Is there a RESTful way to determine whether a POST (or any other non-idempotent verb) will succeed? This would seem to be useful in cases where you essentially need to do multiple idempotent requests against different services, any of which might fail. It would be nice if these requests could be done in a "transaction" (i.e. with support for rollback), but since this is impossible, an alternative is to check whether each of the requests will succeed before actually performing them.
For example suppose I'm building an ecommerce system that allows people to buy t-shirts with custom text printed on them, and this system requires integrating with two different services: a t-shirt printing service, and a payment service. Each of these has a RESTful API, and either might fail. (e.g. the printing company might refuse to print certain words on a t-shirt, say, and the bank might complain if the credit card has expired.) Is there any way to speculatively perform these two requests, so my system will only proceed with them if both requests appear valid?
If not, can this problem be solved in a different way? Creating a resource via a POST with status = pending, and changing this to status = complete if all requests succeed? (DELETE is more tricky...)
HTTP defines the 202 status code for exactly your scenario:
202 Accepted
The request has been accepted for processing, but the processing has not been completed. The request might or might not eventually be acted upon, as it might be disallowed when processing actually takes place. There is no facility for re-sending a status code from an asynchronous operation such as this.
The 202 response is intentionally non-committal. Its purpose is to allow a server to accept a request for some other process (perhaps a batch-oriented process that is only run once per day) without requiring that the user agent's connection to the server persist until the process is completed. The entity returned with this response SHOULD include an indication of the request's current status and either a pointer to a status monitor or some estimate of when the user can expect the request to be fulfilled.
Source: HTTP 1.1 Status Code Definition
This is similar to 201 Created, except that you are indicating that the request has not been completed and the entity has not yet been created. Your response would contain a URL to the resource representing the "order request", so clients can check the status of the order through this URL.
To answer your question more directly: There is no way to "test" whether a request will succeed before you make it, because you're asking for clairvoyance.
It's not possible to foresee the range of technical problems that could occur when you attempt to make a request in the future. The network may be unavailable, the server may not be able to access its database or external systems it depends on for functioning, there may be a power-cut and the server is offline, a stray neutrino could wander into your memory and bump a 0 to a 1 causing a catastrophic kernel fault.
In order to consume a remote service you need to account for possible failures of any request in isolation of any other processes.
For your specific problem, if the services have no transactional safety, you can't bake any in there and you have to deal with this in a more real-world way. A few options off the top of my head:
Get the T-Shirt company to give you a "test" mechanism, so you can see whether they'll process any given order without actually placing it. It could be that placing an order with them is a two-phase operation, where you construct the order in the first phase (at which time they validate its creation) and then you subsequently ask the order to be processed (after you have taken payment successfully).
Take the credit-card payment first and move your order into a "paid" state. Then attempt to fulfil the order with the T-Shirt service as an asynchronous process. If fulfilment fails and you can identify that the customer tried to get something printed the company is not prepared to produce, you will have to contact them to change their order or produce a refund.
Most organizations will adopt the second approach, due to its technical simplicity and reduced risk to the business. It also has the benefit of being able to cope with the T-Shirt service not being available; the asynchronous process simply waits until the service is available and completes the order at that time.
Exactly. That can be done as you suggest in your last sentence. The idea would be to decopule resource creation (that will always work unless network failures) that represents an "ongoing request" of the "order acceptation", that can be later decided. As POST returns a "Location" header, you can then retrieve in any moment the "status" of your request.
At some point it may become either accepted or rejected. This may be intantaneous or it may take some time, so you have to design your service with these restrictions (i.e. allowing the client to check if his/her order is accepted, or running some kind of hourly/daily service that collect accepted requests).
Related
I have an endpoint in my api that supports writes. The resource in question is collaborative, so it is reasonable to expect that there will be parallel write requests arriving concurrently.
If the number of writes is small, then this is relatively straight forward to do with a simple lambda - read the current state, compute the new state, compare and swap, spin until the swap succeeds or until we give up. In either case, we compute the appropriate http response and return it to the caller.
If the API is successful, then eventually the waste of conflicting writes becomes expensive enough to address.
It looks as though the natural response is to copy the requests into a queue, with a function that consumes batches; within each batch, we process the requests in sequence, storing the new write, and computing the appropriate response to the request.
What are the options for getting those computed responses copied into the http responses, and what are the trade offs to be be considered?
