Is graphql schema circular reference an anti-pattern? - graphql

graphql schema like this:
type User {
id: ID!
location: Location
}
type Location {
id: ID!
user: User
}
Now, the client sends a graphql query. Theoretically, the User and Location can circular reference each other infinitely.
I think it's an anti-pattern. For my known, there is no middleware or way to limit the nesting depth of query both in graphql and apollo community.
This infinite nesting depth query will cost a lot of resources for my system, like bandwidth, hardware, performance. Not only server-side, but also client-side.
So, if graphql schema allow circular reference, there should be some middlewares or ways to limit the nesting depth of query. Or, add some constraints for the query.
Maybe do not allow circular reference is a better idea?
I prefer to sending another query and doing multiple operations in one query. It's much more simple.
Update
I found this library: https://github.com/slicknode/graphql-query-complexity. If graphql doesn't limit circular reference. This library can protect your application against resource exhaustion and DoS attacks.

It depends.
It's useful to remember that the same solution can be a good pattern in some contexts and an antipattern in others. The value of a solution depends on the context that you use it. — Martin Fowler
It's a valid point that circular references can introduce additional challenges. As you point out, they are a potential security risk in that they enable a malicious user to craft potentially very expensive queries. In my experience, they also make it easier for client teams to inadvertently overfetch data.
On the other hand, circular references allow an added level of flexibility. Running with your example, if we assume the following schema:
type Query {
user(id: ID): User
location(id: ID): Location
}
type User {
id: ID!
location: Location
}
type Location {
id: ID!
user: User
}
it's clear we could potentially make two different queries to fetch effectively the same data:
{
# query 1
user(id: ID) {
id
location {
id
}
}
# query 2
location(id: ID) {
id
user {
id
}
}
}
If the primary consumers of your API are one or more client teams working on the same project, this might not matter much. Your front end needs the data it fetches to be of a particular shape and you can design your schema around those needs. If the client always fetches the user, can get the location that way and doesn't need location information outside that context, it might make sense to only have a user query and omit the user field from the Location type. Even if you need a location query, it might still not make sense to expose a user field on it, depending on your client's needs.
On the flip side, imagine your API is consumed by a larger number of clients. Maybe you support multiple platforms, or multiple apps that do different things but share the same API for accessing your data layer. Or maybe you're exposing a public API designed to let third-party apps integrate with your service or product. In these scenarios, your idea of what a client needs is much blurrier. Suddenly, it's more important to expose a wide variety of ways to query the underlying data to satisfy the needs of both current clients and future ones. The same could be said for an API for a single client whose needs are likely to evolve over time.
It's always possible to "flatten" your schema as you suggest and provide additional queries as opposed to implementing relational fields. However, whether doing so is "simpler" for the client depends on the client. The best approach may be to enable each client to choose the data structure that fits their needs.
As with most architectural decisions, there's a trade-off and the right solution for you may not be the same as for another team.
If you do have circular references, all hope is not lost. Some implementations have built-in controls for limiting query depth. GraphQL.js does not, but there's libraries out there like graphql-depth-limit that do just that. It'd be worthwhile to point out that breadth can be just as large a problem as depth -- regardless of whether you have circular references, you should look into implementing pagination with a max limit when resolving Lists as well to prevent clients from potentially requesting thousands of records at a time.
As #DavidMaze points out, in addition to limiting the depth of client queries, you can also use dataloader to mitigate the cost of repeatedly fetching the same record from your data layer. While dataloader is typically used to batch requests to get around the "n+1 problem" that arises from lazily loading associations, it can also help here. In addition to batching, dataloader also caches the loaded records. That means subsequent loads for the same record (inside the same request) don't hit the db but are fetched from memory instead.

