Adrian Colyer wrote a great summary of a recent paper by Peter Bailis et al. In the paper the database researchers examine open source Rails applications and observe that the applications apply constraints - foreign key references, uniqueness constraints - in a way that's not very performant or correct.
I was pretty surprised to read about this! For the most part we have avoided problems like this at Shyp, and I didn't realize how widespread this problem is; I certainly have written a lot of bad queries in the past.
So! Let's learn some tips for writing better queries. Everything below will help you write an application that is more correct - it will avoid consistency problems in your data - and more performant - you should be able to achieve the same results as Rails, with fewer queries!
ps - The info below may be really obvious to you! Great! There are a lot of people who aren't familiar with these techniques, as the paper above indicates.
Use database constraints to enforce business logic
Say you define an ActiveRecord class that looks like this:
class User < ActiveRecord::Base validates :email, uniqueness: true end
What actually happens when you try to create a new user? It turns out Rails will make 4 (four!) roundtrips to the database.
BEGIN a transaction.
Perform a SELECT to see if any other users have that email address.
If the SELECT turns up zero rows, perform an INSERT to add the row.
Finally, COMMIT the result.
This is pretty slow! It also increases the load on your application and your database, since you need to make 4 requests for every INSERT. Bailis et al also show that with your database's default transaction isolation level, it's possible to insert two records with the same key. Furthermore, there are some ActiveRecord queries which skip the built-in validations, as Gary Bernhardt discussed in his video, "Where Correctness Is Enforced", way back in 2012. Any query which inserts data and skips the validations can compromise the integrity of your database.
What if I told you you can do the same insert in one query instead of four, and it would be more correct than the Rails version? Instead of Rails's migration, write this:
CREATE TABLE users (email TEXT UNIQUE);
UNIQUE is the key bit there; it adds a unique key on the table. Then,
instead of wrapping the query in a transaction, just try an INSERT.
> insert into users (email) values ('firstname.lastname@example.org'); INSERT 0 1 > insert into users (email) values ('email@example.com'); ERROR: duplicate key value violates unique constraint "users_email_key" DETAIL: Key (email)=(firstname.lastname@example.org) already exists.
You'll probably need to add better error handling around the failure case - at least we did, for the ORM we use. But at any level of query volume, or if speed counts (and it probably does), it's worth it to investigate this.
Just Try the Write
Say you wanted to read a file. You could write this:
if not os.path.isfile(filename): raise ValueError("File does not exist") with open(filename, 'r') as f: f.read() ...
But that would still be vulnerable to a race! What if the OS or another thread
deleted the file between the
isfile check and the
with open line - the
latter would throw an
IOError, which won't be handled. Far better to just try
to read the file and handle errors appropriately.
try: with open(filename, 'r') as f: f.read() ... except IOError: raise ValueError("File does not exist")
Say you have a foreign key reference -
phone_numbers.user_id refers to
users.id, and you want to validate that the
user_id is valid. You could do:
def write_phone_number(number, user_id): user = Users.find_by_id(user_id) if user is None: raise NotFoundError("User not found") Number.create(number=number, user_id=user_id)
Just try to write the number! If you have a foreign key constraint in the database, and the user doesn't exist, the database will tell you so. Otherwise you have a race between the time you fetch the user and the time you create the number.
def write_phone_number(number, user_id): try Number.create(number=number, user_id=user_id) except DatabaseError as e: if is_foreign_key_error(e): raise NotFoundError("Don't know that user id")
Updates Should Compose
Let's say you have the following code to charge a user's account.
def charge_customer(account_id, amount=20): account = Accounts.get_by_id(account_id) account.balance = account.balance - 20 if account.balance <= 0: throw new ValueError("Negative account balance") else account.save()
Under the hood, here's what that will generate:
SELECT * FROM accounts WHERE id = ? UPDATE accounts SET balance = 30 WHERE id = ?;
So far, so good. But what happens if two requests come in to charge the account at the same time? Say the account balance is $100
Thread 1 wants to charge $30. It reads the account balance at $100.
Thread 2 wants to charge $15. It reads the account balance at $100.
Thread 1 subtracts $30 from $100 and gets a new balance of $70.
Thread 2 subtracts $15 from $100 and gets a new balance of $85.
Thread 1 attempts to UPDATE the balance to $70.
Thread 2 attempts to UPDATE the balance to $85.
This is clearly wrong! The balance after $45 of charges should be $55, but it was $70, or $85, depending on which UPDATE went last. There are a few ways to deal with this:
create some kind of locking service to lock the row before the read and after you write the balance. The other thread will wait for the lock before it reads/writes the balance. These are hard to get right and will carry a latency penalty.
