1 API call successful<\/li>\n<\/ul>\n\n\n\nFinal project statement you can give students<\/h2>\n\n\n\n
Deploy a small Flask web app on Ubuntu and make it fully observable in Datadog using Agent, Infrastructure Monitoring, APM, Logs, Synthetics, RUM, Monitors, Dashboard, and one Datadog API call. Then generate normal, slow, and failing traffic and show how Datadog helps detect each condition.<\/strong><\/p>\n\n\n\nMy recommendation<\/h2>\n\n\n\n
Keep the project to one host + one app + one browser page + one synthetic test<\/strong>. <\/p>\n\n\n\n
\n\n\n\nHow to download code?<\/p>\n\n\n\n
sudo wget -O datadog-starter-lab-python-app.zip \\\nhttps://projectxxxxxxxx.s3.ap-south-1.amazonaws.com\/datadog-starter-lab-python-app-main.zip\n\nsudo apt install zip\n\nsudo unzip datadog-starter-lab-python-app-main.zip<\/code><\/pre>\n\n\n\n
\n\n\n\nDatadog Starter Observability Lab<\/h1>\n\n\n\n
This project is a tiny Flask app for beginner Datadog training on Ubuntu.<\/p>\n\n\n\n
Files<\/h2>\n\n\n\n\napp.py<\/code> – main web app<\/li>\n\n\n\ntemplates\/index.html<\/code> – homepage with optional Datadog Browser RUM snippet<\/li>\n\n\n\nrequirements.txt<\/code> – Python dependencies<\/li>\n<\/ul>\n\n\n\nQuick start<\/h2>\n\n\n\nsudo apt update\nsudo apt install -y python3 python3-venv python3-pip curl\nmkdir -p ~\/datadog-lab\ncd ~\/datadog-lab<\/code><\/pre>\n\n\n\nCopy these project files into the folder, then run:<\/p>\n\n\n\n
python3 -m venv venv\nsource venv\/bin\/activate\npip install -r requirements.txt\nsudo mkdir -p \/var\/log\/studentapp\nsudo chown -R $USER:$USER \/var\/log\/studentapp\npython3 app.py<\/code><\/pre>\n\n\n\nOpen:<\/p>\n\n\n\n
\n- http:\/\/SERVER_IP:5000\/<\/li>\n\n\n\n
- http:\/\/SERVER_IP:5000\/health<\/li>\n\n\n\n
- http:\/\/SERVER_IP:5000\/slow<\/li>\n\n\n\n
- http:\/\/SERVER_IP:5000\/error<\/li>\n\n\n\n
- http:\/\/SERVER_IP:5000\/api\/items<\/li>\n<\/ul>\n\n\n\n
Run with Datadog tracing<\/h2>\n\n\n\n
After the Datadog Agent is installed:<\/p>\n\n\n\n
export DD_SERVICE=studentapp\nexport DD_ENV=lab\nexport DD_VERSION=1.0.0\nexport DD_LOGS_INJECTION=true\nexport DD_TRACE_128_BIT_TRACEID_GENERATION_ENABLED=true\nexport DD_AGENT_HOST=127.0.0.1\n\nddtrace-run python3 app.py<\/code><\/pre>\n\n\n\nEnable Browser RUM<\/h2>\n\n\n\n
Set these before starting the app:<\/p>\n\n\n\n
export DD_RUM_ENABLED=true\nexport DD_CLIENT_TOKEN=YOUR_CLIENT_TOKEN\nexport DD_APPLICATION_ID=YOUR_RUM_APPLICATION_ID\nexport DD_SITE=datadoghq.com<\/code><\/pre>\n\n\n\nThen restart the app.<\/p>\n\n\n\n
Sample traffic generator<\/h2>\n\n\n\n
In another terminal:<\/p>\n\n\n\n
for i in {1..10}; do curl -s http:\/\/127.0.0.1:5000\/ > \/dev\/null; done\nfor i in {1..5}; do curl -s http:\/\/127.0.0.1:5000\/api\/items > \/dev\/null; done\nfor i in {1..3}; do curl -s http:\/\/127.0.0.1:5000\/slow > \/dev\/null; done\nfor i in {1..2}; do curl -s http:\/\/127.0.0.1:5000\/error > \/dev\/null; done<\/code><\/pre>\n\n\n\n
\n\n\n\nNote – You need to do the project without much trainer help.<\/h2>\n\n\n\n
I\u2019ve packaged a ready-to-use beginner lab<\/strong> with working code and a simple README:
