We are using spray-cache (can't move to akka-http yet) to cache results from a downstream service we are calling. The effect we want is, if the data is more than 15 minutes old, do the call, otherwise return the cached data.
Our problem is that, if the service call fails, spray-cache will remove the entry from the cache. What we need is to return the old cached data (even if it's stale), and retry the downstream request when the next request comes in.
It looks like Spray does not ship with a default cache implementation that does what you want. According to the spray-caching docs there are two implementations to the Cache trait: SimpleLruCache and ExpiringLruCache.
What you want is a Cache that distinguishes entry expiration (removal of the entry from the cache) from entry refresh (fetching or calculating a more recent copy of the entry).
Since both default implementations merge these two concepts into a single timeout value I think your best bet will be a write a new Cache implementation that distinguishes refresh from expiration.
Related
REST POST is used to create resources.
Let's say we have resource url
"http://example.com/cars"
We want to create a new car.
We POST to "http://example.com/cars" with JSON payload containing car properties (color, weight, model, etc).
Server receives the request, creates a new car, sends a response over the network.
At this point network fails (let's say router stops working properly and ignores every packet).
Client fails with TCP timeout (like 90 seconds).
Client has no idea whether car was created or not.
Also client haven't received car resource id, so it can't GET it to check if it was created.
Now what?
How do you handle this?
You can't simply retry creating, because retrying will just create a duplicate (which is bad).
REST POST is used to create resources.
HTTP POST is used for lots of things. REST doesn't particularly care; it just wants resources that support a uniform interface, and hypermedia.
At this point network fails
Bummer!
Now what? How do you handle this? You can't simply retry creating, because retrying will just create a duplicate (which is bad).
This is a general messaging concern, not directly related to REST. The most common solution is to use the Idempotent Receiver pattern. In short, you
need to define your messages so that the receiver has enough information to recognize the request as something that has already been done.
Ideally, this is being supported at the business level.
Idempotent collections of values are often straight forward; we just need to be thinking sets, rather than lists.
Idempotent collections of entities are trickier; if the request includes an identifier for the new entity, or if we can compute one from the data provided, then we can think of our collection as a hash.
If none of those approaches fits, then there's another possibility. Instead of performing an idempotent mutation of the collection, we make the mutation of the collection itself idempotent. Think "compare and swap" - we encode into the request information that identifies the current state of the collection; is that state is still current when the request arrives, then the mutation is applied. If the condition does not hold, then the request becomes a no-op.
Translating this into HTTP, we make a small modification to the protocol for updating the collection resource. First, we GET the current representation; and in the meta data the server provides validators that can be used in subsquent requests. Having obtained the validator, the client evaluates the current representation of the resource to determine if it needs to be changed. If the client decides to make a change, then submits the change with an If-Match or an If-Unmodified-Since header including the validator. The server, before processing the requests, then considers the validator, immediately abandoning the request with 412 Precondition Failed.
Thus, if a conditional state-changing request is lost, the client can at its own discretion repeat the request without concern that server will misunderstand the client's intent.
Retry it a limited number of times, with increasing delays between the attempts, and make sure the transaction concerned is idempotent.
because retrying will just create a duplicate (which is bad).
It is indeed, and it needs fixing, see above. It should be impossible in your system to create two entries with the same attributes. This is easily accomplished at the database level. You can attain idempotence by having the transaction return the same thing whether the entry already existed or was newly created. Or else just have it return EXISTS if the entry already exists, and adjust your client accordingly.
Need some guidance on best practices for building a RESTful API in node.js
Let's say I have a person record like so:
{
id: 1,
name: 'Jon',
age: 25,
recordVersion: 1
}
If I need to increment the recordVersion every time a value gets changed, would I still use a HTTP PUT to update this record? I've researched on how PUT should be idempotent and should contain the newly-updated representation of the original resource, so I am no sure of what to do.
I could increment the recordVersion property on the first PUT call and send an error on the second PUT call with the same versionNumber of 1 (because it would have incremented to 2 at that point), but does this follow RESTful API standards?
Representation != State
The resources sent over the wire are a representation of the state, not the actual state.
It's perfectly fine to remove the recordVersion and to update it behind the scenes - however if you do that, it would be best to remove it from the representation returned by a GET to that resource as well. To understand why: idempotency is all about what would happen if you applied the operation multiple times in a row (it isn't guaranteed if other operations happen in between...), and about observable side effects.
PUT the data without the version
the data is updated
version code incremented
if you did a GET you would get the data you had PUT (with no version)
PUT the same data again without the version
the data is updated
version code incremented
if you did a GET you would get the same data you had PUT (with no version)
Idempotent, because the resource representation has not changed as a result of calling PUT twice, even though the internal entity state has changed - no observable side effects.
See http://restcookbook.com/HTTP%20Methods/idempotency/ for a bit more detail.
Using version codes to detect conflicts
As you note, you could use inspect the version and throw an error if it has changed - and in fact this is very RESTful, and in my opinion the best way to approach PUT as it helps avoid (often inexplicable) concurrency errors. If you detect this case, it would be appropriate to return a 409 Conflict http status code.
How this would work is:
PUT the data with the version (v1)
the data is updated
version code incremented
if you did a GET you would get the data you had PUT with the new version (v2) (this is a side effect, but it's ok to have a side effect from the first time you do an operation).
