The normal MO for creating items in a database is to let the database control the generation of the primary key (id). That's usually true whether you're using auto-incremented integer ids or UUIDs.
I'm building a clientside app (Angular but the tech is irrelevant) that I want to be able to build offline behaviour into. In order to allow allow offline object creation (and association) I need the the client appplication to generate primary keys for new objects. This is both to allow for associations with other objects created offline and also to allow for indempotence (making sure I don't accidentally save the same object to the server twice due to a network issue).
The challenge though is what happens when that object gets sent to the server. Do you use a temporary clientside ID which you then replace with the ID that the server subsequently generates or you use some sort of ID translation layer between the client and the server - this is what Trello did when building their offline functionality.
However, it occurred to me that there may be a third way. I'm using UUIDs for all tables on the back end. And so this made me realise that I could in theory insert a UUID into the back end that was generated on the front end. The whole point of UUIDs is that they're universally unique so the front end doesn't need to know the server state to generate one. In the unlikely event that they do collide then the uniqueness criteria on the server would prevent a duplicate.
Is this a legitimate approach? The risk seems to be 1. Collisions and 2. any form of security that I haven't anticipated. Collisons seem to be taken care of by the way that UUIDs are generated but I can't tell if there are risks in allowing a client to choose the ID of an inserted object.
However, it occurred to me that there may be a third way. I'm using UUIDs for all tables on the back end. And so this made me realise that I could in theory insert a UUID into the back end that was generated on the front end. The whole point of UUIDs is that they're universally unique so the front end doesn't need to know the server state to generate one. In the unlikely event that they do collide then the uniqueness criteria on the server would prevent a duplicate.
Yes, this is fine. Postgres even has a UUID type.
Set the default ID to be a server-generated UUID if the client does not send one.
Collisions.
UUIDs are designed to not collide.
Any form of security that I haven't anticipated.
Avoid UUIDv1 because...
This involves the MAC address of the computer and a time stamp. Note that UUIDs of this kind reveal the identity of the computer that created the identifier and the time at which it did so, which might make it unsuitable for certain security-sensitive applications.
You can instead use uuid_generate_v1mc which obscures the MAC address.
Avoid UUIDv3 because it uses MD5. Use UUIDv5 instead.
UUIDv4 is simplest, it's a 122 bit random number, and built into Postgres (the others are in the commonly available uuid-osp extension). However, it depends on the strength of the random number generator of each client. But even a bad UUIDv4 generator is better than incrementing an integer.
When building distributed systems, it must be ensured the client and the server eventually ends up with consistent view of the data they are operating on, i.e they never get out of sync. Extra care is needed, because network can not be considered reliable. In other words, in the case of network failure, client never knows if the operation was successful, and may decide to retry the call.
Consider a microservice, which exposes simple CRUD API, and unbounded set of clients, maintained in-house by the same team, by different teams and by different companies also.
In the example, client request a creation of new entity, which the microservice successfully creates and persists, but the network fails and client connection times out. The client will most probably retry, unknowingly persisting the same entity second time. Here is one possible solution to this I came up with:
Use client-generated identifier to prevent duplicate post
This could mean the primary key as it is, the half of the client and server -generated composite key, or the token issued by the service. A service would either persist the entity, or reply with OK message in the case the entity with that identifier is already present.
But there is more to this: What if the client gives up after network failure (but entity got persisted), mutates it's internal view of the entity, and later decides to persist it in the service with the same id. At this point and generally, would it be reasonable for the service just silently:
Update the existing entity with the state that client posted
Or should the service answer with some more specific status code about what happened? The point is, developer of the service couldn't really influence the client design solutions.
So, what are some sensible practices to keep the state consistent across distributed systems and avoid most common pitfalls in the case of network and system failure?
There are some things that you can do to minimize the impact of the client-server out-of-sync situation.
The first measure that you can take is to let the client generate the entity IDs, for example by using GUIDs. This prevents the server to generate a new entity every time the client retries a CreateEntityCommand.
In addition, you can make the command handing idempotent. This means that if the server receives a second CreateEntityCommand, it just silently ignores it (i.e. it does not throw an exception). This depends on every use case; some commands cannot be made idempotent (like updateEntity).
Another thing that you can do is to de-duplicate commands. This means that every command that you send to a server must be tagged with an unique ID. This can also be a GUID. When the server receives a command with an ID that it already had processed then it ignores it and gives a positive response (i.e. 200), maybe including some meta-information about the fact that the command was already processed. The command de-duplication can be placed on top of the stack, as a separate layer, independent of the domain (i.e. in front of the Application layer).
Hello Internet Denizens,
I was reading through a nice database design article and the final determination on how to properly generate DB primary keys was ...
So, in reality, the right solution is probably: use UUIDs for keys,
and don’t ever expose them. The external/internal thing is probably
best left to things like friendly-url treatments, and then (as Medium
does) with a hashed value tacked on the end.
That is, use UUIDs for internal purposes like db joins, but use a friendly-url for external purposes (like a REST API).
My question is ... how do you make uniquely identifiable (and friendly) keys for external purposes?
I've used several APIs: Stripe, QuickBooks, Amazon, etc. and it seems like they use straight up sequential IDs for things like customers, report IDs, etc for retrieving information. It makes me wonder if exposing UUIDs as a security risk is a little overblown b/c in theory you should be able to append a where clause to your queries.
SELECT * FROM products where UUID = <supplied uuid> AND owner/role/group/etc = <logged in user>
The follow-up question is: If you expose a primary key, how do people efficiently restrict access to that resource in a database environment? Assign an owner to a db row?
Interested in the design responses.
Potential Relevant Posts for Further Reading:
Should I use UUIDs for resources in my public API?
It is not a good idea to expose your internal ids to the outside. You should either encode them (with some algorithm) or have a look up table.
Also, do not append parameters provided by user (or URL) to your SQL query (UUIDS or not), this is prone to SQL injection. Use parameterized SQL queries for that.
Background:I'm a new developer fresh out of college at a company that uses RPC architectural style for a lot its internal services.They also seem to change which tool they use behind the scenes pretty frequently, so the tight coupling between the client and server implementations in RPC is problematic. I was tasked with rewriting one of the services, and I feel a RESTful api would be a good match because the backing technology can only deal with files anyway, but I have a few questions.My understanding of REST so far is that you break operations up as much as possible and shift the focus to resources, so both the client and the server together make a state machine with the server mainly handling the transitions through hypermedia.Example:say you have a service that takes a file and splits it in two byte-wise.I would design the sequence for this likethe client would POST the file they want split,server splits the fileserver writes both result pieces to a temp folderserver returns that the client should GET and both files URI'sthe client sends a GET for the pieceserver returns the piece and that the client should DELETE the URIthe client sends a DELETE for the URI
and 2 and 3 are done for both pieces.My question is: How do you ensure that the pieces get deleted at the end?a client could just not follow step 3if you combine step 2&3, a malicious (or negligent) client could just stop after step 1but if you combine them all, isn't that just RPC over HTTP?
If the 2 pieces in question are inseparable, then they are in fact just properties of a single resource.
And yes, if a POST/PUT must be followed by a DELETE, then you're probably just trying to shoehorn RPC into a REST-style architecture.
There's no real definition of what "REST" actually is, but if the one thing certain about it is that it MUST be stateless; i.e. every separate request must be self-sufficient - it cannot depend on a previous request, and cannot mandate subsequent requests.
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