App is related to an f&b business, I have following scenarios (api calls):
Create Order - on creating an order of any food item (let it be X), inventory of which is updated at back-end (this value is under test, let it be P1)
Get Inventory - (this call will fetch me the updated value of inventory of ordered item (X) i.e. inventory value, P1)
Cancel Order - this will cancel the order, i created in my first call, and hence P1 should be rolled back.
Get Inventory - Again i will hit this call to get the value, so as to verify that the inventory value of P1 has been updated properly.
In API call, (2) I extract P1 value using jp#gc Json Extractor and same I did for same call (4).
Now as per my expectations, value obtained in both these extractors should be equal as order has been cancelled now.
To assert these values, I am using JSON Assertion , either I am making use of wrong assertion or lacking a big amount of information here.
May be there is something like I can save the value first in some variable, and then assert.
Image of my test suite:
You can add JSR223 Assertion with checking different variables e.g. a and b:
if (!vars.get("a").equals(vars.get("b"))) {
AssertionResult.setFailureMessage("message");
AssertionResult.setFailure(true);
}
The script can check various aspects of the SampleResult. If an error is detected, the script should use AssertionResult.setFailureMessage("message") and AssertionResult.setFailure(true)
I've looked through all of the Simperium API docs for all of the different programming languages and can't seem to find this. Is there any documentation for the data returned from an ".all" call (e.g. api.todo.all(:cv=>nil, :data=>false, :username=>false, :most_recent=>false, :timeout=>nil) )?
For example, this is some data returned:
{"ccid"=>"10101010101010101010101010110101010",
"o"=>"M",
"cv"=>"232323232323232323232323232",
"clientid"=>"ab-123123123123123123123123",
"v"=>{
"date"=>{"o"=>"+", "v"=>"2015-08-20T00:00:00-07:00"},
"calendar"=>{"o"=>"+", "v"=>false},
"desc"=>{"o"=>"+", "v"=>"<p>test</p>\r\n"},
"location"=>{"o"=>"+", "v"=>"Los Angeles"},
"id"=>{"o"=>"+", "v"=>43}
},
"ev"=>1,
"id"=>"abababababababababababababab/10101010101010101010101010110101010"}
I can figure out some of it just from context or from the name of the key but a lot of it is guesswork and trial and error. The one that concerns me is the value returned for the "o" key. I assume that a value of "M" is modify and a value of "+" is add. I've also run into "-" for delete and just recently discovered that there is also a "! '-'" which is also a delete but don't know what else it signifies. What other values can be returned in the "o" key? Are there other keys/values that can be returned but are rare? Is there documentation that details what can be returned (that would be the most helpful)?
If it matters, I am using the Ruby API but I think this is a question that, if answered, can be helpful for all APIs.
The response you are seeing is a list of all of the changes which have occurred in the given bucket since some point in its history. In the case where cv is blank, it tries to get the full history.
You can find some of the details in the protocol documentation though it's incomplete and focused on the WebSocket message syntax (the operations are the same however as with the HTTP API).
The information provided by the v parameter is the result of applying the JSON-diff algorithm to the data between changes. With this diff information you can reconstruct the data at any given version as the changes stream in.
I'm new with Couchbase. I'm working on reduce function and realize in all of my cases, the rereduce parameters is always false. I've read document about rereduce. They says when the reduce function is called after the previous reduce phase, the rereduce is true. It's confusing me.
My question is how to get rereduce = true ???
Rereduce will become true if you have more than one server in your cluster. I'll explain on _count function example.
When you have one server both operations: map and reduce are processed on one server. I.e. if you have such array : after map: [1:null, 2:null, ... , 5:null] and you need to count them in reduce it will return 5 in one step.
But if you have more than one server map and reduce functions will be executed on each server. So you get i.e. [1:null,3:null] from first server in map, and [2:null,4:null,5:null] from another. Then reduce function will be also called on both servers and it will return [2] from first server and [3] from another. That values will be passed to reduce again on one server, so on rereduce you'll get in value param an array [[2],[3]] and here you need to add that values to get correct count.
So my understanding of currying (based on SO questions) is that it lets you partially set parameters of a function and return a "truncated" function as a result.
If you have a big hairy function takes 10 parameters and looks like
function (location, type, gender, jumpShot%, SSN, vegetarian, salary) {
//weird stuff
}
and you want a "subset" function that will let you deal with presets for all but the jumpShot%, shouldn't you just break out a class that inherits from the original function?