PUT the same data again with version (v1)
conflict is detected because v1 != v2
409 Conflict returned
if you did a GET you would get the same as the result of the first operation - the data you originally PUT with the version v2
This is idempotent, because there have been no observable side effects as a result of calling the operation twice.
The client should, in response to a 409, do another GET to get the latest version code, and possibly offer to the user the opportunity to merge their changes with whatever else has changed in the meantime.
Often people confuse idempotency with thinking that the response to the operation must be the same as a result of multiple calls, but that is not the case - it is about there being no observable side effects as a result of multiple sequential calls.
I'd like to do a TTL based memoization with active refresh asynchronously in scala.
ScalaCache example in the documentation allows for TTL based memoization as follows:
import scalacache._
import memoization._
implicit val scalaCache = ScalaCache(new MyCache())
def getUser(id: Int): User = memoize(60 seconds) {
// Do DB lookup here...
User(id, s"user${id}")
}
Curious whether the DB lookup gets triggered after TTL expires for existing value, synchronously and lazily during the next getUser invocation, or if the refresh happens aggressively and asynchronously - even before the next getUser call.
If the ScalaCache implementation is synchronous, is there an alternate library that provides ability to refresh cache actively and asynchronously ?
Expiration and refresh are closely related but different mechanisms. An expired entry is considered stale and cannot be used, so it must be discarded and refetched. An entry eligible for being refreshed means that the content is still valid to use, but the data should be refetched as it may be out of date. Guava provides these TTL policies under the names expireAfterWrite and refreshAfterWrite, which may be used together if the refresh time is smaller than the expiration time.
The design of most caches prefer discarding unused content. An active refresh would require a dedicated thread that reloads entries regardless of whether they have been used. Therefore most caching libraries do not provide active refresh themselves, but make it easy for applications to add that customization on top.
When a read in Guava detects that the entry is eligible for refresh, that caller will perform the operation. All subsequent reads while the refresh is in progress will obtain the current value. This means that to the refresh is performed synchronously on the user's thread that triggered it, and asynchronously to other threads reading that value. A refresh may be fully asynchronous if CacheLoader.reload is overridden to perform the work on an executor.
Caffeine is a rewrite of Guava's cache and differs slightly by always performing the refresh asynchronously to a user's thread. The cache delegates the operation to an executor, by default ForkJoinPool.commonPool which is a JVM-wide executor. The Policy api provides means of inspecting the runtime state of the cache, such as the age of an entry, for adding application-specific custom behavior.
For other ScalaCache backends support is mixed. Ehcache has a RefreshAheadCache decorator that refreshes lazily using its own threadpool. Redis and memcached do not refresh as they are not aware of the system of record. LruMap has expiration support grafted on and does not have any refresh capabilities.
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 have a RESTful web service which represent processes and activities. Each activity is inside one and only one process.
I would like to represent a "move" operation of activity between the process it is currently in and another process.
I've look at forums and found people suggest to use MOVE operation which is not very standard and other suggest to use PUT but then I'm not sure how to tell the difference between PUT that update and PUT that moves which looks semantically wrong.
Any ideas?
One way might be to represent the move itself as, say, a "transfer" resource (transfer as a noun), and POST a new one:
POST /transfer
With an entity containing:
activity: /activities/4
toProcess: /processes/13
This way, clients are creating new "transfers" which, on the server, handle validating and transferring the activity.
This gives you the ability to add information about the transfer, too. If you wanted to keep a history for auditing, you could add a transferredBy property to the resource, or a transferredOn date.
If using PUTs, you can tell the difference by whether the process of the existing entity matches the new one.
PUT /process1/activity2
process: 2
some_data: and_stuff
To which the logical response (if successful) is
303 See Other
Location: /process2/activity2
Given the available answers I'm not really satisfied with the proposals.
POST is an all purpose method that should be used if none of the other operations fit the bill. The semantics of a payload received are defined by the service/API only and may therefore a solution for one API but not for most ones. It further lacks the property of idempotency which in case of a network issue will leave the client in an uncertainty whether the request received the server and only the response got lost mid way or if the request failed to reach the server at all. A consecutive request might therefore lead to unexpected results or further actions required.
PUT has the semantics of replace the current representation obtainable from the resource (may be empty) with the representation provided in the payload. Servers are free to modify the received representation to a more fitting one or to append or remove further data. PUT may even have side effects on other resources as well, i.e. if a versioning mechanism for a document update is provided. While providing the above-mentioned idempotency property, PUT actually does not fit the semantics of the requested action. This might have serious implications on the interoperability as standard HTTP servers wont be able to server you correctly.
One might use a combination of POST to create the new representation on the new endpoint first and afterwards remove the old one via DELETE. However, this are two separate operations where the first one might fail and if not handled correctly lead to an immediate deletion of the original resource in worst case. There is no real transactional behavior in these set of operations unfortunately.
Instead of using the above mentioned operations I'd suggest to use PATCH. PATCH is a serious of changes calculated by the client necessary to transform a current representation to a desiered one. A server supporting PATCH will have to apply these instructions atomically. Either all of them are applied or none of them at all. PATCH can have side effects and is thus the most suitable fit to perform a move in HTTP currently. To properly use this method, however, a certain media-types should be used. One might orientate on JSON Patch (more reader-friendly) i.e., though this only defines the semantics of operations to modify state of JSON based representations and does not deal with multiple resources AFAIK.