In the late 1970s, Jim Gray, the computer scientist who went missing in 2007 and never found, defined a set of properties for database transactions. These properties were Atomicity, Consistency, Isolation and Durability, thus ACID.
What are the ACID properties? How important are they? Can we trade these properties off for performance? Or should we architecture our software around those and make sure they all are met in all instances?
This blog post and the following posts will attempt to answer these questions and clarify each property with hopefully a relatable example. I will try to be consistent and use one example to explain all four properties. I picked Instagram.
Instagram is a popular photo sharing service that is available on mobile devices. You can post a picture, and people who follow you can like or comment on those pictures.
Note: We are going to make a lot of assumption about Instagram database model which may not necessary be the actual implementation. However, we will try to make assumptions that yield highest performance.
Consider this database design, we have a Picture table, and Like table.
The picture table has the LikesCount field which keeps track of how many likes a picture got. This is a performance tweak in order to avoid querying the Like table every time we want to get the likes count for a picture. We notice that picture 1 has 1 like only by Jon and picture 3 has two likes by Reek and Sansa. Picture 2 has no likes. The Picture_ID is the primary key in the Picture table, while both the Picture_ID and the User constitute the primary key for the Like table. There are many fields that we can add but for simplicity we are sticking with these now.
Assume Jon, when he is not guarding the Wall, likes Picture 2. That is equivalent to one update query to the Picture table to increment the count of likes by 1 and one insert query to add a row in the Like table.
To achieve atomicity, those two queries should succeed together or they should fail together. They can't be one success and one fail or else we will end up in a inconsistent state where the likes rows do not match the count of the likes. Obviously its not a big deal if the like count did not match and here is where the trade off can happen. However, in some circumstances, like banking for instance, we can't tolerate such errors. The database solution should support atomic transactions where we can wrap those two queries in one transaction and then rollback in case one of them failed.
You might wonder, what could cause a query to fail? Network connectivity is one, hardware failure is another, but there are other constraints that we can put in place where the database can actually refuse to execute a query. Referential integrity and unique constraints for instance which brings us to the second property.
Assume this, Jon really likes picture 2 he double clicked it again sending another like. Obviously this is wrong, but we have a constraint in the system to prevent that thank fully.
The first statement will go through updating the likes count..
The second query will fail because of unique constraint that we have put in place (Jon and 2) unique key already exists. This will cause the transaction to rollback and undo the first update as well, restoring the system to a consistent state. The atomicity kicked in here and saved the day in order to achieve consistency. If we didn't have an atomic transaction, we will end up with wrong results. Again not the end of the world.
So there is a lot of room for discussions here right, we can implement this logic at the client side to prevent Jon from sending the second like. But that mean an additional cost for reading that Like table and making sure Jon has already liked that. However, leaving a database constraints will lead to a lot of unnecessary traffic to the database that could be prevented.You guys can chip in for a better design here.
Another point I guess I should mention is a consistent state is different than a correct state. I would like to quote C.J. Date on this.
Consistent mean "satisfying all known integrity constraints" Observe, therefore, that consistent does not necessarily mean correct; a correct state must necessarily be consistent, but a consistent state might still be incorrect, in the sense that it does not accurately reflect the true state of affairs in the real world. "Consistent" might be defined as "correct as far as the system is concerned". - An introduction to Database system.
Consistency as you see is expensive to maintain, so some systems now especially distributed systems prefer a different consistency model called Eventual Consistency. Which we will touch upon on another topic.
I want to handle Isolation in another post since it is a long topic.