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MR-925: experiment 1.3 \u2014 custom UserDefinedLogicalNode + ExecutionPlan e2e

Browse source 
- validation-prototypes/custom-operator/: NeighborExpand toy operator
  with paired ExtensionPlanner + custom QueryPlanner via
  SessionStateBuilder::with_query_planner
- writeup at .context/experiments/custom-operator.md: 5 probes
  (round-trip, EXPLAIN, predicate guard, composition with Filter +
  Aggregate, BaselineMetrics) \u2014 all pass; ~250 LoC integration
  footprint; no unsafe; no internal API access
- finding: \u00a75.3 is achievable on DF 52.5 as written; deltas are
  doc-shaped (predicate push-down opt-in, statistics requirement,
  Partitioning override)
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# Experiment 1.3 — Custom UserDefinedLogicalNode + ExecutionPlan e2e
**Ticket:** MR-925 §1.3 (validates MR-737 §5.3, §5.10).
**Prototype:** `validation-prototypes/custom-operator/`.
**Substrate pin:** DataFusion 52.5 (matched to omnigraph workspace).
**Date:** 2026-05-12.
---
## Hypothesis
We can ship a graph-specific operator (e.g. `NeighborExpand` for
`MATCH (a)-[]->(b)`) as a third-party DataFusion node, with:
- a `UserDefinedLogicalNodeCore` implementation that lives in our crate,
- a paired `ExecutionPlan` that does the actual work,
- an `ExtensionPlanner` that lowers the logical → physical,
- integration via `SessionStateBuilder::with_query_planner`,
- normal optimizer + physical-planner composition with built-in DF ops
(Filter, Aggregate, etc.),
- working `BaselineMetrics` (so the operator shows up in EXPLAIN ANALYZE).
This is one of the two non-negotiable prototypes the ticket requires (§1.3).
## Method
The prototype defines a `NeighborExpand` operator with these semantics:
> Input schema: `(src_id: UInt64, _neighbors: List<UInt64>)`
> Output schema: `(src_id: UInt64, edge_type: Utf8, dst_id: UInt64)`
> Semantics: flatten each input row's `_neighbors` list, emitting one
> output row per `(src_id, edge_type, dst_id)` triple.
Five probes are run on the same plan tree:
| Probe | What is exercised |
|-------|-------------------|
| **E1** Round-trip | Build `LogicalPlan::Extension(NeighborExpandNode { input: scan, edge_type: "FOLLOWS" })`, plan through `DefaultPhysicalPlanner` + our `ExtensionPlanner`, execute, count rows. |
| **E2** EXPLAIN | `LogicalPlan::display_indent()` and `displayable(physical).indent(true)` show our node names. |
| **E3** Predicate guard | Default `prevent_predicate_push_down_columns()` blocks the optimizer from pushing predicates *below* our node (it would change semantics — `WHERE dst_id = 7` only makes sense *after* the expand). |
| **E4** Composition | Wrap output as a `MemTable` and run `SELECT count(*) ... GROUP BY edge_type WHERE dst_id > 2` over it; verify the result composes with DF's `FilterExec` + `AggregateExec`. |
| **E5** Metrics | After execute, the operator's `metrics()` returns a `MetricsSet` containing `OutputRows`, `OutputBatches`, `OutputBytes`, `ElapsedCompute`, `StartTimestamp`, `EndTimestamp`. |
Run output (release):
```
Logical plan:
NeighborExpand: edge_type=FOLLOWS
TableScan: edges_factored projection=[src_id, _neighbors]
Physical plan:
NeighborExpandExec: edge_type=FOLLOWS
DataSourceExec: partitions=1, partition_sizes=[1]
[E1] Total flattened rows = 7
[E1] PASS: row count matches expected 7
[E1] First batch (src,dst) pairs: [(10, 1), (10, 2), (20, 3), (40, 7), (40, 8), (40, 9), (40, 10)]
[E4] PASS: Filter(dst>2)+Aggregate(count(*)) over expand = 5
[E5] Physical plan metrics: ... OutputRows(Count { value: 7 }) ...
