Ri

Ripoff – Generate fake data in PostgreSQL from YAML

Hacker News

Ripoff – Generate fake data in PostgreSQL from YAML

Hello all! This is my first time sharing a project here - I built ripoff, a fake data generator for PostgreSQL that goes from templated yaml files to rows in your database. In my personal and professional work I found that fake data was usually either generated in the application layer, which is awkward and slow(er than SQL), or way too focused on random generation resulting in a database full of data that’s unsavory for humans. ripoff is built for cases where you know the shape of your data, like local development, integration testing, and setting up demos. Unlike other fake data generators ripoff isn’t aware of your schema or app, so it feels more like writing templated SQL than using a DSL. The yaml format is a map of unique identifiers to column values, where column values can be literal strings, references to other rows, or functions that generate random data. All random data generated by ripoff requires an explicit seed, which is neat because re-running ripoff will always generate the same content. That determinism enables ripoff to perform upserts when re-run on the same database, so you don't have to wipe your DB after editing fake data. Here’s a complex but real world example of what a ripoff file might look like: https://github.com/mortenson/ripoff/blob/main/testdata/real_... I just published it so there’s some rough edges, but you should be able to give it a try with any project that uses PostgreSQL! Information on installation and use can be found in the README. Thanks and let me know what you think!

Share card

Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real world · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Fa
Fakid – generate fake identities58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Fakid – generate fake identities

Hacker News10
I
I made a website to generate 1000s of rows of fake data56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I made a website to generate 1000s of rows of fake data

Hacker News1
Pl
Plait.py – a fake data modeler49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Plait.py – a fake data modeler

Hacker News85
Gl
Glassdoor without fake data: KnowYourWorth64%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Glassdoor without fake data: KnowYourWorth

Hacker News11
Cu
Curtain – data management system for PostgreSQL51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Curtain – data management system for PostgreSQL

Hacker News3
Ka
Kafka FDW for PostgreSQL65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Kafka FDW for PostgreSQL

Hacker News2
es
es2postgres – ElasticSearch to PostgreSQL Loader71%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

es2postgres – ElasticSearch to PostgreSQL Loader

Hacker News3
Me
Metagration: PostgreSQL Migrator in PostgreSQL56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Metagration: PostgreSQL Migrator in PostgreSQL

Hacker News1
PR
PRQL in PostgreSQL73%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PRQL in PostgreSQL

Hacker News267
Fa
Faker.jl: generator of fake data for julia58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Faker.jl: generator of fake data for julia

Hacker News2