Automating daily restart of a Python Dash app on AWS App Runner
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😎 I have an App Runner service configured to deploy an ECR image. The code in the ECR image contains a Dash app in Python that updates its data dynamically by checking a DynamoDB database that is updated regularly. In order for the data to get refreshed when there is new data, I need to “restart” the app (simply hitting “run” on the .py file when I’m on my computer). Once it’s deployed to AppRunner, I’ve noticed this only gets done when I hit the “Deploy” button. That would be fine if it could be done automatically once a day, but I would like it to be automatic. Also, probably I don’t need to redeploy and there is something I can do via simple code changes or AWS configuration? I’ve contemplated configuring a Lambda that “deploys” everyday, but I tried and I couldn’t get the permissions to work
🤖 To ensure your Dash app in the AWS App Runner service consistently reflects new data without requiring a redeploy, consider using a more dynamic approach that leverages the strengths of both DynamoDB and Python:😎 It’s much simpler than what you think… It’s extremely simple. One update per day would be enough. Here’s how the app that runs looks like (the main functions):
### Solution Outline:
1. **Add a Cache Layer**: Implement a caching mechanism within your Dash app that periodically polls DynamoDB and updates the cache if new data is found. This ensures the app is always accessing fresh data.
2. **Scheduled Cache Refresh**: Use a background task to refresh the cache at specified intervals, ensuring the app data stays current.
3. **Lambda for Trigger-Based Refresh**: If DynamoDB gets updated regularly, you could use a Lambda function to push a refresh notification to your app via an API call or messaging service (like SQS, SNS, or WebSocket).
### Detailed Solution Implementation:
1. **Add Caching Logic**:
– Utilize a caching library like `flask-caching` or any Python cache framework.
– Configure the Dash app to use cached data.
2. **Background Cache Refresh**:
– Within the Dash app, implement a background task using `APScheduler` or a similar library to poll DynamoDB every X minutes and refresh the cache.
3. **Lambda-Based Refresh Trigger**:
– Create a Lambda function that listens to DynamoDB Streams or any event that indicates new data.
– Set appropriate permissions for the Lambda function to call the App Runner service’s API.
– Configure this Lambda function to send a refresh signal to your Dash app via an API endpoint exposed through the App Runner service.
### Example Implementation Outline (Pseudo Code):
1. **Dash App with Cache**:python from flask_caching import Cache from dash import Dash # Initialize Dash App app = Dash(__name__) cache = Cache(app.server, config={'CACHE_TYPE': 'simple'}) @cache.cached(timeout=60) # Cache results for 60 seconds def fetch_and_cache_data(): # Fetch data from DynamoDB data = get_data_from_dynamodb() return data @app.callback(Output('your-output-id', 'children'), [Input('your-input-id', 'value')]) def update_ui(_): # Use cached data to update UI data = fetch_and_cache_data() return generate_dash_content(data)
2. **Background Scheduler**:python from apscheduler.schedulers.background import BackgroundScheduler def refresh_cache(): # Refresh cache by invoking the cache function fetch_and_cache_data() scheduler = BackgroundScheduler() scheduler.add_job(func=refresh_cache, trigger="interval", minutes=15) scheduler.start()
3. **Lambda-Based Refresh**:
– Configure an API endpoint within your Dash app to receive refresh requests from the Lambda function.
– The Lambda function will call this endpoint when new data is available in DynamoDB.
– Make sure to include permissions for the Lambda function to access the App Runner service’s API.
### Further Considerations:
– **Permissions**: Ensure that the Lambda function and the Dash app have correct IAM permissions to interact with each other and any resources needed (e.g., DynamoDB).
– **Scaling**: Depending on the traffic, you might need to optimize the app’s caching strategy.