TLDR; Circular references are an anti-pattern for non-rate-limited GraphQL APIs. APIs with rate limiting can safely use them.
Long Answer: Yes, true circular references are an anti-pattern on smaller/simpler APIs ... but when you get to the point of rate-limiting your API you can use that limiting to "kill two birds with one stone".
A perfect example of this was given in one of the other answers: Github's GraphQL API let's you request a repository, with its owner, with their repositories, with their owners ... infinitely ... or so you might think from the schema.
If you look at the API though (https://developer.github.com/v4/object/user/) you'll see their structure isn't directly circular: there are types in-between. For instance, User doesn't reference Repository, it references RepositoryConnection. Now, RepositoryConnection does have a RepositoryEdge, which does have a nodes property of type [Repository] ...
... but when you look at the implementation of the API: https://developer.github.com/v4/guides/resource-limitations/ you'll see that the resolvers behind the types are rate-limited (ie. no more than X nodes per query). This guards both against consumers who request too much (breadth-based issues) and consumers who request infinitely (depth-based issues).
Whenever a user requests a resource on GitHub it can allow circular references because it puts the burden on not letting them be circular onto the consumer. If the consumer fails, the query fails because of the rate-limiting.
This lets responsible users ask for the user, of the repository, owned by the same user ... if they really need that ... as long as they don't keep asking for the repositories owned by the owner of that repository, owned by ...
Thus, GraphQL APIs have two options:
avoid circular references (I think this is the default "best practice")
allow circular references, but limit the total nodes that can be queried per call, so that infinite circles aren't possible
If you don't want to rate-limit, GraphQL's approach of using different types can still give you a clue to a solution.
Let's say you have users and repositories: you need two types for both, a User and UserLink (or UserEdge, UserConnection, UserSummary ... take your pick), and a Repository and RepositoryLink.
Whenever someone requests a user via a root query, you return the User type. But that User type would not have:
repositories: [Repository]
it would have:
repositories: [RepositoryLink]
RepositoryLink would have the same "flat" fields as Repository has, but none of its potentically circular object fields. Instead of owner: User, it would have owner: ID.

The pattern you show is fairly natural for a "graph" and I don't think it's especially discouraged in GraphQL. The GitHub GraphQL API is the thing I often look at when I wonder "how do people build larger GraphQL APIs", and there are routinely object cycles there: a Repository has a RepositoryOwner, which can be a User, which has a list of repositories.
At least graphql-ruby has a control to limit nesting depth. Apollo doesn't obviously have this control, but you might be able to build a custom data source or use the DataLoader library to avoid repeatedly fetching objects you already have.

The above answers provide good theoretical discussion on the question. I would like to add more practical considerations that occur in software development.
As #daniel-rearden points out, a consequence of circular references is that it allows for multiple query documents to retrieve the same data. In my experience, this is a bad practice because it makes client-side caching of GraphQL requests less predictable and more difficult, since a developer would have to explicitly specify that the documents are returning the same data in a different structure.
Furthermore, in unit testing, it is difficult to generate mock data for objects whose fields/properties contain circular references to the parent. (at least in JS/TS; if there are languages that support this easily out-of-the-box, I'd love to hear it in a comment)
Maintenance of a clear data hierarchy seems to be the clear choice for understandable and maintainable schemas. If a reference to a field's parent is frequently needed, it is perhaps best to build a separate query.
Aside: Truthfully, if it were not for the practical consequences of circular references, I would love to use them. It would be beautiful and amazing to represent data structures as a "mathematically perfect" directed graph.

