While working on my previous note on how to deploy from Azure DevOps to an AWS EC2 instance, I came across another use case –how to deploy from Azure DevOps to an AWS S3 bucket. This note is about the steps I followed to do so. Here is a recap so that we’re all on the same page. Azure DevOps is Microsoft’s solution to a software development process that aids collaboration, traceability, and visibility using components like Azure Boards (work items), Azure Repos (code repository), Azure Pipelines (build and deploy), Azure Artifacts (package management), Azure Test Plans along with a plug and play module to integrate an awesome number of third party tools like Docker, Terraform, etc AWS S3 stands for simple storage service and as the name suggests it is used to store and retrieve artifacts -files and folder. Here is a link to the official documentation.
Coming back to the use case at hand, I wanted to create a release definition (pipeline) to store artifacts from Azure DevOps into an AWS S3 bucket. This can be broken down into three steps: Step 1: Create an AWS IAM user with appropriate permissions Step 2: Create a service connection in Azure DevOps Step 3: Create a release definition
Please note that there are a couple of pre-requisites to this use case. -knowledge about AWS IAM -how to create/manage a user -knowledge about AWS S3 bucket -how to create/manage a bucket -knowledge about Azure DevOps pipelines -how to create a build and release definition
Step 1: Create an AWS IAM user with appropriate permissions As we know, to work with resources in AWS, we need appropriate access -read/modify. In this case, we need an IAM user with programmatic access permission (full access) to S3. Please attach appropriate policy (AmazonS3FullAccess) and store the Access key ID and Secret Access key securely. We need those in the next step.
Note: I am purposefully staying out of going into details about IAM -user, group, policy, and role in this post.
Step 2: Create a service connection in Azure DevOps Now let’s go back to our Azure DevOps portal and launch the project where we want to set up the connection. Click on the gear icon at the bottom left corner. This will bring up the project settings and under Pipelines, we find the Service connections options. If you do not have any existing connections, you’ll get a welcome message.
Click on Create service connection. This will launch a new side panel with all the new connections available. Chances are that the AWS connection is not available, as I have in the below image.
This means that we’ll have to install the AWS Connection from the marketplace. Let us close the new service connection panel and return to our project portal. On the top right corner you’ll see a shopping bag icon -Marketplace -> Browse marketplace. This launches a new page where we may add extensions for our Azure DevOps project. Search for AWS here.
Click on the icon to install the extension to get it for free. Make sure you are logged into the right organization when you are installing this service connection (ensure your email ID displayed at the top is the same email ID tied to your Azure DevOps project). As instructed on the next page, select the Azure DevOps organization for which you want to install the extension. and click on Install. After installation, you’ll see a message to “Proceed to organization”. Let’s click on that. Now, lets again navigate to the project and click on the gear icon at the bottom left followed by Service connections and then “Create service connection”. This time we see AWS as the first option to connect to. Yay! Select AWS and click on Next. On this page, we are requested to provided mandatory authentication details along with a few optional details. For now, we’ll proceed with Access Key ID and Secret Access Key that we saved from the previous step when we created our IAM user along with the Service Connection name and description.
Once you have the connection created, you’ll see that under Service connections. That brings us to the end of step 2 and now we proceed with creating a release definition to copy artifacts (files and folders) from Azure DevOps to an S3 bucket in AWS. How exciting!
Step 3: Create a release definition Let’s navigate back to our project portal by clicking on Overview (vertical right panel) and then click on Pipelines -> Releases. It’d be good if we already have a Pipeline/Build definition created or some code in the repository that we can upload to S3. Assuming you have, we proceed to the step when we add a new stage to a release definition. Click on the + to add a task to agent job and search for S3.
This will list two tasks. We select “Amazon S3 Upload” and click on Add. Under this task, we will be required to fill details like the AWS Credential, Region, BucketName, and Source Folder. Once that is done, save the release definition and trigger a run. Note: Here is an image of the task group from a separate project with all values.
After a successful run of the release definition, I was able to view the artifacts in the S3 bucket.
Conclusion: There are multiple options available when it comes to populating AWS S3 bucket from Azure DevOps depending on what the specific use case is. Similar to uploading to S3, we can also download contents from an S3 bucket by using the “Amazon S3 download” task.