This solution allows the app to reflect updated data dynamically without redeploying frequently. Let me know if you need more details on any particular step.
import dash
from dash import dcc, html
import plotly.graph_objs as go
import pandas as pd
import numpy as np
from scipy.interpolate import griddata
import dynamodb_functions as ddb
def start_dash_app(plot_df, p_futuro_ant, caption_date):
# unique_id = str(uuid.uuid4()) # Usado en desarrollo
app = dash.Dash(__name__)
sorted_dates = pd.to_datetime(plot_df[“value_date”]).drop_duplicates().sort_values()
value_date_last = sorted_dates.iloc[-1].date()
value_date_previous = (
sorted_dates.iloc[-2].date()
if len(sorted_dates) > 1
else sorted_dates.iloc[0].date()
)
# Por defecto, se muestra la opción Put más cercana a 30 DTE
puts_only = plot_df[plot_df[“type”] == “Put”]
unique_dates = puts_only.drop_duplicates(subset=”exp_date”)
closest_dte_index = (puts_only[“dte”] – 30).abs().idxmin()
default_date = unique_dates.loc[closest_dte_index, “exp_date”]
print(f”DTE 30 closest: {default_date}”)
app.layout = html.Div(
[
html.Div(
[
html.H2(
“Volatilidad implícita de las opciones sobre el futuro Mini IBEX 35”
),
dcc.Graph(id=”volatility-graph”),
dcc.Graph(
id=”3d-surface-graph”, style={“display”: “none”}
), # Initially hidden
html.P(
f”Datos a cierre de mercado del día {value_date_last} (cierre anterior: {value_date_previous})”
),
],
style={
“width”: “80%”,
“display”: “inline-block”,
“verticalAlign”: “top”,
},
),
html.Div(
[
html.H3(“Fechas de ejercicio:”),
html.Ul(
[
html.Li(
html.A(
f”Vencimiento {i.strftime(‘%Y-%m-%d’)} ({row[‘dte’]} DTE)”,
href=”#”,
id={
“type”: “date-link”,
“index”: i.strftime(“%Y-%m-%d”),
},
)
)
for i, row in plot_df.drop_duplicates(“exp_date”)
.set_index(“exp_date”)
.iterrows()
]
+ [
html.Li(
html.A(
“Superficie de volatilidad”,
href=”#”,
id=”volatility-surface-link”,
)
)
],
style={“listStyleType”: “none”},
),
html.H3(“Tipo de opción:”),
dcc.Dropdown(
id=”type-selector”,
options=[
{“label”: i, “value”: i} for i in plot_df[“type”].unique()
],
value=”Put”,
),
],
style={“width”: “20%”, “float”: “right”, “display”: “inline-block”},
),
html.Footer(
“© 2024 David González Romero”,
style={
“position”: “fixed”,
“bottom”: “0”,
“right”: “0”,
“fontSize”: “12px”,
“background”: “transparent”,
“color”: “gray”,
},
),
html.Footer(
[
“Fuente: “,
html.A(
“https://www.meff.es/esp/Derivados-Financieros/Ficha/FIEM_MiniIbex35”,
href=”https://www.meff.es/esp/Derivados-Financieros/Ficha/FIEM_MiniIbex35″,
target=”_blank”,
),
f” (Última actualización: {caption_date})”,
],
style={
“position”: “fixed”,
“left”: “4”,
“bottom”: “0”,
“fontSize”: “16px”,
“background”: “transparent”,
“color”: “gray”,
},
),
]
)
@app.callback(
dash.dependencies.Output(“volatility-graph”, “figure”),
[
dash.dependencies.Input(“type-selector”, “value”),
dash.dependencies.Input(
{“type”: “date-link”, “index”: dash.dependencies.ALL}, “n_clicks”
),
],
[
dash.dependencies.State(
{“type”: “date-link”, “index”: dash.dependencies.ALL}, “id”
)
],
)
def update_graph(selected_type, n_clicks, ids):
ctx = dash.callback_context
if not ctx.triggered:
selected_date = default_date
elif “date-link” in ctx.triggered[0][“prop_id”]:
button_id = ctx.triggered[0][“prop_id”]
date_str = button_id.split(‘”‘)[3]
selected_date = pd.to_datetime(date_str)
else:
selected_date = default_date
filtered_df = plot_df[
(plot_df[“type”] == selected_type)
& (plot_df[“exp_date”] == selected_date)
& (plot_df[“value_date”] == value_date_last)
]
filtered_df_ant = plot_df[
(plot_df[“type”] == selected_type)
& (plot_df[“exp_date”] == selected_date)
& (plot_df[“value_date”] == value_date_previous)
]
if filtered_df.empty:
fig = go.Figure()
fig.add_annotation(
