lance_index/scalar/

expression.rs

// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The Lance Authors

use std::{
    ops::Bound,
    sync::{Arc, LazyLock},
};

use arrow::array::BinaryBuilder;
use arrow_array::{Array, RecordBatch, UInt32Array};
use arrow_schema::{DataType, Field, Schema, SchemaRef};
use async_recursion::async_recursion;
use async_trait::async_trait;
use datafusion_common::ScalarValue;
use datafusion_expr::{
    Between, BinaryExpr, Expr, Operator, ReturnFieldArgs, ScalarUDF,
    expr::{InList, Like, ScalarFunction},
};
use tokio::try_join;

use super::{
    AnyQuery, BloomFilterQuery, LabelListQuery, MetricsCollector, SargableQuery, ScalarIndex,
    SearchResult, TextQuery, TokenQuery,
};
#[cfg(feature = "geo")]
use super::{GeoQuery, RelationQuery};
use lance_core::{
    Error, Result,
    utils::mask::{NullableRowAddrMask, RowAddrMask},
};
use lance_datafusion::{expr::safe_coerce_scalar, planner::Planner};
use roaring::RoaringBitmap;
use tracing::instrument;

const MAX_DEPTH: usize = 500;

/// An indexed expression consists of a scalar index query with a post-scan filter
///
/// When a user wants to filter the data returned by a scan we may be able to use
/// one or more scalar indices to reduce the amount of data we load from the disk.
///
/// For example, if a user provides the filter "x = 7", and we have a scalar index
/// on x, then we can possibly identify the exact row that the user desires with our
/// index.  A full-table scan can then turn into a take operation fetching the rows
/// desired.  This would create an IndexedExpression with a scalar_query but no
/// refine.
///
/// If the user asked for "type = 'dog' && z = 3" and we had a scalar index on the
/// "type" column then we could convert this to an indexed scan for "type='dog'"
/// followed by an in-memory filter for z=3.  This would create an IndexedExpression
/// with both a scalar_query AND a refine.
///
/// Finally, if the user asked for "z = 3" and we do not have a scalar index on the
/// "z" column then we must fallback to an IndexedExpression with no scalar_query and
/// only a refine.
///
/// Two IndexedExpressions can be AND'd together.  Each part is AND'd together.
/// Two IndexedExpressions cannot be OR'd together unless both are scalar_query only
///   or both are refine only
/// An IndexedExpression cannot be negated if it has both a refine and a scalar_query
///
/// When an operation cannot be performed we fallback to the original expression-only
/// representation
#[derive(Debug, PartialEq)]
pub struct IndexedExpression {
    /// The portion of the query that can be satisfied by scalar indices
    pub scalar_query: Option<ScalarIndexExpr>,
    /// The portion of the query that cannot be satisfied by scalar indices
    pub refine_expr: Option<Expr>,
}

pub trait ScalarQueryParser: std::fmt::Debug + Send + Sync {
    /// Visit a between expression
    ///
    /// Returns an IndexedExpression if the index can accelerate between expressions
    fn visit_between(
        &self,
        column: &str,
        low: &Bound<ScalarValue>,
        high: &Bound<ScalarValue>,
    ) -> Option<IndexedExpression>;
    /// Visit an in list expression
    ///
    /// Returns an IndexedExpression if the index can accelerate in list expressions
    fn visit_in_list(&self, column: &str, in_list: &[ScalarValue]) -> Option<IndexedExpression>;
    /// Visit an is bool expression
    ///
    /// Returns an IndexedExpression if the index can accelerate is bool expressions
    fn visit_is_bool(&self, column: &str, value: bool) -> Option<IndexedExpression>;
    /// Visit an is null expression
    ///
    /// Returns an IndexedExpression if the index can accelerate is null expressions
    fn visit_is_null(&self, column: &str) -> Option<IndexedExpression>;
    /// Visit a comparison expression
    ///
    /// Returns an IndexedExpression if the index can accelerate comparison expressions
    fn visit_comparison(
        &self,
        column: &str,
        value: &ScalarValue,
        op: &Operator,
    ) -> Option<IndexedExpression>;
    /// Visit a scalar function expression
    ///
    /// Returns an IndexedExpression if the index can accelerate the given scalar function.
    /// For example, an ngram index can accelerate the contains function.
    fn visit_scalar_function(
        &self,
        column: &str,
        data_type: &DataType,
        func: &ScalarUDF,
        args: &[Expr],
    ) -> Option<IndexedExpression>;

    /// Visit a LIKE expression
    ///
    /// Returns an IndexedExpression if the index can accelerate LIKE expressions.
    /// For prefix patterns (e.g., "foo%"):
    /// - ZoneMaps prune zones based on min/max statistics
    /// - BTrees use range query conversion `[prefix, next_prefix)`
    ///
    /// For patterns with wildcards in the middle (e.g., "foo%bar%"), the leading prefix
    /// can still be used for pruning, with the full pattern as a refine expression.
    ///
    /// # Arguments
    /// * `column` - The column name
    /// * `like` - The full LIKE expression (for constructing refine_expr if needed)
    /// * `pattern` - The LIKE pattern as ScalarValue (e.g., "foo%")
    fn visit_like(
        &self,
        _column: &str,
        _like: &Like,
        _pattern: &ScalarValue,
    ) -> Option<IndexedExpression> {
        None
    }

    /// Visits a potential reference to a column
    ///
    /// This function is a little different from the other visitors.  It is used to test if a potential
    /// column reference is a reference the index handles.
    ///
    /// Most indexes are designed to run on references to the indexed column.  For example, if a query
    /// is "x = 7" and we have a scalar index on "x" then we apply the index to the "x" column reference.
    ///
    /// However, some indexes are designed to run on projections of the indexed column.  For example,
    /// if a query is "json_extract(json, '$.name') = 'books'" and we have a JSON index on the "json" column
    /// then we apply the index to the projection of the "json" column.
    ///
    /// This function is used to test if a potential column reference is a reference the index handles.
    /// The default implementation matches column references but this can be overridden by indexes that
    /// handle projections.
    ///
    /// The function is also passed in the data type of the column and should return the data type of the
    /// reference.  Normally this is the same as the input for a direct column reference and possibly something
    /// different for a projection.  E.g. a JSON column (LargeBinary) might be projected to a string or float
    ///
    /// Note: higher logic in the expression parser already limits references to either Expr::Column or Expr::ScalarFunction
    /// where the first argument is an Expr::Column.  If your projection doesn't fit that mold then the
    /// expression parser will need to be modified.
    fn is_valid_reference(&self, func: &Expr, data_type: &DataType) -> Option<DataType> {
        match func {
            Expr::Column(_) => Some(data_type.clone()),
            _ => None,
        }
    }
}

/// A generic parser that wraps multiple scalar query parsers
///
/// It will search each parser in order and return the first non-None result
#[derive(Debug)]
pub struct MultiQueryParser {
    parsers: Vec<Box<dyn ScalarQueryParser>>,
}

impl MultiQueryParser {
    /// Create a new MultiQueryParser with a single parser
    pub fn single(parser: Box<dyn ScalarQueryParser>) -> Self {
        Self {
            parsers: vec![parser],
        }
    }

    /// Add a new parser to the MultiQueryParser
    pub fn add(&mut self, other: Box<dyn ScalarQueryParser>) {
        self.parsers.push(other);
    }
}

impl ScalarQueryParser for MultiQueryParser {
    fn visit_between(
        &self,
        column: &str,
        low: &Bound<ScalarValue>,
        high: &Bound<ScalarValue>,
    ) -> Option<IndexedExpression> {
        self.parsers
            .iter()
            .find_map(|parser| parser.visit_between(column, low, high))
    }
    fn visit_in_list(&self, column: &str, in_list: &[ScalarValue]) -> Option<IndexedExpression> {
        self.parsers
            .iter()
            .find_map(|parser| parser.visit_in_list(column, in_list))
    }
    fn visit_is_bool(&self, column: &str, value: bool) -> Option<IndexedExpression> {
        self.parsers
            .iter()
            .find_map(|parser| parser.visit_is_bool(column, value))
    }
    fn visit_is_null(&self, column: &str) -> Option<IndexedExpression> {
        self.parsers
            .iter()
            .find_map(|parser| parser.visit_is_null(column))
    }
    fn visit_comparison(
        &self,
        column: &str,
        value: &ScalarValue,
        op: &Operator,
    ) -> Option<IndexedExpression> {
        self.parsers
            .iter()
            .find_map(|parser| parser.visit_comparison(column, value, op))
    }
    fn visit_scalar_function(
        &self,
        column: &str,
        data_type: &DataType,
        func: &ScalarUDF,
        args: &[Expr],
    ) -> Option<IndexedExpression> {
        self.parsers
            .iter()
            .find_map(|parser| parser.visit_scalar_function(column, data_type, func, args))
    }
    fn visit_like(
        &self,
        column: &str,
        like: &Like,
        pattern: &ScalarValue,
    ) -> Option<IndexedExpression> {
        self.parsers
            .iter()
            .find_map(|parser| parser.visit_like(column, like, pattern))
    }
    /// TODO(low-priority): This is maybe not quite right.  We should filter down the list of parsers based
    /// on those that consider the reference valid.  Instead what we are doing is checking all parsers if any one
    /// parser considers the reference valid.
    ///
    /// This will be a problem if the user creates two indexes (e.g. btree and json) on the same column and those two
    /// indexes have different reference schemes.
    fn is_valid_reference(&self, func: &Expr, data_type: &DataType) -> Option<DataType> {
        self.parsers
            .iter()
            .find_map(|parser| parser.is_valid_reference(func, data_type))
    }
}

/// A parser for indices that handle SARGable queries
#[derive(Debug)]
pub struct SargableQueryParser {
    index_name: String,
    index_type: String,
    needs_recheck: bool,
}

impl SargableQueryParser {
    pub fn new(index_name: String, index_type: String, needs_recheck: bool) -> Self {
        Self {
            index_name,
            index_type,
            needs_recheck,
        }
    }
}

impl ScalarQueryParser for SargableQueryParser {
    fn is_valid_reference(&self, func: &Expr, data_type: &DataType) -> Option<DataType> {
        match func {
            Expr::Column(_) => Some(data_type.clone()),
            // Also accept get_field expressions for nested field access
            Expr::ScalarFunction(udf) if udf.name() == "get_field" => Some(data_type.clone()),
            _ => None,
        }
    }

    fn visit_between(
        &self,
        column: &str,
        low: &Bound<ScalarValue>,
        high: &Bound<ScalarValue>,
    ) -> Option<IndexedExpression> {
        if let Bound::Included(val) | Bound::Excluded(val) = low
            && val.is_null()
        {
            return None;
        }
        if let Bound::Included(val) | Bound::Excluded(val) = high
            && val.is_null()
        {
            return None;
        }
        let query = SargableQuery::Range(low.clone(), high.clone());
        Some(IndexedExpression::index_query_with_recheck(
            column.to_string(),
            self.index_name.clone(),
            self.index_type.clone(),
            Arc::new(query),
            self.needs_recheck,
        ))
    }

    fn visit_in_list(&self, column: &str, in_list: &[ScalarValue]) -> Option<IndexedExpression> {
        if in_list.iter().any(|val| val.is_null()) {
            return None;
        }
        let query = SargableQuery::IsIn(in_list.to_vec());
        Some(IndexedExpression::index_query_with_recheck(
            column.to_string(),
            self.index_name.clone(),
            self.index_type.clone(),
            Arc::new(query),
            self.needs_recheck,
        ))
    }

    fn visit_is_bool(&self, column: &str, value: bool) -> Option<IndexedExpression> {
        Some(IndexedExpression::index_query_with_recheck(
            column.to_string(),
            self.index_name.clone(),
            self.index_type.clone(),
            Arc::new(SargableQuery::Equals(ScalarValue::Boolean(Some(value)))),
            self.needs_recheck,
        ))
    }

    fn visit_is_null(&self, column: &str) -> Option<IndexedExpression> {
        Some(IndexedExpression::index_query_with_recheck(
            column.to_string(),
            self.index_name.clone(),
            self.index_type.clone(),
            Arc::new(SargableQuery::IsNull()),
            self.needs_recheck,
        ))
    }

    fn visit_comparison(
        &self,
        column: &str,
        value: &ScalarValue,
        op: &Operator,
    ) -> Option<IndexedExpression> {
        if value.is_null() {
            return None;
        }
        let query = match op {
            Operator::Lt => SargableQuery::Range(Bound::Unbounded, Bound::Excluded(value.clone())),
            Operator::LtEq => {
                SargableQuery::Range(Bound::Unbounded, Bound::Included(value.clone()))
            }
            Operator::Gt => SargableQuery::Range(Bound::Excluded(value.clone()), Bound::Unbounded),
            Operator::GtEq => {
                SargableQuery::Range(Bound::Included(value.clone()), Bound::Unbounded)
            }
            Operator::Eq => SargableQuery::Equals(value.clone()),
            // This will be negated by the caller
            Operator::NotEq => SargableQuery::Equals(value.clone()),
            _ => unreachable!(),
        };
        Some(IndexedExpression::index_query_with_recheck(
            column.to_string(),
            self.index_name.clone(),
            self.index_type.clone(),
            Arc::new(query),
            self.needs_recheck,
        ))
    }

    fn visit_scalar_function(
        &self,
        column: &str,
        _data_type: &DataType,
        func: &ScalarUDF,
        args: &[Expr],
    ) -> Option<IndexedExpression> {
        // Handle starts_with(col, 'prefix') -> convert to LikePrefix query
        if func.name() == "starts_with" && args.len() == 2 {
            // Extract the prefix from the second argument
            let prefix = match &args[1] {
                Expr::Literal(ScalarValue::Utf8(Some(s)), _) => ScalarValue::Utf8(Some(s.clone())),
                Expr::Literal(ScalarValue::LargeUtf8(Some(s)), _) => {
                    ScalarValue::LargeUtf8(Some(s.clone()))
                }
                _ => return None,
            };

            let query = SargableQuery::LikePrefix(prefix);
            return Some(IndexedExpression::index_query_with_recheck(
                column.to_string(),
                self.index_name.clone(),
                self.index_type.clone(),
                Arc::new(query),
                self.needs_recheck,
            ));
        }

