uni_query/query/df_graph/

common.rs

// SPDX-License-Identifier: Apache-2.0
// Copyright 2024-2026 Dragonscale Team

//! Common helpers shared across graph execution plan implementations.
//!
//! This module provides shared utilities to reduce code duplication across
//! the df_graph module's execution plan implementations.

use arrow_array::{ArrayRef, RecordBatch};
use arrow_schema::{DataType, Field, Schema, SchemaRef};
use datafusion::arrow::array::Array;
use datafusion::common::Result as DFResult;
use datafusion::physical_expr::{EquivalenceProperties, Partitioning};
use datafusion::physical_plan::PlanProperties;
use datafusion::prelude::SessionContext;
use futures::TryStreamExt;
use parking_lot::RwLock;
use std::collections::HashMap;
use std::sync::Arc;
use uni_common::Value;
use uni_common::core::schema::Schema as UniSchema;
use uni_cypher::ast::{BinaryOp, CypherLiteral, Expr};
use uni_store::storage::manager::StorageManager;

use super::GraphExecutionContext;
use super::procedure_call::map_yield_to_canonical;
use super::unwind::arrow_to_json_value;
use crate::query::df_graph::MutationContext;
use crate::query::df_planner::HybridPhysicalPlanner;
use crate::query::executor::core::{OperatorStats, collect_plan_metrics};
use crate::query::planner::LogicalPlan;

/// Convert an `ArrowError` into a `DataFusionError`.
///
/// Wraps the Arrow-level error into DataFusion's `ArrowError` variant. Use this
/// with `.map_err(arrow_err)` when calling raw Arrow compute kernels from
/// DataFusion execution plans.
pub fn arrow_err(e: arrow::error::ArrowError) -> datafusion::error::DataFusionError {
    datafusion::error::DataFusionError::ArrowError(Box::new(e), None)
}

/// Compute standard plan properties for graph operators.
///
/// All graph operators use the same plan properties:
/// - Unknown partitioning with 1 partition
/// - Incremental emission type
/// - Bounded execution
pub fn compute_plan_properties(schema: SchemaRef) -> Arc<PlanProperties> {
    Arc::new(PlanProperties::new(
        EquivalenceProperties::new(schema),
        Partitioning::UnknownPartitioning(1),
        datafusion::physical_plan::execution_plan::EmissionType::Incremental,
        datafusion::physical_plan::execution_plan::Boundedness::Bounded,
    ))
}

/// Return the Arrow `DataType` for `_labels` columns: `List<Utf8>`.
///
/// This is used across scan, traverse, bind, and other modules whenever a
/// `_labels` field needs to be declared in a schema. Centralizing the
/// definition avoids divergence and reduces boilerplate.
pub fn labels_data_type() -> DataType {
    DataType::List(Arc::new(Field::new("item", DataType::Utf8, true)))
}

/// Extract a `UInt64Array` of vertex/edge IDs from an Arrow column.
///
/// Accepts both `UInt64` (native VID type) and `Int64` (from parameter
/// injection where `arrow_to_json_value` round-trips through `Value::Int`).
/// For `Int64` columns the values are cast to `UInt64`.
///
/// # Errors
///
/// Returns a `DataFusionError::Execution` if the column is neither `UInt64`
/// nor `Int64`.
pub fn column_as_vid_array(
    col: &dyn arrow_array::Array,
) -> datafusion::error::Result<std::borrow::Cow<'_, arrow_array::UInt64Array>> {
    use arrow_array::{Int64Array, StructArray, UInt64Array};
    use arrow_schema::DataType;

    if let Some(arr) = col.as_any().downcast_ref::<UInt64Array>() {
        return Ok(std::borrow::Cow::Borrowed(arr));
    }

    if let Some(arr) = col.as_any().downcast_ref::<Int64Array>() {
        let cast: UInt64Array = arr.iter().map(|v| v.map(|i| i as u64)).collect();
        return Ok(std::borrow::Cow::Owned(cast));
    }

    // Support entity-struct aliases (e.g., WITH coalesce(b, c) AS x) where
    // traversal inputs may provide the source as a Struct with an "_vid" field.
    if let Some(arr) = col.as_any().downcast_ref::<StructArray>()
        && let DataType::Struct(fields) = arr.data_type()
        && let Some((vid_idx, _)) = fields.find("_vid")
    {
        return column_as_vid_array(arr.column(vid_idx).as_ref());
    }

    // Support CypherValue-encoded Node values in LargeBinary columns
    // (e.g., from list comprehension loop variables over node collections)
    // Also handles JSON round-tripped nodes (Value::Map with _id field)
    if let Some(arr) = col.as_any().downcast_ref::<arrow_array::LargeBinaryArray>() {
        let vids = vids_from_large_binary(arr);
        return Ok(std::borrow::Cow::Owned(vids));
    }

    // OPTIONAL MATCH can produce all-null columns with Arrow Null type
    if *col.data_type() == DataType::Null {
        let vids: UInt64Array = (0..col.len()).map(|_| None::<u64>).collect();
        return Ok(std::borrow::Cow::Owned(vids));
    }

    Err(datafusion::error::DataFusionError::Execution(format!(
        "VID column has type {:?}, expected UInt64 or Int64",
        col.data_type()
    )))
}

/// Extract a VID from a CypherValue.
///
/// Handles both `Value::Node` (native node) and `Value::Map` with `_id` field
/// (JSON round-tripped node from `cv_array_to_large_list`).
fn extract_vid_from_value(val: &Value) -> Option<u64> {
    match val {
        Value::Node(node) => Some(node.vid.as_u64()),
        Value::Map(map) => {
            // Handle round-tripped nodes that became Maps.
            // Path nodes use struct fields (_vid, _label, properties) which
            // round-trip through arrow_to_json_value as { "_vid": Int(N), ... }.
            // Value::Node → serde_json uses { "_id": "N", ... }.
            // Check both keys to handle either path.

            // Check _vid first (from path struct → arrow_to_json_value round-trip)
            if let Some(Value::Int(vid)) = map.get("_vid") {
                return Some(*vid as u64);
            }
            // Also check _id (from Value::Node → serde_json round-trip)
            if let Some(Value::String(id_str)) = map.get("_id") {
                return id_str
                    .strip_prefix("Vid(")
                    .and_then(|s| s.strip_suffix(')'))
                    .unwrap_or(id_str)
                    .parse::<u64>()
                    .ok();
            }
            if let Some(Value::Int(id)) = map.get("_id") {
                return Some(*id as u64);
            }
            None
        }
        _ => None,
    }
}

/// Extract VIDs from a `LargeBinaryArray` of CypherValue-encoded values.
///
/// Decodes each element and delegates to [`extract_vid_from_value`].
/// Null elements and decode failures produce null VID entries.
fn vids_from_large_binary(arr: &arrow_array::LargeBinaryArray) -> arrow_array::UInt64Array {
    use uni_common::cypher_value_codec;

