Better Know A Ruby Thing: On The Use of Private Methods

Noel Rappin writes thoughtfully about private methods. I actually approach writing classes in the opposite way that he does, and I also do it because of past experience. I currently work on a smallish team, but I used to work on a team that had ~200 engineers committing to the same monolith. Effectively, the cost of writing public-by-default methods was essentially the same as maintaining a library: It wasn’t clear to me who would use my class, when, or how. My approach is private-by-default: Make the API as small as possible.

If, in fact, there is a future need to re-use some abstraction that is currently used in a private method, I am happy to refactor later and make that functionality available purposefully.

Inline RBS type declaration

Experimental syntax, but this mode of type declarations are something I can get on board with!

Since it’s using #:: and # @rbs as special comments, they can probably be recognized by syntax highlighters and other tooling.

Lesser Known Rails Helpers to Write Cleaner View Code

I’ve been writing Rails for over 12 years, and some of these are new to me! I wonder if I’ll remember that they exist next time I have opportunity to use them!

Why PostgreSQL Is the Bedrock for the Future of Data

The take is not exactly surprising, since Timescale is all-in on Postgres, and the last part of the article feels a bit like an ad. That said, Postgres is fantastic and getting better with each release.

Free Yourself, Build the Future, Embrace PostgreSQL

Why, after 6 years, I’m over GraphQL

Well reasoned critique of GraphQL, with practical examples of the issues of working with GraphQL. Instead, the author wants to keep the strict typing, but go back to REST endpoints.

(Bi)Temporal Tables, PostgreSQL and SQL Standard

This is exciting! The article says that temporal support (and later bi-temporal support) is coming natively to Postgres! That is welcome news! I am excited to learn how will temporal modeling be supported and how can Rails (and other) frameworks can take advantage of it.

An unfair advantage: multi-tenant queues in Postgres

An interesting post on allocating jobs in queues “fair” for multi-tenants. The algorithm described does the job distribution at write time, leaving a simpler dequeueing process.

Ruby Heredocs

Good reference on how to use ruby HEREDOCs. Depending on how often you use them, it’s easy to forget the specific syntax.

When Do We Stop Finding New Music? A Statistical Analysis

Not a lot of complicated stats here, except some slicing and averaging. However, the conclusion resonates. Your musical tastes stop evolving. I certainly find many new less artists than I did before. That said, my experience is not exactly like what is described: While I still like the music that I listened to when I was 13-15, I listen more to music that I didn’t really get into until much later, in my late 20s and early 30s.

Postgres has support for creating views that are themselves updatable, as if they are tables. By default, this is only possible for simple views, as defined in the documentation. More advanced views, can also be updatable, with a bit more work. Lets examine how.

For the sake of this example¹, lets say we have a user_last_logins table that captures the last date and time that the user has logged into a system:

CREATE TABLE user_last_logins (
  user_id integer NOT NULL,
  logged_in_at timestamp NOT NULL
);
-- CREATE TABLE
-- Time: 10.102 ms

CREATE UNIQUE INDEX idex_user_id_on_user_last_logins
ON user_last_logins(user_id);
-- CREATE INDEX
-- Time: 7.783 ms

Somewhere in the application code, when the user logs in, we update the time that a user has logged in.

INSERT INTO user_last_logins(user_id, logged_in_at)
VALUES
  (1, NOW()), (2, NOW()), (3, NOW());
-- INSERT 0 3
-- Time: 5.007 ms

SELECT * FROM user_last_logins;
--  user_id |        logged_in_at
-- ---------+----------------------------
--        1 | 2024-04-29 14:36:11.765157
--        2 | 2024-04-29 14:36:11.765157
--        3 | 2024-04-29 14:36:11.765157
-- (3 rows)
--
-- Time: 0.481 ms


UPDATE user_last_logins
SET logged_in_at = NOW()
WHERE user_id = 1;
-- UPDATE 1
-- Time: 5.463 ms

SELECT * FROM user_last_logins;
--  user_id |        logged_in_at
-- ---------+----------------------------
--        2 | 2024-04-29 14:36:11.765157
--        3 | 2024-04-29 14:36:11.765157
--        1 | 2024-04-29 14:36:22.268137
-- (3 rows)
--
-- Time: 0.973 ms

Notice how the timestamp for the row with user_id = 1 was updated.

Now lets imagine that our requirements change. We are now set to record the time of every login by users, as opposed to the last time. We also have different client applications that might not be on the same schedule, and would like to keep the current database functionality intact. Our first order of business is to create our new table and populated with the information we have at hand:

BEGIN;

CREATE TABLE user_logins (
  user_id integer NOT NULL,
  logged_in_at timestamp NOT NULL
);

INSERT INTO user_logins (user_id, logged_in_at)
SELECT * FROM user_last_logins;

COMMIT;
-- BEGIN
-- Time: 0.129 ms
-- CREATE TABLE
-- Time: 4.720 ms
-- INSERT 0 3
-- Time: 0.842 ms
-- COMMIT
-- Time: 0.339 ms

SELECT * from user_logins;
--
--  user_id |        logged_in_at
-- ---------+----------------------------
--        2 | 2024-04-29 14:36:11.765157
--        3 | 2024-04-29 14:36:11.765157
--        1 | 2024-04-29 14:36:22.268137
-- (3 rows)
--
-- Time: 0.816 ms

The new table has the same structure as the old table, but has no unique index on user_id, because we want to allow multiple rows for each user. I am aware that I could have renamed the table instead of creating a new one and copying data. I choose to do it this way for didactical purposes.