        None
    }

    fn visit_like(
        &self,
        column: &str,
        like: &Like,
        pattern: &ScalarValue,
    ) -> Option<IndexedExpression> {
        // Case-insensitive LIKE (ILIKE) cannot be efficiently pruned with zone maps
        if like.case_insensitive {
            return None;
        }

        // Extract the pattern string
        let pattern_str = match pattern {
            ScalarValue::Utf8(Some(s)) => s.as_str(),
            ScalarValue::LargeUtf8(Some(s)) => s.as_str(),
            _ => return None,
        };

        // Try to extract a prefix from the LIKE pattern
        let (prefix, needs_refine) = extract_like_leading_prefix(pattern_str, like.escape_char)?;

        // Create the prefix ScalarValue with the same type as the pattern
        let prefix_value = match pattern {
            ScalarValue::Utf8(_) => ScalarValue::Utf8(Some(prefix)),
            ScalarValue::LargeUtf8(_) => ScalarValue::LargeUtf8(Some(prefix)),
            _ => return None,
        };

        let query = SargableQuery::LikePrefix(prefix_value);
        let scalar_query = Some(ScalarIndexExpr::Query(ScalarIndexSearch {
            column: column.to_string(),
            index_name: self.index_name.clone(),
            index_type: self.index_type.clone(),
            query: Arc::new(query),
            needs_recheck: self.needs_recheck,
        }));

        // If the pattern has wildcards beyond simple prefix, add refine expression
        let refine_expr = if needs_refine {
            Some(Expr::Like(like.clone()))
        } else {
            None
        };

        Some(IndexedExpression {
            scalar_query,
            refine_expr,
        })
    }
}

/// Extract the leading literal prefix from a LIKE pattern.
///
/// Returns `Some((prefix, needs_refine))` where:
/// - `prefix` is the leading literal portion before any wildcards
/// - `needs_refine` is true if the pattern has wildcards beyond a simple trailing `%`
///
/// Returns `None` if the pattern starts with a wildcard (no leading literal).
///
/// Examples:
/// - "foo%" -> Some(("foo", false)) - pure prefix, no recheck needed
/// - "foo%bar%" -> Some(("foo", true)) - can use prefix for pruning, needs recheck
/// - "foo_bar%" -> Some(("foo", true)) - _ is a wildcard, needs recheck
/// - "foo\%bar%" with escape '\' -> Some(("foo%bar", false)) - escaped %, pure prefix
/// - "%foo" -> None - starts with wildcard, cannot prune
/// - "foo" -> None - no wildcard at all, use equality instead
fn extract_like_leading_prefix(pattern: &str, escape_char: Option<char>) -> Option<(String, bool)> {
    let chars: Vec<char> = pattern.chars().collect();
    let len = chars.len();

    if len == 0 {
        return None;
    }

    // DataFusion's starts_with simplification escapes special characters with backslash
    // but doesn't set escape_char. Use backslash as default escape character.
    // Pattern: starts_with(col, 'test_ns$') -> col LIKE 'test\_ns$%' (escape_char: None)
    // See: https://github.com/apache/datafusion/issues/XXXX
    let effective_escape_char = escape_char.or(Some('\\'));

    // Helper to check if a character at position i is escaped
    let is_escaped = |i: usize| -> bool {
        if let Some(esc) = effective_escape_char {
            if i > 0 && chars[i - 1] == esc {
                // Check if the escape char itself is escaped
                if i >= 2 && chars[i - 2] == esc {
                    false // Escape was escaped, so this char is NOT escaped
                } else {
                    true // This char is escaped
                }
            } else {
                false
            }
        } else {
            // No escape character defined - nothing can be escaped
            false
        }
    };

    // Pattern must contain at least one unescaped wildcard
    let has_wildcard = chars.iter().enumerate().any(|(i, &c)| {
        if c != '%' && c != '_' {
            return false;
        }
        !is_escaped(i)
    });

    if !has_wildcard {
        return None; // No wildcards, should use equality
    }

    // Check if pattern starts with an unescaped wildcard
    if chars[0] == '%' || chars[0] == '_' {
        return None; // Starts with wildcard, cannot prune
    }

    // Extract the leading literal prefix (everything before first unescaped wildcard)
    let mut prefix = String::new();
    let mut i = 0;
    let mut found_wildcard = false;

    while i < len {
        let c = chars[i];

        // Check for escape character (using effective escape char which may be inferred)
        if let Some(esc) = effective_escape_char
            && c == esc
            && i + 1 < len
        {
            let next = chars[i + 1];
            if next == '%' || next == '_' || next == esc {
                // Escaped character - add the literal character
                prefix.push(next);
                i += 2;
                continue;
            }
        }

        // Check for unescaped wildcard
        if c == '%' || c == '_' {
            found_wildcard = true;
            break;
        }

        prefix.push(c);
        i += 1;
    }

    if prefix.is_empty() {
        return None;
    }

    // Check if pattern is just a simple prefix (ends with single % and nothing after)
    let needs_refine = if found_wildcard && i < len {
        // Check if we're at a % wildcard
        if chars[i] == '%' && i + 1 == len {
            // Pattern is "prefix%" - pure prefix match, no refine needed
            false
        } else {
            // Pattern has more after first wildcard, or has _ wildcard
            true
        }
    } else {
        // No wildcard found (shouldn't happen due to earlier check)
        false
    };

    Some((prefix, needs_refine))
}

/// A parser for bloom filter indices that only support equals, is_null, and is_in operations
#[derive(Debug)]
pub struct BloomFilterQueryParser {
    index_name: String,
    index_type: String,
    needs_recheck: bool,
}

impl BloomFilterQueryParser {
    pub fn new(index_name: String, index_type: String, needs_recheck: bool) -> Self {
        Self {
            index_name,
            index_type,
            needs_recheck,
        }
    }
}

impl ScalarQueryParser for BloomFilterQueryParser {
    fn visit_between(
        &self,
        _: &str,
        _: &Bound<ScalarValue>,
        _: &Bound<ScalarValue>,
    ) -> Option<IndexedExpression> {
        // Bloom filters don't support range queries
        None
    }

    fn visit_in_list(&self, column: &str, in_list: &[ScalarValue]) -> Option<IndexedExpression> {
        let query = BloomFilterQuery::IsIn(in_list.to_vec());
        Some(IndexedExpression::index_query_with_recheck(
            column.to_string(),
            self.index_name.clone(),
            self.index_type.clone(),
            Arc::new(query),
            self.needs_recheck,
        ))
    }

    fn visit_is_bool(&self, column: &str, value: bool) -> Option<IndexedExpression> {
        Some(IndexedExpression::index_query_with_recheck(
            column.to_string(),
            self.index_name.clone(),
            self.index_type.clone(),
            Arc::new(BloomFilterQuery::Equals(ScalarValue::Boolean(Some(value)))),
            self.needs_recheck,
        ))
    }

    fn visit_is_null(&self, column: &str) -> Option<IndexedExpression> {
        Some(IndexedExpression::index_query_with_recheck(
            column.to_string(),
            self.index_name.clone(),
            self.index_type.clone(),
            Arc::new(BloomFilterQuery::IsNull()),
            self.needs_recheck,
        ))
    }

    fn visit_comparison(
        &self,
        column: &str,
        value: &ScalarValue,
        op: &Operator,
    ) -> Option<IndexedExpression> {
        let query = match op {
            // Bloom filters only support equality comparisons
            Operator::Eq => BloomFilterQuery::Equals(value.clone()),
            // This will be negated by the caller
            Operator::NotEq => BloomFilterQuery::Equals(value.clone()),
            // Bloom filters don't support range operations
            _ => return None,
        };
        Some(IndexedExpression::index_query_with_recheck(
            column.to_string(),
            self.index_name.clone(),
            self.index_type.clone(),
            Arc::new(query),
            self.needs_recheck,
        ))
    }

    fn visit_scalar_function(
        &self,
        _: &str,
        _: &DataType,
        _: &ScalarUDF,
        _: &[Expr],
    ) -> Option<IndexedExpression> {
        // Bloom filters don't support scalar functions
        None
    }
}

/// A parser for indices that handle label list queries
#[derive(Debug)]
pub struct LabelListQueryParser {
    index_name: String,
    index_type: String,
}

impl LabelListQueryParser {
    pub fn new(index_name: String, index_type: String) -> Self {
        Self {
            index_name,
            index_type,
        }
    }
}

impl ScalarQueryParser for LabelListQueryParser {
    fn visit_between(
        &self,
        _: &str,
        _: &Bound<ScalarValue>,
        _: &Bound<ScalarValue>,
    ) -> Option<IndexedExpression> {
        None
    }

    fn visit_in_list(&self, _: &str, _: &[ScalarValue]) -> Option<IndexedExpression> {
        None
    }

    fn visit_is_bool(&self, _: &str, _: bool) -> Option<IndexedExpression> {
        None
    }

    fn visit_is_null(&self, _: &str) -> Option<IndexedExpression> {
        None
    }

    fn visit_comparison(
        &self,
        _: &str,
        _: &ScalarValue,
        _: &Operator,
    ) -> Option<IndexedExpression> {
        None
    }

    fn visit_scalar_function(
        &self,
        column: &str,
        data_type: &DataType,
        func: &ScalarUDF,
        args: &[Expr],
    ) -> Option<IndexedExpression> {
        if args.len() != 2 {
            return None;
        }
        // DataFusion normalizes array_contains to array_has
        if func.name() == "array_has" {
            let inner_type = match data_type {
                DataType::List(field) | DataType::LargeList(field) => field.data_type(),
                _ => return None,
            };
            let scalar = maybe_scalar(&args[1], inner_type)?;
            // array_has(..., NULL) returns no matches in datafusion, but the index would
            // match rows containing NULL. Fallback to match datafusion behavior.
            if scalar.is_null() {
                return None;
            }
            let query = LabelListQuery::HasAnyLabel(vec![scalar]);
            return Some(IndexedExpression::index_query(
                column.to_string(),
                self.index_name.clone(),
                self.index_type.clone(),
                Arc::new(query),
            ));
        }

        let label_list = maybe_scalar(&args[1], data_type)?;
        if let ScalarValue::List(list_arr) = label_list {
            let list_values = list_arr.values();
            if list_values.is_empty() {
                return None;
            }
            let mut scalars = Vec::with_capacity(list_values.len());
            for idx in 0..list_values.len() {
                scalars.push(ScalarValue::try_from_array(list_values.as_ref(), idx).ok()?);
            }
            if func.name() == "array_has_all" {
                let query = LabelListQuery::HasAllLabels(scalars);
                Some(IndexedExpression::index_query(
                    column.to_string(),
                    self.index_name.clone(),
                    self.index_type.clone(),
                    Arc::new(query),
                ))
            } else if func.name() == "array_has_any" {
                let query = LabelListQuery::HasAnyLabel(scalars);
                Some(IndexedExpression::index_query(
                    column.to_string(),
                    self.index_name.clone(),
                    self.index_type.clone(),
                    Arc::new(query),
                ))
            } else {
                None
            }
        } else {
            None
        }
    }
}

/// A parser for indices that handle string contains queries
#[derive(Debug, Clone)]
pub struct TextQueryParser {
    index_name: String,
    index_type: String,
    needs_recheck: bool,
}

impl TextQueryParser {
    pub fn new(index_name: String, index_type: String, needs_recheck: bool) -> Self {
        Self {
            index_name,
            index_type,
            needs_recheck,
        }
    }
}

impl ScalarQueryParser for TextQueryParser {
    fn visit_between(
        &self,
        _: &str,
        _: &Bound<ScalarValue>,
        _: &Bound<ScalarValue>,
    ) -> Option<IndexedExpression> {
        None
    }

    fn visit_in_list(&self, _: &str, _: &[ScalarValue]) -> Option<IndexedExpression> {
        None
    }

    fn visit_is_bool(&self, _: &str, _: bool) -> Option<IndexedExpression> {
        None
    }

    fn visit_is_null(&self, _: &str) -> Option<IndexedExpression> {
        None
    }

    fn visit_comparison(
        &self,
        _: &str,
        _: &ScalarValue,
        _: &Operator,
    ) -> Option<IndexedExpression> {
        None
    }

    fn visit_scalar_function(
        &self,
        column: &str,
        data_type: &DataType,
        func: &ScalarUDF,
        args: &[Expr],
    ) -> Option<IndexedExpression> {
        if args.len() != 2 {
            return None;
        }
        let scalar = maybe_scalar(&args[1], data_type)?;
        match scalar {
            ScalarValue::Utf8(Some(scalar_str)) | ScalarValue::LargeUtf8(Some(scalar_str)) => {
                if func.name() == "contains" {
                    let query = TextQuery::StringContains(scalar_str);
                    Some(IndexedExpression::index_query_with_recheck(
                        column.to_string(),
                        self.index_name.clone(),
                        self.index_type.clone(),
                        Arc::new(query),
                        self.needs_recheck,
                    ))
                } else {
                    None
                }
            }
            _ => {
                // If the scalar is not a string, we cannot handle it
                None
            }
        }
    }
}

/// A parser for indices that handle queries with the contains_tokens function
#[derive(Debug, Clone)]
pub struct FtsQueryParser {
    index_name: String,
    index_type: String,
}

impl FtsQueryParser {
    pub fn new(name: String, index_type: String) -> Self {
        Self {
            index_name: name,
            index_type,
        }
    }
}

impl ScalarQueryParser for FtsQueryParser {
    fn visit_between(
        &self,
        _: &str,
        _: &Bound<ScalarValue>,
        _: &Bound<ScalarValue>,
    ) -> Option<IndexedExpression> {
        None
    }

    fn visit_in_list(&self, _: &str, _: &[ScalarValue]) -> Option<IndexedExpression> {
        None
    }

    fn visit_is_bool(&self, _: &str, _: bool) -> Option<IndexedExpression> {
        None
    }

    fn visit_is_null(&self, _: &str) -> Option<IndexedExpression> {
        None
    }

    fn visit_comparison(
        &self,
        _: &str,
        _: &ScalarValue,
        _: &Operator,
    ) -> Option<IndexedExpression> {
        None
    }

    fn visit_scalar_function(
        &self,
        column: &str,
        data_type: &DataType,
        func: &ScalarUDF,
        args: &[Expr],
    ) -> Option<IndexedExpression> {
        if args.len() != 2 {
            return None;
        }
        let scalar = maybe_scalar(&args[1], data_type)?;
        if let ScalarValue::Utf8(Some(scalar_str)) = scalar
            && func.name() == "contains_tokens"
        {
            let query = TokenQuery::TokensContains(scalar_str);
            return Some(IndexedExpression::index_query(
                column.to_string(),
                self.index_name.clone(),
                self.index_type.clone(),
                Arc::new(query),
            ));
        }
        None
    }
}

/// A parser for geo indices that handles spatial queries
#[cfg(feature = "geo")]
#[derive(Debug, Clone)]
pub struct GeoQueryParser {
    index_name: String,
    index_type: String,
}

#[cfg(feature = "geo")]
impl GeoQueryParser {
    pub fn new(index_name: String, index_type: String) -> Self {
        Self {
            index_name,
            index_type,
        }
    }
}

#[cfg(feature = "geo")]
impl ScalarQueryParser for GeoQueryParser {
    fn visit_between(
        &self,
        _: &str,
        _: &Bound<ScalarValue>,
        _: &Bound<ScalarValue>,
    ) -> Option<IndexedExpression> {
        None
    }

    fn visit_in_list(&self, _: &str, _: &[ScalarValue]) -> Option<IndexedExpression> {
        None
    }

    fn visit_is_bool(&self, _: &str, _: bool) -> Option<IndexedExpression> {
        None
    }

    fn visit_is_null(&self, column: &str) -> Option<IndexedExpression> {
        Some(IndexedExpression::index_query_with_recheck(
            column.to_string(),
            self.index_name.clone(),
            self.index_type.clone(),
            Arc::new(GeoQuery::IsNull),
            true,
        ))
    }

    fn visit_comparison(
        &self,
        _: &str,
        _: &ScalarValue,
        _: &Operator,
    ) -> Option<IndexedExpression> {
        None
    }

    fn visit_scalar_function(
        &self,
        column: &str,
        _data_type: &DataType,
        func: &ScalarUDF,
        args: &[Expr],
    ) -> Option<IndexedExpression> {
        if (func.name() == "st_intersects"
            || func.name() == "st_contains"
            || func.name() == "st_within"
            || func.name() == "st_touches"
            || func.name() == "st_crosses"
            || func.name() == "st_overlaps"
            || func.name() == "st_covers"
            || func.name() == "st_coveredby")
            && args.len() == 2
        {
            let left_arg = &args[0];
            let right_arg = &args[1];
            return match (left_arg, right_arg) {
                (Expr::Literal(left_value, metadata), Expr::Column(_)) => {
                    let mut field = Field::new("_geo", left_value.data_type(), false);
                    if let Some(metadata) = metadata {
                        field = field.with_metadata(metadata.to_hashmap());
                    }
                    let query = GeoQuery::IntersectQuery(RelationQuery {
                        value: left_value.clone(),
                        field,
                    });
                    Some(IndexedExpression::index_query_with_recheck(
                        column.to_string(),
                        self.index_name.clone(),
                        self.index_type.clone(),
                        Arc::new(query),
                        true,
                    ))
                }
                (Expr::Column(_), Expr::Literal(right_value, metadata)) => {
                    let mut field = Field::new("_geo", right_value.data_type(), false);
                    if let Some(metadata) = metadata {
                        field = field.with_metadata(metadata.to_hashmap());
                    }
                    let query = GeoQuery::IntersectQuery(RelationQuery {
                        value: right_value.clone(),
                        field,
                    });
                    Some(IndexedExpression::index_query_with_recheck(
                        column.to_string(),
                        self.index_name.clone(),
                        self.index_type.clone(),
                        Arc::new(query),
                        true,
                    ))
                }
                _ => None,
            };
        }
        None
    }
}

impl IndexedExpression {
    /// Create an expression that only does refine
    fn refine_only(refine_expr: Expr) -> Self {
        Self {
            scalar_query: None,
            refine_expr: Some(refine_expr),
        }
    }

    /// Create an expression that is only an index query
    fn index_query(
        column: String,
        index_name: String,
        index_type: String,
        query: Arc<dyn AnyQuery>,
    ) -> Self {
        Self {
            scalar_query: Some(ScalarIndexExpr::Query(ScalarIndexSearch {
                column,
                index_name,
                index_type,
                query,
                needs_recheck: false, // Default to false, will be set by parser
            })),
            refine_expr: None,
        }
    }

    /// Create an expression that is only an index query with explicit needs_recheck
    fn index_query_with_recheck(
        column: String,
        index_name: String,
        index_type: String,
        query: Arc<dyn AnyQuery>,
        needs_recheck: bool,
    ) -> Self {
        Self {
            scalar_query: Some(ScalarIndexExpr::Query(ScalarIndexSearch {
                column,
                index_name,
                index_type,
                query,
                needs_recheck,
            })),
            refine_expr: None,
        }
    }

    /// Try and negate the expression
    ///
    /// If the expression contains both an index query and a refine expression then it
    /// cannot be negated today and None will be returned (we give up trying to use indices)
    fn maybe_not(self) -> Option<Self> {
        match (self.scalar_query, self.refine_expr) {
            (Some(_), Some(_)) => None,
            (Some(scalar_query), None) => {
                if scalar_query.needs_recheck() {
                    return None;
                }
                Some(Self {
                    scalar_query: Some(ScalarIndexExpr::Not(Box::new(scalar_query))),
                    refine_expr: None,
                })
            }
            (None, Some(refine_expr)) => Some(Self {
                scalar_query: None,
                refine_expr: Some(Expr::Not(Box::new(refine_expr))),
            }),
            (None, None) => panic!("Empty node should not occur"),
        }
    }

    /// Perform a logical AND of two indexed expressions
    ///
    /// This is straightforward because we can just AND the individual parts
    /// because (A && B) && (C && D) == (A && C) && (B && D)
    fn and(self, other: Self) -> Self {
        let scalar_query = match (self.scalar_query, other.scalar_query) {
            (Some(scalar_query), Some(other_scalar_query)) => Some(ScalarIndexExpr::And(
                Box::new(scalar_query),
                Box::new(other_scalar_query),
            )),
            (Some(scalar_query), None) => Some(scalar_query),
            (None, Some(scalar_query)) => Some(scalar_query),
            (None, None) => None,
        };
        let refine_expr = match (self.refine_expr, other.refine_expr) {
            (Some(refine_expr), Some(other_refine_expr)) => {
                Some(refine_expr.and(other_refine_expr))
            }
            (Some(refine_expr), None) => Some(refine_expr),
            (None, Some(refine_expr)) => Some(refine_expr),
            (None, None) => None,
        };
        Self {
            scalar_query,
            refine_expr,
        }
    }

    /// Try and perform a logical OR of two indexed expressions
    ///
    /// This is a bit tricky because something like:
    ///   (color == 'blue' AND size < 20) OR (color == 'green' AND size < 50)
    /// is not equivalent to:
    ///   (color == 'blue' OR color == 'green') AND (size < 20 OR size < 50)
    fn maybe_or(self, other: Self) -> Option<Self> {
        // If either expression is missing a scalar_query then we need to load all rows from
        // the database and so we short-circuit and return None
        let scalar_query = self.scalar_query?;
        let other_scalar_query = other.scalar_query?;
        let scalar_query = Some(ScalarIndexExpr::Or(
            Box::new(scalar_query),
            Box::new(other_scalar_query),
        ));

        let refine_expr = match (self.refine_expr, other.refine_expr) {
            // TODO
            //
            // To handle these cases we need a way of going back from a scalar expression query to a logical DF expression (perhaps
            // we can store the expression that led to the creation of the query)
            //
            // For example, imagine we have something like "(color == 'blue' AND size < 20) OR (color == 'green' AND size < 50)"
            //
            // We can do an indexed load of all rows matching "color == 'blue' OR color == 'green'" but then we need to
            // refine that load with the full original expression which, at the moment, we no longer have.
            (Some(_), Some(_)) => {
                return None;
            }
            (Some(_), None) => {
                return None;
            }
            (None, Some(_)) => {
                return None;
            }
            (None, None) => None,
        };
        Some(Self {
            scalar_query,
            refine_expr,
        })
    }

    fn refine(self, expr: Expr) -> Self {
        match self.refine_expr {
            Some(refine_expr) => Self {
                scalar_query: self.scalar_query,
                refine_expr: Some(refine_expr.and(expr)),
            },
            None => Self {
                scalar_query: self.scalar_query,
                refine_expr: Some(expr),
            },
        }
    }
}

/// A trait implemented by anything that can load indices by name
///
/// This is used during the evaluation of an index expression
#[async_trait]
pub trait ScalarIndexLoader: Send + Sync {
    /// Load the index with the given name
    async fn load_index(
        &self,
        column: &str,
        index_name: &str,
        metrics: &dyn MetricsCollector,
    ) -> Result<Arc<dyn ScalarIndex>>;
}

/// This represents a search into a scalar index
#[derive(Debug, Clone)]
pub struct ScalarIndexSearch {
    /// The column to search (redundant, used for debugging messages)
    pub column: String,
    /// The name of the index to search
    pub index_name: String,
    /// The type of the index being searched (e.g. "BTree", "Bitmap"), used for display purposes
    pub index_type: String,
    /// The query to search for
    pub query: Arc<dyn AnyQuery>,
    /// If true, the query results are inexact and will need a recheck
    pub needs_recheck: bool,
}

impl PartialEq for ScalarIndexSearch {
    fn eq(&self, other: &Self) -> bool {
        self.column == other.column
            && self.index_name == other.index_name
            && self.query.as_ref().eq(other.query.as_ref())
    }
}