    (0..arr.len())
        .map(|i| {
            if arr.is_null(i) {
                return None;
            }
            cypher_value_codec::decode(arr.value(i))
                .ok()
                .as_ref()
                .and_then(extract_vid_from_value)
        })
        .collect()
}

/// Extract VIDs from a column of CypherValue-encoded Node values.
///
/// Takes a `LargeBinary` array where each element is a CypherValue-encoded
/// value and extracts VIDs from Node values. Non-Node values produce nulls.
/// Also handles JSON round-tripped node Maps from `cv_array_to_large_list`.
pub fn extract_vids_from_cypher_value_column(col: &dyn Array) -> DFResult<arrow_array::ArrayRef> {
    let binary_col = col
        .as_any()
        .downcast_ref::<arrow_array::LargeBinaryArray>()
        .ok_or_else(|| {
            datafusion::error::DataFusionError::Execution(
                "extract_vids_from_cypher_value_column: expected LargeBinary column".to_string(),
            )
        })?;
    Ok(Arc::new(vids_from_large_binary(binary_col)) as arrow_array::ArrayRef)
}

/// Extract a typed value from a column at a given row index.
///
/// Looks up `col_name` in the batch schema, downcasts to `T`, and applies
/// `extract_fn` if the value is valid. Returns `None` if the column is missing,
/// the downcast fails, or the value is null.
pub(crate) fn extract_column_value<T: arrow_array::Array + 'static, R>(
    batch: &RecordBatch,
    col_name: &str,
    row_idx: usize,
    extract_fn: impl FnOnce(&T, usize) -> R,
) -> Option<R> {
    let (idx, _) = batch.schema().column_with_name(col_name)?;
    let col = batch.column(idx);
    let arr = col.as_any().downcast_ref::<T>()?;
    if arr.is_valid(row_idx) {
        Some(extract_fn(arr, row_idx))
    } else {
        None
    }
}

/// Build the standard node struct fields for path structures.
///
/// Used when materializing path objects containing nodes.
/// Fields: `_vid`, `_labels`, `properties`
pub fn node_struct_fields() -> arrow_schema::Fields {
    arrow_schema::Fields::from(vec![
        Field::new("_vid", DataType::UInt64, false),
        Field::new("_labels", labels_data_type(), true),
        Field::new("properties", DataType::LargeBinary, true),
    ])
}

/// Build the standard edge struct fields for path structures.
///
/// Used when materializing path objects containing edges.
/// Fields: `_eid`, `_type_name`, `_src`, `_dst`, `properties`
pub fn edge_struct_fields() -> arrow_schema::Fields {
    arrow_schema::Fields::from(vec![
        Field::new("_eid", DataType::UInt64, false),
        Field::new("_type_name", DataType::Utf8, false),
        Field::new("_src", DataType::UInt64, false),
        Field::new("_dst", DataType::UInt64, false),
        Field::new("properties", DataType::LargeBinary, true),
    ])
}

/// Encode a properties HashMap to CypherValue bytes for LargeBinary columns.
///
/// Used when materializing path properties that need to be stored in LargeBinary
/// columns. Converts the HashMap into a `Value::Map` and encodes it using the
/// CypherValue codec.
pub fn encode_props_to_cv(props: &std::collections::HashMap<String, uni_common::Value>) -> Vec<u8> {
    let val = uni_common::Value::Map(props.clone());
    uni_common::cypher_value_codec::encode(&val)
}

/// Build edge list field for schema with given step variable name.
///
/// Creates a list of edge structs for the relationship variable in VLP patterns.
/// For example, `r` in `MATCH (a)-[r*1..3]->(b)` gets a `List<EdgeStruct>`.
pub fn build_edge_list_field(step_var: &str) -> Field {
    let edge_item = Field::new("item", DataType::Struct(edge_struct_fields()), true);
    // Field must be nullable to support OPTIONAL MATCH unmatched (r = NULL)
    Field::new(step_var, DataType::List(Arc::new(edge_item)), true)
}

/// Build path struct field for schema with given path variable name.
///
/// Creates a struct field with `nodes` and `relationships` lists.
pub fn build_path_struct_field(path_var: &str) -> Field {
    let node_item = Field::new("item", DataType::Struct(node_struct_fields()), true);
    let nodes_field = Field::new("nodes", DataType::List(Arc::new(node_item)), true);

    let edge_item = Field::new("item", DataType::Struct(edge_struct_fields()), true);
    let relationships_field =
        Field::new("relationships", DataType::List(Arc::new(edge_item)), true);

    Field::new(
        path_var,
        DataType::Struct(arrow_schema::Fields::from(vec![
            nodes_field,
            relationships_field,
        ])),
        true,
    )
}

/// Extend an input schema with a path struct field.
///
/// Clones the fields from `input_schema` and appends a path struct field
/// using [`build_path_struct_field`].
pub fn extend_schema_with_path(input_schema: SchemaRef, path_variable: &str) -> SchemaRef {
    let mut fields: Vec<Arc<Field>> = input_schema.fields().to_vec();
    fields.push(Arc::new(build_path_struct_field(path_variable)));
    Arc::new(Schema::new(fields))
}

/// Build a path struct array from nodes and relationships list arrays.
///
/// Combines the nodes and relationships arrays into a single `StructArray` with
/// the standard path structure (`nodes`, `relationships`), applying the given
/// validity mask.
pub fn build_path_struct_array(
    nodes_array: ArrayRef,
    rels_array: ArrayRef,
    path_validity: Vec<bool>,
) -> DFResult<arrow_array::StructArray> {
    Ok(arrow_array::StructArray::try_new(
        arrow_schema::Fields::from(vec![
            Arc::new(Field::new("nodes", nodes_array.data_type().clone(), true)),
            Arc::new(Field::new(
                "relationships",
                rels_array.data_type().clone(),
                true,
            )),
        ]),
        vec![nodes_array, rels_array],
        Some(arrow::buffer::NullBuffer::from(path_validity)),
    )?)
}

/// Create a `ListBuilder<StructBuilder>` for building edge list arrays.
///
/// Used when materializing edge lists for step variables (`r` in `[r*1..3]`)
/// and path relationship arrays. Returns a builder whose struct fields match
/// `edge_struct_fields()`.
pub fn new_edge_list_builder()
-> arrow_array::builder::ListBuilder<arrow_array::builder::StructBuilder> {
    use arrow_array::builder::{LargeBinaryBuilder, StringBuilder, StructBuilder, UInt64Builder};
    arrow_array::builder::ListBuilder::new(StructBuilder::new(
        edge_struct_fields(),
        vec![
            Box::new(UInt64Builder::new()),
            Box::new(StringBuilder::new()),
            Box::new(UInt64Builder::new()),
            Box::new(UInt64Builder::new()),
            Box::new(LargeBinaryBuilder::new()),
        ],
    ))
}

/// Create a `ListBuilder<StructBuilder>` for building node list arrays.
///
/// Used when materializing path node arrays. Returns a builder whose struct
/// fields match `node_struct_fields()`.
pub fn new_node_list_builder()
-> arrow_array::builder::ListBuilder<arrow_array::builder::StructBuilder> {
    use arrow_array::builder::{
        LargeBinaryBuilder, ListBuilder, StringBuilder, StructBuilder, UInt64Builder,
    };
    arrow_array::builder::ListBuilder::new(StructBuilder::new(
        node_struct_fields(),
        vec![
            Box::new(UInt64Builder::new()),
            Box::new(ListBuilder::new(StringBuilder::new())),
            Box::new(LargeBinaryBuilder::new()),
        ],
    ))
}

/// Append a single edge to an edge struct builder.
///
/// Writes `_eid`, `_type_name`, `_src`, `_dst`, and `properties` fields,
/// then appends the struct row. The `query_ctx` is used to look up edge
/// properties from the L0 visibility chain.
pub fn append_edge_to_struct(
    struct_builder: &mut arrow_array::builder::StructBuilder,
    eid: uni_common::core::id::Eid,
    type_name: &str,
    src_vid: u64,
    dst_vid: u64,
    query_ctx: &uni_store::runtime::context::QueryContext,
) {
    use arrow_array::builder::{LargeBinaryBuilder, StringBuilder, UInt64Builder};
    use uni_store::runtime::l0_visibility;

    struct_builder
        .field_builder::<UInt64Builder>(0)
        .unwrap()
        .append_value(eid.as_u64());
    struct_builder
        .field_builder::<StringBuilder>(1)
        .unwrap()
        .append_value(type_name);
    struct_builder
        .field_builder::<UInt64Builder>(2)
        .unwrap()
        .append_value(src_vid);
    struct_builder
        .field_builder::<UInt64Builder>(3)
        .unwrap()
        .append_value(dst_vid);
    let props_builder = struct_builder
        .field_builder::<LargeBinaryBuilder>(4)
        .unwrap();
    if let Some(props) = l0_visibility::get_edge_properties(eid, query_ctx) {
        let cv_bytes = encode_props_to_cv(&props);
        props_builder.append_value(&cv_bytes);
    } else {
        props_builder.append_null();
    }
    struct_builder.append(true);
}

/// Append a null edge struct row (placeholder values + null validity).
///
/// Arrow struct builders require all field builders to advance even for null rows.
/// This appends default placeholder values and marks the struct row as null.
fn append_null_edge_struct(struct_builder: &mut arrow_array::builder::StructBuilder) {
    use arrow_array::builder::{LargeBinaryBuilder, StringBuilder, UInt64Builder};

    struct_builder
        .field_builder::<UInt64Builder>(0)
        .unwrap()
        .append_value(0);
    struct_builder
        .field_builder::<StringBuilder>(1)
        .unwrap()
        .append_value("");
    struct_builder
        .field_builder::<UInt64Builder>(2)
        .unwrap()
        .append_value(0);
    struct_builder
        .field_builder::<UInt64Builder>(3)
        .unwrap()
        .append_value(0);
    struct_builder
        .field_builder::<LargeBinaryBuilder>(4)
        .unwrap()
        .append_null();
    struct_builder.append(false);
}

/// Append an edge to a struct builder, handling the `Option<Eid>` case.
///
/// When `eid` is `Some`, resolves the type name from `batch_type_name` (primary)
/// or L0 visibility (fallback), then delegates to [`append_edge_to_struct`].
/// When `eid` is `None`, appends a null struct row.
pub fn append_edge_to_struct_optional(
    struct_builder: &mut arrow_array::builder::StructBuilder,
    eid: Option<uni_common::core::id::Eid>,
    src_vid: u64,
    dst_vid: u64,
    batch_type_name: Option<String>,
    query_ctx: &uni_store::runtime::context::QueryContext,
) {
    match eid {
        Some(e) => {
            use uni_store::runtime::l0_visibility;
            let type_name = batch_type_name
                .or_else(|| l0_visibility::get_edge_type(e, query_ctx))
                .unwrap_or_default();
            append_edge_to_struct(struct_builder, e, &type_name, src_vid, dst_vid, query_ctx);
        }
        None => append_null_edge_struct(struct_builder),
    }
}

/// Append a single node to a node struct builder.
///
/// Writes `_vid`, `_labels`, and `properties` fields, then appends the struct
/// row. The `query_ctx` is used to look up labels and properties from the L0
/// visibility chain.
pub fn append_node_to_struct(
    struct_builder: &mut arrow_array::builder::StructBuilder,
    vid: uni_common::core::id::Vid,
    query_ctx: &uni_store::runtime::context::QueryContext,
) {
    use arrow_array::builder::{LargeBinaryBuilder, ListBuilder, StringBuilder, UInt64Builder};
    use uni_store::runtime::l0_visibility;

    struct_builder
        .field_builder::<UInt64Builder>(0)
        .unwrap()
        .append_value(vid.as_u64());
    let labels = l0_visibility::get_vertex_labels(vid, query_ctx);
    let labels_builder = struct_builder
        .field_builder::<ListBuilder<StringBuilder>>(1)
        .unwrap();
    let values = labels_builder.values();
    for lbl in &labels {
        values.append_value(lbl);
    }
    labels_builder.append(true);
    let props_builder = struct_builder
        .field_builder::<LargeBinaryBuilder>(2)
        .unwrap();
    if let Some(props) = l0_visibility::get_vertex_properties(vid, query_ctx) {
        let cv_bytes = encode_props_to_cv(&props);
        props_builder.append_value(&cv_bytes);
    } else {
        props_builder.append_null();
    }
    struct_builder.append(true);
}

/// Append a null node struct row (placeholder values + null validity).
///
/// Arrow struct builders require all field builders to advance even for null rows.
/// This appends default placeholder values and marks the struct row as null.
fn append_null_node_struct(struct_builder: &mut arrow_array::builder::StructBuilder) {
    use arrow_array::builder::{LargeBinaryBuilder, ListBuilder, StringBuilder, UInt64Builder};

    struct_builder
        .field_builder::<UInt64Builder>(0)
        .unwrap()
        .append_value(0);
    struct_builder
        .field_builder::<ListBuilder<StringBuilder>>(1)
        .unwrap()
        .append(true);
    struct_builder
        .field_builder::<LargeBinaryBuilder>(2)
        .unwrap()
        .append_null();
    struct_builder.append(false);
}

/// Append a node to a struct builder, handling the `Option<Vid>` case.
///
/// When `vid` is `Some`, delegates to [`append_node_to_struct`].
/// When `vid` is `None`, appends a null struct row.
pub fn append_node_to_struct_optional(
    struct_builder: &mut arrow_array::builder::StructBuilder,
    vid: Option<uni_common::core::id::Vid>,
    query_ctx: &uni_store::runtime::context::QueryContext,
) {
    match vid {
        Some(v) => append_node_to_struct(struct_builder, v, query_ctx),
        None => append_null_node_struct(struct_builder),
    }
}

/// Re-encode a `LargeListArray` of CypherValue elements into a `LargeBinaryArray` of CypherValue arrays.
///
/// Each row in the input `LargeListArray` contains zero or more `LargeBinary`
/// elements that are individually CypherValue-encoded values. This function decodes
/// each element, wraps them into a `serde_json::Value::Array`, and re-encodes
/// the whole array as a single CypherValue blob in the output `LargeBinaryArray`.
///
/// Null rows in the input produce null entries in the output.
///
/// # Errors
///
/// Returns a `DataFusionError::Execution` if the input is not a
/// `LargeListArray` or if CypherValue decoding fails.
pub fn large_list_of_cv_to_cv_array(
    list: &datafusion::arrow::array::LargeListArray,
) -> datafusion::error::Result<Arc<dyn datafusion::arrow::array::Array>> {
    use datafusion::arrow::array::{LargeBinaryArray, LargeBinaryBuilder};

    let values = list.values();
    let binary_values = values
        .as_any()
        .downcast_ref::<LargeBinaryArray>()
        .ok_or_else(|| {
            datafusion::error::DataFusionError::Execution(
                "large_list_of_cv_to_cv_array: inner values must be LargeBinaryArray".to_string(),
            )
        })?;

    let mut builder = LargeBinaryBuilder::new();

    for row_idx in 0..list.len() {
        if list.is_null(row_idx) {
            builder.append_null();
            continue;
        }

        let start = list.offsets()[row_idx] as usize;
        let end = list.offsets()[row_idx + 1] as usize;

        let mut json_elements = Vec::with_capacity(end - start);
        for elem_idx in start..end {
            if binary_values.is_null(elem_idx) {
                json_elements.push(serde_json::Value::Null);
            } else {
                let blob = binary_values.value(elem_idx);
                match uni_common::cypher_value_codec::decode(blob) {
                    Ok(uni_val) => {
                        let json_val: serde_json::Value = uni_val.into();
                        json_elements.push(json_val);
                    }
                    Err(_) => json_elements.push(serde_json::Value::Null),
                }
            }
        }

        let uni_val: uni_common::Value = serde_json::Value::Array(json_elements).into();
        let bytes = uni_common::cypher_value_codec::encode(&uni_val);
        builder.append_value(&bytes);
    }

    Ok(Arc::new(builder.finish()))
}

/// Convert a single Arrow array element at `idx` to `serde_json::Value`.
///
/// Handles the common scalar types (UInt64, Int64, Float64, Utf8, Boolean, LargeBinary).
/// Returns `serde_json::Value::Null` for null values or unsupported types.
fn arrow_element_to_json(
    col: &dyn datafusion::arrow::array::Array,
    idx: usize,
) -> serde_json::Value {
    use datafusion::arrow::array::{
        BooleanArray, Float64Array, Int64Array, StringArray, UInt64Array,
    };

    if col.is_null(idx) {
        return serde_json::Value::Null;
    }

    if let Some(arr) = col.as_any().downcast_ref::<UInt64Array>() {
        serde_json::Value::Number(serde_json::Number::from(arr.value(idx)))
    } else if let Some(arr) = col.as_any().downcast_ref::<Int64Array>() {
        serde_json::Value::Number(serde_json::Number::from(arr.value(idx)))
    } else if let Some(arr) = col.as_any().downcast_ref::<Float64Array>() {
        serde_json::Number::from_f64(arr.value(idx))
            .map(serde_json::Value::Number)
            .unwrap_or(serde_json::Value::Null)
    } else if let Some(arr) = col.as_any().downcast_ref::<StringArray>() {
        serde_json::Value::String(arr.value(idx).to_string())
    } else if let Some(arr) = col.as_any().downcast_ref::<BooleanArray>() {
        serde_json::Value::Bool(arr.value(idx))
    } else if let Some(arr) = col.as_any().downcast_ref::<arrow_array::LargeBinaryArray>() {
        uni_common::cypher_value_codec::decode(arr.value(idx))
            .map(|v| v.into())
            .unwrap_or(serde_json::Value::Null)
    } else {
        serde_json::Value::Null
    }
}

/// Convert a typed `LargeListArray` to a `LargeBinaryArray` of CypherValue arrays.
///
/// Each row in the input `LargeListArray` contains zero or more elements of a
/// specific type (Int64, Float64, Utf8, Boolean, or nested LargeBinary). This
/// function converts each row into a JSON array and encodes it as a CypherValue blob.
///
/// If the inner type is already `LargeBinary` (CypherValue), delegates to
/// `large_list_of_cv_to_cv_array()`.
///
/// Null rows in the input produce null entries in the output.
///
/// # Errors
///
/// Returns a `DataFusionError::Execution` if CypherValue encoding fails.
pub fn typed_large_list_to_cv_array(
    list: &datafusion::arrow::array::LargeListArray,
) -> datafusion::error::Result<Arc<dyn datafusion::arrow::array::Array>> {
    use datafusion::arrow::array::{LargeBinaryBuilder, StructArray};

    let values = list.values();

    // If inner type is LargeBinary, delegate to existing function
    if values.data_type() == &DataType::LargeBinary {
        return large_list_of_cv_to_cv_array(list);
    }

    // Build the element-to-JSON converter closure. For Struct arrays, we need
    // to iterate over fields; for scalar arrays, use arrow_element_to_json directly.
    let elem_to_json: Box<dyn Fn(usize) -> serde_json::Value> = match values.data_type() {
        DataType::UInt64
        | DataType::Int64
        | DataType::Float64
        | DataType::Utf8
        | DataType::Boolean => {
            let values = values.clone();
            Box::new(move |idx| arrow_element_to_json(values.as_ref(), idx))
        }
        DataType::Struct(_) => {
            let typed = values
                .as_any()
                .downcast_ref::<StructArray>()
                .ok_or_else(|| {
                    datafusion::error::DataFusionError::Execution(
                        "Expected StructArray".to_string(),
                    )
                })?;
            let fields: Vec<_> = typed.fields().iter().cloned().collect();
            let columns: Vec<_> = (0..typed.num_columns())
                .map(|i| typed.column(i).clone())
                .collect();
            let nulls = typed.nulls().cloned();
            Box::new(move |idx| {
                if nulls.as_ref().is_some_and(|n| n.is_null(idx)) {
                    return serde_json::Value::Null;
                }
                let mut map = serde_json::Map::new();
                for (field_idx, field) in fields.iter().enumerate() {
                    let value = arrow_element_to_json(columns[field_idx].as_ref(), idx);
                    map.insert(field.name().clone(), value);
                }
                serde_json::Value::Object(map)
            })
        }
        other => {
            return Err(datafusion::error::DataFusionError::Execution(format!(
                "Unsupported element type for typed_large_list_to_cv_array: {:?}",
                other
            )));
        }
    };

    let mut builder = LargeBinaryBuilder::new();

    for row_idx in 0..list.len() {
        if list.is_null(row_idx) {
            builder.append_null();
            continue;
        }

        let start = list.offsets()[row_idx] as usize;
        let end = list.offsets()[row_idx + 1] as usize;
        let json_elements: Vec<serde_json::Value> = (start..end).map(&elem_to_json).collect();

        let uni_val: uni_common::Value = serde_json::Value::Array(json_elements).into();
        let bytes = uni_common::cypher_value_codec::encode(&uni_val);
        builder.append_value(&bytes);
    }