Related

Child service data access to other services with Apollo Federation configuration

We've been using Apollo Federation for about 1.5 years as our main api. Behind the federation gateway are 6 child graphql services which are all combined at the gateway. This configuration really works excellent when you have a result set of data which spans the different services. E.g. A list of tickets which references the user who purchased and event it is associated with it, etc.
One place we have experienced this breaking down is when a pre-set of data is needed which is already defined in another child service (or across other child services) (resolver/path). There is no way (that has been discovered by us) to query the federation from a child service to get a federated set of data for use by a resolver to work with that data.
For example, say we have a graphql query defined which queries all tickets for an event, and through federation returns the purchaser's data, the event's data and the products data. If I need this data set from a resolver, I would need to make all those queries again myself duplicating dataSource logic and having to match up the data in code.
One crazy thought which came up is to setup apollo-datasource-rest dataSource to make queries against our gateway end point as a dataSource for our resolvers. This way we can request the data we need and let Apollo Federation stitch all the data together as it is designed to do. So instead of the resolver querying the database for all the different pieces of data and then matching them up, we would request the data from our graphql gateway where this query is already defined.
What we are trying to avoid by doing this is having a repeated set of queries in child services to get the details which are already available in (or across) other services.
The question
Is this a really bad idea?
Is it a plausible idea?
Has anyone tried something like this before?
Yes we would have to ensure that there aren't circular dependencies on the resolvers. In our case I see the "dataSource accessing the gateway" utilized in gathering initial data in mutations.
Example of a federated query. In this query, event, allocatedTo, purchasedBy, and product are all types in other services. event is an Event type, allocatedTo and purchasedBy are a Profile type, and product is a Product type. This query provides me with all the data I would use to say, send an email notification to the people in the result set. Though to get this data from a resolver in a mutation to queue up those emails means I need to make many queries and align all the data through code myself instead of using the Gateway/federation which does this already with the already established query. The thought around using apollo-datasource-rest to query our own gateway is get at this data in this form. Not through separate queries and code to align id's etc.
query getRegisteredUsers($eventId: ID!) {
communications {
event(eventId: $eventId) {
registered {
event {
name
}
isAllocated,
hasCheckedIn,
lastUpdatedAt,
allocatedTo {
firstName
lastName
email
}
purchasedBy {
id
firstName
lastName
}
product {
__typename
...on Ticket {
id
name
}
}
}
}
}
}
FYI, I didn't quite understand the question until I looked at your edits, which had some examples.
Is this a really bad idea?
In my experience, yes. Not as an idea, as you're in good company with other very smart people who have done this.
Is it a plausible idea?
Absolutely it's plausible, but I don't recommend it.
Has anyone tried something like this before?
Yes, but I hope you don't.
Your Question
Having resolvers make requests back to the Gateway:
I do not recommend this. I've seen this happen, and I've personally worked to help companies out of the mess this takes you into. Circular dependencies are going to happen. Latency is just going to skyrocket as you have more and more hops, TLS handshakes, etc. Do orchestration instead. It feels weird to introduce non-GraphQL, but IMO in the end it's way simpler, faster, and more maintainable than where "just talk to the gateway" takes you.
What then?
When you're dealing with some mutations which require data from across multiple data sources to be able to process a single thing (like sending a transaction email to a person), you have some choices. Something that helped me figure this out was the question "how would I have done this before GraphQL?"
Orchestration: you have a single "orchestration service", which takes the mutation and makes calls (preferably non-GraphQL, so REST, gRPC, Lambda?) to the owner services to collect the data. The orchestration layer does NOT own data, but it can speak with the other services. It's like Federation, but for sending the data into the request, instead of into the response.
Choreography: you trigger roughly the same thing, but via an event stream. (doesn't work as well with the request / response model of GraphQL)
CQRS (projections): Copies of database data, used for things like reporting. CQRS is basically "the way you read data doesn't have to be the same as the way you write it", and it allows for things like event-sourced data. If all of your data sources actually share the same database, you don't even need "projections" as much as you would just want a read replica. If you're not at enough scale to do replicas, just skip it and promise never to write data that your current domain doesn't own.
What I Do
Where I work, I have gotten us to:
Queries
queries always start with "one database call".
if the "one database call" goes to one domain of data (most often true), that query goes into one service, and Federation fills in the leaves of your tree. If you really follow CQRS, this could go the same way as #3, but we don't.
if your "one database call" needs data from across domains (e.g. get all orders with Product X in it, but sorted by the customer's first name), you need a database projection. Preferably this can be handled by a "reporting service": it doesn't OWN any data, but it READS all data.
Mutations
if your top-level mutation modifies acts only within one domain, the mutation goes in a service, it can use database transactions, and Federation fills in the leaves
if your mutation is required to write across multiple domains and requires immediate consistency (placing an order with inventory, payments, etc), we chose orchestration to write across multiple services (and roll-back when necessary, since we don't have database transactions to do it for us).
if your mutation requires data from many places to send further into the request (like sending an email), we chose orchestration to pull from the multiple services and to push that data down. This feels very much like Federation, but in reverse.

Shall I use a DTO or not?

