diff --git a/src/sqlite_provider.rs b/src/sqlite_provider.rs
index b636093..2405b02 100644
--- a/src/sqlite_provider.rs
+++ b/src/sqlite_provider.rs
@@ -24,8 +24,14 @@ use async_trait::async_trait;
 use datafusion::catalog::{Session, TableProvider};
 use datafusion::common::Result as DFResult;
 use datafusion::error::DataFusionError;
+use datafusion::execution::{SendableRecordBatchStream, TaskContext};
 use datafusion::logical_expr::{Expr, TableType};
-use datafusion::physical_plan::ExecutionPlan;
+use datafusion::physical_expr::EquivalenceProperties;
+use datafusion::physical_plan::execution_plan::{Boundedness, EmissionType};
+use datafusion::physical_plan::stream::RecordBatchStreamAdapter;
+use datafusion::physical_plan::{
+    DisplayAs, DisplayFormatType, ExecutionPlan, Partitioning, PlanProperties,
+};
 use rusqlite::{Connection, types::Value as SqlValue};
 use tokio::sync::Semaphore;
 
@@ -311,10 +317,262 @@ impl TableProvider for SqliteLookupProvider {
         _filters: &[Expr],
         _limit: Option<usize>,
     ) -> DFResult<Arc<dyn ExecutionPlan>> {
-        Err(DataFusionError::NotImplemented(
-            "SqliteLookupProvider does not support full table scans; use fetch_by_keys".into(),
-        ))
+        Ok(Arc::new(SqliteFullScanExec::new(
+            self.pool.clone(),
+            self.sem.clone(),
+            self.table_name.clone(),
+            self.schema.clone(),
+        )))
+    }
+}
+
+// ── Full-scan execution plan ──────────────────────────────────────────────────
+
+/// Batch size used when streaming rows from SQLite during a full table scan.
+/// Larger values reduce round-trip overhead; smaller values reduce peak memory.
+const SCAN_BATCH_SIZE: usize = 1024;
+
+/// Physical execution plan that streams all rows from a SQLite table in
+/// [`SCAN_BATCH_SIZE`]-row batches.  Used by the adaptive filtered path in
+/// `USearchExec` to evaluate WHERE-clause predicates without loading the
+/// entire table into memory at once.
+#[derive(Debug)]
+struct SqliteFullScanExec {
+    pool: Arc<Mutex<Vec<Connection>>>,
+    sem: Arc<Semaphore>,
+    table_name: String,
+    schema: SchemaRef,
+    properties: PlanProperties,
+}
+
+impl SqliteFullScanExec {
+    fn new(
+        pool: Arc<Mutex<Vec<Connection>>>,
+        sem: Arc<Semaphore>,
+        table_name: String,
+        schema: SchemaRef,
+    ) -> Self {
+        let properties = PlanProperties::new(
+            EquivalenceProperties::new(schema.clone()),
+            Partitioning::UnknownPartitioning(1),
+            EmissionType::Incremental,
+            Boundedness::Bounded,
+        );
+        Self {
+            pool,
+            sem,
+            table_name,
+            schema,
+            properties,
+        }
+    }
+}
+
+impl DisplayAs for SqliteFullScanExec {
+    fn fmt_as(&self, _t: DisplayFormatType, f: &mut fmt::Formatter) -> fmt::Result {
+        write!(f, "SqliteFullScanExec: table={}", self.table_name)
+    }
+}
+
+impl ExecutionPlan for SqliteFullScanExec {
+    fn name(&self) -> &str {
+        "SqliteFullScanExec"
+    }
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+    fn properties(&self) -> &PlanProperties {
+        &self.properties
+    }
+    fn children(&self) -> Vec<&Arc<dyn ExecutionPlan>> {
+        vec![]
+    }
+    fn with_new_children(
+        self: Arc<Self>,
+        children: Vec<Arc<dyn ExecutionPlan>>,
+    ) -> DFResult<Arc<dyn ExecutionPlan>> {
+        if children.is_empty() {
+            Ok(self)
+        } else {
+            Err(DataFusionError::Internal(
+                "SqliteFullScanExec is a leaf node and takes no children".into(),
+            ))
+        }
     }
+
+    fn execute(
+        &self,
+        _partition: usize,
+        _ctx: Arc<TaskContext>,
+    ) -> DFResult<SendableRecordBatchStream> {
+        let pool = self.pool.clone();
+        let sem = Arc::clone(&self.sem);
+        let table_name = self.table_name.clone();
+        let schema = self.schema.clone();
+
+        // Bounded channel: backpressure limits how many batches are buffered
+        // ahead of the consumer, keeping peak memory to O(batch_size × 2).
+        let (tx, rx) = tokio::sync::mpsc::channel::<DFResult<RecordBatch>>(2);
+
+        let schema_task = schema.clone();
+        tokio::spawn(async move {
+            // Acquire a semaphore permit so the scan counts against the
+            // same concurrency limit as fetch_by_keys.
+            let _permit = match sem.acquire_owned().await {
+                Ok(p) => p,
+                Err(e) => {
+                    let _ = tx
+                        .send(Err(DataFusionError::Execution(e.to_string())))
+                        .await;
+                    return;
+                }
+            };
+
+            let conn = match pool.lock() {
+                Ok(mut g) => g.pop().ok_or_else(|| {
+                    DataFusionError::Execution("SqliteFullScanExec: connection pool empty".into())
+                }),
+                Err(e) => Err(DataFusionError::Execution(format!(
+                    "connection pool mutex poisoned: {e}"
+                ))),
+            };
+            let conn = match conn {
+                Ok(c) => c,
+                Err(e) => {
+                    let _ = tx.send(Err(e)).await;
+                    return;
+                }
+            };
+
+            let pool_c = pool.clone();
+            let tx_c = tx.clone();
+            if let Err(e) = tokio::task::spawn_blocking(move || {
+                let guard = ConnGuard::new(pool_c, conn);
