# Podcast Reviews Example

This is an example data application where we produce text insights from podcast review data. It is made up of N datasets:

- Raw reviews `(date, podcast, text, rating)`
- Podcasts `(podcast, title, category)`
- Categorized review text `(date, category, podcast, text)`
- Phrase models `(date, category, hash, ngram, score)`
- Podcast phrase stats `(date, category, podcast, ngram, count, rating)`
- Podcast daily summary `(date, category, podcast, phrase_stats, recent_reviews)`

```mermaid
flowchart LR
    raw_reviews[(Raw Reviews)] & podcasts[(Podcasts)] --> categorize_text --> categorized_texts[(Categorized Texts)]
    categorized_texts --> phrase[Phrase Modeling] --> phrase_models[(Phrase Models)]
    phrase_models & raw_reviews --> phrase_stats --> podcast_phrase_stats[(Podcast Phrase Stats)]
    podcast_phrase_stats & raw_reviews --> calc_summary --> podcast_daily_summary[(Podcast Daily Summary)]
```

## Input Data

Get it from [here](https://www.kaggle.com/datasets/thoughtvector/podcastreviews/versions/28?select=database.sqlite)! (and put it in `examples/podcast_reviews/data/ingest/database.sqlite`)

## `phrase` Dependency

This relies on [`soaxelbrooke/phrase`](https://github.com/soaxelbrooke/phrase) for phrase extraction - check out its [releases](https://github.com/soaxelbrooke/phrase/releases) to get a relevant binary.

## Building Output Partitions

### CLI Build
Use the DataBuild CLI to build specific partitions:

```bash
bazel build //:podcast_reviews_graph.build
# Builds bazel-bin/podcast_reviews_graph.build

# Build raw reviews for a specific date
bazel-bin/podcast_reviews_graph.build "reviews/date=2020-01-01"

# Build categorized reviews
bazel-bin/podcast_reviews_graph.build "categorized_reviews/category=Technology/date=2020-01-01"

# Build phrase models
bazel-bin/podcast_reviews_graph.build "phrase_models/category=Technology/date=2020-01-01"

# Build daily summaries
bazel-bin/podcast_reviews_graph.build "daily_summaries/category=Technology/date=2020-01-01"

# Build all podcasts data
bazel-bin/podcast_reviews_graph.build "podcasts/all"
```

### Service Build
Use the Build Graph Service for HTTP API access:

```bash
# Start the service
bazel build //:podcast_reviews_graph.service
bazel-bin/podcast_reviews_graph.service

# Submit build request for reviews
curl -X POST http://localhost:8080/api/v1/builds \
  -H "Content-Type: application/json" \
  -d '{"partitions": ["reviews/date=2020-01-01"]}'

# Submit build request for daily summary (builds entire pipeline)
curl -X POST http://localhost:8080/api/v1/builds \
  -H "Content-Type: application/json" \
  -d '{"partitions": ["daily_summaries/category=Technology/date=2020-01-01"]}'

# Check build status
curl http://localhost:8080/api/v1/builds/BUILD_REQUEST_ID

# Get partition status
curl http://localhost:8080/api/v1/partitions/reviews%2Fdate%3D2020-01-01/status

# Get partition events
curl http://localhost:8080/api/v1/partitions/reviews%2Fdate%3D2020-01-01/events

# Analyze build graph (planning only)
curl -X POST http://localhost:8080/api/v1/analyze \
  -H "Content-Type: application/json" \
  -d '{"partitions": ["daily_summaries/category=Technology/date=2020-01-01"]}'
```

### Partition Reference Patterns

The following partition reference patterns are supported:

- `reviews/date=YYYY-MM-DD` - Raw reviews for a specific date
- `podcasts/all` - All podcasts metadata
- `categorized_reviews/category=CATEGORY/date=YYYY-MM-DD` - Categorized reviews
- `phrase_models/category=CATEGORY/date=YYYY-MM-DD` - Phrase models
- `phrase_stats/category=CATEGORY/date=YYYY-MM-DD` - Phrase statistics
- `daily_summaries/category=CATEGORY/date=YYYY-MM-DD` - Daily summaries (the "output")