I am building a microservice that has two separate services: a user service and a comments service. The user service stores the user details like email, first/last name job title, etc, and the comments service stores all comments made by the user.
In the UI, I need to populate the comments (via a REST API) and show the first/last name, email, and job title of the user.
Is it recommended that we store all these user details in the comments database?
If yes, then every time a user changes their details first/last name or job title then I will have to update their details in all the comments (I don't think this is a good idea )
If no, then if I store just the userid in the comments DB, how am I supposed to get the user details for each comment? Let's say we want to show 20 comments per page in the UI.
First, challenge architecture. Let's assume that the both services in the question are part of a larger ecosystem of microservices that all make use of the user information. Else separation will most certainly be overengineered. But from the word "comments" we can at least guess that there is at least one other class of objects, that is the things being commented. So let's assume a "user service" is a meaningful crumb to break out into a microservice, because at least some other crumbs get the necessary weight to justify the microservice breakup.
In that case I suggest the following strategy:
Second, implement an abstraction layer into your comments service right away so that most of the code will not have to care about where the user comes from (i.e. don't join or $lookup). This is also a great opportunity for local testing, because you can just create a collection with the data you need and run service level integration tests against it.
Third, for integration with the user service, get the data from there via API (which should support bulk data selection in any case) every time you need it. Because you have the abstraction layer, you can add caching, cache timeout and displacement strategies and whatever you may need below this abstraction without caring in the main portion of the code. Add such on an as needed basis. Keep it simple.
Fourth, when things really go heavyweight and you have to care with tens of thousands of users, tons of comments and many requests per second the comments service could, still below the abstraction, implement an upfront replication pattern to get the full user database locally. This will usually be done based on an asynchronous message being sent by the user service to all subscribers when something changes in te user base. When it suits the subscribers (i.e. the comment service), they can trigger full or (from time to time) delta replication of the changes. Suitable collections will be already in place from what you did for caching. And it will probably be considerably less info you need in the comments service, than is stored in user service (let alone the hashed password, other login options or accounting information).
Fifth, should you still hit performance challenges, you can break the abstraction for the few cases you need to and do the join or $lookup.
Follow the steps in order, and stop as soon as the overall assembly works fine. Every step adds considerable complexity, and when you don't need it, don't implement it.
This is a question related to designing command handling with Axon 4.
Let say I've a domain that model the concept of a Payment.
The actual payment will be done by an external Partner. I want to track it in my system via the following events: a Payment Request Was Issued followed by either
Partner Agreed the Payment or Partner Declined the Payment.
Every events issued by the command should be enrolled in the same database transaction.
What would be the best practice to actually call my partner in Axon 4 ?
Here's what I've done so far:
Have one command named RequestPaymentCommand
This command will be handled by a Payment Aggregate like this:
do some checks
apply the event PaymentRequestWasIssued
and then, call the external partner and given the result it will apply either PaymentAccepted or PaymentRefused
In this answer from stackoverflow, it is said that
All the data that you need to apply the event should normally be available in the command
With this statement in mind, I understand that I should create as much Commands as Events ? But In this case, what is the point of all theses commands ? Should I end up with something like:
My command RequestPaymentCommand will generate the PaymentRequestWasIssued event.
Then from somewhere I call my partner and then send another command (how to name it ?) that will generate the event given the result from the partner ?
The actual payment will be done by an external Partner
This means that your application is not the source of truth and it should not try to behave like one. This means that it should only observe what is happening in the remote system and possible react to remote events. To "observe" could mean to duplicate/copy the remote events in local databases, without modifications, just for cache reasons or for display reasons. Your system should not directly give other interpretations to these events, other than those given by their source.
After the remote events are copied locally, your system could react to them. This could mean that a Saga, after receives the Partner Agreed the Payment it sends a UnlockFeature command to a local Aggregate (see DDD).
