Monday, July 17, 2017

What is Docker?

Docker is a software platform that allows you to build, test, and deploy applications quickly. Docker packages software into standardized units called containers that have everything the software needs to run including libraries, system tools, code, and runtime. Using Docker, you can quickly deploy and scale applications into any environment and know your code will run. Amazon ECS uses Docker images in task definitions to launch containers on EC2 instances in your clusters.
Running Docker on AWS provides developers and admins a highly reliable, low-cost way to build, ship, and run distributed applications at any scale. AWS supports both Docker licensing models: open source Docker Community Edition (CE) and subscription-based Docker Enterprise Edition (EE).
Docker is available on many different operating systems, including most modern Linux distributions, like Ubuntu, and even Mac OSX and Windows.
Docker Benefits
Ship More Software Faster
Docker users on average ship software 7x more frequently than non-Docker users. Docker enables developers to ship isolated services as often as needed by eliminating the headaches of software dependencies.
Improve Developer Productivity
Docker reduces the time spent setting up new environments or troubleshooting differences between environments.
Seamlessly Move Applications
Docker-based applications can be seamlessly moved from local development machines to production deployments on AWS.
Standardize Application Operations
Small containerized applications make it easy to deploy, identify issues, and roll back for remediation.
Docker Use Cases
Continuous Integration & Delivery
Accelerate application delivery by standardizing environments and removing conflicts between language stacks and versions.
Data Processing
Provide big data processing as a service. Package data and analytics packages into portable containers that can be executed by non-technical users
Containers as a Service
Build and ship distributed applications with content and infrastructure that is IT-managed and secured.

Tuesday, June 27, 2017

Amazon CloudWatch

Amazon CloudWatch monitors your Amazon Web Services (AWS) resources and the applications you run on AWS in real-time.You can use CloudWatch to collect and track metrics, which are the variables you want to measure for your resources and applications. CloudWatch alarms send notifications or automatically make changes to the resources you are monitoring based on rules that you define.
Amazon CloudWatch Architecture
Metrics
A metric is the fundamental concept in CloudWatch and represents a time-ordered set of data points. These data points can be either your custom metrics or metrics from other services in AWS. You or AWS products publish metric data points into CloudWatch and you retrieve statistics about those data points as an ordered set of time-series data. You can use metrics to calculate statistics and present the data graphically in the CloudWatch console. Metrics exist only in the region in which they are created.

Namespaces
CloudWatch namespaces are containers for metrics. Metrics in different namespaces are isolated from each other, so that metrics from different applications are not mistakenly aggregated into the same statistics.

Dimensions
A dimension is a name/value pair that helps you to uniquely identify a metric. Every metric has specific characteristics that describe it, and you can think of dimensions as categories for those characteristics. Dimensions help you design a structure for your statistics plan. Because dimensions are part of the unique identifier for a metric, whenever you add a unique name/value pair to one of your metrics, you are creating a new metric.

DynamoDB Data Model

In Amazon DynamoDB, a table is a collection of items and each item is a collection of attributes. In a relational database, a table has a predefined schema such as the table name, primary key, list of its column names and their data types. All records stored in the table must have the same set of columns. In contrast, DynamoDB only requires that a table has a primary key, but does not require you to define all of the attribute names and data types in advance. Individual items in a DynamoDB table can have any number of attributes, although there is a limit of 400 KB on the item size. An item size is the sum of lengths of its attribute names and values (binary and UTF-8 lengths).
Each attribute in an item is a name-value pair. An attribute can be a scalar (single-valued), a JSON document, or a set. For example, consider storing a catalog of products in DynamoDB.You can create a table, ProductCatalog, with the Id attribute as its primary key. The primary key uniquely identifies each item, so that no two products in the table can have the same Id.

