Prática de Shadowing: Distributed Transactions Explained: 2 Phase Commit vs Saga Pattern - Aprenda a falar inglês com o YouTube
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When you're first building an application, transactions are easy.
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When you're first building an application, transactions are easy.
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You have a database, and when a customer places an order,
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you wrap the whole thing in the transaction.
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Charge their card, reserve the inventory,
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record a ledger entry for accounting.
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If any of those rights fails,
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the database rolls everything back automatically,
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and it's just like nothing happened.
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You probably don't even think about it much.
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Your database gives you what are called asset guarantees,
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which basically means two things that matter here.
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First, atomicity.
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Either all three of those rights happen together,
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or none of them do.
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There's no world where the card gets charged,
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but the inventory doesn't get reserved.
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Second is isolation.
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While that transaction is in progress,
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no other part of your system can see the half-finished state.
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Another query checking the customer's balance won't see a charge for an order that hasn't fully processed yet.
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The database handles all of this behind the scenes,
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and you just write your SQL and move on.
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But when your application grows,
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you start to get more traffic, more data, more writes.
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And eventually, that single database starts hitting its limits.
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So you do what everyone does at this point.
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You split things up.
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Maybe you shard the database to spread write load across multiple machines,
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or maybe you break up your monolith into microservices,
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where each service now owns its own database.
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The specifics can vary, but the result is the same.
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Your data now lives on multiple independent machines instead of one.
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And at this point, everything changes.
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The payment flow that used to be one transaction against one database
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is now three completely separate operations against three separate databases on three separate machines.
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The card gets charged to the payment database,
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inventory gets reserved in the inventory database,
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and that ledger entry gets recorded in the accounting database.
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You can't wrap a transaction across these independent databases because they don't know about each other.
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So if the card charge commits,
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but then the inventory reservation fails because the item is out of stock,
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There's no database-level rollback that can undo that charge.
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It's already committed and a completely different database on a completely different machine.
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When you're processing thousands of transactions a second across distributed infrastructure,
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partial failures like these aren't edge cases,
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they actually become pretty routine.
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Now this whole class of problem is what's called a distributed transaction.
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A single logical operation that needs to span multiple independent databases or services,
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where all the steps need to either succeed together or be cleaned up when something goes wrong.
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The textbooks give us two approaches to distributed transactions,
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two-phase commit and a SOGA pattern.
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In practice, the industry has overwhelmingly chosen one over the other,
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and understanding why will save you a lot of pain.
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The two-phase commit is the classic academic solution to distributed transactions.
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The idea is to introduce a new component called a coordinator whose entire job is to make sure
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that all participants in a transaction agree on the outcome before any make their changes permanent.
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It works in two phases,
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which is where the name comes from of course.
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In the first phase, called the prepare phase,
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the coordinator sends a message to every participant asking,
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can you commit this transaction?
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Each participating database then does the actual work.
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It processes the request, durably records the changes so that nothing is lost if it crashes,
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and locks the affected rows so that no other transactions can modify them in the meantime.
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Then it responds to the coordinator with either yes,
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I'm ready to commit, or no, something went wrong.
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If any single participant votes no,
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the coordinator tells everyone to abort and release their locks.
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If every participant votes yes on the other hand,
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then the coordinator moves to phase 2.
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It sends a commit message to everyone.
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Each participant makes its changes permanent and releases those locks,
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and the transaction is now complete.
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What this gives you is strong consistency,
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the same guarantee you had with a single database.
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Every participant agrees on the outcome before anything is finalized,
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so there's no window where the system is in a partial or inconsistent state.
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On paper, this is exactly what you want,
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but the problems show up when you try to run this in production.
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The fundamental problem with two-phase commit or 2PC is that it's a blocking protocol,
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and blocking in a distributed system is dangerous because you're now dependent on multiple machines all staying healthy at the same time.
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I want you to picture this.
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The coordinator collects all three yes votes from the participants,
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but then it crashes.
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Right there after collecting the votes,
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but before it gets a chance to send the commit decision.
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Now the participants are all stuck.
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They're all sitting there with locks held on the rows they prepared and they have no idea what to do next.
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They can't go ahead and commit on their own,
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because maybe the coordinator was about to tell them to abort.
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They can't abort on their own either,
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because maybe the coordinator was about to tell them to commit and the other participants already went through with it.
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So they just wait.
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And every other transaction in your system that needs to touch any of those locked rows is now blocked too,
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waiting for the locks that nobody can release.
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Crashes aren't even the only problem with 2PC.
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A single slow participant holds up the entire transaction.
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So if the ledger service,
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for example, were to take 10 seconds to respond to the prepared message,
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the card service
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and the inventory service are both just sitting there with their locks held for those full 10 seconds doing nothing.
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That means the entire system moves at the speed of the slowest participant.
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And if a network partition means the coordinator can't reach the participant at all,
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there's no safe default.
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It can't tell whether the message got through or not.
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This is why almost nobody uses two-phase commit across services in productions.
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Pat Helland wrote a really influential paper called Life Beyond Distributed Transactions.
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In that paper, he argues exactly this point.
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Distributed transactions across autonomous services don't work at internet scale.
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The industry took this lesson to heart.
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2PC does exist in production,
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but only inside distributed databases like Google Spanner or YugoBiteDB,
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where the coordinator and the participants are tightly coupled within the same system.
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This is where the database handles the complexity internally so that you as the caller don't have to.
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But across independent services with different deployment schedules and different failure characteristics,
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that's where it all falls apart.
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So what should you do instead?
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Well, when companies need to coordinate work across multiple services,
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the saga pattern is what they reach for.
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Uber, Netflix, Amazon, DoorDash, they all use this pattern in production.
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Sagas start from a very different assumption than 2PC.
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You don't actually need all or nothing atomicity spanning multiple services.
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You just need a way to eventually get to a consistent state,
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even when things go wrong along the way.
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Instead of coordinating one big distributed transaction with locks held across services,
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you break the work into a chain of independent local transactions.
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So each service does its piece of work and commits to its own database on its own terms.
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When something fails further down the chain,
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there's no way to roll back to earlier steps since they've already been committed to that separate database.
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So instead, you run what is called a compensating action.
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These are business-level undoes that reverse the effects of what already happened.
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So a refund instead of a rollback,
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a cancellation instead of an abort.
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Something needs to detect that failure and trigger those compensations
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and how that works is a key design decision we'll get into in just a moment.
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But the trade-off is that instead of getting the strong consistency you get with 2PC,
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Saga gives you what's called eventual consistency.
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The system might be temporarily in an inconsistent state while compensations are running.
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A customer might briefly see a charge on their card before the refund goes through,
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but it always converges to a correct state and nothing is blocked while that convergence is happening.
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Other transactions can keep flowing normally that entire time.
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Now there are two ways to implement sagas,
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and the choice between them determines who is responsible for detecting failures and running compensations.
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The first approach is called choreography,
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and it's the decentralized option.
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It uses a publish subscribe pattern where each service broadcasts an event when it finishes its work,
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and any interested service can pick it up and react.
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So the card service charges the card and then publishes a card charged event.
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The inventory service is listening for that event,
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so when it arrives, it reserves the stock and publishes an inventory reserved event.
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The ledger service picks that up and then records the entry.
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If something fails, the failing service publishes a failure event,
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and the upstream services react by running their own compensations.
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This works well when you have a simple flow with just two or three steps.
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But once you get to five or six services all publishing and reacting to each other's events,
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figuring out the current state of any given transaction becomes really difficult.
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Where exactly did it fail?
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Which compensating actions have already run?
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Did the refund actually go through?
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Without a central place tracking all of this,
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you end up digging through logs across a dozen different services,
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trying to piece together what happened.
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The second approach is called orchestration,
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and it's what most teams end up using once they reach any serious scale.
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Instead of services reacting to each other's events,
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you have a dedicated orchestrator service that controls the entire flow.
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It tells each service what to do one step at a time.
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Card service, charge the card.
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It waits for confirmation.
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Inventory service, reserve the stock.
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Wait for confirmation.
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If something fails, the orchestrator knows exactly what steps failed and can run the right compensating action in the right order.
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Tools like Temporal, which was created by the engineer behind Uber's Cadence workflow engine,
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or AWS Step Functions, are purpose-built for exactly this kind of orchestration.
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It's what we use here at Hello Interview to coordinate our payment and fulfillment flows,
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and it's the pattern we'd recommend that most teams use.
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The important difference between Saga orchestration and 2PC coordinators is what happens when it crashes.
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The orchestrator doesn't leave locks dangling across your system.
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It's durable.
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When it restarts, it reads its own state from a database and it picks up exactly where it left off.
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So no rows are locked in the meantime and no other transactions are blocked during that recovery period.
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Saga solved the blocking problem that makes 2PC impractical,
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but they introduce a different kind of complexity.
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the compensating action themselves.
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The idea of just undo the previous step sounds clean,
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but in practice, it gets pretty messy quickly.
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Say the card charge went through and committed,
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and then the inventory reservation fails because the item is out of stock.
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The compensation is to issue a refund on the card,
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but unlike a database rollback,
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that refund is visible to the customer.
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They see an actual charge show up on their card,
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and then a few seconds later, they see a refund.
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Their bank might even send them a push notification for each one.
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It works correctly, but it's not the invisible cleanup that a database rollback gives you.
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And some actions are genuinely hard to undo at all.
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If one of the steps in your flow is to send a confirmation email,
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you can't unsend that email.
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If you fired a webhook to a third-party payment system,
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you can send a follow-up cancellation,
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but you can't guarantee that they'll process it in time or at all.
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Each step in your saga needs a well-defined compensating action,
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and some of those compensations are inherently imperfect.
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On top of that, compensating actions can themselves fail.
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What if the refund API is down when you need to issue that refund?
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Now you need retry logic for your compensations.
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And if you're retrying a refund,
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you need the operation to be idempotent,
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meaning it produces the same result whether you run it once or 10 times,
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so that a retry doesn't accidentally refund the customer twice.
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You end up needing the same level of reliability engineering for your failure handling as you do for your happy paths.
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Even with solid compensation logic in place,
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there's one more failure mode that catches teams off guard.
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When your card service finishes charging the card,
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it needs to do two things.
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Save the result to its own database
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and publish an event to a message broker so that the next service in the chain knows it's time to proceed.
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The problem is that those are two completely separate writes to two completely separate systems.
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This is called the dual write problem.
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If the database write succeeds but the event published fails,
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the next step in your saga never gets triggered and the whole flow stalls.
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If the event publishes successfully but the database write fails,
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now downstream services are reacting to something that didn't actually happen in your database.
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You can fix this with something called a transactional outbox pattern.
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Instead of writing to your database and publishing an event as two separate operations,
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you write both your data and the outgoing event into the same database via a single local transaction.
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The event goes into a special outboxed table right alongside your regular data at right,
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so they either both commit or neither do.
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Then, a separate background process watches the outboxed table and publishes those events to your message broker.
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That background process can use change data capture,
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which means it tails the database's own transaction logs to pick up new entries,
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or it can simply pull the outboxed table on a regular interval.
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Before you reach for either of these patterns,
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the first question to ask yourself is whether you actually need a distributed transaction at all.
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If you can design your service boundaries so that the data that transacts together lives in the same database, do that.
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This is easier to get right up front than to try to retrofit later,
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so it's worth thinking about early.
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For example, move the inventory and ledger table into the same database that has the payments table,
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if they always need to be updated together.
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This way a local database transaction is simpler,
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faster, and more reliable than any distributed alternative,
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and this is always the best answer when you can make it work.
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If you genuinely can't avoid distributing the transaction across services,
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you're going to use a saga.
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That's not really a debate in the industry anymore.
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The question is which flavor of saga makes sense for your situation.
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Choreography is usually where teams start,
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and for simple flows it works great.
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If you have three or four steps,
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the services are truly independent,
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and you don't need centralized visibility into where each transaction stands,
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choreography keeps things simple.
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Something like an e-commerce notification system where an order is placed,
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event triggers, an email or push notification independently,
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this is a natural fit.
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Most teams eventually outgrow it as their flows get more complex,
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but there's no reason to over-engineer from day one.
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For anything more complex than that,
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orchestration is the way to go.
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Complex flows with branching logic,
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flows where you need to see exactly where a transaction is stuck,
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flows where the compensation logic is tricky
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and you want it defined in one clear place rather than scattered across half a dozen services.
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Most teams end up here,
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and tools like temporal or AWS step functions make it very practical to implement.
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One last note, if eventual consistency truly is not acceptable for a particular piece of your system,
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consider whether that data can live in a single distributed database like Spanner
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or YugaByteDB that handle that strong consistency internally for you.
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That's a very different thing from trying to build 2PC yourself across independent services.
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At the end of the day,
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the pattern that you'll see at most companies operating at scale is saga with orchestration,
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independent operations at every step so the retries are always safe,
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and a transactional outbox to make sure events are as reliable as database rights.
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It means accepting eventual consistency,
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but that's a trade-off the industry has made deliberately.
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And it's the architecture that Uber,
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Netflix, and Amazon actually run in production today.
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Praticar a conversação em inglês com vídeos como este é uma abordagem eficaz para desenvolver suas habilidades de fala e compreensão auditiva. Ao analisar transcrições de vídeos educativos, você pode identificar estruturas linguísticas importantes e aprender vocabulário técnico que é relevante no mundo da tecnologia e dos negócios. O tópico de transações distribuídas é um exemplo excelente, pois oferece um contexto real onde a linguagem técnica é usada. Além disso, o ato de repetir e reproduzir partes do vídeo permite que você utilize a técnica de shadowspeak, uma prática que tem mostrado melhorar significativamente a fluência e a pronúncia.
Gramática & Expressões em Contexto
- Atomicity: Essa palavra refere-se à garantia de que todas as partes de uma transação ocorram juntas. Você pode praticar essa expressão em frases, como "A atomicidade é crucial para a integridade dos dados".
- Isolation: Usada para descrever que, durante uma transação, os dados não podem ser acessados por outros processos. Frases como "O isolamento impede a visão de dados incompletos" são boas para praticar.
- Rollback: Um termo técnico que significa reverter uma transação. Você pode usar em situações do dia a dia, como "Se houver um erro, precisamos fazer o rollback da operação".
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Durante o vídeo, algumas palavras podem ser desafiadoras para pronunciar corretamente. Por exemplo, transaction é uma palavra que muitos falantes não nativos podem pronunciar incorretamente. Tente focar na sílaba tônica e na transição entre os sons. Além disso, a palavra inventory pode ser difícil, especialmente em sua forma rapidinha durante o discurso. Praticar essas palavras em voz alta, talvez usando shadow speech como técnica de apoio, pode ajudá-lo a melhorar sua pronúncia.
Ao se envolver com conteúdos que exigem atenção especial às nuances de pronúncia e gramática, você não só melhora suas habilidades em inglês, mas também se torna mais confiante ao falar sobre tópicos complexos.
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