/// This represents a lookup into one or more scalar indices
///
/// This is a tree of operations because we may need to logically combine or
/// modify the results of scalar lookups
#[derive(Debug, Clone)]
pub enum ScalarIndexExpr {
    Not(Box<Self>),
    And(Box<Self>, Box<Self>),
    Or(Box<Self>, Box<Self>),
    Query(ScalarIndexSearch),
}

impl PartialEq for ScalarIndexExpr {
    fn eq(&self, other: &Self) -> bool {
        match (self, other) {
            (Self::Not(l0), Self::Not(r0)) => l0 == r0,
            (Self::And(l0, l1), Self::And(r0, r1)) => l0 == r0 && l1 == r1,
            (Self::Or(l0, l1), Self::Or(r0, r1)) => l0 == r0 && l1 == r1,
            (Self::Query(l_search), Self::Query(r_search)) => l_search == r_search,
            _ => false,
        }
    }
}

impl std::fmt::Display for ScalarIndexExpr {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            Self::Not(inner) => write!(f, "NOT({})", inner),
            Self::And(lhs, rhs) => write!(f, "AND({},{})", lhs, rhs),
            Self::Or(lhs, rhs) => write!(f, "OR({},{})", lhs, rhs),
            Self::Query(search) => write!(
                f,
                "[{}]@{}({})",
                search.query.format(&search.column),
                search.index_name,
                search.index_type
            ),
        }
    }
}

/// When we evaluate a scalar index query we return a batch with three columns and two rows
///
/// The first column has the block list and allow list
/// The second column tells if the result is least/exact/more (we repeat the discriminant twice)
/// The third column has the fragments covered bitmap in the first row and null in the second row
pub static INDEX_EXPR_RESULT_SCHEMA: LazyLock<SchemaRef> = LazyLock::new(|| {
    Arc::new(Schema::new(vec![
        Field::new("result".to_string(), DataType::Binary, true),
        Field::new("discriminant".to_string(), DataType::UInt32, true),
        Field::new("fragments_covered".to_string(), DataType::Binary, true),
    ]))
});

#[derive(Debug)]
enum NullableIndexExprResult {
    Exact(NullableRowAddrMask),
    AtMost(NullableRowAddrMask),
    AtLeast(NullableRowAddrMask),
}

impl From<SearchResult> for NullableIndexExprResult {
    fn from(result: SearchResult) -> Self {
        match result {
            SearchResult::Exact(mask) => Self::Exact(NullableRowAddrMask::AllowList(mask)),
            SearchResult::AtMost(mask) => Self::AtMost(NullableRowAddrMask::AllowList(mask)),
            SearchResult::AtLeast(mask) => Self::AtLeast(NullableRowAddrMask::AllowList(mask)),
        }
    }
}

impl std::ops::BitAnd<Self> for NullableIndexExprResult {
    type Output = Self;

    fn bitand(self, rhs: Self) -> Self {
        match (self, rhs) {
            (Self::Exact(lhs), Self::Exact(rhs)) => Self::Exact(lhs & rhs),
            (Self::Exact(lhs), Self::AtMost(rhs)) | (Self::AtMost(lhs), Self::Exact(rhs)) => {
                Self::AtMost(lhs & rhs)
            }
            (Self::Exact(exact), Self::AtLeast(_)) | (Self::AtLeast(_), Self::Exact(exact)) => {
                // We could do better here, elements in both lhs and rhs are known
                // to be true and don't require a recheck.  We only need to recheck
                // elements in lhs that are not in rhs
                Self::AtMost(exact)
            }
            (Self::AtMost(lhs), Self::AtMost(rhs)) => Self::AtMost(lhs & rhs),
            (Self::AtLeast(lhs), Self::AtLeast(rhs)) => Self::AtLeast(lhs & rhs),
            (Self::AtMost(most), Self::AtLeast(_)) | (Self::AtLeast(_), Self::AtMost(most)) => {
                Self::AtMost(most)
            }
        }
    }
}

impl std::ops::BitOr<Self> for NullableIndexExprResult {
    type Output = Self;

    fn bitor(self, rhs: Self) -> Self {
        match (self, rhs) {
            (Self::Exact(lhs), Self::Exact(rhs)) => Self::Exact(lhs | rhs),
            (Self::Exact(lhs), Self::AtMost(rhs)) | (Self::AtMost(rhs), Self::Exact(lhs)) => {
                // We could do better here, elements in lhs are known to be true
                // and don't require a recheck.  We only need to recheck elements
                // in rhs that are not in lhs
                Self::AtMost(lhs | rhs)
            }
            (Self::Exact(lhs), Self::AtLeast(rhs)) | (Self::AtLeast(rhs), Self::Exact(lhs)) => {
                Self::AtLeast(lhs | rhs)
            }
            (Self::AtMost(lhs), Self::AtMost(rhs)) => Self::AtMost(lhs | rhs),
            (Self::AtLeast(lhs), Self::AtLeast(rhs)) => Self::AtLeast(lhs | rhs),
            (Self::AtMost(_), Self::AtLeast(least)) | (Self::AtLeast(least), Self::AtMost(_)) => {
                Self::AtLeast(least)
            }
        }
    }
}

impl NullableIndexExprResult {
    pub fn drop_nulls(self) -> IndexExprResult {
        match self {
            Self::Exact(mask) => IndexExprResult::Exact(mask.drop_nulls()),
            Self::AtMost(mask) => IndexExprResult::AtMost(mask.drop_nulls()),
            Self::AtLeast(mask) => IndexExprResult::AtLeast(mask.drop_nulls()),
        }
    }
}

#[derive(Debug)]
pub enum IndexExprResult {
    // The answer is exactly the rows in the allow list minus the rows in the block list
    Exact(RowAddrMask),
    // The answer is at most the rows in the allow list minus the rows in the block list
    // Some of the rows in the allow list may not be in the result and will need to be filtered
    // by a recheck.  Every row in the block list is definitely not in the result.
    AtMost(RowAddrMask),
    // The answer is at least the rows in the allow list minus the rows in the block list
    // Some of the rows in the block list might be in the result.  Every row in the allow list is
    // definitely in the result.
    AtLeast(RowAddrMask),
}

impl IndexExprResult {
    pub fn row_addr_mask(&self) -> &RowAddrMask {
        match self {
            Self::Exact(mask) => mask,
            Self::AtMost(mask) => mask,
            Self::AtLeast(mask) => mask,
        }
    }

    pub fn discriminant(&self) -> u32 {
        match self {
            Self::Exact(_) => 0,
            Self::AtMost(_) => 1,
            Self::AtLeast(_) => 2,
        }
    }

    pub fn from_parts(mask: RowAddrMask, discriminant: u32) -> Result<Self> {
        match discriminant {
            0 => Ok(Self::Exact(mask)),
            1 => Ok(Self::AtMost(mask)),
            2 => Ok(Self::AtLeast(mask)),
            _ => Err(Error::invalid_input_source(
                format!("Invalid IndexExprResult discriminant: {}", discriminant).into(),
            )),
        }
    }

    #[instrument(skip_all)]
    pub fn serialize_to_arrow(
        &self,
        fragments_covered_by_result: &RoaringBitmap,
    ) -> Result<RecordBatch> {
        let row_addr_mask = self.row_addr_mask();
        let row_addr_mask_arr = row_addr_mask.into_arrow()?;
        let discriminant = self.discriminant();
        let discriminant_arr =
            Arc::new(UInt32Array::from(vec![discriminant, discriminant])) as Arc<dyn Array>;
        let mut fragments_covered_builder = BinaryBuilder::new();
        let fragments_covered_bytes_len = fragments_covered_by_result.serialized_size();
        let mut fragments_covered_bytes = Vec::with_capacity(fragments_covered_bytes_len);
        fragments_covered_by_result.serialize_into(&mut fragments_covered_bytes)?;
        fragments_covered_builder.append_value(fragments_covered_bytes);
        fragments_covered_builder.append_null();
        let fragments_covered_arr = Arc::new(fragments_covered_builder.finish()) as Arc<dyn Array>;
        Ok(RecordBatch::try_new(
            INDEX_EXPR_RESULT_SCHEMA.clone(),
            vec![
                Arc::new(row_addr_mask_arr),
                Arc::new(discriminant_arr),
                Arc::new(fragments_covered_arr),
            ],
        )?)
    }
}

impl ScalarIndexExpr {
    /// Evaluates the scalar index expression
    ///
    /// This will result in loading one or more scalar indices and searching them
    ///
    /// TODO: We could potentially try and be smarter about reusing loaded indices for
    /// any situations where the session cache has been disabled.
    #[async_recursion]
    async fn evaluate_impl(
        &self,
        index_loader: &dyn ScalarIndexLoader,
        metrics: &dyn MetricsCollector,
    ) -> Result<NullableIndexExprResult> {
        match self {
            Self::Not(inner) => {
                let result = inner.evaluate_impl(index_loader, metrics).await?;
                // Flip certainty: NOT(AtMost) → AtLeast, NOT(AtLeast) → AtMost
                Ok(match result {
                    NullableIndexExprResult::Exact(mask) => NullableIndexExprResult::Exact(!mask),
                    NullableIndexExprResult::AtMost(mask) => {
                        NullableIndexExprResult::AtLeast(!mask)
                    }
                    NullableIndexExprResult::AtLeast(mask) => {
                        NullableIndexExprResult::AtMost(!mask)
                    }
                })
            }
            Self::And(lhs, rhs) => {
                let lhs_result = lhs.evaluate_impl(index_loader, metrics);
                let rhs_result = rhs.evaluate_impl(index_loader, metrics);
                let (lhs_result, rhs_result) = try_join!(lhs_result, rhs_result)?;
                Ok(lhs_result & rhs_result)
            }
            Self::Or(lhs, rhs) => {
                let lhs_result = lhs.evaluate_impl(index_loader, metrics);
                let rhs_result = rhs.evaluate_impl(index_loader, metrics);
                let (lhs_result, rhs_result) = try_join!(lhs_result, rhs_result)?;
                Ok(lhs_result | rhs_result)
            }
            Self::Query(search) => {
                let index = index_loader
                    .load_index(&search.column, &search.index_name, metrics)
                    .await?;
                let search_result = index.search(search.query.as_ref(), metrics).await?;
                Ok(search_result.into())
            }
        }
    }

    #[instrument(level = "debug", skip_all)]
    pub async fn evaluate(
        &self,
        index_loader: &dyn ScalarIndexLoader,
        metrics: &dyn MetricsCollector,
    ) -> Result<IndexExprResult> {
        Ok(self
            .evaluate_impl(index_loader, metrics)
            .await?
            .drop_nulls())
    }

    pub fn to_expr(&self) -> Expr {
        match self {
            Self::Not(inner) => Expr::Not(inner.to_expr().into()),
            Self::And(lhs, rhs) => {
                let lhs = lhs.to_expr();
                let rhs = rhs.to_expr();
                lhs.and(rhs)
            }
            Self::Or(lhs, rhs) => {
                let lhs = lhs.to_expr();
                let rhs = rhs.to_expr();
                lhs.or(rhs)
            }
            Self::Query(search) => search.query.to_expr(search.column.clone()),
        }
    }

    pub fn needs_recheck(&self) -> bool {
        match self {
            Self::Not(inner) => inner.needs_recheck(),
            Self::And(lhs, rhs) | Self::Or(lhs, rhs) => lhs.needs_recheck() || rhs.needs_recheck(),
            Self::Query(search) => search.needs_recheck,
        }
    }
}

// Extract a column from the expression, if it is a column, or None
fn maybe_column(expr: &Expr) -> Option<&str> {
    match expr {
        Expr::Column(col) => Some(&col.name),
        _ => None,
    }
}

// Extract the full nested column path from a get_field expression chain
// For example: get_field(get_field(metadata, "status"), "code") -> "metadata.status.code"
fn extract_nested_column_path(expr: &Expr) -> Option<String> {
    let mut current_expr = expr;
    let mut parts = Vec::new();

    // Walk up the get_field chain
    loop {
        match current_expr {
            Expr::ScalarFunction(udf) if udf.name() == "get_field" => {
                if udf.args.len() != 2 {
                    return None;
                }
                // Extract the field name from the second argument
                // The Literal now has two fields: ScalarValue and Option<FieldMetadata>
                if let Expr::Literal(ScalarValue::Utf8(Some(field_name)), _) = &udf.args[1] {
                    parts.push(field_name.clone());
                } else {
                    return None;
                }
                // Move up to the parent expression
                current_expr = &udf.args[0];
            }
            Expr::Column(col) => {
                // We've reached the base column
                parts.push(col.name.clone());
                break;
            }
            _ => {
                return None;
            }
        }
    }

    // Reverse to get the correct order (parent.child.grandchild)
    parts.reverse();

    // Format the path correctly
    let field_refs: Vec<&str> = parts.iter().map(|s| s.as_str()).collect();
    Some(lance_core::datatypes::format_field_path(&field_refs))
}