    Ok(Arc::new(builder.finish()))
}

/// Convert a `LargeBinaryArray` of CypherValue-encoded arrays into a `LargeListArray`.
///
/// Each element in the input array is a CypherValue blob encoding a JSON array (e.g. `[1,2,3]`).
/// Elements are converted to the specified `element_type`. For example, if `element_type`
/// is `Int64`, CypherValue numbers are parsed as i64 values.
///
/// Non-array CypherValue values and nulls produce empty lists.
pub fn cv_array_to_large_list(
    array: &dyn datafusion::arrow::array::Array,
    element_type: &DataType,
) -> datafusion::error::Result<Arc<dyn datafusion::arrow::array::Array>> {
    use datafusion::arrow::array::LargeBinaryArray;
    use datafusion::arrow::buffer::{OffsetBuffer, ScalarBuffer};

    let binary_arr = array
        .as_any()
        .downcast_ref::<LargeBinaryArray>()
        .ok_or_else(|| {
            datafusion::error::DataFusionError::Execution(
                "cv_array_to_large_list: expected LargeBinaryArray".to_string(),
            )
        })?;

    // Collect all JSON elements across all rows
    let num_rows = binary_arr.len();
    let mut all_elements: Vec<Vec<serde_json::Value>> = Vec::with_capacity(num_rows);
    let mut nulls = Vec::with_capacity(num_rows);

    for i in 0..num_rows {
        if binary_arr.is_null(i) {
            all_elements.push(Vec::new());
            nulls.push(false);
            continue;
        }

        let blob = binary_arr.value(i);
        let uni_val = match uni_common::cypher_value_codec::decode(blob) {
            Ok(v) => v,
            Err(_) => {
                all_elements.push(Vec::new());
                nulls.push(false);
                continue;
            }
        };
        let json_val_decoded: serde_json::Value = uni_val.into();

        match json_val_decoded {
            serde_json::Value::Array(elements) => {
                all_elements.push(elements);
                nulls.push(true);
            }
            _ => {
                all_elements.push(Vec::new());
                nulls.push(true);
            }
        }
    }

    // Build typed values array and offsets
    let mut offsets: Vec<i64> = Vec::with_capacity(num_rows + 1);
    offsets.push(0);

    let values_array: Arc<dyn datafusion::arrow::array::Array> = match element_type {
        DataType::Int64 => {
            let mut builder = datafusion::arrow::array::builder::Int64Builder::new();
            for elems in &all_elements {
                for elem in elems {
                    if let serde_json::Value::Number(n) = elem {
                        if let Some(i) = n.as_i64() {
                            builder.append_value(i);
                        } else if let Some(f) = n.as_f64() {
                            builder.append_value(f as i64);
                        } else {
                            builder.append_null();
                        }
                    } else {
                        builder.append_null();
                    }
                }
                offsets.push(offsets.last().unwrap() + elems.len() as i64);
            }
            Arc::new(builder.finish())
        }
        DataType::Float64 => {
            let mut builder = datafusion::arrow::array::builder::Float64Builder::new();
            for elems in &all_elements {
                for elem in elems {
                    if let serde_json::Value::Number(n) = elem
                        && let Some(f) = n.as_f64()
                    {
                        builder.append_value(f);
                    } else {
                        builder.append_null();
                    }
                }
                offsets.push(offsets.last().unwrap() + elems.len() as i64);
            }
            Arc::new(builder.finish())
        }
        DataType::Utf8 | DataType::LargeUtf8 => {
            let mut builder = datafusion::arrow::array::builder::StringBuilder::new();
            for elems in &all_elements {
                for elem in elems {
                    match elem {
                        serde_json::Value::String(s) => builder.append_value(s),
                        serde_json::Value::Null => builder.append_null(),
                        other => builder.append_value(other.to_string()),
                    }
                }
                offsets.push(offsets.last().unwrap() + elems.len() as i64);
            }
            Arc::new(builder.finish())
        }
        DataType::Boolean => {
            let mut builder = datafusion::arrow::array::builder::BooleanBuilder::new();
            for elems in &all_elements {
                for elem in elems {
                    if let serde_json::Value::Bool(b) = elem {
                        builder.append_value(*b);
                    } else {
                        builder.append_null();
                    }
                }
                offsets.push(offsets.last().unwrap() + elems.len() as i64);
            }
            Arc::new(builder.finish())
        }
        // Fallback: keep as CypherValue LargeBinary blobs
        _ => {
            let mut builder = datafusion::arrow::array::builder::LargeBinaryBuilder::new();
            for elems in &all_elements {
                for elem in elems {
                    let uni_val: uni_common::Value = elem.clone().into();
                    let bytes = uni_common::cypher_value_codec::encode(&uni_val);
                    builder.append_value(&bytes);
                }
                offsets.push(offsets.last().unwrap() + elems.len() as i64);
            }
            Arc::new(builder.finish())
        }
    };

    let field = Arc::new(Field::new("item", element_type.clone(), true));
    let offset_buffer = OffsetBuffer::new(ScalarBuffer::from(offsets));
    let null_buffer = datafusion::arrow::buffer::NullBuffer::from(nulls);

    let large_list = datafusion::arrow::array::LargeListArray::new(
        field,
        offset_buffer,
        values_array,
        Some(null_buffer),
    );

    Ok(Arc::new(large_list))
}

/// Collect all record batches from all partitions of an execution plan.
///
/// Iterates over each partition, executes it, and collects all resulting
/// batches into a single `Vec`. Shared by `execute_subplan` and `run_apply`.
pub async fn collect_all_partitions(
    plan: &Arc<dyn datafusion::physical_plan::ExecutionPlan>,
    task_ctx: Arc<datafusion::execution::TaskContext>,
) -> DFResult<Vec<RecordBatch>> {
    let partition_count = plan.properties().output_partitioning().partition_count();

    let mut all_batches = Vec::new();
    for partition in 0..partition_count {
        let stream = plan.execute(partition, task_ctx.clone())?;
        let batches: Vec<RecordBatch> = stream.try_collect().await?;
        all_batches.extend(batches);
    }
    Ok(all_batches)
}