Primary Key
When you create a table, in addition to the table name, you must specify the primary key of the table. The primary key uniquely identifies each item in the table, so that no two items can have the same key. DynamoDB supports two different kinds of primary keys: 
Partition Key – A simple primary key, composed of one attribute known as the partition key. DynamoDB uses the partition key's value as input to an internal hash function; the output from the hash function determines the partition where the item will be stored. No two items in a table can have the same partition key value.
Partition Key and Sort Key – A composite primary key, composed of two attributes. The first attribute is the partition key, and the second attribute is the sort key. DynamoDB uses the partition key value as input to an internal hash function; the output from the hash function determines the partition where the item will be stored. All items with the same partition key are stored together, in sorted order by sort key value. It is possible for two items to have the same partition key value, but those two items must have different sort key values.

Secondary Indexes
When you create a table with a composite primary key (partition key and sort key), you can optionally define one or more secondary indexes on that table. A secondary index lets you query the data in the table using an alternate key, in addition to queries against the primary key.

DynamoDB Data Types
Amazon DynamoDB supports the following data types:
Scalar types – Number, String, Binary, Boolean, and Null.
Document types – List and Map.
Set types – String Set, Number Set, and Binary Set.

Item Distribution
DynamoDB stores data in partitions. A partition is an allocation of storage for a table, backed by solid state drives (SSDs) and automatically replicated across three facilities within an AWS region. Partition management is handled entirely by DynamoDB—customers never need to manage partitions themselves. If your storage requirements exceed a partition's capacity, DynamoDB allocates additional partitions automatically.

Monday, May 15, 2017

AWS CodeStar

AWS CodeStar is a cloud-based service for creating, managing, and working with software development projects on AWS. You can quickly develop, build, and deploy applications on AWS with an AWS CodeStar project. An AWS CodeStar project creates and integrates AWS services for your project development toolchain. Depending on your choice of AWS CodeStar project template, that toolchain might include source control, build, deployment, virtual servers or serverless resources, and more. AWS CodeStar also manages the permissions required for project users (called team members). By adding users as team members to an AWS CodeStar project, project owners can quickly and simply grant each team member role-appropriate access to a project and its resources.
AWS CodeStar provides a unified user interface, enabling you to easily manage your software development activities in one place. With AWS CodeStar, you can set up your entire continuous delivery toolchain in minutes, allowing you to start releasing code faster. AWS CodeStar makes it easy for your whole team to work together securely, allowing you to easily manage access and add owners, contributors, and viewers to your projects. Each AWS CodeStar project comes with a project management dashboard, including an integrated issue tracking capability powered by Atlassian JIRA Software. With the AWS CodeStar project dashboard, you can easily track progress across your entire software development process, from your backlog of work items to teams’ recent code deployments.

Wednesday, May 10, 2017

Amazon DynamoDB Accelerator (DAX)

DynamoDB Accelerator (DAX) is a new caching service for DynamoDB.
Amazon DynamoDB Accelerator (DAX) is a fully managed, highly available, in-memory cache for DynamoDB that delivers up to a 10x performance improvement – from milliseconds to microseconds – even at millions of requests per second. DAX does all the heavy lifting required to add in-memory acceleration to your DynamoDB tables, without requiring developers to manage cache invalidation, data population, or cluster management.
Amazon DynamoDB is designed for scale and performance. In most cases, the DynamoDB response times can be measured in single-digit milliseconds. However, there are certain use cases that require response times in microseconds. For these use cases, DynamoDB Accelerator (DAX) delivers fast response times for accessing eventually consistent data.
DAX is a DynamoDB-compatible caching service that enables you to benefit from fast in-memory performance for demanding applications. DAX addresses three core scenarios:
  • As an in-memory cache, DAX reduces the response times of eventually-consistent read workloads by an order of magnitude, from single-digit milliseconds to microseconds.
  • DAX reduces operational and application complexity by providing a managed service that is API-compatible with Amazon DynamoDB, and thus requires only minimal functional changes to use with an existing application.
  • For read-heavy or bursty workloads, DAX provides increased throughput and potential operational cost savings by reducing the need to over-provision read capacity units. This is especially beneficial for applications that require repeated reads for individual keys.

Friday, April 7, 2017

JSON Schema

In computing, JSON (JavaScript Object Notation) is an open-standard format that uses human-readable text to transmit data objects consisting of attribute–value pairs. It is the most common data format used for asynchronous browser/server communication, largely replacing XML, and is used by AJAX.
JSON is a language-independent data format. It was derived from JavaScript, but as of 2017 many programming languages include code to generate and parse JSON-format data. The official Internet media type for JSON is application/json. JSON filenames use the extension .json.
JSON Schema specifies a JSON-based format to define the structure of JSON data for validation, documentation, and interaction control. A JSON Schema provides a contract for the JSON data required by a given application, and how that data can be modified. JSON Schema is based on the concepts from XML Schema (XSD), but is JSON-based. The JSON data schema can be used to validate JSON data. As in XSD, the same serialization/deserialization tools can be used both for the schema and data. The schema is self-describing. There are no standard file extension, but some suggested .schema.json.
The official MIME type for JSON text is "application/json". Although most modern implementations have adopted the official MIME type, many applications continue to provide legacy support for other MIME types. Many service providers, browsers, servers, web applications, libraries, frameworks, and APIs use, expect, or recognize the (unofficial) MIME type "text/json" or the content-type "text/javascript".
Example:
{
  "$schema": "http://json-schema.org/schema#",
  "title": "Product",
  "type": "object",
  "required": ["id", "name", "price"],
  "properties": {
    "id": {
      "type": "number",
      "description": "Product identifier"
    },
    "name": {
      "type": "string",
      "description": "Name of the product"
    },
    "price": {
      "type": "number",
      "minimum": 0
    },
    "tags": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "stock": {
      "type": "object",
      "properties": {
        "warehouse": {
          "type": "number"
        },
        "retail": {
          "type": "number"
        }
      }
    }
  }
}
There are several websites which allows us to generate JSON schema file online. An example is : http://jsonschema.net/

Tuesday, April 4, 2017

OpenAPI Specification : Swagger

The OpenAPI Specification (originally known as the Swagger Specification) is a specification for machine-readable interface files for describing, producing, consuming, and visualizing RESTful Web services. A variety of tools can generate code, documentation and test cases given an interface file. Development of the OpenAPI Specification (OAS) is overseen by the Open API Initiative, an open source collaborative project of the Linux Foundation.
Swagger is a project used to describe and document RESTful APIs. The Swagger specification defines a set of files required to describe such an API. These files can then be used by the Swagger-UI project to display the API and Swagger-Codegen to generate clients in various languages. Additional utilities can also take advantage of the resulting files, such as testing tools.
Applications implemented based on OpenAPI interface files can automatically generate documentation of methods, parameters and models. This helps keep the documentation, client libraries, and source code in sync.
Features:
  • The OpenAPI Specification is language-agnostic. It is also extensible into new technologies and protocols beyond HTTP.
  • With Swagger's declarative resource specification, clients can understand and consume services without knowledge of server implementation or access to the server code.
  • The Swagger UI framework allows both developers and non-developers to interact with the API in a sandbox UI that gives clear insight into how the API responds to parameters and options. Swagger may utilize both JSON and XML.
  • Swagger-Codegen contains a template-driven engine to generate documentation, API clients and server stubs in different languages by parsing the OpenAPI/Swagger definition.
How to start:
If you're an API provider and want to use Swagger to describe your APIs - there are several approaches available:
  • A top-down approach where you would use the Swagger Editor to create your Swagger definition and then use the integrated Swagger Codegen tools to generate server implementation.
  • A bottom-up approach where you have an existing REST API for which you want to create a Swagger definition. Either you create the definition manually (using the same Swagger Editor mentioned above), or if you are using one of the supported frameworks (JAX-RS, node.js, etc), you can get the Swagger definition generated automatically for you. If you're doing JAX-RS have a look at the example at https://github.com/swagger-api/swagger-core/wiki/Swagger-Core-JAX-RS-Project-Setup-1.5.X.
If on the other hand you're an API Consumer who wants to integrate with an API that has a Swagger definition you can use the online version of the Swagger UI to explore the API (given that you have a URL to the APIs Swagger definition) - and then use Swagger Codegen to generate the client library of your choice.