I'm building a web application with Spring, and I'm at the point where I have an Entity, a Repository, a RestController, and I can access endpoints in my browser.
I'm now trying to return JSON data to the browser, and I'm seeing all of this stuff about DTOs in various guides.
Do I really need a DTO? Can't I just put the serialization logic on the entity itself?
I think, this is a little bit debatable question, where the short answer would be:
It depends.
Little longer answer
There are plenty of people, who, in plenty of cases, would prefer one approach (using DTOs) over another (using bare entities), and vice versa; however, there is no the single source of truth on which is better to use.
It very much depends on the requirements, architectural approach you decide to stick with, (even on) personal preference and other (project-related) specific details.
Some even claim that DTO is an anti-pattern; some love using them; some think, that data refinement/adjustment should happen on the consumer/client side (for various reasons, out of which, one can be No Policy for API changes).
That being said, YES, you can simply return the #Entity instance (or list of entities) right from your controller and there is no problem with this approach. I would even say, that this does not necessarily violate something from SOLID or Clean Code principles.again, it depends on what do you use a response for, what representation of data do you need, what should be the capacity and purpose of the object in question, and etc..
DTO is generally a good practice in the following scenarios:
When you want to aggregate the data for your object from different resources, i.e. you want to put some object transformation logic between the Persistence Layer and the Business(or Web) Layer:
Imagine you fetch from your database a List<Employee>; however, from another 3rd party web-service, you also receive some complementary-to-employee data for each Employee object, which you have to aggregate in the Employee objects (aggregate, or do some calculation, or etc. point is that you want to combine the data from different resources). This is a good case when you might want to use DTO pattern. It is reusable, it conforms to Single-Responsibility Principle, and it is well segregated from other layers;
When you don't necessarily combine data received from different sources, but you want to modify the entity which you will be returning:
Imagine you have a very big Entity (with a lot of fields), and the client, which calls the corresponding endpoint (Front-End application, Mobile, or any client), has no need of receiving this huge entity (or list of entities). If you, despite the client's requirement, will still be sending the original/unchanged entity, you will end up consuming network bandwidth/load inefficiently (more than enough), performance will be weaker, and generally, you will be just wasting computing resources for no good reason. In this case, you might want to transform your original Entity to the DTO object, which the client needs (only with required fields). Here, you might even want to implement different DTO classes, for one entity, for different consumers/clients.
However, if you are sure, that your table/relation representations (instances of #Entity classes) are exactly what the client needs, I see no necessity of introducing DTOs.
Supporting further the idea, that #Entity can be returned to the presentation layer without DTO
Java Persistence with Hibernate, Second Edition, in §3.3.2, even motivates it explicitly, that:
You can reuse persistent classes outside the context of persistence, in unit tests or in the presentation layer, for example. You can create instances in any runtime environment with the regular Java new operator, preserving testability and reusability;
Hibernate entities do not need to be explicitly Serializable;
You might also want to have a look at this question.
In general, it’s up to you to decide. If your application is relatively simple and you don’t expose any sensitive information, an response is y ambiguous for the client, there is nothing criminal in returning back the whole entity. If your client expect a small slice of entity, eg only 2-3 fields from 30 fields entity, then it make sense to do the translation or consider different protocol such as GraphQL.
It is ideal design where you should not expose the entity.
It is a good design to convert your entity to DTO before you pass the same to web layer.
These days RestJpacontrollers are also available.
But again it all varies from application to application which one to use.
If your application does a need only read only operation then make sense to use RestJpacontrollers and can use entity at web layer.
In other case where application modifies data frequently then in that case better option to opt DTO and use it at the UI layer.
Another case is of multiple requests are required to bring data for a particular task. In the same case data to be brought can be combined in a DTO so that only one request can bring all the required data.
We can use data of multiple entities data into one DTO.
This DTO can be used for the front end or in the rest API.
Do I really need a DTO? Can't I just put the serialization logic on the entity itself?
I'd say you don't, but it is better to use them, according to SOLID principles, namely single responsibility one. Entities are ORM should be used to interact with database, not being serialized and passed to the other layers.

Am I misusing GraphQL if I must decompose REST data, then re-aggregate it?

