All example are based on Angular2 RC 1
HTTP and Routing - Get in HTTP
HTTP and Routing - Basic Routing
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Monday, 23 May 2016
Part 4 of 5: Notes on Dependency Injection in Angular2
All example are based on Angular2 RC 1
DI - Class constructor injection
DI - Building a service
DI - Provider registration at Bootstrap and The Inject decorator
DI - The opaque token
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Part 3 of 5: Notes on Forms and Pipes in Angular2
All example are based on Angular2 RC 1
Forms - Template Driven Forms
Forms - Model Driven Forms
Forms - Validation—built in
Forms - Validation—custom
Forms - Error handling
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Part 2 of 5: Notes on Directive and Pipes in Angular2
All example are based on Angular2 RC 1
Directive and Pipes - Attribute directives—custom
Directive and Pipes - Using directive values
Directive and Pipes - Working with events in directives
Directive and Pipes - Angular pipes -- custom
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Part 1 of 5: Component in Angular2
All example are based on Angular2 RC 1
Components - Displaying data in our templates
Components - Working with Events
Components - Using Property
Components - Using more complex data
Components - Using Sub-Component
Components - Getting data to the component with input
Components - Subscribing to component events with output
Components - Getting data to the component with @input
Components - Subscribing to component events with @output
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Sunday, 15 May 2016
My Notes from lynda.com (Learn AngularJS 2: The Basics)
All example are based on Angular2 RC 1
Displaying data in our templates
Working with Events
Using Property
Using more complex data
Using Sub-Component
Getting data to the component with input
Subscribing to component events with output
Getting data to the component with @input
Subscribing to component events with @output
Saturday, 7 May 2016
What is Apache Hadoop ?
Hadoop brings the ability to cheaply process large amounts of data, regardless of its structure.
The Core of Hadoop: MapReduce
The important innovation of MapReduce is the ability to take a query over a dataset, divide it, and run in parallel over multiple nodes. Distributation the computation solves the issue of data too large to fit onto a single machine. Combine this technique with commodity Linux server and you have a cost-effective alternative to massive computing arrays.
Programming Hadoop at the MapReduce level is a case of working with the Java APIs, and manually loading data files into HDFS (Hadoop Distributed File System).
Programmability
Hadoop offers two solutions for making Hadoop programming easier.
Programming Pig
Pis is a programming language that simplifies the common tasks of working with Hadoop: loading data, expressing transformations on the data, and storing the final results.
Programming Hive
Hive enables Hadoop to operate as a data warehouse. It superimposes structure on data in HDFS and then permits queries over the data using a familiar SQL-like syntax. As with Pig, Hive's core capabilities are extensible.
Choosing between Hive and Pig can be confusing. Hive is more suitable for data warehousing tasks, with predominatly static structure and the need for frequent analysis. Hive's closeness to SQL makes it an ideal point of integration between Hadoop and other business intelligence tools.
Pig gives the developer more agility for the exploration of large datasets, allowing the development of succinct scripts for transforming data flows for incorporation into larger applications.
The Hadoop Bestiary
- Ambari Deployment, configuration and monitoring
- Flume Collection and import of log and event data
- HBase Column-oriented database scaling to billions of rows
- HCatalog Schema and data type sharing over Pig,Hive and MapReduce
- HDFS Distributed redundant file system for Hadoop
- Hive Data warehouse with SQL-Like access
- Mahout Library if machine learning and data mining algorithms
- MapReduce Parallel computation on server clusters
- Pig High-level programming language for Hadoop computations
- Oozie Orchestration and workflow management
- Sqoop Imports data from relational databases
- Whirr Cloud-agnostic deployment of clusters
- Zoookeeper Configuration management and coodination
Getting data in and out: Sqoop and Flume
Improved interoperability with the rest of the data world is provided by Sqoop and Flume. Sqoop is a tool designed to import data from relational databases into Hadoop, either directly into HDFS or into Hive. Flume is designed to import streaming flows of log data directly into HDFS.
Hive's SQL friendliness means that it can be used as a point of integration with vast universe of database tools capable of making connections via JBDC or ODBC database drivers.
Coordination and Workflow: Zookeeper and Oozie
As cmputing nodes can come and go, members of the cluster need to synchronize with each other, know where to access services, and know how they should be configured. This is the purpose of Zookeper
The Oozie component provides features to manage the workflow and dependencies, removing the need for developers to code custom solutions.
Management and Deployment: Ambari and Whirr
Ambari is intended to help system administrators deploy and configure Hadoop, upgrade clusters, and monitor services. Through and API, it may be integrated with other system management tools.
Whirr is a highly complementary componentary component. It offers a way of running services, including Hadoop, on cloude pltforms. Ehirr is cloud neutral and currently supports. Whirr is cloud neutral and currently supports the Amazon EC2 and Rackspace services.
Machine Learning: Mahout
Every organization's data are diverse and particular to their needs. However, there is much less diversity in the kinds of analyses performes on the data. The Mahout project is a library of Hadoop implementations of common analytical computations. Use cases include user collaborative filtering, user recommendations, clustering, and classification.
