The software industry as a whole contains a lot of people doing a lot of different things, but for every developer working on new embedded firmware, there’s about ten building the linchpin of modern software – CRUD apps that serve requests over HTTP. A lot of these apps are backed by MVC frameworks like Ruby on Rails or ASP.NET, and backed by ACID-compliant relational databases like Postgres or SQL Server.

Sharp edges in production can lead to all kinds of unexpected cases during the execution of an HTTP request – client disconnects, application bugs that fail a request midway through, and timeouts are all extraordinary conditions that will occur regularly given enough request volume. Databases can protect applications against integrity problems with their transactions, and it’s worth taking a little time to think about how to make best use of them.

There’s a surprising symmetry between an HTTP request and a database’s transaction. Just like the transaction, an HTTP request is a transactional unit of work – it’s got a clear beginning, end, and result. The client generally expects a request to execute atomically and will behave as if it will (although that of course varies based on implementation). Here we’ll look at an example service to see how HTTP requests and transactions apply nicely to one another.

I’m going to make the case that for a common idempotent HTTP request, requests should map to backend transactions at 1:1. For every request, all operations are committed or aborted as part of a single transaction within it.

Transactions (tx1, tx2, tx3) mapped to HTTP requests at a 1:1 ratio.

At first glance requiring idempotency may sound like a sizeable caveat, but in many APIs operations can be made to be idempotent by massaging endpoint verbs and behavior, and moving non-idempotent operations like network calls to background jobs.

Some APIs can’t be made idempotent and those will need a little extra consideration. We’ll look at what to do about them in more detail later as a follow up to this article.

Let’s build a simple test service with a single “create user” endpoint. A client hits it with an email parameter, and the endpoint responds with status 201 Created to signal that the user’s been created. The endpoint is also idempotent so that if a client hits the endpoint again with the same parameter, it responds with status 200 OK to signal that everything is still fine.

PUT /users?email=jane@example.com

On the backend, we’re going to do three things:

Check if the user already exists, and if so, break and do nothing. Insert a new record for the user. Insert a new “user action” record. It’ll serve as an audit log which comes with a reference to a user’s ID, an action name, and a timestamp.

We’ll build our implementation with Postgres, Ruby, and an ORM in the style of ActiveRecord or Sequel, but these concepts apply beyond any specific technology.

The service defines a simple Postgres schema containing tables for its users and user actions :

CREATE TABLE users ( id BIGSERIAL PRIMARY KEY, email TEXT NOT NULL CHECK (char_length(email) <= 255) ); -- our "user action" audit log CREATE TABLE user_actions ( id BIGSERIAL PRIMARY KEY, user_id BIGINT NOT NULL REFERENCES users (id), action TEXT NOT NULL CHECK (char_length(action) < 100), occurred_at TIMESTAMPTZ NOT NULL DEFAULT now() );

The server route checks to see if the user exists. If so, it returns immediately. If not, it creates the user and user action, and returns. In both cases, the transaction commits successfully.

put "/users/:email" do |email| DB.transaction(isolation: :serializable) do user = User.find(email) halt(200, 'User exists') unless user.nil? # create the user user = User.create(email: email) # create the user action UserAction.create(user_id: user.id, action: 'created') # pass back a successful response [201, 'User created'] end end

The SQL that’s generated in the case of a successful insertion looks roughly like:

START TRANSACTION ISOLATION LEVEL SERIALIZABLE; SELECT * FROM users WHERE email = 'jane@example.com'; INSERT INTO users (email) VALUES ('jane@example.com'); INSERT INTO user_actions (user_id, action) VALUES (1, 'created'); COMMIT;

Readers with sharp eyes may have noticed a potential problem: our users table doesn’t have a UNIQUE constraint on its email column. The lack of one could potentially allow two interleaved transactions to run their SELECT phase one concurrently and get empty results. They’d both follow up with an INSERT , leaving a duplicated row.

A data race causing two concurrent HTTP requests to insert the same row.

Luckily, in this example we’ve used an even more powerful mechanism than UNIQUE to protect our data’s correctness. Invoking our transaction with DB.transaction(isolation: :serializable) starts it in SERIALIZABLE ; an isolation level so powerful that its guarantees might seem practically magical. It emulates serial transaction execution as if each outstanding transaction had been executed one after the other, rather than concurrently. In cases like the above where a race condition would have caused one transaction to taint the results of another, one of the two will fail to commit with a message like this one:

ERROR: could not serialize access due to read/write dependencies among transactions DETAIL: Reason code: Canceled on identification as a pivot, during commit attempt. HINT: The transaction might succeed if retried.

