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PostgreSQL Templates (supabase.io)
197 points by kiwicopple 35 days ago | hide | past | favorite | 33 comments

We are using PostgreSQL templates to speed up our integration tests. This allow us to have full blown (and non mocked) databases which are isolated to each parallel running test.

We have recently open-sourced our core-server managing this (go): https://github.com/allaboutapps/integresql

Projects already utilizing Go and PostgreSQL may easily adapt this concept for their testing via https://github.com/allaboutapps/integresql-client-go

We have found H2 in Postgres mode to be the fastest way to test db stuff, at least in Postgres and Java. In Go you might lose most of the speed advantage since its not running "in JVM". It also doesn't support many of pg's advanced features

I'm generally not a fan of emulating database behavior through a mocking layer while testing/implementing. Even minor version changes of PostgreSQL plus it's extensions (e.g. PostGIS) may introduce slight differences, e.g. how indices are used, function deprecations, query planner, etc. . It might not even be an erroneous result, just performance regressions or slight sorting differences in the returned query result.

We try to approximate local/test and live as close as possible, therefore using the same database, with the same extensions in their exact same version is a hard requirement for us while implementing/testing locally.

However, I have no experience with H2 (and the modern Java ecosystem in general), so cannot talk about how close their emulation implementation resembles PostgreSQL behavior.

We do the same thing, with the addendum that nearly all of our tests use this mechanism.

A full test suite run recreates the template db from flyway migrations; every test run clones the template db. One nice thing about this is that it's easy to examine the state of the database on test failures. One downside is that there's no "drop multiple databases" command so cleanup can take a while depending on how often you run the script.

Yep, inspecting the actual database state after multiple test failures directly is very nice (we log the used test-database after before every test, so it's easy to connect afterwards).

Regarding cleanup: We run configure a max pool size in integresql after which "dirty"-flagged test databases are automatically removed (and then recreated on demand). We may also support auto-deletion (e.g. after a successful test) in the future, however currently it just does not seem necessary even when we have 5000+ test-db, it's just a disk-space concern.

This is super nice! I have seen similar solutions for Python in the past, but having an easy API + Docker makes this generic and language agnostics.

Have you looked to tweak PSQL parameters to make it faster for test runs e.g. by disabling disk writes?

Yep, disabling fsync, synchronous_commit and full_page_writes ONLY for this PostgreSQL integration testing strategy gives us an additional boost of ~35% in overall testing speed. https://github.com/allaboutapps/go-starter/blob/master/docke...

Regarding being language agnostic: YES, clients for languages other than go are very welcome. The integresql server actually provides a trivial RESTful JSON api and should be fairly easy to integrate with any language. This is our second iteration regarding this PostgreSQL test strategy, the last version came embedded with our legacy Node.js backend stack and wasn't able to handle tests in parallel, which became an additional hard requirement for us.

See our go-starter project for infos on how we are using integresql, e.g. our WithTestDatabase/WithTestServer utility function we use in all tests to "inject" the specific database: https://github.com/allaboutapps/go-starter/blob/master/inter...

How does this compare (performance-wise) to simply truncating all tables?

We did several benchmarks back in the day and it definitely depends on the size of your migrations + fixtures that you need in every test. Performance increases were simply huge for us, despite the serial execution of our tests in our legacy backends.

A new database scaffolded from a predefined template becomes available in several milliseconds or few seconds VS. truncating, migrating up and seeding (fixtures) would always take several seconds. We initially experimented with recreating these test-databases directly via sql dumps, then binary dumps until we finally realized that PostgreSQL templates are the best option in these testing scenarios.

Sorry, I've got no benchmarks to share at this time.

We're using PostgreSQL teams to create a database from a template for every integration test worker (Jest).

This way every worker works on their own database. The speedup was 5x-7x as tests are now running in parallel.

Do you store test data in the template?

My team uses a migration framework (in our case Flyway) such that we can easily create completely fresh DBs on a factory fresh(+ user roles) instance of Postgres. We do this for our testing as well. This means the schema tools are always tested as well. Is there a benefit to using templates? They're faster perhaps?

I'm using templates as well for the speedup in testing. We build a postgresql docker image with the migrations + seed data, then create a few databases for parallel testing.

When migration or seed files have changed, rules rebuild the image. That way the price of migration (about 2 minutes for our Django app) isn't paid on every test run.

Creating a DB from template is 2 seconds.

We use flyway to create a template. Then each test that mutates the DB automatically invokes a cleanup routine which recreates the test DB from the template. You get into hot water around the connection pooling, but it's much, much faster than refreshing using flyway.

For 10 workers it takes <0.5 seconds to create 10 databases from the template. Test data is created from within tests.

I’ve seen two people mention this for integration tests and that sounds great, but how do you handle system tests? As an example of the difference, my team would use an integration to test a repository class individually and a system test to check everything from the top level api endpoint and down. I think the top level thing is important because it makes sure we are getting the right connection string from our database provider and what not.

Where do you see problems with system tests? Shouldn't they work the same with one database per worker/thread?

I've used this in the past for testing depending on how much seeding was required prior to the test, and how much parallelism was required. Other than that I haven't been able to find any useful real world scenarios to exploit this functionality. I suppose if you wanted to have a source database and a bunch of children and could prevent connections to the source it might be helpful... somehow?

I worked on a SaaS project who's customers are companies in an old-fashioned industry. A common requirement was that their data shouldn't be stored in the same database tables as any other customer. They were fine with it being co-located on the same cloud servers, using the same credentials... as long as their data wasn't on the same tables. So I used postgres templates to make a database per customer.