As I like to state at the end of my notes, I hope you enjoyed reading the article as much as enjoyed writing it. And if you have any questions or clarifications to seek, please do not hesitate. I will be glad to explore those with you.
For information about storage pricing, open AWS Pricing
Lets take a look at the what AWS has to offer.
Amazon EC2 provides you with flexible, cost effective, and easy-to-use data storage options for your instances. Each option has a unique combination of performance and durability. These storage options can be used independently or in combination to suit your requirements.
After reading this section, you should have a good understanding about how you can use the data storage options supported by Amazon EC2 to meet your specific requirements. These storage options include the following:
Amazon EBS provides durable, block-level storage volumes that you can attach to a running instance. You can use Amazon EBS as a primary storage device for data that requires frequent and granular updates. For example, Amazon EBS is the recommended storage option when you run a database on an instance.
Points to remember !!
in a single Availability Zones.
need to attach to only one EC2 at a time
need to be a Root Volume/ Boot Volume.
need to have IOPS up to 260,000
data need to be persisted after restart/termination.
when latency of milliseconds are not important
additional cost are to be incurred apart from ec2.
can be configured to be either deleted or not when ec2 terminates
Amazon EBS provides the following volume types
Solid state drives (SSD) — Optimized for transactional workloads involving frequent read/write operations with small I/O size, where the dominant performance attribute is IOPS. example : gp2/gp3,io2
Hard disk drives (HDD) — Optimized for large streaming workloads where the dominant performance attribute is throughput.
Previous generation — Hard disk drives that can be used for workloads with small datasets where data is accessed infrequently and performance is not of primary importance. (not talked about here)
Many instances can access storage from disks that are physically attached to the host computer. This disk storage is referred to as instance store. Instance store provides temporary block-level storage for instances. The data on an instance store volume persists only during the life of the associated instance; if you stop, hibernate, or terminate an instance, any data on instance store volumes is lost.
Points to remember !!
Its a Temporary storage and is volatile.
All data are lost when the machine is restarted/terminated.
Its specific to Availability Zones.
It can be attached to only one EC2 at a time
It can be a Root Volume/ Boot Volume.
It can have IOPS up to 1.2 million
when latency on milliseconds are important
The instance type determines the size of the instance store available and the type of hardware used for the instance store volumes.
Instance store volumes are included as part of the instance’s usage cost.
Amazon EFS provides scalable file storage for use with Amazon EC2. You can create an EFS file system and configure your instances to mount the file system. You can use an EFS file system as a common data source for workloads and applications running on multiple instances.
Points to remember !!
All data are Persists and can be shared among ec2.
Its specific to Region.
It can be attached to multiple EC2 at the same time
It cannot be a Root Volume/ Boot Volume.
Every time you launch an instance from an AMI, a root storage device is created for that instance. The root storage device contains all the information necessary to boot the instance. You can specify storage volumes in addition to the root device volume when you create an AMI or launch an instance using block device mapping. For more information, see Block device mappings.
You can also attach EBS volumes to a running instance.
First of all, In addition to requesting SSL/TLS certificates provided by AWS Certificate Manager (ACM), you can import certificates that you obtained outside of AWS. You might do this because you already have a certificate from a third-party certificate authority (CA), or because you have application-specific requirements that are not met by ACM issued certificates.
You are responsible for monitoring the expiration date of your imported certificates and for renewing them before they expire. You can simplify this task by using Amazon CloudWatch Events to send notices when your imported certificates approach expiration. For more information, see Using CloudWatch Events.
The above diagram shows the complete flow but we will just be covering the part of invoking the Lambda via Event Rule and create both rule and lambda via CloudFormation.
Understanding the Event.
An event such as Lambda receives is displayed under
We have all heard about s3 a lot, and has been use by many enterprise organization in the past and we have seen that the solution has turned out to be awesome. So, Let take a deeper look and understand what s3 is and how can I harness the power it provides us with.
Amazon Simple Storage Service (S3) is an object storage service
So, to start with one on the most interesting feature for any developer is that it is free to use up to an extent, Let understand what is means.
12-Months Free: These free tier offers are only available to new AWS customers, and are available for 12 months following your AWS sign-up date. When your 12 month free usage term expires or if your application use exceeds the tiers, you simply pay standard, pay-as-you-go service rates
Always Free: These free tier offers do not automatically expire at the end of your 12 month AWS Free Tier term, but are available to both existing and new AWS customers indefinitely.