We’re not going to look into how SERIALIZABLE works, but sufficed to say it may detect a number of different data races for us, and if it does it’ll abort a transaction when it tries to commit.

Even though in our example a race should be rare, we’d prefer to handle it correctly in our application code so that it doesn’t bubble up as a 500 to a client. This is possible by wrapping the request’s core operations in a loop:

MAX_ATTEMPTS = 2 put "/users/:email" do |email| MAX_ATTEMPTS.times do begin DB.transaction(isolation: :serializable) do ... end # Success! Leave the loop. break rescue Sequel::SerializationFailure log.error "Failed to commit serially: #{$!}" # Failure: fall through to the next loop. end end end

In this case, we might have more than one of the same transaction mapped to the HTTP request like so:

An aborted transaction being retried within the same request.

These loops will be more expensive than usual, but again, we’re protecting ourselves against an unusual race. In practice, unless callers are particularly contentious, they’ll rarely occur.

Gems like Sequel can handle this for you automatically (this code will behave similarly to the loop above):

DB.transaction(isolation: :serializable, retry_on: [Sequel::SerializationFailure]) do ... end

I’ve taken the opportunity to demonstrate the power of a serializable transaction, but in real life you’d want to put in a UNIQUE constraint on email even if you intended to use the serializable isolation level. Although SERIALIZABLE will protect you from a duplicate insert, an added UNIQUE will act as one more check to protect your application against incorrectly invoked transactions or buggy code. It’s worth having it in there.

It’s a common pattern to add jobs to a background queue during an HTTP request so that they can be worked out-of-band and a waiting client doesn’t have to block on an expensive operation.

Let’s add one more step to our user service above. In addition to creating user and user action records, we’ll also make an API request to an external support service to tell it that a new account’s been created. We’ll do that by queuing a background job because there’s no reason that it has to happen in-band with the request.

put "/users/:email" do |email| DB.transaction(isolation: :serializable) do ... # enqueue a job to tell an external support service # that a new user's been created enqueue(:create_user_in_support_service, email: email) ... end end

If we used a common job queue like Sidekiq to do this work, then in the case of a transaction rollback (like we talked about above where two transactions conflict), we could end up with an invalid job in the queue. It’s referencing data that no longer exists, so no matter how many times job workers retried it, it can never succeed.

A way around this is to create a job staging table into our database. Instead of sending jobs to the queue directly, they’re sent to a staging table first, and an enqueuer pulls them out in batches and puts them to the job queue.

CREATE TABLE staged_jobs ( id BIGSERIAL PRIMARY KEY, job_name TEXT NOT NULL, job_args JSONB NOT NULL );

The enqueuer selects jobs, enqueues them, and then removes them from the staging table . Here’s a rough implementation:

loop do DB.transaction do # pull jobs in large batches job_batch = StagedJobs.order('id').limit(1000) if job_batch.count > 0 # insert each one into the real job queue job_batch.each do |job| Sidekiq.enqueue(job.job_name, *job.job_args) end # and in the same transaction remove these records StagedJobs.where('id <= ?', job_batch.last).delete end end end

Because jobs are inserted into the staging table from within a transaction, its isolation property (ACID’s “I”) guarantees that they’re not visible to any other transaction until after the inserting transaction commits. A staged job that’s rolled back is never seen by the enqueuer, and doesn’t make it to the job queue.

I call this pattern a transactionally-staged job drain.

It’s also possible to just put the job queue directly in the database itself with a library like Que, but because bloat can be potentially dangerous in systems like Postgres, this probably isn’t as good of an idea.

What we’ve covered here works nicely for HTTP requests that are idempotent. That’s probably a healthy majority given a well-designed API, but there are always going to be some endpoints that are not idempotent. Examples include calling out to an external payment gateway with a credit card, requesting a server to be provisioned, or anything else that needs to make a synchronous network request.

For these types of requests we’re going to need to build something a little more sophisticated, but just like in this simpler case, our database has us covered. In part two of this series we’ll look at how to implement idempotency keys on top of multi-stage transactions.