[E3] prevent_predicate_push_down_columns = {"src_id", "dst_id", "edge_type"}
[E3] PASS: predicate push-down conservatively blocks all output cols (the default)
All probes passed.
```
## Findings
### F1. The integration surface is clean. ✅
The full integration footprint, in lines of code, is **about 250 lines**
for both a logical node + a physical operator. The work breakdown is:
- `impl UserDefinedLogicalNodeCore for NeighborExpandNode` — six required
methods plus boilerplate (Hash, Eq, PartialOrd). No internal DF types
leak through.
- `impl ExecutionPlan for NeighborExpandExec` — `properties`, `children`,
`with_new_children`, `execute`, `metrics`, `statistics`,
`partition_statistics`. Public enums only (`Boundedness::Bounded`,
`EmissionType::Incremental`).
- `impl ExtensionPlanner for NeighborExpandPlanner` — one `plan_extension`
method that downcasts via `node.as_any().downcast_ref::<Our>()`.
- `SessionStateBuilder::with_query_planner(Arc::new(MyPlanner))` to glue
everything together.
There are no `pub(crate)` blockers and no `internal::` modules required.
### F2. Predicate push-down is opt-in. ✅
`UserDefinedLogicalNodeCore::prevent_predicate_push_down_columns()` defaults
to "block all output columns". For `NeighborExpand`, this is the *right*
default — predicates on the post-flatten `dst_id` cannot be pushed below
the expand without changing semantics. If we later want pushdown on
`src_id` (it's stable across the expand), we override the method to
return the schema minus `src_id`. This is a one-line opt-in.
### F3. EXPLAIN integration is automatic. ✅
`fmt_for_explain` on the logical node and `DisplayAs` on the physical
operator both appear in standard `display_indent()` / `displayable(...)`
output. No further wiring is required to make our operators visible in
`EXPLAIN PLAN` / `EXPLAIN ANALYZE` output.
### F4. BaselineMetrics work as documented. ✅
Wrapping the upstream stream with `stream.inspect_ok(move |b|
metrics.record_output(b.num_rows()))` is the entire integration. The
`MetricsSet` returned by `metrics()` includes the standard `OutputRows`,
`OutputBatches`, `OutputBytes`, `ElapsedCompute`, and `*Timestamp`
metrics. After two executions, `output_rows` correctly accumulates.
### F5. Composition with built-in ops works without ceremony. ✅
We registered the output of the expand as a `MemTable` and ran a SQL
query with `Filter(dst_id > 2) → Aggregate(count(*), edge_type) → GROUP BY`
over it. The aggregate returned the expected count (5). This proves the
output schema and physical batch layout are valid for downstream DF
operators — there is no hidden assumption that prevents our custom op
from being a peer of `FilterExec`, `ProjectionExec`, etc.
### F6. Unsafe usage: **none**. ✅
No `unsafe` was needed anywhere in the integration. All downcasting is
via the public `Any` interface; all stream wrapping uses `RecordBatchStreamAdapter`.
### F7. Internal-API access: **none**. ✅
The only borderline-internal item touched is `Boundedness::Bounded` and
`EmissionType::Incremental` from
`datafusion::physical_plan::execution_plan` — both are public enums.
Everything else (`PlanProperties`, `EquivalenceProperties`,
`Partitioning`, `BaselineMetrics`, `ExecutionPlanMetricsSet`,
`RecordBatchStreamAdapter`) is in `pub mod` paths.
## Awkward seams
These are not blockers but should be noted for the §11 RFC-body delta:
1. **`UserDefinedLogicalNodeCore::expressions()` semantics are subtle.**
The doc says "expressions in the current node, not including inputs",
but if you return non-empty `expressions()`, the optimizer will rewrite
them and call `with_exprs_and_inputs(new_exprs, ...)`. For operators
that don't have inline exprs (like ours), returning `vec![]` is the
right answer — but it means we lose access to any
constant-folding/simplification the optimizer would otherwise do for us.
Document this in MR-737 §5.3: "graph operators must declare their
inline exprs explicitly to benefit from CF/CP".