Trials: These free tier offers are short term trial offers that start from the time of first usage begins. Once the trial period expires you simply pay standard, pay-as-you-go service rates (see each service page for full pricing details).
Storage classes – Amazon S3 offers a range of storage classes designed for different use cases.
Storage management – Amazon S3 has storage management features that you can use to manage costs, meet regulatory requirements, reduce latency, and save multiple distinct copies of your data for compliance requirements.
Access management – Amazon S3 provides features for auditing and managing access to your buckets and objects. By default, S3 buckets and the objects in them are private.
Data processing – To transform data and trigger workflows to automate a variety of other processing activities at scale
Storage logging and monitoring – Amazon S3 provides logging and monitoring tools that you can use to monitor and control how your Amazon S3 resources are being used.
Analytics and insights – Amazon S3 offers features to help you gain visibility into your storage usage, which empowers you to better understand, analyze, and optimize your storage at scale.
Strong consistency – Amazon S3 provides strong read-after-write consistency for PUT and DELETE requests of objects in your Amazon S3 bucket in all AWS Regions.
How Amazon S3 works ?
Amazon S3 is an object storage service that stores data as objects within buckets. An object is a file and any metadata that describes the file. A bucket is a container for objects.
To store your data in Amazon S3, you first create a bucket and specify a bucket name and AWS Region. Then, you upload your data to that bucket as objects in Amazon S3. Each object has a key (or key name), which is the unique identifier for the object within the bucket.
S3 provides features that you can configure to support your specific use case. For example, you can use S3 Versioning to keep multiple versions of an object in the same bucket, which allows you to restore objects that are accidentally deleted or overwritten.
Buckets and the objects in them are private and can be accessed only if you explicitly grant access permissions. You can use bucket policies, AWS Identity and Access Management (IAM) policies, access control lists (ACLs), and S3 Access Points to manage access.
A bucket is a container for objects stored in Amazon S3. You can store any number of objects in a bucket and can have up to 100 buckets in your account. To request an increase, visit the Service Quotas Console.
Every object is contained in a bucket. For example, if the object named photos/puppy.jpg is stored in the DOC-EXAMPLE-BUCKET bucket in the US West (Oregon) Region, then it is addressable using the URL https://DOC-EXAMPLE-BUCKET.s3.us-west-2.amazonaws.com/photos/puppy.jpg. For more information, see Accessing a Bucket.
When you create a bucket, you enter a bucket name and choose the AWS Region where the bucket will reside. After you create a bucket, you cannot change the name of the bucket or its Region. Bucket names must follow the bucket naming rules. You can also configure a bucket to use S3 Versioning or other storage management features.
Organize the Amazon S3 namespace at the highest level.
Identify the account responsible for storage and data transfer charges.
Provide access control options, such as bucket policies, access control lists (ACLs), and S3 Access Points, that you can use to manage access to your Amazon S3 resources.
Serve as the unit of aggregation for usage reporting.
Objects are the fundamental entities stored in Amazon S3. Objects consist of object data and metadata. The metadata is a set of name-value pairs that describe the object. These pairs include some default metadata, such as the date last modified, and standard HTTP metadata, such as Content-Type. You can also specify custom metadata at the time that the object is stored.
An object key (or key name) is the unique identifier for an object within a bucket. Every object in a bucket has exactly one key. The combination of a bucket, object key, and optionally, version ID (if S3 Versioning is enabled for the bucket) uniquely identify each object. So you can think of Amazon S3 as a basic data map between “bucket + key + version” and the object itself.
You can use S3 Versioning to keep multiple variants of an object in the same bucket. With S3 Versioning, you can preserve, retrieve, and restore every version of every object stored in your buckets. You can easily recover from both unintended user actions and application failures.
A bucket policy is a resource-based AWS Identity and Access Management (IAM) policy that you can use to grant access permissions to your bucket and the objects in it. Only the bucket owner can associate a policy with a bucket. The permissions attached to the bucket apply to all of the objects in the bucket that are owned by the bucket owner. Bucket policies are limited to 20 KB in size.
