# Log Analytics on Google Cloud (Solution) ## **Overview** In this lab you will learn about the features and tools provided by Cloud Logging to gain insight of your applications. ### What you'll learn * How to use Cloud Logging effectively and get insight about applications running on GKE * How to effectively build and run queries using log analytics ## **The demo application used in the lab** To use a concrete example, you will work through a scenario based on this [microservices demo](https://github.com/GoogleCloudPlatform/microservices-demo) sample app deployed to a GKE cluster. In this demo app, there are many microservices and dependencies among them. ![chart demonstrating many microservices and their dependencies](https://cdn.qwiklabs.com/dgo2Yut%2FH5g9gXqK6M0OAwtFK4Ji82rTqV%2FtSIwkGos%3D align="left") ## **Setup and Requirements** ### Before you click the Start Lab button Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click **Start Lab**, shows how long Google Cloud resources will be made available to you. This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab. To complete this lab, you need: * Access to a standard internet browser (Chrome browser recommended). **Note:** Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account. * Time to complete the lab---remember, once you start, you cannot pause a lab. **Note:** If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account. ### How to start your lab and sign in to the Google Cloud console 1. Click the **Start Lab** button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the **Lab Details** panel with the following: * The **Open Google Cloud console** button * Time remaining * The temporary credentials that you must use for this lab * Other information, if needed, to step through this lab 2. Click **Open Google Cloud console** (or right-click and select **Open Link in Incognito Window** if you are running the Chrome browser). The lab spins up resources, and then opens another tab that shows the **Sign in** page. ***Tip:*** Arrange the tabs in separate windows, side-by-side. **Note:** If you see the **Choose an account** dialog, click **Use Another Account**. 3. If necessary, copy the **Username** below and paste it into the **Sign in** dialog. ```apache student-03-8896045b197b@qwiklabs.net ``` You can also find the **Username** in the **Lab Details** panel. 4. Click **Next**. 5. Copy the **Password** below and paste it into the **Welcome** dialog. ```apache aTSQrVepqDKh ``` You can also find the **Password** in the **Lab Details** panel. 6. Click **Next**. **Important:** You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. **Note:** Using your own Google Cloud account for this lab may incur extra charges. 7. Click through the subsequent pages: * Accept the terms and conditions. * Do not add recovery options or two-factor authentication (because this is a temporary account). * Do not sign up for free trials. After a few moments, the Google Cloud console opens in this tab. **Note:** To view a menu with a list of Google Cloud products and services, click the **Navigation menu** at the top-left. ![Navigation menu icon](https://cdn.qwiklabs.com/nUxFb6oRFr435O3t6V7WYJAjeDFcrFb16G9wHWp5BzU%3D align="left") ### Activate Cloud Shell Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources. 1. Click **Activate Cloud Shell** ![Activate Cloud Shell icon](https://cdn.qwiklabs.com/ep8HmqYGdD%2FkUncAAYpV47OYoHwC8%2Bg0WK%2F8sidHquE%3D align="left") at the top of the Google Cloud console. When you are connected, you are already authenticated, and the project is set to your **Project\_ID**, `qwiklabs-gcp-01-aff4d91c33dd`. The output contains a line that declares the **Project\_ID** for this session: ```apache Your Cloud Platform project in this session is set to qwiklabs-gcp-01-aff4d91c33dd ``` `gcloud` is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion. 2. (Optional) You can list the active account name with this command: ```apache gcloud auth list ``` 3. Click **Authorize**. **Output:** ```apache ACTIVE: * ACCOUNT: student-03-8896045b197b@qwiklabs.net To set the active account, run: $ gcloud config set account `ACCOUNT` ``` 4. (Optional) You can list the project ID with this command: ```apache gcloud config list project ``` **Output:** ```apache [core] project = qwiklabs-gcp-01-aff4d91c33dd ``` **Note:** For full documentation of `gcloud`, in Google Cloud, refer to [the gcloud CLI overview guide](https://cloud.google.com/sdk/gcloud). ## **Task 1. Infrastructure setup** ### Verify the GKE cluster Connect to a Google Kubernetes Engine cluster and validate that it's been created correctly. 