I suppose what I'm looking for is a use case for this pattern. Thanks!
Currying has many uses. From simply specifying default parameters for functions you use often to returning specialized functions that serve a specific purpose.
But let me give you this example:
function log_message(log_level, message){}
log_error = curry(log_message, ERROR)
log_warning = curry(log_message, WARNING)
log_message(WARNING, 'This would be a warning')
log_warning('This would also be a warning')
In javascript I do currying on callback functions (because they cannot be passed any parameters after they are called (from the caller)
So something like:
...
var test = "something specifically set in this function";
onSuccess: this.returnCallback.curry(test).bind(this),
// This will fail (because this would pass the var and possibly change it should the function be run elsewhere
onSuccess: this.returnCallback.bind(this,test),
...
// this has 2 params, but in the ajax callback, only the 'ajaxResponse' is passed, so I have to use curry
returnCallback: function(thePassedVar, ajaxResponse){
// now in here i can have 'thePassedVar', if
}
I'm not sure if that was detailed or coherent enough... but currying basically lets you 'prefill' the parameters and return a bare function call that already has data filled (instead of requiring you to fill that info at some other point)
When programming in a functional style, you often bind arguments to generate new functions (in this example, predicates) from old. Pseudo-code:
filter(bind_second(greater_than, 5), some_list)
might be equivalent to:
filter({x : x > 5}, some_list)
where {x : x > 5} is an anonymous function definition. That is, it constructs a list of all values from some_list which are greater than 5.
In many cases, the parameters to be omitted will not be known at compile time, but rather at run time. Further, there's no limit to the number of curried delegates that may exist for a given function. The following is adapted from a real-world program.
I have a system in which I send out command packets to a remote machine and receive back response packets. Every command packet has an index number, and each reply bears the index number of the command to which it is a response. A typical command, translated into English, might be "give me 128 bytes starting at address 0x12300". A typical response might be "Successful." along with 128 bytes of data.
To handle communication, I have a routine which accepts a number of command packets, each with a delegate. As each response is received, the corresponding delegate will be run on the received data. The delegate associated with the command above would be something like "Confirm that I got a 'success' with 128 bytes of data, and if so, store them into my buffer at address 0x12300". Note that multiple packets may be outstanding at any given time; the curried address parameter is necessary for the routine to know where the incoming data should go. Even if I wanted to write a "store data to buffer" routine which didn't require an address parameter, it would have no way of knowing where the incoming data should go.
What is an idempotent operation?
In computing, an idempotent operation is one that has no additional effect if it is called more than once with the same input parameters. For example, removing an item from a set can be considered an idempotent operation on the set.
In mathematics, an idempotent operation is one where f(f(x)) = f(x). For example, the abs() function is idempotent because abs(abs(x)) = abs(x) for all x.
These slightly different definitions can be reconciled by considering that x in the mathematical definition represents the state of an object, and f is an operation that may mutate that object. For example, consider the Python set and its discard method. The discard method removes an element from a set, and does nothing if the element does not exist. So:
my_set.discard(x)
has exactly the same effect as doing the same operation twice:
my_set.discard(x)
my_set.discard(x)
Idempotent operations are often used in the design of network protocols, where a request to perform an operation is guaranteed to happen at least once, but might also happen more than once. If the operation is idempotent, then there is no harm in performing the operation two or more times.
See the Wikipedia article on idempotence for more information.
The above answer previously had some incorrect and misleading examples. Comments below written before April 2014 refer to an older revision.
An idempotent operation can be repeated an arbitrary number of times and the result will be the same as if it had been done only once. In arithmetic, adding zero to a number is idempotent.
Idempotence is talked about a lot in the context of "RESTful" web services. REST seeks to maximally leverage HTTP to give programs access to web content, and is usually set in contrast to SOAP-based web services, which just tunnel remote procedure call style services inside HTTP requests and responses.
REST organizes a web application into "resources" (like a Twitter user, or a Flickr image) and then uses the HTTP verbs of POST, PUT, GET, and DELETE to create, update, read, and delete those resources.
