyboardInterrupt exception in your main thread.

Here's how you can implement this:

Example:

import threading
import time

def worker(stop_event):
    while not stop_event.is_set():
        print("Thread running...")
        time.sleep(1)

if __name__ == "__main__":
    stop_event = threading.Event()
    worker_thread = threading.Thread(target=watch_k8s_events, args=("namespace", stop_event, deque(maxlen=20)))

    worker_thread.start()
    print("Thread started. Press Ctrl+C to stop.")

    try:
        while worker_thread.is_alive():
            worker_thread.join(timeout=1)
    except KeyboardInterrupt:
        print("Keyboard interrupt received. Stopping thread...")
        stop_event.set()
        worker_thread.join()
        print("Thread stopped gracefully.")

Explanation:

• **
  AI: **: Used to signal the thread to stop gracefully.
• Keyboard Interrupt (Ctrl+C): Captured by the main thread using a try-except block.
• Graceful Shutdown: When Ctrl+C is pressed, the
  AI: is set, signaling the thread to exit its loop and terminate cleanly.

This approach ensures your thread exits gracefully without abruptly killing it, allowing for proper resource cleanup.
User: how can we ensure the process and watch thread end for sure with in certain time?
print("starting watch thread")
watch_thread.start()
print("starting helm release+ install"+str(datetime.now()))
print(cmd_helm_install)
process = Popen(cmd_helm_install, stdout=PIPE, stderr=PIPE)
_, stderr = process.communicate()
print("process completed"+ str(datetime.now()))
stop_event.set()
print("stop event set"+ str(datetime.now()))
watch_thread.join(timeout=onboarding_timeout +20)
print("watch thread joined"+ str(datetime.now()))
AI: To ensure both the subprocess (
AI: ) and the watch thread (
AI: ) terminate within a certain time, you should explicitly handle timeouts and forcefully terminate them if they exceed the allowed duration.

Here's how you can modify your existing code to guarantee termination within a specified timeout:

Recommended Approach:

import subprocess
import threading
import time
from datetime import datetime

# Define your timeout (in seconds)
total_timeout = int(onboarding_timeout) + 20

print("starting watch thread")
watch_thread.start()

print("starting helm release install", datetime.now())
print(cmd_helm_install)

# Start the subprocess without blocking
process = subprocess.Popen(cmd_helm_install, stdout=subprocess.PIPE, stderr=subprocess.PIPE)

try:
    # Wait for the subprocess to complete within the timeout
    stdout, stderr = process.communicate(timeout=total_timeout)
except subprocess.TimeoutExpired:
    print("Helm install subprocess timed out. Terminating process.")
    process.kill()
    stdout, stderr = process.communicate()

print("process completed", datetime.now())

# Signal the watch thread to stop
stop_event.set()
print("stop event set", datetime.now())

# Wait for the watch thread to finish within a short timeout
watch_thread.join(timeout=10)

# If the watch thread is still alive after the timeout, log a warning
if watch_thread.is_alive():
    print("Watch thread did not terminate gracefully within timeout.")
else:
    print("Watch thread joined successfully.")

print("watch thread joined", datetime.now())

# Handle subprocess return code
if process.returncode != 0:
    error_msg = stderr.decode("utf-8")
    print("Helm install failed:", error_msg)
    print("Captured Kubernetes events:", list(k8s_events_buffer))

Explanation:

• Subprocess Timeout:

AI: ensures the subprocess doesn't run indefinitely. If it exceeds the timeout, a
AI: exception is raised.

• In the exception handler,
  AI: forcefully terminates the subprocess.

• Watch Thread Timeout:

  • After signaling the watch thread to stop (
    AI: ),
    AI: waits briefly for the thread to finish.
  • If the thread doesn't terminate within this short timeout, you log a warning. Python threads can't be forcefully killed, so the thread must periodically check the
    AI: and exit gracefully.

Important Notes:

• Python threads cannot be forcefully terminated externally. The thread must periodically check the
  AI: and exit gracefully.
• The subprocess can be forcefully terminated using
  AI: if it exceeds the timeout.

This approach ensures your subprocess and watch thread do not run indefinitely and guarantees termination within a predictable timeframe.
User: in the testhelm.py, the stop event is set at
stop event set2025-03-29 16:35:16.021897
but the thread by itself realised it at
stop event is set, breaking2025-03-29 16:38:20.615466
why is there a a delay
AI: The delay you're seeing between setting the
AI: and the thread actually noticing it is due to the blocking nature of the Kubernetes client's
AI: method.