[Download the starter project ZIP] – https:\/\/github.com\/devopsschool-assignment-projects\/datadog-starter-lab-python-app
[View the README] – https:\/\/github.com\/devopsschool-assignment-projects\/datadog-starter-lab-python-app
[View the Flask app]- https:\/\/github.com\/devopsschool-assignment-projects\/datadog-starter-lab-python-app<\/p>\n\n\n\nWhat students will build<\/h2>\n\n\n\n
A tiny Ubuntu-hosted web app with:<\/p>\n\n\n\n
\n\/health<\/code><\/li>\n\n\n\n\/api\/items<\/code><\/li>\n\n\n\n\/slow<\/code><\/li>\n\n\n\n\/error<\/code><\/li>\n\n\n\n- one simple HTML page for RUM<\/strong><\/li>\n<\/ul>\n\n\n\n
Then they will connect it to:<\/p>\n\n\n\n
\n- Datadog Agent<\/li>\n\n\n\n
- Infra Monitoring<\/li>\n\n\n\n
- APM<\/li>\n\n\n\n
- Logs<\/li>\n\n\n\n
- Synthetic Monitoring<\/li>\n\n\n\n
- RUM<\/li>\n\n\n\n
- Alerts<\/li>\n\n\n\n
- Dashboard<\/li>\n\n\n\n
- API<\/li>\n<\/ul>\n\n\n\n
Datadog\u2019s docs support this exact flow: install the Agent on Linux, enable traces, turn on log collection in datadog.yaml<\/code>, add a custom log file config under conf.d<\/code>, create Browser RUM with a clientToken<\/code> and applicationId<\/code>, add Synthetic API assertions like status code and response time, and use API\/Application keys for API access. ([Datadog][1])<\/p>\n\n\n\nExact student steps<\/h2>\n\n\n\n1) Prepare Ubuntu<\/h3>\n\n\n\n
Run this:<\/p>\n\n\n\n
sudo apt update\nsudo apt install -y python3 python3-venv python3-pip curl unzip<\/code><\/pre>\n\n\n\nFlask\u2019s official docs support installing Flask with pip<\/code> inside a virtual environment. ([Flask Documentation][2])<\/p>\n\n\n\n2) Unzip the project<\/h3>\n\n\n\n
Copy datadog-starter-lab.zip<\/code> to the Ubuntu machine, then run:<\/p>\n\n\n\nmkdir -p ~\/datadog-lab\ncd ~\/datadog-lab\nunzip datadog-starter-lab.zip\ncd datadog-starter-lab<\/code><\/pre>\n\n\n\n3) Create Python environment and install packages<\/h3>\n\n\n\npython3 -m venv venv\nsource venv\/bin\/activate\npip install -r requirements.txt<\/code><\/pre>\n\n\n\n4) Prepare app log folder<\/h3>\n\n\n\nsudo mkdir -p \/var\/log\/studentapp\nsudo chown -R $USER:$USER \/var\/log\/studentapp<\/code><\/pre>\n\n\n\n5) Install Datadog Agent on Ubuntu<\/h3>\n\n\n\n
For a Linux host, Datadog\u2019s tutorial shows this install pattern; replace the API key and adjust DD_SITE<\/code> if your org is not on datadoghq.com<\/code>. ([Datadog][3])<\/p>\n\n\n\nDD_AGENT_MAJOR_VERSION=7 DD_API_KEY=YOUR_DATADOG_API_KEY DD_SITE=\"datadoghq.com\" bash -c \"$(curl -L https:\/\/install.datadoghq.com\/scripts\/install_script_agent7.sh)\"<\/code><\/pre>\n\n\n\nStart it:<\/p>\n\n\n\n
sudo service datadog-agent start\nsudo datadog-agent status<\/code><\/pre>\n\n\n\nDatadog\u2019s docs say Linux Agent install is done with the generated Linux script, and their tracing tutorial shows sudo service datadog-agent start<\/code> on Linux. ([Datadog][1])<\/p>\n\n\n\n6) Enable log collection in the Agent<\/h3>\n\n\n\n
Datadog says log collection is not enabled by default<\/strong> and you must set logs_enabled: true<\/code> in datadog.yaml<\/code>. ([Datadog][4])<\/p>\n\n\n\nEdit:<\/p>\n\n\n\n
sudo nano \/etc\/datadog-agent\/datadog.yaml<\/code><\/pre>\n\n\n\nFind this line and change it to:<\/p>\n\n\n\n
logs_enabled: true<\/code><\/pre>\n\n\n\n7) Tell Datadog Agent to read the app log file<\/h3>\n\n\n\n
Datadog\u2019s host log docs say to create a new folder under conf.d\/<\/code>, place a conf.yaml<\/code> there, then restart the Agent. ([Datadog][5])<\/p>\n\n\n\nRun:<\/p>\n\n\n\n
sudo mkdir -p \/etc\/datadog-agent\/conf.d\/studentapp.d\nsudo nano \/etc\/datadog-agent\/conf.d\/studentapp.d\/conf.yaml<\/code><\/pre>\n\n\n\nPaste this:<\/p>\n\n\n\n
logs:\n - type: file\n path: \/var\/log\/studentapp\/app.log\n service: studentapp\n source: python<\/code><\/pre>\n\n\n\nRestart the Agent:<\/p>\n\n\n\n
sudo systemctl restart datadog-agent\nsudo datadog-agent status<\/code><\/pre>\n\n\n\nDatadog also notes that on Linux the Agent runs as the datadog-agent<\/code> user, so log file permissions matter. ([Datadog][6])<\/p>\n\n\n\n8) Run the app with APM enabled<\/h3>\n\n\n\n
Datadog\u2019s Python tracing docs support starting a Python app with ddtrace-run<\/code>, and DD_SERVICE<\/code> \/ DD_VERSION<\/code> are valid config env vars for framework integrations like Flask. ([Datadog][7])<\/p>\n\n\n\nRun:<\/p>\n\n\n\n
source venv\/bin\/activate\n\nexport DD_SERVICE=studentapp\nexport DD_ENV=lab\nexport DD_VERSION=1.0.0\nexport DD_LOGS_INJECTION=true\nexport DD_AGENT_HOST=127.0.0.1\n\nddtrace-run python3 app.py<\/code><\/pre>\n\n\n\n9) Open the app<\/h3>\n\n\n\n
On the Ubuntu machine:<\/p>\n\n\n\n
curl http:\/\/127.0.0.1:5000\/health<\/code><\/pre>\n\n\n\nIn a browser:<\/p>\n\n\n\n
http://YOUR_UBUNTU_IP:5000\/<\/code><\/pre>\n\n\n\n10) Generate traffic for APM and Logs<\/h3>\n\n\n\n
Open another terminal and run:<\/p>\n\n\n\n
for i in {1..10}; do curl -s http:\/\/127.0.0.1:5000\/ > \/dev\/null; done\nfor i in {1..5}; do curl -s http:\/\/127.0.0.1:5000\/api\/items > \/dev\/null; done\nfor i in {1..3}; do curl -s http:\/\/127.0.0.1:5000\/slow > \/dev\/null; done\nfor i in {1..2}; do curl -s http:\/\/127.0.0.1:5000\/error > \/dev\/null; done<\/code><\/pre>\n\n\n\nNow students should see:<\/p>\n\n\n\n
\n- host metrics from the Agent<\/li>\n\n\n\n
- traces for the Flask app<\/li>\n\n\n\n
- logs from
\/var\/log\/studentapp\/app.log<\/code><\/li>\n<\/ul>\n\n\n\nDatadog\u2019s APM getting-started flow is: set up tracing, run the app, generate traffic, then explore collected traces. ([Datadog][8])<\/p>\n\n\n\n
Exact RUM setup<\/h2>\n\n\n\n
Datadog\u2019s Browser RUM docs say you first create a JS application in Datadog, which generates a clientToken<\/code> and an applicationId<\/code>, then add the SDK snippet to the page. ([Datadog][9])<\/p>\n\n\n\n11) Create the RUM app in Datadog<\/h3>\n\n\n\n
In Datadog UI:<\/p>\n\n\n\n
\n- Go to Digital Experience<\/strong><\/li>\n\n\n\n
- Click Add an Application<\/strong><\/li>\n\n\n\n
- Choose JavaScript (JS)<\/strong><\/li>\n\n\n\n
- Create the app<\/li>\n\n\n\n
- Copy:<\/li>\n\n\n\n
clientToken<\/code><\/li>\n\n\n\napplicationId<\/code><\/li>\n<\/ul>\n\n\n\n12) Restart the app with RUM enabled<\/h3>\n\n\n\n