TLDR: Transactions are important but can be complex and confusing when getting started. This article provides an introduction to the terms and thinking behind transactions.
Transactions Are Important
The famous “pennies for everyone” scheme in the movie Office Space was based on transferring fractions of a cent to an account with every transaction a bank performs. So obviously transactions are important, at least to the plot of that movie.
But banking software that didn’t have transactions would be in even worse shape. Every transfer from one account to another involves changes to two different data items in different places. If only one of those changes happens, someone is not happy. If I’m transferring money to a savings account, and my checking account doesn’t get reduced, I’m happy but the bank is not. If the money doesn’t make it into the savings account, the bank has an angry customer.
So if the bank didn’t have transactions, our movie characters could accumulate money very quickly by just adding a bug that caused one half of a few transfers to fail. Then they could exploit that bug to create “free money”.
Since I’m old enough to have played games on Bulletin Board Systems, I remember a similar example from the famous game Trade Wars. One of its exploitable bugs was the ability to “clone” a planet, complete with all its resources, by taking the right series of steps when displaying planet data. First, you loaded a planet’s data into the right memory structure, then skipped over to another planet in a way that wouldn’t load its data. Then, you took one more step that would write the first planet’s data to the second planet’s storage location. Again, free money.
This, too, is a bug related to transactions. The act of changing a data store needs to be encapsulated; if it happens as a side effect, then it is not “transactional” and there is a risk of data integrity issues.
Nowadays, when we often choose NoSQL document or graph stores, transactions are still important. If anything, they are more important, because when we’re dealing with a distributed database that is “eventually consistent”, we can live with the idea that we are reading old data, but we can’t live with the idea that we are dealing with partially updated data. Even the recent MtGox Bitcoin debacle can be laid at the feet of transaction issues, specifically failure to identify and properly roll back an invalid transaction.
A transaction is really just a way to combine multiple actions so either they all happen, or none of them happen. There is no magic that makes sure that the parts of a transaction will succeed; the transaction just creates a way to box them up so that, if any one fails, no changes are made.
The simplest kind of transaction takes place within a database. When making changes in a transaction, the database keeps track of the multiple SQL statements we issue, and holds those changes back. While we’re continuing to make those changes, anyone who reads the same tables will see the data as if none of our statements had been issued. Then, when we perform the “commit”, all of the changes are made at once. If done correctly, there is no “window” where someone reading from the database would see some but not all of the changes. We use the term “atomic”, coming from the Greek word “atomos”, which means “indivisible”. (Turns out the atom itself wasn’t atomic, but our transactions should be.)
This same concept applies to distributed databases with eventual consistency. It might take a relatively long time for my local copy of the database to reflect changes that were made on the other side of the network, but I should never be able to see part of a data update.
That simple approach to transactions works fine as long as there is a single database. It even works with a distributed database as long as any one node can accept changes on behalf of all of the nodes. Where it doesn’t work is when multiple services get involved across a distributed system. For example, we can introduce the idea of coordinating a change with a remote system. In this case, not only do I need to make changes to my local database, I need to make sure the remote system has accepted the change and makes it to the remote system as well. Otherwise, I risk having data integrity issues where I think something went through, but the other system thinks it didn’t.
To address this, we need a way to carry out the transaction in a distributed way across all of the systems involved. We start by informing all of the systems of the start of the transaction. Then we tell the systems what the changes are, giving each system a chance to acknowledge or reject the change. Then we tell everyone that the transaction is committed. This way, if there is some problem in the request to the remote system, we find out about it before committing the local change, and no one is updated. Also, if we find some issue in our local change, we can tell the remote system to roll back the transaction, and it will not change.
Two Phase Commit
There is one small problem left with this approach, which is that it leaves a window where a system accepts a change, but that change later becomes invalid. This can happen because there’s no guarantee that a transaction will be done “quickly”; the whole point of transactions is that we can take our time, because no one will see our changes until we commit.
For example, let’s say that we accepted a change to update a row in a table. Then, before that change is committed, some really fast transaction comes through (probably running on a nicer computer) and deletes the row. We can’t update it any more, and it wouldn’t be right to just pretend we updated it before it was deleted, because the order of the operations might matter. (Picture someone closing their account while a money transfer is in progress. Another chance for free money!)
Instead, what we need is a way to reject the transaction just at the point it’s committed. This would be fine if the transaction is local to us. But by that point, everyone else involved has also been told to commit, so it’s too late for us to back out now.
To address this, distributed transaction processing, like XA transactions from the Open Group, splits transactions up into two phases. In the first phase, like Col. Sanders in Spaceballs, everyone is told to “prepare” to commit. This is the time to check to make sure we can still perform the change we accepted previously. It is also a time to lock down any resources we are going to change, because the commit is coming quickly. (“Sir, hadn’t you better sit down?”)
If we reject the prepare step, the transaction is rolled back, and no one changes. If we accept the prepare step, we are agreeing that when the transaction is committed, we definitely are going to be able to perform it. No waffling around. When the commit does come, then, all the interested systems can be confident that everyone else also performed the change.
The SMOD Scenario
I’m sure you can think of scenarios where even this level of protection isn’t enough. What if there’s a bug and the change isn’t made correctly? What if the power goes out on the data center just as the transaction is half committed? What if the Sweet Meteor O’ Death wins the 2016 U.S. presidential election and destroys us all?
Of course, the answer is, sometimes bad things happen to good computers. In these cases, there are a few things we can do. We can keep good logs (including transaction logs, which are just statements about the changes we intend to make), so we can go back, forensic scientist style, to figure out what happened and how to manually make it right.
We can build in “compensatory” logic, so if we issue a commit and don’t hear back from everyone that was supposed to do the commit, we have a way to “undo” the change. (Of course, that presents its own issues, because the absence of evidence is not evidence of absence. Just because the other system didn’t acknowledge the commit doesn’t mean it didn’t do it. Maybe it was the acknowledgement that got lost.)
Overall, fortunately, these are extreme edge cases, and we often have human beings involved to check that things were done correctly, especially for very important transactions. There are multiple reasons that banks have a daily limit on ATM withdrawls; a computer bug is definitely one of them.
If you’ve never had to write code that uses transactions, or if you just followed the usual rules for transactional code (i.e. if anything goes wrong, throw any handy exception to cause a rollback), I hope this was a useful explanation of why that transactional code is important and why it needs to be as complex as it is. If you’re already familiar with transactions, but you read to the end, I hope the literary and cultural references were entertaining enough to be worth your time.