+                let conn = guard.conn.as_ref().unwrap();
+
+                let col_list = schema_task
+                    .fields()
+                    .iter()
+                    .map(|f| quote_ident(f.name()))
+                    .collect::<Vec<_>>()
+                    .join(", ");
+                // No ORDER BY — the adaptive filter doesn't require ordering.
+                let sql = format!("SELECT {col_list} FROM {}", quote_ident(&table_name));
+
+                let mut stmt = match conn.prepare(&sql) {
+                    Ok(s) => s,
+                    Err(e) => {
+                        let _ = tx_c.blocking_send(Err(DataFusionError::Execution(e.to_string())));
+                        return;
+                    }
+                };
+
+                let mut rows = match stmt.query([]) {
+                    Ok(r) => r,
+                    Err(e) => {
+                        let _ = tx_c.blocking_send(Err(DataFusionError::Execution(e.to_string())));
+                        return;
+                    }
+                };
+
+                let n_cols = schema_task.fields().len();
+                let mut col_bufs: Vec<Vec<SqlValue>> = (0..n_cols)
+                    .map(|_| Vec::with_capacity(SCAN_BATCH_SIZE))
+                    .collect();
+                let mut rows_in_batch = 0usize;
+
+                loop {
+                    match rows.next() {
+                        Ok(Some(row)) => {
+                            let mut row_ok = true;
+                            for (ci, buf) in col_bufs.iter_mut().enumerate() {
+                                match row.get::<_, SqlValue>(ci) {
+                                    Ok(v) => buf.push(v),
+                                    Err(e) => {
+                                        let _ = tx_c.blocking_send(Err(
+                                            DataFusionError::Execution(e.to_string()),
+                                        ));
+                                        row_ok = false;
+                                        break;
+                                    }
+                                }
+                            }
+                            if !row_ok {
+                                // Error already sent on the channel — skip the
+                                // final flush entirely to avoid sending Ok after Err.
+                                return;
+                            }
+                            rows_in_batch += 1;
+                            if rows_in_batch >= SCAN_BATCH_SIZE {
+                                let drained: Vec<Vec<SqlValue>> = col_bufs
+                                    .iter_mut()
+                                    .map(|b| {
+                                        std::mem::replace(b, Vec::with_capacity(SCAN_BATCH_SIZE))
+                                    })
+                                    .collect();
+                                rows_in_batch = 0;
+                                match build_scan_batch(&schema_task, drained) {
+                                    Ok(batch) => {
+                                        if tx_c.blocking_send(Ok(batch)).is_err() {
+                                            return; // consumer dropped
+                                        }
+                                    }
+                                    Err(e) => {
+                                        let _ = tx_c.blocking_send(Err(e));
+                                        return;
+                                    }
+                                }
+                            }
+                        }
+                        Ok(None) => break,
+                        Err(e) => {
+                            let _ =
+                                tx_c.blocking_send(Err(DataFusionError::Execution(e.to_string())));
+                            return;
+                        }
+                    }
+                }
+
+                // Flush the last partial batch.
+                if rows_in_batch > 0 {
+                    match build_scan_batch(&schema_task, col_bufs) {
+                        Ok(batch) => {
+                            let _ = tx_c.blocking_send(Ok(batch));
+                        }
+                        Err(e) => {
+                            let _ = tx_c.blocking_send(Err(e));
+                        }
+                    }
+                }
+            })
+            .await
+            {
+                let _ = tx
+                    .send(Err(DataFusionError::Execution(format!(
+                        "scan task panicked: {e}"
+                    ))))
+                    .await;
+            }
+        });
+
+        // Convert the channel receiver into a RecordBatch stream.
+        let stream = futures::stream::unfold(rx, |mut rx| async move {
+            rx.recv().await.map(|item| (item, rx))
+        });
+        Ok(Box::pin(RecordBatchStreamAdapter::new(schema, stream)))
+    }
+}
+
+/// Build a [`RecordBatch`] from column buffers of [`SqlValue`]s.
+fn build_scan_batch(schema: &SchemaRef, col_bufs: Vec<Vec<SqlValue>>) -> DFResult<RecordBatch> {
+    let arrays: Vec<ArrayRef> = schema
+        .fields()
+        .iter()
+        .zip(col_bufs)
+        .map(|(field, values)| sql_values_to_arrow(field.data_type(), values))
+        .collect::<DFResult<_>>()?;
+    RecordBatch::try_new(schema.clone(), arrays)
+        .map_err(|e| DataFusionError::ArrowError(Box::new(e), None))
 }
 
 // ── Build helpers ─────────────────────────────────────────────────────────────
diff --git a/tests/sqlite_provider_test.rs b/tests/sqlite_provider_test.rs
index a08121d..c7dfb53 100644
--- a/tests/sqlite_provider_test.rs
+++ b/tests/sqlite_provider_test.rs
@@ -2,7 +2,7 @@
 
 use std::sync::Arc;
 
-use arrow_array::{RecordBatch, StringArray, UInt64Array};
+use arrow_array::{Array, RecordBatch, StringArray, UInt64Array};
 use arrow_schema::{DataType, Field, Schema};
 use datafusion::catalog::TableProvider;
 use datafusion::prelude::SessionContext;
@@ -140,23 +140,43 @@ async fn test_empty_key_slice() {
     assert!(batches.is_empty());
 }
 
-/// Regression test for the silent-empty-scan bug:
-/// scan() used to return an empty MemTable, producing zero rows with no error.
-/// It must now return NotImplemented so callers get a clear failure.
+/// scan() returns a streaming ExecutionPlan that yields all rows in batches.
 #[tokio::test]
-async fn test_scan_returns_not_implemented() {
+async fn test_scan_streams_all_rows() {
+    use datafusion::execution::TaskContext;
+    use futures::StreamExt;
+
     let dir = tempdir().unwrap();
     let provider = make_provider(&dir);
 
     let ctx = SessionContext::new();
     let state = ctx.state();
-    let result = provider.scan(&state, None, &[], None).await;
-    assert!(result.is_err());
-    let err = result.unwrap_err().to_string();
-    assert!(
-        err.contains("not support full table scans"),
-        "expected NotImplemented error, got: {err}"
-    );
+    let plan = provider.scan(&state, None, &[], None).await.unwrap();
+
+    let task_ctx = Arc::new(TaskContext::default());
+    let mut stream = plan.execute(0, task_ctx).unwrap();
+
+    let mut total_rows = 0usize;
+    let mut all_names: Vec<String> = Vec::new();
+    while let Some(batch) = stream.next().await {
+        let batch = batch.unwrap();
+        total_rows += batch.num_rows();
+
+        let names_col = batch
+            .column_by_name("name")
+            .unwrap()
+            .as_any()
+            .downcast_ref::<StringArray>()
+            .unwrap();
+        for i in 0..names_col.len() {
+            all_names.push(names_col.value(i).to_string());
+        }
+    }
+
+    assert_eq!(total_rows, 3);
+    assert!(all_names.contains(&"alice".to_string()));
+    assert!(all_names.contains(&"bob".to_string()));
+    assert!(all_names.contains(&"carol".to_string()));
 }
 
 /// Regression test for the SQL injection fix via quote_ident: