THEORY: Distributed Transactions and why you should avoid them (2 Phase Commit , Saga Pattern, TCC, Idempotency etc)

avoid-distributed-transactions.md

Distributed Transactions and why you should avoid them

1. Modern technologies won't support it (RabbitMQ, Kafka, etc.);
2. This is a form of using Inter-Process Communication in a synchronized way and this reduces availability;
3. All participants of the distributed transaction need to be avaiable for a distributed commit, again: reduces availability.

Implementing business transactions that span multiple services is not straightforward. Distributed transactions are best avoided because of the CAP theorem. Moreover, many modern (NoSQL) databases don’t support them. The best solution is to use the Saga Pattern.

[...]

One of the most well-known patterns for distributed transactions is called Saga. The first paper about it was published back in 1987 and has it been a popular solution since then.

There are a couple of different ways to implement a saga transaction, but the two most popular are:

• Events/Choreography: When there is no central coordination, each service produces and listen to other service’s events and decides if an action should be taken or not;
• Command/Orchestration: when a coordinator service is responsible for centralizing the saga’s decision making and sequencing business logic;

Video: Reliable Messaging Without Distributed Transactions

Udi Dahan explains how you can still do reliable messaging even if you can't use (or don't want) distributed transactions (DTC).

Reliable Messaging in Distributed Systems by Aleksei (@fairday)

In this article, we reviewed several approaches for building reliable messaging in distributed systems. There are several recommendations we might consider while building systems with these characteristics

1. Always develop idempotent consumers since network failure is unavoidable.
2. Carefully use the First-Local-Commit-Then-Publish with a clear understanding of guarantee requirements.
3. Never use the First-Publish-Then-Local-Commit approach since it may lead to severe data inconsistency in your system.
4. If existing database choice decision very likely may change or technical strategy implies to select the best storage solution for the problem — don’t build shared libraries by binding to database solutions like CDC.
5. Use the Transactional Outbox approach as a standard solution for achieving at least once guarantees.
6. Consider using the Listen to yourself approach when No-SQL databases are leveraged.

Strict Ordering and Best-Effort Ordering

Tweet do Marcelo Costa no Bluesky:

Regras que adoto:

• Strict ordering: Se a ordem das mensagens for crítica para o sucesso do processamento;
• Best effort ordering: Se vc prioriza desempenho e escalabilidade e pode lidar com reordenação;

Lidar com reordenação —> aplicar alguma técnica de SEDA.
Tem um mundo ai para explorar

Comparação entre Strict Ordering e Best-Effort Ordering

Tweet do Marcelo Costa no Bluesky

Tem uma tentativa de livro que eu e uns amigos estamos tentando publicar que a gente fala sobre isso:

A good example of designing an Outbox(er) component in Go:
Achieving reliable dual writes in distributed systems

In my opinion, they basically implemented a job scheduler under the hood:

The Reactive Principles

These are the foundational principles of implementing distributed systems that make an application Reactive:

1. I. Stay Responsive
2. II. Accept Uncertainty
3. III. Embrace Failure
4. IV. Assert Autonomy
5. V. Tailor Consistency
6. VI. Decouple Time
7. VII. Decouple Space
8. VIII. Handle Dynamics

II. Accept Uncertainty

As soon as we cross the boundary of the local machine, or of the container, we enter a vast and endless ocean of nondeterminism: the world of distributed systems. It is a scary world in which systems can fail in the most spectacular and intricate ways, where information becomes lost, reordered, and corrupted, and where failure detection is a guessing game. It’s a world of uncertainty.

This has a lot of implications: we can’t always trust time as measured by clocks and timestamps, or order (causality new tab might not even exist). Accepting this uncertainty, we have to use strategies to cope with it. For example: rely on logical clocks new tab (such as vector clocks new tab); when appropriate use eventual consistency new tab (e.g. certain NoSQL new tab databases and CRDTs new tab); and make sure our communication protocols are associative new tab (batch-insensitive), commutative new tab (order-insensitive), and idempotent new tab (duplication-insensitive).

The key is to manage uncertainty directly in the application architecture. To design resilient autonomous components that publish their protocols to the world—protocols that clearly define what they can promise, what commands and events will be accepted, and, as a result of that, what behavior will trigger and how the data model should be used. The timeliness and assessed accuracy of underlying information should be visible to other components where appropriate so that they — or the end-user — can judge the reliability of the current system state.

VI. Decouple Time

[...] This is a fragile assumption in the context of distributed systems, where we can’t ensure the availability or reachability of all components in a system at all times. By introducing temporal decoupling in our communication protocols, one component does not need to assume and require the availability of the other components. It makes the components more independent and autonomous and, as a consequence, the overall system more reliable. Popular techniques to implement temporal decoupling include durable message queues, append-only journals, and publish-subscribe topics with a retention duration.

With temporal decoupling, we give the caller the option to perform other work, asynchronously new tab, rather than be blocked waiting on the resource to become available. This can be achieved by allowing the caller to put its request on a queue, register a callback new tab to be notified later, return immediately, and continue execution (e.g., non-blocking I/O new tab). A great way to orchestrate callbacks is to use a Finite State Machine new tab (FSM), other techniques include Futures/Promises new tab, Dataflow Variables, new tab Async/Await new tab, Coroutines new tab, and composition of asynchronous functional combinators new tab in streaming libraries.

💡 Top insights (and one point I don't agree with) about the Gregor Hohpe's article "Event-driven = Loosely coupled? Not so fast!":

TLDR
This is fantastic post from Gregor for reading slowly and deep thinking. It gets to the core of buzzwords such as EDA and Coupling used daily without really analysing direct and second-level consequences.

https://www.linkedin.com/posts/bibryam_i-love-blog-posts-that-make-me-think-gregor-activity-7218910524107878400-ZMy_/

I love blog posts that make me think. Gregor Hohpe hits the nail on the head 100% with this one. Here are the top insights (and one point I don't agree with):

1. Messages vs. Events
   Messaging is an interaction style, whereas "Event" describes the semantics of a message.
   Commands, Events, Documents are all Messages.

2. Messages vs. Channels
   We must delineate which characteristics of an event-driven system derive from the properties of the channel rather than the message intent.
   Message + Channel = Some Decoupling

3. Events vs. Coupling
   This is the core of the article, introduces a unique perspective about coupling for the different interaction styles. We can now explore the Interaction Styles (RPC, P2P, Pub/Sub) with different Coupling types (change propagation).

4. Now vs Later
   I love the second-level effects analysis too

Folks pitching EDA as decoupled may be the ones early in the lifecycle (like vendors) and won't have to live with the consequences of their (coupling) decisions. The more you take advantage of your ability to add recipients, the harder it will become to make a change. Thus, taking advantage of one dimension (add recipients) of decoupling exposes you more to another dimension of coupling (ex: schema changes).

⭐️ 5. What I don't fully agree with Gregor

"Location Coupling propagates location changes of a recipient to a sender (or vice versa). Message channels decouple location changes because they are (mostly) logical constructs... RPC can achieve some decoupling through DNS or active load balancers, but it's much more likely that a change affects the sender."

⭐️ 5.1.
IMO, one of the main reasons for using RPC is the decoupling of service consumer and provider, so they can evolve in isolation but also start/stop, scale, move, independently. Otherwise, there’s no reason to split a monolith into services and in-memory calls should be preferred over RPC.

⭐️ 5.2.
Today, no caller has a hard-coded callee address; it’s almost always a logical name:

• Kubernetes service name, not Pod IP
• Dapr sidecar/Istio proxy, with the logical name of the target service
• AWS API Gateway address, rather than Lambda URL
• Client-side service discovery that looks up the location, still not affecting the client
• IMO, RPC today always involves some kind of proxy

⭐️ 5.3.
In all these cases, the provider can scale based on demand or move without affecting the client. You might say the client needs to know the seed location (K8s service, AWS gateway, sidecar, etc.), but that’s true for the location of the messaging channel too. Even if the topic is a logical name the broker URL is with fixed location.

How Complex Systems Fail

5. Complex systems run in degraded mode.
A corollary to the preceding point is that complex systems run as broken systems. The system continues to function because it contains so many redundancies and because people can make it function, despite the presence of many flaws. [...]

16. Safety is a characteristic of systems and not of their components
Safety is an emergent property of systems; it does not reside in a person, device or department of an organization or system. Safety cannot be purchased or manufactured; it is not a feature that is separate from the other components of the system.

Outbox Pattern - by Unico

• The interesting part is they use Protobuf as a content type when sending events to the broker. Still, for some reason that's unclear in the article, they serialize this Protobuf data into JSON format before persisting it in the outbox table. I guess they do so because they use Debezium under the hood.
• They also use the CloudEvents (v1.0.2) spec for defining the format of event data;

This is the Protobuf message using the CloudEvent spec:

syntax = "proto3";
import "google/protobuf/timestamp.proto";
import "google/protobuf/any.proto";  
message OutboxEvent {
  string specversion = 1;  
  string type = 2;  
  string source = 3;  
  string subject = 4;  
  string id = 5;  
  google.protobuf.Timestamp time = 6;  
  string datacontenttype = 7;  
  string dataschema = 8;  
  google.protobuf.Any data = 9;
}

And this is an example:

{
  "specversion": "1.0",
  "type": "someevent",
  "source": "integration",
  "subject": "1ec07712-79b7-485a-a0e2-0a1c33fd1016",
  "time": "2020-04-30T04:00:00Z",
  "datacontenttype": "application/json",
  "dataschema": "http://<schemapath>",
  "data": {
    "transactionId": "1ec07712-79b7-485a-a0e2-0a1c33fd1016",
    "doc": "123.123.123-00",
    "image_id": "ea02254f-28f4-4b31-99a5-957bb024f78d"
  }
}

Fidelis blog: System Design - Saga Pattern 🇧🇷 - artigo sobre Saga e Outbox Pattern escrito pelo Matheus Fidelis.

Do Caos a Consistência: A Ordem das Mensagens em Sistemas Distribuídos

River's blog: Building an idempotent email API with River unique jobs