adriangb commented on code in PR #161:
URL: https://github.com/apache/datafusion-site/pull/161#discussion_r2972220216
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content/blog/2026-03-20-writing-table-providers.md:
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+---
+layout: post
+title: Writing Custom Table Providers in Apache DataFusion
+date: 2026-03-20
+author: Tim Saucer (rerun.io)
+categories: [tutorial]
+---
+<!--
+{% comment %}
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+(the "License"); you may not use this file except in compliance with
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+
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+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
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+{% endcomment %}
+-->
+
+[TOC]
+
+One of DataFusion's greatest strengths is its extensibility. If your data lives
+in a custom format, behind an API, or in a system that DataFusion does not
+natively support, you can teach DataFusion to read it by implementing a
+**custom table provider**. This post walks through the three layers you need to
+understand and explains where your work should actually happen.
+
+## The Three Layers
+
+---
+
+When DataFusion executes a query against a table, three abstractions
collaborate
+to produce results:
+
+1. **[`TableProvider`]** -- Describes the table (schema, capabilities) and
+ produces an execution plan when queried.
+2. **[`ExecutionPlan`]** -- Describes *how* to compute the result:
partitioning,
+ ordering, and child plan relationships.
+3. **[`SendableRecordBatchStream`]** -- The async stream that *actually does
the
+ work*, yielding `RecordBatch`es one at a time.
+
+Think of these as a funnel: `TableProvider::scan()` is called once during
+planning to create an `ExecutionPlan`, then `ExecutionPlan::execute()` is
called
+once per partition to create a stream, and those streams are where rows are
+actually produced during execution.
+
+[`TableProvider`]:
https://docs.rs/datafusion/latest/datafusion/catalog/trait.TableProvider.html
+[`ExecutionPlan`]:
https://docs.rs/datafusion/latest/datafusion/physical_plan/trait.ExecutionPlan.html
+[`SendableRecordBatchStream`]:
https://docs.rs/datafusion/latest/datafusion/execution/type.SendableRecordBatchStream.html
+
+## Layer 1: TableProvider
+
+---
+
+A [`TableProvider`] represents a queryable data source. For a minimal read-only
+table, you need four methods:
+
+```rust
+impl TableProvider for MyTable {
+ fn as_any(&self) -> &dyn Any { self }
+
+ fn schema(&self) -> SchemaRef {
+ Arc::clone(&self.schema)
+ }
+
+ fn table_type(&self) -> TableType {
+ TableType::Base
+ }
+
+ async fn scan(
+ &self,
+ state: &dyn Session,
+ projection: Option<&Vec<usize>>,
+ filters: &[Expr],
+ limit: Option<usize>,
+ ) -> Result<Arc<dyn ExecutionPlan>> {
+ // Build and return an ExecutionPlan -- keep this lightweight!
+ Ok(Arc::new(MyExecPlan::new(
+ Arc::clone(&self.schema),
+ projection,
+ limit,
+ )))
+ }
+}
+```
+
+The `scan` method is the heart of `TableProvider`. It receives three pushdown
+hints from the optimizer, each reducing the amount of data your source needs
+to produce:
+
+- **`projection`** -- Which columns are needed. This reduces the **width** of
+ the output. If your source supports it, read only these columns rather than
+ the full schema.
+- **`filters`** -- Predicates the engine would like you to apply during the
+ scan. This reduces the **number of rows** by skipping data that does not
+ match. Implement `supports_filters_pushdown` to advertise which filters you
+ can handle.
+- **`limit`** -- A row count cap. This also reduces the **number of rows** --
+ if you can stop reading early once you have produced enough rows, this avoids
+ unnecessary work.
+
+### Keep `scan()` Lightweight
+
+This is a critical point: **`scan()` runs during planning, not execution.** It
+should return quickly. Best practices are to avoid performing I/O, network
+calls, or heavy computation here. The `scan` method's job is to *describe* how
+the data will be produced, not to produce it. All the real work belongs in the
+stream (Layer 3).
+
+A common pitfall is to fetch data or open connections in `scan()`. This blocks
+the planning thread and can cause timeouts or deadlocks, especially if the
query
+involves multiple tables or subqueries that all need to be planned before
+execution begins.
Review Comment:
I want to agree with this but in practice doesn't
[ListingTable](https://github.com/apache/datafusion/blob/646d183e1d6f6e9884cb078fc5cad6a93c15cd8d/datafusion/catalog-listing/src/table.rs#L515-L521),
some of our examples only avoid this because they pre-load all of the data
(which is not realistic for a production system)
([e.g.](https://github.com/apache/datafusion/blob/646d183e1d6f6e9884cb078fc5cad6a93c15cd8d/datafusion-examples/examples/data_io/parquet_embedded_index.rs#L268)).
We might also want to document `scan_with_args()` instead of `scan()`.
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