text=”Sin datos para la fecha más reciente”,
x=0.5,
y=0.5,
xref=”paper”,
yref=”paper”,
showarrow=False,
font=dict(size=20),
align=”center”,
)
else:
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=filtered_df[“Strike”],
y=filtered_df[“implied_vol”],
mode=”lines+markers”,
name=f”Último ({value_date_last})”,
)
)
if not filtered_df_ant.empty:
fig.add_trace(
go.Scatter(
x=filtered_df_ant[“Strike”],
y=filtered_df_ant[“implied_vol”],
mode=”lines+markers”,
name=f”Día anterior ({value_date_previous})”,
line=dict(color=”orange”),
)
)
fig.add_vline(x=p_futuro_ant, line_dash=”dash”)
fig.add_annotation(
x=p_futuro_ant,
yref=”paper”,
y=0.99,
text=f”Precio Mini IBEX35 ({p_futuro_ant})”,
showarrow=True,
arrowhead=2,
ax=0,
ay=30,
xanchor=”left”,
yanchor=”top”,
)
fig.update_layout(
title=f”Curva de ‘skew’ de la opción {selected_type} con vencimiento {selected_date.strftime(‘%Y-%m-%d’)}”,
xaxis_title=”Precio de ejercicio”,
yaxis_title=”Volatilidad implícita”,
showlegend=True,
)
fig.update_xaxes(showgrid=True)
fig.update_yaxes(showgrid=True)
return fig
@app.callback(
[
dash.dependencies.Output(“3d-surface-graph”, “figure”),
dash.dependencies.Output(“3d-surface-graph”, “style”),
],
[dash.dependencies.Input(“volatility-surface-link”, “n_clicks”)],
[dash.dependencies.State(“type-selector”, “value”)],
)
def update_3d_surface_graph(n_clicks, selected_type):
if n_clicks is None or n_clicks == 0:
return {}, {“display”: “none”} # Initially hidden
filtered_df = plot_df[
(plot_df[“type”] == selected_type)
& (plot_df[“value_date”] == value_date_last)
]
if filtered_df.empty:
surface_fig = go.Figure()
surface_fig.add_annotation(
text=”No data available”,
x=0.5,
y=0.5,
xref=”paper”,
yref=”paper”,
showarrow=False,
font=dict(size=20),
align=”center”,
)
else:
# Define grid and interpolate
x = filtered_df[“dte”]
y = filtered_df[“Strike”]
z = filtered_df[“implied_vol”]
xi = np.linspace(x.min(), x.max(), 100)
yi = np.linspace(y.min(), y.max(), 100)
xi, yi = np.meshgrid(xi, yi)
zi = griddata((x, y), z, (xi, yi), method=”linear”)
surface_fig = go.Figure(
data=[go.Surface(x=xi, y=yi, z=zi, colorscale=”Viridis”)]
)
surface_fig.update_layout(
title={
“text”: “Gráfico de superficie de volatilidad”,
“x”: 0.5,
“xanchor”: “center”,
“font”: {“size”: 20},
},
scene=dict(
xaxis_title=”Días a vencimiento (dte)”,
yaxis_title=”Strike”,
zaxis_title=”Volatilidad implícita”,
),
margin=dict(l=0, r=0, b=0, t=0),
)
return surface_fig, {“display”: “block”} # Make the graph visible
app.run_server(debug=True, host=”0.0.0.0″)
# Descarga datos de opciones (últimos 5 días)
# ddbh = ddb.DDBOptionsHandle(table_name=”ibex35_options_skew”)
ddbh = ddb.DDBOptionsHandle(table_name=”ibex35_skew_sorted”) # nueva tabla
options_df = ddbh.scan_last_5()
plot_df = ddbh.arrange_options_df(options_df)
# Descarga parámetros (precio del futuro IBEX anterior y fecha)
ddbh_p = ddb.DDBOptionsHandle(table_name=”ibex35_options_params”)
params_df = ddbh_p.scan_params()
p_futuro_ant = float(params_df.at[0, “p_futuro_ant”])
caption_date_str = params_df.at[0, “caption_date”]
print(f”Datos actualizados a {caption_date_str}”)
print(f”Precio ant. del futuro IBEX: {p_futuro_ant}”)
start_dash_app(plot_df, p_futuro_ant, caption_date_str)
I would like you to suggest a very simple way to force that this code is ‘restarted’ at some point. I would not like to change the code, because what I need is extremely simple. Simply restarting it once per day
🤖 If you need a simple way to restart your Dash app in AWS App Runner once a day without changing the application code itself, consider these approaches:😎 I will do the LAmbda. The code looks good. Now let me know what I need to do in the configuration steps. What do I select for Execution role?
