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# DataFrame and SQL

DataFusion Java supports two query interfaces: SQL strings via
`SessionContext.sql(String)`, and a programmatic DataFrame API.

## SQL

```java
try (DataFrame df = ctx.sql("SELECT a, b FROM t WHERE a > 10")) {
    df.show();
}
```

`sql(String)` plans the query and returns a `DataFrame`. Execution does
not start until you pull results.

## DataFrame transformations

The DataFrame API exposes `select`, `filter`, `limit`, `distinct`,
`dropColumns`, and `withColumnRenamed`.

```java
try (DataFrame df = ctx.readParquet("/path/to/orders.parquet")) {
    try (DataFrame filtered = df.filter("o_orderpriority = '1-URGENT'")) {
        filtered.show();
    }
}
```

Each transformation returns a new `DataFrame` that must be closed.

## Pulling results

Three patterns are available:

**Stream as Arrow batches.** Use `collect(allocator)` to pull the result
set as Arrow record batches via the [Arrow C Data Interface]:

```java
try (DataFrame df = ctx.sql("SELECT ...");
     ArrowReader reader = df.collect(allocator)) {
    while (reader.loadNextBatch()) {
        var batch = reader.getVectorSchemaRoot();
        // process batch...
    }
}
```

[Arrow C Data Interface]: https://arrow.apache.org/docs/format/CDataInterface.html

**Count rows.** `df.count()` returns the row count without materializing
the rows in the JVM.

**Print for inspection.** `df.show()` and `df.show(int n)` print results
to standard output. Useful for exploration; not appropriate for
production code paths.

## Schema introspection

To get the schema of a registered table without running a query:

```java
org.apache.arrow.vector.types.pojo.Schema schema = ctx.tableSchema("orders");
```

## Plan input

A DataFusion logical plan can be deserialized from `datafusion-proto`
bytes via `SessionContext.fromProto(byte[])`. The `datafusion-proto` Java
classes are generated by the Maven build. This is useful for accepting
plans produced by other DataFusion-aware tooling.