/// Execute a logical plan using a fresh HybridPhysicalPlanner with the given params.
///
/// Shared by `RecursiveCTEExec`, `GraphApplyExec`, and `ExistsExecExpr`.
/// Pass `mutation_ctx = Some(...)` to enable writes inside the sub-plan
/// (`CALL { ... SET/CREATE/MERGE/DELETE }`). `None` is appropriate for
/// read-only sub-plans (Locy rule evaluation, EXISTS predicate).
#[expect(
    clippy::too_many_arguments,
    reason = "Threading mutation_ctx for CALL subquery writes"
)]
pub async fn execute_subplan(
    plan: &LogicalPlan,
    params: &HashMap<String, Value>,
    outer_values: &HashMap<String, Value>,
    graph_ctx: &Arc<GraphExecutionContext>,
    session_ctx: &Arc<RwLock<SessionContext>>,
    storage: &Arc<StorageManager>,
    schema_info: &Arc<UniSchema>,
    mutation_ctx: Option<&Arc<MutationContext>>,
) -> DFResult<Vec<RecordBatch>> {
    let (batches, _plan) = execute_subplan_with_outer_vars(
        plan,
        params,
        outer_values,
        graph_ctx,
        session_ctx,
        storage,
        schema_info,
        &std::collections::HashSet::new(),
        mutation_ctx,
    )
    .await?;
    Ok(batches)
}

/// Like [`execute_subplan`], but also returns the per-operator metrics of the
/// clause-body physical plan for profiling.
///
/// Used by the Locy `profile()` path to capture a Cypher-style operator tree
/// ([`OperatorStats`]) for each rule clause body, per fixpoint iteration. The
/// metrics are read from the plan's `BaselineMetrics` after execution via
/// `collect_plan_metrics`, so this MUST run the plan to completion first
/// (which it does, like `execute_subplan`). The non-profiled path calls
/// [`execute_subplan`] instead and pays nothing.
#[expect(
    clippy::too_many_arguments,
    reason = "Mirrors execute_subplan's parameter list; profiling adds only the return value"
)]
pub async fn execute_subplan_collecting(
    plan: &LogicalPlan,
    params: &HashMap<String, Value>,
    outer_values: &HashMap<String, Value>,
    graph_ctx: &Arc<GraphExecutionContext>,
    session_ctx: &Arc<RwLock<SessionContext>>,
    storage: &Arc<StorageManager>,
    schema_info: &Arc<UniSchema>,
    mutation_ctx: Option<&Arc<MutationContext>>,
) -> DFResult<(Vec<RecordBatch>, Vec<OperatorStats>)> {
    let (batches, plan) = execute_subplan_with_outer_vars(
        plan,
        params,
        outer_values,
        graph_ctx,
        session_ctx,
        storage,
        schema_info,
        &std::collections::HashSet::new(),
        mutation_ctx,
    )
    .await?;
    let operators = collect_plan_metrics(&plan);
    Ok((batches, operators))
}

#[expect(
    clippy::too_many_arguments,
    reason = "Threading outer_entity_vars for nested EXISTS and mutation_ctx for CALL writes"
)]
/// Like `execute_subplan`, but with outer entity variable names for nested EXISTS.
///
/// The `outer_entity_vars` set is threaded to the `HybridPhysicalPlanner` so its
/// expression compiler can distinguish fresh pattern bindings from correlated
/// references in nested pattern-predicate EXISTS.
pub async fn execute_subplan_with_outer_vars(
    plan: &LogicalPlan,
    params: &HashMap<String, Value>,
    outer_values: &HashMap<String, Value>,
    graph_ctx: &Arc<GraphExecutionContext>,
    session_ctx: &Arc<RwLock<SessionContext>>,
    storage: &Arc<StorageManager>,
    schema_info: &Arc<UniSchema>,
    outer_entity_vars: &std::collections::HashSet<String>,
    mutation_ctx: Option<&Arc<MutationContext>>,
) -> DFResult<(
    Vec<RecordBatch>,
    Arc<dyn datafusion::physical_plan::ExecutionPlan>,
)> {
    let mut planner = HybridPhysicalPlanner::with_l0_context(
        session_ctx.clone(),
        storage.clone(),
        graph_ctx.l0_context().clone(),
        graph_ctx.property_manager().clone(),
        schema_info.clone(),
        params.clone(),
        outer_values.clone(),
    );
    planner.set_outer_entity_vars(outer_entity_vars.clone());

    // Propagate registries from parent context so procedures remain available
    // inside correlated subqueries (Apply operator).
    if let Some(registry) = graph_ctx.algo_registry() {
        planner = planner.with_algo_registry(registry.clone());
    }
    if let Some(registry) = graph_ctx.procedure_registry() {
        planner = planner.with_procedure_registry(registry.clone());
    }
    if let Some(runtime) = graph_ctx.xervo_runtime() {
        planner = planner.with_xervo_runtime(runtime.clone());
    }

    // Propagate the outer MutationContext so writes inside CALL { ... }
    // subqueries (SET, CREATE, MERGE, DELETE) and RecursiveCTE bodies can
    // route through the same transaction's L0 buffer.
    if let Some(mc) = mutation_ctx {
        planner = planner.with_mutation_context(mc.clone());
    }

    let execution_plan = planner.plan(plan).map_err(|e| {
        datafusion::error::DataFusionError::Execution(format!("Sub-plan error: {e}"))
    })?;

    let task_ctx = session_ctx.read().task_ctx();
    let all_batches = collect_all_partitions(&execution_plan, task_ctx).await?;

    Ok((all_batches, execution_plan))
}

/// Extract a single row from a RecordBatch as a HashMap of column name → Value.
///
/// Used to build parameters for correlated subqueries (Apply, EXISTS).
pub fn extract_row_params(batch: &RecordBatch, row_idx: usize) -> HashMap<String, Value> {
    let schema = batch.schema();
    (0..batch.num_columns())
        .map(|col_idx| {
            let col_name = schema.field(col_idx).name().clone();
            let val = arrow_to_json_value(batch.column(col_idx).as_ref(), row_idx);
            (col_name, val)
        })
        .collect()
}

/// Infer the output schema of a ProcedureCall logical plan node.
///
/// This is a simplified version of `GraphProcedureCallExec::build_schema()` that
/// doesn't require target_properties or graph_ctx. It covers common procedure types
/// with basic scalar type inference. For unknown procedures or complex node expansions,
/// it falls back to Utf8.
fn infer_procedure_call_schema(
    procedure_name: &str,
    yield_items: &[(String, Option<String>)],
    _schema_info: &UniSchema,
) -> SchemaRef {
    let infer_type = |name: &str| -> DataType {
        match procedure_name {
            "uni.schema.labels" => match name {
                "propertyCount" | "nodeCount" | "indexCount" => DataType::Int64,
                _ => DataType::Utf8,
            },
            "uni.schema.edgeTypes" | "uni.schema.relationshipTypes" => match name {
                "propertyCount" => DataType::Int64,
                _ => DataType::Utf8,
            },
            "uni.schema.constraints" => match name {
                "enabled" => DataType::Boolean,
                _ => DataType::Utf8,
            },
            "uni.schema.labelInfo" => match name {
                "nullable" | "indexed" | "unique" => DataType::Boolean,
                _ => DataType::Utf8,
            },
            // Node-yield procedures (search family): infer scalar yield
            // types via the canonical yield-name → Arrow-type table.
            // Node expansion happens at execution time in
            // `GraphProcedureCallExec`.
            n if super::procedure_call::is_node_yield_procedure_static(n) => {
                super::procedure_call::canonical_search_type(map_yield_to_canonical(name))
            }
            // uni.schema.indexes, unknown procedures, and fallback: all Utf8
            _ => DataType::Utf8,
        }
    };

    let fields: Vec<Field> = yield_items
        .iter()
        .map(|(name, alias)| {
            let col_name = alias.as_ref().unwrap_or(name);
            Field::new(col_name, infer_type(name), true)
        })
        .collect();

