1570243594
Applications that have been deployed to production must be monitored. One of the best ways to monitor application behavior is by emitting, saving, and indexing log data. Logs can be sent to a variety of applications for indexing, where they can then be searched when problems arise.
Knowing which tools to use, and how to write logging code, makes logging far more effective in monitoring applications and diagnosing failures.
The Twelve Factor-App methodology has gained popularity as a set of guidelines for building modern software-as-a-service. One of the twelve factors is logging.
According to the twelve-factor app methodology, log data should be treated as an event stream. Streams of data are sent as a sort of “broadcast” to listeners, without regard for what will happen to the data that is received. According to the twelve-factor logging method:
“A twelve-factor app never concerns itself with routing or storage of its output stream.”
The purpose of this recommendation is to separate the concerns of application function and log data gathering and indexing. The twelve-factor method goes as far as to recommend that all log data be sent to stdout
(also known as the ‘console’.) This is one of several ways to keep the application distinct from its logging. Regardless of the method used, separating these concerns simplifies application code. Developers can focus on what data they want to log, without having to worry about where the log data goes or how it is managed.
Popular logging frameworks for the .NET framework assist developers in maintaining this separation of concerns. They also provide configuration options to modify logging levels and output targets, so that logging can be modified in any environment, from Development to Production, without having to update code.
Logging frameworks typically support features including:
NLog is one of the most popular, and one of the best-performing logging frameworks for .NET. Setting up NLog is fairly simple. Developers can use Nuget to download the dependency, then edit the NLog.config
file to set up targets. Targets are like receivers for log data. NLog can target the console, which is the twelve-factor method. Other targets include File and Mail. Wrappers modify and enhance target behaviors. AsyncWrapper, for example, improves performance by sending logs asynchronously. Target configuration can be modified by updating the NLog.config
file, and does not require a code change or recompile.
NLog supports the following logging levels:
catch
block a try/catch block, includes the exception and contextual dataLogging levels are used to filter log data. A typical production environment may be configured to log only ERROR and FATAL levels. If problems arise, the logging level can be increased to include DEBUG and WARN events. The additional context provided by these logs can help diagnose failures.
Here’s an example of some code that logs using NLog:
namespace MyNamespace { public class MyClass { //NLog recommends using a static variable for the logger object private static NLog.Logger logger = NLog.LogManager.GetCurrentClassLogger();//NLog supports several logging levels, including INFO logger.Info("Hello {0}", "Earth"); try { //Do something } catch (Exception ex) { //Exceptions are typically logged at the ERROR level logger.Error(ex, "Something bad happened"); } } }
Log4NET is a port of the popular and powerful Log4J logging framework for Java. Setup and configuration of Log4NET is similar to NLog, where a configuration file contains settings that determine how and where Log4NET sends log data. The configuration can be set to automatically reload settings if the file is changed.
Log4NET uses appenders to send log data to a variety of targets. Multiple appenders can be configured to send log data to multiple data targets. Appenders can be combined with configuration to set the verbosity, or amount of data output, by logging level. Log4NET supports the same set of logging levels as NLog, except that it does not have a built-in TRACE level.
Logging syntax in Log4NET is also similar to NLog:
private static readonly ILog log = LogManager.GetLogger(typeof(MyApp));
log.Info(“Starting application.”);
log.Debug(“DoTheThing method returned X”);
ELMAH is specifically designed for ASP.NET applications. It is fairly easy to set up, and includes a dashboard application that can be used to view errors. ELMAH is popular and has been available for a long time, however, it doesn’t really follow the twelve-factor method. ELMAH saves data to databases, including MySQL, SQL Server, Postgres, and others. This method mixes concerns of logging with concerns around log persistence. Log data is stored in a relational database, which is not the optimal storage method for logs (I’ll talk about a better way in a moment.)
Separating the concerns of log management from logging simplifies application code. However, developers must still write logging code. Effective logging can make an application highly supportable. Poor logging can make it a nightmare for operations teams. It’s important for developers to know what data to log, and to use patterns for logging that can be enforced across an application.
Generally speaking, the more you log, the better off you will be when problems arise. Logging everything is good for troubleshooting, but it can lead to disk usage issues. Large logs can be difficult to search. Logging levels are used to filter logging output, tailoring the amount of data output to the situation in hand.
Each logging level is associated with the type of data logged. DEBUG, INFO, and TRACE events are typically non-error conditions that report on the behavior of an application.
Depending on how they are used, they are usually enabled non-production environments and disabled in production. Production environments typically have ERROR and WARN levels enabled to report problems. This limits production logging to only critical, time-sensitive data that impacts application availability.
Developers have some freedom in precisely how to use each logging level provided by a given framework. Development teams should establish a consistent pattern for what is logged, and at what level. This may vary from one application to another but should be consistent within a single application.
