Move LHOL and LEDA to Cloud9. Update whole lab to use same C9 (#95)

* YOLO

* LEDA with proper C9 template

* Further work on C9 for DynamoDB ID

* LADV broad changes to remove EE and add C9/WS

* Tying together C9 across labs

* LEDA updated too

Author: switch180

.github/scripts/build-assets.py

@@ -22,7 +22,9 @@
 #Move static assets
 Path(dest_root, 'assets').mkdir(parents=True, exist_ok=False)
 data_files = ['design-patterns/cloudformation/lab.yaml',
+    'design-patterns/cloudformation/C9.yaml',
     'design-patterns/cloudformation/UserData.sh',
+    'design-patterns/cloudformation/UserDataC9.sh',
     'event-driven/event-driven-cfn.yaml',
     'static/files/hands-on-labs/migration-env-setup.yaml',
     'static/files/hands-on-labs/migration-dms-setup.yaml']

.github/workflows/main.yml

@@ -46,6 +46,8 @@ jobs:
       run: aws s3 sync public s3://$STEP_S3_BUCKET --delete
     - name: SetS3Acl
       run: aws s3api put-object-acl --grant-read uri=http://acs.amazonaws.com/groups/global/AllUsers --bucket $STEP_S3_BUCKET --key assets/lab.yaml
+    - name: SetS3Acl
+      run: aws s3api put-object-acl --grant-read uri=http://acs.amazonaws.com/groups/global/AllUsers --bucket $STEP_S3_BUCKET --key assets/C9.yaml
     - name: SetS3AclED1
       run: aws s3api put-object-acl --grant-read uri=http://acs.amazonaws.com/groups/global/AllUsers --bucket $STEP_S3_BUCKET --key assets/GeneratorLambdaPackage.zip
     - name: SetS3AclED2

config.toml

@@ -29,7 +29,7 @@ pygmentsStyle = "monokai"
   disableShortcutsTitle = true
   disableInlineCopyToClipBoard = true
   #AWS custom params
-  design_patterns_s3_lab_yaml = "https://s3.amazonaws.com/amazon-dynamodb-labs.com/assets/lab.yaml"
+  design_patterns_s3_lab_yaml = "https://s3.amazonaws.com/amazon-dynamodb-labs.com/assets/C9.yaml"
   lhol_migration_setup_yaml = "https://s3.amazonaws.com/amazon-dynamodb-labs.com/assets/migration-env-setup.yaml"
   lhol_migration_dms_setup_yaml = "https://s3.amazonaws.com/amazon-dynamodb-labs.com/assets/migration-dms-setup.yaml"
   event_driven_architecture_lab_yaml = "https://s3.amazonaws.com/amazon-dynamodb-labs.com/assets/event-driven-cfn.yaml"

content/design-patterns/_index.en.md

@@ -2,7 +2,7 @@
 title = "Advanced Design Patterns for Amazon DynamoDB"
 chapter = true
 description = "300 level: Hands-on exercise using Python and DynamoDB best practices."
-pre = "<b>Workshop: </b>"
+pre = "<b>LADV: </b>"
 
 weight = 1
 +++

content/design-patterns/ex1capacity/Step2.en.md

@@ -7,7 +7,7 @@ weight = 3
 
 Now that you have created the table, you can load some sample data into the table by running the following Python script.
 ```bash
-cd /home/ec2-user/workshop
+cd /home/ubuntu/workshop
 python load_logfile.py logfile ./data/logfile_small1.csv
 ```
 The parameters in the preceding command: 1) Table name = `logfile` 2) File name = `logfile_small1.csv`

content/design-patterns/ex8streams/Step6.en.md

@@ -55,10 +55,10 @@ NEXTTOKEN       eyJFeGNsdXNpdmVTdGFydEtleSI6IG51bGwsICJib3RvX3RydW5jYXRlX2Ftb3Vu
 
 ## Congratulations, you have successfully completed all the exercises in the workshop!
 
-If you ran the lab on your own AWS account, you should delete all the tables made during these exercises. If you are at an AWS event using the AWS Workshop platform (the Event Engine), you do not need to delete your tables.
+If you ran the lab on your own AWS account, you should delete all the tables made during these exercises. If you are at an AWS event using the AWS Workshop platform (Workshop Studio), you do not need to delete your tables.
 
 {{% notice warning %}}
-During the course of the lab, you created DynamoDB tables that will incur a cost that could approach tens or hundreds of dollars per day. You must delete the DynamoDB tables using the DynamoDB console to clean up the lab. In addition, if you are not part of an AWS event or you are running this lab in your own account, make sure you delete the CloudFormation stack as soon as the lab is complete. If you're using event engine, you don't need to delete the CloudFormation stack.
+During the course of the lab, you created DynamoDB tables that will incur a cost that could approach tens or hundreds of dollars per day. You must delete the DynamoDB tables using the DynamoDB console to clean up the lab. In addition, if you are not part of an AWS event or you are running this lab in your own account, make sure you delete the CloudFormation stack as soon as the lab is complete. If you're using Workshop Studio Event Delivery, you don't need to delete the CloudFormation stack.
 {{% /notice %}}
 