Bucket policies use JSON-based access policy language that is standard across AWS. You can use bucket policies to add or deny permissions for the objects in a bucket. Bucket policies allow or deny requests based on the elements in the policy, including the requester, S3 actions, resources, and aspects or conditions of the request (for example, the IP address used to make the request). For example, you can create a bucket policy that grants cross-account permissions to upload objects to an S3 bucket while ensuring that the bucket owner has full control of the uploaded objects. For more information, see Bucket policy examples.
Access control lists (ACLs)
As a general rule, we recommend using S3 resource-based policies (bucket policies and access point policies) or IAM policies for access control instead of ACLs. ACLs are an access control mechanism that predates resource-based policies and IAM. For more information about when you’d use ACLs instead of resource-based policies or IAM policies, see Access policy guidelines.
You can use ACLs to grant read and write permissions for individual buckets and objects to authorized users. Each bucket and object has an ACL attached to it as a subresource. The ACL defines which AWS accounts or groups are granted access and the type of access. For more information, see Access control list (ACL) overview.
S3 Access Points
Amazon S3 Access Points are named network endpoints with dedicated access policies that describe how data can be accessed using that endpoint. Access Points simplify managing data access at scale for shared datasets in Amazon S3. Access Points are named network endpoints attached to buckets that you can use to perform S3 object operations, such as GetObject and PutObject.
Each access point has its own IAM policy. You can configure Block Public Access settings for each access point. To restrict Amazon S3 data access to a private network, you can also configure any access point to accept requests only from a virtual private cloud (VPC).
You can choose the geographical AWS Region where Amazon S3 stores the buckets that you create. You might choose a Region to optimize latency, minimize costs, or address regulatory requirements. Objects stored in an AWS Region never leave the Region unless you explicitly transfer or replicate them to another Region. For example, objects stored in the Europe (Ireland) Region never leave it.
Amazon S3 data consistency model
Amazon S3 provides strong read-after-write consistency for PUT and DELETE requests of objects in your Amazon S3 bucket in all AWS Regions. This behavior applies to both writes to new objects as well as PUT requests that overwrite existing objects and DELETE requests. In addition, read operations on Amazon S3 Select, Amazon S3 access controls lists (ACLs), Amazon S3 Object Tags, and object metadata (for example, the HEAD object) are strongly consistent.
Updates to a single key are atomic. For example, if you make a PUT request to an existing key from one thread and perform a GET request on the same key from a second thread concurrently, you will get either the old data or the new data, but never partial or corrupt data.
Amazon S3 achieves high availability by replicating data across multiple servers within AWS data centers. If a PUT request is successful, your data is safely stored. Any read (GET or LIST request) that is initiated following the receipt of a successful PUT response will return the data written by the PUT request. Here are examples of this behavior:
A process writes a new object to Amazon S3 and immediately lists keys within its bucket. The new object appears in the list.
A process replaces an existing object and immediately tries to read it. Amazon S3 returns the new data.
A process deletes an existing object and immediately tries to read it. Amazon S3 does not return any data because the object has been deleted.
A process deletes an existing object and immediately lists keys within its bucket. The object does not appear in the listing.
Amazon S3 does not support object locking for concurrent writers. If two PUT requests are simultaneously made to the same key, the request with the latest timestamp wins. If this is an issue, you must build an object-locking mechanism into your application.
Updates are key-based. There is no way to make atomic updates across keys. For example, you cannot make the update of one key dependent on the update of another key unless you design this functionality into your application.
Bucket configurations have an eventual consistency model. Specifically, this means that:
If you delete a bucket and immediately list all buckets, the deleted bucket might still appear in the list.
If you enable versioning on a bucket for the first time, it might take a short amount of time for the change to be fully propagated. We recommend that you wait for 15 minutes after enabling versioning before issuing write operations (PUT or DELETE requests) on objects in the bucket.
This section provides examples of behavior to be expected from Amazon S3 when multiple clients are writing to the same items.
In this example, both W1 (write 1) and W2 (write 2) finish before the start of R1 (read 1) and R2 (read 2). Because S3 is strongly consistent, R1 and R2 both return color = ruby.
In the next example, W2 does not finish before the start of R1. Therefore, R1 might return color = ruby or color = garnet. However, because W1 and W2 finish before the start of R2, R2 returns color = garnet.