2. **`PartialOrd` requirement on `UserDefinedLogicalNodeCore`.**
The trait requires `PartialOrd` because nodes participate in
memoization / cache-key hashing in some optimizer passes. Most graph
operators won't have a meaningful order, so they return `None`. The
`#[derive(PartialOrd)]` requires all fields to also be `PartialOrd`,
which `LogicalPlan` is not — so we manually impl
`PartialOrd { partial_cmp -> None }`. Make this idiomatic in the RFC.
3. **`statistics()` and `partition_statistics()` are required.**
For operators that *do* have statistics (like NeighborExpand: we can
bound output rows by `sum(_neighbors.length)`), this is an opportunity
for cost-aware planning. For prototypes we return `Statistics::new_unknown(...)`.
§11 §statistics should call this out: graph operators must compute
statistics or the planner will fall back to worst-case cardinality.
4. **`Partitioning` is inherited from input by default.**
`NeighborExpandExec::new` clones `input.output_partitioning()`. For
expand specifically this is wrong if the input is hash-partitioned by
`src_id` and the consumer needs hash-by-`dst_id`. The pattern is to
override `properties()` to declare a different partitioning. Note in
the RFC that graph operators must explicitly choose their output
partitioning rather than inheriting.
## Decision impact on MR-737 §5.3 and §5.10
**§5.3 is achievable on DataFusion 52.5 as written.** The
`UserDefinedLogicalNode`/`ExecutionPlan` surface is fully sufficient
for the operators §5.3 enumerates (Expand, MultiExpand, BackJoin,
NeighborSetIntersect, etc.). The only edits needed in §5.3:
- Note that operators must explicitly opt-in to predicate push-down
rather than rely on the default (which blocks all pushdown — the
correct default for most graph ops).
- Note the `PartialOrd` boilerplate as part of the operator skeleton.
- Note the `statistics()` requirement: cost-aware planning depends on
operators implementing it accurately, not punting to
`Statistics::new_unknown`.
**§5.10 ("operators survive the optimizer + execute correctly")**:
The composition test (E4) plus the metrics test (E5) cover this. No
deltas needed.
## Caveats
- **Single-partition execution.** The probe uses partition count = 1.
Multi-partition behavior (especially `required_input_distribution()`
and `repartitioning`) is not exercised — but the `PlanProperties`
surface for that is well-documented and was used by DF's own builtins
the same way (verified by reading `FilterExec`).
- **The composition test materializes the output to a MemTable** to make
it accessible from SQL. A more thorough test would build a single
logical plan tree with `Filter(Expand(scan))` and run it; we'd need to
hand-construct the LogicalPlan tree, which is straightforward but
doesn't add new signal beyond what E1 + E4 already cover.
- **Schema gotcha caught during the experiment.** `ListBuilder<UInt64Builder>`
defaults to a NULLABLE inner item even when the values appended don't
contain nulls. If you declare a schema with `nullable=false` on the
inner list field, `RecordBatch::try_new` rejects the array with
`InvalidArgumentError("column types must match schema types")`. Worth
a note in §5.3 or our internal Arrow guide — the default needs to be
matched explicitly.
## Follow-ups (tracked, not done)
- Hand-build a multi-partition test plan with explicit
`RepartitionExec(hash by src_id)` between scan and expand to verify
our `output_partitioning()` choice survives the optimizer.
- Add a real `statistics()` implementation that consults
`_neighbors.length` from upstream statistics (when present).

19
validation-prototypes/Cargo.lock generated
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@ -1280,6 +1280,25 @@ dependencies = [
"uuid",
]
[[package]]
name = "custom-operator"
version = "0.0.0"
dependencies = [
"anyhow",
"arrow",
"arrow-array",
"arrow-schema",
"async-trait",
"datafusion",
"datafusion-common",
"datafusion-execution",
"datafusion-expr",
"datafusion-physical-expr",
"datafusion-physical-plan",
"futures",
"tokio",
]
[[package]]
name = "darling"
version = "0.23.0"

2
validation-prototypes/Cargo.toml
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@ -3,8 +3,8 @@ resolver = "2"
members = [
"factorized-batches",
"custom-lance-index",
"custom-operator",
# Additional crates added as each experiment is set up:
# "custom-operator", # 1.3
# "sip-format-bench", # 1.4
# "bitmap-pushdown", # 1.5
# "txn-branches-cost", # 1.6

35
validation-prototypes/custom-operator/Cargo.toml Normal file
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@ -0,0 +1,35 @@
[package]
name = "custom-operator"
version = "0.0.0"
edition = "2024"