Run the update in a transaction; this will create an implicit lock on the row. If the transaction runs at the SERIALIZABLE or REPEATABLE READ levels, this is safe. Note most databases will set the default transaction level to READ COMMITTED, which won't protect against the issue referenced above.
- Skip the SELECT and write a single UPDATE query that looks like this:
UPDATE accounts SET balance = balance - 20 WHERE id = ?;
That last UPDATE is composable. You can run a million balance updates in any
order, and the end balance will be exactly the same, every time. Plus you don't
need a transaction or a locking service; it's exactly one write (and faster
.save() version above!)
But if I do just one UPDATE, I can't check whether the balance will go below zero! You can - you just need to enforce the nonnegative constraint in the database, not the application.
CREATE TABLE accounts ( id integer primary key, balance integer CHECK (balance >= 0), );
That will throw any time you try to write a negative balance, and you can handle the write failure in the application layer.
Update: Apparently MySQL accepts check constraints as valid syntax, but does not execute them, so you might need to take a different approach. Thanks olivier for pointing this out!
The key point is that your updates should be able to run in any order without
breaking the application. Use relative ranges -
balance = balance - 20 for
example - if you can. Or, only apply the UPDATE if the previous state of the
database is acceptable, via a WHERE clause. The latter technique is very useful
for state machines:
UPDATE pickups SET status='submitted' WHERE status='draft' AND id=?;
That update will either succeed (if the pickup was in draft), or return zero rows. If you have a million threads try that update at the same time, only one will succeed - an incredibly valuable property!
Beware of save()
save() function in an ORM is really unfortunate for two reasons. First,
.save(), you have to retrieve an instance of the object via a
SELECT call. If you have an object's ID and some fields to read, you can
avoid needing to do the read by just trying the UPDATE. This introduces more
latency and the possibility of writing stale data.
Second, some implementations of
.save() will issue an UPDATE and update
This can lead to updates getting clobbered. Say two requests come in, one to
update a user's phone number, and the other to update a user's email address,
and both call
.save() on the record.
UPDATE users SET email@example.com', phone_number='newnumber' WHERE id = 1; UPDATE users SET firstname.lastname@example.org', phone_number='oldnumber' WHERE id = 1;
In this scenario the first UPDATE gets clobbered, and the old email gets
persisted. This is really bad! We told the first thread that we updated the
email address, and then we overwrote it. Your users and your customer service
team will get really mad, and this will be really hard to reproduce. Be wary of
.save - if correctness is important (and it should be!), use an UPDATE with
only the column that you want.
If you thought the previous section was interesting, check this out. Say we have a pickups table. Each pickup has a driver ID and a status (DRAFT, ASSIGNED, QUEUED, etc).
CREATE TABLE pickups ( id integer, driver_id INTEGER REFERENCES drivers(id), status TEXT );
We want to enforce a rule that a given driver can only have one ASSIGNED pickup at a time. You can do this in the application by using transactions and writing very, very careful code... or you can ask Postgres to do it for you:
CREATE UNIQUE INDEX "only_one_assigned_driver" ON pickups(driver_id) WHERE status = 'ASSIGNED';
Now watch what happens if you attempt to violate that constraint:
> INSERT INTO pickups (id, driver_id, status) VALUES (1, 101, 'ASSIGNED'); INSERT 0 1 > INSERT INTO pickups (id, driver_id, status) VALUES (2, 101, 'DRAFT'); INSERT 0 1 -- OK, because it's draft; doesn't hit the index. > INSERT INTO pickups (id, driver_id, status) VALUES (3, 101, 'ASSIGNED'); ERROR: duplicate key value violates unique constraint "only_one_assigned_driver" DETAIL: Key (driver_id)=(101) already exists.
We got a duplicate key error when we tried to insert a second
record! Because you can trust the database to not ever screw this up, you have
more flexibility in your application code. Certainly you don't have to be as
careful to preserve the correctness of the system, since it's impossible to put
in a bad state!
In many instances your ORM may be generating a query that's both slow, and can lead to concurrency errors. That's the bad news. The good news is you can write database queries that are both faster and more correct!
A good place to start is by reversing the traditional model of ORM development. Instead of starting with the code in the ORM and working backwards to the query, start with the query you want, and figure out how to express than in your ORM. You'll probably end up using the lower-level methods offered by your ORM a lot more, and you'll probably discover defects in the way that your ORM handles database errors. That's okay! You are on a much happier path.
Liked what you read? I am looking for work.