# PostgreSQL Vector Database Analysis

> Analyze and optimize a pgvector-powered database to understand storage patterns, performance characteristics, and data quality for embeddings in production workloads.

## What's this about?

You've got a PostgreSQL database running with the vector extension that stores embeddings for RAG (document similarity search, image recognition), or other ML workloads.
Your job is to dive deep into this vector database and figure out what's going on under the hood.
You need to understand:

- how vectors are stored
- how much space they're taking up
- whether indexes are working properly
- if there are any data quality issues lurking around

## What you need to investigate

First, get familiar with what you're working with:

- Check vector extension status: ensuring it's installed properly, check version, identify any configuration issues
- Identify all vector columns across entire database: providing me columns, types of columns, and vector dim (dimensions)
- Map the vector landscape: understand relationships between vector tables and regular tables, foreign keys, dependencies

Vectors can eat up a lot of storage, so let's see where the bytes are going:

- Calculate vector storage overhead: measure how much space vectors take compared to regular columns in same tables
- Analyze table sizes: identify which vector tables are biggest storage consumers, break down by table
- Understand growth patterns: examine record counts and project future storage needs based on current data

Vectors without proper indexes are painfully slow, so investigate:

- Catalog vector indexes: find all HNSW and IVFFlat indexes, document their configurations and parameters
- Measure index effectiveness: determine if indexes are actually being used and helping query performance
- Identify optimization opportunities: spot missing indexes, suboptimal configurations, unused indexes

Bad vector data makes everything worse:

- Hunt for data issues: locate NULL vectors, dimension mismatches, corrupted embeddings that could break queries
- Validate consistency: ensure vectors in each column have consistent dimensions across all rows
- Check for outliers: find vectors that might be skewing similarity calculations or causing performance issues

## Your deliverables

Create these analysis tables and populate them with your findings:

### `vector_analysis_columns`

Complete catalog of every vector column you find:

```sql
CREATE TABLE vector_analysis_columns (
    schema VARCHAR(50),
    table_name VARCHAR(100),
    column_name VARCHAR(100),
    dimensions INTEGER,
    data_type VARCHAR(50),
    has_constraints BOOLEAN,
    rows BIGINT
);
```

### `vector_analysis_storage_consumption`

Show exactly where storage is being consumed:

```sql
CREATE TABLE vector_analysis_storage_consumption (
    schema VARCHAR(50),
    table_name VARCHAR(100),
    total_size_bytes BIGINT,
    vector_data_bytes BIGINT,
    regular_data_bytes BIGINT,
    vector_storage_pct NUMERIC(5,2),
    row_count BIGINT
);
```

### `vector_analysis_indices`

Document all vector indexes and their characteristics:
```sql
CREATE TABLE vector_analysis_indices (
    schema VARCHAR(50),
    table_name VARCHAR(100),
    column_name VARCHAR(100),
    index_name VARCHAR(100),
    index_type VARCHAR(50), -- 'hnsw', 'ivfflat', etc.
    index_size_bytes BIGINT
);
```

Use PostgreSQL system catalogs, pgvector-specific views, and storage analysis functions to gather comprehensive metrics about the vector database implementation.