It’s now possible to insert new records for each users:

INSERT INTO user_logins VALUES (1, NOW());
-- INSERT 0 1
-- Time: 1.613 ms

SELECT * from user_logins;
--  user_id |        logged_in_at
-- ---------+----------------------------
--        2 | 2024-04-29 14:36:11.765157
--        3 | 2024-04-29 14:36:11.765157
--        1 | 2024-04-29 14:36:22.268137
--        1 | 2024-04-29 14:37:23.696786
-- (4 rows)
--
-- Time: 0.505 ms

We still have a problem: There are clients to this application that can’t change their code right away. Let’s tackle first the clients that want to read an up-to-date user_last_logins relation:

BEGIN;

DROP TABLE user_last_logins;

CREATE OR REPLACE VIEW user_last_logins AS
SELECT user_id, MAX(logged_in_at) as logged_in_at
FROM user_logins
GROUP BY 1;

COMMIT;
-- BEGIN
-- Time: 0.103 ms
-- DROP TABLE
-- Time: 3.219 ms
-- CREATE VIEW
-- Time: 6.703 ms
-- COMMIT
-- Time: 1.338 ms

SELECT * FROM user_last_logins;
--  user_id |        logged_in_at
-- ---------+----------------------------
--        3 | 2024-04-29 14:36:11.765157
--        2 | 2024-04-29 14:36:11.765157
--        1 | 2024-04-29 14:37:23.696786
-- (3 rows)
--
-- Time: 1.273 ms

We’ve created a view that produced the same information that used to be in user_last_logins from the underlying user_logins table, with the same guarantees that a user_id will only show up in a single row. Read clients can continue working without a hitch. However, write clients won’t be able to update as before:

UPDATE user_last_logins
SET logged_in_at = NOW()
WHERE user_id = 1;
-- ERROR:  cannot update view "user_last_logins"
-- DETAIL:  Views containing GROUP BY are not automatically updatable.
-- HINT:  To enable updating the view, provide an INSTEAD OF UPDATE trigger or an unconditional ON UPDATE DO INSTEAD rule.
-- Time: 0.975 ms

Conceptually, we know that the underlying table has the same structure, and that the insert to the view can be forwarded to the underlying table. The error even gives us a hint at what to do: Use and INSTEAD OF UPDATE trigger.

BEGIN;

CREATE OR REPLACE FUNCTION instead_function_insert_user_last_logins() RETURNS TRIGGER AS
$BODY$
BEGIN

INSERT INTO user_logins(user_id, logged_in_at)
VALUES (NEW.user_id, NEW.logged_in_at);

RETURN NEW;
END;
$BODY$
LANGUAGE plpgsql;

CREATE TRIGGER instead_trigger_user_last_logins
INSTEAD OF UPDATE ON user_last_logins
FOR EACH ROW
EXECUTE PROCEDURE instead_function_insert_user_last_logins();

COMMIT;
-- BEGIN
-- Time: 0.077 ms
-- CREATE FUNCTION
-- Time: 6.508 ms
-- CREATE TRIGGER
-- Time: 2.049 ms
-- COMMIT
-- Time: 0.427 ms

We’ve defined a trigger that runs on each attempted update of user_last_logins and instead runs a function. The body of the function plainly inserts instead into user_logins. And because user_logins powers the user_last_logins view, the insert appears to work as requested.

UPDATE user_last_logins
SET logged_in_at = NOW()
WHERE user_id = 1;
-- UPDATE 1
-- Time: 6.153 ms

SELECT * FROM user_last_logins;
--  user_id |        logged_in_at
-- ---------+----------------------------
--        3 | 2024-04-29 14:36:11.765157
--        2 | 2024-04-29 14:36:11.765157
--        1 | 2024-04-29 14:38:39.369368
-- (3 rows)
--
-- Time: 0.696 ms


SELECT * FROM user_logins;
--  user_id |        logged_in_at
-- ---------+----------------------------
--        2 | 2024-04-29 14:36:11.765157
--        3 | 2024-04-29 14:36:11.765157
--        1 | 2024-04-29 14:36:22.268137
--        1 | 2024-04-29 14:37:23.696786
--        1 | 2024-04-29 14:38:39.369368
-- (5 rows)
--
-- Time: 0.826 ms

Now our existing clients can migrate to use user_logins at their own pace. For this particular case, we only dealt with INSTEAD OF UPDATE, but in the same vein we could have defined an INSTEAD OF INSERT trigger.

Conclusions

The INSTEAD OF trigger allows us a complex view to remain updatable. In this made-up-case this allows views to function as an abstraction for tables that no longer exist, and de-couples the need from client code changing at the same time as the database schema.

Bash Debugging

Another gem by Julia Evans. I always learn something from her zines, and this is no exception: You can have a step debugger in bash!

How Figma’s Databases Team Lived to Tell the Scale

Very interesting article on how Figma managed to scale Postgres. Having a DB proxy that checks the queries and routes to the correct shard (and even aggregates among shards) is wild.

The use of logical partitioning to prove their work before doing actual physical partitioning is very clever.

Before going out and building a custom replication scheme, remember that there are out-of-the-box solutions out there that most organizations are better choosing over custom solutions.

jnv: interactive JSON filter using jq

jnv looks like a nice tool for interactively exploring the a JSON file.