We are considering using GraphQL on top of a REST service (using the
FHIR standard for medical records).
I understand that the pattern with GraphQL is to aggregate the results
of multiple, independent resolvers into the final result. But a
FHIR-compliant REST server offers batch endpoints that already aggregate
data. Sometimes we’ll need à la carte data—a patient’s age or address
only, for example. But quite often, we’ll need most or all of the data
available about a particular patient.
So although we can get that kind of plenary data from a single REST call
that knits together multiple associations, it seems we will need to
fetch it piecewise to do things the GraphQL way.
An optimization could be to eager load and memoize all the associated
data anytime any resolver asks for any data. In some cases this would be
appropriate while in other cases it would be serious overkill. But
discerning when it would be overkill seems impossible given that
resolvers should be independent. Also, it seems bloody-minded to undo
and then redo something that the REST service is already perfectly
capable of doing efficiently.
So—
Is GraphQL the wrong tool when it sits on top of a REST API that can
efficiently aggregate data?
If GraphQL is the right tool in this situation, is eager-loading and
memoization of associated data appropriate?
If eager-loading and memoization is not the right solution, is there
an alternative way to take advantage of the REST service’s ability
to aggregate data?
My question is different from
this
question and
this
question because neither touches on how to take advantage of another
service’s ability to aggregate data.
An alternative approach would be to parse the request inside the resolver for a particular query. The fourth parameter passed to a resolver is an object containing extensive information about the request, including the selection set. You could then await the batched request to your API endpoint based on the requested fields, and finally return the result of the REST call, and let your lower level resolvers handle parsing it into the shape the data was requested in.
Parsing the info object can be a PITA, although there's libraries out there for that, at least in the Node ecosystem.

Micro Services and noSQL - Best practice to enrich data in micro service architecture