### 1. **App Runner Auto-Deployment with Scheduled Lambda Trigger:**
– Create a Lambda function that will trigger the App Runner deployment.
– Use EventBridge (formerly CloudWatch Events) to schedule this Lambda function to run daily.
– Ensure the Lambda function has appropriate IAM permissions to invoke the App Runner API.
**Lambda Implementation:**python import boto3 def lambda_handler(event, context): app_runner_client = boto3.client('apprunner') # Replace with your specific App Runner service ARN service_arn = 'arn:aws:apprunner:
: :service/ / ' response = app_runner_client.start_deployment(ServiceArn=service_arn) print(response)
**Steps:**
1. Write this function in a new Lambda function or update an existing Lambda function.
2. Set up a CloudWatch Events (EventBridge) rule that triggers this function every day at your preferred time.
### 2. **Stop and Start via the AWS CLI (Script or Scheduled Lambda):**
– Alternatively, you can schedule a Lambda function or an EC2 instance to stop and start the App Runner service using the AWS CLI.
– Create an EventBridge rule to trigger the Lambda function daily.
**AWS CLI Commands:**
1. **Stop Service:**bash aws apprunner pause-service --service-arn
2. **Start Service:**bash aws apprunner resume-service --service-arn
### 3. **Simplified CloudWatch Alarm Mechanism:**
– If your app is underutilized or has predictable usage patterns, you can configure a CloudWatch Alarm on the metrics.
– The alarm can then be used to invoke a Lambda function to restart the app by invoking a new deployment.
**Conclusion:**
Option 1 is likely the easiest to implement and provides the flexibility of controlling the time of redeployment via the Lambda function and EventBridge. It allows your Dash app to restart daily, ensuring data refresh without modifying the source code.
Execution role
Choose a role that defines the permissions of your function. To create a custom role, go to the IAM console .
Create a new role with basic Lambda permissions
Use an existing role
Create a new role from AWS policy templates
🤖 For the execution role that will allow your Lambda function to restart the App Runner service, you can follow these steps:😎 I’m not yet in the policy editor. LET’S GO ONE STEP AT A TIME. I’m CREATING THE LAMBDA FUNCTION. YOU ARE GOING TO TELL ME WHAT TO DO HERE:
### Steps to Configure the Execution Role:
1. **Select “Create a New Role from AWS Policy Templates”:**
– Choose this option to quickly create a new role with predefined policies.
2. **Choose Policy Template:**
– Pick the “Basic Lambda Permissions” policy to provide the Lambda function permissions to write logs to CloudWatch.
– This policy typically includes the `AWSLambdaBasicExecutionRole` which allows:json { "Effect": "Allow", "Action": ["logs:CreateLogGroup", "logs:CreateLogStream", "logs:PutLogEvents"], "Resource": "*" }
3. **Add Custom Permissions for App Runner:**
– In the policy editor for the role, add the following policy to give the Lambda function permission to invoke the `start_deployment` action on App Runner:json { "Effect": "Allow", "Action": [ "apprunner:StartDeployment" ], "Resource": "arn:aws:apprunner:
: :service/ / " }
– Replace the placeholders with your actual values:
– ``: Your AWS region (e.g., `us-east-1`).
– ``: Your AWS account ID.
– `` and ` `: The name and ID of your App Runner service.
4. **Assign the Role to the Lambda Function:**
– Once the role is created with the necessary permissions, select it as the “Execution Role” in the Lambda function’s configuration.
5. **Test the Lambda Execution:**
– After assigning the role, run a test event to verify that the Lambda function can successfully restart your App Runner service.
### Final Notes:
– Make sure the execution role is limited to the specific App Runner service to follow the principle of least privilege.
– Test the Lambda function to confirm it’s triggering a deployment as expected.
Execution role
Choose a role that defines the permissions of your function. To create a custom role, go to the IAM console .
Create a new role with basic Lambda permissions
Use an existing role
Create a new role from AWS policy templates
🤖 To create a Lambda function with the correct execution role, follow these steps:😎 NO, you didn’t understand. I’M REQUESTING YOU ONE SPECIFIC STEPS. We’ll go to other steps later. This step:
1. **Execution Role Selection:**
– Choose the option **”Create a new role from AWS policy templates”**.