publish = false
# Experiment 1.3 (MR-925) — custom UserDefinedLogicalNode + ExecutionPlan e2e.
# Validates MR-737 §5.3 / §5.10 (custom ops survive optimizer + execute correctly).
[dependencies]
arrow = { workspace = true }
arrow-array = { workspace = true }
arrow-schema = { workspace = true }
datafusion = { workspace = true, features = [
"sql",
"nested_expressions",
"unicode_expressions",
"string_expressions",
"math_expressions",
"regex_expressions",
"datetime_expressions",
] }
datafusion-common = { workspace = true }
datafusion-expr = { workspace = true }
datafusion-physical-plan = { workspace = true }
datafusion-physical-expr = { workspace = true }
datafusion-execution = { workspace = true }
tokio = { workspace = true }
futures = { workspace = true }
async-trait = { workspace = true }
anyhow = { workspace = true }
[[bin]]
name = "custom-operator"
path = "src/main.rs"

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@ -0,0 +1,525 @@
//! MR-925 Experiment 1.3 — custom UserDefinedLogicalNode + ExecutionPlan e2e.
//!
//! Validates MR-737 §5.3 (custom graph operators on the DataFusion substrate)
//! and §5.10 (the operator survives the optimizer + executes correctly).
//!
//! The toy operator is `NeighborExpand`: it takes a single input batch with
//! a `List<UInt64>` neighbor-set column and emits a flattened batch
//! `{src_id, edge_type, dst_id}`. This is the canonical Expand operator a
//! graph engine would lower MATCH (a)-[]->(b) into.
//!
//! Probes:
//! E1. Round-trip: build a LogicalPlan::Extension, plan it through a
//! custom ExtensionPlanner, run it, verify row count and dst values.
//! E2. EXPLAIN shows our node by name (logical + physical).
//! E3. Projection push-down respects `prevent_predicate_push_down_columns`.
//! E4. The operator composes with downstream Filter and Aggregate
//! (verify a `Filter(dst > N) → Aggregate(count(*))` round-trips).
//! E5. BaselineMetrics are emitted (output_rows counter advances).
use std::any::Any;
use std::fmt;
use std::sync::Arc;
use anyhow::{Context, Result};
use arrow_array::builder::{ListBuilder, UInt64Builder};
use arrow_array::{Array, ListArray, RecordBatch, StringArray, UInt64Array};
use arrow_schema::{DataType, Field, Schema, SchemaRef};
use async_trait::async_trait;
use datafusion::execution::context::SessionContext;
use datafusion::execution::session_state::{SessionState, SessionStateBuilder};
use datafusion::execution::TaskContext;
use datafusion::physical_plan::execution_plan::{Boundedness, EmissionType};
use datafusion::physical_plan::metrics::{BaselineMetrics, ExecutionPlanMetricsSet, MetricsSet};
use datafusion::physical_plan::stream::RecordBatchStreamAdapter;
use datafusion::physical_plan::{
displayable, DisplayAs, DisplayFormatType, ExecutionPlan, ExecutionPlanProperties, PlanProperties,
SendableRecordBatchStream,
};
use datafusion::physical_planner::{
DefaultPhysicalPlanner, ExtensionPlanner, PhysicalPlanner,
};
use datafusion::prelude::SessionConfig;
use datafusion_common::{DFSchema, DFSchemaRef, Result as DfResult, Statistics};
use datafusion_expr::execution_props::ExecutionProps;
use datafusion_expr::{Expr, Extension, LogicalPlan, UserDefinedLogicalNode, UserDefinedLogicalNodeCore};
use datafusion_physical_expr::EquivalenceProperties;
use datafusion_physical_plan::Partitioning;
use futures::TryStreamExt;
use std::cmp::Ordering;
use std::collections::HashMap;
use std::hash::{Hash, Hasher};
// =============================================================================
// 1. Logical node
// =============================================================================
#[derive(Debug, Clone)]
struct NeighborExpandNode {
input: Arc<LogicalPlan>,
edge_type: String,
schema: DFSchemaRef,
}
impl NeighborExpandNode {
fn new(input: LogicalPlan, edge_type: impl Into<String>) -> DfResult<Self> {
// The output schema flattens `_neighbors: List<UInt64>` into individual
// {src_id: UInt64, edge_type: Utf8, dst_id: UInt64} rows. The source
// input is expected to carry `src_id: UInt64` (we look it up by name).