I want to plan a solution that manages enriched data in my architecture.
To be more clear, I have dozens of micro services.
let's say - Country, Building, Floor, Worker.
All running over a separate NoSql data store.
When I get the data from the worker service I want to present also the floor name (the worker is working on), the building name and country name.
Solution1.
Client will query all microservices.
Problem - multiple requests and making the client be aware of the structure.
I know multiple requests shouldn't bother me but I believe that returning a json describing the entity in one single call is better.
Solution 2.
Create an orchestration that retrieves the data from multiple services.
Problem - if the data (entity names, for example) is not stored in the same document in the DB it is very hard to sort and filter by these fields.
Solution 3.
Before saving the entity, e.g. worker, call all the other services and fill the relative data (Building Name, Country name).
Problem - when the building name is changed, it doesn't reflect in the worker service.
solution 4.
(This is the best one I can come up with).
Create a process that subscribes to a broker and receives all entities change.
For each entity it updates all the relavent entities.
When an entity changes, let's say building name changes, it updates all the documents that hold the building name.
Problem:
Each service has to know what can be updated.
When a trailing update happens it shouldnt update the broker again (recursive update), so this can complicate to the microservices.
solution 5.
Keeping everything normalized. Fileter and sort in ElasticSearch.
Problem: keeping normalized data in ES is too expensive performance-wise
One thing I saw Netflix do (which i like) is create intermediary services for stuff like this. So maybe a new intermediary service that can call the other services to gather all the data then create the unified output with the Country, Building, Floor, Worker.
You can even go one step further and try to come up with a scheme for providing as input which resources you want to include in the output.
So I guess this closely matches your solution 2. I notice that you mention for solution 2 that there are concerns with sorting/filtering in the DB's. I think that if you are using NoSQL then it has to be for a reason, and more often then not the reason is for performance. I think if this was done wrong then yeah you will have problems but if all the appropriate fields that are searchable are properly keyed and indexed (as #Roman Susi mentioned in his bullet points 1 and 2) then I don't see this as being a problem. Yeah this service will only be as fast as the culmination of your other services and data stores, so they have to be fast.
Now you keep your individual microservices as they are, keep the client calling one service, and encapsulate the complexity of merging the data into this new service.
This is the video that I saw this in (https://www.youtube.com/watch?v=StCrm572aEs)... its a long video but very informative.
It is hard to advice on the Solution N level, but certain problems can be avoided by the following advices:
Use globally unique identifiers for entities. For example, by assigning key values some kind of URI.
The global ids also simplify updates, because you track what has actually changed, the name or the entity. (entity has one-to-one relation with global URI)
CAP theorem says you can choose only two from CAP. Do you want a CA architecture? Or CP? Or maybe AP? This will strongly affect the way you distribute data.
For "sort and filter" there is MapReduce approach, which can distribute the load of figuring out those things.
Think carefully about the balance of normalization / denormalization. If your services operate on URIs, you can have a service which turns URIs to labels (names, descriptions, etc), but you do not need to keep the redundant information everywhere and update it. Do not do preliminary optimization, but try to keep data normalized as long as possible. This way, worker may not even need the building name but it's global id. And the microservice looks up the metadata from another microservice.
In other words, minimize the number of keys, shared between services, as part of separation of concerns.
Focus on the underlying model, not the JSON to and from. Right modelling of the data in your system(s) gains you more than saving JSON calls.
As for NoSQL, take a look at Riak database: it has adjustable CAP properties, IIRC. Even if you do not use it as such, reading it's documentation may help to come up with suitable architecture for your distributed microservices system. (Of course, this applies if you have essentially parallel system)
First of all, thanks for your question. It is similar to Main Problem Of Document DBs: how to sort collection by field from another collection? I have my own answer for that so i'll try to comment all your solutions:
Solution 1: It is good if client wants to work with Countries/Building/Floors independently. But, it does not solve problem you mentioned in Solution 2 - sorting 10k workers by building gonna be slow
Solution 2: Similar to Solution 1 if all client wants is a list enriched workers without knowing how to combine it from multiple pieces
Solution 3: As you said, unacceptable because of inconsistent data.
Solution 4: Gonna be working, most of the time. But:
Huge data duplication. If you have 20 entities, you are going to have x20 data.
Large complexity. 20 entities -> 20 different procedures to update related data
High cohesion. All your services must know each other. Data model change will propagate to every service because of update procedures
Questionable eventual consistency. It can be done so data will be consistent after failures but it is not going to be easy
Solution 5: Kind of answer :-)
But - you do not want everything. Keep separated services that serve separated entities and build other services on top of them.
If client wants enriched data - build service that returns enriched data, as in Solution 2.
If client wants to display list of enriched data with filtering and sorting - build a service that provides enriched data with filtering and sorting capability! Likely, implementation of such service will contain ES instance that contains cached and indexed data from lower-level services. Point here is that ES does not have to contain everything or be shared between every service - it is up to you to decide better balance between performance and infrastructure resources.
This is a case where Linked Data can help you.
Basically the Floor attribute for the worker would be an URI (a link) to the floor itself. And Any other linked data should be expressed as URIs as well.
Modeled with some JSON-LD it would look like this:
worker = {
'#id': '/workers/87373',
name: 'John',
floor: {
'#id': '/floors/123'
}
}
floor = {
'#id': '/floor/123',
'level': 12,
building: { '#id': '/buildings/87' }
}
building = {
'#id': '/buildings/87',
name: 'John's home',
city: { '#id': '/cities/908' }
}
This way all the client has to do is append the BASE URL (like api.example.com) to the #id and make a simple GET call.
To remove the extra calls burden from the client (in case it's a slow mobile device), we use the gateway pattern with micro-services. The gateway can expand those links with very little effort and augment the return object. It can also do multiple calls in parallel.
So the gateway will make a GET /floor/123 call and replace the floor object on the worker with the reply.

