Q1: What is the Kernel version of your Docker container?
Q2: Why the first process inside the Docker container run as PID 1?
Q3: How much default memory is allocated to my Docker container?
Q4: Is there is any way to restrict how much amount of memory we can allocate to the container?
Q5: How does container get its IP or able to communicate to the external world?
These are the question which pops up in my mind when I first start exploring docker, you may not have a answer to these question if you are new to Docker but if you already start thinking in this direction you are heading in right direction 🙂
But we haven’t logged into the container, now it’s a time to logged into that container. Last time the issue we faced that once we logged out of the container it got shutdown, let see how we can deal with this problem
We have this container up and running
$ docker container ls
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES3afb4a8cfeb7 nginx "nginx -g 'daemon of…" 37 hours ago Up 3 seconds 80/tcp mytestserver
It’s time to log into this container but this time using docker exec and now I am inside my docker container.
$ docker container exec -it 3afb4a8cfeb7 bash
root@3afb4a8cfeb7:/#
What exec will do
exec Run a command in a running container
-i, --interactive Keep STDIN open even if not attached-t, --tty Allocate a pseudo-TTY
Let’s dig more into it and see the difference -i and -t makes
This time let start with -i flag only
$ docker container exec -i 3afb4a8cfeb7 bashlsbinboot
dev
etc
home
lib
mnt
As you can see with -i, I am only getting an interactive session but not the terminal
Let’s try out the same command but this time only with -t
$ docker container exec -t 3afb4a8cfeb7 bash
root@3afb4a8cfeb7:/# ls
As you can see here, we are only getting terminal here but I am not able to interact with it
So this needs to be built as a part of your muscle memory that we need to use -i and -t in tandem when we are trying to login to any container.
Type the below command to run your first docker container
docker container run hello-world
$ docker container run hello-world
Hello from Docker!
This message shows that your installation appears to be working correctly.
To generate this message, Docker took the following steps:
1. The Docker client contacted the Docker daemon.
2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
(amd64)
3. The Docker daemon created a new container from that image which runs the
executable that produces the output you are currently reading.
4. The Docker daemon streamed that output to the Docker client, which sent it
to your terminal.
To try something more ambitious, you can run an Ubuntu container with:
$ docker run -it ubuntu bash
Share images, automate workflows, and more with a free Docker ID:
https://hub.docker.com/
For more examples and ideas, visit:
https://docs.docker.com/engine/userguide/
Let see what happen behind the scene
As this is my first container, docker engine tried to find an image named hello-world.
But as we just get started, there is no such image stored locally
Unable to find image 'hello-world:latest' locally
Docker engine goes to DockerHub(For the time being think DockerHub as a GitHub for Docker containers), looking for this image named hello-world
Docker is an open platform for developing, shipping and running application. Its main benefit is to package applications in containers, allowing them to be portable to any system running a Linux, Mac or Windows operating system (OS).
It follows the build once and runs anywhere approach.
Docker Engine
Docker Engine is a client-server application with these major components:
A server which is a type of long-running program called a daemon process (the dockerd command).
A REST API that specifies interfaces that programs can use to talk to the daemon and instruct it what to do.
A command line interface (CLI) client (the docker command).
The CLI uses the Docker REST API to control or interact with the Docker daemon through scripting or direct CLI commands. Many other Docker applications use the underlying API and CLI.
The daemon creates and manages Docker objects, such as images, containers, networks, and volumes.
As I promised earlier that I will come up with something better in the next few months which is not the full-fledge 100days but breaking down into small components and this time 21 Days of Docker.
Starting from Oct 7, I am starting a Program, which is called “21 Days of Docker” and the main idea behind this is to spend at least one hour of every day for next 21 days in Sharing Docker knowledge and then share progress via
Finally, with limping and crawling, we reached to the Day 100 of 100 days of DevOps. I apologize for not being consistent in the latter half especially after Day97 but I learned a lot and I believe you guys also got a chance to learn something out of my blogs.
I will promise that I will come up with something better in the next few months which is not the full-fledge 100days but breaking down into small components eg: 30 Days of DevOps.
Once again thank you, everyone, who followed me, I will continue to post my blog .