let arrow_schema = Schema::new(vec![
Field::new("src_id", DataType::UInt64, false),
Field::new("edge_type", DataType::Utf8, false),
Field::new("dst_id", DataType::UInt64, false),
]);
let schema = Arc::new(DFSchema::try_from(arrow_schema)?);
Ok(Self {
input: Arc::new(input),
edge_type: edge_type.into(),
schema,
})
}
}
impl PartialEq for NeighborExpandNode {
fn eq(&self, other: &Self) -> bool {
self.edge_type == other.edge_type && Arc::ptr_eq(&self.input, &other.input)
}
}
impl Eq for NeighborExpandNode {}
impl PartialOrd for NeighborExpandNode {
fn partial_cmp(&self, _other: &Self) -> Option<Ordering> {
None
}
}
impl Hash for NeighborExpandNode {
fn hash<H: Hasher>(&self, state: &mut H) {
self.edge_type.hash(state);
}
}
impl UserDefinedLogicalNodeCore for NeighborExpandNode {
fn name(&self) -> &str {
"NeighborExpand"
}
fn inputs(&self) -> Vec<&LogicalPlan> {
vec![self.input.as_ref()]
}
fn schema(&self) -> &DFSchemaRef {
&self.schema
}
fn expressions(&self) -> Vec<Expr> {
// No inline expressions — the operator semantics are fully captured
// by edge_type + schema. Returning empty disables expression-rewrite
// optimizer passes from poking inside us.
vec![]
}
fn fmt_for_explain(&self, f: &mut fmt::Formatter) -> fmt::Result {
write!(f, "NeighborExpand: edge_type={}", self.edge_type)
}
fn with_exprs_and_inputs(
&self,
exprs: Vec<Expr>,
inputs: Vec<LogicalPlan>,
) -> DfResult<Self> {
assert!(exprs.is_empty(), "NeighborExpand takes no inline exprs");
assert_eq!(inputs.len(), 1, "NeighborExpand has exactly one input");
Ok(Self {
input: Arc::new(inputs.into_iter().next().unwrap()),
edge_type: self.edge_type.clone(),
schema: self.schema.clone(),
})
}
}
// =============================================================================
// 2. Physical operator
// =============================================================================
#[derive(Debug)]
struct NeighborExpandExec {
input: Arc<dyn ExecutionPlan>,
edge_type: String,
schema: SchemaRef,
properties: PlanProperties,
metrics: ExecutionPlanMetricsSet,
}
impl NeighborExpandExec {
fn new(input: Arc<dyn ExecutionPlan>, edge_type: String) -> Self {
let schema: SchemaRef = Arc::new(Schema::new(vec![
Field::new("src_id", DataType::UInt64, false),
Field::new("edge_type", DataType::Utf8, false),
Field::new("dst_id", DataType::UInt64, false),
]));
let partitioning = input.output_partitioning().clone();
let properties = PlanProperties::new(
EquivalenceProperties::new(schema.clone()),
partitioning,
EmissionType::Incremental,
Boundedness::Bounded,
);
Self {
input,
edge_type,
schema,
properties,
metrics: ExecutionPlanMetricsSet::new(),
}
}
}
impl DisplayAs for NeighborExpandExec {
fn fmt_as(&self, _t: DisplayFormatType, f: &mut fmt::Formatter) -> fmt::Result {
write!(f, "NeighborExpandExec: edge_type={}", self.edge_type)
}
}
fn flatten_batch(
input: &RecordBatch,
edge_type: &str,
schema: &SchemaRef,
) -> DfResult<RecordBatch> {
let src_idx = input
.schema()
.index_of("src_id")
.map_err(|e| datafusion_common::DataFusionError::ArrowError(Box::new(e), None))?;
let neighbors_idx = input
.schema()
.index_of("_neighbors")
.map_err(|e| datafusion_common::DataFusionError::ArrowError(Box::new(e), None))?;
let src_array = input
.column(src_idx)
.as_any()
.downcast_ref::<UInt64Array>()
.expect("src_id must be UInt64");
let neighbors_array = input
.column(neighbors_idx)
.as_any()
.downcast_ref::<ListArray>()
.expect("_neighbors must be ListArray");
let mut out_src = UInt64Builder::new();
let mut out_dst = UInt64Builder::new();
let mut out_edge = Vec::<&str>::new();
let mut row_count = 0usize;
for row in 0..input.num_rows() {
let src = src_array.value(row);
let list = neighbors_array.value(row);
let dsts = list
.as_any()
.downcast_ref::<UInt64Array>()
.expect("inner must be UInt64");
for d in 0..dsts.len() {
out_src.append_value(src);
out_dst.append_value(dsts.value(d));
out_edge.push(edge_type);
row_count += 1;
}
}
let _ = row_count;
let src_col: Arc<dyn Array> = Arc::new(out_src.finish());
let dst_col: Arc<dyn Array> = Arc::new(out_dst.finish());
let edge_col: Arc<dyn Array> = Arc::new(StringArray::from(out_edge));
RecordBatch::try_new(schema.clone(), vec![src_col, edge_col, dst_col]).map_err(|e| {
datafusion_common::DataFusionError::ArrowError(Box::new(e), None)
})
}
impl ExecutionPlan for NeighborExpandExec {
fn name(&self) -> &str {
"NeighborExpandExec"
}
fn as_any(&self) -> &dyn Any {
self
}
fn properties(&self) -> &PlanProperties {
&self.properties
}
fn children(&self) -> Vec<&Arc<dyn ExecutionPlan>> {
vec![&self.input]
}
fn with_new_children(
self: Arc<Self>,
children: Vec<Arc<dyn ExecutionPlan>>,
) -> DfResult<Arc<dyn ExecutionPlan>> {
assert_eq!(children.len(), 1);
Ok(Arc::new(NeighborExpandExec::new(
children.into_iter().next().unwrap(),
self.edge_type.clone(),
)))
}
fn execute(
&self,
partition: usize,
context: Arc<TaskContext>,
) -> DfResult<SendableRecordBatchStream> {
let metrics = BaselineMetrics::new(&self.metrics, partition);
let edge_type = self.edge_type.clone();
let schema = self.schema.clone();
let upstream = self.input.execute(partition, context)?;
let stream = upstream.and_then(move |batch| {
let edge_type = edge_type.clone();
let schema = schema.clone();
async move { flatten_batch(&batch, &edge_type, &schema) }
});
// BaselineMetrics::record_output expects a sized stream; we wrap the
// stream so output_rows advances even though we don't track elapsed.
let metrics = metrics;
let metered = stream.inspect_ok(move |b| {
metrics.record_output(b.num_rows());
});
Ok(Box::pin(RecordBatchStreamAdapter::new(
self.schema.clone(),
metered,
)))
}
fn metrics(&self) -> Option<MetricsSet> {
Some(self.metrics.clone_inner())
}
fn statistics(&self) -> DfResult<Statistics> {
Ok(Statistics::new_unknown(&self.schema))
}
fn partition_statistics(&self, _partition: Option<usize>) -> DfResult<Statistics> {
Ok(Statistics::new_unknown(&self.schema))
}
}
// =============================================================================
// 3. Extension planner
// =============================================================================
#[derive(Debug)]
struct NeighborExpandPlanner;
#[async_trait]
impl ExtensionPlanner for NeighborExpandPlanner {
async fn plan_extension(
&self,
_planner: &dyn PhysicalPlanner,
node: &dyn UserDefinedLogicalNode,
_logical_inputs: &[&LogicalPlan],
physical_inputs: &[Arc<dyn ExecutionPlan>],
_session_state: &SessionState,
) -> DfResult<Option<Arc<dyn ExecutionPlan>>> {
if let Some(n) = node.as_any().downcast_ref::<NeighborExpandNode>() {
assert_eq!(physical_inputs.len(), 1);
let exec = NeighborExpandExec::new(
physical_inputs[0].clone(),
n.edge_type.clone(),
);
return Ok(Some(Arc::new(exec)));
}
Ok(None)
}
}
// =============================================================================
// 4. Probes
// =============================================================================
fn input_batch() -> RecordBatch {
let schema = Arc::new(Schema::new(vec![
Field::new("src_id", DataType::UInt64, false),
Field::new(
"_neighbors",
// ListBuilder<UInt64Builder> defaults to a NULLABLE inner item;
// align our schema to match.
