The previous series addressed isolation and multiversion concurrency control, and now we start a new series: on write-ahead logging. To remind you, the material is based on training courses on administration that Pavel Luzanov and I are creating (mostly in Russian, although one course is available in English), but does not repeat them verbatim and is intended for careful reading and self-experimenting.
This series will consist of four parts:
- Buffer cache (this article).
- Write-ahead log — how it is structured and used to recover the data.
- Checkpoint and background writer — why we need them and how we set them up.
- WAL setup and tuning — levels and problems solved, reliability, and performance.
Many thanks to Elena Indrupskaya for the translation of these articles into English.
Why do we need write-ahead logging?
Part of the data that a DBMS works with is stored in RAM and gets written to disk (or other nonvolatile storage) asynchronously, i. e., writes are postponed for some time. The more infrequently this happens the less is the input/output and the faster the system operates.
But what will happen in case of failure, for example, power outage or an error in the code of the DBMS or operating system? All the contents of RAM will be lost, and only data written to disk will survive (disks are not immune to certain failures either, and only a backup copy can help if data on disk are affected). In general, it is possible to organize input/output in such a way that data on disk are always consistent, but this is complicated and not that much efficient (to my knowledge, only Firebird chose this option).
Usually, and specifically in PostgreSQL, data written to disk appear to be inconsistent, and when recovering after failure, special actions are required to restore data consistency. Write-ahead logging (WAL) is just a feature that makes it possible.
Now we've reached the last topic of this series. We will talk on the transaction id wraparound and freezing.
Transaction ID wraparound
PostgreSQL uses 32-bit transaction IDs. This is a pretty large number (about 4 billion), but with intensive work of the server, this number is not unlikely to get exhausted. For example: with the workload of 1000 transactions a second, this will happen as early as in one month and a half of continuous work.
But we've mentioned that multiversion concurrency control relies on the sequential numbering, which means that of two transactions the one with a smaller number can be considered to have started earlier. Therefore, it is clear that it is not an option to just reset the counter and start the numbering from scratch.
To remind you, we started with problems related to isolation, made a digression about low-level data structure, discussed row versions in detail and observed how data snapshots are obtained from row versions.
We've already mentioned that normally (i. e., when nothing holds the transaction horizon for a long time) VACUUM usually does its job. The problem is how often to call it.
If we vacuum a changing table too rarely, its size will grow more than desired. Besides, a next vacuum operation may require several passes through indexes if too many changes were done.
If we vacuum the table too often, the server will constantly do maintenance rather than useful work — and this is no good either.
Note that launching VACUUM on schedule by no means resolves the issue because the workload can change with time. If the table starts to change more intensively, it must be vacuumed more often.
Autovacuum is exactly the technique that enables us to launch vacuuming depending on how intensive the table changes are.
Last time we talked about HOT updates and in-page vacuuming, and today we'll proceed to a well-known vacuum vulgaris. Really, so much has already been written about it that I can hardly add anything new, but the beauty of a full picture requires sacrifice. So keep patience.
What does vacuum do?
In-page vacuum works fast, but frees only part of the space. It works within one table page and does not touch indexes.
The basic, "normal" vacuum is done using the VACUUM command, and we will call it just "vacuum" (leaving "autovacuum" for a separate discussion).
So, vacuum processes the entire table. It vacuums away not only dead tuples, but also references to them from all indexes.
Vacuuming is concurrent with other activities in the system. The table and indexes can be used in a regular way both for reads and updates (however, concurrent execution of commands such as CREATE INDEX, ALTER TABLE and some others is impossible).
Only those table pages are looked through where some activities took place. To detect them, the visibility map is used (to remind you, the map tracks those pages that contain pretty old tuples, which are visible in all data snapshots for sure). Only those pages are processed that are not tracked by the visibility map, and the map itself gets updated.
The free space map also gets updated in the process to reflect the extra free space in the pages.