Boto3 is the Amazon Web Services (AWS) SDK for Python. It enables Python developers to create, configure, and manage AWS services, such as EC2 and S3. Boto3 provides an easy to use, object-oriented API, as well as low-level access to AWS services.
Boto3 is built on the top of a library called Botocore, which is shared by the AWS CLI. Botocore provides the low level clients, session and credentials and configuration data. Boto3 built on the top of Botocore by providing its own session, resources, collections, waiters and paginators.
With AWS Lambda, you can run code without provisioning or managing servers. You pay only for the compute time that you consume — there’s no charge when your code isn’t running. You can run code for virtually any type of application or backend service — all with zero administration. Just upload your code and Lambda takes care of everything required to run and scale your code with high availability. You can set up your code to automatically trigger from other AWS services or call it directly from any web or mobile app.
* Author from scratch: Which is self explanatory, i.e you are writing your own function
* Use a blueprint: Build a lambda application from sample code and configuration preset for common use cases(Provided by AWS)
* Browse serverless app repository: Deploy a sample lambda application from the AWS Serverless Application Repository(Published by other developers and AWS Patners)
Function name: HelloWorld
Runtime: Choose Python3.7 from the dropdown
Permission: For the time being choose the default permission
Click Create Function
Invoking Lambda Function
When building applications on AWS Lambda the core components are Lambda functions and event sources. An event source is the AWS service or custom application that publishes events, and a Lambda function is the custom code that processes the events
* Amazon S3 Pushes Events * AWS Lambda Pulls Events from a Kinesis Stream * HTTP API requests through API Gateway * CloudWatch Schedule Events
def lambda_handler(event, context): # TODO implement print(event) <-------- return { 'statusCode': 200, 'body': json.dumps('Hello from Lambda!') }
Add this entry, which simply means we are trying to print the event
Again save it
Let’s try to set a simple test event, Click on Test
Under Event template, search for Amazon CloudWatch
Event Name: Give your event some name and test it
Go back and this time Click on Monitoring
Click on View logs in CloudWatch
Click on the log stream and you will see the same logs you see in Lambda console
Lambda Programming Model
Lambda supports a bunch of programming languages
You write code for your Lambda function in one of the languages AWS Lambda supports. Regardless of the language you choose, there is a common pattern to writing code for a Lambda function that includes the following core concepts.
* Handler: Handler is the function AWS Lambda calls to start execution of your Lambda function, it act as an entry point.
As you can see handle start with lambda_function which is a Python Script Name and then lambda_handler which is a function and act as an entry point for event and context
Event: We already saw in the previous example where we passed the CloudWatch Event to our code
Context — AWS Lambda also passes a context object to the handler function, as the second parameter. Via this context object, your code can interact with AWS Lambda. For example, your code can find the execution time remaining before AWS Lambda terminates your Lambda function.
Logging — Your Lambda function can contain logging statements. AWS Lambda writes these logs to CloudWatch Logs.
Exceptions — Your Lambda function needs to communicate the result of the function execution to AWS Lambda. Depending on the language you author your Lambda function code, there are different ways to end a request successfully or to notify AWS Lambda an error occurred during the execution.
One more thing, I want to highlight is the timeout
You can now set the timeout value for a function to any value up to 15 minutes. When the specified timeout is reached, AWS Lambda terminates execution of your Lambda function. As a best practice, you should set the timeout value based on your expected execution time to prevent your function from running longer than intended.
