Ask The Game, the Build Log

Why I Spent My Weekend Renaming Database Tables Instead of Building Features

I just spent my weekend doing something that probably sounds incredibly boring: renaming database tables and columns. But for me, it was fun.

When I started building my system, I was moving fast. Really fast. I named my main database table the_game_podcast and had columns like episode_title and episode_audio_url. It worked, so I kept going.

Then I added transcription chunks and called the table the_game_podcast_chunks with a column called chunk_text. Then speaker analysis went into speaker_embeddings_vectors. You see where this is going.

Fast forward six months and 915 episodes later, my database looked like this:

the_game_podcast.episode_title
the_game_podcast_chunks.chunk_text  
speaker_embeddings_vectors.speaker_label
chunk_segments.segment_decision

Every time I wrote a query, I had to mentally translate between what the data actually was (episodes, transcripts, speakers) and what I'd hastily named the tables a month ago.

I was spending more time remembering my own naming decisions than actually building intelligence on top of the podcast content.

Easy Migration

I decided to standardize everything to follow actual database naming conventions. Industry standard stuff, meaning plural table names, no redundant prefixes, snake_case columns that describe the data clearly.

The plan was:

I expected this to take a couple of days and possibly break things. Instead, it took about two hours and worked well. Here's how I approached it:

Step 1: Create new tables with clean names

Instead of trying to rename everything in place, I created brand new tables with the correct schema. This meant I could test everything without risking the existing data.

Step 2: Migrate data with column mapping

The tricky part was that some data types didn't match. Like sentiment_score was stored as JSONB containing a number, but my new schema expected a proper numeric column. I had to convert text confidence levels like "high" to actual numbers (0.9, 0.7, 0.5).

Step 3: Update application code

This is where the real work was. I had to update my database service layer, configuration files, and key scripts to use the new table and column names.

Step 4: Test with real pipeline

The moment of truth - I ran my complete podcast processing pipeline on a real episode to see if everything still worked.

What Broke and What I Learned

The pipeline test caught exactly the kind of issue I was worried about. When the system tried to save transcription chunks to the new transcripts table, it failed because the new schema required UUID primary keys, but my old code was expecting auto-generated integers.

ERROR: null value in column "id" of relation "transcripts" violates not-null constraint

This is precisely why I tested with a real episode instead of just assuming the migration worked. The fix was simply to generate UUIDs for new transcript records; however, identifying the issue through real testing saved me from breaking the production system.

The Immediate Payoff

Now when I write queries, they actually make sense:

-- Before (confusing)
SELECT episode_title, chunk_text 
FROM the_game_podcast 
JOIN the_game_podcast_chunks ON episode_id = episode_id

-- After (clear)
SELECT title, text
FROM episodes 
JOIN transcripts ON episodes.id = transcripts.episode_id

But the real benefit isn't just cleaner code. It's that I can think about my data in terms of what it actually represents instead of translating through my old naming decisions.

Technical Debt

I'm glad I did this migration now rather than waiting until I had even more data and more complex features built on top of the messy schema. And with the help of AI, it's easier to create a migration plan and a monitoring system. I'm about to process 915 episodes with thousands of transcript segments, and the migration will have to work flawlessly on all of it.

What's Next

Now that I have a solid foundation, I'm returning to add more features. Actually, no, I think I will process all the episodes.

I was recently accepted into the [Deepgram Startup Program](Deepgram Startup Program), so I now have the budget to transcribe all of it.

Pipeline heating up.

– Benoit Meunier