Why this happens:

• The
  AI: method internally makes a blocking HTTP request to the Kubernetes API server. It waits for new events or until the server-side timeout occurs (default is typically around 5 minutes).
• When you set the
  AI: , the thread doesn't immediately notice because it's still blocked waiting for the next event or timeout from the Kubernetes API server.
• Only after the server-side timeout or when a new event arrives does the thread get a chance to check the
  AI: again.

How to fix this:

To ensure the thread responds promptly to the
AI: , you should explicitly set a shorter client-side timeout (timeout_seconds) in the
AI: call. For example:

for event in w.stream(v1.list_namespaced_event, namespace=namespace, timeout_seconds=10):

Recommended modification to your existing code:

def watch_k8s_events(namespace, stop_event, buffer, limit=20):
    config.load_kube_config()
    v1 = client.CoreV1Api()
    w = watch.Watch()

    while not stop_event.is_set():
        try:
            # Set a short timeout to ensure quick responsiveness
            for event in w.stream(v1.list_namespaced_event, namespace=namespace, timeout_seconds=10):
                if stop_event.is_set():
                    print("stop event is set, breaking" + str(datetime.now()))
                    break

                obj = event['object']
                if obj.type not in ("Warning", "Error"):
                    continue

                event_obj = {
                    "type": obj.type,
                    "reason": obj.reason,
                    "message": obj.message,
                    "involvedObject": obj.involved_object.name,
                    "timestamp": obj.last_timestamp
                }
                print("event_obj", event_obj)
                if event_obj not in buffer:
                    buffer.append(event_obj)
                    if len(buffer) > limit:
                        buffer.popleft()
        except Exception as e:
            print(f"⚠️ Connection error: {e}. Retrying in 5 seconds...")
            time.sleep(5)  # Wait before retrying
    print("end of watch k8s event" + str(datetime.now()))

What this achieves:

• The thread checks for the
  AI: at least every 10 seconds (or whenever an event arrives), ensuring a maximum delay of about 10 seconds.
• This prevents the long delay you're currently experiencing (several minutes) and ensures your thread terminates promptly after the
  AI: is set.

This approach is the recommended way to handle responsiveness when using Kubernetes watch streams.
User: how to query only warnings in the above watch
AI: To query only warnings in your Kubernetes event watcher, you can use the field_selector parameter provided by the Kubernetes API. Specifically, you can filter events by their
AI: field, setting it to "Warning".

Here's how you can modify your existing
AI: function to watch only warning events:

Updated

AI: function:

def watch_k8s_events(namespace, stop_event, buffer, limit=20):
    config.load_kube_config()  # or load_incluster_config()
    v1 = client.CoreV1Api()
    w = watch.Watch()

    while not stop_event.is_set():
        try:
            print("start of watch k8s stream" + str(datetime.now()))
            # Add field_selector to filter only Warning events
            for event in w.stream(
                v1.list_namespaced_event,
                namespace=namespace,
                field_selector="type=Warning",
                timeout_seconds=10
            ):
                if stop_event.is_set():
                    print("stop event is set, breaking" + str(datetime.now()))
                    break

                obj = event['object']

                event_obj = {
                    "type": obj.type,
                    "reason": obj.reason,
                    "message": obj.message,
                    "involvedObject": obj.involved_object.name,
                    "timestamp": obj.last_timestamp
                }
                print("event_obj", event_obj)
                if event_obj not in buffer:
                    buffer.append(event_obj)
                    if len(buffer) > limit:
                        buffer.popleft()
        except Exception as e:
            print(f"⚠️ Connection error: {e}. Retrying in 5 seconds...")
            time.sleep(5)  # Wait before retrying
    print("end of watch k8s event" + str(datetime.now()))

Explanation:

• field_selector="type=Warning":
  This parameter instructs the Kubernetes API to return only events whose type is "Warning". This reduces the amount of data transferred and processed, improving efficiency.

• timeout_seconds=10:
  This ensures the watcher periodically checks the
  AI: and exits promptly when requested.

Result:

With this modification, your watcher will only receive and process Kubernetes events of type "Warning".
User: will it not make it to retrieve the same events again and again
AI: No, it won't repeatedly retrieve the same events again and again indefinitely, but it can retrieve duplicate events across multiple watch cycles if the same event is still present in the Kubernetes event list.

Why duplicates can occur:

• Kubernetes events are stored for a certain duration (typically 1 hour by default). Each time your watch stream reconnects (after the timeout of 10 seconds), it queries the Kubernetes API again, which returns the current set of events matching your selector (
  AI: ).
• If the same event still exists in Kubernetes, it will be retrieved again, causing duplicates in your buffer.

How your current code handles duplicates:

Your current code already has logic to prevent duplicates from being stored repeatedly in the buffer:

if event_obj not in buffer:
    buffer.append(event_obj)
    if len(buffer) > limit:
        buffer.popleft()

This ensures that the same event object (with identical fields) won't be added multiple times to your buffer. However, if the event timestamp or message slightly changes, it will be considered a new event and added again.

Recommended improvement to avoid duplicates more robustly:

To avoid duplicates more reliably, you can track events by their unique identifiers (UIDs). Kubernetes events have a unique metadata.uid field. You can use this UID to ensure uniqueness:

Here's how you can modify your code:

def watch_k8s_events(namespace, stop_event, buffer, limit=20):
    config.load_kube_config()
    v1 = client.CoreV1Api()
    w = watch.Watch()

    seen_event_uids = set()  # Track seen event UIDs to avoid duplicates

    while not stop_event.is_set():
        try:
            print("start of watch k8s stream" + str(datetime.now()))
            for event in w.stream(v1.list_namespaced_event, namespace=namespace,
                                  field_selector="type=Warning",
                                  timeout_seconds=10):
                if stop_event.is_set():
                    print("stop event is set, breaking" + str(datetime.now()))
                    break

                obj = event['object']
                event_uid = obj.metadata.uid

                if event_uid in seen_event_uids:
                    continue  # Skip already seen events

                event_obj = {
                    "type": obj.type,
                    "reason": obj.reason,
                    "message": obj.message,
                    "involvedObject": obj.involved_object.name,
                    "timestamp": obj.last_timestamp
                }

                print("event_obj", event_obj)
                buffer.append(event_obj)
                seen_event_uids.add(event_uid)

                if len(buffer) > limit:
                    buffer.popleft()

        except Exception as e:
            print(f"⚠️ Connection error: {e}. Retrying in 5 seconds...")
            time.sleep(5)  # Wait before retrying

    print("end of watch k8s event" + str(datetime.now()))

What this achieves:

• Unique tracking: By using metadata.uid, you ensure each event is processed exactly once.
• No duplicates: Even if the watch reconnects and retrieves the same event again, it won't be added to your buffer repeatedly.

This approach is the recommended way to reliably avoid duplicate Kubernetes events.
User: can we make the filter to retrieve the latest events instead of all the events ? may be last 20 as we want to capture the last 20 warnings only
AI: Yes, you can achieve this by using the Kubernetes API's
AI: method with the
AI: parameter and sorting by timestamp. However, the Kubernetes API itself doesn't directly support sorting or limiting events by count in the watch stream. Instead, you can retrieve the latest events by initially fetching the most recent events using a regular API call (without watch), and then start watching from the latest resource version.

Here's how you can modify your existing
AI: function to achieve this:

Recommended Implementation:

def watch_k8s_events(namespace, stop_event, buffer, limit=20):
    config.load_kube_config()
    v1 = client.CoreV1Api()
    w = watch.Watch()

    # First, get the latest events to initialize buffer and resource_version
    try:
        print("Fetching initial events snapshot at", datetime.now())
        events_list = v1.list_namespaced_event(
            namespace=namespace,
            field_selector="type=Warning",
            limit=limit,
            _preload_content=True
        )

        # Sort events by timestamp to ensure latest events are captured
        sorted_events = sorted(
            events.items,
            key=lambda x: x.last_timestamp or x.event_time or x.metadata.creation_timestamp,
            reverse=False
        )

        for obj in sorted_events:
            event_obj = {
                "type": obj.type,
                "reason": obj.reason,
                "message": obj.message,
                "involvedObject": obj.involved_object.name,
                "timestamp": obj.last_timestamp
            }
            buffer.append(event_obj)

        # Get the latest resource version to start watching from
        resource_version = events.metadata.resource_version

        v1 = client.CoreV1Api()
        w = watch.Watch()

        while not stop_event.is_set():
            try:
                print("start of watch k8s stream" + str(datetime.now()))
                for event in w.stream(
                    v1.list_namespaced_event,
                    namespace=namespace,
                    field_selector="type=Warning",
                    resource_version=resource_version,
                    timeout_seconds=10
                ):
                    if stop_event.is_set():
                        print("stop event is set, breaking" + str(datetime.now()))
                        break

                    obj = event['object']
                    event_obj = {
                        "type": obj.type,
                        "reason": obj.reason,
                        "message": obj.message,
                        "involvedObject": obj.involved_object.name,
                        "timestamp": obj.last_timestamp
                    }
                    print("event_obj", event_obj)
                    if event_obj not in buffer:
                        buffer.append(event_obj)
                        if len(buffer) > limit:
                            buffer.popleft()

            except Exception as e:
                print(f"⚠️ Connection error: {e}. Retrying in 5 seconds...")
                time.sleep(5)  # Wait before retrying

    except Exception as e:
        print(f"⚠️ Error fetching initial events: {e}")

    print("end of watch k8s event" + str(datetime.now()))

Explanation:

• Initial Fetch:

  • First, you fetch the latest 20 warning events using
    AI: with
    AI: .
  • Sort these events by timestamp to ensure you have the latest events.
  • Populate your buffer with these initial events.

• Resource Version:

  • You retrieve the latest resource_version from the initial fetch.
  • This ensures the watch stream starts from the latest point, avoiding older events.

• Watch Stream:

  • The watch stream (
    AI: ) starts from the latest resource_version, ensuring only new events after this point are retrieved.

This approach ensures you capture only the latest 20 warning events initially and then continue watching for new events, avoiding duplicates and older events.
User: is resource version tied to event or the resource
AI: The resourceVersion is tied to the Kubernetes resource itself, not to individual events. Specifically:

• Resource Version:

  • Represents the version of the resource object in the Kubernetes API server.
  • Changes whenever the resource is modified (created, updated, or deleted).
  • Used by Kubernetes clients to watch for changes starting from a specific point in time.

• Events:

  • Kubernetes events are themselves resources (
    AI: objects).
  • Each event has its own metadata.resourceVersion, which increments whenever the event object itself is created or updated.
  • The resourceVersion of an event is not directly tied to the resource it describes (e.g., a Pod or Deployment). Instead, it is specific to the event object itself.

Practical Implication for Your Code:

When you use the Kubernetes watch API (
AI: ) with a resource_version parameter, you're instructing Kubernetes to stream events starting from that specific resource version. This means:

• If you specify a resource_version from a previous event list call, you'll receive events that occurred after that resource version.
• If you don't specify a resource_version, the watch stream starts from the current state.

Recommended Usage in Your Scenario:

To avoid duplicates and ensure you only get new events, you should:

1. Initially fetch the latest events using
   AI: and store the latest resource_version.
2. Start your watch stream from that resource_version.

Example:

# Initial fetch to get latest events and resource_version
events_list = v1.list_namespaced_event(
    namespace=namespace,
    field_selector="type=Warning",
    limit=20
)
resource_version = events_list.metadata.resource_version

# Start watching from this resource_version
for event in w.stream(
    v1.list_namespaced_event,
    namespace=namespace,
    field_selector="type=Warning",
    resource_version=resource_version,
    timeout_seconds=10
):
    # process events

This ensures you only receive new events after the initial snapshot, avoiding duplicates and older events.
User: I am still not seeing the advantage of resource version as we anyway fetch the latest events only
AI: You're correct—if your goal is simply to fetch the latest events periodically (e.g., every 10 seconds), using resource_version doesn't provide a significant advantage. Let me clarify why:

What is resource_version?

• Resource version is a Kubernetes internal mechanism to track changes to resources. When you use a watch with a specific resource_version, Kubernetes streams events that occurred after that version.
• It's useful when you want to continuously watch for incremental updates without missing events or receiving duplicates.

Why isn't it helpful in your scenario?

• Your current implementation uses a short timeout (
  AI: ) and repeatedly reconnects to the Kubernetes API. Each time you reconnect, you're fetching the latest events again.
• Since you're only interested in the latest warnings (e.g., the last 20), and you're already filtering duplicates in your buffer, using resource_version doesn't add much value. You're effectively polling rather than continuously watching.

When would resource_version be beneficial?

• If you were maintaining a continuous, uninterrupted watch stream (without frequent reconnects), resource_version would ensure you don't miss events or receive duplicates.
• If you wanted to precisely track incremental changes over a long period without polling, resource_version would be essential.

Recommended approach for your scenario:

Given your current approach (short timeout, periodic reconnects, and fetching the latest events), your existing implementation is already suitable. You don't need to use resource_version.

However, if you want to optimize further and avoid repeatedly fetching the same events, you could:

1. Fetch initial events once (to populate your buffer).
2. Start a continuous watch from the latest resource_version to receive only new events as they occur, without periodic reconnects.

