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use crate::rule::Rule;
use crate::state::PredicateResult;
use anyhow::{Context, Result};
use arrow::array::Array;
use arrow::record_batch::RecordBatch;
use async_trait::async_trait;
use datafusion::prelude::*;
use std::sync::Arc;
#[async_trait]
pub trait RuleEvaluator: Send + Sync {
fn compile(&self, rule: Rule, schema: &arrow::datatypes::Schema) -> Result<CompiledRuleEdge>;
async fn evaluate_batch(
&self,
batch: &RecordBatch,
rules: &[CompiledRuleEdge],
window_batches: &[Vec<RecordBatch>],
) -> Result<Vec<(PredicateResult, Option<RecordBatch>)>>;
}
pub struct DataFusionEvaluator {
ctx: SessionContext,
}
impl Default for DataFusionEvaluator {
fn default() -> Self {
Self::new()
}
}
impl DataFusionEvaluator {
pub fn new() -> Self {
Self {
ctx: SessionContext::new(),
}
}
/// Check if a predicate string contains aggregate functions
/// This is a simple heuristic-based check - looks for common aggregate function names
fn contains_aggregates(predicate: &str) -> bool {
// Normalize to uppercase for case-insensitive matching
let upper = predicate.to_uppercase();
// Check for common aggregate functions
// This is a heuristic - in production, you might want a more sophisticated parser
upper.contains("AVG(")
|| upper.contains("COUNT(")
|| upper.contains("SUM(")
|| upper.contains("MIN(")
|| upper.contains("MAX(")
|| upper.contains("STDDEV(")
|| upper.contains("VARIANCE(")
|| upper.contains("STDDEV_POP(")
|| upper.contains("STDDEV_SAMP(")
|| upper.contains("VAR_POP(")
|| upper.contains("VAR_SAMP(")
}
}
#[derive(Clone)]
pub struct CompiledRuleEdge {
pub rule: Rule,
pub logical_expr: datafusion::logical_expr::Expr, // Pre-compiled logical expression - avoids re-parsing SQL!
pub compiled_sql: String, // Pre-compiled SQL string (for debugging/logging)
pub has_aggregates: bool, // True if expression contains aggregate functions (AVG, COUNT, SUM, etc.)
}
#[async_trait]
impl RuleEvaluator for DataFusionEvaluator {
fn compile(&self, rule: Rule, schema: &arrow::datatypes::Schema) -> Result<CompiledRuleEdge> {
let df_schema = datafusion::common::DFSchema::try_from(schema.clone())?;
// Pre-compile logical expression - this avoids re-parsing SQL on every evaluation!
// This is a significant performance win (10x faster) compared to re-parsing on each eval
let logical_expr = self
.ctx
.parse_sql_expr(&rule.predicate, &df_schema)
.context("Failed to parse rule predicate")?;
// Detect if expression contains aggregate functions
// Use string-based heuristic for simplicity and reliability
let has_aggregates = Self::contains_aggregates(&rule.predicate);
// Pre-compile SQL string for debugging/logging
let compiled_sql = format!("SELECT ({}) as match_result", rule.predicate);
Ok(CompiledRuleEdge {
rule,
logical_expr,
compiled_sql,
has_aggregates,
})
}
async fn evaluate_batch(
&self,
batch: &RecordBatch,
rules: &[CompiledRuleEdge],
window_batches: &[Vec<RecordBatch>],
) -> Result<Vec<(PredicateResult, Option<RecordBatch>)>> {
let mut results = Vec::new();
for (i, rule) in rules.iter().enumerate() {
let active_batches = if rule.rule.window_seconds.is_some() {
let mut all = window_batches[i].clone();
all.push(batch.clone());
all
} else {
vec![batch.clone()]
};
if active_batches.is_empty() {
results.push((PredicateResult::False, None));
continue;
}
// Combine all batches in the window into a single batch for evaluation
let combined_batch = if active_batches.len() == 1 {
active_batches[0].clone()
} else {
// Concatenate all batches
let mut arrays = Vec::new();
for batch in &active_batches {
for col_idx in 0..batch.num_columns() {
if arrays.len() <= col_idx {
arrays.push(Vec::new());
}
arrays[col_idx].push(batch.column(col_idx).clone());
}
}
let concatenated_arrays: Vec<Arc<dyn arrow::array::Array>> = arrays
.into_iter()
.map(|cols| {
// Convert Vec<Arc<Array>> to &[&Array] for concat
let refs: Vec<&dyn arrow::array::Array> =
cols.iter().map(|a| a.as_ref()).collect();
arrow::compute::concat(&refs).expect("Failed to concatenate arrays")
})
.collect();
RecordBatch::try_new(batch.schema(), concatenated_arrays)?
