21 February 2023
Normally, when you drop a column from PostgreSQL, it doesn’t have to do anything to the data in the table. It just marks the column as no longer alive in the system catalogs, and gets on with business.
There is, however, a big exception to this:
ALTER TABLE … SET WITHOUT OIDS. This pops up when using
pg_upgrade to upgrade a database to a version of PostgreSQL that doesn’t support table OIDs (if you don’t know what and why user tables in PostgreSQL had OIDs, that’s a topic for a different time).
ALTER TABLE … SET WITHOUT OIDS rewrites the whole table, and reindexes the table as well. This can take up quite a bit of secondary storage space:
- On the tablespace that the current table lives in, it can take up to the size of the table as it rewrites the table.
- On temporary file storage (
pg_tmp), it can take significant storage doing the reindexing, since it may need to spill the required sorts to disk. This can be mitigated by increasing
So, plan for some extended table locking if you do this. If you have a very large database to upgrade, and it still has tables with OIDs, this may be an opportunity to upgrade via logical replication rather than
16 February 2023
This topic pops up very frequently: “Should we use
bigints as primary keys?”
One of the reasons that the question gets so many conflicting answers is that there are really two different questions being asked:
- “Should our keys be random or sequential?”
- “Should our keys be 64 bits, or larger?”
Let’s take them independently.
Should our keys be random or sequential?
There are strong reasons for either one. The case for random keys is:
They’re more-or-less self-hashing, if the randomness is truly random. This means that if an outside party sees that you have a customer number 109248310948109, they can’t rely on you having a customer number 109248310948110. This can be handy if keys are exposed in URLs or inside of web pages, for example. You can expose
66ee0ea6-dad8-4b0b-af1c-bdc55ccd45e to the world with a pretty high level of confidence you haven’t given an attacker useful information.
It’s much easier to merge databases or tables together if the keys are random (and highly unlikely to collide) than if the keys are serials starting at 1.
The case for sequential keys is:
Sequential keys are (sometimes much) faster to generate than random keys.
Sequential keys have much better interaction with B-tree indexes than random keys, since inserting a new key doesn’t have to consult as many pages as it does in a random key. Different tests have come up with different results on how big the performance difference is, but random keys are always going to be slower than sequential ones in this case. (Note, however, that the tests almost always compare
UUID, and that’s conflating both the sequential vs random and 64-bit vs 128-bit properties.)
As we note below, “sequential” doesn’t automatically mean
bigint! There are implementations of UUIDs (or, at least, 128-bit UUID-like values) that have high order sequential bits but low order random bits. This avoids the index locality problems of purely random keys, while preserving (to an extent) the self-hashing behavior of random keys.
Should our keys be 64 bits, or larger?
It’s often just taken for granted than when we say “random” keys, we mean “
UUIDs”, but there’s nothing intrinsic about
bigint keys that means they have to be sequential, or (as we noted above) about
UUID keys that require they be purely random.
bigint values will be more performant in PostgreSQL than 128 bit values. Of course, one reason is just that PostgreSQL has to move twice as much data (and store twice as much data on disk). A more subtle reason is the internal storage model PostgreSQL uses for values. The
Datum type that represents a single value is the “natural” word length of the processor (64 bits on a 64 bit processor). If the value fits in 64 bits, the
Datum is just the value. If it’s larger than 64 bits, the
Datum is a pointer to the value. Since
UUIDs are 128 bits, this adds a level of indirection and memory management to handling one internally. How big is this performance issue? Not large, but it’s not zero, either.
So, if you don’t think you need 128 bits of randomness (really, 124 bits plus a type field) that a
UUID provides, consider using a 64 bit value even if it is random, or if it is (for example) 16 bits of sequence plus 48 bits of randomness.
Other considerations about sequential keys
If you are particularly concerned about exposing information, one consideration is that keys that have sequential properties, even just in the high bits, can expose the rate of growth of a table and the total size of it. This may be something you don’t want run the risk of leaking; a new social media network probably doesn’t want the outside world keeping close track of the size of the
user table. Purely random keys avoid this, and may be a good choice if the key is exposed to the public in an API or URL. Limiting the number of high-order sequential bits can also mitigate this, and a (probably small) cost in locality for B-tree indexes.
15 February 2023
I’ll be speaking on Database Antipatterns and How to Find Them at SCaLE 2023, March 9-12, 2023 in Pasadena, CA.
I’m very happy that I’ll be presenting “Extreme PostgreSQL” at PgDay/MED in Malta (yay, Malta!) on 13 April 2023.
8 February 2023
The slides from my talk at the February 2023 SFPUG Meeting are now available.
30 January 2023
I’m very pleased to be talking about real-life logical replication at Nordic PgDay 2023, in beautiful Stockholm.
18 January 2023
There’s a particular anti-pattern in database design that PostgreSQL handles… not very well.
For example, let’s say you are building something like Twitch. (The real Twitch doesn’t work this way! At least, not as far as I know!) So, you have streams, and you have users, and users watch streams. So, let’s do a schema!
