A Case for Upserts

It looks like upserts yet again will not land in Postgres. This is deeply unsatisfying because over the last couple of years it has become more than obvious for me that this functionality is not just useful but a necessity. Interestingly enough it seems like a lot of people are suggesting that depending on upserts means that the design of the application is bad but I would argue that anything other than upserts means that the application is actually deeply unreliable or badly planned.

What's an Upsert Upserts probably got a bad reputation because they were popularized by MySQL which is often regarded as a bad SQL database. However MySQL also became popular as a driving force behind web applications so some of its design decisions work really well in a web environment. In MySQL there are two types of upserts: replaces (which can be emulated in Postgres) and the "on duplicate" clause which can be used to react on an already existing row. The latter is really hard to efficiently implement in Postgres at the moment. I will go into the details of why upserts are important in a bit, I just want to show the two types of it first.

Replaces The simple upsert in MySQL Is the replace insert. It's an insert that will implicitly delete an already existing row before it does anything. The way it's implemented is an extension to the insert syntax: replace into my_table ( key , value ) values ( 'key' , 'value' ); With an alternative syntax that works similar to updates (which to be fair, MySQL also has for insert and which I find much pleasant because it mirrors the update syntax): replace my_table set key = 'key' , value = 'value' ; The way both of these work is by inserting a value and deleting an already existing record for a given primary key or unique constraint. The most common case for this safely treating a table as a key value store. In this particular case you can also use a transaction safely in Postgres and you can just send a delete before the insert: begin ; delete from my_table where key = 'key' ; insert into my_table ( key , value ) values ( 'key' , 'value' ); commit ; The obvious problem with this however is that a transaction that happens concurrently will not see the row any more. You can easily test this by just not committing and attempting to update from another shell. The update will block until the commit happens but once it finishes it will tell you that zero rows were updated even though there were clearly rows available to update. The reason for this is that transactions just serialize the execution, they don't guarantee any atomicity of independent row updates. After the delete happens the second transaction gets a chance to run and the update will fail because it no longer sees a row. Now if you're thinking the solution is to just raise the isolation level, you're in for a disappointment. Even with the highest isolation level the result is the same. However an explicit table lock gets around this. To be honest, this probably has the same characteristics as doing the same operation on MyISAM where MySQL got its initial upserts from: begin ; lock my_table in exclusive mode ; delete from my_table where key = 'key' ; insert into my_table ( key , value ) values ( 'key' , 'value' ); commit ; This will work and still allow concurrent reads, but it will definitely block any concurrent modifications from happening.

On Duplicate Clauses The more interesting upsert in MySQL is the ON DUPLICATE KEY UPDATE clause. For some reason this got a bad reputation similar to basic's on error resume next even though it's not just useful but also the only safe way to handle concurrency with uniques. The way it works is by defining an insert as well as an update in the same statement. For instance this is the way to implement safe counters that are not initialized upfront: insert into counters ( counter_name , seq ) values ( 'my_counter' , 1 ) on duplicate key update seq = seq + 1 ; If the key 'my_counter' does not exist yet, the row is created by inserting it with the value 1 for seq otherwise it will execute the update statement on that particular row. There is an obvious question what happens if there are two unique constraints on a table, and the answer is quite simply that it breaks down in that case. If there are two uniques MySQL will not give an error but just execute the operation as if it was happening on the first row that matches. Essentially the where is extended to be be an or for any of the unique constraints compared to the values from the insert statement. This is obviously dangerous and definitely not optional. In a purely theoretical case the better way would be to be explicit about the uniqueness. This could work like this (if a database would support it): insert into counters ( counter_name , seq ) values ( 'my_counter' , 1 ) on duplicate key ( counter_name ) update seq = seq + 1 ; The standard SQL way is to be super explicit about everything and very confusing MERGE STATEMENT.