My sense is that in handling the http request, after (synchronously) enqueue the message, I need to block/poll on something that will eventually be populated with the response to the request.
I'm not sure if this will count an an answer, but I do not agree that the natural response is to copy/queue/block; that feels like you're just trading optimistic concurrency control for a kind of pessimistic one (and you'd probably have an easier time just implementing a lock using e.g. Redis - not to mention there are other issues with Lambda itself that would make the approach you describe even more difficult).
Users probably do not want an API like this as it would have high latency.
In my opinion an API that is well designed for collaborate modification of some shared state has higher order constructs that make the API successful: thinking of a conversation as an example, you would decompose the chat in to individual messages, where each message is in reply to some other message; the concurrent modification to the conversation is append-only for the most part (you might allow a user to edit an individual message but that's not a point of resource contention) and you might do things like count the number of messages within the conversation asynchronously such that it is eventually consistent.
You can look at the domain of your API and see if there's a way to expose modification to it in such a way that reduces contention by making modifications target sub-entities (even if the API represents this as a single resource, the storage engine does not have to).
Another option is looking in to a model like event sourcing, where the changes themselves are literally appended and you derive the state from some snapshot plus recent changes.
Let's assume we host two microservices: RealEstate and Candidate.
The RealEstate service is responsible for managing rental properties, landlords and so forth.
The Candidate service provides commands to apply for a rental property.
There would be a CandidateForRentalProperty command which requires the RentalPropertyId and all necessary Candidate information.
Now the crucial point: Different types of RentalPropertys require a different set of Candidate information.
Therefore the commands and aggregates got splitten up:
Commands: CandidateForParkingLot, CandidateForFlat, and so forth.
Aggregates: ParkingLotCandidature, FlatCandidature, and so forth.
The UI asks the read model to decide which command has to be called.
It's reasonable for me to validate the Candidate information and all the business logic involved with that in the Candidate domain layer, but leave out validation whether the correct command got called based on the given RentalPropertyId. Reason: Multiple aggregates are involved in this validation.
The microservice should be autonomous and it's read model consumes events from the RealEstate domain, hence it's not guaranteed to be up to date. We don't want to reject candidates based on that but rather use eventual consistency.
Yes, this could lead to inept Candidate information used for a certain kind of RentalProperty. Someone could just call the CandidateForFlat command with a parking lot rental property id.
But how do we handle the cases in which this happens?
The RealEstate domain does not know anything about Candidates.
Would there be an event handler which checks if there is something wrong and execute an appropriate command to compensate?
On the other hand, this "mapping" is domain logic and I'd like to accomodate it in the domain layer. But I don't know who's accountable for this kind of compensating measures. Would the Candidate aggregate be informed, like IneptApplicationTypeUsed or something like that?
As an aside - commands are usually imperative verbs. ApplyForFlat might be a better spelling than CandidateForFlat.
The pattern you are probably looking for here is that of an exception report; when the candidate service matches a CandidateForFlat message with a ParkingLot identifier, then the candidate service emits as an output a message saying "hey, we've got a problem here".
If a follow up message fixes the problem -- the candidate service gets an updated message that fixes the identifier in the CandidateForFlat message, or the candidate service gets an update from real estate announcing that the identifier actually points to a Flat, then the candidate service can emit another message "never mind, the problem has been fixed"
I tend to find in this pattern that the input commands to the service are really all just variations of handle(Event); the user submitted, the http request arrived; the only question is whether or not the microservice chooses to track that event. In other words, the "command" stream is just another logical event source that the microservice is subscribed to.
As you said, validation of commands should be performed at the point of command generation - at client side - where read models are available.
Command processing is performed by aggregate, so it cannot and should not check validity or existence of other aggregates. So it should trust a command issuer.
If commands comes from an untrusted environment like public API, then your API gateway becomes a client, and it should have necessary read models to validate references.
If you want to accept a command fast and check it later, then log events like ClientAppliedForParkingLot, and have a Saga/Process manager handle further workflow by keeping its internal state, and issuing commands like AcceptApplication or RejectApplication.
I understand the need for validation but I don't think the example you gave calls for cross-Aggregate (or cross-microservice for that matter) compensating measures as stated in the Q title.
Verifications like checking that the ID the client gave along with the flat rental command matches a flat and not a parking lot, that the client has permission to do that, and so forth, are legitimate. But letting the client create such commands in the wild and waiting for an external actor to come around and enforce these rules seems subpar because the rules could be made intrinsic properties of the object originating the process.