Example of optimized continuous watch (optional):

def watch_k8s_events(namespace, stop_event, buffer, limit=20):
    config.load_kube_config()
    v1 = client.CoreV1Api()
    w = watch.Watch()

    # Initial fetch to populate buffer and get resource_version
    events_list = v1.list_namespaced_event(
        namespace=namespace,
        field_selector="type=Warning",
        limit=limit
    )

    # Populate buffer initially
    sorted_events = sorted(
        events_list.items,
        key=lambda x: x.last_timestamp or x.event_time or x.metadata.creation_timestamp
    )
    for obj in sorted_events:
        event_obj = {
            "type": obj.type,
            "reason": obj.reason,
            "message": obj.message,
            "involvedObject": obj.involved_object.name,
            "timestamp": obj.last_timestamp
        }
        buffer.append(event_obj)

    resource_version = events_list.metadata.resource_version

    # Continuous watch from resource_version
    while not stop_event.is_set():
        try:
            for event in w.stream(
                v1.list_namespaced_event,
                namespace=namespace,
                field_selector="type=Warning",
                resource_version=resource_version,
                timeout_seconds=60  # longer timeout for continuous watch
            ):
                if stop_event.is_set():
                    break

                obj = event['object']
                event_obj = {
                    "type": obj.type,
                    "reason": obj.reason,
                    "message": obj.message,
                    "involvedObject": obj.involved_object.name,
                    "timestamp": obj.last_timestamp
                }

                buffer.append(event_obj)
                if len(buffer) > limit:
                    buffer.popleft()

                # Update resource_version to latest
                resource_version = obj.metadata.resource_version

        except Exception as e:
            print(f"⚠️ Connection error: {e}. Retrying in 5 seconds...")
            time.sleep(5)

    print("end of watch k8s event" + str(datetime.now()))

Conclusion:

• If you're comfortable with periodic polling and filtering duplicates, your current approach is fine.
• If you prefer a continuous, incremental watch without duplicates, using resource_version as shown above is beneficial.

In short, the advantage of resource_version is primarily realized in continuous, incremental watches—not in periodic polling scenarios like your current implementation.
User: implement this.
AI: Coding agent session cancelled.
User: I am trying to bring in the latest changes from upstream to my local fork.. as I am preparing to send a PR with my local fork\release branch

C:\repos\git\connectedk8s>git remote -v
origin https://github.com/AzureArcForKubernetes/connectedk8s/ (fetch)
origin https://github.com/AzureArcForKubernetes/connectedk8s/ (push)
upstream https://github.com/Azure/azure-cli-extensions.git (fetch)
upstream https://github.com/Azure/azure-cli-extensions.git (push)

TITLE: Updating Local Fork with Upstream Changes

USER INTENT: The user aims to bring the latest changes from the upstream repository into their local fork's release branch in preparation for submitting a pull request (PR).

TASK DESCRIPTION: The user needs to synchronize their local fork with the upstream repository to ensure they have the latest updates before creating a PR.

EXISTING: The user has already set up their local repository with two remotes:

• origin: Points to their fork at https://github.com/AzureArcForKubernetes/connectedk8s/
• upstream: Points to the original repository at https://github.com/Azure/azure-cli-extensions.git

PENDING: The user needs to perform the following steps to update their local release branch with the latest changes from the upstream repository:

1. Fetch the latest changes from the upstream repository.
2. Merge those changes into their local release branch.
3. Resolve any potential merge conflicts.
4. Push the updated release branch to their fork on GitHub.

CODE STATE: The user is currently in the command line interface of their local repository.

RELEVANT CODE/DOCUMENTATION SNIPPETS:

1. Fetch upstream changes:

   git fetch upstream

2. Checkout the local release branch:

   git checkout release

3. Merge upstream changes into local release branch:

   git merge upstream/main  # or upstream/release if that's the branch to merge from

4. Push changes to the origin:

   git push origin release

OTHER NOTES:

• The user should ensure they are merging from the correct branch of the upstream repository (e.g., main or release).
• If there are merge conflicts, Git will prompt the user to resolve them before completing the merge.
• After resolving conflicts, the user can continue the merge process with git add <resolved_files> followed by git commit.

This summary provides a clear path for the user to update their local fork with the latest changes from the upstream repository in preparation for their PR.