Didn't make much sense to me either but whatever pays the bills I guess.

There are good reasons for that. At an old company I was at, over a couple years I was around there were multiple (~5) instances of clients asking for custom reports or data dumps, and they were sent data from other clients (as well as their own) because a tenant id was missed in some joins, or something similar. Yes, sloppy practices all around allowed that to happen. That being said, it would have been much less likely to happen if data segregation were enforced at the database level (using either RLS, or separate database/schema/tables per-client).

It's really hard to join customer A's addresses table with customer B's invoices when they are different databases.

There's no reason to join across customers in this situation and it saves you from making that type of mistake.

Colocating this on the same database and using the same users/credentials just makes it easier operationally. It's about isolating the data.

Just don't fall into the trap of creating a database schema per customer. Having different databases is better (as TXID is not shared)

Can you elucidate or provide some reference about this? I’m not familiar with any problems in this space (it’s not clear to me what problems a shared TXID could cause), and schemas seemed neat.

That all depends on how many customers you expect to have in a single database, and how many tables each one needs. EDIT: also, transaction volume.

Ok, so let's imagine your application requires 100 database tables.

If you have 1 customer, you have 100 tables. Every transaction performed in this database (including updates) will increase the age(datfrozenxid) by one. You now have 100 tables you need to vacuum.

If you only had one schema, that's no problem at all. Even with the defaults and a high number of writes, the default 3 autovacuum workers will quickly deal with it. Even though they can only vacuum a table at the time.

Now your business is growing. 1 customer becomes 10, 100... 10000?

At 10k customers, you now have 1 million tables (maybe more, as TOAST tables are counted). Every single transaction increases the 'age' of every table by one. It doesn't matter that 99% of all tables have low TXIDs, the overall DB "age" will be on the table with the highest count.

The default 3 autovacuum workers will definitely NOT cut it at this point(even with PG launching anti-wraparound autovaccums). You can augment by "manually" running vacuum, but in that case the overall database age will only decrease once it's done vacuuming all 1 million tables, so start early(before you reach the ~2.1 billion mark)

Alternatively, you could write a script that will sort tables by age and vacuum individually. This may not help as much as it seems, as the distribution will depend on how much work your autovacuum workers have been able to do so far. Not to mention, even getting the list of tables will be slow at that point.

So now you have to drastically increase the number of workers (and other settings, like work_mem) – which may also means higher costs – and you'll still have to watch this like a hawk (or rather, your monitoring system has to). There's no clear indication that the workers are falling behind, you can only get an approximation by trending TXID age.

You can make this work, but it is a pain. Even more so if you haven't prepared for it. For a handful of customers or a small number of tables or a small number of transactions this may not matter. Our production systems (~200 tables, excluding TOAST) started to fall behind after a few thousand customers. We have had at least one outage because we didn't even track this at the time and the database shutdown. 20/20, but nobody remembered to add this to the monitoring system.

Another unrelated problems with multiple schemas is: database migrations are much more complex now, you have to upgrade all schemas. This is both a blessing and a curse. Database dumps also tend to take forever.

i use templates to create a "quick save" backup of a database quickly during development:

createdb -T old_db new_db

Neat trick. Is this any faster than pg_dump and pg_load? Note: there's plenty of optimization you can apply to pg_dump and even use pg_restore, none applied below.

Like so:

    function save_database() {
        mkdir -p /tmp/database-snapshots
        pg_dump -Fc "$DATABASE_NAME" >"/tmp/database-snapshots/$DATABASE_NAME.dump"

    function restore_database() {

        if [ ! -f "$DATABASE_DUMP" ]; then
            echo "No dump to restore"
            psql postgres -c 'SELECT pg_terminate_backend(pid) FROM pg_stat_activity WHERE pid <> pg_backend_pid()'
            psql postgres -c "DROP DATABASE IF EXISTS \"$DATABASE_NAME\""
            psql postgres -c "CREATE DATABASE \"$DATABASE_NAME\""
            pg_restore -Fc -j 8 -d "$DATABASE_NAME" "$DATABASE_DUMP"

Yes, it's significantly faster because it doesn't require rebuilding indexes (among other reasons probably). Downside is it consumes more disk space.

Stellar is a tool that wraps the template mechanism, and has some benchmarks: https://github.com/fastmonkeys/stellar

Oh another key downside I remembered of the template mechanism, at least with Postgres 9.6, is that you can't have any connections open to the source database when making a snapshot copy.

My understanding has always been that creating a database is essentially a series of file copy operations from the template.

That may be true! The point is the template has the indexes already built ready to copy. Where the dump file does not.

Another approach is to use thin clones of Postgres to create disposable databases for fast iterations.

We've made Database Lab tool working on top of CoW file systems (ZFS, LVM) that is capable of provision multiterabyte Postgres instances in seconds. The goal is to use such clones to verify database migrations and optimize SQL queries against production-size data. See our repo: https://gitlab.com/postgres-ai/database-lab.

Is this similar to doing something like `rails db:schema:load` and then `rails db:seed`? i.e. set a brand new db to a certain schema + preload a set of fixtures into it?

The effect will be relatively similar -- you arrive at a new database with the proper schema/structure in place, but the difference is in execution. Doing this _within_ the database engine is likely 10x faster than loading the schema through a client library making a series of CREATE and ALTER statements (ala the schema:load rake task).

Does anybody know why template1 isn't utf8 by default? Because of that I always need to create new databases from template0 to use utf8 f.e like this: `createdb -T template0 -E UTF8 newdb`

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