Common Use case of Lambda
Terraform Code
All the steps we have performed manually. let’s try to automate it using terraform
Step1: Create your test Python function
def lambda_handler(event, context):
print ("Hello from terraform world")
return "hello from terraform world"
Now let’s zip it up
$ zip lambda.zip lambda.py
adding: lambda.py (deflated 27%)
Step2: Define your Lambda resource
resource "aws_lambda_function" "test_lambda" { filename = "lambda.zip" function_name = "lambda_handler" role = "${aws_iam_role.iam_for_lambda.arn}" handler = "lambda.lambda_handler" # The filebase64sha256() function is available in Terraform 0.11.12 and later # For Terraform 0.11.11 and earlier, use the base64sha256() function and the file() function: # source_code_hash = "${base64sha256(file("lambda_function_payload.zip"))}" source_code_hash = "${base64sha256("lambda.zip")}" runtime = "python2.7" }
filename: Is the name of the file, you zipped in the previous step
function name: Is the name of the function you defined in your python code
role: IAM role attached to the Lambda Function. This governs both who / what can invoke your Lambda Function, as well as what resources our Lambda Function has access to
handler: Function entry point in our code(python code filename.method name) (filename: lambda.py we don’t need to include file extension) and (lambda function is lambda_handler def lambda_handler(event, context))
source_code_hash: Used to trigger updates. Must be set to a base64-encoded SHA256 hash of the package file specified with either filename or s3_key. The usual way to set this is filebase64sha256("file.zip") (Terraform 0.11.12 and later) or base64sha256(file("file.zip")) (Terraform 0.11.11 and earlier), where “file.zip” is the local filename of the lambda function source archive.
Step4: Terraform Init: Initialize a Terraform working directory, containing Terraform configuration files. This is the first command that should be run after writing a new Terraform configuration or cloning an existing one from version control.
$ terraform init
Initializing provider plugins...
- Checking for available provider plugins on https://releases.hashicorp.com...
- Downloading plugin for provider "aws" (2.23.0)...
The following providers do not have any version constraints in configuration,
so the latest version was installed.
To prevent automatic upgrades to new major versions that may contain breaking
changes, it is recommended to add version = "..." constraints to the
corresponding provider blocks in configuration, with the constraint strings
suggested below.
* provider.aws: version = "~> 2.23"
Terraform has been successfully initialized!
You may now begin working with Terraform. Try running "terraform plan" to see
any changes that are required for your infrastructure. All Terraform commands
should now work.
If you ever set or change modules or backend configuration for Terraform,
rerun this command to reinitialize your working directory. If you forget, other
commands will detect it and remind you to do so if necessary.
Step5: Terraform plan: The terraform plan command is used to create an execution plan. Terraform performs a refresh, unless explicitly disabled, and then determines what actions are necessary to achieve the desired state specified in the configuration files.
$ terraform plan
Refreshing Terraform state in-memory prior to plan...
The refreshed state will be used to calculate this plan, but will not be
persisted to local or remote state storage.
------------------------------------------------------------------------
An execution plan has been generated and is shown below.
Resource actions are indicated with the following symbols:
+ create
Terraform will perform the following actions:
+ aws_cloudwatch_log_group.example
id: <computed>
arn: <computed>
name: "/aws/lambda/lambda_handler"
retention_in_days: "14"
+ aws_iam_policy.lambda_logging
id: <computed>
arn: <computed>
description: "IAM policy for logging from a lambda"
name: "lambda_logging"
path: "/"
policy: "{\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Action\": [\n \"logs:CreateLogStream\",\n \"logs:PutLogEvents\"\n ],\n \"Resource\": \"arn:aws:logs:*:*:*\",\n \"Effect\": \"Allow\"\n }\n ]\n}\n"
+ aws_iam_role.iam_for_lambda
id: <computed>
arn: <computed>
assume_role_policy: "{\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Action\": \"sts:AssumeRole\",\n \"Principal\": {\n \"Service\": \"lambda.amazonaws.com\"\n },\n \"Effect\": \"Allow\",\n \"Sid\": \"\"\n }\n ]\n}\n"
create_date: <computed>
force_detach_policies: "false"
max_session_duration: "3600"
name: "iam_for_lambda"
path: "/"
unique_id: <computed>
+ aws_iam_role_policy_attachment.lambda_logs
id: <computed>
policy_arn: "${aws_iam_policy.lambda_logging.arn}"
role: "iam_for_lambda"
+ aws_lambda_function.test_lambda
id: <computed>
arn: <computed>
filename: "lambda.zip"
function_name: "lambda_handler"
handler: "lambda.lambda_handler"
invoke_arn: <computed>
last_modified: <computed>
memory_size: "128"
publish: "false"
qualified_arn: <computed>
reserved_concurrent_executions: "-1"
role: "${aws_iam_role.iam_for_lambda.arn}"
runtime: "python2.7"
source_code_hash: "Gpu07NPcj26NrKv0Ne6BbZkfDRuM3ozHHqCFUWH9Sqg="
source_code_size: <computed>
timeout: "3"
tracing_config.#: <computed>
version: <computed>
Plan: 5 to add, 0 to change, 0 to destroy.