Original prompt

I am trying to bring in the latest changes from upstream to my local fork.. as I am preparing to send a PR with my local fork\release branch

C:\repos\git\connectedk8s>git remote -v
origin https://github.com/AzureArcForKubernetes/connectedk8s/ (fetch)
origin https://github.com/AzureArcForKubernetes/connectedk8s/ (push)
upstream https://github.com/Azure/azure-cli-extensions.git (fetch)
upstream https://github.com/Azure/azure-cli-extensions.git (push)
e_config(kube_config)

This is the definitive point where the kubeconfig path is first set or adjusted in your 
AI:  file.
User: how to get current time in python 
AI: To get the current time in Python, you can use the built-in `datetime` module:

```python
from datetime import datetime

# Get current local time
current_time = datetime.now()
print("Current local time:", current_time)

# Get current UTC time
current_utc_time = datetime.utcnow()
print("Current UTC time:", current_utc_time)

# Format the current time as a string
formatted_time = current_time.strftime("%Y-%m-%d %H:%M:%S")
print("Formatted time:", formatted_time)

Explanation:

• datetime.now() returns the current local date and time.
• datetime.utcnow() returns the current UTC date and time.
• strftime() formats the datetime object into a readable string.

Example Output:

Current local time: 2025-03-29 14:30:15.123456
Current UTC time: 2025-03-29 18:30:15.123456
Formatted time: 2025-03-29 14:30:15

In your provided code, you have a function call
AI: . If you want to implement a similar helper function, you can define it as follows:

def utctimenow():
    return datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S")

Then you can use it directly:

print("Current UTC time:", utctimenow())

User: how to end watch_thread.join() with time
AI: To end the
AI: with a timeout, you can pass a timeout parameter to the
AI: method. This allows the main thread to wait for the specified time, and if the thread hasn't finished by then, the main thread continues execution.

Here's how you can modify your existing code:

# Existing code:
stop_event.set()
print("stop event set", datetime.now())

# Replace this line:
watch_thread.join()

# With this line (e.g., timeout after 30 seconds):
watch_thread.join(timeout=30)

# Check if thread is still alive after timeout
if watch_thread.is_alive():
    print("Watch thread did not finish within timeout.")
else:
    print("Watch thread joined successfully.")

Explanation:

AI: waits up to 30 seconds for the thread to finish.

• After the timeout, the main thread continues execution regardless of whether the watch thread has completed.
• You can check if the thread is still running using
  AI: and handle accordingly.

This approach ensures your main thread doesn't block indefinitely if the watch thread doesn't terminate promptly.
User: how to stop python thread with keyboard input
AI: To stop a Python thread gracefully using keyboard input (e.g., pressing Ctrl+C), you can use a
AI: object and handle the KeyboardInterrupt exception in your main thread.

Here's how you can implement this:

Example:

import threading
import time

def worker(stop_event):
    while not stop_event.is_set():
        print("Thread running...")
        time.sleep(1)

if __name__ == "__main__":
    stop_event = threading.Event()
    worker_thread = threading.Thread(target=watch_k8s_events, args=("namespace", stop_event, deque(maxlen=20)))

    worker_thread.start()
    print("Thread started. Press Ctrl+C to stop.")

    try:
        while worker_thread.is_alive():
            worker_thread.join(timeout=1)
    except KeyboardInterrupt:
        print("Keyboard interrupt received. Stopping thread...")
        stop_event.set()
        worker_thread.join()
        print("Thread stopped gracefully.")

Explanation:

• **
  AI: **: Used to signal the thread to stop gracefully.
• Keyboard Interrupt (Ctrl+C): Captured by the main thread using a try-except block.
• Graceful Shutdown: When Ctrl+C is pressed, the
  AI: is set, signaling the thread to exit its loop and terminate cleanly.

This approach ensures your thread exits gracefully without abruptly killing it, allowing for proper resource cleanup.
User: how can we ensure the process and watch thread end for sure with in certain time?
print("starting watch thread")
watch_thread.start()
print("starting helm release+ install"+str(datetime.now()))
print(cmd_helm_install)
process = Popen(cmd_helm_install, stdout=PIPE, stderr=PIPE)
_, stderr = process.communicate()
print("process completed"+ str(datetime.now()))
stop_event.set()
print("stop event set"+ str(datetime.now()))
watch_thread.join(timeout=onboarding_timeout +20)
print("watch thread joined"+ str(datetime.now()))
AI: To ensure both the subprocess (
AI: ) and the watch thread (
AI: ) terminate within a certain time, you s...

Created from VS Code via the GitHub Pull Request extension.

---

✨ Let Copilot coding agent set things up for you — coding agent works faster and does higher quality work when set up for your repo.