With this statement in mind, I understand that I should create as much Commands as Events ? But In this case, what is the point of all theses commands ?
This is an indication that those are not your events: you should not emit them from your code; in the worst case you store them and react to them (in a Saga/Process manager). This means that you should discover the local business processes and model them as such: they react to events by sending commands.
I need some guidance on how to properly build out a system that will be able to scale. I will give you some information about what I am trying to do and then ask my specific question.
I have a site where I want visitors to send some data to be processed. They input the data into a textarea or upload it in a file. Simple. The data is somewhat preprocessed on the client side before a POST request is made to a REST endpoint.
What I am stuck on is what is a good way to take this posted data store it and then associate an id with it that references the user since I cannot process the data fast enough for it to be returned to the user in a reasonable amount of time?
This question is a bit vague and open to opinion, I admit it. I just need a push in the right direction to keep moving. What I have been considering is throwing the data into a message queue and then having some workers process the data elsewhere and when the data is processed alert the user as to where to find it with some sort of link to an S3 bucket or just a URL to a file. The other idea was to just run the request for each item to be processed against another end-point that already processes individual records in some sort of loop client side. The problem is as follows with this idea:
To process the data it may take somewhere from 30 minutes to 2 hours depending upon the amount that they want processed. It's not ideal for them to just sit there and wait for that to finish depending on the amount of records they need processed, so I have ruled this out mostly.
Any guidance would be very much appreciated as I don't have any coworkers to bounce things off of, nor do I know many people with the domain knowledge that I could freely ask. If this isn't the right place to ask this, could you point me in the right direction as to where it should be asked?
Chris
If I've got you right, your pipeline is:
Accept item from user
Possibly preprocess/validate it (?)
Put into some queue
Process data
Return result.
You man use one or several queues on stage (3). Entity from user gets added to one of the queues. If it's big enough, it could be stored in S3 or storage alike, and only info about it put into the queue: link, add date, user id (or email of alike). Processors can pull items from queue and give feedback to users.
If you have no strict requirements on order, things get much simpler: you don't need any sync between them. Treat all the components: upload acceptors, queues, storages and processors as independent pools of processes. Monitor each pool separately. If there's some bottlenecks - add machines to that pool.
Let's say we have a User, Wallet REST microservices and an API gateway that glues things together. When Bob registers on our website, our API gateway needs to create a user through the User microservice and a wallet through the Wallet microservice.
Now here are a few scenarios where things could go wrong:
User Bob creation fails: that's OK, we just return an error message to the Bob. We're using SQL transactions so no one ever saw Bob in the system. Everything's good :)
User Bob is created but before our Wallet can be created, our API gateway hard crashes. We now have a User with no wallet (inconsistent data).
User Bob is created and as we are creating the Wallet, the HTTP connection drops. The wallet creation might have succeeded or it might have not.
What solutions are available to prevent this kind of data inconsistency from happening? Are there patterns that allow transactions to span multiple REST requests? I've read the Wikipedia page on Two-phase commit which seems to touch on this issue but I'm not sure how to apply it in practice. This Atomic Distributed Transactions: a RESTful design paper also seems interesting although I haven't read it yet.
Alternatively, I know REST might just not be suited for this use case. Would perhaps the correct way to handle this situation to drop REST entirely and use a different communication protocol like a message queue system? Or should I enforce consistency in my application code (for example, by having a background job that detects inconsistencies and fixes them or by having a "state" attribute on my User model with "creating", "created" values, etc.)?
What doesn't make sense:
distributed transactions with REST services. REST services by definition are stateless, so they should not be participants in a transactional boundary that spans more than one service. Your user registration use case scenario makes sense, but the design with REST microservices to create User and Wallet data is not good.
What will give you headaches:
EJBs with distributed transactions. It's one of those things that work in theory but not in practice. Right now I'm trying to make a distributed transaction work for remote EJBs across JBoss EAP 6.3 instances. We've been talking to RedHat support for weeks, and it didn't work yet.