------------------------------------------------------------------------
Note: You didn't specify an "-out" parameter to save this plan, so Terraform
can't guarantee that exactly these actions will be performed if
"terraform apply" is subsequently run.
Step6: terraform apply: The terraform apply command is used to apply the changes required to reach the desired state of the configuration, or the pre-determined set of actions generated by a terraform plan execution plan.
$ terraform apply
An execution plan has been generated and is shown below.
Resource actions are indicated with the following symbols:
+ create
Terraform will perform the following actions:
+ aws_cloudwatch_log_group.example
id: <computed>
arn: <computed>
name: "/aws/lambda/lambda_handler"
retention_in_days: "14"
+ aws_iam_policy.lambda_logging
id: <computed>
arn: <computed>
description: "IAM policy for logging from a lambda"
name: "lambda_logging"
path: "/"
policy: "{\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Action\": [\n \"logs:CreateLogStream\",\n \"logs:PutLogEvents\"\n ],\n \"Resource\": \"arn:aws:logs:*:*:*\",\n \"Effect\": \"Allow\"\n }\n ]\n}\n"
+ aws_iam_role.iam_for_lambda
id: <computed>
arn: <computed>
assume_role_policy: "{\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Action\": \"sts:AssumeRole\",\n \"Principal\": {\n \"Service\": \"lambda.amazonaws.com\"\n },\n \"Effect\": \"Allow\",\n \"Sid\": \"\"\n }\n ]\n}\n"
create_date: <computed>
force_detach_policies: "false"
max_session_duration: "3600"
name: "iam_for_lambda"
path: "/"
unique_id: <computed>
+ aws_iam_role_policy_attachment.lambda_logs
id: <computed>
policy_arn: "${aws_iam_policy.lambda_logging.arn}"
role: "iam_for_lambda"
+ aws_lambda_function.test_lambda
id: <computed>
arn: <computed>
filename: "lambda.zip"
function_name: "lambda_handler"
handler: "lambda.lambda_handler"
invoke_arn: <computed>
last_modified: <computed>
memory_size: "128"
publish: "false"
qualified_arn: <computed>
reserved_concurrent_executions: "-1"
role: "${aws_iam_role.iam_for_lambda.arn}"
runtime: "python2.7"
source_code_hash: "Gpu07NPcj26NrKv0Ne6BbZkfDRuM3ozHHqCFUWH9Sqg="
source_code_size: <computed>
timeout: "3"
tracing_config.#: <computed>
version: <computed>
Plan: 5 to add, 0 to change, 0 to destroy.
Do you want to perform these actions?
Terraform will perform the actions described above.
Only 'yes' will be accepted to approve.
Enter a value: yes
aws_iam_policy.lambda_logging: Creating...
arn: "" => "<computed>"
description: "" => "IAM policy for logging from a lambda"
name: "" => "lambda_logging"
path: "" => "/"
policy: "" => "{\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Action\": [\n \"logs:CreateLogStream\",\n \"logs:PutLogEvents\"\n ],\n \"Resource\": \"arn:aws:logs:*:*:*\",\n \"Effect\": \"Allow\"\n }\n ]\n}\n"
aws_iam_role.iam_for_lambda: Creating...
arn: "" => "<computed>"
assume_role_policy: "" => "{\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Action\": \"sts:AssumeRole\",\n \"Principal\": {\n \"Service\": \"lambda.amazonaws.com\"\n },\n \"Effect\": \"Allow\",\n \"Sid\": \"\"\n }\n ]\n}\n"
create_date: "" => "<computed>"
force_detach_policies: "" => "false"
max_session_duration: "" => "3600"
name: "" => "iam_for_lambda"
path: "" => "/"
unique_id: "" => "<computed>"
aws_iam_policy.lambda_logging: Still creating... (10s elapsed)
aws_iam_role.iam_for_lambda: Still creating... (10s elapsed)
aws_iam_role.iam_for_lambda: Creation complete after 10s (ID: iam_for_lambda)
aws_lambda_function.test_lambda: Creating...
