![]() When querying data, PostgreSQL utilizes the metadata to quickly navigate to the relevant page and tuple. A simple representation of a PostgreSQL page containing metadata about the page and tuples stored in the page What happens when querying data? There’s also a special area called the Line Pointer Array, which provides the offsets where each tuple begins. PostgreSQL reads and writes data in page units.Įach page in PostgreSQL consists of a page header (which contains metadata about the page, such as page layout versions, page flags, and so on) and actual data (including tuples). A page is the unit of storage in PostgreSQL, typically 8 kB in size, that holds one or more tuples.Each element in a tuple corresponds to a specific column in that table, containing the actual data value for that column. You'll generally see the terms tuple and row used interchangeably. A tuple is the physical representation of an entry in a table.Essential PostgreSQL Storage Concepts How does PostgreSQL store data?Īt a high level, there are two terms you need to understand: tuples and pages. This last point deserves a deeper dive into how data is actually stored in PostgreSQL and what is causing the problem, so let’s briefly cover some essential PostgreSQL storage concepts. This is something that often gets overlooked, but maintaining PostgreSQL storage usage is paramount to keeping large PostgreSQL tables fast. The second problem that comes with not optimizing your PostgreSQL storage usage is that this situation can lead to bad performance, with queries running slower and your I/O operations increasing. But yes, this also works the opposite way: if you don’t pay attention to managing your storage, your storage bill will increase. Usage-based models are a great incentive to actually optimize your PostgreSQL database size as much as possible since you’ll see immediate reductions in your bill. Timescale charges by the amount of storage you use: you don't need to worry about allocating storage or managing storage plans, which really simplifies things-and the less storage you use, the less it costs. In a way, these issues are mitigated by usage-based models. In other PostgreSQL providers, when you run out of storage space, you must upgrade and pay for the next available plan or storage tier, meaning that you’ll see a considerably higher bill overnight. This model assumes that you’ll need to predetermine how much disk space you’ll need in the future and then pay for it, regardless of whether you end up using it or not, and without the chance of downscaling. If you’re running PostgreSQL in an EBS instance in AWS or in RDS, for example, you’ll be charged through an allocation-based model. ![]() The first problem, and the most obvious, is the cost. Are you running PostgreSQL in RDS? Raise the storage limits. Are you running servers on-prem? Slap another hard drive on that bad boy. Indeed, resigning yourself to simply using more storage is the most straightforward way to tackle an increasingly growing PostgreSQL database. “My PostgreSQL provider is actually usage-based ( like Timescale), and I don’t have the problem of being locked into a large disk.” ![]() “Storage is cheap these days, and optimizing a PostgreSQL database takes time and effort. Let’s spend a couple of minutes addressing this question first. Why Is PostgreSQL Storage Optimization Important? This article explores several strategies that will help you reduce your PostgreSQL database size considerably and sustainably. But you’re going to need a better strategy in the long run to optimize your PostgreSQL storage use, or you’ll keep paying more and more money.ĭoes your PostgreSQL database really need to be that large? Is there something you can do to optimize your storage use? Okay, if it comes down to that situation, you should probably remedy it ASAP by adding more storage. A monitor went off at work-your PostgreSQL database is slowly but steadily reaching its maximum storage space. Your phone buzzes in the middle of the night.
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