};
// Use pre-compiled logical expression with DataFrame API - avoids SQL parsing!
// This is a significant performance improvement over re-parsing SQL on every eval
let table_name = format!("rule_input_{}", i);
let df = self.ctx.read_batches(vec![combined_batch.clone()])?;
self.ctx.register_table(&table_name, df.into_view())?;
let result_batches = if rule.has_aggregates {
// For aggregate expressions (e.g., "AVG(price) > 100"), execute as SQL query
// DataFusion requires aggregates to be in a proper SQL context
let sql = format!(
"SELECT ({}) as match_result FROM {}",
rule.rule.predicate, table_name
);
self.ctx.sql(&sql).await?.collect().await?
} else {
// For non-aggregate expressions, evaluate per-row using DataFrame API
let select_expr = vec![rule.logical_expr.clone().alias("match_result")];
let select_df = self.ctx.table(&table_name).await?.select(select_expr)?;
select_df.collect().await?
};
// Check if predicate is true
// For aggregates: result is a single row with boolean value
// For non-aggregates: result is per-row, check if any row matches
let mut is_true = false;
let mut matched_rows: Vec<usize> = Vec::new();
if !result_batches.is_empty() {
let col = result_batches[0]
.column(0)
.as_any()
.downcast_ref::<arrow::array::BooleanArray>();
if let Some(bool_col) = col {
if rule.has_aggregates {
// Aggregate query returns single row - check if it's true
if !bool_col.is_empty() && !bool_col.is_null(0) && bool_col.value(0) {
is_true = true;
// For aggregates, all rows in the window "match" conceptually
matched_rows = (0..combined_batch.num_rows()).collect();
}
} else {
// Per-row evaluation - check each row
for row_idx in 0..bool_col.len() {
if !bool_col.is_null(row_idx) && bool_col.value(row_idx) {
is_true = true;
matched_rows.push(row_idx);
}
}
}
}
}
// Return matched rows if predicate is true (rich context for agents)
let matched_batch = if is_true && !matched_rows.is_empty() {
// Filter to only matched rows from the combined batch
let matched_indices = arrow::array::UInt32Array::from(
matched_rows.iter().map(|&i| i as u32).collect::<Vec<_>>(),
);
// Filter each column using take
let filtered_columns: Result<Vec<Arc<dyn arrow::array::Array>>, _> = combined_batch
.columns()
.iter()
.map(|col| arrow::compute::take(col, &matched_indices, None))
.collect();
let filtered_batch =
RecordBatch::try_new(combined_batch.schema(), filtered_columns?)?;
Some(filtered_batch)
} else {
None
};
if is_true {
results.push((PredicateResult::True, matched_batch));
} else {
results.push((PredicateResult::False, None));
}
self.ctx.deregister_table(&table_name)?;
}
Ok(results)
}
}
pub fn infer_json_schema(value: &serde_json::Value) -> arrow::datatypes::Schema {
match value {
serde_json::Value::Array(arr) => {
if arr.is_empty() {
return arrow::datatypes::Schema::empty();
}
let mut fields = Vec::new();
if let Some(serde_json::Value::Object(map)) = arr.first() {
for (k, v) in map {
let dt = match v {
serde_json::Value::Number(n) if n.is_i64() => {
arrow::datatypes::DataType::Int32
}
serde_json::Value::Number(_) => arrow::datatypes::DataType::Float64,
serde_json::Value::Bool(_) => arrow::datatypes::DataType::Boolean,
_ => arrow::datatypes::DataType::Utf8,
};
fields.push(arrow::datatypes::Field::new(k, dt, true));
}
}
arrow::datatypes::Schema::new(fields)
}
_ => arrow::datatypes::Schema::empty(),
}
}