REST - complex applications

I'm struggling to apply RESTful principles to a new web application I'm working on. In particular, it's the idea that to be RESTful, each HTTP request should carry enough information by itself for its recipient to process it to be in complete harmony with the stateless nature of HTTP.
The application allows users to search for medications. The search accepts filters as input, for example, return discontinued medicines, include complimentary therapy etc..etc. In total there are around 30 filters that can be applied.
Additionally, patient details can be entered including the patients age, gender, current medications etc.
To be Restful, should all this information be included with every request? This seems to place a huge overhead on the network. Also, wouldn't the restrictions on URL length, at least for GET, make this unfeasible?
The "Filter As Resource" is a perfect tact for this.
You can PUT the filter definition to the filter resource, and it can return the filter ID.
PUT is idempotent, so even if the filter is already there, you just need to detect that you've seen the filter before, so you can return the proper ID for the filter.
Then, you can add a filter parameter to your other requests, and they can grab the filter to use for the queries.
GET /medications?filter=1234&page=4&pagesize=20
I would run the raw filters through some sort of canonicalization process, just to have a normalized set, so that, e.g. filter "firstname=Bob lastname=Eubanks" is identical to "lastname=Eubanks firstname=Bob". That's just me though.
The only real concern is that, as time goes on, you may need to obsolete some filters. You can simply error out the request should someone make a request with a missing or obsolete filter.
Edit answering question...
Let's start with the fundamentals.
Simply, you want to specify a filter for use in queries, but these filters are (potentially) involved and complicated. If it was simple /medications/1234, this wouldn't be a problem.
Effectively, you always need to send the filter to the query. The question is how to represent that filter.
The fundamental issue with things like sessions in REST systems is that they're typically managed "out of band". When you, say, go and create a medication, you PUT or POST to the medications resource, and you get a reference back to that medication.
With a session, you would (typically) get back a cookie, or perhaps some other token to represent that session. If your PUT to the medications resource created a session also, then, in truth, your request created two resources: a medication, and a session.
Unfortunately, when you use something like a cookie, and you require that cookie for your request, the resource name is no longer the true representation of the resource. Now it's the resource name (the URL), and the cookie.
So, if I do a GET on the resource named /medications/search, and the cookie represents a session, and that session happens to have a filter in it, you can see how in effect, that resource name, /medications/search, isn't really useful at all. I don't have all of the information I need to make effective use, because of the side effect of the cookie and the session and the filter therein.
Now, you could perhaps rewrite the name: /medications/search?session=ABC123, effectively embedding the cookie in the resource name.
But now you run in to the typical contract of sessions, notably that they're short lived. So, that named resource is less useful, long term, not useless, just less useful. Right now, this query gives me interesting data. Tomorrow? Probably not. I'll get some nasty error about the session being gone.
The other problem is that sessions typically are not managed as a resource. For example, they're usually a side effect, vs explicitly managed via GET/PUT/DELETE. Sessions are also the "garbage heap" of web app state. In this case, we're just kind of hoping that the session is properly populated with what is needed for this request. We actually don't really know. Again, it's a side effect.
Now, let's turn it on its head a little bit. Let's use /medications/search?filter=ABC123.
Obviously, casually, this looks identical. We just changed the name from 'session' to 'filter'. But, as discussed, Filters, in this case, ARE a "first class resource". They need to be created, managed, etc. the same as a medication, a JPEG, or any other resource in your system. This is the key distinction.
Certainly, you could treat "sessions" as a first class resource, creating them, putting stuff in them directly, etc. But you can see how, at least from a clarity point of view, a "first class" session isn't really a good abstraction for this case. Using a session, its like going to the cleaners and handing over your entire purse or briefcase. "Yea, the ticket is in there somewhere, dig out what you want, give me my clothes", especially compared to something explicit like a filter.
So, you can see how, at 30,000 feet, there's not a lot of difference in the case between a filter and a session. But when you zoom in, they're quite different.
With the filter resource, you can choose to make them a persistent thing forever and ever. You can expire them, you can do whatever you want. Sessions tend to have pre-conceived semantics: short live, duration of the connection, etc. Filters can have any semantics you want. They're completely separate from what comes with a session.
If I were doing this, how would I work with filters?
I would assume that I really don't care about the content of a filter. Specifically, I doubt I would ever query for "all filters that search by first name". At this juncture, it seems like uninteresting information, so I won't design around it.
Next, I would normalize the filters, like I mentioned above. Make sure that equivalent filters truly are equivalent. You can do this by sorting the expressions, ensuring fieldnames are all uppercase, or whatever.