1.In Cloud Shell, set the zone in `gcloud`: ```apache gcloud config set compute/zone us-central1-c ``` 2. Use the following command to see the cluster's status: ```apache gcloud container clusters list ``` You should see a similar status: ```apache NAME: day2-ops LOCATION: us-central1-c MASTER_VERSION: 1.24.3-gke.2100 MASTER_IP: 34.68.183.6 MACHINE_TYPE: e2-standard-2 NODE_VERSION: 1.24.3-gke.2100 NUM_NODES: 4 STATUS: RUNNING ``` The cluster status will say RUNNING. If it's still PROVISIONING, wait a moment and run the command above again. Repeat until the status is RUNNING. You can also check the progress in the Cloud Console - **Navigation menu** > **Kubernetes Engine** > **Clusters**. 3. Once your cluster has RUNNING status, get the cluster credentials: ```apache gcloud container clusters get-credentials day2-ops --region us-central1 ``` (Output) ```apache Fetching cluster endpoint and auth data. kubeconfig entry generated for day2-ops. ``` 4. Verify that the nodes have been created: ```apache kubectl get nodes ``` Your output should look like this: ```apache NAME STATUS ROLES AGE VERSION gke-day2-ops-default-pool-b3081059-bskv Ready 3m32s v1.24.3-gke.2100 gke-day2-ops-default-pool-b3081059-dt73 Ready 3m30s v1.24.3-gke.2100 gke-day2-ops-default-pool-b3081059-g1m3 Ready 3m31s v1.24.3-gke.2100 gke-day2-ops-default-pool-b3081059-wq4b Ready 3m31s v1.24.3-gke.2100 ``` ## **Task 2. Deploy application** Next, you will deploy a microservices application called Online Boutique to your cluster to create an actual workload you can monitor. 1. Run the following to clone the repo: ```apache git clone https://github.com/GoogleCloudPlatform/microservices-demo.git ``` 2. Change to the `microservices-demo` directory: ```apache cd microservices-demo ``` 3. Install the app using `kubectl`: ```apache kubectl apply -f release/kubernetes-manifests.yaml ``` 4. Confirm everything is running correctly: ```apache kubectl get pods ``` The output should look similar to the output below. Re-run the command until all pods are reporting a Running status before moving to the next step. ```apache NAME READY STATUS RESTARTS AGE adservice-55f94cfd9c-4lvml 1/1 Running 0 20m cartservice-6f4946f9b8-6wtff 1/1 Running 2 20m checkoutservice-5688779d8c-l6crl 1/1 Running 0 20m currencyservice-665d6f4569-b4sbm 1/1 Running 0 20m emailservice-684c89bcb8-h48sq 1/1 Running 0 20m frontend-67c8475b7d-vktsn 1/1 Running 0 20m loadgenerator-6d646566db-p422w 1/1 Running 0 20m paymentservice-858d89d64c-hmpkg 1/1 Running 0 20m productcatalogservice-bcd85cb5-d6xp4 1/1 Running 0 20m recommendationservice-685d7d6cd9-pxd9g 1/1 Running 0 20m redis-cart-9b864d47f-c9xc6 1/1 Running 0 20m shippingservice-5948f9fb5c-vndcp 1/1 Running 0 20m ``` 5. Run the following to get the **external** IP of the application. An IP address is only returned once the service has been deployed. So, you may need to repeat the commands until there's an external IP address assigned: ```apache export EXTERNAL_IP=$(kubectl get service frontend-external -o jsonpath="{.status.loadBalancer.ingress[0].ip}") echo $EXTERNAL_IP ``` (Example output) ```apache 35.222.235.86 ``` 6. Finally, confirm that the app is up and running: ```apache curl -o /dev/null -s -w "%{http_code}\n" http://${EXTERNAL_IP} ``` Your confirmation will look like this: ```apache 200 ``` After the application is deployed, you can also go to the Cloud Console and view the status. 