So what I'd recommend is to change the entry point into the operation - to create the Candidature Aggregate Root as part of another Aggregate Root's behavior. If that other Aggregate (RentalProperty in our case) lives in another Bounded Context/microservice, you can maintain a list of RentalProperties in the Candidate Bounded Context with just the amount of info needed, and initiate the Candidature from there.
So you would have
FlatCandidatureHandler ==loads==> RentalProperty ==creates==> FlatCandidature
or
FlatCandidatureHandler ==checks existence==> local RentalProperty data
==creates==> FlatCandidature
As a side note, what could actually necessitate compensating actions are factors extrinsic to the root object of the process. For instance, if the property becomes unavailable in the mean time. Then whatever Aggregate holds that information should emit an event when that happens and the compensation should be initiated.
I am learning to develop microservices using DDD, CQRS, and ES. It is HTTP RESTful service. The microservices is about online shop. There are several domains like products, orders, suppliers, customers, and so on. The domains built in separate services. How to do the validation if the command payload relates to other domains?
For example, here is the addOrderItemCommand payload in the order service (command-side).
{
"customerId": "CUST111",
"productId": "SKU222",
"orderId":"SO333"
}
How to validate the command above? How to know that the customer is really exists in database (query-side customer service) and still active? How to know that the product is exists in database and the status of the product is published? How to know whether the customer eligible to get the promo price from the related product?
Is it ok to call API directly (like point-to-point / ajax / request promise) to validate this payload in order command-side service? But I think, the performance will get worse if the API called directly just for validation. Because, we have developed an event processor outside the command-service that listen from the event and apply the event to the materalized view.
Thank you.
As there are more than one bounded contexts that need to be queried for the validation to pass you need to consider eventual consistency. That being said, there is always a chance that the process as a whole can be in an invalid state for a "small" amount of time. For example, the user could be deactivated after the command is accepted and before the order is shipped. An online shop is a complex system and exceptions could appear in any of its subsystems. However, being implemented as an event-driven system helps; every time the ordering process enters an invalid state you can take compensatory actions/commands. For example, if the user is deactivated in the meantime you can cancel all its standing orders, release the reserved products, announce the potential customers that have those products in the wishlist that they are not available and so on.
There are many kinds of validation in DDD but I follow the general rule that the validation should be done as early as possible but without compromising data consistency. So, in order to be early you could query the readmodel to reject the commands that couldn't possible be valid and in order for the system to be consistent you need to make another check just before the order is shipped.
Now let's talk about your specific questions:
How to know that the customer is really exists in database (query-side customer service) and still active?
You can query the readmodel to verify that the user exists and it is still active. You should do this as a command that comes from an invalid user is a strong indication of some kind of attack and you don't want those kind of commands passing through your system. However, even if a command passes this check, it does not necessarily mean that the order will be shipped as other exceptions could be raised in between.
How to know that the product is exists in database and the status of the product is published?
Again, you can query the readmodel in order to notify the user that the product is not available at the moment. Or, depending on your business, you could allow the command to pass if you know that those products will be available in less than 24 hours based on some previous statistics (for example you know that TV sets arrive daily in your stock). Or you could let the customer choose whether it waits or not. In this case, if the products are not in stock at the final phase of the ordering (the shipping) you notify the customer that the products are not in stock anymore.
How to know whether the customer eligible to get the promo price from the related product?
You will probably have to query another bounded context like Promotions BC to check this. This depends on how promotions are validated/used.
Is it ok to call API directly (like point-to-point / ajax / request promise) to validate this payload in order command-side service? But I think, the performance will get worse if the API called directly just for validation.
This depends on how resilient you want your system to be and how fast you want to reject invalid commands.
Synchronous call are simpler to implement but they lead to a less resilient system (you should be aware of cascade failures and use technics like circuit breaker to stop them).
Asynchronous (i.e. using events) calls are harder to implement but make you system more resilient. In order to have async calls, the ordering system can subscribe to other systems for events and maintain a private state that can be queried for validation purposes as the commands arrive. In this way, the ordering system continues to work even of the link to inventory or customer management systems are down.
In any case, it really depends on your business and none of us can tell you exaclty what to do.
As always everything depends on the specifics of the domain but as a general principle cross domain validation should be done via the read model.
In this case, I would maintain a read model within each microservice for use in validation. Of course, that brings with it the question of eventual consistency.