Then, I would store the filter as an XML or JSON document, whichever is more comfortable/appropriate for the application. I would give each filter a unique key (naturally), but I would also store a hash for the actual document with the filter.
I would do this to be able to quickly find if the filter is already stored. Since I'm normalizing it, I "know" that the XML (say) for logically equivalent filters would be identical. So, when someone goes to PUT, or insert a new filter, I would do a check on the hash to see if it has been stored before. I may well get back more than one (hashes can collide, of course), so I'll need to check the actual XML payloads to see whether they match.
If the filters match, I return a reference to the existing filter. If not, I'd create a new one and return that.
I also would not allow a filter UPDATE/POST. Since I'm handing out references to these filters, I would make them immutable so the references can remain valid. If I wanted a filter by "role", say, the "get all expire medications filter", then I would create a "named filter" resource that associates a name with a filter instance, so that the actual filter data can change but the name remain the same.
Mind, also, that during creation, you're in a race condition (two requests trying to make the same filter), so you would have to account for that. If your system has a high filter volume, this could be a potential bottleneck.
Hope this clarifies the issue for you.
To be Restful, should all this information be included with every request?
No. If it looks like your server is sending (or receiving) too much information, chances are that there are one or more resources you haven't yet identified.
The first and most important step in designing a RESTful system is to identify and name your resources. How would you do that for your system?
From your description, here's one possible set of resources:
User - a user of the system (maybe a doctor or patient (?) - Role might need to be exposed as a resource here)
Medication - the stuff in the bottle, but it also might represent the kind of bottle (quantity and contents), or it might represent a particular bottle - depending on if you're a pharmacy or just a help desk.
Disease - the condition for which a Patient might want to take a Medication.
Patient - a person who might take a Medication
Recommendation - a Medication that might be beneficial to a Patient based on a Disease they suffer from.
Then you could look for relationships among resources;
User has and belongs to many Roles
Medication has and belongs to many Diseases
Disease has many Recommendations.
Patient has and belongs to many Medications and Diseases (poor chap)
Patient has many Recommendations
Recommendation has one Patient and has one Disease
The specifics are probably not right for your particular problem, but the idea is simple: create a network of relationships among your resources.
At this point it might be helpful to think about URI structure, although keep in mind that REST APIs must be hypertext-driven:
# view all Recommendations for the patient
GET http://server.com/patients/{patient}/recommendations
# view all Recommendations for a Medication
GET http://servier.com/medications/{medication}/recommendations
# add a new Recommendation for a Patient
PUT http://server.com/patients/{patient}/recommendations
Because this is REST, you'll spend most of your time defining the media types used to transfer representations of your resources between client and server.
By exposing more resources, you can cut down on the amount of data that needs to be transferred during each request. Also notice there are no query parameters in the URIs. The server can be as stateful as it needs to be to keep track of it all, and each request can be fully self-contained.
REST is for APIs, not (typical) applications. Don't try to wedge a fundamentally stateful interaction into a stateless model just because you read about it on wikipedia.
To be Restful, should all this information be included with every request? This seems to place a huge overhead on the network. Also, wouldn't the restrictions on URL length, at least for GET, make this unfeasible?
The size of parameters is usually insignificant compared to the size of resources the server sends. If you're using such large parameters that they are a network burden, place them on the server once and then use them as resources.
There are no significant restrictions on URL length -- if your server has such a limit, upgrade it. It's probably years old and chock-full of security vulnerabilities anyway.
No all of that does not have to be in every request.
Each resource (medication, patient history, etc) should have a canonical URI that uniquely identifies it. In some applications (eg, Rails-based ones) this will be something like "/patients/1234" or "/drugs/5678" but the URL format is unimportant.
A client that has previously obtained the URI for a resource (such as from a search, or from a link embedded in another resource) can retrieve it using this URI.
Are you working on a RESTful API that other apps will use to search your data? Or are you building a end-user focused web application where users will log in and perform these searches?
If your users are logging in, then you're already stateful as you'll have some type of session cookie to maintain the logged in state. I would go ahead and create a session object that contains all the search filters. If a user hasn't set any filters, then this object will be empty.
Here's a great blog post about using GET vs POST. It mentions a URL length limit set by Internet Explorer of 2,048 characters, so you want to use POST for long requests.
http://carsonified.com/blog/dev/the-definitive-guide-to-get-vs-post/

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