7. In the **Kubernetes Engine** > **Workloads** page you'll see that all the pods are OK. ![workloads page with pods in OK status](https://cdn.qwiklabs.com/xiB9uesxmJg31lu%2FZsh5ZzP23oDzc3RtqbzGge6KoVs%3D align="left") 8. Now click **Gateways, Services & Ingress**, verify that all services are OK. ### Open the application Under **Gateways, Services & Ingress**, click the **Endpoint** IP of the service **frontend-external:** ![highlight of the frontend-external service's external IP address](https://cdn.qwiklabs.com/VBfOAIJnZIbR4EBY1hFpP1OcKKVKlij3zRYfggdbspc%3D align="left") It should open the application and you will have a page like the following: ![demo application web page](https://cdn.qwiklabs.com/vqr0npRJcTFimM37tOokhA1HiGtdqQsTluZcBOu7I7M%3D align="left") Click **Check my progress** to verify the objective. Deploy application Check my progress ## **Task 3. Manage log buckets** There are two ways you can enable Log Analytics. One way is to upgrade an existing bucket. The other is to create a new log bucket with Log Analytics enabled. ### Upgrade an existing bucket You can use the following steps to upgrade an existing log bucket. 1. On the Navigation menu, click **Logging**, then click **Logs Storage**. 2. Click **UPGRADE** for an existing bucket, for example, the `_Default` bucket. ![bucket details panel with 2 checkboxes marked](https://cdn.qwiklabs.com/au5qJlHbvP3WVa83SXQc3nXzteNYy7BPtHtZaPFYXAo%3D align="left") 3. Click **UPGRADE** in the popup window. 4. Wait for the upgrade to complete. Initially the status changes to **Not eligible for upgrade** before you see the **Upgraded** status. 5. Click the **OPEN** dropdown button. 6. Select the view `_AllLogs`, the Log Analytics page will open for you. ### Create a new Log bucket Alternatively, you can configure Cloud Logging to create a new log bucket with Log Analytics enabled. 1. On the left-hand menu open **Logging,** then click **Logs Storage.** 2. Click **CREATE LOG BUCKET** at top. 3. Provide a name such as **day2ops-log** to the bucket. 4. Check both **Upgrade to use Log Analytics** and **Create a new BigQuery dataset that links to this bucket**. 5. Type in a dataset name such as: **day2ops\_log** ![bucket details panel with 2 checkboxes marked](https://cdn.qwiklabs.com/XOvhjT9y0%2B5ggf12ojlNjPF4wNxRNzTWpf5w4O49o7w%3D align="left") Selecting *Create a linked dataset in BigQuery* will create a dataset for you in BigQuery if it does not exist. This way you can run queries in BigQuery if you want to. 6. Finally, click **Create bucket** to create the log bucket. Click **Check my progress** to verify the objective. Create a Log bucket Check my progress ### **Write to the new Log bucket** You can create a log sink to use the new log bucket. You can do it from the **Logs Router** directly. Another easy way to do this is from **Logs Explorer**. You can run log queries to select and filter the logs you are interested in and create a sink. Once nice thing from that approach is the log query will be automatically copied to the sing configuration as the filter. 1. On the **Navigation menu**, click **Logging**, then click **Logs Explorer**. 2. In the top-right of **Logs Explorer**, enable **Show query** and run the following query in the query field: ```apache resource.type="k8s_container" ``` 2. Click on **Actions** then click **Create sink**. ![Logs Explorer page with Create sink button highlighted](https://cdn.qwiklabs.com/Ph1NEDUzqNasIL8%2Fli0wPq7XDEVoHMf%2Fwx2H1pwqvDE%3D align="left") 3. Provide a name such as **day2ops-sink** as the sink name. 4. Click **NEXT**. 5. Select **Logging bucket** in the sink service dropdown list. 6. Select the new log bucket you just created. 7. Click **NEXT**. You should see the resource type query already in the filter. ![sink details with resouce type displayed](https://cdn.qwiklabs.com/AopQuXp0Ix1e%2BreKkvI%2BJIGsCQzEImhmtImqvvoLFk8%3D align="left") 8. Click **CREATE SINK**. Wait a little bit and your sink should be created. Click **Check my progress** to verify the objective. create the log sink Check my progress ### **Read from the new Log bucket** Go back to the Logs Explorer. Notice that there are many different resource types for the logs as highlighted in the screenshot below. ![resouce types and refine scopes button highlighted](https://cdn.qwiklabs.com/uAHIdq8nf1IdlLnTuk6fSlcaALXqPOjdbg0i0L2Ze54%3D align="left") 1. To view the logs in the new log bucket, click **REFINE SCOPE** at the top of the page. 2. Select **Log view** and the new log bucket you just created. 