 #### Reporting issues

content/design-patterns/setup/Step1.en.md

@@ -5,32 +5,32 @@ weight = 10
 #TODO mod-xxx is not the same name when running in EC2
 +++
 
-1. Once you've gained access to the AWS Management Console for the lab, double check the region is correct and one of **TeamRole** OR **WSParticipantRole** appears on the top right of the console.
+1. Once you've gained access to the AWS Management Console for the lab, double check the region is correct and the role name **WSParticipantRole** appears on the top right of the console.
 1. In the services search bar, search for **Systems Manager** and click on it to open the AWS Systems Manager section of the AWS Management Console.
 1. In the AWS Systems Manager console, locate the menu in the left, identify the section **Node Management** and select **Session Manager** from the list.
 1. Choose **Start session** to launch a shell session.
-1. Click the radio button to select the EC2 instance for the lab. If you see no instance, wait a few minutes and then click refresh. Wait until an ec2 instance with name of `mod-XXXXXXXXXXXXXXXX` OR `ddb` is available before continuing. You will see only one of the two names.
-1. Click in the **Start Session** button (This action will open a new tab in your browser with a new black shell).
-1. In the new black shell, switch to the ec2-user account by running `sudo su - ec2-user`
+1. Click the radio button to select the EC2 instance for the lab. If you see no instance, wait a few minutes and then click refresh. Wait until an ec2 instance with name of `DynamoDBC9` is available before continuing. Select the instance.
+1. Click the **Start Session** button (This action will open a new tab in your browser with a new black shell).
+1. In the new black shell, switch to the ubuntu account by running `sudo su - ubuntu`
    ```bash
-   sudo su - ec2-user
+   sudo su - ubuntu
    ```
-1. run `shopt login_shell` and be sure it says `login_shell on`. Then, change into the workshop directory to begin: `cd ~/workshop`
-
+1. run `shopt login_shell` and be sure it says `login_shell on` and then change into the workshop directory. 
    ```bash
    #Verify login_shell is 'on'
    shopt login_shell
    #Change into the workshop directory
-   cd ~/workshop
+   cd ~/workshop/
    ```
 
-   The output of your commands in the Session Manager session should look like the following:
 
+The output of your commands in the Session Manager session should look like the following:
    ```bash
-   sh-4.2$ sudo su - ec2-user
-   [ec2-user@ip-172-31-24-0 ~]$ #Verify login_shell is 'on'
-   [ec2-user@ip-172-31-24-0 ~]$ shopt login_shell
+   $ sudo su - ubuntu
+   :~ $ #Verify login_shell is 'on'
+   shopt login_shell
+   #Change into the workshop directory
+   cd ~/workshop/
    login_shell     on
-   [ec2-user@ip-172-31-24-0 ~]$ #Change into the workshop directory
-   [ec2-user@ip-172-31-24-0 ~]$ cd ~/workshop
+   :~/workshop $
    ```

content/design-patterns/setup/Step2.en.md

@@ -14,8 +14,9 @@ python --version
 Output:
 
 ```plain
-Python 3.6.12
+Python 3.10.12
 ```
+**Note: The major and minor version of Python may vary from what you see above**
 
 Run the following command to check the AWS CLI on your EC2 instance:
 
@@ -28,9 +29,9 @@ Sample output:
 
 ```bash
 #Note that your linux kernel version may differ from the example.
-aws-cli/1.18.139 Python/3.6.12 Linux/4.14.193-113.317.amzn1.x86_64 botocore/1.17.62
+aws-cli/2.13.26 Python/3.11.6 Linux/6.2.0-1013-aws exe/x86_64.ubuntu.22 prompt/off
 ```
 
 {{% notice note %}}
-_Make sure you have AWS CLI version 1.18.139 and python 3.6.12 before proceeding. If you do not have these versions, review [Step 1]({{< ref "design-patterns/setup/Step1" >}}) to ensure you have completed each command correctly._
+_Make sure you have AWS CLI version 2.x or higher and python 3.10 or higher before proceeding. If you do not have these versions, you may have difficultly successfully completing the lab._
 {{% /notice %}}

content/design-patterns/setup/Step4.en.md

@@ -7,7 +7,7 @@ weight = 40
 On the EC2 instance, go to the workshop folder and run the ls command:
 
 ```bash
-cd /home/ec2-user/workshop
+cd /home/ubuntu/workshop
 ls -l .
 ```
 

content/design-patterns/setup/_index.en.md

@@ -12,7 +12,6 @@ description = "Setup the lab environment and connect to the lab instance on Amaz
 To set up this workshop, choose one of the following links, depending on whether you are:
 - […running the workshop on your own (in your own account)]({{< ref "start-here/user-account/" >}}), or
 - […attending an AWS-hosted event (using AWS-provided access codes)]({{< ref "start-here/aws-ws-event/" >}}), or
-- (legacy)[…attending an AWS-hosted event (using AWS-provided hashes)]({{< ref "start-here/aws-event/" >}})
 
 Once you have completed with either setup, continue on to:
 - [Exercise 1: DynamoDB Capacity Units and Partitioning]({{< ref "/design-patterns/ex1capacity" >}})