arn: "" => "<computed>"
filename: "" => "lambda.zip"
function_name: "" => "lambda_handler"
handler: "" => "lambda.lambda_handler"
invoke_arn: "" => "<computed>"
last_modified: "" => "<computed>"
memory_size: "" => "128"
publish: "" => "false"
qualified_arn: "" => "<computed>"
reserved_concurrent_executions: "" => "-1"
role: "" => "arn:aws:iam::XXXXXX:role/iam_for_lambda"
runtime: "" => "python2.7"
source_code_hash: "" => "Gpu07NPcj26NrKv0Ne6BbZkfDRuM3ozHHqCFUWH9Sqg="
source_code_size: "" => "<computed>"
timeout: "" => "3"
tracing_config.#: "" => "<computed>"
version: "" => "<computed>"
aws_iam_policy.lambda_logging: Creation complete after 11s (ID: arn:aws:iam::355622012945:policy/lambda_logging)
aws_iam_role_policy_attachment.lambda_logs: Creating...
policy_arn: "" => "arn:aws:iam::XXXXXX:policy/lambda_logging"
role: "" => "iam_for_lambda"
aws_iam_role_policy_attachment.lambda_logs: Creation complete after 0s (ID: iam_for_lambda-20190814010350932300000001)
aws_lambda_function.test_lambda: Still creating... (10s elapsed)
aws_lambda_function.test_lambda: Still creating... (20s elapsed)
aws_lambda_function.test_lambda: Still creating... (30s elapsed)
aws_lambda_function.test_lambda: Still creating... (40s elapsed)
aws_lambda_function.test_lambda: Creation complete after 41s (ID: lambda_handler)
aws_cloudwatch_log_group.example: Creating...
arn: "" => "<computed>"
name: "" => "/aws/lambda/lambda_handler"
retention_in_days: "" => "14"
aws_cloudwatch_log_group.example: Still creating... (10s elapsed)
aws_cloudwatch_log_group.example: Creation complete after 11s (ID: /aws/lambda/lambda_handler)
Apply complete! Resources: 5 added, 0 changed, 0 destroyed.
I wrote the AWS Certified Solution Architect exam almost 8 months back and after clearing that exam I decided to write my second AWS exam in the next three months but those three months became six. A couple of weeks back I watched the below youtube video “Inside the mind of a master procrastinator | Tim Urban” and was able to co-relate myself to this guy. That was the time when the Panic Monster hit inside my brain and told me this is the correct time to write the next exam.
Panic Monster
YAY I cleared the exam!
WARNING: Some House Keeping task, before reading this blog 🙂 🙂
1: As everyone needs to sign NDA with AWS, I can’t tell you the exact question asked during the exam neither I have GB of memory, but I can give you the pointers what to expect in the exam.
2: As we all know AWS infrastructure updates everyday, so some of the stuff might not be relevant after a few days/weeks/months.
3: Please don’t ask for any exam dumps or question, that defeats the whole purpose of the exam.
Exam Preparation
I highly recommend the Linux Academy Course to everyone, Adrian Cantrill did an excellent job in explaining all the concepts and going into the in-depth of all topics.
Now coming back to the exam, the entire exam is divided into five main topics.
Based on my experience, you must need to know these four services to clear this exam.
KMS
VPC
IAM
Identity Federation(This is a surprise package to me, I saw almost 5–6 questions related to Identity Federation).
Domain 1: Incident Response
What steps you will perform if your ACCESS_KEY and SECRET_ACCESS_KEY got leaked accidentally on GitHub(Tips: You need to rotate the key immediately, update your application which is using this key(good idea to use Role) and then disable/delete this key).
What steps to follow if your EC2 instance got compromised(Tips: Take the snapshot of EBS volume, Build instance in your forensic subnet or isolate this instance).
Make sure you understand that same Cloudtrail can be applied to all regions, the question will trick you, do you create one trail per region or the same trail can be applied to multiple regions? What will happen to all the future region, can the same trail will be applied or do we need to create a new trail?