3. Click **APPLY**. You will see Kubernetes Containers is now the only resource type and there are much less log entries now. That's because only filtered logs will be sent to the bucket. ## **Task 4. Log analysis** * On the left side, under **Logging**, click **Log Analytics** to access the feature. You should see something like the following: ![a7afa6a91515d206.png](https://cdn.qwiklabs.com/Y1CWp0xRLshWSrYUyGqM40wsXJr6pBF5O5%2BHs7LjNwg%3D align="left") If your query field is empty or you forget which table you want to use, you can click the **Query** button to get the sample query back. Now you can run your own queries in the query field. The following are some examples. **Important:** The log view name in the `FROM` clause is different for the log buckets. You need to make sure you use the correct view name. You can use the previous step to verify. ### **To find the most recent errors:** You want to find the most recent errors for from the containers: ```sql SELECT TIMESTAMP, JSON_VALUE(resource.labels.container_name) AS container, json_payload FROM `qwiklabs-gcp-01-aff4d91c33dd.global.day2ops-log._AllLogs` WHERE severity="ERROR" AND json_payload IS NOT NULL ORDER BY 1 DESC LIMIT 50 ``` After run the query, you should see the output like the following: ![query results to find recent errors](https://cdn.qwiklabs.com/Qm%2FDh%2BPyushfZSDu1%2B3GgXh6Msp8zpvaQjcaq6KNxNY%3D align="left") ### **To find the min, max, and average latency:** You can view the min, max, and average latencies in a timeframe for the frontend service: ```sql SELECT hour, MIN(took_ms) AS min, MAX(took_ms) AS max, AVG(took_ms) AS avg FROM ( SELECT FORMAT_TIMESTAMP("%H", timestamp) AS hour, CAST( JSON_VALUE(json_payload, '$."http.resp.took_ms"') AS INT64 ) AS took_ms FROM `qwiklabs-gcp-01-aff4d91c33dd.global.day2ops-log._AllLogs` WHERE timestamp > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 24 HOUR) AND json_payload IS NOT NULL AND SEARCH(labels, "frontend") AND JSON_VALUE(json_payload.message) = "request complete" ORDER BY took_ms DESC, timestamp ASC ) GROUP BY 1 ORDER BY 1 ``` After run the query, you should see the output like the following: ![query results to find min, max, and average latency](https://cdn.qwiklabs.com/Ut%2FAW7nRaCvVmuMvCs01pCK8kUH%2BMf%2Bw9fic9CP5WOw%3D align="left") ### **Product page visit number** You want to know how many times users visit a certain product page in the past hour: ```sql SELECT count(*) FROM `qwiklabs-gcp-01-aff4d91c33dd.global.day2ops-log._AllLogs` WHERE text_payload like "GET %/product/L9ECAV7KIM %" AND timestamp > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 HOUR) ``` After run the query, you should see the output like the following: ![query results for product page visits](https://cdn.qwiklabs.com/HCmbdCO4mIbL9zTh3mkI%2FiQqD3ArcsQsBfahDo3Ykck%3D align="left") ### **Sessions with shopping cart checkout** You can run the following query to view how many sessions end up with checkout (POST call to the /cart/checkout service): ```sql SELECT JSON_VALUE(json_payload.session), COUNT(*) FROM `qwiklabs-gcp-01-aff4d91c33dd.global.day2ops-log._AllLogs` WHERE JSON_VALUE(json_payload['http.req.method']) = "POST" AND JSON_VALUE(json_payload['http.req.path']) = "/cart/checkout" AND timestamp > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 HOUR) GROUP BY JSON_VALUE(json_payload.session) ``` After run the query, you should see the output like the following: ![query result for shopping cart checkout](https://cdn.qwiklabs.com/2pIvbMIOVQ6WmTsDWDs9ASaZNGwGqX8D73Q5L1PVlmg%3D align="left") --- ## Solution of Lab %[https://www.youtube.com/watch?v=l7UOU56TWzk] Run the following Commands in **CloudShell** ```apache curl -LO raw.githubusercontent.com/QUICK-GCP-LAB/2-Minutes-Labs-Solutions/main/Log%20Analytics%20on%20Google%20Cloud/gsp1088.sh sudo chmod +x gsp1088.sh ./gsp1088.sh ``` * For the bucket name paste the following: ```apache day2ops-log ``` * For the dataset name paste the following: ```apache day2ops_log ``` ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1734938739590/e6f014e6-b1c1-4f44-9524-daec62e80ead.png align="center")