Idempotence plays an important role in REST. If you GET a representation of a REST resource (eg, GET a jpeg image from Flickr), and the operation fails, you can just repeat the GET again and again until the operation succeeds. To the web service, it doesn't matter how many times the image is gotten. Likewise, if you use a RESTful web service to update your Twitter account information, you can PUT the new information as many times as it takes in order to get confirmation from the web service. PUT-ing it a thousand times is the same as PUT-ing it once. Similarly DELETE-ing a REST resource a thousand times is the same as deleting it once. Idempotence thus makes it a lot easier to construct a web service that's resilient to communication errors.
Further reading: RESTful Web Services, by Richardson and Ruby (idempotence is discussed on page 103-104), and Roy Fielding's PhD dissertation on REST. Fielding was one of the authors of HTTP 1.1, RFC-2616, which talks about idempotence in section 9.1.2.
No matter how many times you call the operation, the result will be the same.
Idempotence means that applying an operation once or applying it multiple times has the same effect.
Examples:
Multiplication by zero. No matter how many times you do it, the result is still zero.
Setting a boolean flag. No matter how many times you do it, the flag stays set.
Deleting a row from a database with a given ID. If you try it again, the row is still gone.
For pure functions (functions with no side effects) then idempotency implies that f(x) = f(f(x)) = f(f(f(x))) = f(f(f(f(x)))) = ...... for all values of x
For functions with side effects, idempotency furthermore implies that no additional side effects will be caused after the first application. You can consider the state of the world to be an additional "hidden" parameter to the function if you like.
Note that in a world where you have concurrent actions going on, you may find that operations you thought were idempotent cease to be so (for example, another thread could unset the value of the boolean flag in the example above). Basically whenever you have concurrency and mutable state, you need to think much more carefully about idempotency.
Idempotency is often a useful property in building robust systems. For example, if there is a risk that you may receive a duplicate message from a third party, it is helpful to have the message handler act as an idempotent operation so that the message effect only happens once.
A good example of understanding an idempotent operation might be locking a car with remote key.
log(Car.state) // unlocked
Remote.lock();
log(Car.state) // locked
Remote.lock();
Remote.lock();
Remote.lock();
log(Car.state) // locked
lock is an idempotent operation. Even if there are some side effect each time you run lock, like blinking, the car is still in the same locked state, no matter how many times you run lock operation.
An idempotent operation produces the result in the same state even if you call it more than once, provided you pass in the same parameters.
An idempotent operation is an operation, action, or request that can be applied multiple times without changing the result, i.e. the state of the system, beyond the initial application.
EXAMPLES (WEB APP CONTEXT):
IDEMPOTENT:
Making multiple identical requests has the same effect as making a single request. A message in an email messaging system is opened and marked as "opened" in the database. One can open the message many times but this repeated action will only ever result in that message being in the "opened" state. This is an idempotent operation. The first time one PUTs an update to a resource using information that does not match the resource (the state of the system), the state of the system will change as the resource is updated. If one PUTs the same update to a resource repeatedly then the information in the update will match the information already in the system upon every PUT, and no change to the state of the system will occur. Repeated PUTs with the same information are idempotent: the first PUT may change the state of the system, subsequent PUTs should not.
NON-IDEMPOTENT:
If an operation always causes a change in state, like POSTing the same message to a user over and over, resulting in a new message sent and stored in the database every time, we say that the operation is NON-IDEMPOTENT.
NULLIPOTENT:
If an operation has no side effects, like purely displaying information on a web page without any change in a database (in other words you are only reading the database), we say the operation is NULLIPOTENT. All GETs should be nullipotent.
When talking about the state of the system we are obviously ignoring hopefully harmless and inevitable effects like logging and diagnostics.
Just wanted to throw out a real use case that demonstrates idempotence. In JavaScript, say you are defining a bunch of model classes (as in MVC model). The way this is often implemented is functionally equivalent to something like this (basic example):
function model(name) {
function Model() {
this.name = name;
}
return Model;
}
You could then define new classes like this:
var User = model('user');
var Article = model('article');
But if you were to try to get the User class via model('user'), from somewhere else in the code, it would fail:
var User = model('user');
// ... then somewhere else in the code (in a different scope)
var User = model('user');
Those two User constructors would be different. That is,
model('user') !== model('user');
To make it idempotent, you would just add some sort of caching mechanism, like this:
var collection = {};
function model(name) {
if (collection[name])
return collection[name];
function Model() {
this.name = name;
}
collection[name] = Model;
return Model;
}
By adding caching, every time you did model('user') it will be the same object, and so it's idempotent. So:
model('user') === model('user');
Quite a detailed and technical answers. Just adding a simple definition.
