I have a REST API based on Onion Architecture.
But I have some challenges to apply this way of building a server. Concretely with what should be the behaviour of an Interface if has some data to check before giving structured data to a Usecase.
That is one of my problems:
I have some methods in the Interface that catch info about timers from the request. But I'm facing always the same question. Must I catch all and give to the Usecase and do all checks there, or instead of that, first I have to check if a timer exists in the DB (if i'm updating a timer) and after that do what I need?
This type of checks like Role of who is requesting and what is allowed to do or not, if timers exist, if user exists, if an user already exists and you can't create someone with the same username (I want an unique username restriction) etc, are annoying me because depending on where I'm doing the check, following strictly the Onion Architecture or not, I'm executing more or less code that sometimes is unnecessary.
If I check some things in the Interface, I am avoiding executing code that would be unnecesary. But I'm not following this Architecture correctly, and viceversa.
Any thoughts?
I am using Spring-Boot, Spring-Data/JPA with Hazelcast client/server topology. In parts of my test application, I am calculating time when performing CRUD operations on the client side (the server is the one interacting with a relational db). I configured the map(Store) to be write-behind by setting write-delay-seconds to 10.
Spring-Data's save() returns the persisted entity. In the client app, therefore, the application flow will be blocked until the (server) returns the persisted entity.
Would like to know is there is an alternative in which case the client does NOT have to wait for the entity to persist. Was under the impression that once new data is stored in the Map, persisting to the backed happens asynchronously -> the client app would NOT have to wait.
Map config in hazelast.xml:
<map name="com.foo.MyMap">
<map-store enabled="true" initial-mode="EAGER">
<class-name>com.foo.MyMapStore</class-name>
<write-delay-seconds>10</write-delay-seconds>
</map-store>
</map>
#NeilStevenson I don't find your response particularly helpful. I asked on an earlier post about where and how to generate the Map keys. You pointed me to the documentation which fails to shed any light on this topic. Same goes for the hazelcast (and other) examples.
The point of having the cache in the 1st place, is to avoid hitting the database. When we add data (via save()), we need to also generate an unique key for the Map. This key also becomes the Entity.Id in the database table. Since, again, its the hazelcast client that generates these Ids, there is no need to wait for the record to be persisted in the backend.
The only reason to wait for save() to return the persisted object would be to catch any exceptions NOT because of the ID.
That unfortunately is how it is meant to work, see https://docs.spring.io/spring-data/commons/docs/current/api/org/springframework/data/repository/CrudRepository.html#save-S-.
Potentially the external store mutates the saved entry in some way.
Although you know it won't do this, there isn't a variant on the save defined.
So the answer seems to be this is not currently available in the general purpose Spring repository definition. Why not raise a feature request for the Spring Data team ?
I have been using POST in a REST API to create objects. Every once in a while, the server will create the object, but the client will be disconnected before it receives the 201 Created response. The client only sees a failed POST request, and tries again later, and the server happily creates a duplicate object...
Others must have had this problem, right? But I google around, and everyone just seems to ignore it.
I have 2 solutions:
A) Use PUT instead, and create the (GU)ID on the client.
B) Add a GUID to all objects created on the client, and have the server enforce their UNIQUE-ness.
A doesn't match existing frameworks very well, and B feels like a hack. How does other people solve this, in the real world?
Edit:
With Backbone.js, you can set a GUID as the id when you create an object on the client. When it is saved, Backbone will do a PUT request. Make your REST backend handle PUT to non-existing id's, and you're set.
Another solution that's been proposed for this is POST Once Exactly (POE), in which the server generates single-use POST URIs that, when used more than once, will cause the server to return a 405 response.
The downsides are that 1) the POE draft was allowed to expire without any further progress on standardization, and thus 2) implementing it requires changes to clients to make use of the new POE headers, and extra work by servers to implement the POE semantics.
By googling you can find a few APIs that are using it though.
Another idea I had for solving this problem is that of a conditional POST, which I described and asked for feedback on here.
There seems to be no consensus on the best way to prevent duplicate resource creation in cases where the unique URI generation is unable to be PUT on the client and hence POST is needed.
I always use B -- detection of dups due to whatever problem belongs on the server side.
Detection of duplicates is a kludge, and can get very complicated. Genuine distinct but similar requests can arrive at the same time, perhaps because a network connection is restored. And repeat requests can arrive hours or days apart if a network connection drops out.
All of the discussion of identifiers in the other anwsers is with the goal of giving an error in response to duplicate requests, but this will normally just incite a client to get or generate a new id and try again.
A simple and robust pattern to solve this problem is as follows: Server applications should store all responses to unsafe requests, then, if they see a duplicate request, they can repeat the previous response and do nothing else. Do this for all unsafe requests and you will solve a bunch of thorny problems. Repeat DELETE requests will get the original confirmation, not a 404 error. Repeat POSTS do not create duplicates. Repeated updates do not overwrite subsequent changes etc. etc.
"Duplicate" is determined by an application-level id (that serves just to identify the action, not the underlying resource). This can be either a client-generated GUID or a server-generated sequence number. In this second case, a request-response should be dedicated just to exchanging the id. I like this solution because the dedicated step makes clients think they're getting something precious that they need to look after. If they can generate their own identifiers, they're more likely to put this line inside the loop and every bloody request will have a new id.
Using this scheme, all POSTs are empty, and POST is used only for retrieving an action identifier. All PUTs and DELETEs are fully idempotent: successive requests get the same (stored and replayed) response and cause nothing further to happen. The nicest thing about this pattern is its Kung-Fu (Panda) quality. It takes a weakness: the propensity for clients to repeat a request any time they get an unexpected response, and turns it into a force :-)
I have a little google doc here if any-one cares.