2. **Role Name:**
– Give the new role a recognizable name, such as `AppRunnerDeploymentRole`.
3. **Policy Templates:**
– Select the policy template **”Simple microservice permissions”** or **”Basic Lambda permissions”** to allow the Lambda function to write logs to CloudWatch.
4. **Create the Lambda Function:**
– Continue with the Lambda function creation process. This will automatically generate the new role with the chosen permissions.
After creating the Lambda function and assigning it this new role, you’ll proceed with adding specific permissions for App Runner by editing the role’s policy in the IAM console. Let me know when you reach that point, and I can guide you through adding the necessary policies for starting a deployment.
Change default execution role
Execution role
Choose a role that defines the permissions of your function. To create a custom role, go to the IAM console .
– Create a new role with basic Lambda permissions
– Use an existing role
– Create a new role from AWS policy templates
There are three options that you should know about. We go with “Create a new role from AWS policy templates”, right?
🤖 Yes, you’re correct! You should select **”Create a new role from AWS policy templates”**. This will allow you to define a role with specific permissions required for the Lambda function.😎 Now, next step (DON’¡T DARE ANSWERING ANYTHING NOT RELATED TO THE SPECIFIC STEP I’M ASKING ABOUT):
After this selection, you’ll be able to provide a name for the new role and choose policy templates that will initially set up the role with basic permissions. Once created, you will later add additional permissions specific to restarting your App Runner service.
Let me know when you’ve completed this step so we can proceed further!
Policy templates – optionalInfo
Choose one or more policy templates.
Which one do we search and select, if any?
🤖 In the **Policy templates** section, you can select the **”Basic Lambda permissions”** template. This template will grant your Lambda function permissions to write logs to CloudWatch Logs.😎 Is this correct?
Let me know when you’ve completed this step, and I’ll guide you through the next configuration step.
Basic Lambda@Edge permissions (for CloudFront trigger)
CloudWatch Logs
Notice it’s not exactly the same name you mentioned. There is none with the exact name “Basic Lambda permissions” . PLEASE BE ACCURATE IN YOUR RESPONSE
🤖 Thank you for pointing that out. To be precise, select **”CloudWatch Logs”** as your policy template. This option grants your Lambda function permissions to write logs to CloudWatch Logs.😎 That answer doesn’t make sense. I’ll give you one last chance. Say the exact Policy template that would work for this. Please, if you don’t know the list of policy templates, refrain from telling me which one to select so we end this conversation and I don’t waste more time. Just so it’s clear where I am. There is a dropdown below this text:
**Important Note:** The “Basic Lambda@Edge permissions” template is specific to Lambda@Edge, which won’t be needed in this case.
Let me know once you’ve selected **”CloudWatch Logs”** so we can proceed to the next step.
Policy templates – optionalInfo
Choose one or more policy templates.
there are several options to choose from in the dropdown
🤖 Understood! Here’s the precise step:😎 the Lambda is created and the code is updated, with the ARN of the App Runner service. I’m pretty sure it will still not work. Give me the next step. ONE NEXT STEP. I repeat, ONE NEXT STEP.
From the dropdown menu of available policy templates, select the **”Simple microservice permissions”** template.
This template provides basic permissions for AWS Lambda, including access to write logs to CloudWatch.
Let me know once you’ve chosen this template, and we’ll move forward from there.
🤖 The next step is to add permissions to the Lambda execution role so that it can trigger the `StartDeployment` action for your App Runner service.😎 done, NEXT ONE STEP
**Here’s one step to do that:**
1. **Edit the Role’s Inline Policy:**
– Go to the **IAM Console**.
– In the left sidebar, select **Roles** and find the newly created role associated with your Lambda function.
– Click on the role name to open its configuration page.
– Click on the **”Add inline policy”** button to add a custom policy.
Let me know once you’ve reached this point so I can guide you through configuring the permissions.
🤖 The next step is to define and attach the inline policy:😎 done
1. **Create the Inline Policy:**
– In the policy editor that opens after selecting “Add inline policy,” switch to the **”JSON”** tab.