// Extract a column from the expression, if it is a column, and we have an index for that column, or None
//
// There's two ways to get a column.  First, the obvious way, is a
// simple column reference (e.g. x = 7).  Second, a more complex way,
// is some kind of projection into a column (e.g. json_extract(json, '$.name')).
// Third way is nested field access (e.g. get_field(metadata, "status.code"))
fn maybe_indexed_column<'b>(
    expr: &Expr,
    index_info: &'b dyn IndexInformationProvider,
) -> Option<(String, DataType, &'b dyn ScalarQueryParser)> {
    // First try to extract the full nested column path for get_field expressions
    if let Some(nested_path) = extract_nested_column_path(expr)
        && let Some((data_type, parser)) = index_info.get_index(&nested_path)
        && let Some(data_type) = parser.is_valid_reference(expr, data_type)
    {
        return Some((nested_path, data_type, parser));
    }

    match expr {
        Expr::Column(col) => {
            let col = col.name.as_str();
            let (data_type, parser) = index_info.get_index(col)?;
            if let Some(data_type) = parser.is_valid_reference(expr, data_type) {
                Some((col.to_string(), data_type, parser))
            } else {
                None
            }
        }
        Expr::ScalarFunction(udf) => {
            if udf.args.is_empty() {
                return None;
            }
            // For non-get_field functions, fall back to old behavior
            let col = maybe_column(&udf.args[0])?;
            let (data_type, parser) = index_info.get_index(col)?;
            if let Some(data_type) = parser.is_valid_reference(expr, data_type) {
                Some((col.to_string(), data_type, parser))
            } else {
                None
            }
        }
        _ => None,
    }
}

// Extract a literal scalar value from an expression, if it is a literal, or None
fn maybe_scalar(expr: &Expr, expected_type: &DataType) -> Option<ScalarValue> {
    match expr {
        Expr::Literal(value, _) => safe_coerce_scalar(value, expected_type),
        // Some literals can't be expressed in datafusion's SQL and can only be expressed with
        // a cast.  For example, there is no way to express a fixed-size-binary literal (which is
        // commonly used for UUID).  As a result the expression could look like...
        //
        // col = arrow_cast(value, 'fixed_size_binary(16)')
        //
        // In this case we need to extract the value, apply the cast, and then test the casted value
        Expr::Cast(cast) => match cast.expr.as_ref() {
            Expr::Literal(value, _) => {
                let casted = value.cast_to(&cast.data_type).ok()?;
                safe_coerce_scalar(&casted, expected_type)
            }
            _ => None,
        },
        Expr::ScalarFunction(scalar_function) => {
            if scalar_function.name() == "arrow_cast" {
                if scalar_function.args.len() != 2 {
                    return None;
                }
                match (&scalar_function.args[0], &scalar_function.args[1]) {
                    (Expr::Literal(value, _), Expr::Literal(cast_type, _)) => {
                        let target_type = scalar_function
                            .func
                            .return_field_from_args(ReturnFieldArgs {
                                arg_fields: &[
                                    Arc::new(Field::new("expression", value.data_type(), false)),
                                    Arc::new(Field::new("datatype", cast_type.data_type(), false)),
                                ],
                                scalar_arguments: &[Some(value), Some(cast_type)],
                            })
                            .ok()?;
                        let casted = value.cast_to(target_type.data_type()).ok()?;
                        safe_coerce_scalar(&casted, expected_type)
                    }
                    _ => None,
                }
            } else {
                None
            }
        }
        _ => None,
    }
}

// Extract a list of scalar values from an expression, if it is a list of scalar values, or None
fn maybe_scalar_list(exprs: &Vec<Expr>, expected_type: &DataType) -> Option<Vec<ScalarValue>> {
    let mut scalar_values = Vec::with_capacity(exprs.len());
    for expr in exprs {
        match maybe_scalar(expr, expected_type) {
            Some(scalar_val) => {
                scalar_values.push(scalar_val);
            }
            None => {
                return None;
            }
        }
    }
    Some(scalar_values)
}

fn visit_between(
    between: &Between,
    index_info: &dyn IndexInformationProvider,
) -> Option<IndexedExpression> {
    let (column, col_type, query_parser) = maybe_indexed_column(&between.expr, index_info)?;
    let low = maybe_scalar(&between.low, &col_type)?;
    let high = maybe_scalar(&between.high, &col_type)?;

    let indexed_expr =
        query_parser.visit_between(&column, &Bound::Included(low), &Bound::Included(high))?;

    if between.negated {
        indexed_expr.maybe_not()
    } else {
        Some(indexed_expr)
    }
}

fn visit_in_list(
    in_list: &InList,
    index_info: &dyn IndexInformationProvider,
) -> Option<IndexedExpression> {
    let (column, col_type, query_parser) = maybe_indexed_column(&in_list.expr, index_info)?;
    let values = maybe_scalar_list(&in_list.list, &col_type)?;

    let indexed_expr = query_parser.visit_in_list(&column, &values)?;

    if in_list.negated {
        indexed_expr.maybe_not()
    } else {
        Some(indexed_expr)
    }
}

fn visit_is_bool(
    expr: &Expr,
    index_info: &dyn IndexInformationProvider,
    value: bool,
) -> Option<IndexedExpression> {
    let (column, col_type, query_parser) = maybe_indexed_column(expr, index_info)?;
    if col_type != DataType::Boolean {
        None
    } else {
        query_parser.visit_is_bool(&column, value)
    }
}

// A column can be a valid indexed expression if the column is boolean (e.g. 'WHERE on_sale')
fn visit_column(
    col: &Expr,
    index_info: &dyn IndexInformationProvider,
) -> Option<IndexedExpression> {
    let (column, col_type, query_parser) = maybe_indexed_column(col, index_info)?;
    if col_type != DataType::Boolean {
        None
    } else {
        query_parser.visit_is_bool(&column, true)
    }
}

fn visit_is_null(
    expr: &Expr,
    index_info: &dyn IndexInformationProvider,
    negated: bool,
) -> Option<IndexedExpression> {
    let (column, _, query_parser) = maybe_indexed_column(expr, index_info)?;
    let indexed_expr = query_parser.visit_is_null(&column)?;
    if negated {
        indexed_expr.maybe_not()
    } else {
        Some(indexed_expr)
    }
}

fn visit_not(
    expr: &Expr,
    index_info: &dyn IndexInformationProvider,
    depth: usize,
) -> Result<Option<IndexedExpression>> {
    let node = visit_node(expr, index_info, depth + 1)?;
    Ok(node.and_then(|node| node.maybe_not()))
}

fn visit_comparison(
    expr: &BinaryExpr,
    index_info: &dyn IndexInformationProvider,
) -> Option<IndexedExpression> {
    let left_col = maybe_indexed_column(&expr.left, index_info);
    if let Some((column, col_type, query_parser)) = left_col {
        let scalar = maybe_scalar(&expr.right, &col_type)?;
        query_parser.visit_comparison(&column, &scalar, &expr.op)
    } else {
        // Datafusion's query simplifier will canonicalize expressions and so we shouldn't reach this case.  If, for some reason, we
        // do reach this case we can handle it in the future by inverting expr.op and swapping the left and right sides
        None
    }
}

fn maybe_range(
    expr: &BinaryExpr,
    index_info: &dyn IndexInformationProvider,
) -> Option<IndexedExpression> {
    let left_expr = match expr.left.as_ref() {
        Expr::BinaryExpr(binary_expr) => Some(binary_expr),
        _ => None,
    }?;
    let right_expr = match expr.right.as_ref() {
        Expr::BinaryExpr(binary_expr) => Some(binary_expr),
        _ => None,
    }?;

    let (left_col, dt, parser) = maybe_indexed_column(&left_expr.left, index_info)?;
    let right_col = maybe_column(&right_expr.left)?;

    if left_col != right_col {
        return None;
    }

    let left_value = maybe_scalar(&left_expr.right, &dt)?;
    let right_value = maybe_scalar(&right_expr.right, &dt)?;

    let (low, high) = match (left_expr.op, right_expr.op) {
        // x >= a && x <= b
        (Operator::GtEq, Operator::LtEq) => {
            (Bound::Included(left_value), Bound::Included(right_value))
        }
        // x >= a && x < b
        (Operator::GtEq, Operator::Lt) => {
            (Bound::Included(left_value), Bound::Excluded(right_value))
        }
        // x > a && x <= b
        (Operator::Gt, Operator::LtEq) => {
            (Bound::Excluded(left_value), Bound::Included(right_value))
        }
        // x > a && x < b
        (Operator::Gt, Operator::Lt) => (Bound::Excluded(left_value), Bound::Excluded(right_value)),
        // x <= a && x >= b
        (Operator::LtEq, Operator::GtEq) => {
            (Bound::Included(right_value), Bound::Included(left_value))
        }
        // x <= a && x > b
        (Operator::LtEq, Operator::Gt) => {
            (Bound::Excluded(right_value), Bound::Included(left_value))
        }
        // x < a && x >= b
        (Operator::Lt, Operator::GtEq) => {
            (Bound::Included(right_value), Bound::Excluded(left_value))
        }
        // x < a && x > b
        (Operator::Lt, Operator::Gt) => (Bound::Excluded(right_value), Bound::Excluded(left_value)),
        _ => return None,
    };

    parser.visit_between(&left_col, &low, &high)
}

fn visit_and(
    expr: &BinaryExpr,
    index_info: &dyn IndexInformationProvider,
    depth: usize,
) -> Result<Option<IndexedExpression>> {
    // Many scalar indices can efficiently handle a BETWEEN query as a single search and this
    // can be much more efficient than two separate range queries.  As an optimization we check
    // to see if this is a between query and, if so, we handle it as a single query
    //
    // Note: We can't rely on users writing the SQL BETWEEN operator because:
    //   * Some users won't realize it's an option or a good idea
    //   * Datafusion's simplifier will rewrite the BETWEEN operator into two separate range queries
    if let Some(range_expr) = maybe_range(expr, index_info) {
        return Ok(Some(range_expr));
    }

    let left = visit_node(&expr.left, index_info, depth + 1)?;
    let right = visit_node(&expr.right, index_info, depth + 1)?;
    Ok(match (left, right) {
        (Some(left), Some(right)) => Some(left.and(right)),
        (Some(left), None) => Some(left.refine((*expr.right).clone())),
        (None, Some(right)) => Some(right.refine((*expr.left).clone())),
        (None, None) => None,
    })
}

fn visit_or(
    expr: &BinaryExpr,
    index_info: &dyn IndexInformationProvider,
    depth: usize,
) -> Result<Option<IndexedExpression>> {
    let left = visit_node(&expr.left, index_info, depth + 1)?;
    let right = visit_node(&expr.right, index_info, depth + 1)?;
    Ok(match (left, right) {
        (Some(left), Some(right)) => left.maybe_or(right),
        // If one side can use an index and the other side cannot then
        // we must abandon the entire thing.  For example, consider the
        // query "color == 'blue' or size > 10" where color is indexed but
        // size is not.  It's entirely possible that size > 10 matches every
        // row in our database.  There is nothing we can do except a full scan
        (Some(_), None) => None,
        (None, Some(_)) => None,
        (None, None) => None,
    })
}

fn visit_binary_expr(
    expr: &BinaryExpr,
    index_info: &dyn IndexInformationProvider,
    depth: usize,
) -> Result<Option<IndexedExpression>> {
    match &expr.op {
        Operator::Lt | Operator::LtEq | Operator::Gt | Operator::GtEq | Operator::Eq => {
            Ok(visit_comparison(expr, index_info))
        }
        // visit_comparison will maybe create an Eq query which we negate
        Operator::NotEq => Ok(visit_comparison(expr, index_info).and_then(|node| node.maybe_not())),
        Operator::And => visit_and(expr, index_info, depth),
        Operator::Or => visit_or(expr, index_info, depth),
        _ => Ok(None),
    }
}

fn visit_scalar_fn(
    scalar_fn: &ScalarFunction,
    index_info: &dyn IndexInformationProvider,
) -> Option<IndexedExpression> {
    if scalar_fn.args.is_empty() {
        return None;
    }
    let (col, data_type, query_parser) = maybe_indexed_column(&scalar_fn.args[0], index_info)?;
    query_parser.visit_scalar_function(&col, &data_type, &scalar_fn.func, &scalar_fn.args)
}

fn visit_like_expr(
    like: &Like,
    index_info: &dyn IndexInformationProvider,
) -> Option<IndexedExpression> {
    let (column, _, query_parser) = maybe_indexed_column(&like.expr, index_info)?;