In the last example, W2 begins before W1 has received an acknowledgement. Therefore, these writes are considered concurrent. Amazon S3 internally uses last-writer-wins semantics to determine which write takes precedence. However, the order in which Amazon S3 receives the requests and the order in which applications receive acknowledgements cannot be predicted because of various factors, such as network latency. For example, W2 might be initiated by an Amazon EC2 instance in the same Region, while W1 might be initiated by a host that is farther away. The best way to determine the final value is to perform a read after both writes have been acknowledged.
Accessing Amazon S3
You can work with Amazon S3 in any of the following ways:
AWS Management Console
The console is a web-based user interface for managing Amazon S3 and AWS resources. If you’ve signed up for an AWS account, you can access the Amazon S3 console by signing into the AWS Management Console and choosing S3 from the AWS Management Console home page.
AWS Command Line Interface
You can use the AWS command line tools to issue commands or build scripts at your system’s command line to perform AWS (including S3) tasks.
AWS provides SDKs (software development kits) that consist of libraries and sample code for various programming languages and platforms (Java, Python, Ruby, .NET, iOS, Android, and so on). The AWS SDKs provide a convenient way to create programmatic access to S3 and AWS. Amazon S3 is a REST service. You can send requests to Amazon S3 using the AWS SDK libraries. which wrap the underlying Amazon S3 REST API and simplify your programming tasks. For example, the SDKs take care of tasks such as calculating signatures, cryptographically signing requests, managing errors, and retrying requests automatically. For information about the AWS SDKs, including how to download and install them, see Tools for AWS.
Every interaction with Amazon S3 is either authenticated or anonymous. If you are using the AWS SDKs, the libraries compute the signature for authentication from the keys that you provide. For more information about how to make requests to Amazon S3, see Making requests.
Amazon S3 REST API
The architecture of Amazon S3 is designed to be programming language-neutral, using AWS-supported interfaces to store and retrieve objects. You can access S3 and AWS programmatically by using the Amazon S3 REST API. The REST API is an HTTP interface to Amazon S3. With the REST API, you use standard HTTP requests to create, fetch, and delete buckets and objects.
To use the REST API, you can use any toolkit that supports HTTP. You can even use a browser to fetch objects, as long as they are anonymously readable.
The REST API uses standard HTTP headers and status codes, so that standard browsers and toolkits work as expected. In some areas, we have added functionality to HTTP (for example, we added headers to support access control). In these cases, we have done our best to add the new functionality in a way that matches the style of standard HTTP usage.
If you make direct REST API calls in your application, you must write the code to compute the signature and add it to the request. For more information about how to make requests to Amazon S3, see Making requests.Note
SOAP API support over HTTP is deprecated, but it is still available over HTTPS. Newer Amazon S3 features are not supported for SOAP. We recommend that you use either the REST API or the AWS SDKs.
PCI DSS compliance
Amazon S3 supports the processing, storage, and transmission of credit card data by a merchant or service provider, and has been validated as being compliant with Payment Card Industry (PCI) Data Security Standard (DSS). For more information about PCI DSS, including how to request a copy of the AWS PCI Compliance Package, see PCI DSS Level 1.
A relational database is a type of database that stores and provides access to data points that are related to one another. Relational databases are based on the relational model, an intuitive, straightforward way of representing data in tables. In a relational database, each row in the table is a record with a unique ID called the key. The columns of the table hold attributes of the data, and each record usually has a value for each attribute, making it easy to establish the relationships among data points.
ACID properties and RDBMS
Four crucial properties define relational database transactions: atomicity, consistency, isolation, and durability—typically referred to as ACID.
Atomicity defines all the elements that make up a complete database transaction.
Consistency defines the rules for maintaining data points in a correct state after a transaction.
Isolation keeps the effect of a transaction invisible to others until it is committed, to avoid confusion.
Durability ensures that data changes become permanent once the transaction is committed.
Relational Databases Provided by AWS
Features of RDS
RDS has two key features
Multi -AZ – For DR – Automatic Failover(AWS automatically points the DNS to the IP.)
Read Replica – For Performance – Heavy read – Read Replicas to Read Replica
Encryption at Rest – Security – if enabled snapshots/backups/read replicas are also encrypted.
Backups – Automatic Backup / Manual Snapshots
Non Relational Databases
Non-relational databases (often called NoSQL databases) are different from traditional relational databases in that they store their data in a non-tabular form. Instead, non-relational databases might be based on data structures like documents. A document can be highly detailed while containing a range of different types of information in different formats. This ability to digest and organize various types of information side-by-side makes non-relational databases much more flexible than relational databases.