Two-phase commit solutions in general. I think the 2PC protocol is a great algorithm (many years ago I implemented it in C with RPC). It requires comprehensive fail recovery mechanisms, with retries, state repository, etc. All the complexity is hidden within the transaction framework (ex.: JBoss Arjuna). However, 2PC is not fail proof. There are situations the transaction simply can't complete. Then you need to identify and fix database inconsistencies manually. It may happen once in a million transactions if you're lucky, but it may happen once in every 100 transactions depending on your platform and scenario.
Sagas (Compensating transactions). There's the implementation overhead of creating the compensating operations, and the coordination mechanism to activate compensation at the end. But compensation is not fail proof either. You may still end up with inconsistencies (= some headache).
What's probably the best alternative:
Eventual consistency. Neither ACID-like distributed transactions nor compensating transactions are fail proof, and both may lead to inconsistencies. Eventual consistency is often better than "occasional inconsistency". There are different design solutions, such as:
You may create a more robust solution using asynchronous communication. In your scenario, when Bob registers, the API gateway could send a message to a NewUser queue, and right-away reply to the user saying "You'll receive an email to confirm the account creation." A queue consumer service could process the message, perform the database changes in a single transaction, and send the email to Bob to notify the account creation.
The User microservice creates the user record and a wallet record in the same database. In this case, the wallet store in the User microservice is a replica of the master wallet store only visible to the Wallet microservice. There's a data synchronization mechanism that is trigger-based or kicks in periodically to send data changes (e.g., new wallets) from the replica to the master, and vice-versa.
But what if you need synchronous responses?
Remodel the microservices. If the solution with the queue doesn't work because the service consumer needs a response right away, then I'd rather remodel the User and Wallet functionality to be collocated in the same service (or at least in the same VM to avoid distributed transactions). Yes, it's a step farther from microservices and closer to a monolith, but will save you from some headache.
This is a classic question I was asked during an interview recently How to call multiple web services and still preserve some kind of error handling in the middle of the task. Today, in high performance computing, we avoid two phase commits. I read a paper many years ago about what was called the "Starbuck model" for transactions: Think about the process of ordering, paying, preparing and receiving the coffee you order at Starbuck... I oversimplify things but a two phase commit model would suggest that the whole process would be a single wrapping transaction for all the steps involved until you receive your coffee. However, with this model, all employees would wait and stop working until you get your coffee. You see the picture ?
Instead, the "Starbuck model" is more productive by following the "best effort" model and compensating for errors in the process. First, they make sure that you pay! Then, there are message queues with your order attached to the cup. If something goes wrong in the process, like you did not get your coffee, it is not what you ordered, etc, we enter into the compensation process and we make sure you get what you want or refund you, This is the most efficient model for increased productivity.
Sometimes, starbuck is wasting a coffee but the overall process is efficient. There are other tricks to think when you build your web services like designing them in a way that they can be called any number of times and still provide the same end result. So, my recommendation is:
Don't be too fine when defining your web services (I am not convinced about the micro-service hype happening these days: too many risks of going too far);
Async increases performance so prefer being async, send notifications by email whenever possible.
Build more intelligent services to make them "recallable" any number of times, processing with an uid or taskid that will follow the order bottom-top until the end, validating business rules in each step;
Use message queues (JMS or others) and divert to error handling processors that will apply operations to "rollback" by applying opposite operations, by the way, working with async order will require some sort of queue to validate the current state of the process, so consider that;
In last resort, (since it may not happen often), put it in a queue for manual processing of errors.
Let's go back with the initial problem that was posted. Create an account and create a wallet and make sure everything was done.
Let's say a web service is called to orchestrate the whole operation.
Pseudo code of the web service would look like this:
Call Account creation microservice, pass it some information and a some unique task id 1.1 Account creation microservice will first check if that account was already created. A task id is associated with the account's record. The microservice detects that the account does not exist so it creates it and stores the task id. NOTE: this service can be called 2000 times, it will always perform the same result. The service answers with a "receipt that contains minimal information to perform an undo operation if required".