    // Extract the pattern as a ScalarValue
    let pattern = match like.pattern.as_ref() {
        Expr::Literal(scalar, _) => scalar.clone(),
        _ => return None,
    };

    query_parser.visit_like(&column, like, &pattern)
}

fn visit_node(
    expr: &Expr,
    index_info: &dyn IndexInformationProvider,
    depth: usize,
) -> Result<Option<IndexedExpression>> {
    if depth >= MAX_DEPTH {
        return Err(Error::invalid_input(format!(
            "the filter expression is too long, lance limit the max number of conditions to {}",
            MAX_DEPTH
        )));
    }
    match expr {
        Expr::Between(between) => Ok(visit_between(between, index_info)),
        Expr::Alias(alias) => visit_node(alias.expr.as_ref(), index_info, depth),
        Expr::Column(_) => Ok(visit_column(expr, index_info)),
        Expr::InList(in_list) => Ok(visit_in_list(in_list, index_info)),
        Expr::IsFalse(expr) => Ok(visit_is_bool(expr.as_ref(), index_info, false)),
        Expr::IsTrue(expr) => Ok(visit_is_bool(expr.as_ref(), index_info, true)),
        Expr::IsNull(expr) => Ok(visit_is_null(expr.as_ref(), index_info, false)),
        Expr::IsNotNull(expr) => Ok(visit_is_null(expr.as_ref(), index_info, true)),
        Expr::Not(expr) => visit_not(expr.as_ref(), index_info, depth),
        Expr::BinaryExpr(binary_expr) => visit_binary_expr(binary_expr, index_info, depth),
        Expr::ScalarFunction(scalar_fn) => Ok(visit_scalar_fn(scalar_fn, index_info)),
        Expr::Like(like) => {
            if like.negated {
                // NOT LIKE cannot be efficiently pruned with zone maps
                Ok(None)
            } else {
                Ok(visit_like_expr(like, index_info))
            }
        }
        _ => Ok(None),
    }
}

/// A trait to be used in `apply_scalar_indices` to inform the function which columns are indexeds
pub trait IndexInformationProvider {
    /// Check if an index exists for `col` and, if so, return the data type of col
    /// as well as a query parser that can parse queries for that column
    fn get_index(&self, col: &str) -> Option<(&DataType, &dyn ScalarQueryParser)>;
}

/// Attempt to split a filter expression into a search of scalar indexes and an
///   optional post-search refinement query
pub fn apply_scalar_indices(
    expr: Expr,
    index_info: &dyn IndexInformationProvider,
) -> Result<IndexedExpression> {
    Ok(visit_node(&expr, index_info, 0)?.unwrap_or(IndexedExpression::refine_only(expr)))
}

#[derive(Clone, Default, Debug)]
pub struct FilterPlan {
    pub index_query: Option<ScalarIndexExpr>,
    /// True if the index query is guaranteed to return exact results
    pub skip_recheck: bool,
    pub refine_expr: Option<Expr>,
    pub full_expr: Option<Expr>,
}

impl FilterPlan {
    pub fn empty() -> Self {
        Self {
            index_query: None,
            skip_recheck: true,
            refine_expr: None,
            full_expr: None,
        }
    }

    pub fn new_refine_only(expr: Expr) -> Self {
        Self {
            index_query: None,
            skip_recheck: true,
            refine_expr: Some(expr.clone()),
            full_expr: Some(expr),
        }
    }

    pub fn is_empty(&self) -> bool {
        self.refine_expr.is_none() && self.index_query.is_none()
    }

    pub fn all_columns(&self) -> Vec<String> {
        self.full_expr
            .as_ref()
            .map(Planner::column_names_in_expr)
            .unwrap_or_default()
    }

    pub fn refine_columns(&self) -> Vec<String> {
        self.refine_expr
            .as_ref()
            .map(Planner::column_names_in_expr)
            .unwrap_or_default()
    }

    /// Return true if this has a refine step, regardless of the status of prefilter
    pub fn has_refine(&self) -> bool {
        self.refine_expr.is_some()
    }

    /// Return true if this has a scalar index query
    pub fn has_index_query(&self) -> bool {
        self.index_query.is_some()
    }

    pub fn has_any_filter(&self) -> bool {
        self.refine_expr.is_some() || self.index_query.is_some()
    }

    pub fn make_refine_only(&mut self) {
        self.index_query = None;
        self.refine_expr = self.full_expr.clone();
    }

    /// Return true if there is no refine or recheck of any kind and there is an index query
    pub fn is_exact_index_search(&self) -> bool {
        self.index_query.is_some() && self.refine_expr.is_none() && self.skip_recheck
    }
}

pub trait PlannerIndexExt {
    /// Determine how to apply a provided filter
    ///
    /// We parse the filter into a logical expression.  We then
    /// split the logical expression into a portion that can be
    /// satisfied by an index search (of one or more indices) and
    /// a refine portion that must be applied after the index search
    fn create_filter_plan(
        &self,
        filter: Expr,
        index_info: &dyn IndexInformationProvider,
        use_scalar_index: bool,
    ) -> Result<FilterPlan>;
}

impl PlannerIndexExt for Planner {
    fn create_filter_plan(
        &self,
        filter: Expr,
        index_info: &dyn IndexInformationProvider,
        use_scalar_index: bool,
    ) -> Result<FilterPlan> {
        let logical_expr = self.optimize_expr(filter)?;
        if use_scalar_index {
            let indexed_expr = apply_scalar_indices(logical_expr.clone(), index_info)?;
            let mut skip_recheck = false;
            if let Some(scalar_query) = indexed_expr.scalar_query.as_ref() {
                skip_recheck = !scalar_query.needs_recheck();
            }
            Ok(FilterPlan {
                index_query: indexed_expr.scalar_query,
                refine_expr: indexed_expr.refine_expr,
                full_expr: Some(logical_expr),
                skip_recheck,
            })
        } else {
            Ok(FilterPlan {
                index_query: None,
                skip_recheck: true,
                refine_expr: Some(logical_expr.clone()),
                full_expr: Some(logical_expr),
            })
        }
    }
}

#[cfg(test)]
mod tests {
    use std::collections::HashMap;

    use arrow_schema::{Field, Schema};
    use chrono::Utc;
    use datafusion_common::{Column, DFSchema};
    use datafusion_expr::simplify::SimplifyContext;
    use lance_datafusion::exec::{LanceExecutionOptions, get_session_context};

    use crate::scalar::json::{JsonQuery, JsonQueryParser};

    use super::*;

    struct ColInfo {
        data_type: DataType,
        parser: Box<dyn ScalarQueryParser>,
    }

    impl ColInfo {
        fn new(data_type: DataType, parser: Box<dyn ScalarQueryParser>) -> Self {
            Self { data_type, parser }
        }
    }

    struct MockIndexInfoProvider {
        indexed_columns: HashMap<String, ColInfo>,
    }

    impl MockIndexInfoProvider {
        fn new(indexed_columns: Vec<(&str, ColInfo)>) -> Self {
            Self {
                indexed_columns: HashMap::from_iter(
                    indexed_columns
                        .into_iter()
                        .map(|(s, ty)| (s.to_string(), ty)),
                ),
            }
        }
    }

    impl IndexInformationProvider for MockIndexInfoProvider {
        fn get_index(&self, col: &str) -> Option<(&DataType, &dyn ScalarQueryParser)> {
            self.indexed_columns
                .get(col)
                .map(|col_info| (&col_info.data_type, col_info.parser.as_ref()))
        }
    }

    fn check(
        index_info: &dyn IndexInformationProvider,
        expr: &str,
        expected: Option<IndexedExpression>,
        optimize: bool,
    ) {
        let schema = Schema::new(vec![
            Field::new("color", DataType::Utf8, false),
            Field::new("size", DataType::Float32, false),
            Field::new("aisle", DataType::UInt32, false),
            Field::new("on_sale", DataType::Boolean, false),
            Field::new("price", DataType::Float32, false),
            Field::new("json", DataType::LargeBinary, false),
        ]);
        let df_schema: DFSchema = schema.try_into().unwrap();

        let ctx = get_session_context(&LanceExecutionOptions::default());
        let state = ctx.state();
        let mut expr = state.create_logical_expr(expr, &df_schema).unwrap();
        if optimize {
            let simplify_context = SimplifyContext::default()
                .with_schema(Arc::new(df_schema))
                .with_query_execution_start_time(Some(Utc::now()));
            let simplifier =
                datafusion::optimizer::simplify_expressions::ExprSimplifier::new(simplify_context);
            expr = simplifier.simplify(expr).unwrap();
        }

        let actual = apply_scalar_indices(expr.clone(), index_info).unwrap();
        if let Some(expected) = expected {
            assert_eq!(actual, expected);
        } else {
            assert!(actual.scalar_query.is_none());
            assert_eq!(actual.refine_expr.unwrap(), expr);
        }
    }

    fn check_no_index(index_info: &dyn IndexInformationProvider, expr: &str) {
        check(index_info, expr, None, false)
    }

    fn check_simple(
        index_info: &dyn IndexInformationProvider,
        expr: &str,
        col: &str,
        query: impl AnyQuery,
    ) {
        check(
            index_info,
            expr,
            Some(IndexedExpression::index_query(
                col.to_string(),
                format!("{}_idx", col),
                "BTree".to_string(),
                Arc::new(query),
            )),
            false,
        )
    }

    fn check_range(
        index_info: &dyn IndexInformationProvider,
        expr: &str,
        col: &str,
        query: SargableQuery,
    ) {
        check(
            index_info,
            expr,
            Some(IndexedExpression::index_query(
                col.to_string(),
                format!("{}_idx", col),
                "BTree".to_string(),
                Arc::new(query),
            )),
            true,
        )
    }

    fn check_simple_negated(
        index_info: &dyn IndexInformationProvider,
        expr: &str,
        col: &str,
        query: SargableQuery,
    ) {
        check(
            index_info,
            expr,
            Some(
                IndexedExpression::index_query(
                    col.to_string(),
                    format!("{}_idx", col),
                    "BTree".to_string(),
                    Arc::new(query),
                )
                .maybe_not()
                .unwrap(),
            ),
            false,
        )
    }

    #[test]
    fn test_expressions() {
        let index_info = MockIndexInfoProvider::new(vec![
            (
                "color",
                ColInfo::new(
                    DataType::Utf8,
                    Box::new(SargableQueryParser::new(
                        "color_idx".to_string(),
                        "BTree".to_string(),
                        false,
                    )),
                ),
            ),
            (
                "aisle",
                ColInfo::new(
                    DataType::UInt32,
                    Box::new(SargableQueryParser::new(
                        "aisle_idx".to_string(),
                        "BTree".to_string(),
                        false,
                    )),
                ),
            ),
            (
                "on_sale",
                ColInfo::new(
                    DataType::Boolean,
                    Box::new(SargableQueryParser::new(
                        "on_sale_idx".to_string(),
                        "BTree".to_string(),
                        false,
                    )),
                ),
            ),
            (
                "price",
                ColInfo::new(
                    DataType::Float32,
                    Box::new(SargableQueryParser::new(
                        "price_idx".to_string(),
                        "BTree".to_string(),
                        false,
                    )),
                ),
            ),
            (
                "json",
                ColInfo::new(
                    DataType::LargeBinary,
                    Box::new(JsonQueryParser::new(
                        "$.name".to_string(),
                        Box::new(SargableQueryParser::new(
                            "json_idx".to_string(),
                            "BTree".to_string(),
                            false,
                        )),
                    )),
                ),
            ),
        ]);

        check_simple(
            &index_info,
            "json_extract(json, '$.name') = 'foo'",
            "json",
            JsonQuery::new(
                Arc::new(SargableQuery::Equals(ScalarValue::Utf8(Some(
                    "foo".to_string(),
                )))),
                "$.name".to_string(),
            ),
        );

        check_no_index(&index_info, "size BETWEEN 5 AND 10");
        // Cast case.  We will cast 5 (an int64) to Int16 and then coerce to UInt32
        check_simple(
            &index_info,
            "aisle = arrow_cast(5, 'Int16')",
            "aisle",
            SargableQuery::Equals(ScalarValue::UInt32(Some(5))),
        );
        // 5 different ways of writing BETWEEN (all should be recognized)
        check_range(
            &index_info,
            "aisle BETWEEN 5 AND 10",
            "aisle",
            SargableQuery::Range(
                Bound::Included(ScalarValue::UInt32(Some(5))),
                Bound::Included(ScalarValue::UInt32(Some(10))),
            ),
        );
        check_range(
            &index_info,
            "aisle >= 5 AND aisle <= 10",
            "aisle",
            SargableQuery::Range(
                Bound::Included(ScalarValue::UInt32(Some(5))),
                Bound::Included(ScalarValue::UInt32(Some(10))),
            ),
        );

        check_range(
            &index_info,
            "aisle <= 10 AND aisle >= 5",
            "aisle",
            SargableQuery::Range(
                Bound::Included(ScalarValue::UInt32(Some(5))),
                Bound::Included(ScalarValue::UInt32(Some(10))),
            ),
        );

