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# PyPaimon Rust

This project builds the Rust-powered core for [PyPaimon](https://paimon.apache.org/docs/master/pypaimon/overview/) while also providing DataFusion integration for querying Paimon tables.

Install via PyPI:

```
pip install pypaimon-rust
```

If you want to use the native Python DataFusion `SessionContext`, install `datafusion` as well.

## Query Paimon Tables with DataFusion

The recommended way to query Paimon tables is through `SQLContext`, which supports
multi-catalog registration, DDL, DML, and all Paimon-specific SQL extensions:

```python
from pypaimon_rust.datafusion import SQLContext

ctx = SQLContext()
ctx.register_catalog("paimon", {
    "warehouse": "/path/to/warehouse",
})

# DDL
ctx.sql("CREATE SCHEMA paimon.my_db")
ctx.sql("CREATE TABLE paimon.my_db.users (id INT, name STRING, PRIMARY KEY (id))")

# DML
ctx.sql("INSERT INTO paimon.my_db.users VALUES (1, 'alice'), (2, 'bob')")

# Query tables via SQL (catalog.database.table)
batches = ctx.sql("SELECT * FROM paimon.my_db.users")
```

### Multimodal SQL Helpers

`SQLContext` also registers Python scalar UDFs for common BLOB media and vector
workflows. Install the optional media dependencies when you need image or video
decoding:

```shell
pip install "pypaimon-rust[video]"
```

```python
batches = ctx.sql("""
    SELECT
        id,
        media_info(content) AS info_json,
        media_thumbnail(content, 160, 90) AS preview_png,
        video_snapshot(content, 1000) AS frame_png
    FROM paimon.my_db.assets
""")
```

For vector search, `vector_from_json` converts JSON-encoded embeddings into
Arrow `List<Float32>` values that can be used from a temporary table or subquery:

```python
batches = ctx.sql("""
    WITH queries AS (
        SELECT id, vector_from_json(embedding_json) AS embedding
        FROM paimon.my_db.query_embeddings
    )
    SELECT q.id AS query_id, r.id AS result_id
    FROM queries q
    CROSS JOIN LATERAL vector_search(
        'paimon.my_db.items',
        'embedding',
        q.embedding,
        10
    ) AS r
""")
```

### Temporary Tables

You can register temporary in-memory tables programmatically. Names support the same resolution rules as SQL: bare names use the current catalog and database, partially qualified names use the current catalog, and fully qualified names specify catalog.database.table.

Register a single PyArrow RecordBatch as a temporary table:

```python
import pyarrow as pa

batch = pa.record_batch([[1, 2], ["alice", "bob"]], names=["id", "name"])

ctx.register_batch("paimon.default.my_temp", batch)

batches = ctx.sql("SELECT * FROM paimon.default.my_temp")

# Drop it via SQL when no longer needed
ctx.sql("DROP TEMPORARY TABLE paimon.default.my_temp")
```

Alternatively, if you want to use the native Python DataFusion `SessionContext`,
install `datafusion` and register a `PaimonCatalog`:

```python
from datafusion import SessionContext
from pypaimon_rust.datafusion import PaimonCatalog

catalog = PaimonCatalog({
    "warehouse": "/path/to/warehouse",
})

ctx = SessionContext()
ctx.register_catalog_provider("paimon", catalog)

# Query tables via SQL (catalog.database.table)
df = ctx.sql("SELECT * FROM paimon.default.my_table LIMIT 10")
df.show()
```

### REST Catalog

```python
from datafusion import SessionContext
from pypaimon_rust.datafusion import PaimonCatalog

catalog = PaimonCatalog({
    "metastore": "rest",
    "uri": "http://localhost:8080",
    "warehouse": "my_warehouse",
})

ctx = SessionContext()
ctx.register_catalog_provider("paimon", catalog)
```

Time travel queries are not supported in the Python binding at this time.
