alpha31476/spotify-metadata-parquet

Spotify metadata and audio features

This is a lossless Parquet export of four supplied Spotify SQLite databases. It retains all 13 tables and all original columns. It includes track, artist, album, genre, popularity, file metadata, and audio measurements.

Storage: 67.08 GiB in 9,659 Zstd-compressed Parquet files, compared with 278.65 GiB of SQLite source files. This reduces file size by 75.93%. The tables contain 1,762,433,614 rows in total. That total includes relationship rows and derived tables, not unique songs.

The repository includes the conversion, validation, query, audit, benchmark, packaging, and upload code. It also includes the supplied PowerShell, Python, SQL, and notebooks. See the code guide.

Tables

Each table has its own Hugging Face configuration. tracks is the default. The train split contains the full table. It is not a curated training split.

Table	Rows	Original columns	Parquet GiB
tracks_ranking	256,039,007	8	5.3705
track_audio_features	255,594,909	17	9.0907
track_files	255,966,403	27	32.3770
albums	58,590,982	15	2.8377
artists	15,430,442	6	0.4035
tracks	256,039,007	13	13.3302
album_images	175,809,992	4	1.4521
artist_images	26,777,840	4	0.2402
artist_genres	2,229,947	2	0.0094
artist_albums	111,808,828	5	0.5943
track_artists	348,055,756	2	1.3705
available_markets	89,501	2	0.0020
new_table_name	1,000	11	0.0000

Each exported table also contains __sqlite_rowid. This retains the internal SQLite row number, including for tables without an explicit row ID column. The query helper hides that added column. Original rowid columns remain. The schema and original index definitions are in manifest.json. SQLite index structures are not copied into Parquet.

Audio measurements and audio files

track_audio_features retains tempo, energy, danceability, loudness, speechiness, acousticness, instrumentalness, liveness, valence, key, mode, time signature, and duration. Measurements use float64 where needed. No measurements are rounded. Missing values remain NULL.

These databases contain metadata, not full-song audio recordings. Image fields contain URLs. Spotify file IDs identify files but do not contain them. The prefixed_ogg_packet field contains a short binary prefix.

Stream a table

Install datasets separately to use this optional loading method:

python -m pip install datasets

from datasets import load_dataset

rows = load_dataset(
    "alpha31476/spotify-metadata-parquet",
    name="track_audio_features",
    split="train",
    streaming=True,
)
print(next(iter(rows)))

Select tracks, artists, albums, or any table name listed above as name. Streaming avoids downloading the full repository first.

Query a local copy

Use Python 3.11 or later. Install the core dependencies from the repository:

python -m pip install -r requirements.txt
python query_spotify.py --sql "SELECT rowid, name, popularity FROM tracks LIMIT 10"
python query_spotify.py --sql "SELECT tempo, energy, danceability FROM track_audio_features LIMIT 10"

The helper creates DuckDB views over the Parquet files. It does not import all rows into another database. The example queries show album, artist, genre, and audio-feature joins. Large joins can still need time, memory, and temporary disk space.

Validation

During conversion, every part was read back and compared with its source Arrow values. Floating-point measurements were compared by their binary representation. NULL positions, binary values, source row counts, and source file identities were checked.

The final check confirmed all table counts, schemas, part sizes, NULL statistics, row ID ranges, and Zstd compression. See validation.json. release_inventory.json records SHA256 checksums for the staged files. It excludes itself and local runtime files.

The portable export code was also tested on two real rows from each of the 13 source tables. See release_checks.json.

python verify_export.py
python upload_dataset.py --verify-hashes

The first command checks Parquet metadata. The second reads all files to check their checksums. Neither command uploads data.

Reproduce or publish

The original SQLite files are inputs to the export tool. They are not included in this repository. The exporter opens them read-only. See the code guide for the full commands and dependency notes.

The uploader runs a local check by default. Publication requires --publish. It uses the normal Hugging Face login. No token is stored in this repository. Interrupted uploads can be resumed with --publish --resume on the same prepared release. Local upload cache and state files are excluded.

Source and limits

The supplied source files are spotify_clean.sqlite3, spotify_clean_audio_features.sqlite3, spotify_clean_track_files.sqlite3, and spotify_tracks_ranking.sqlite3. Source table counts and sample rows are included under metadata.

tracks_ranking is a derived ranking table. new_table_name is a local derived sample of 1,000 rows. These are retained to preserve the supplied databases. They are not independent sets of source songs.

Popularity and availability are historical snapshots. They are not current Spotify values. Missing data remain missing. Spotify IDs and archive paths do not establish that the matching audio files are present.

License

No explicit redistribution license was supplied with the local source files. No new data license is assigned by this conversion. See LICENSE-NOTICE.txt. The format change does not grant new rights over upstream content.