What is Big Data ?
Big data is data that exceeds the processing capacity of conventional database systems.
The value of big data to an organization falls into two categories: analytical use and enabling new product.
What Does Big Data Look Like?
Input data to big data systems could be chatter from social networks, web server logs, traffic flow sensors, sattellite imagery, broadcast audio streams, banking transactions, MP3s of rock music, the content of web pages, scans of goverments, GPS trails, telemetru from automobiles, financial market data, the list goes on.
To clarify matters, the three V's of Volume, Velocity and Variety are commonly used to characterize different aspects of big data.
Volume
The benifit gained from the ability to process large amount of information is the main attraction of big data analytics.
Many companies already have large amount of archived data, perhaps in the form of logs, but not the capacity to process it.
Velocity
It's not just the velocity of the incoming data that's the issue: it's possible to stream fast-moving data into bulk storage for later batch processing.
Variety
Rarely does data present itself in a form perfectly ordered and ready for processing. A common theme in big data systems is that the source data is diverse, and doesn't fall into neat relational structures. It could be text from social networks, image data, a raw feed directly from a sensor source. None of these things come ready for integration into an application.
Tuesday, 3 May 2016
(Part 1 of 2) How to Create Simple Windows Service and Log with Log4Net
Our windows service will support two modes
- Interval Mode Executes a task at regular intervals after some delay
- Daly Mode Executes a task at specific time of day
Create New Windows Service Project and Add aap.config file
Add Reference (system.configuration)
Code Your Service
Adding an Installer to the Windows Service and Write Code
Setting the Windows Service Name and StartType
Making the Windows Service Automatically start after Installation
Compile and Install Service
Test Our Window Service
Get Source Code GitHub
Monday, 2 May 2016
Three Moral Code for Designing WEB API (Security, Stability, Documentation)
Security
There are many methods to Secure your api but two are most widely used. Token-based authentication and OAuth 2 + SSL
Token-based authentication
For most APIs, I prefer a simple token-based authentication, where the token is a random hash assigned to the user and they can reset it at any point if it has been stolen. Allow the token to be passed in through POST or an HTTP header.
OAuth 2 + SSL
Another very good option is OAuth 2 + SSL. You should be using SSL anyway, but OAuth 2 is reasonably simple to implement on the server side, and libraries are available for many common programming languages.
Here are some other important things to keep in mind:
- Whitelisting Functionality. APIs generally allow you to do basic create, read, update, and delete operations on data. But you don’t want to allow these operations for every entity, so make sure each has a whitelist of allowable actions. Make sure, for example, that only authorized users can run commands like
/user/delete/{id}. If it doesn’t, then send back an error message such as a 406 Not Acceptable response. - Protect yourself against Cross-Site Request Forgery (CSRF). If you are allowing session or cookie authentication, you need to make sure that you’re protecting yourself from CSRF attacks.
- Validate access to resources. In every request, you need to verify that a user is in fact allowed access to the specific item they are referencing.
Stability and Consistency
Let's say you have a api http://niisar.com/api/friendlist and it response JSON Data. This seems fine at first. But what happen when you need to modify the format of JSON? Everyone that’s already integrated with you is going to break. Oops.
So do some planning ahead, and version your API from the outset, explicitly incorporating a version number into the URL like http://niisar.com/api/v1/friendlistso that people rely on v1 of API.
Also use inheritance or a shared architecture to reuse the same naming conventions and data handling consistently throughout your API.
Finally, you need to record and publish a changelog to show differences between versions of your API so that users know exactly how to upgrade.
Documentation and Support
Documentation may be boring but if you want anyone to use your API, documentation is essential. You’ve simply got to get this right. It’s the first thing users will see, so in some ways it’s like the gift wrap. Present well, and people are more likely to use your API.
Fortunately, there are number of software tools that facilitate and simplify the task of generating documentation. Or you can write something yourself for your API
But what separates great documentation from adequate documentation is the inclusion of usage examples and, ideally, tutorials. This is what helps the user understand your API and where to start. It orients them and helps them load your API into their brain.
Make sure that API can get up and running with at least a basic implementation of your API, even if it’s just following a tutorial, within a few minutes. I think 15 minutes is a good goal.
Some specific recommendations to ease and facilitate adoption of your API:
- Make sure people can actually use your API and that it works the first time, every time.
- Keep it simple. so that developers only have to learn your API, not your API + 10 obscure new technologies.
- Provide language-specific libraries to interface with your service.
- Simplify any necessary signup.
- Provide excellent support. A big barrier to adoption is lack of support. How will you handle and respond to a bug report? What about unclear documentation? An unsophisticated user? Forums, bug trackers, and email support are fantastic starts, but do make sure that when someone posts a bug, you really address it. Nobody wants to see a ghost town forum or a giant list of bugs that haven’t been addressed
How To Set Up A Print Style Sheet
You can use CSS to change the appearance of your web page when it's printed on a paper. You can specify one font for the screen version and another for the print version.
You just need to press Ctrl + P to print or call Print function from javascript window.print(); Both are same thing.
The css for printing looks like
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