What is Data warehousing ?
A data warehouse is a type of data management system that is designed to enable and support business intelligence (BI) activities, especially analytics. Data warehouses are solely intended to perform queries and analysis and often contain large amounts of historical data.
Difference OLTP Vs OLAP
Online Transaction Processing(OLTP) differs from Online Analytics Processing(OLAP) in terms of the type of query you run. Online transaction processing (OLTP) captures, stores, and processes data from transactions in real time. Online analytical processing (OLAP) uses complex queries to analyze aggregated historical data from OLTP systems.
AWS Data warehousing Solution is Red Shift
What is ElastiCache ?
Amazon ElastiCache is a fully managed, in-memory caching service supporting flexible, real-time use cases. You can use ElastiCache for caching, which accelerates application and database performance, or as a primary data store for use cases that don’t require durability like session stores, gaming leaderboards, streaming, and analytics. ElastiCache is compatible with
In this article, I’m going to explain to you how you can Host Static website on S3 using a custom domain.
Few things to keep in mind
You can use Amazon S3 to host a static website. On a static website, individual webpages include static content. They might also contain client-side scripts.
By contrast, a dynamic website relies on server-side processing, including server-side scripts such as PHP, JSP, or ASP.NET. Amazon S3 does not support server-side scripting
Buckets must have a globally unique name.
Buckets are defined at the regional level
Before you begin
As you follow the steps in this example, you work with the following services:
Amazon Route 53 – You use Route 53 to register domains and to define where you want to route internet traffic for your domain. The example shows how to create Route 53 alias records that route traffic for your domain (example.com) and subdomain (www.example.com) to an Amazon S3 bucket that contains an HTML file.
Amazon S3 – You use Amazon S3 to create buckets, upload a sample website page, configure permissions so that everyone can see the content, and then configure the buckets for website hosting.
Step 1: Register a custom domain with Route 53 or Godaddy
If you don’t already have a registered domain name, such as example.com, register one with Route 53. For more information, see Registering a new domain in the Amazon Route 53 Developer Guide. After you register your domain name, you can create and configure your Amazon S3 buckets for website hosting.
You can also purchase a domain from GoDaddy or any other provider and use it.
When you purchase using any other provider say goDaddy they are currently managing you DNS. What we need to do is go to Godaddy and under your your domain find the option to Manage DNS.
You would see something like a below screenshot.
Just remember we need to change these name server to tell Godaddy the you don’t need to managed my nameservers.
Step 2: Create two buckets
To support requests from both the root domain and subdomain, you create two buckets.
Domain bucket – example.com
Subdomain bucket – www.example.com
These bucket names must match your domain name exactly. In this example, the domain name is example.com. You host your content out of the root domain bucket (example.com). You create a redirect request for the subdomain bucket (www.example.com). If someone enters www.example.com in their browser, they are redirected to example.com and see the content that is hosted in the Amazon S3 bucket with that name.
Note: Choose a Region that is geographically close to you to minimize latency and costs, or to address regulatory requirements
Step 3: Configure your root domain bucket for website hosting
In this step, you configure your root domain bucket (example.com) as a website. This bucket will contain your website content. When you configure a bucket for website hosting, you can access the website using the Website endpoints.
Step 4: Configure your subdomain bucket for website redirect
After you configure your root domain bucket for website hosting, you can configure your subdomain bucket to redirect all requests to the domain. In this example, all requests for www.example.com are redirected to example.com.
Step 5: Upload index and website content
In this step, you upload your index document and optional website content to your root domain bucket.
When you enable static website hosting for your bucket, you enter the name of the index document (for example, index.html). After you enable static website hosting for the bucket, you upload an HTML file with this index document name to your bucket.
Step 6 : Add a Public Hosted Zone for you domain
You need to create a hosted Zone for the purchased Domain. Domain name will be the same name as that of you domain.
Step 7: Add alias records for your domain and subdomain
In this step, you create the alias records that you add to the hosted zone for your domain maps example.com and www.example.com. Instead of using IP addresses, the alias records use the Amazon S3 website endpoints. Amazon Route 53 maintains a mapping between the alias records and the IP addresses where the Amazon S3 buckets reside. You create two alias records, one for your root domain and one for your subdomain.