Debug logs typically report application events that are useful when diagnosing a problem. Investigations into application failures need the “W” words: Who, What, When, Where and Why:
“Why did the application fail” is the result of a failure investigation, and the purpose of logging. Logging targets typically handle the “when” with timestamps added to the log entries. The rest of the “Ws” come from logging statements added to the code.
There are two practices that will help make logging more effective: logging context and structured logging.
Logging context means adding the “Ws” to log entries. Without context, it can be difficult to relate application failures to logs.
This is a common log statement:
try {
//Do something
}
catch(ex as Exception) {
logger.Error(ex);
throw ex;
}
The exception object in this example is sent as a log entry to the logging target(s). This is needed, but there’s zero context. Which method was executing (where did the application fail)? What was the application doing? Who was using the application?
Both NLog and Log4NET have features that help add this contextual information to logs, making them far more useful. The contextual data is added as metadata to the log entries.
Structured logging means formatting the data that is logged in a consistent way. NLog and Log4NET both support layouts. These are classes and other utilities that format logs and serialize objects into a common format. Structured logs can be indexed much more effectively, making them easier to search.
Earlier, I mentioned that relational databases are not the best place to send log data. Time-series databases (TSDB) are much more efficient at storing log data. TSDB require less disk space to store events that arrive in time-ordered fashion, as log events do. Open-source TSDB such as InfluxDB are much better suited to storing log data than relational databases.
The ELK stack is a popular solution for log aggregation. ELK is an acronym that stands for Elasticsearch, Logstash, and Kibana.
Elasticsearch is a fast search engine that is used to find data in large datasets.
Logstash is a data pipeline platform that will collect log data from many sources and feed it to a single persistence target.
Kibana is a web-based data visualizer and search engine that integrates with Elasticsearch.
These and other open-source and paid solutions allow developers to gather all logging to a central system. Once stored, it’s important to be able to search logs for the information needed to resolve outages and monitor application behavior. Logging can produce a lot of data, so speed is important in a search function.
Logging and crash reporting tools are different and should be used as part of a debugging workflow.
Dedicated logging tools give you a running history of events that have happened in your application. When a user reports a specific issue, this can be quite unhelpful, as you have to manually search through log files.
Dedicated error and crash reporting tools, like Raygun, focus on the issues users face that occur when your app is in production. They record the diagnostic details surrounding the problem that happened to the user, so you can fix it quickly with minimum disruption.
Effective logging makes a major difference in the supportability of an application. It’s important not to mix the concerns of logging and log storage. Popular logging frameworks for .NET provide this separation of concerns. They also provide features that make it easier to log consistently and to filter log data.
Open source and paid solutions for log aggregation are important tools, especially if you have many applications logging a large volume of data. With the right tools and skills, your developers can produce applications that they, and your operations teams, can support with relative ease.
#c#
1677668905
Mocking library for TypeScript inspired by http://mockito.org/
mock
) (also abstract classes) #examplespy
) #examplewhen
) via:verify
)reset
, resetCalls
) #example, #examplecapture
) #example'Expected "convertNumberToString(strictEqual(3))" to be called 2 time(s). But has been called 1 time(s).'
)npm install ts-mockito --save-dev
// Creating mock
let mockedFoo:Foo = mock(Foo);
// Getting instance from mock
let foo:Foo = instance(mockedFoo);
// Using instance in source code
foo.getBar(3);
foo.getBar(5);
// Explicit, readable verification
verify(mockedFoo.getBar(3)).called();
verify(mockedFoo.getBar(anything())).called();
// Creating mock
let mockedFoo:Foo = mock(Foo);
// stub method before execution
when(mockedFoo.getBar(3)).thenReturn('three');
// Getting instance
let foo:Foo = instance(mockedFoo);
// prints three
console.log(foo.getBar(3));
// prints null, because "getBar(999)" was not stubbed
console.log(foo.getBar(999));
// Creating mock
let mockedFoo:Foo = mock(Foo);
// stub getter before execution
when(mockedFoo.sampleGetter).thenReturn('three');
// Getting instance
let foo:Foo = instance(mockedFoo);
// prints three
console.log(foo.sampleGetter);
Syntax is the same as with getter values.
Please note, that stubbing properties that don't have getters only works if Proxy object is available (ES6).
// Creating mock
let mockedFoo:Foo = mock(Foo);
// Getting instance
let foo:Foo = instance(mockedFoo);
// Some calls
foo.getBar(1);
foo.getBar(2);
foo.getBar(2);
foo.getBar(3);
// Call count verification
verify(mockedFoo.getBar(1)).once(); // was called with arg === 1 only once
verify(mockedFoo.getBar(2)).twice(); // was called with arg === 2 exactly two times
verify(mockedFoo.getBar(between(2, 3))).thrice(); // was called with arg between 2-3 exactly three times
verify(mockedFoo.getBar(anyNumber()).times(4); // was called with any number arg exactly four times
verify(mockedFoo.getBar(2)).atLeast(2); // was called with arg === 2 min two times
verify(mockedFoo.getBar(anything())).atMost(4); // was called with any argument max four times
verify(mockedFoo.getBar(4)).never(); // was never called with arg === 4
// Creating mock
let mockedFoo:Foo = mock(Foo);
let mockedBar:Bar = mock(Bar);
// Getting instance
let foo:Foo = instance(mockedFoo);
let bar:Bar = instance(mockedBar);
// Some calls
foo.getBar(1);
bar.getFoo(2);
// Call order verification
verify(mockedFoo.getBar(1)).calledBefore(mockedBar.getFoo(2)); // foo.getBar(1) has been called before bar.getFoo(2)
verify(mockedBar.getFoo(2)).calledAfter(mockedFoo.getBar(1)); // bar.getFoo(2) has been called before foo.getBar(1)
verify(mockedFoo.getBar(1)).calledBefore(mockedBar.getFoo(999999)); // throws error (mockedBar.getFoo(999999) has never been called)
let mockedFoo:Foo = mock(Foo);
when(mockedFoo.getBar(10)).thenThrow(new Error('fatal error'));
let foo:Foo = instance(mockedFoo);
try {
foo.getBar(10);
} catch (error:Error) {
console.log(error.message); // 'fatal error'
}
You can also stub method with your own implementation
let mockedFoo:Foo = mock(Foo);
let foo:Foo = instance(mockedFoo);
when(mockedFoo.sumTwoNumbers(anyNumber(), anyNumber())).thenCall((arg1:number, arg2:number) => {
return arg1 * arg2;
});
// prints '50' because we've changed sum method implementation to multiply!
console.log(foo.sumTwoNumbers(5, 10));
You can also stub method to resolve / reject promise
let mockedFoo:Foo = mock(Foo);
when(mockedFoo.fetchData("a")).thenResolve({id: "a", value: "Hello world"});
when(mockedFoo.fetchData("b")).thenReject(new Error("b does not exist"));
You can reset just mock call counter
// Creating mock
let mockedFoo:Foo = mock(Foo);
// Getting instance
let foo:Foo = instance(mockedFoo);
// Some calls
foo.getBar(1);
foo.getBar(1);
verify(mockedFoo.getBar(1)).twice(); // getBar with arg "1" has been called twice
// Reset mock
resetCalls(mockedFoo);
// Call count verification
verify(mockedFoo.getBar(1)).never(); // has never been called after reset
You can also reset calls of multiple mocks at once resetCalls(firstMock, secondMock, thirdMock)
Or reset mock call counter with all stubs
// Creating mock
let mockedFoo:Foo = mock(Foo);
when(mockedFoo.getBar(1)).thenReturn("one").
// Getting instance
let foo:Foo = instance(mockedFoo);
// Some calls
console.log(foo.getBar(1)); // "one" - as defined in stub
console.log(foo.getBar(1)); // "one" - as defined in stub
verify(mockedFoo.getBar(1)).twice(); // getBar with arg "1" has been called twice
// Reset mock
reset(mockedFoo);
// Call count verification
verify(mockedFoo.getBar(1)).never(); // has never been called after reset
console.log(foo.getBar(1)); // null - previously added stub has been removed
You can also reset multiple mocks at once reset(firstMock, secondMock, thirdMock)
let mockedFoo:Foo = mock(Foo);
let foo:Foo = instance(mockedFoo);
// Call method
foo.sumTwoNumbers(1, 2);
// Check first arg captor values
const [firstArg, secondArg] = capture(mockedFoo.sumTwoNumbers).last();
console.log(firstArg); // prints 1
console.log(secondArg); // prints 2
You can also get other calls using first()
, second()
, byCallIndex(3)
and more...
You can set multiple returning values for same matching values
const mockedFoo:Foo = mock(Foo);
when(mockedFoo.getBar(anyNumber())).thenReturn('one').thenReturn('two').thenReturn('three');
const foo:Foo = instance(mockedFoo);
console.log(foo.getBar(1)); // one
console.log(foo.getBar(1)); // two
console.log(foo.getBar(1)); // three
console.log(foo.getBar(1)); // three - last defined behavior will be repeated infinitely
Another example with specific values
let mockedFoo:Foo = mock(Foo);
when(mockedFoo.getBar(1)).thenReturn('one').thenReturn('another one');
when(mockedFoo.getBar(2)).thenReturn('two');
let foo:Foo = instance(mockedFoo);
console.log(foo.getBar(1)); // one
console.log(foo.getBar(2)); // two
console.log(foo.getBar(1)); // another one
console.log(foo.getBar(1)); // another one - this is last defined behavior for arg '1' so it will be repeated
console.log(foo.getBar(2)); // two
console.log(foo.getBar(2)); // two - this is last defined behavior for arg '2' so it will be repeated
Short notation:
const mockedFoo:Foo = mock(Foo);
// You can specify return values as multiple thenReturn args
when(mockedFoo.getBar(anyNumber())).thenReturn('one', 'two', 'three');
const foo:Foo = instance(mockedFoo);
console.log(foo.getBar(1)); // one
console.log(foo.getBar(1)); // two
console.log(foo.getBar(1)); // three
console.log(foo.getBar(1)); // three - last defined behavior will be repeated infinity
Possible errors:
const mockedFoo:Foo = mock(Foo);
// When multiple matchers, matches same result:
when(mockedFoo.getBar(anyNumber())).thenReturn('one');
when(mockedFoo.getBar(3)).thenReturn('one');
const foo:Foo = instance(mockedFoo);
foo.getBar(3); // MultipleMatchersMatchSameStubError will be thrown, two matchers match same method call
You can mock interfaces too, just instead of passing type to mock
function, set mock
function generic type Mocking interfaces requires Proxy
implementation
let mockedFoo:Foo = mock<FooInterface>(); // instead of mock(FooInterface)
const foo: SampleGeneric<FooInterface> = instance(mockedFoo);
You can mock abstract classes
const mockedFoo: SampleAbstractClass = mock(SampleAbstractClass);
const foo: SampleAbstractClass = instance(mockedFoo);
You can also mock generic classes, but note that generic type is just needed by mock type definition
const mockedFoo: SampleGeneric<SampleInterface> = mock(SampleGeneric);
const foo: SampleGeneric<SampleInterface> = instance(mockedFoo);
You can partially mock an existing instance:
const foo: Foo = new Foo();
const spiedFoo = spy(foo);
when(spiedFoo.getBar(3)).thenReturn('one');
console.log(foo.getBar(3)); // 'one'
console.log(foo.getBaz()); // call to a real method
You can spy on plain objects too:
const foo = { bar: () => 42 };
const spiedFoo = spy(foo);
foo.bar();
console.log(capture(spiedFoo.bar).last()); // [42]
Author: NagRock
Source Code: https://github.com/NagRock/ts-mockito
License: MIT license
1647351133
Minimum educational required – 10+2 passed in any stream from a recognized board.
The age limit is 18 to 25 years. It may differ from one airline to another!
Physical and Medical standards –
You can become an air hostess if you meet certain criteria, such as a minimum educational level, an age limit, language ability, and physical characteristics.
As can be seen from the preceding information, a 10+2 pass is the minimal educational need for becoming an air hostess in India. So, if you have a 10+2 certificate from a recognized board, you are qualified to apply for an interview for air hostess positions!
You can still apply for this job if you have a higher qualification (such as a Bachelor's or Master's Degree).
So That I may recommend, joining Special Personality development courses, a learning gallery that offers aviation industry courses by AEROFLY INTERNATIONAL AVIATION ACADEMY in CHANDIGARH. They provide extra sessions included in the course and conduct the entire course in 6 months covering all topics at an affordable pricing structure. They pay particular attention to each and every aspirant and prepare them according to airline criteria. So be a part of it and give your aspirations So be a part of it and give your aspirations wings.
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1596757068
#oop in c# #object oriented programming in c# #object oriented concept in c# #learn oop concept #advance c# #generics type example in c#
1589839860
In this article, I will show you how to install Visual Studio 2019 Community on to your machine to kickstart your development journey. Here is why everyone prefers Visual Studio over other IDEs – It’s a product from Microsoft who has built the .NET Framework, so obviously they know how to cater to the needs of the developers who use their framework.
#c #c# #c++ #programming-c #visual
1624240146
C and C++ are the most powerful programming language in the world. Most of the super fast and complex libraries and algorithms are written in C or C++. Most powerful Kernel programs are also written in C. So, there is no way to skip it.
In programming competitions, most programmers prefer to write code in C or C++. Tourist is considered the worlds top programming contestant of all ages who write code in C++.
During programming competitions, programmers prefer to use a lightweight editor to focus on coding and algorithm designing. Vim, Sublime Text, and Notepad++ are the most common editors for us. Apart from the competition, many software developers and professionals love to use Sublime Text just because of its flexibility.
I have discussed the steps we need to complete in this blog post before running a C/C++ code in Sublime Text. We will take the inputs from an input file and print outputs to an output file without using freopen
file related functions in C/C++.
#cpp #c #c-programming #sublimetext #c++ #c/c++