    Arc::new(Schema::new(fields))
}

/// Infer the output schema of a logical plan using UniSchema property metadata.
///
/// This is needed because correlated subqueries reference outer variables that
/// don't exist as physical columns at planning time, so we can't dry-run plan
/// the subquery to get its schema. Instead we walk the logical plan and use
/// `UniSchema` property metadata to infer types.
pub fn infer_logical_plan_schema(plan: &LogicalPlan, schema_info: &UniSchema) -> SchemaRef {
    // Walk to outermost Project
    if let LogicalPlan::Project { projections, .. } = plan {
        let fields: Vec<Field> = projections
            .iter()
            .map(|(expr, alias)| {
                let name = alias.clone().unwrap_or_else(|| expr.to_string_repr());
                let dt = infer_expr_type(expr, schema_info);
                Field::new(name, dt, true)
            })
            .collect();
        return Arc::new(Schema::new(fields));
    }

    // For non-Project plans, walk through wrapping nodes
    match plan {
        LogicalPlan::Sort { input, .. }
        | LogicalPlan::Limit { input, .. }
        | LogicalPlan::Filter { input, .. }
        | LogicalPlan::Distinct { input } => infer_logical_plan_schema(input, schema_info),

        LogicalPlan::ProcedureCall {
            procedure_name,
            yield_items,
            ..
        } => infer_procedure_call_schema(procedure_name, yield_items, schema_info),

        _ => {
            // Fallback: empty schema
            Arc::new(Schema::empty())
        }
    }
}

/// Infer Arrow DataType for a Cypher expression using schema metadata.
fn infer_expr_type(expr: &Expr, schema_info: &UniSchema) -> DataType {
    match expr {
        Expr::Property(base, key) => {
            if let Expr::Variable(_) = base.as_ref() {
                // Look up key across all labels/edge types in schema
                for props in schema_info.properties.values() {
                    if let Some(meta) = props.get(key.as_str()) {
                        return meta.r#type.to_arrow();
                    }
                }
                DataType::LargeBinary
            } else {
                DataType::LargeBinary
            }
        }
        Expr::BinaryOp { left, op, right } => match op {
            BinaryOp::Add | BinaryOp::Sub | BinaryOp::Mul | BinaryOp::Div | BinaryOp::Mod => {
                let lt = infer_expr_type(left, schema_info);
                let rt = infer_expr_type(right, schema_info);
                numeric_promotion(&lt, &rt)
            }
            BinaryOp::Eq
            | BinaryOp::NotEq
            | BinaryOp::Lt
            | BinaryOp::LtEq
            | BinaryOp::Gt
            | BinaryOp::GtEq
            | BinaryOp::And
            | BinaryOp::Or => DataType::Boolean,
            _ => DataType::LargeBinary,
        },
        Expr::Literal(lit) => match lit {
            CypherLiteral::Integer(_) => DataType::Int64,
            CypherLiteral::Float(_) => DataType::Float64,
            CypherLiteral::String(_) => DataType::Utf8,
            CypherLiteral::Bool(_) => DataType::Boolean,
            CypherLiteral::Null => DataType::Null,
            CypherLiteral::Bytes(_) => DataType::LargeBinary,
        },
        Expr::Variable(_) => DataType::LargeBinary,
        Expr::FunctionCall { name, args, .. } => match name.to_lowercase().as_str() {
            "count" => DataType::Int64,
            "sum" | "avg" => {
                if let Some(arg) = args.first() {
                    let arg_type = infer_expr_type(arg, schema_info);
                    if matches!(arg_type, DataType::Float32 | DataType::Float64) {
                        DataType::Float64
                    } else {
                        DataType::Int64
                    }
                } else {
                    DataType::Int64
                }
            }
            "min" | "max" => {
                if let Some(arg) = args.first() {
                    infer_expr_type(arg, schema_info)
                } else {
                    DataType::LargeBinary
                }
            }
            "tostring" | "trim" | "ltrim" | "rtrim" | "tolower" | "toupper" | "left" | "right"
            | "substring" | "replace" | "reverse" | "type" => DataType::Utf8,
            "tointeger" | "toint" | "size" | "length" | "id" => DataType::Int64,
            "tofloat" => DataType::Float64,
            // Numeric statistical aggregates always yield Float64.
            "stdev" | "stddev" | "stdevp" | "stddevp" | "variance" | "variancep" => {
                DataType::Float64
            }
            "toboolean" => DataType::Boolean,
            _ => DataType::LargeBinary,
        },
        _ => DataType::LargeBinary,
    }
}

/// Numeric type promotion for binary arithmetic.
fn numeric_promotion(left: &DataType, right: &DataType) -> DataType {
    match (left, right) {
        (DataType::Float64, _) | (_, DataType::Float64) => DataType::Float64,
        (DataType::Float32, _) | (_, DataType::Float32) => DataType::Float64,
        (DataType::Int64, _) | (_, DataType::Int64) => DataType::Int64,
        (DataType::Int32, _) | (_, DataType::Int32) => DataType::Int64,
        _ => DataType::Int64,
    }
}

/// Evaluate a simple expression to get a `uni_common::Value`.
///
/// Supports:
/// - Literal values
/// - Parameter references ($param)
/// - Variable references (node/edge variables from MATCH)
/// - Literal lists
/// - Literal maps ({key: value, ...})
pub(crate) fn evaluate_simple_expr(
    expr: &Expr,
    params: &HashMap<String, Value>,
    outer_values: &HashMap<String, Value>,
) -> DFResult<Value> {
    match expr {
        Expr::Literal(lit) => Ok(lit.to_value()),

        Expr::Parameter(name) => params.get(name).cloned().ok_or_else(|| {
            datafusion::error::DataFusionError::Execution(format!("Parameter '{}' not found", name))
        }),

        Expr::Variable(name) => {
            // Node variables are stored as "{name}._vid" in outer_values
            let vid_key = format!("{}._vid", name);
            if let Some(val) = outer_values.get(&vid_key) {
                return Ok(val.clone());
            }
            // Fall back to plain name (edge variables, scalar columns)
            outer_values.get(name).cloned().ok_or_else(|| {
                datafusion::error::DataFusionError::Execution(format!(
                    "Variable '{}' not found in scope (looked for '{}' and '{}')",
                    name, vid_key, name
                ))
            })
        }