        check_range(
            &index_info,
            "5 <= aisle AND 10 >= aisle",
            "aisle",
            SargableQuery::Range(
                Bound::Included(ScalarValue::UInt32(Some(5))),
                Bound::Included(ScalarValue::UInt32(Some(10))),
            ),
        );

        check_range(
            &index_info,
            "10 >= aisle AND 5 <= aisle",
            "aisle",
            SargableQuery::Range(
                Bound::Included(ScalarValue::UInt32(Some(5))),
                Bound::Included(ScalarValue::UInt32(Some(10))),
            ),
        );
        check_range(
            &index_info,
            "aisle <= 10 AND aisle > 5",
            "aisle",
            SargableQuery::Range(
                Bound::Excluded(ScalarValue::UInt32(Some(5))),
                Bound::Included(ScalarValue::UInt32(Some(10))),
            ),
        );
        check_range(
            &index_info,
            "aisle < 10 AND aisle >= 5",
            "aisle",
            SargableQuery::Range(
                Bound::Included(ScalarValue::UInt32(Some(5))),
                Bound::Excluded(ScalarValue::UInt32(Some(10))),
            ),
        );
        check_simple(
            &index_info,
            "on_sale IS TRUE",
            "on_sale",
            SargableQuery::Equals(ScalarValue::Boolean(Some(true))),
        );
        check_simple(
            &index_info,
            "on_sale",
            "on_sale",
            SargableQuery::Equals(ScalarValue::Boolean(Some(true))),
        );
        check_simple_negated(
            &index_info,
            "NOT on_sale",
            "on_sale",
            SargableQuery::Equals(ScalarValue::Boolean(Some(true))),
        );
        check_simple(
            &index_info,
            "on_sale IS FALSE",
            "on_sale",
            SargableQuery::Equals(ScalarValue::Boolean(Some(false))),
        );
        check_simple_negated(
            &index_info,
            "aisle NOT BETWEEN 5 AND 10",
            "aisle",
            SargableQuery::Range(
                Bound::Included(ScalarValue::UInt32(Some(5))),
                Bound::Included(ScalarValue::UInt32(Some(10))),
            ),
        );
        // Small in-list (in-list with 3 or fewer items optimizes into or-chain)
        check_simple(
            &index_info,
            "aisle IN (5, 6, 7)",
            "aisle",
            SargableQuery::IsIn(vec![
                ScalarValue::UInt32(Some(5)),
                ScalarValue::UInt32(Some(6)),
                ScalarValue::UInt32(Some(7)),
            ]),
        );
        check_simple_negated(
            &index_info,
            "NOT aisle IN (5, 6, 7)",
            "aisle",
            SargableQuery::IsIn(vec![
                ScalarValue::UInt32(Some(5)),
                ScalarValue::UInt32(Some(6)),
                ScalarValue::UInt32(Some(7)),
            ]),
        );
        check_simple_negated(
            &index_info,
            "aisle NOT IN (5, 6, 7)",
            "aisle",
            SargableQuery::IsIn(vec![
                ScalarValue::UInt32(Some(5)),
                ScalarValue::UInt32(Some(6)),
                ScalarValue::UInt32(Some(7)),
            ]),
        );
        check_simple(
            &index_info,
            "aisle IN (5, 6, 7, 8, 9)",
            "aisle",
            SargableQuery::IsIn(vec![
                ScalarValue::UInt32(Some(5)),
                ScalarValue::UInt32(Some(6)),
                ScalarValue::UInt32(Some(7)),
                ScalarValue::UInt32(Some(8)),
                ScalarValue::UInt32(Some(9)),
            ]),
        );
        check_simple_negated(
            &index_info,
            "NOT aisle IN (5, 6, 7, 8, 9)",
            "aisle",
            SargableQuery::IsIn(vec![
                ScalarValue::UInt32(Some(5)),
                ScalarValue::UInt32(Some(6)),
                ScalarValue::UInt32(Some(7)),
                ScalarValue::UInt32(Some(8)),
                ScalarValue::UInt32(Some(9)),
            ]),
        );
        check_simple_negated(
            &index_info,
            "aisle NOT IN (5, 6, 7, 8, 9)",
            "aisle",
            SargableQuery::IsIn(vec![
                ScalarValue::UInt32(Some(5)),
                ScalarValue::UInt32(Some(6)),
                ScalarValue::UInt32(Some(7)),
                ScalarValue::UInt32(Some(8)),
                ScalarValue::UInt32(Some(9)),
            ]),
        );
        check_simple(
            &index_info,
            "on_sale is false",
            "on_sale",
            SargableQuery::Equals(ScalarValue::Boolean(Some(false))),
        );
        check_simple(
            &index_info,
            "on_sale is true",
            "on_sale",
            SargableQuery::Equals(ScalarValue::Boolean(Some(true))),
        );
        check_simple(
            &index_info,
            "aisle < 10",
            "aisle",
            SargableQuery::Range(
                Bound::Unbounded,
                Bound::Excluded(ScalarValue::UInt32(Some(10))),
            ),
        );
        check_simple(
            &index_info,
            "aisle <= 10",
            "aisle",
            SargableQuery::Range(
                Bound::Unbounded,
                Bound::Included(ScalarValue::UInt32(Some(10))),
            ),
        );
        check_simple(
            &index_info,
            "aisle > 10",
            "aisle",
            SargableQuery::Range(
                Bound::Excluded(ScalarValue::UInt32(Some(10))),
                Bound::Unbounded,
            ),
        );
        // In the future we can handle this case if we need to.  For
        // now let's make sure we don't accidentally do the wrong thing
        // (we were getting this backwards in the past)
        check_no_index(&index_info, "10 > aisle");
        check_simple(
            &index_info,
            "aisle >= 10",
            "aisle",
            SargableQuery::Range(
                Bound::Included(ScalarValue::UInt32(Some(10))),
                Bound::Unbounded,
            ),
        );
        check_simple(
            &index_info,
            "aisle = 10",
            "aisle",
            SargableQuery::Equals(ScalarValue::UInt32(Some(10))),
        );
        check_simple_negated(
            &index_info,
            "aisle <> 10",
            "aisle",
            SargableQuery::Equals(ScalarValue::UInt32(Some(10))),
        );
        // // Common compound case, AND'd clauses
        let left = Box::new(ScalarIndexExpr::Query(ScalarIndexSearch {
            column: "aisle".to_string(),
            index_name: "aisle_idx".to_string(),
            index_type: "BTree".to_string(),
            query: Arc::new(SargableQuery::Equals(ScalarValue::UInt32(Some(10)))),
            needs_recheck: false,
        }));
        let right = Box::new(ScalarIndexExpr::Query(ScalarIndexSearch {
            column: "color".to_string(),
            index_name: "color_idx".to_string(),
            index_type: "BTree".to_string(),
            query: Arc::new(SargableQuery::Equals(ScalarValue::Utf8(Some(
                "blue".to_string(),
            )))),
            needs_recheck: false,
        }));
        check(
            &index_info,
            "aisle = 10 AND color = 'blue'",
            Some(IndexedExpression {
                scalar_query: Some(ScalarIndexExpr::And(left.clone(), right.clone())),
                refine_expr: None,
            }),
            false,
        );
        // Compound AND's and not all of them are indexed columns
        let refine = Expr::Column(Column::new_unqualified("size")).gt(datafusion_expr::lit(30_i64));
        check(
            &index_info,
            "aisle = 10 AND color = 'blue' AND size > 30",
            Some(IndexedExpression {
                scalar_query: Some(ScalarIndexExpr::And(left.clone(), right.clone())),
                refine_expr: Some(refine.clone()),
            }),
            false,
        );
        // Compounded OR's where ALL columns are indexed
        check(
            &index_info,
            "aisle = 10 OR color = 'blue'",
            Some(IndexedExpression {
                scalar_query: Some(ScalarIndexExpr::Or(left.clone(), right.clone())),
                refine_expr: None,
            }),
            false,
        );
        // Compounded OR's with one or more unindexed columns
        check_no_index(&index_info, "aisle = 10 OR color = 'blue' OR size > 30");
        // AND'd group of OR
        check(
            &index_info,
            "(aisle = 10 OR color = 'blue') AND size > 30",
            Some(IndexedExpression {
                scalar_query: Some(ScalarIndexExpr::Or(left, right)),
                refine_expr: Some(refine),
            }),
            false,
        );
        // Examples of things that are not yet supported but should be supportable someday

        // OR'd group of refined index searches (see IndexedExpression::or for details)
        check_no_index(
            &index_info,
            "(aisle = 10 AND size > 30) OR (color = 'blue' AND size > 20)",
        );

        // Non-normalized arithmetic (can use expression simplification)
        check_no_index(&index_info, "aisle + 3 < 10");

        // Currently we assume that the return of an index search tells us which rows are
        // TRUE and all other rows are FALSE.  This will need to change but for now it is
        // safer to not support the following cases because the return value of non-matched
        // rows is NULL and not FALSE.
        check_no_index(&index_info, "aisle IN (5, 6, NULL)");
        // OR-list with NULL (in future DF version this will be optimized repr of
        // small in-list with NULL so let's get ready for it)
        check_no_index(&index_info, "aisle = 5 OR aisle = 6 OR NULL");
        check_no_index(&index_info, "aisle IN (5, 6, 7, 8, NULL)");
        check_no_index(&index_info, "aisle = NULL");
        check_no_index(&index_info, "aisle BETWEEN 5 AND NULL");
        check_no_index(&index_info, "aisle BETWEEN NULL AND 10");
    }

    #[tokio::test]
    async fn test_not_flips_certainty() {
        use lance_core::utils::mask::{NullableRowAddrSet, RowAddrTreeMap};

        // Test that NOT flips certainty for inexact index results
        // This tests the implementation in evaluate_impl for Self::Not

        // Helper function that mimics the NOT logic we just fixed
        fn apply_not(result: NullableIndexExprResult) -> NullableIndexExprResult {
            match result {
                NullableIndexExprResult::Exact(mask) => NullableIndexExprResult::Exact(!mask),
                NullableIndexExprResult::AtMost(mask) => NullableIndexExprResult::AtLeast(!mask),
                NullableIndexExprResult::AtLeast(mask) => NullableIndexExprResult::AtMost(!mask),
            }
        }

        // AtMost: superset of matches (e.g., bloom filter says "might be in [1,2]")
        let at_most = NullableIndexExprResult::AtMost(NullableRowAddrMask::AllowList(
            NullableRowAddrSet::new(RowAddrTreeMap::from_iter(&[1, 2]), RowAddrTreeMap::new()),
        ));
        // NOT(AtMost) should be AtLeast (definitely NOT in [1,2], might be elsewhere)
        assert!(matches!(
            apply_not(at_most),
            NullableIndexExprResult::AtLeast(_)
        ));

        // AtLeast: subset of matches (e.g., definitely in [1,2], might be more)
        let at_least = NullableIndexExprResult::AtLeast(NullableRowAddrMask::AllowList(
            NullableRowAddrSet::new(RowAddrTreeMap::from_iter(&[1, 2]), RowAddrTreeMap::new()),
        ));
        // NOT(AtLeast) should be AtMost (might NOT be in [1,2], definitely elsewhere)
        assert!(matches!(
            apply_not(at_least),
            NullableIndexExprResult::AtMost(_)
        ));

        // Exact should stay Exact
        let exact = NullableIndexExprResult::Exact(NullableRowAddrMask::AllowList(
            NullableRowAddrSet::new(RowAddrTreeMap::from_iter(&[1, 2]), RowAddrTreeMap::new()),
        ));
        assert!(matches!(
            apply_not(exact),
            NullableIndexExprResult::Exact(_)
        ));
    }

    #[tokio::test]
    async fn test_and_or_preserve_certainty() {
        use lance_core::utils::mask::{NullableRowAddrSet, RowAddrTreeMap};

        // Test that AND/OR correctly propagate certainty
        let make_at_most = || {
            NullableIndexExprResult::AtMost(NullableRowAddrMask::AllowList(
                NullableRowAddrSet::new(
                    RowAddrTreeMap::from_iter(&[1, 2, 3]),
                    RowAddrTreeMap::new(),
                ),
            ))
        };

        let make_at_least = || {
            NullableIndexExprResult::AtLeast(NullableRowAddrMask::AllowList(
                NullableRowAddrSet::new(
                    RowAddrTreeMap::from_iter(&[2, 3, 4]),
                    RowAddrTreeMap::new(),
                ),
            ))
        };

        let make_exact = || {
            NullableIndexExprResult::Exact(NullableRowAddrMask::AllowList(NullableRowAddrSet::new(
                RowAddrTreeMap::from_iter(&[1, 2]),
                RowAddrTreeMap::new(),
            )))
        };