When you create a hosted zone by default there are two records already created for you. We need to add two more for both
When creating the above two records in route 53 we need to add a type of type A or you can select Alias, and choose S# website endpoint.
Remember to chose the correct region where the bucket is hosted.
Step 8: Update the NS in Godaddy
We need to go to the provider for domain and update the name server as provided by the Route 53 for your domain.
Step 9: Test the website
Verify that the website and the redirect work correctly. In your browser, enter your URLs. In this example, you can try the following URLs:
Domain (http://example.com) – Displays the index document in the example.com bucket.
Subdomain (http://www.example.com) – Redirects your request to http://example.com. You see the index document in the example.com bucket.
If your website or redirect links don’t work, you can try the following:
Clear cache – Clear the cache of your web browser.
Check name servers – If your web page and redirect links don’t work after you’ve cleared your cache, you can compare the name servers for your domain and the name servers for your hosted zone. If the name servers don’t match, you might need to update your domain name servers to match those listed under your hosted zone. For more information, see Adding or changing name servers and glue records for a domain.
When I try to invoke my Amazon Cognito user pools API, I get an “Unable to verify secret hash for client <client-id>” error. How do I resolve the error?
As per documentation
When a user pool app client is configured with a client secret in the user pool, a SecretHash value is required in the API’s query argument. If a secret hash isn’t provided in the APIs query argument, then Amazon Cognito returns an Unable to verify secret hash for client <client-id> error.
– AWS Docs
But still even after passing the SECRET_HASH this still doesn’t behave correctly when using ADMIN_NO_SRP_AUTH. This should not be generated when using the ADMIN_NO_SRP_AUTH
I am trying to do this via AWS Lambda here.
So, There is a simple solution to this.
Go to App Clients in your cognito.
You should see a list of client as there can be more than on app client in an user pool.
Now, click on show details, and you should see a text box showing CLIENT SECRET
If this shows up with a secret key the work arround is to delete the app client for the user pool and create a new one
We can now remove the Client secret from the Auth Parameters
The request would now look like shown below.
public AdminInitiateAuthRequest CreateAdminInitiateAuthRequest(AdminInitiateAuthRequestModel apiRequest)
if (String.IsNullOrEmpty(apiRequest.Username) || String.IsNullOrEmpty(apiRequest.Password))
throw new Exception("Bad Request");
AdminInitiateAuthRequest authRequest = new AdminInitiateAuthRequest();
authRequest.UserPoolId = _poolId;
authRequest.ClientId = _appClientId;
authRequest.AuthFlow = AuthFlowType.ADMIN_NO_SRP_AUTH;
authRequest.AuthParameters = new Dictionary<string, string>();
Many developers are at least marginally familiar with AWS Lambda functions. They’re reasonably straightforward to set up, but the vast AWS landscape can make it hard to see the big picture. With so many different pieces it can be daunting, and frustratingly hard to see how they fit seamlessly into a normal web application.
The Serverless framework is a huge help here. It streamlines the creation, deployment, and most significantly, the integration of Lambda functions into a web app. To be clear, it does much, much more than that, but these are the pieces I’ll be focusing on. Hopefully, this post strikes your interest and encourages you to check out the many other things Serverless supports. If you’re completely new to Lambda you might first want to check out this AWS intro.
Before we get to cool things like file uploads and S3 buckets, let’s create a basic Lambda function, connect it to an HTTP endpoint, and call it from an existing web app. The Lambda won’t do anything useful or interesting, but this will give us a nice opportunity to see how pleasant it is to work with Serverless. First, let’s create our service. For me, it’s my lambda folder. Whatever directory you choose, cd into it from terminal and run the following command:
sls create -t aws-nodejs --path hello-world
That creates a new directory called hello-world. Let’s crack it open and see what’s in there.
If you look in handler.js, you should see an async function that returns a message. We could hit sls deploy in our terminal right now, and deploy that Lambda function, which could then be invoked. But before we do that, let’s make it callable over the web.
Working with AWS manually, we’d normally need to go into the AWS API Gateway, create an endpoint, then create a stage, and tell it to proxy to our Lambda. With serverless, all we need is a little bit of config.
Still in the hello-world directory? Open the serverless.yaml file that was created in there.
The config file actually comes with boilerplate for the most common setups. Let’s uncomment the http entries, and add a more sensible path.