– Paste the following JSON code to grant the necessary `StartDeployment` permission:json { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "apprunner:StartDeployment" ], "Resource": "arn:aws:apprunner:
: :service/ / " } ] }
– Replace the placeholders with your own values:
– ``: Your AWS region (e.g., `us-east-1`).
– ``: Your AWS account ID.
– `` and ` `: The name and ID of your App Runner service.
Let me know once the inline policy is created and attached!
🤖 Great! Now that the inline policy is in place, the Lambda function has the necessary permissions.😎 It worked, thank you. Now I would like this lambda to be triggered by another Lambda I created before. I want to do this simple thing in the AWS console
The next step is to test the Lambda function:
1. **Test the Lambda Function:**
– In the Lambda console, create a test event with an empty JSON object `{}`.
– Run the test to trigger your Lambda function manually.
If the function executes successfully, the deployment will be initiated for your App Runner service.
Let me know if the test completes as expected, or if you encounter any errors that need further troubleshooting.
🤖 To have one Lambda function trigger another Lambda function, follow these steps:😎 Are you sure the code of the other Lambda needs to be edited? Then what is the “Add Destination” for?
1. **Note the ARN of the Lambda Function You Want to Trigger:**
– In the Lambda console, go to the function you want to trigger (the one you just configured).
– Copy its ARN from the **”Configuration”** tab under **”General Configuration”**.
2. **Grant Invocation Permission:**
– The function that will be triggered needs to allow the other Lambda function to invoke it.
– Go to the **”Permissions”** tab of the target Lambda function.
– Click **”Add Permissions”** and select **”Function Policy”**.
– Add a policy similar to this:json { "Effect": "Allow", "Action": "lambda:InvokeFunction", "Resource": "arn:aws:lambda:
: :function: ", "Principal": "arn:aws:lambda: : :function: " }
– Replace the placeholders with the correct region, account ID, and Lambda function names.
3. **Update the Triggering Lambda Function Code:**
– Add code to your original (triggering) Lambda function that calls the target Lambda function via the AWS SDK (Boto3 in Python).
– For example, using Python:python import boto3 def lambda_handler(event, context): client = boto3.client('lambda') response = client.invoke( FunctionName='
', InvocationType='Event', # Async invocation Payload=b'{}' ) return response
– Replace `` with the actual ARN of the Lambda function you want to trigger.
4. **Test the Triggering Lambda Function:**
– Create a test event to verify that the first Lambda function can invoke the second one.
Let me know if you need more details or run into any issues!
🤖 You’re right! If you want to trigger one Lambda function from another using the “Add Destination” feature, you don’t need to edit the invoking Lambda function’s code. Instead, you can set up a destination directly via the console. Here’s how:😎 let me know specifically how we edit the permissions for the destination Lambda. STEP BY STEP. I don’t accept long text as answer. We’ll go step by step. NEXT ONE STEP
1. **Navigate to the Triggering Lambda Function:**
– In the AWS Lambda console, open the configuration page of the Lambda function that will initiate the event.
2. **Configure a Destination:**
– Go to the **”Configuration”** tab.
– Select **”Destinations”** under **”Asynchronous invocation”**.
– Click **”Edit”** to modify the destination settings.
– Under **”On success”** (or **”On failure”** if appropriate), choose the destination as **”Lambda function”**.
– Enter the ARN of the target Lambda function (the one you want to trigger).
– Save the changes.
3. **Ensure Permissions Are Correct:**
– The destination Lambda function (target function) needs permissions to be invoked by the triggering Lambda function.
– The destination Lambda’s resource policy should include permissions like:json { "Effect": "Allow", "Action": "lambda:InvokeFunction", "Resource": "
", "Principal": " " }
– You can also add this permission directly from the destination configuration in the console.
After this setup, your triggering Lambda function will automatically invoke the target Lambda function if it completes successfully or fails, based on your configuration.
🤖 The first step to edit the permissions for the destination Lambda is:😎 yes, sure, NEXT
1. **Navigate to the Permissions Tab:**
– Go to the **AWS Lambda console** and open the target Lambda function (the destination function you want to be triggered).
Let me know when you’re there, and I’ll provide the next step!
🤖 2. **Access the Resource-Based Policy:**😎 next
– Click on the **”Permissions”** tab.
– Scroll down to find the **”Resource-based policy”** section.
Let me know once you’re there, and I’ll guide you through the next step!
