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;

Starbucks Does Not Use Two-Phase Commit

Excellent articles from 2004 and 2005 written by Gregor Hohpe, co-creator of the EIP book, about error handling in distributed systems:

• EIP: Starbucks Does Not Use Two-Phase Commit
• Your Coffee Shop Doesn't Use Two-Phase Commit
  • This magazine article is the same as the blog's but it’s more didact and better written;

Error-handling strategies for loosely coupled systems

Basically, both articles say that when we try to write 2 or more systems in a business transaction and one of them fails, we have 4 strategies to do error handling:

1. Write-off: just ignore, do nothing;
2. Retry: try to send the message again. Retry is a plausible option if there’s a realistic chance that the retry will succeed;
3. Compensating Action: try to undo already completed operations to return the system to a consistent state;
4. Transaction coordinator: follow the Two-Phase Commit (2PC) approach. Although a two-phase commit can make life a lot simpler, it can also hurt the free flow of messages (and therefore scalability) because it requires the allocation of a stateful transaction resource across the flow of multiple, asynchronous actions. When amounts and stakes are larger, a pessimistic two-phase-commit approach is more appropriate;

Those strategies are exactly the ones discussed in this amazing talk: Six Little Lines of Fail

Other contents

• Welcome to the Serverlesspresso workshop
• Welcome to the Serverlesspresso workshop > Overview

Some talks on how Nubank has designed and implemented consistency in their distributed systems with Kafka and Datomic:

• Kafka Summit 2019: Maintaining Consistency for a Financial Event-Driven Architecture
  • First principle: "Whenever an event happens, the entire world must eventually be in a state where the event happened exactly once. Not zero times, not more than once, exactly once."
    (The idea here is to have a generic solution to maintain consistency among the systems using techniques such as DLQs, background jobs, and retries to allow replay events. In scenarios of outages, it is also important to have circuit breakers well configured between services)
  • Second principle: "Every event shall be idempotent."
    (Once all events (and operations) are idempotents, everything becomes too much easier to implement the first principle)
• Clojure South 2019
  • Maintaining integrity in distributed systems with a side effect machine (🇧🇷 pt_BR)
  • Challenges of Building an Event Sourced Banking Account | Rafael Ring
    • This talk is about how Nubank implements Event Sourcing and deals with its hard parts, such as eventual consistency, concurrency, reads, and stale calculation performance;
    • Concurrency: Make sure to have idempotent services and whenever possible, rely on uniqueness checks from your database to avoid duplicating data;
    • Eventual Consistency: Try to present the data as eventual for the customer, use distributed tracing, and ensure that the system knows how to deal with failure;
    • Reads: BFFs can help aggregating data for the customer views, and polling can be used to remove the async part of reads;
    • State calculating performance: For very intensive calculation, building a cache with the same event source principles may help; The up-to-date balance feature is the main example of using a cache inside the same database leveraging the ACID properties to guarantee that after each transaction (like a deposit) everything is synchronized atomically;

Excellent documentation about idempotency and request flows supported by Powertools for AWS Lambda (Python):

• Powertools for AWS Lambda (Python) > Idempotency
• Powertools for AWS Lambda (Python) > Idempotency request flow
• Youtube - Let them Retry: Idempotency for the Rest of Us - by Heitor Lessa

⭐️ Cache Consistency with Database by Daniel Wu

It’s often impossible to implement a linearizability consistency model with distributed cache and database systems considering all kinds of errors and failures. Every cache pattern has its limitation and in some cases you cannot get sequential consistency, or sometimes you get unexpected latency between cache and database. With all the solutions I showed in this article, there are always corner cases that you might encounter with high concurrency. So, there is no silver bullet for this, know the limitation and define your consistency requirement before you choose a solution. If you want linearizability consistency with fault-tolerance, you’d better not use cache at all.

Excellent content by Tyler Treat:

Building Reliable Systems

• Slides: Push It to the Limit: Considerations for Building Reliable Systems - by Tyler Treat
• Designed to Fail

  • With monolithic systems, we care more about preventing failure from occurring. [...] With highly distributed microservice architectures where failure is all but guaranteed, we embrace it.

  • What does it mean to embrace failure? Anticipating failure is understanding the behavior when things go wrong, building systems to be resilient to it, and having a game plan for when it happens, either manual or automated.

  • The key to being highly available is learning to be partially available. Frequently, one of the requirements for partial availability is telling the client “no.”.

  • [...] we made explicit decisions to drop messages on the floor if we detect the system is becoming overloaded. As queues become backed up, incoming messages are discarded, a statsd counter is incremented, and a backpressure notification is sent to the client. Upon receiving this notification, the client can respond accordingly by failing fast, exponentially backing off, or using some other flow-control strategy.

  • If an overloaded service is not essential to core business, we fail fast on calls to it to prevent availability or latency problems upstream. For example, a spam-detection service is not essential to an email system, so if it’s unavailable or overwhelmed, we can simply bypass it.

  • By isolating and controlling it, we can prevent failure from becoming widespread and unpredictable. By building in backpressure mechanisms and other types of intentional “failure” modes, we can ensure better availability and reliability for our systems through graceful degradation.

Distributed Systems Are a UX Problem

• Blog: Distributed Systems Are a UX Problem
• Video: Distributed systems are a UX problem

  • We need to shift the focus from system properties and guarantees to business rules and application behavior.

• Slides: Distributed Systems Are a UX Problem - by Tyler Treat

  • We choose consistency over availability, or we can choose availability by making local decisions with the knowledge at hand and designing the UX accordingly.

  • Managing partial failure is a matter of dealing with partial knowledge and managing risk.

  • Systems don't make decisions, they make guesses.

  • Systems need the capacity to apologize.

  • Customers judge you not by your failures, but by how you handle your failures.

  • Businesses need both code and people to manage apoligies.

Some very good articles about distributed systems and messaging by Kevin Sookocheff:

• Fault-Tolerance and Data Consistency Using Distributed Sagas
• Publish-Subscribe Messaging Using Amazon SQS

Life beyond Distributed Transactions: an Apostate’s Opinion

• Paper: Life beyond Distributed Transactions: an Apostate’s Opinion (2007) by Pat Helland
• Updated version of the paper - Life Beyond Distributed Transactions

  • This is an updated and abbreviated version of a paper by the same name first published in CIDR (Conference on Innovative Database Research) 2007.

• Article: Life Beyond Distributed Transactions: An Apostate's Implementation - A Primer
• Youtube: Life Beyond Distributed Transactions: An Apostate's Implementation - Jimmy Bogard (2019)

  • In this session, we'll look at how to implement coordination between non-transactional resources using Pat's paper as a guide, with examples in Azure Cosmos DB, Azure Service Bus, and Azure SQL Server. We'll look at a real-world example where a codebase assumed everything would be transactional and always succeed, but production proved us wrong! Finally, we'll look at advanced coordination workflows such as Sagas to achieve robust, scalable coordination across the enterprise.

• Youtube: Sean T. Allen on Life Beyond Distributed Transactions: An Apostate’s Opinion [PWL SF] 07/2018

  • While the paper explicitly addresses distributed database design, I'll show that the ideas are far more widely applicable, particularly in scaling stateful applications. In particular, how we implemented some of the ideas from the paper in our distributed stream processor Wallaroo.

YugabyteDB avoids bloat with the outbox pattern

• ⭐️ Fault-Tolerance and Data Consistency Using Distributed Sagas

  • It’s important to note that a compensating transaction Ci does not necessarily return the database to the original state it was in before the transaction Ti was run. The compensation only needs to be semantically equivalent.

• Youtube: Distributed Sagas: A Protocol for Coordinating Microservices - Caitie McCaffrey - JOTB17
• Feral Concurrency Control: An Empirical Investigation of Modern Application Integrity

Exactly-once message processsing

Distributed algorithms are difficult. If you find yourself struggling to understand one of them, we assure you – you are not alone. We have spent last couple of years researching ways to ensure exactly-once message processing in systems that exchange messages in an asynchronous and durable way (a.k.a. message queues) and you know what? We still struggle and make silly mistakes. The reason is that even a very simple distributed algorithm generates vast numbers of possible execution paths.

Very good article, Exactly-once intuition, about a set of heuristics that are very helpful in sketching the structure of an algorithm to achieve exactly-once message processing. Below there're a summary of those heuristics:

1. The transaction and the side effects: The outcome of processing a message consists of two parts. There is a transactional part and a side effects part. The transaction consists of application state change and of marking the incoming message as processed. The side effects include things like creating objects in non-transactional data stores (e.g. uploading a blob) and sending messages.;
2. Until the transaction is committed, nothing happened: In order for an algorithm to behave correctly, it has to guarantee that until a transaction is committed, no effects of the processing are visible to the external observers.
3. Prepare - Commit - Publish: [...] For this reason any correct algorithm has to make sure the side effects are made durable, but not visible (prepared), before the transaction is committed. Then, after the commit, the side affects are published.
4. Side effects stored in each processing attempt are isolated: [...] In our PDF example each processing attempt would generate its own PDF document but only the attempt that succeeded to commit would publish its outgoing messages, announcing to the world the true location of the PDF.
5. Register - Cleanup: Although we can’t avoid generating garbage, a well-behaved algorithm ensures that the garbage is eventually cleaned up.
6. Concurrency control ensures serialization of processing: [...] the outbox record also contains the side effects information. It can exist in only two states: created and dispatched. The transition from created to dispatched does not generate any new information so it does not require concurrency control to prevent lost writes.

Não confie em Exactly-Once Message Processing

Scaling Shared Data in Distributed Systems

• Consistency, by definition, requires linearizability. In multi-threaded programs, we achieve this with mutexes. In distributed systems, we use transactions and distributed locking. Intuitively, both involve performance trade-offs.

• There are several different strategies, each with their own pros and cons: Immutable Data > Last-Write Wins > Application-Level Conflict Resolution > Causal Ordering > Distributed Data Types

• Use weakly consistent models when you can because they afford you high availability and low latency, and rely on stronger models only when absolutely necessary. Do what makes sense for your system.

Some interesting articles from LittleHorse blog:

• Integration Patterns: Transactional Outbox
• Integration Patterns: Saga Transactions

While Saga is very hard to implement, it's simple to describe:

• Try to perform the actions across the multiple systems.
• If one of the actions fails, then run a compensation for all previously-executed tasks.

The compensation is simply an action that "undoes" the previous action. For example, the compensation for a payment task might be to issue a refund.

• The Basics of Workflow

But what is a workflow engine?

It is a system that allows you to reliably execute a series of steps while being robust to technical failures (network outages, crashes) and business process failures. A step in a workflow can be calling a piece of code on a server, reaching out to an external API, waiting for a callback from a person or external system, or more.

A core challenge when automating a business process is Failure and Exception Handling: figuring out what to do when something doesn't happen, happens with an unexpected outcome, or plain simply fails. This is often difficult to reason about, leaving your applications vulnerable to uncaught exceptions, incomplete business workflows, or data loss.

A workflow engine standardizes how to throw an exception, where the exception is logged, and the logic around when/how to retry. This gives you peace of mind that once a workflow run is started, it will reliably complete.

Sequin

Sequin is a tool for capturing changes and streaming data out of your Postgres database.

• No such thing as exactly-once delivery

  • Processing is the the full message lifecycle: the message was delivered to the receiver, the receiver did its job, and then the receiver acknowledged the message.

    With that definition, SQS, Kafka, and Sequin are all systems that guarantee exactly-once processing. The term processing captures both the delivery of the message and the successful acknowledgment of the message.

  • In my mind, the terms at-most-once and at-least-once delivery help us distinguish between delivery mechanics. And the term "exactly-once processing" indicates it's a messaging system with at-least-once delivery and acknowledgments.

  • A debate over a Github issue - At the end of the day, perfect exactly-once mechanics are a platonic ideal. And a system can only bring you so far, at some point you must implement idempotency on the client if your requirements demand it.