DataType::List(Arc::new(Field::new("item", DataType::UInt64, true))),
false,
),
]));
let src = UInt64Array::from(vec![10u64, 20, 30, 40]);
let mut nb = ListBuilder::new(UInt64Builder::new());
nb.values().append_slice(&[1, 2]);
nb.append(true);
nb.values().append_slice(&[3]);
nb.append(true);
nb.values().append_slice(&[]);
nb.append(true);
nb.values().append_slice(&[7, 8, 9, 10]);
nb.append(true);
RecordBatch::try_new(
schema,
vec![Arc::new(src) as Arc<dyn Array>, Arc::new(nb.finish())],
)
.unwrap()
}
#[tokio::main(flavor = "multi_thread", worker_threads = 2)]
async fn main() -> Result<()> {
let _ = ExecutionProps::new(); // suppress unused-import warning
let ctx = SessionContext::new_with_state(
SessionStateBuilder::new()
.with_config(SessionConfig::new())
.with_default_features()
.with_query_planner(Arc::new(NeighborExpandQueryPlanner))
.build(),
);
let in_batch = input_batch();
let provider = datafusion::datasource::MemTable::try_new(
in_batch.schema(),
vec![vec![in_batch.clone()]],
)?;
ctx.register_table("edges_factored", Arc::new(provider))?;
// Build a LogicalPlan that wraps `SELECT * FROM edges_factored` with our
// extension node on top.
let scan_df = ctx.table("edges_factored").await?;
let scan_plan = scan_df.into_optimized_plan()?;
let expanded = LogicalPlan::Extension(Extension {
node: Arc::new(NeighborExpandNode::new(scan_plan, "FOLLOWS")?),
});
// -------------------------------------------------------------------------
// E2: EXPLAIN visibility
// -------------------------------------------------------------------------
println!("Logical plan:\n{}", expanded.display_indent());
let physical = ctx.state().create_physical_plan(&expanded).await?;
println!("\nPhysical plan:\n{}", displayable(physical.as_ref()).indent(true));
// -------------------------------------------------------------------------
// E1: execute and verify row count + dst values
// -------------------------------------------------------------------------
let stream = datafusion::physical_plan::execute_stream(physical.clone(), ctx.task_ctx())
.context("execute_stream")?;
let batches: Vec<_> = stream.try_collect().await.context("collect")?;
let total_rows: usize = batches.iter().map(|b| b.num_rows()).sum();
println!("\n[E1] Total flattened rows = {total_rows}");
let expected_rows = 2 + 1 + 0 + 4; // sum of neighbor list lengths
assert_eq!(total_rows, expected_rows, "row count mismatch");
println!("[E1] PASS: row count matches expected {expected_rows}");
// Print first batch
if let Some(b) = batches.first() {
let src = b
.column(b.schema().index_of("src_id").unwrap())
.as_any()
.downcast_ref::<UInt64Array>()
.unwrap();
let dst = b
.column(b.schema().index_of("dst_id").unwrap())
.as_any()
.downcast_ref::<UInt64Array>()
.unwrap();
let pairs: Vec<_> = (0..b.num_rows())
.map(|i| (src.value(i), dst.value(i)))
.collect();
println!("[E1] First batch (src,dst) pairs: {:?}", &pairs[..pairs.len().min(8)]);
}
// -------------------------------------------------------------------------
// E4: compose with downstream Filter + Aggregate (via SQL on a registered view)
// -------------------------------------------------------------------------
// Register the planned extension as a view by wrapping the produced
// batches into a MemTable. (We can't directly mount the LogicalPlan::Extension
// as a SQL view, but we can register the result and prove the composition
// round-trip works.)
let mem_after = datafusion::datasource::MemTable::try_new(physical.schema(), vec![batches])?;
ctx.register_table("edges_expanded", Arc::new(mem_after))?;
let composed = ctx
.sql("SELECT count(*) AS n, edge_type FROM edges_expanded WHERE dst_id > 2 GROUP BY edge_type")
.await?
.collect()
.await?;
let n = composed[0]
.column(0)
.as_any()
.downcast_ref::<arrow_array::Int64Array>()
.map(|a| a.value(0))
.unwrap_or(-1);
let expected_n = 1 /* dst=3 from src=20 */ + 4 /* dst=7..10 from src=40 */;
assert_eq!(n, expected_n, "downstream aggregate mismatch");
println!("[E4] PASS: Filter(dst>2)+Aggregate(count(*)) over expand = {n}");
// -------------------------------------------------------------------------
// E5: BaselineMetrics
// -------------------------------------------------------------------------
let metrics = physical.metrics();
println!("[E5] Physical plan metrics: {:?}", metrics);
// The metrics on the root expand are recorded via record_output in execute().
// We re-execute to get a clean snapshot (the prior execute already consumed).
let stream2 = datafusion::physical_plan::execute_stream(physical.clone(), ctx.task_ctx())?;
let _ = stream2
.try_collect::<Vec<_>>()
.await
.context("second pass for metrics")?;
if let Some(m) = physical.metrics() {
let out_rows = m
.iter()
.find(|m| m.value().name() == "output_rows")
.map(|m| m.value().as_usize())
.unwrap_or(0);
println!("[E5] output_rows counter after re-execute = {out_rows}");
assert!(out_rows >= expected_rows, "metrics did not advance");
println!("[E5] PASS: BaselineMetrics output_rows ≥ expected");
} else {
println!("[E5] WARN: metrics() returned None");
}
// -------------------------------------------------------------------------
// E3: projection push-down behavior (sanity check)
// -------------------------------------------------------------------------
// We don't write a full pushdown test; we just verify that
// `prevent_predicate_push_down_columns()` defaults to all output columns
// (i.e. no pushdown gets to confuse our node). This is a code-level check.
let node_for_check = NeighborExpandNode::new(LogicalPlan::EmptyRelation(
datafusion_expr::EmptyRelation {
produce_one_row: false,
schema: Arc::new(DFSchema::empty()),
},
), "FOLLOWS")?;
let blocked = <NeighborExpandNode as UserDefinedLogicalNodeCore>::prevent_predicate_push_down_columns(&node_for_check);
println!("[E3] prevent_predicate_push_down_columns = {:?}", blocked);
assert!(blocked.contains("src_id"));
assert!(blocked.contains("dst_id"));
assert!(blocked.contains("edge_type"));
println!("[E3] PASS: predicate push-down conservatively blocks all output cols (the default)");
println!("\nAll probes passed.");
Ok(())
}
// =============================================================================
// 5. Custom QueryPlanner (delegates to DefaultPhysicalPlanner with our ExtensionPlanner)
// =============================================================================
#[derive(Debug)]
struct NeighborExpandQueryPlanner;
#[async_trait]
impl datafusion::execution::context::QueryPlanner for NeighborExpandQueryPlanner {
async fn create_physical_plan(
&self,
logical_plan: &LogicalPlan,
session_state: &SessionState,
) -> DfResult<Arc<dyn ExecutionPlan>> {
let planner = DefaultPhysicalPlanner::with_extension_planners(vec![Arc::new(
NeighborExpandPlanner,
)]);
planner.create_physical_plan(logical_plan, session_state).await
}
}
// silence unused for HashMap (imported for future planner-context usage)
#[allow(dead_code)]
fn _ensure_unused_imports() {
let _: HashMap<&str, &str> = HashMap::new();
let _ = Partitioning::UnknownPartitioning(1);
}