        Expr::List(items) => {
            let values: Vec<Value> = items
                .iter()
                .map(|item| evaluate_simple_expr(item, params, outer_values))
                .collect::<DFResult<_>>()?;
            Ok(Value::List(values))
        }

        Expr::Map(entries) => {
            let map: HashMap<String, Value> = entries
                .iter()
                .map(|(k, v)| {
                    evaluate_simple_expr(v, params, outer_values).map(|val| (k.clone(), val))
                })
                .collect::<DFResult<_>>()?;
            Ok(Value::Map(map))
        }

        _ => Err(datafusion::error::DataFusionError::Execution(format!(
            "Unsupported expression type for procedure argument: {:?}",
            expr
        ))),
    }
}

/// Merge edge property metadata across multiple edge types.
///
/// When a traversal spans several edge types, property columns must accommodate
/// all of them. This function collects property metadata from each type and
/// resolves conflicts: if two types define the same property with different
/// data types, the merged type widens to `CypherValue`. Nullability is merged
/// with OR (if either is nullable, the result is nullable).
pub fn merged_edge_schema_props(
    uni_schema: &UniSchema,
    edge_type_ids: &[u32],
) -> HashMap<String, uni_common::core::schema::PropertyMeta> {
    let mut merged: HashMap<String, uni_common::core::schema::PropertyMeta> = HashMap::new();
    let mut sorted_ids = edge_type_ids.to_vec();
    sorted_ids.sort_unstable();

    for edge_type_id in sorted_ids {
        if let Some(edge_type_name) = uni_schema.edge_type_name_by_id_unified(edge_type_id)
            && let Some(props) = uni_schema.properties.get(edge_type_name.as_str())
        {
            for (prop_name, meta) in props {
                match merged.get_mut(prop_name) {
                    Some(existing) => {
                        if existing.r#type != meta.r#type {
                            existing.r#type = uni_common::core::schema::DataType::CypherValue;
                        }
                        existing.nullable |= meta.nullable;
                    }
                    None => {
                        merged.insert(prop_name.clone(), meta.clone());
                    }
                }
            }
        }
    }

    merged
}

// ---------------------------------------------------------------------------
// Shared key extraction for Locy operators (Priority, Fold, BestBy, Fixpoint)
// ---------------------------------------------------------------------------

/// A hashable scalar key extracted from an Arrow array row.
///
/// Used across Locy operators for grouping and deduplication.
#[derive(Clone, Debug, PartialEq, Eq, Hash)]
pub(crate) enum ScalarKey {
    Null,
    Bool(bool),
    Int64(i64),
    Utf8(String),
    Binary(Vec<u8>),
}

/// Extract a composite key from a row of a `RecordBatch`.
///
/// For each column index in `key_indices`, reads the scalar value at `row_idx`
/// and converts it to a `ScalarKey`. Float64 values are hashed by their bit
/// representation for exact grouping.
pub(crate) fn extract_scalar_key(
    batch: &RecordBatch,
    key_indices: &[usize],
    row_idx: usize,
) -> Vec<ScalarKey> {
    use arrow::array::Array;
    key_indices
        .iter()
        .map(|&col_idx| {
            let col = batch.column(col_idx);
            if col.is_null(row_idx) {
                return ScalarKey::Null;
            }
            match col.data_type() {
                arrow_schema::DataType::Boolean => {
                    let arr = col
                        .as_any()
                        .downcast_ref::<arrow_array::BooleanArray>()
                        .unwrap();
                    ScalarKey::Bool(arr.value(row_idx))
                }
                arrow_schema::DataType::Int64 => {
                    let arr = col
                        .as_any()
                        .downcast_ref::<arrow_array::Int64Array>()
                        .unwrap();
                    ScalarKey::Int64(arr.value(row_idx))
                }
                arrow_schema::DataType::Utf8 => {
                    let arr = col
                        .as_any()
                        .downcast_ref::<arrow_array::StringArray>()
                        .unwrap();
                    ScalarKey::Utf8(arr.value(row_idx).to_string())
                }
                arrow_schema::DataType::LargeBinary => {
                    let arr = col
                        .as_any()
                        .downcast_ref::<arrow_array::LargeBinaryArray>()
                        .unwrap();
                    ScalarKey::Binary(arr.value(row_idx).to_vec())
                }
                arrow_schema::DataType::Float64 => {
                    // Hash f64 as bits for grouping
                    let arr = col
                        .as_any()
                        .downcast_ref::<arrow_array::Float64Array>()
                        .unwrap();
                    ScalarKey::Int64(arr.value(row_idx).to_bits() as i64)
                }
                arrow_schema::DataType::LargeUtf8 => {
                    let arr = col
                        .as_any()
                        .downcast_ref::<arrow_array::LargeStringArray>()
                        .unwrap();
                    ScalarKey::Utf8(arr.value(row_idx).to_string())
                }
                _ => {
                    // Fallback (including Struct): use arrow display formatter
                    let formatter = arrow::util::display::ArrayFormatter::try_new(
                        col.as_ref(),
                        &arrow::util::display::FormatOptions::default(),
                    );
                    match formatter {
                        Ok(f) => ScalarKey::Utf8(f.value(row_idx).to_string()),
                        Err(_) => ScalarKey::Utf8(format!("opaque@{row_idx}")),
                    }
                }
            }
        })
        .collect()
}

// `calculate_score` moved to `uni-query-functions::similar_to` (a pure
// leaf module). Re-export for back-compat with code that imports it via
// `df_graph::common::calculate_score`.
pub use uni_query_functions::similar_to::calculate_score;

#[cfg(test)]
mod tests {
    use super::*;
    use arrow_array::{LargeBinaryArray, UInt64Array};
    use arrow_schema::Schema;

    #[test]
    fn test_extract_row_params_loses_uint64_to_int() {
        let schema = Arc::new(Schema::new(vec![Field::new(
            "n._vid",
            DataType::UInt64,
            true,
        )]));
        let batch = RecordBatch::try_new(schema, vec![Arc::new(UInt64Array::from(vec![Some(7)]))])
            .expect("batch should be valid");

        let params = extract_row_params(&batch, 0);
        assert_eq!(params.get("n._vid"), Some(&Value::Int(7)));
    }

    #[test]
    fn test_extract_row_params_decodes_largebinary_to_map() {
        let encoded = uni_common::cypher_value_codec::encode(&Value::Map(HashMap::new()));
        let schema = Arc::new(Schema::new(vec![Field::new(
            "m._all_props",
            DataType::LargeBinary,
            true,
        )]));
        let batch = RecordBatch::try_new(
            schema,
            vec![Arc::new(LargeBinaryArray::from(vec![Some(
                encoded.as_slice(),
            )]))],
        )
        .expect("batch should be valid");

        let params = extract_row_params(&batch, 0);
        assert_eq!(
            params.get("m._all_props"),
            Some(&Value::Map(HashMap::new()))
        );
    }
}