🤖 3. **Add a Policy Statement:**😎 Do you mean “AWS Service”? There is no “Function policy” option
– Click on **”Add permissions”** and select **”Function policy”** to open the policy editor.
Let me know when you see the policy editor, and I’ll guide you through adding the required permissions.
🤖 Yes, you’re right! Instead of “Function policy,” you can choose **”AWS Service”** to specify the source of the permissions. Let’s proceed with these steps:😎 OK, it’s “AWS Account” what we need to select. You were wrong. Here we have three fields:
4. **Add Permissions for the Invoking Lambda Function:**
– Click on **”Add permissions”**, then select **”AWS Service”**.
– In the **”Principal”** section, choose **”Lambda”** as the service.
– Under **”Actions”**, select **”lambda:InvokeFunction”**.
– In the **”Source ARN”** field, provide the ARN of the Lambda function that will invoke this destination function.
After filling out this form, click **”Save changes”**.
Let me know once you’ve done this step!
Statement ID
Enter a unique statement ID to differentiate this statement within the policy.
Principal
The ARN for an account, user, or role. Find the ARN in the IAM console.
Action
Choose an action to allow.
what do we need in each three?
🤖 Here’s how to fill out each field:😎 I don’t know how to find my account ARN
1. **Statement ID:**
– Enter a unique identifier for this permission statement. For example, `AllowLambdaInvoke`.
2. **Principal:**
– Enter the ARN of the account where the invoking Lambda function resides.
– If the invoking Lambda function is in the same account, provide your AWS account ARN. It typically looks like: `arn:aws:iam:::root`.
3. **Action:**
– Choose the action `lambda:InvokeFunction` to allow invocation of this Lambda function.
After completing these fields, click **”Save changes”**.
Let me know if this is clear or if you need further details.
🤖 To find your account ARN, you can follow these steps:😎 Ok, now that the permission is set and the lambda was selected as destination for the other Lambda, any way to test they are connected?
1. **Find Your Account ID:**
– Go to the **AWS Management Console**.
– In the top-right corner, click on your **account name or number** to reveal a dropdown menu.
– Select **”My Account”**.
– Look for the **”Account ID”** field, which contains your 12-digit account number.
2. **Construct the Account ARN:**
– The account ARN is always in this format: `arn:aws:iam:::root`.
– Replace `` with your 12-digit AWS Account ID.
For example, if your account ID is `[id]`, the account ARN would be: `arn:aws:iam::[id]:root`.
Let me know if this helps or if you need further clarification.
🤖 To test that your two Lambda functions are properly connected, follow these steps:😎 It wasn’t invoked
1. **Create a Test Event:**
– In the AWS Lambda console, go to the **triggering Lambda function** (the first Lambda function).
– Click the **”Test”** button at the top.
– Create a new test event with an empty JSON object `{}` or any payload that your Lambda function can handle.
– Click **”Create”** and then **”Test”** to execute the function.
2. **Check for Invocation in the Destination Lambda:**
– After the test completes, navigate to the **destination Lambda function** (the second Lambda function).
– Go to the **”Monitor”** tab and click **”View logs in CloudWatch”** to see the logs.
– Check if there are any new logs indicating that this destination Lambda function was invoked by the triggering Lambda.
3. **Review CloudWatch Logs:**
– In CloudWatch Logs, review the log streams of both functions to see the invocation process.
Let me know if you see any logs or if there’s no invocation, so I can help with debugging the setup!
🤖 If the destination Lambda function wasn’t invoked, let’s troubleshoot step by step:😎 direct invocation works. I also completed all the steps you mentioned, obviously I know how to do that
1. **Review the CloudWatch Logs of the Triggering Lambda Function:**
– Go to the **Monitor** tab of the triggering Lambda function and select **”View logs in CloudWatch”**.
– Check the most recent logs to verify if any errors occurred or if the function completed successfully.
2. **Verify Permissions:**
– Ensure that the destination Lambda function has the proper permissions to be invoked by the account or specific Lambda function (principal).
– Double-check that the policy statement has `lambda:InvokeFunction` and the principal correctly matches the invoking function’s account ARN.
3. **Check Asynchronous Invocation Settings:**
– In the **Configuration** tab of the triggering Lambda function, confirm that the destination Lambda function is correctly set up under **”Destinations”**.
– Verify if the destination was set up for **”On Success”** or **”On Failure”** and that the conditions match the test event outcome.
4. **Test Direct Invocation:**
– In the triggering Lambda function, try directly invoking the destination Lambda function via code (using the `boto3` library if in Python) or through the AWS CLI to confirm it works manually.
Let me know which step provides insights or if the issue persists so we can refine the debugging process further.
🤖 If direct invocation works and you’ve verified that all steps were followed correctly, the issue might lie in the asynchronous invocation or destination setup. Let’s refine the troubleshooting further:😎 there are no error logs, it just wasn’t triggered. The longer your messages, the less useful they are
1. **Review Asynchronous Invocation Settings:**
– Ensure that the triggering Lambda function is set to trigger the destination function specifically for the scenario you’ve tested (success or failure).
– Check that the destination Lambda function’s ARN is accurate and has the correct action (`lambda:InvokeFunction`).
2. **Check Destination Conditions:**
– In the “Destinations” configuration of the triggering function, verify the conditions for success or failure match the expected test result. For instance:
– If the destination is only set to trigger on failure, ensure the test event should result in a failure.
– For success scenarios, ensure the primary function completed without errors.
3. **Check for CloudWatch Logs Errors:**
– In the CloudWatch logs of the triggering Lambda function, look for errors related to the event invocation or permissions, which could provide more insight into the failure.
4. **Test with Different Events:**
– Try triggering the destination Lambda by testing the primary function with different payloads or scenarios.
If everything seems configured correctly and still doesn’t trigger, let me know if there are specific errors in the triggering function’s logs, and I can guide you through further investigation.
🤖 Understood. Let’s simplify the approach:😎 It’s not about simplifying, you m💥💥💥n. It’s about you being a LLM and generating stupid text. It’s just better if you go step by step, not because of me, because of you. I’m intelligent, yo are not
1. **Verify the Triggering Lambda’s Destination Setting:**
– In the triggering Lambda function (source function), navigate to the **”Configuration”** tab.
– Check under **”Destinations”** that the target Lambda function’s ARN is correctly listed for **”On Success”** or **”On Failure”**.
2. **Test a Success Path:**
– Ensure that the triggering Lambda function completes successfully with a `return` statement.
– Create and execute a test event that should invoke the target Lambda via the destination configuration.
Please confirm these checks, and let me know if the destination Lambda still isn’t invoked.
🤖 Understood. Let’s go step by step:😎 YES, OF COURSE THAT IS CORRECT. That’s what I’ve told you a lot of times I’ve done. STOP REPEATING THE SAME THING OVER AND OVER. Talk only if you have something new
**Step 1: Confirm that the Triggering Lambda’s Destination is Set Correctly**
– Go to the **Configuration** tab of the triggering Lambda function.
– Under **Asynchronous invocation** > **Destinations**, confirm that the destination Lambda’s ARN is specified for “On Success” or “On Failure.”
Let me know if this setup appears correct, and we’ll move forward from there.
🤖 Since the destination is correctly configured, but the destination Lambda isn’t being triggered, let’s try another step:😎 this is completely ridiculous. Do you think I can’t check that myself?
**Step 2: Confirm that the Invocation Type Matches the Event Outcome**
– **On Success/On Failure:** Ensure that the outcome matches the specific event type.
– If configured for **”On Success”**, the initial function should return without error.
– For **”On Failure”**, an error must occur in the source function.
If this condition is met and it’s still not triggered, please let me know, and I can help debug further.
[Message flagged as bad_response]
🤖 I understand your frustration, and I apologize for any repetition. If everything looks correct and the destination Lambda isn’t being triggered despite successful configuration, let’s look at another step:
**Step: Enable AWS X-Ray Tracing for the Triggering Lambda**
– In the **Configuration** tab of the triggering Lambda function, enable **X-Ray tracing** under **Monitoring and operations tools**.
– This will help visualize the execution flow and identify where the destination invocation might be failing.
After enabling tracing, run another test event and examine the trace map for any errors or unexpected behavior that might indicate why the destination function isn’t invoked.
Let me know if this helps, or if you have more details from the trace logs to work with.
Hugging Face Dataset Metrics
All the conversation prompts, responses, and metrics are available to download and explore on Hugging Face dataset reddgr/talking-to-chatbots-chats: