Shadowing Practice: Distributed Transactions Explained: 2 Phase Commit vs Saga Pattern - Learn English Speaking with 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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About This Lesson
In this lesson, you will practice your English speaking skills by exploring the concept of distributed transactions in technology. You will learn how transactions operate in a simple database versus how they function in a more complex, distributed system. By understanding these intricate details, you can improve your ability to discuss technical topics in English, helping you become more fluent and confident in your communication. This exercise is perfect for learners who want to learn English with YouTube and improve their vocabulary in the tech field.
Key Vocabulary & Phrases
- Transaction: An action that involves multiple steps, where either all actions succeed or none do.
- Atomicity: A property that ensures all parts of a transaction are completed or none at all.
- Isolation: Ensures transactions do not interfere with each other while processing.
- Distributed transaction: A transaction that requires multiple independent databases to work together.
- Rollback: The process of reverting a database to its previous state when a transaction fails.
- Microservices: Architectural design that breaks applications into small services, each with its own database.
- Partial failure: A situation where some parts of a process succeed while others fail.
Practice Tips
To effectively use the shadowing technique while watching this video, pay close attention to the speaker's tone and speed. The speaker utilizes a clear and informative style, which makes it easier to mimic. Start by playing the video at a slower speed if necessary, then gradually increase the speed as you gain confidence. Use a shadowing app to record yourself as you repeat phrases, focusing on matching the speaker’s intonation and rhythm. This will help you develop a more natural flow and improve your understanding of technical vocabulary. Regularly practicing through this method will enable you to enhance your fluency in discussing complex topics and strengthen your ability to use specialized vocabulary effectively. Consider using a shadowing site that allows you to compare your recordings with the original audio to identify areas for improvement.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.
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