Must try CloudTrail multi-account scenario(Where you can create one central S3 bucket and can push trail from different accounts)(Common issues: Not able to push logs from the particular account? Does the S3 bucket policy looks correct? Do we have an IAM Resource defined for that particular account?)
CloudWatch
How to troubleshoot if cloud watch agent is not sending logs to CloudWatch Log Group(Some Tips: Is cloudwatch agent running? Does EC2 Instance Role have sufficient permission to push logs to CloudWatch Logs)
Cloudwatch metrics to filter events and create an alert?(eg: Failed logins or someone trying to break-in with root credentials)
Must remember this point, VPC Flow Logs is not for deep packet inspection or analysis(it only hold metadata), for a deep packet inspection you need third party tool(eg: Wireshark)
Understand the format of VPC Flow Log and check some sample flow logs(Pay special attention to ACCEPT vs REJECT field, where packets are getting REJECTED at Security Group Level or NACL)
A rules package is a collection of security checks that can be configured as part of an assessment template and assessment run.
Amazon Inspector has two types of rules packages, the
network reachability rules package that checks for
network accessibility of your Amazon EC2 instances,
and host assessment rules packages that check for
vulnerabilities and insecure configurations on the
Amazon EC2 instance. Host assessment rules packages
include Common Vulnerabilities and Exposures (CVE),
Center for Internet Security (CIS) Operating System
configuration benchmarks, and security best practices.
Domain 3: Infrastructure Security
CloudFront
Try to create CloudFront Distribution and make a note of each step.
What is the difference when you use your own SSL cert vs CloudFront Provided cert.
Use of System Manager Parameter Store(eg: Which service you will use to store a secret in AWS, if the question is related to DB then prefer Secret Manager)
How to use System Manager for Patching(Question could be to meet the compliance requirement you need to regularly patch your server, which AWS Service you can use)
Domain 4: Identity and Access Management
You will see a bunch of questions related to IAM Policies and what the particular policy do?
Make sure you are comfortable and understand the difference between IAM Policies vs Resource Policies(Especially S3 bucket policies and KMS).
Use of AWS Organization(Remember Service Control Policy(SCP) can deny access only, they cannot allow)
Understand how AWS Secure Token Service(STS), this is not only important for the exam but also as a part of your daily job.
Active Directory
5–6 questions related to active directory.
Please brush up your concept related to Web Identity Federation and SAML.
In which case you prefer HSM(look for key term like satisfying the compliance requirement)over KMS
Understand how key Rotation works in case of AWS Managed Key(Automatically Rotated after 3 years) vs Customer Manager(Automatically rotated after 365 days — disabled by default) vs Customer Manager imported key material (No automatic rotation)
KMS Grant: With grants, you can programmatically delegate the use of KMS customer master keys (CMKs) to other AWS principals. You can use them to allow access, but not deny it. Grants are typically used to provide temporary permissions or more granular permissions.
Other Key Topics
Macie
Whenever question asked about PII(personally identifiable information) your best bet is Macie
Amazon Macie recognizes sensitive data such as personally identifiable information (PII) or intellectual property and provides you with dashboards and alerts that give visibility into how this data is being accessed or moved. For more info
Remember this point, when you enable the secret manager it will rotate credentials immediately. Make sure all your application instances are configured to use Secret Manager before enabling credentials rotations
Port 25 is the default but EC2 throttles email traffic on port 25
To avoid timeout either use port 587 or 2587
AWS Lambda
Understand the difference between Function Policy(helpful in troubleshooting if cloud watch doesn’t get invoked) vs Lambda Execution Role(Where Lambda need to perform some action eg: Stopping any EC2 instance)
AWS Glacier Vault Lock
Initiate the lock by attaching a vault lock policy to your vault, which sets the lock to an in-progress state and returns a lock ID. While in the in-progress state, you have 24 hours to validate your vault lock policy before the lock ID expires.
Your data will live in the storage system for an indefinite period of time after you terminate the volume but will be wiped prior to being available to another user.
As this is the Speciality exam, you will except this exam to be much more difficult as compared to other exam and on the top of it you need to know so many aws services not just skimming through it but you need to know in-depth but in the end you will learn so much out of it. So keep calm and write this exam and let me know in case if you have any question.
