Microsoft Orleans Grains Concurrency Handling

Microsoft Orleans Grains Concurrency Handling

Few weeks ago I explained the benefits of using Microsoft Orleans. One of them was the implementation of the actor pattern. Today I will dig deeper into it by revisiting a common scenario present in today systems and how it can be solved with Orleans grains.
This post will be composed by 3 parts:

1 - Traditional system
2 - The problems
3 - Grain solution

1. Traditional system

In a traditional system, we can often see a N-tier architecture (usually 3-tier) composed by the following:

Front end -> Service tier -> Database

The front end being the webserver like ASP NET. The service tier being a set of stateless services containing business logic. They ensure the consistency of the system by extracting the latest state of business object from the database, validate business rules and write back to database ~ the last tier. In this sort of architecture, the database is the source of truth.

This can be illustrated with the following example of a bank account with 4 rules:

1. The user can deposit and withdraw from a bank account.
2. Withdrawal is only permitted if the amount is higher or equal to the balance of the account.
3. Deposit is always allowed.
4. Only the owner can withdraw money.
class BankAccountService
    IStorage _storage;

    public BankAccount(IStorage storage)
        _storage = storage;

    public void SetOwner(Guid accountId, string ownerName)
        var owner = _storage.GetOwner(accountId);
        owner.Name = ownerName;

    public void Deposit(Guid accountId, decimal amount)
        var bankAccount = _storage.GetBankAccount(accountId);
        bankAccount.Balance += amount;

    public void Withdraw(Guid accountId, string ownerName, decimal amount)
        var bankAccount = _storage.GetBankAccount(accountId);
        var owner = _storage.GetOwner(accountId);

        if (bankAccount.Balance < amount)
            throw new ValidationException("Amount withdrawn must be lower or equal to balance.");

        if (owner.Name != ownerName)
            throw new ValidationException("Only the owner is allowed to withdraw.");

        bankAccount.Balance -= amount;

Behind IStorage, the implementation fetch and write to the database.

The second business rule is ensured by doing the following:

  1. The storage hits the db to get the latest status,
  2. then we ensure that there is enough money before we deduct the amount,
  3. then save the account back to database

This service, even though stateless, has a major drawback, it isn’t thread safe. Meaning this service is not guaranteed to yield predictable results if multiple threads call its functions concurrently.

2. The problems

Thread safety is one of the main problem modern system face. How can we allow our system to execute multiple concurrent transactions while keeping a consistent output?

One common scenario, starting from a zero balance, could be if a Deposit happens slightly before a Withdrawal, both running on different threads. There will be 2 possible results:

  • Saving deposit amount happens before withdraw validation is done and the withdraw can be executed
  • Withdraw validation happens before deposit therefore a validation exception is thrown

Without thread safety, it is impossible to predicte the result.

In order to bring back the consistency in the system, a concurrency control must be implemented:

Optimistic concurrency assumes that the changes done on the same resource do not happen frequently therefore instead of locking the resource, it tracks if the resource changed from the time it was read. Depending on the way we handle the result, we can either overwrite the value or just abort the changes. Most ORMs come with OCC build in, when an entity is retrieved from the ORM, it is tracked and when wrote back, it is checked for changes. If we want to ensure that calls come sequentially, we will need to introduce a queuing system.

Pessimistic concurrency control is used when frequent read and write to a resource are required. It is pessimistic in the sense that we assume the worse therefore lock the resource for each transaction. There are two type of locks read and write locks:

  • Write lock are needed for read and write on a particular row, write lock means: I will be changing this row so don't let others read this row until I finish updating it.
  • Read lock are needed to lock the the row for read, read lock means: I need the data in this row to stay the same during my transaction so don't let anyone change it. But you can let others read if they need.

In our example, we will need to implement a locking mechanism on all 3 functions, SetOwner, Deposit and Withdraw. In withdraw we will acquire a write lock on the bank account and a read lock on the owner. With that, we will ensure that no other thread can access the resource while it is in the current transaction.

The main problem with pessimistic concurrency is that it creates contention as locking is invovled. The other main problem is that it is the responsability of the developer to create the complex logic around the locking mechanism.

Therefore three major problems can be seen in a N-tier application:

  1. the state of the application is stored in the database involving two round trips are needed, one to fetch the latest state and a second one to write the state after update
  2. the database is the source of truth making the system highly reliant on the data saved, the fetch is required to prevent validating business rules on stale data
  3. the services contaning the business logic isn’t thread safe forcing developers to implement a complex locking mechanism to handle multi threads

The actor pattern addresses this three problems.

3. Grain solution

In Microsoft Orleans, the implementation of an actor is called a Grain.
Orleans enters into the N-tier by replacing the middle layer and the storage layer by

Front end -> Orleans grains -> Orleans grains storage

Instead of having stateless services, Orleans comes with stateful actors.

There implementation is very similar to the service we implemented earlier. To implement a bank account grain, we would start first by the interface:

interface IBankAccount: IGrainWithGuidKey
    Task SetOwner(string ownerName);
    Task Deposit(decimal amount);
    Task Withdraw(string ownerName, decimal amount);

Then the grain:

class BankAccountService: Grain, IBankAccount
    string _owner;
    decimal _balance;

    public Task SetOwner(string ownerName)
        _owner = ownerName;
        return Task.CompletedTask;

    public Task Deposit(decimal amount)
        _balance += amount;
        return Task.CompletedTask;

    public Task Withdraw(string ownerName, decimal amount)
        if (_balance < amount)

        if (_owner != ownerName)

        _balance -= amount;
        return Task.CompletedTask;

By moving to Orleans grains, the visible benefit we eliminated the first and second problems:

  1. The state is no longer stored in the database, the actor holds the state therefore no trip is needed, the state is always available in memory
  2. The database is no longer the source of truth, the truth is the actor itself which makes the code much closer to OOP as an actor can be seen as an object with behaviours

The last benefit is actually not visible as it is handled by the Orleans runtime:

  1. Grains are thread safe in themselves

All grains are assured to be thread safe as each functions of the grain is assured to be called in sequential order synchronously. It is ensured by the Orleans runtime which queues calls. All grains calls are asynchronous and must return a Task.
It means that if one client calls Deposit and another client few second after calls Withdraw, Deposit will be assured to complete first regardless of when Withdraw is called.


Today we saw the benefit of the actor pattern by moving from a N-tier system to a Microsoft Orleans application.
Thanks to the Orleans grains, we have eliminated the three major problems found in the service tier of a N-tier system. We, developer, do not need to think about concurrency, it is completed abstracted by Orleans runtime. This allows us to focus on business logic and simplify the code in the grain. Hope you liked this post, if you have any question do not hesitate! See you next time!


  1. Hi Kimserey, your blog has helped me answer 'Why should I use Orleans?'. Thank you for that. I thought I had posted a comment earlier but I think I might have missed to publish it so I just wanted to drop few questions that I'm battling with myself regarding Orleans.

    1. As you demonstrated in your blog, Grain is single threaded. While this would resolve thread conflicts, wouldn't this hurt performance eventually? Using the Bank Application example you gave, let's say we received a request of creating thousands of Bank Accounts in which the logic resides inside BankAccount grain. Since grain activation is single threaded, wouldn't this result in slow performance because each call will block other subsequent calls?

    2. Let's decide to decompose the application int to two grains to solve the above issue. Bank and Account grains. Bank grain will call Account grain to create account. But wouldn't this still block each initial Bank grain call until it finishes processing account creation by activating the Account grain?

    What I'm trying to wrap my head around is, even though we can scale out our application in multi node (silos) cluster wouldn't the grain activation distribution be only advantageous in terms of unique grains and not in terms of distributing multiple instances of a grain? I think there would be far more number of grain instances than a number of unique grains (actors) in the application.

    I hope you understand what I'm battling about.

    Thank you!


Post a Comment

Popular posts from this blog

Manage assets and static files with Angular CLI

Prime NG data table for Angular

A complete SignalR with ASP Net Core example with WSS, Authentication, Nginx