Call Wallet creation, giving it the account ID and task id. Let's say a condition is not valid and the wallet creation cannot be performed. The call returns with an error but nothing was created.
The orchestrator is informed of the error. It knows it needs to abort the Account creation but it will not do it itself. It will ask the wallet service to do it by passing its "minimal undo receipt" received at the end of step 1.
The Account service reads the undo receipt and knows how to undo the operation; the undo receipt may even include information about another microservice it could have called itself to do part of the job. In this situation, the undo receipt could contain the Account ID and possibly some extra information required to perform the opposite operation. In our case, to simplify things, let's say is simply delete the account using its account id.
Now, let's say the web service never received the success or failure (in this case) that the Account creation's undo was performed. It will simply call the Account's undo service again. And this service should normaly never fail because its goal is for the account to no longer exist. So it checks if it exists and sees nothing can be done to undo it. So it returns that the operation is a success.
The web service returns to the user that the account could not be created.
This is a synchronous example. We could have managed it in a different way and put the case into a message queue targeted to the help desk if we don't want the system to completly recover the error". I've seen this being performed in a company where not enough hooks could be provided to the back end system to correct situations. The help desk received messages containing what was performed successfully and had enough information to fix things just like our undo receipt could be used for in a fully automated way.
I have performed a search and the microsoft web site has a pattern description for this approach. It is called the compensating transaction pattern:
Compensating transaction pattern
All distributed systems have trouble with transactional consistency. The best way to do this is like you said, have a two-phase commit. Have the wallet and the user be created in a pending state. After it is created, make a separate call to activate the user.
This last call should be safely repeatable (in case your connection drops).
This will necessitate that the last call know about both tables (so that it can be done in a single JDBC transaction).
Alternatively, you might want to think about why you are so worried about a user without a wallet. Do you believe this will cause a problem? If so, maybe having those as separate rest calls are a bad idea. If a user shouldn't exist without a wallet, then you should probably add the wallet to the user (in the original POST call to create the user).
IMHO one of the key aspects of microservices architecture is that the transaction is confined to the individual microservice (Single responsibility principle).
In the current example, the User creation would be an own transaction. User creation would push a USER_CREATED event into an event queue. Wallet service would subscribe to the USER_CREATED event and do the Wallet creation.
If my wallet was just another bunch of records in the same sql database as the user then I would probably place the user and wallet creation code in the same service and handle that using the normal database transaction facilities.
It sounds to me you are asking about what happens when the wallet creation code requires you touch another other system or systems? Id say it all depends on how complex and or risky the creation process is.
If it's just a matter of touching another reliable datastore (say one that can't participate in your sql transactions), then depending on the overall system parameters, I might be willing to risk the vanishingly small chance that second write won't happen. I might do nothing, but raise an exception and deal with the inconsistent data via a compensating transaction or even some ad-hoc method. As I always tell my developers: "if this sort of thing is happening in the app, it won't go unnoticed".
As the complexity and risk of wallet creation increases you must take steps to ameliorate the risks involved. Let's say some of the steps require calling multiple partner apis.
At this point you might introduce a message queue along with the notion of partially constructed users and/or wallets.
A simple and effective strategy for making sure your entities eventually get constructed properly is to have the jobs retry until they succeed, but a lot depends on the use cases for your application.
I would also think long and hard about why I had a failure prone step in my provisioning process.
One simple Solution is you create user using the User Service and use a messaging bus where user service emits its events , and Wallet Service registers on the messaging bus, listens on User Created event and create Wallet for the User. In the mean time , if user goes on Wallet UI to see his Wallet, check if user was just created and show your wallet creation is in progress, please check in some time
What solutions are available to prevent this kind of data inconsistency from happening?
