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CORBA vs. the Fallacies of Distributed Computing

Michael Whittaker
April 17, 2023


Service Weaver is a programming framework for writing distributed applications in Go. A Service Weaver application is implemented as a set of actor-like entities called components. A component implements a set of methods, and components interact by calling these methods. When a Service Weaver application is deployed, some components are co-located in the same process, and others are run in separate processes, potentially on separate machines. As a result, some method calls are performed locally, and some are performed remotely.

This paradigm might sound familiar to you. It's similar to the distributed object model used by CORBA, DCOM, Java RMI, and countless other frameworks that have come and gone. It is widely believed that these frameworks failed because they attempted to make remote method calls look and act like local method calls. There are whole papers explaining that frameworks trying to unify local and remote objects are "doomed to failure". Does this mean that Service Weaver is doomed to fail just like CORBA?


The hypothesis that CORBA failed because method calls looked like local calls but behaved like remote calls is incorrect. CORBA actually failed for different reasons, which we explain below. Many successful systems have made remote calls look local (e.g., TensorFlow, Microsoft Orleans). We hope for Service Weaver to join this list of successful systems as it gets adopted.

In this blog post, we explain that remote method calls in Service Weaver do look like local calls, but they don't act like them. We'll also explain why CORBA failed for unrelated reasons.

Looking Local

When we say a remote method call looks like a local method call, we mean they syntactically look the same. This is contrast to the calls' semantics, or the way they act, which we discuss in the next section.

Typically, remote method calls are syntactically pronounced. When you issue an RPC with gRPC, for example, you have to provide protocol buffers as arguments, making it clear that the arguments are being serialized and sent over the network. Some frameworks add additional syntax to remote calls even when it is otherwise not needed. Ray, for example, requires that a remote call to function f(...) be written as f.remote(...).

# An invocation of the remote function square in Ray. Note the remote keyword.
futures = [square.remote(i) for i in range(4)]

However, placing a syntactic burden on developers when making remote method calls is unnecessary. Even A Note on Distributed Computing, the paper that argues the unification of local and remote objects is "doomed to failure", does "not argue against pleasant programming interfaces."

Some people think that remote method calls require syntactic adornment so that developers can tell when a remote call is happening. However, any syntactic marker is inadequate, as remote method calls are usually performed within functions, and the callers of these functions do not see the syntactic marker that is "required" to tell when a remote method call is happening. For example, is the following key-value store Put local or remote?

// Is this key-value store Put local or remote?
resp, err := cli.Put(ctx, "sample_key", "sample_value")

Syntactically, it's impossible to tell. This example was taken from the etcd client documentation. Only after digging into the implementation of Put will you find that etcd implements Put as a gRPC RPC under the covers:

var resp *pb.PutResponse
r := &pb.PutRequest{Key: op.key, Value: op.val, Lease: int64(op.leaseID), PrevKv: op.prevKV, IgnoreValue: op.ignoreValue, IgnoreLease: op.ignoreLease}
resp, err = kv.remote.Put(ctx, r, kv.callOpts...)
if err == nil {
    return OpResponse{put: (*PutResponse)(resp)}, nil

SQL queries are similar. The following query could be performed locally against a SQLite database or globally against a Spanner database. It's not the syntax of the program, but rather the overall context and semantics that you use to understand how the program executes.

SELECT pid, pname
FROM parts
WHERE color == 'blue'

With Service Weaver, remote method calls are syntactically identical to local method calls. They do look the same, and that's okay.

// A remote method call in Service Weaver.
sum, err := adder.Add(ctx, 1, 2)

Acting Local

When we say a remote method call acts like a local method call, we mean they have the same semantics. This is where frameworks can get into trouble. The fallacies of distributed computing explain that remote method calls have fundamentally different semantics than local method calls. For example:

Because of these fundamental semantic differences, trying to make a remote method call act like a local method call will not end well. Take distributed shared memory, for example. The idea behind distributed shared memory is that a regular single-machine program can be run, without modification, across multiple machines. The program's low-level memory loads and stores are translated to execute against remote memory. Distributed shared memory never took off because hiding the latency and partial failures of remote memory accesses is extremely challenging.

Or consider NFS hard mounts, an example taken from A Note on Distributed Computing. If an operation on a hard mounted NFS volume fails, the NFS client tries to mask the failure by retrying the operation indefinitely until it succeeds. This means that if any NFS server fails, all clients freeze until it is repaired.

While Service Weaver remote method calls look like local method calls, they do not act like them.

Yes, these are a burden on the programmer, but that is precisely the point of A Note on Distributed Computing; it is a mistake to try to isolate the programmer from the fundamental issues of remote calls. We are also working on extending our unit test framework to automatically inject latency and failures to further ensure that Service Weaver applications are resilient to the failures and delays they will encounter in a real deployment.

Why Did CORBA Fail?

Contrary to popular belief, CORBA did not fail because of an inherent flaw with the distributed object paradigm. Michi Henning, co-author of the book Advanced CORBA Programming with C++, wrote an article for ACM Queue in 2006 titled The Rise and Fall of CORBA that documents why CORBA failed. Henning explains that CORBA failed because it was plagued with technical and bureaucratic issues. For example, CORBA had unnecessarily complex APIs, subtle language portability issues, a bloated wire protocol, unsecure and unencrypted network traffic, no versioning support, etc. It was these issues that sank CORBA, not the distributed object paradigm. In fact, projects like TensorFlow, Pathways, Ray, Microsoft Orleans, and Akka prove that similar paradigms can be implemented with incredible success.