You could try a two step approach. You request an object to be created, which returns a token. Then in a second request, ask for a status using the token. Until the status is requested using the token, you leave it in a "staged" state.
If the client disconnects after the first request, they won't have the token and the object stays "staged" indefinitely or until you remove it with another process.
If the first request succeeds, you have a valid token and you can grab the created object as many times as you want without it recreating anything.
There's no reason why the token can't be the ID of the object in the data store. You can create the object during the first request. The second request really just updates the "staged" field.
Server-issued Identifiers
If you are dealing with the case where it is the server that issues the identifiers, create the object in a temporary, staged state. (This is an inherently non-idempotent operation, so it should be done with POST.) The client then has to do a further operation on it to transfer it from the staged state into the active/preserved state (which might be a PUT of a property of the resource, or a suitable POST to the resource).
Each client ought to be able to GET a list of their resources in the staged state somehow (maybe mixed with other resources) and ought to be able to DELETE resources they've created if they're still just staged. You can also periodically delete staged resources that have been inactive for some time.
You do not need to reveal one client's staged resources to any other client; they need exist globally only after the confirmatory step.
Client-issued Identifiers
The alternative is for the client to issue the identifiers. This is mainly useful where you are modeling something like a filestore, as the names of files are typically significant to user code. In this case, you can use PUT to do the creation of the resource as you can do it all idempotently.
The down-side of this is that clients are able to create IDs, and so you have no control at all over what IDs they use.
There is another variation of this problem. Having a client generate a unique id indicates that we are asking a customer to solve this problem for us. Consider an environment where we have a publicly exposed APIs and have 100s of clients integrating with these APIs. Practically, we have no control over the client code and the correctness of his implementation of uniqueness. Hence, it would probably be better to have intelligence in understanding if a request is a duplicate. One simple approach here would be to calculate and store check-sum of every request based on attributes from a user input, define some time threshold (x mins) and compare every new request from the same client against the ones received in past x mins. If the checksum matches, it could be a duplicate request and add some challenge mechanism for a client to resolve this.
If a client is making two different requests with same parameters within x mins, it might be worth to ensure that this is intentional even if it's coming with a unique request id.
This approach may not be suitable for every use case, however, I think this will be useful for cases where the business impact of executing the second call is high and can potentially cost a customer. Consider a situation of payment processing engine where an intermediate layer ends up in retrying a failed requests OR a customer double clicked resulting in submitting two requests by client layer.
Design
Automatic (without the need to maintain a manual black list)
Memory optimized
Disk optimized
Algorithm [solution 1]
REST arrives with UUID
Web server checks if UUID is in Memory cache black list table (if yes, answer 409)
Server writes the request to DB (if was not filtered by ETS)
DB checks if the UUID is repeated before writing
If yes, answer 409 for the server, and blacklist to Memory Cache and Disk
If not repeated write to DB and answer 200
Algorithm [solution 2]
REST arrives with UUID
Save the UUID in the Memory Cache table (expire for 30 days)
Web server checks if UUID is in Memory Cache black list table [return HTTP 409]
Server writes the request to DB [return HTTP 200]
In solution 2, the threshold to create the Memory Cache blacklist is created ONLY in memory, so DB will never be checked for duplicates. The definition of 'duplication' is "any request that comes into a period of time". We also replicate the Memory Cache table on the disk, so we fill it before starting up the server.
In solution 1, there will be never a duplicate, because we always check in the disk ONLY once before writing, and if it's duplicated, the next roundtrips will be treated by the Memory Cache. This solution is better for Big Query, because requests there are not imdepotents, but it's also less optmized.
HTTP response code for POST when resource already exists
I've seen a fair few articles/posts that recommend not having more than one context per request when using EF.
Is it valid to have a second context for logging purposes such as 'user x did y', 'failed login from z' etc.
The rationale behind this is that I'd like these errors to be logged even if there is an error while using the "main" context, ie. foreign key issues etc.
Is there another way to do this or if I head down this road is there any things to try and avoid?
You can always have more context instances if your application logic really needs them and ability to persist log to database even with invalid data in the main context can be considered as such situation. You just need to ensure that your updates do not run in the same transaction (they must use different DB connection as well) - that should be a default behavior unless you use TransactionScope.
I have a Service layer in my MVC3 app, which plays the role of a Repository among other things, as a layer between my Data layer and the actual web application. I have coded all my GetById methods to be robust, using FirstOrDefault and not just First, because the Id is passed in a URL and cannot be guaranteed to be a valid Id.
I now find myself where I'm doing a FirstOrDefault, then only proceeding if the result is not null. I would like to log the event when it is null, and then proceed to do nothing etc. Now, I am already using Elmah to log unhandled exceptions, and I have very little experience with exception handling etc. in MVC3, but it occurs to me that it might be better for me to use a simple First, with Elmah logging the exception if no entity is found.
How should I approach this scenario, where an invalid Id is quite definitely an logic exception, but not a low level CLR exception? This is not like when somebody is asked to enter an Id and no entity is found for their search term, which is a normal logic result.
Generating exceptions can be expensive. You're initial approach of validating user input is more robust. I would recommend using a logging framework such as NLog (http://nlog-project.org) to log the case were an invalid ID is passed in.
If you would like to keep all of your log messages in Elmah, then you can decide to write directly to Elmah's error log instead of bubbling-up an exception.