Traditionally, distributed transaction managers are used. A few years ago in the Java EE world you might have created these services as EJBs which were deployed to different nodes and your API gateway would have made remote calls to those EJBs. The application server (if configured correctly) automatically ensures, using two phase commit, that the transaction is either committed or rolled back on each node, so that consistency is guaranteed. But that requires that all the services be deployed on the same type of application server (so that they are compatible) and in reality only ever worked with services deployed by a single company.
Are there patterns that allow transactions to span multiple REST requests?
For SOAP (ok, not REST), there is the WS-AT specification but no service that I have ever had to integrate has support that. For REST, JBoss has something in the pipeline. Otherwise, the "pattern" is to either find a product which you can plug into your architecture, or build your own solution (not recommended).
I have published such a product for Java EE: https://github.com/maxant/genericconnector
According to the paper you reference, there is also the Try-Cancel/Confirm pattern and associated Product from Atomikos.
BPEL Engines handle consistency between remotely deployed services using compensation.
Alternatively, I know REST might just not be suited for this use case. Would perhaps the correct way to handle this situation to drop REST entirely and use a different communication protocol like a message queue system?
There are many ways of "binding" non-transactional resources into a transaction:
As you suggest, you could use a transactional message queue, but it will be asynchronous, so if you depend on the response it becomes messy.
You could write the fact that you need to call the back end services into your database, and then call the back end services using a batch. Again, async, so can get messy.
You could use a business process engine as your API gateway to orchestrate the back end microservices.
You could use remote EJB, as mentioned at the start, since that supports distributed transactions out of the box.
Or should I enforce consistency in my application code (for example, by having a background job that detects inconsistencies and fixes them or by having a "state" attribute on my User model with "creating", "created" values, etc.)?
Playing devils advocate: why build something like that, when there are products which do that for you (see above), and probably do it better than you can, because they are tried and tested?
In micro-services world the communication between services should be either through rest client or messaging queue. There can be two ways to handle the transactions across services depending on how are you communicating between the services. I will personally prefer message driven architecture so that a long transaction should be a non blocking operation for a user.
Lets take you example to explain it :
Create user BOB with event CREATE USER and push the message to a message bus.
Wallet service subscribed to this event can create a wallet corresponding to the user.
The one thing which you have to take care is to select a robust reliable message backbone which can persists the state in case of failure. You can use kafka or rabbitmq for messaging backbone. There will be a delay in execution because of eventual consistency but that can be easily updated through socket notification. A notifications service/task manager framework can be a service which update the state of the transactions through asynchronous mechanism like sockets and can help UI to update show the proper progress.
Personally I like the idea of Micro Services, modules defined by the use cases, but as your question mentions, they have adaptation problems for the classical businesses like banks, insurance, telecom, etc...
Distributed transactions, as many mentioned, is not a good choice, people now going more for eventually consistent systems but I am not sure this will work for banks, insurance, etc....
I wrote a blog about my proposed solution, may be this can help you....
https://mehmetsalgar.wordpress.com/2016/11/05/micro-services-fan-out-transaction-problems-and-solutions-with-spring-bootjboss-and-netflix-eureka/
Eventual consistency is the key here.
One of the services is chosen to become primary handler of the event.
This service will handle the original event with single commit.
Primary handler will take responsibility for asynchronously communicating the secondary effects to other services.
The primary handler will do the orchestration of other services calls.
The commander is in charge of the distributed transaction and takes control. It knows the instruction to be executed and will coordinate executing them. In most scenarios there will just be two instructions, but it can handle multiple instructions.
The commander takes responsibility of guaranteeing the execution of all instructions, and that means retires.
When the commander tries to effect the remote update and doesn’t get a response, it has no retry.
This way the system can be configured to be less prone to failure and it heals itself.
As we have retries we have idempotence.
Idempotence is the property of being able to do something twice such a way that the end results be the same as if it had been done once only.
We need idempotence at the remote service or data source so that, in the case where it receives the instruction more than once, it only processes it once.
Eventual consistency
This solves most of distributed transaction challenges, however we need to consider couple of points here.
Every failed transaction will be followed by a retry, the amount of attempted retries depends on the context.
Consistency is eventual i.e., while the system is out of consistent state during a retry, for example if a customer has ordered a book, and made a payment and then updates the stock quantity. If the stock update operations fail and assuming that was the last stock available, the book will still be available till the retry operation for the stock updating has succeeded. After the retry is successful your system will be consistent.
Why not use API Management (APIM) platform that supports scripting/programming? So, you will be able to build composite service in the APIM without disturbing micro services. I have designed using APIGEE for this purpose.
Premise:
It is recommended that in CQRS+DDD+ES style applications use task based screens, these screens guide the user and capture intent.
These task screens can also be referred to as Inductive User Interface. Some examples of UI design guidelines that can help you create mondern, user-friendly apps:
Microsoft Inductive User Interface Guidelines and,
Index of UX guidelines
The way I understand it, the tasks, generally speaking, should line up with Commands or Functions waiting on the Server.
For example, if the User makes a change to the Customer's [first name], generally speaking this should be an isolated task where a pop-up window or the like provides a mechanism for this event, and this event only.
Questions:
Part-1:
In the situation where the User is not just making a change to a Customer's [first name], but actually creating a new Customer. Surely the User will not go from [first name] => to [last name] => to [address] => to [email], etc. -- in a wizard like style, where each wizard screen maps to a Command.
a) How are the screens laid out when it's just not practical to isolate a single task? For example when creating a new Customer or Inventory Item.
b) What does the code and/or logic flow related to the Commands look like on the Client and Server in this situation, keeping in mind the obvious pull to stay consistent with the "normal" task based flow of the rest of the system? After all, these all just translate to Activities or Events in the Event Source.
Part-2:
What if the User is not just making a change to a Customer's [first name], but their [last name], [address], and [phone number] -- all the while they User is off-line.
I think ultimately, the User should still be able to do real work on multiple tasks in different areas of the application, while off-line, and perform robust conflict resolution when coming back online.
a) What is the code and/or logic flow and/or artifacts related to the Commands on the Client side while the User is off-line while handling these events locally (IndexDb, queues, etc.)? and
b) What does the connection look like and how does it act when off-line (retries)?
c) What is the code and/or logic flow and/or artifacts related to the Commands on the Client and Server side, when the User comes back on-line?
d) What does the connection look like and how does it act when coming back on-line (reestablish of connection, if it is determined that the Client side ViewModel is stale, WebSockets, etc.)?
Reference diagram:
The way I understand it, the tasks, generally speaking, should line up with Commands or Functions waiting on the Server.
Or sometimes events, but the basic idea is right.
Surely the User will not go from [first name] => to [last name] => to [address] => to [email], etc. -- in a wizard like style, where each wizard screen maps to a Command.
No, we usually want a coarser grain than that. Some tasks do only require a single property, but several properties is a common case.
How are the screens laid out when it's just not practical to isolate a single task?
By grouping together cohesive units; consider the Amazon order workflow -- there are actually several different sets of data collected (the order itself, the selection of payment, specifying new methods of payment, specifying the delivery address, specifying the shipping priority....).
all the while they User is off-line.
See CQRS, not just for server systems; but in broad strokes - treat the data collected from the user as events (FormSubmitted) rather than commands. The offline device is the authority for tracking what the user did while off line; but the unavailable server is still the authority for the consequences of those events. So the server is responsible for the merge when the client reconnects.
The precise details might vary from one domain to another -- for instance, in a warehousing system, where the offline device has been collecting information about inventory, you might handle the inconsistencies that the server observes during the merge by raising exception reports (the device registered this package leaving the warehouse, but we have no record of it entering the warehouse).