How you handle that should come from your understanding of the domain. Factors such as the length of the eventual consistency compared to the frequency of updates should be considered. The cost of getting it wrong for the business compared to the cost of development to minimise the problem. In many cases, just recording the fact there has been a problem is more than adequate for the business.
I have a blog post dedicated to validation which you can find here: How To Validate Commands in a CQRS Application
Using Event-machine and Ruby. Currently I'm making a game were at the end of the turn it checks if other user there. When sending data to the user using ws.send() how can I check if the user actually got the data or is alternative solution?
As the library doesn't provide you with access to the underlying protocol elements, you need to add elements to your application protocol to do this. A typical approach is to add an identifier to each message and response to messages with acknowledgement messages that contain those identifiers.
Note that such an approach will only help you to have a better idea of what has been received by a client. There is no assurance of particular state in the case of errors. An example would be losing a connection after the client as sent an ACK, but the service has not received it.
As a result of the complexity I just mentioned, it is often easier to try to make most operations idempotent - that is able to be replayed without detriment to the system, and to replay readily during/after error conditions. You may additionally find a way to periodically synchronize the relevant state entirely, to avoid the long term continuation of minor errors introduced by loss of data/a connection.
I have a set of resources whose representations are lazily created. The computation to construct these representations can take anywhere from a few milliseconds to a few hours, depending on server load, the specific resource, and the phase of the moon.
The first GET request received for the resource starts the computation on the server. If the computation completes within a few seconds, the computed representation is returned. Otherwise, a 202 "Accepted" status code is returned, and the client must poll the resource until the final representation is available.
The reason for this behavior is the following: If a result is available within a few seconds, it needs to be retrieved as soon as possible; otherwise, when it becomes available is not important.
Due to limited memory and the sheer volume of requests, neither NIO nor long polling is an option (i.e. I can't keep nearly enough connections open, nor even can I even fit all of the requests in memory; once "a few seconds" have passed, I persist the excess requests). Likewise, client limitations are such that they cannot handle a completion callback, instead. Finally, note I'm not interested in creating a "factory" resource that one POSTs to, as the extra roundtrips mean we fail the piecewise realtime constraint more than is desired (moreover, it's extra complexity; also, this is a resource that would benefit from caching).
I imagine there is some controversy over returning a 202 "Accepted" status code in response to a GET request, seeing as I've never seen it in practice, and its most intuitive use is in response to unsafe methods, but I've never found anything specifically discouraging it. Moreover, am I not preserving both safety and idempotency?
So, what do folks think about this approach?
EDIT: I should mention this is for a so-called business web API--not for browsers.
If it's for a well-defined and -documented API, 202 sounds exactly right for what's happening.
If it's for the public Internet, I would be too worried about client compatibility. I've seen so many if (status == 200) hard-coded.... In that case, I would return a 200.
Also, the RFC makes no indication that using 202 for a GET request is wrong, while it makes clear distinctions in other code descriptions (e.g. 200).
The request has been accepted for processing, but the processing has not been completed.
We did this for a recent application, a client (custom application, not a browser) POST'ed a query and the server would return 202 with a URI to the "job" being posted - the client would use that URI to poll for the result - this seems to fit nicely with what was being done.
The most important thing here is anyway to document how your service/API works, and what a response of 202 means.
From what I can recall - GET is supposed to return a resource without modifying the server. Maybe activity will be logged or what have you, but the request should be rerunnable with the same result.
POST on the other hand is a request to change the state of something on the server. Insert a record, delete a record, run a job, something like that. 202 would be appropriate for a POST that returned but isn't finished, but not really a GET request.
It's all very puritan and not well practiced in the wild, so you're probably safe by returning 202. GET should return 200. POST can return 200 if it finished or 202 if it's not done.
http://www.w3.org/Protocols/rfc2616/rfc2616-sec10.html
In case of a resource that is supposed to have a representation of an entity that is clearly specified by an ID (as opposed to a "factory" resource, as described in the question), I recommend staying with the GET method and, in a situation when the entity/representation is not available because of lazy-creation or any other temporary situation, use the 503 Service Unavailable response code that is more appropriate and was actually designed for situations like this one.
Reasoning for this can be found in the RFCs for HTTP itself (please verify the description of the 503 response code), as well as on numerous other resources.
Please compare with HTTP status code for temporarily unavailable pages. Although that question is about a different use case, it actually relates to the exact same feature of HTTP.