Idempotent = Re-runnable
For example,
Create operation in itself is not guaranteed to run without error if executed more than once.
But if there is an operation CreateOrUpdate then it states re-runnability (Idempotency).
Idempotent Operations: Operations that have no side-effects if executed multiple times.
Example: An operation that retrieves values from a data resource and say, prints it
Non-Idempotent Operations: Operations that would cause some harm if executed multiple times. (As they change some values or states)
Example: An operation that withdraws from a bank account
It is any operation that every nth result will result in an output matching the value of the 1st result. For instance the absolute value of -1 is 1. The absolute value of the absolute value of -1 is 1. The absolute value of the absolute value of absolute value of -1 is 1. And so on. See also: When would be a really silly time to use recursion?
An idempotent operation over a set leaves its members unchanged when applied one or more times.
It can be a unary operation like absolute(x) where x belongs to a set of positive integers. Here absolute(absolute(x)) = x.
It can be a binary operation like union of a set with itself would always return the same set.
cheers
In short, Idempotent operations means that the operation will not result in different results no matter how many times you operate the idempotent operations.
For example, according to the definition of the spec of HTTP, GET, HEAD, PUT, and DELETE are idempotent operations; however POST and PATCH are not. That's why sometimes POST is replaced by PUT.
An operation is said to be idempotent if executing it multiple times is equivalent to executing it once.
For eg: setting volume to 20.
No matter how many times the volume of TV is set to 20, end result will be that volume is 20. Even if a process executes the operation 50/100 times or more, at the end of the process the volume will be 20.
Counter example: increasing the volume by 1. If a process executes this operation 50 times, at the end volume will be initial Volume + 50 and if a process executes the operation 100 times, at the end volume will be initial Volume + 100. As you can clearly see that the end result varies based upon how many times the operation was executed. Hence, we can conclude that this operation is NOT idempotent.
I have highlighted the end result in bold.
If you think in terms of programming, let's say that I have an operation in which a function f takes foo as the input and the output of f is set to foo back. If at the end of the process (that executes this operation 50/100 times or more), my foo variable holds the value that it did when the operation was executed only ONCE, then the operation is idempotent, otherwise NOT.
foo = <some random value here, let's say -2>
{ foo = f( foo ) } curly brackets outline the operation
if f returns the square of the input then the operation is NOT idempotent. Because foo at the end will be (-2) raised to the power (number of times operation is executed)
if f returns the absolute of the input then the operation is idempotent because no matter how many multiple times the operation is executed foo will be abs(-2).
Here, end result is defined as the final value of variable foo.
In mathematical sense, idempotence has a slightly different meaning of:
f(f(....f(x))) = f(x)
here output of f(x) is passed as input to f again which doesn't need to be the case always with programming.
my 5c:
In integration and networking the idempotency is very important.
Several examples from real-life:
Imagine, we deliver data to the target system. Data delivered by a sequence of messages.
1. What would happen if the sequence is mixed in channel? (As network packages always do :) ). If the target system is idempotent, the result will not be different. If the target system depends of the right order in the sequence, we have to implement resequencer on the target site, which would restore the right order.
2. What would happen if there are the message duplicates? If the channel of target system does not acknowledge timely, the source system (or channel itself) usually sends another copy of the message. As a result we can have duplicate message on the target system side.
If the target system is idempotent, it takes care of it and result will not be different.
If the target system is not idempotent, we have to implement deduplicator on the target system side of the channel.
For a workflow manager (as Apache Airflow) if an idempotency operation fails in your pipeline the system can retry the task automatically without affecting the system. Even if the logs change, that is good because you can see the incident.
The most important in this case is that your system can retry the task that failed and doesn't mess up the pipeline (e.g. appending the same data in a table each retry)
Let's say the client makes a request to "IstanceA" service which process the request, passes it to DB, and shuts down before sending the response. since the client does not see that it was processed and it will retry the same request. Load balancer will forward the request to another service instance, "InstanceB", which will make the same change on the same DB item.
We should use idempotent tokens. When a client sends a request to a service, it should have some kind of request-id that can be saved in DB to show that we have already executed the request. if the client retries the request, "InstanceB" will check the requestId. Since that particular request already has been executed, it will not make any change to the DB item. Those kinds of requests are called idempotent requests. So we send the same request multiple times, but we won't make any change