        // AtMost & AtMost → AtMost
        assert!(matches!(
            make_at_most() & make_at_most(),
            NullableIndexExprResult::AtMost(_)
        ));

        // AtLeast & AtLeast → AtLeast
        assert!(matches!(
            make_at_least() & make_at_least(),
            NullableIndexExprResult::AtLeast(_)
        ));

        // AtMost & AtLeast → AtMost (superset remains superset)
        assert!(matches!(
            make_at_most() & make_at_least(),
            NullableIndexExprResult::AtMost(_)
        ));

        // AtMost | AtMost → AtMost
        assert!(matches!(
            make_at_most() | make_at_most(),
            NullableIndexExprResult::AtMost(_)
        ));

        // AtLeast | AtLeast → AtLeast
        assert!(matches!(
            make_at_least() | make_at_least(),
            NullableIndexExprResult::AtLeast(_)
        ));

        // AtMost | AtLeast → AtLeast (subset coverage guaranteed)
        assert!(matches!(
            make_at_most() | make_at_least(),
            NullableIndexExprResult::AtLeast(_)
        ));

        // Exact & AtMost → AtMost
        assert!(matches!(
            make_exact() & make_at_most(),
            NullableIndexExprResult::AtMost(_)
        ));

        // Exact | AtLeast → AtLeast
        assert!(matches!(
            make_exact() | make_at_least(),
            NullableIndexExprResult::AtLeast(_)
        ));
    }

    #[test]
    fn test_extract_like_leading_prefix() {
        // Simple prefix patterns (no recheck needed)
        assert_eq!(
            extract_like_leading_prefix("foo%", None),
            Some(("foo".to_string(), false))
        );
        assert_eq!(
            extract_like_leading_prefix("abc%", None),
            Some(("abc".to_string(), false))
        );

        // Patterns with wildcards in the middle (need recheck)
        assert_eq!(
            extract_like_leading_prefix("foo%bar%", None),
            Some(("foo".to_string(), true))
        );
        assert_eq!(
            extract_like_leading_prefix("foo_bar%", None),
            Some(("foo".to_string(), true))
        );
        assert_eq!(
            extract_like_leading_prefix("foo%bar", None),
            Some(("foo".to_string(), true))
        );
        assert_eq!(
            extract_like_leading_prefix("foo_", None),
            Some(("foo".to_string(), true))
        );

        // Not prefix patterns (starts with wildcard)
        assert_eq!(extract_like_leading_prefix("%foo", None), None);
        assert_eq!(extract_like_leading_prefix("_foo%", None), None);
        assert_eq!(extract_like_leading_prefix("%", None), None);

        // No wildcard at all (should use equality)
        assert_eq!(extract_like_leading_prefix("foo", None), None);

        // With escape character
        assert_eq!(
            extract_like_leading_prefix(r"foo\%bar%", Some('\\')),
            Some(("foo%bar".to_string(), false))
        );
        assert_eq!(
            extract_like_leading_prefix(r"foo\_bar%", Some('\\')),
            Some(("foo_bar".to_string(), false))
        );
        assert_eq!(
            extract_like_leading_prefix(r"foo\\bar%", Some('\\')),
            Some(("foo\\bar".to_string(), false))
        );

        // Escaped trailing % is not a wildcard (no wildcards)
        assert_eq!(extract_like_leading_prefix(r"foo\%", Some('\\')), None);

        // With backslash as default escape (for DataFusion starts_with compatibility):
        // "foo\%" means escaped %, no wildcard -> None (should use equality)
        assert_eq!(extract_like_leading_prefix(r"foo\%", None), None);
        // "foo\bar%" - \b is not a valid escape sequence, so \ and b are literals, % is wildcard
        assert_eq!(
            extract_like_leading_prefix(r"foo\bar%", None),
            Some(("foo\\bar".to_string(), false))
        );

        // Empty pattern
        assert_eq!(extract_like_leading_prefix("", None), None);

        // Mixed escaped and unescaped
        assert_eq!(
            extract_like_leading_prefix(r"foo\%bar%baz%", Some('\\')),
            Some(("foo%bar".to_string(), true))
        );
    }

    #[test]
    fn test_like_expression_parsing() {
        // Test that LIKE expressions are parsed correctly with refine_expr for complex patterns

        let index_info = MockIndexInfoProvider::new(vec![(
            "color",
            ColInfo::new(
                DataType::Utf8,
                Box::new(SargableQueryParser::new(
                    "color_idx".to_string(),
                    "BTree".to_string(),
                    false,
                )),
            ),
        )]);

        // Simple prefix pattern: LIKE 'foo%' -> LikePrefix("foo"), no refine_expr
        let schema = Schema::new(vec![Field::new("color", DataType::Utf8, false)]);
        let df_schema: DFSchema = schema.try_into().unwrap();
        let ctx = get_session_context(&LanceExecutionOptions::default());
        let state = ctx.state();

        let expr = state
            .create_logical_expr("color LIKE 'foo%'", &df_schema)
            .unwrap();
        let result = apply_scalar_indices(expr, &index_info).unwrap();

        assert!(result.scalar_query.is_some(), "Should have scalar_query");
        assert!(
            result.refine_expr.is_none(),
            "Simple prefix should not need refine_expr"
        );

        // Extract the query and verify it's LikePrefix
        if let Some(ScalarIndexExpr::Query(search)) = &result.scalar_query {
            let query = search.query.as_any().downcast_ref::<SargableQuery>();
            assert!(query.is_some(), "Query should be SargableQuery");
            match query.unwrap() {
                SargableQuery::LikePrefix(prefix) => {
                    assert_eq!(prefix, &ScalarValue::Utf8(Some("foo".to_string())));
                }
                _ => panic!("Expected LikePrefix query"),
            }
        } else {
            panic!("Expected Query variant");
        }

        // Complex pattern: LIKE 'foo%bar%' -> LikePrefix("foo"), with refine_expr
        let expr = state
            .create_logical_expr("color LIKE 'foo%bar%'", &df_schema)
            .unwrap();
        let result = apply_scalar_indices(expr, &index_info).unwrap();

        assert!(result.scalar_query.is_some(), "Should have scalar_query");
        assert!(
            result.refine_expr.is_some(),
            "Complex pattern should have refine_expr"
        );

        // Verify the query is still LikePrefix("foo")
        if let Some(ScalarIndexExpr::Query(search)) = &result.scalar_query {
            let query = search.query.as_any().downcast_ref::<SargableQuery>();
            assert!(query.is_some(), "Query should be SargableQuery");
            match query.unwrap() {
                SargableQuery::LikePrefix(prefix) => {
                    assert_eq!(prefix, &ScalarValue::Utf8(Some("foo".to_string())));
                }
                _ => panic!("Expected LikePrefix query"),
            }
        }

        // Verify the refine_expr is the original LIKE expression
        let refine = result.refine_expr.unwrap();
        match refine {
            Expr::Like(like) => {
                assert!(!like.negated);
                assert!(!like.case_insensitive);
                if let Expr::Literal(ScalarValue::Utf8(Some(pattern)), _) = like.pattern.as_ref() {
                    assert_eq!(pattern, "foo%bar%");
                } else {
                    panic!("Expected Utf8 literal pattern");
                }
            }
            _ => panic!("Expected Like expression in refine_expr"),
        }

        // Pattern starting with wildcard: LIKE '%foo' -> no index, only refine
        let expr = state
            .create_logical_expr("color LIKE '%foo'", &df_schema)
            .unwrap();
        let result = apply_scalar_indices(expr, &index_info).unwrap();

        assert!(
            result.scalar_query.is_none(),
            "Pattern starting with wildcard should not use index"
        );
        assert!(result.refine_expr.is_some(), "Should fall back to refine");
    }

    #[test]
    fn test_starts_with_with_underscore_after_optimization() {
        // Test that starts_with with underscore in prefix works correctly after DataFusion optimization
        // DataFusion simplifies starts_with(col, 'test_ns$') to col LIKE 'test_ns$%'
        // The underscore in the prefix should NOT be treated as a wildcard!
        let index_info = MockIndexInfoProvider::new(vec![(
            "object_id",
            ColInfo::new(
                DataType::Utf8,
                Box::new(SargableQueryParser::new(
                    "object_id_idx".to_string(),
                    "BTree".to_string(),
                    false,
                )),
            ),
        )]);

        let schema = Schema::new(vec![Field::new("object_id", DataType::Utf8, false)]);
        let df_schema: DFSchema = schema.try_into().unwrap();
        let ctx = get_session_context(&LanceExecutionOptions::default());
        let state = ctx.state();

        // Create the expression with starts_with containing underscore
        let expr = state
            .create_logical_expr("starts_with(object_id, 'test_ns$')", &df_schema)
            .unwrap();

        // Apply DataFusion simplification (this may convert starts_with to LIKE)
        let simplify_context = SimplifyContext::default()
            .with_schema(Arc::new(df_schema))
            .with_query_execution_start_time(Some(Utc::now()));
        let simplifier =
            datafusion::optimizer::simplify_expressions::ExprSimplifier::new(simplify_context);
        let simplified_expr = simplifier.simplify(expr).unwrap();

        // Apply scalar indices
        let result = apply_scalar_indices(simplified_expr, &index_info).unwrap();

        // The prefix should be "test_ns$", NOT "test"
        // This test documents the current (potentially broken) behavior
        if let Some(ScalarIndexExpr::Query(search)) = &result.scalar_query {
            let query = search
                .query
                .as_any()
                .downcast_ref::<SargableQuery>()
                .unwrap();
            match query {
                SargableQuery::LikePrefix(prefix) => {
                    let prefix_str = match prefix {
                        ScalarValue::Utf8(Some(s)) => s.clone(),
                        _ => panic!("Expected Utf8 prefix"),
                    };
                    // Verify the prefix is correctly extracted with underscore as literal
                    assert_eq!(
                        prefix_str, "test_ns$",
                        "Prefix should be 'test_ns$', not 'test' (underscore should not be a wildcard)"
                    );
                }
                _ => panic!("Expected LikePrefix query"),
            }
        } else {
            // If no scalar query, it means the pattern was not recognized
            panic!("Expected scalar_query to be present");
        }
    }

    #[test]
    fn test_starts_with_to_like_conversion() {
        // Test that starts_with(col, 'prefix') is converted to LikePrefix query
        let index_info = MockIndexInfoProvider::new(vec![(
            "color",
            ColInfo::new(
                DataType::Utf8,
                Box::new(SargableQueryParser::new(
                    "color_idx".to_string(),
                    "BTree".to_string(),
                    false,
                )),
            ),
        )]);

        let schema = Schema::new(vec![Field::new("color", DataType::Utf8, false)]);
        let df_schema: DFSchema = schema.try_into().unwrap();
        let ctx = get_session_context(&LanceExecutionOptions::default());
        let state = ctx.state();

        // starts_with(color, 'foo') should be converted to LikePrefix("foo")
        let expr = state
            .create_logical_expr("starts_with(color, 'foo')", &df_schema)
            .unwrap();
        let result = apply_scalar_indices(expr, &index_info).unwrap();

        assert!(
            result.scalar_query.is_some(),
            "starts_with should use index"
        );
        assert!(
            result.refine_expr.is_none(),
            "Pure prefix starts_with should not need refine_expr"
        );

        // Extract the query and verify it's LikePrefix
        if let Some(ScalarIndexExpr::Query(search)) = &result.scalar_query {
            let query = search.query.as_any().downcast_ref::<SargableQuery>();
            assert!(query.is_some(), "Query should be SargableQuery");
            match query.unwrap() {
                SargableQuery::LikePrefix(prefix) => {
                    assert_eq!(prefix, &ScalarValue::Utf8(Some("foo".to_string())));
                }
                _ => panic!("Expected LikePrefix query"),
            }
        } else {
            panic!("Expected Query variant");
        }

        // Both starts_with and LIKE 'prefix%' should produce the same LikePrefix query
        let like_expr = state
            .create_logical_expr("color LIKE 'foo%'", &df_schema)
            .unwrap();
        let like_result = apply_scalar_indices(like_expr, &index_info).unwrap();

        // Compare the queries - both should be LikePrefix("foo")
        if let (
            Some(ScalarIndexExpr::Query(starts_with_search)),
            Some(ScalarIndexExpr::Query(like_search)),
        ) = (&result.scalar_query, &like_result.scalar_query)
        {
            let sw_query = starts_with_search
                .query
                .as_any()
                .downcast_ref::<SargableQuery>()
                .unwrap();
            let like_query = like_search
                .query
                .as_any()
                .downcast_ref::<SargableQuery>()
                .unwrap();
            assert_eq!(
                sw_query, like_query,
                "starts_with and LIKE 'prefix%' should produce identical queries"
            );
        }
    }
}