CREATE TABLE stream (stream_id bigint PRIMARY KEY);
CREATE TABLE "user" (user_id bigint PRIMARY KEY);
CREATE TABLE stream_viewer (
stream_id bigint REFERENCES stream(stream_id),
user_id bigint REFERENCES "user"(user_id),
PRIMARY KEY (stream_id, user_id));
OK, schema complete! Not bad for a day’s work. (Note the double quotes around
USER is a keyword in PostgreSQL, so we have to put it in double quotes to use as a table name. This is not great practice, but more about double quotes some other time.)
Let’s say we persuade a very popular streamer over to our platform. They go on-line, and all 1,252,136 of our users simultaneously log on and start following that stream.
So, we now have to insert 1,252,136 new records into
stream_viewer. That’s pretty bad. But what’s worse is now we have 1,252,136 records with a foreign key relationship to a single record in
stream. During the operation of the
INSERT statement, the transaction that is doing the
INSERT will take a
FOR KEY SHARE lock on that record. This means that at any one moment, several thousand different transactions will have a
FOR KEY SHARE lock on that record.
This is very bad.
If more than one transaction at a time has a lock on a single record, the MultiXact system handles this. MultiXact puts a special transaction ID in the record that’s locked, and then builds an external data structure that holds all of the transaction IDs that have locked the record. This works great… up to a certain size. But that data structure is of fixed size, and when it fills up, it spills onto secondary storage.
As you might imagine, that’s slow. You can see this with lots of sessions suddenly waiting on various
MultiXact* lightweight locks.
You can get around this in a few ways:
- Don’t have that foreign key. Of course, you also then lose referential integrity, so if the
stream record is deleted, there may still be lots of
stream_viewer records that now have an invalid foreign key.
- Batch up the join operations. That way, one big transaction is doing the
INSERTs instead of a large number of small ones. This can make a big difference in both locking behavior, and general system throughput. (For extra credit, use a
COPY instead of an
INSERT to process the batch.)
Not many systems have this particular design issue. (You would never actually build a streaming site using that schema, just to start.) But if you do, this particular behavior is a good thing to avoid.
16 January 2023
Previously, I wrote that you should never lock tables. And you usually shouldn’t! But sometimes, there’s a good reason to. Here’s one.
When you are doing a schema-modifying operation, like adding a column to a table, PostgreSQL needs to take an
ACCESS EXCLUSIVE lock on the table while it is modifying the system catalogs. Unless it needs to rewrite the table, this lock isn’t held for very long.
However, locks in PostgreSQL are first-come, first-served. If the system is busy, there may be conflicting locks on the table that you are attempting to modify. (Even just a
SELECT statement takes lock on the tables it is operating on; it just doesn’t conflict with much.) If the
ALTER TABLE statement can’t get the lock right away, it enters a queue, waiting to get to the front and get the lock.
However, now, every lock after that enters the queue, too, behind that
ALTER TABLE. This can create the result of a long-running
ACCESS EXCLUSIVE lock, even though it’s not granted. On a busy table on a busy system, this can shut things down.
So, what to do?
You can do this:
FOR i IN 1 .. 1000 LOOP
LOCK TABLE t NOWAIT;
ALTER TABLE t ADD COLUMN i BIGINT;
EXCEPTION WHEN lock_not_available THEN
This loops until it can acquire the lock, but doesn’t sit in the queue if it can’t. Once it acquires the lock, it does the modification and exits. If it can’t acquire the lock after a certain number of cycles, it exits with an error (you can set the number of cycles to anything, and you can adjust time it sleeps after failing to get the lock).
15 January 2023
NUMERIC is a variable length type of fixed precision. You can have as many digits as you want (and you want to pay the storage for).
DOUBLE PRECISION is a floating point type, with variable precision.
Sometimes, the question comes up: How much slower is
DOUBLE PRECISION, anyway?
Here’s a quick, highly unscientific benchmark:
Doing a simple test (100 million rows), a straight
SUM() across a
NUMERIC was about 2.2x slower than
OUBLE PRECISION. It went up to about 4x slower if there was a simple calculation,
SUM(n*12). It was about 5x slower if the calculation involved the same type,
SUM(n*n). Of course, these are just on my laptop, but I would expect that the ratios would remain constant on other machines.
Inserting the 100 million rows took 72.2 seconds for
DOUBLE PRECISION, 146.2 seconds for
NUMERIC. The resulting table size was 3.5GB for
DOUBLE PRECISION, 4.2GB for
NUMERIC is slower. But it’s not absurdly slower.
NUMERIC is much slower than
bigint (exercise left to the reader), so using
NUMERIC for things like primary keys is definitely not a good idea.
15 November 2019
If you have something interesting to day about PostgreSQL, we [would love to get a proposal from you]. Even if you have never spoken before, consider responding to the CfP! PgDay 2020 is particularly friendly to first-time and inexperienced speakers. You’re among friends! If you use PostgreSQL, you almost certainly have opinions and experiences that others would love to hear about… go for it!