Why Upserts? So why are upserts important? The reason is concurrency. Without an upsert there is no way to reliably ensure that a row is in a certain state. The only workaround is to move the code that creates the row into a locked (or semi locked) situation. For instance the traditional example of a one-to-one relationship is usually solved in postgres by just ensuring the related inserts have low chance of concurrency. Imagine for instance you have users and user settings. Each row in the users table has also a related row in the user settings table but each user can only ever have one. Since users typically only ever register once you can just hope for the best and just do an insert. In case there is some concurrency happening then the second transaction will just fail with a unique constraint failure. This reason this is a problem is because of lazy creation, network failure and the hope to make all operations idempotent.

Lazy Creation Lazy creation is the obvious example of where the lack of upserts is painful. The counter example was already brought up. In this case a counter gets created on first increment but that's by far not the only example. A much more common case is functionality that did not exist at a certain point in time and is related to bulk data already existing or is just inherently optional. For instance say you have a few million users and all the sudden you want to add a new one-to-one relationship for these users. Say for instance you added a new notification feature and you want to store the notification settings in a new table. If you don't have upserts the only way to safely deal with this is to have a migration script that adds a nulled-out row for each of these users at the time you add this feature to your application. This is expensive and ultimately a waste of space, because many of your users will already be inactive at the time you add this feature. Also not all of those users will actually use notifications. The obviously better way is an upsert: create this row when the user first needs to configure the notification settings.

Network Failure and Idempotency Upserts are especially important for web applications because the network is inherently unreliable. It's true that HTTP is implemented on top of TCP, a reliable transport, but obviously there is one case that TCP (or HTTP) cannot do by themselves: bidirectional acknowledgement of high level operations. An operation is idempotent if it can be applied multiple times without changing the outcome. An example of such an operation is any read-only operation (such as selects). It does not matter how many times you select something, it will still give you the same result. While the data returned for the next select might be slightly different because of concurrent updates, the select itself does not modify the data. Another inherently idempotent operation is obviously a deletion. No matter how many times you delete something, in the end the row is always gone. Again, someone might concurrently re-create the row but that does not make the operation non-idempotent. On the other hand inserts, increments and similar things are not idempotent. The traditional example is usually brought up with purchases but really this problem is not unique to purchases: it can happy for every single non-idempotent HTTP request. Say you click the purchase button but an error occurs: is it safe to submit the payment again or not? How would you know? The simple answer is that you cannot know because of late failure on the network. For instance say there was a badly implemented web store and a user clicks on the submit button and ultimately the website times out or the user gets a connection reset message. However as far as the server was concerned the request was made, the record for the payment was recorded, the transaction was committed and the user was redirect to a result page and the connection was closed after sending the data. Unfortunately all of this went through a proxy server and the proxy server barfed when sending the data back to the client. The only way to make such an operation idempotent is to provide extra information with the initial request to be able to detect a duplicate attempt. The very obvious one is a "nonce". When the client shows the payment page it rolls a large random number. With the purchase the user submits this number and the server will permanently store it. Even if the response to the client fails, the client can safely retry the transmission under the assumption he sends the same number to the server. The server can then look if the nonce was already stored in the database and then not carry out the payment a second time. The unfortunate truth is that network failure is very, very common and can really happen for every single HTTP request. The reason people mostly bring it up with payments is because it's the part where you can lose the most amount distrust in customers. If you charge someone twice for a service you can only deliver once you have a problem.

Constraint Failures Upserts do not solve this problem, but upserts make it much easier to deal with the problem of resubmission in a generic way. A unique constraint gets you quite far, but it means that all code needs to be able to deal with a constraint failure. For instance to go back to the example of notification settings. If each user can only have one notification setting page, then what happens if accidental concurrency happens and one of the two transactions fails with a unique constraint failure? Not handling it will cause an internal server error most likely. So how would code respond to this? The obvious one is to try the insert first, and then if a constraint failure happens to retry the transaction with an update. However this does not solve the problem, because a concurrent delete might now all the sudden make your update fail. So if deletes are allowed, you now need to check if the update worked, and go back to an insert. This ultimately is loop that endlessly retries and has not guarantee of ever succeeding if high concurrency happens.