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delta_datafusion.rs
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delta_datafusion.rs
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//! Datafusion integration for Delta Table
//!
//! Example:
//!
//! ```rust
//! use std::sync::Arc;
//! use datafusion::execution::context::ExecutionContext;
//!
//! async {
//! let mut ctx = ExecutionContext::new();
//! let table = deltalake::open_table("./tests/data/simple_table")
//! .await
//! .unwrap();
//! ctx.register_table("demo", Arc::new(table)).unwrap();
//!
//! let batches = ctx
//! .sql("SELECT * FROM demo").unwrap()
//! .collect()
//! .await.unwrap();
//! };
//! ```
use std::any::Any;
use std::convert::TryFrom;
use std::sync::Arc;
use arrow::datatypes::{DataType as ArrowDataType, Schema as ArrowSchema, TimeUnit};
use datafusion::datasource::{PartitionedFile, TableProvider};
use datafusion::logical_plan::{combine_filters, Expr};
use datafusion::physical_optimizer::pruning::PruningPredicate;
use datafusion::physical_plan::metrics::ExecutionPlanMetricsSet;
use datafusion::physical_plan::parquet::{ParquetExec, ParquetPartition};
use datafusion::physical_plan::ExecutionPlan;
use datafusion::physical_plan::{ColumnStatistics, Statistics};
use datafusion::scalar::ScalarValue;
use crate::delta;
use crate::schema;
impl delta::DeltaTable {
/// Return statistics for Datafusion Table
pub fn datafusion_table_statistics(&self) -> Statistics {
let stats = self
.get_active_add_actions()
.iter()
.fold(
Some(Statistics {
num_rows: Some(0),
total_byte_size: Some(0),
column_statistics: Some(vec![
ColumnStatistics {
null_count: Some(0),
max_value: None,
min_value: None,
distinct_count: None
};
self.schema().unwrap().get_fields().len()
]),
is_exact: true,
}),
|acc, action| {
let acc = acc?;
let new_stats = action.get_stats().unwrap_or(None)?;
Some(Statistics {
num_rows: acc
.num_rows
.map(|rows| rows + new_stats.num_records as usize),
total_byte_size: acc
.total_byte_size
.map(|total_size| total_size + action.size as usize),
column_statistics: acc.column_statistics.map(|col_stats| {
self.schema()
.unwrap()
.get_fields()
.iter()
.zip(col_stats)
.map(|(field, stats)| ColumnStatistics {
null_count: new_stats
.null_count
.get(field.get_name())
.and_then(|x| {
let null_count_acc = stats.null_count?;
let null_count = x.as_value()? as usize;
Some(null_count_acc + null_count)
}),
max_value: new_stats.max_values.get(field.get_name()).and_then(
|x| {
let old_stats = stats.clone();
let max_value = to_scalar_value(x.as_value()?);
match (max_value, old_stats.max_value) {
(Some(max_value), Some(old_max_value)) => {
if left_larger_than_right(
old_max_value.clone(),
max_value.clone(),
) {
Some(old_max_value)
} else {
Some(max_value)
}
}
(Some(max_value), None) => Some(max_value),
(None, old) => old,
}
},
),
min_value: new_stats.min_values.get(field.get_name()).and_then(
|x| {
let old_stats = stats.clone();
let min_value = to_scalar_value(x.as_value()?);
match (min_value, old_stats.min_value) {
(Some(min_value), Some(old_min_value)) => {
if left_larger_than_right(
min_value.clone(),
old_min_value.clone(),
) {
Some(old_min_value)
} else {
Some(min_value)
}
}
(Some(min_value), None) => Some(min_value),
(None, old) => old,
}
},
),
distinct_count: None, // TODO: distinct
})
.collect()
}),
is_exact: true,
})
},
)
.unwrap_or_default();
// Convert column max/min scalar values to correct types based on arrow types.
Statistics {
is_exact: true,
num_rows: stats.num_rows,
total_byte_size: stats.total_byte_size,
column_statistics: stats.column_statistics.map(|col_stats| {
let fields = self.schema().unwrap().get_fields();
col_stats
.iter()
.zip(fields)
.map(|(col_states, field)| {
let dt = (self as &dyn TableProvider)
.schema()
.field_with_name(field.get_name())
.unwrap()
.data_type()
.clone();
ColumnStatistics {
null_count: col_states.null_count,
max_value: col_states
.max_value
.as_ref()
.and_then(|scalar| correct_scalar_value_type(scalar.clone(), &dt)),
min_value: col_states
.min_value
.as_ref()
.and_then(|scalar| correct_scalar_value_type(scalar.clone(), &dt)),
distinct_count: col_states.distinct_count,
}
})
.collect()
}),
}
}
}
impl TableProvider for delta::DeltaTable {
fn schema(&self) -> Arc<ArrowSchema> {
Arc::new(
<ArrowSchema as TryFrom<&schema::Schema>>::try_from(
delta::DeltaTable::schema(self).unwrap(),
)
.unwrap(),
)
}
fn scan(
&self,
projection: &Option<Vec<usize>>,
batch_size: usize,
filters: &[Expr],
limit: Option<usize>,
) -> datafusion::error::Result<Arc<dyn ExecutionPlan>> {
let schema = Arc::new(<ArrowSchema as TryFrom<&schema::Schema>>::try_from(
delta::DeltaTable::schema(self).unwrap(),
)?);
let filenames = self.get_file_uris();
let metrics = ExecutionPlanMetricsSet::new();
let partitions = filenames
.into_iter()
.zip(self.get_active_add_actions())
.enumerate()
.map(|(idx, (fname, action))| {
let statistics = if let Ok(Some(statistics)) = action.get_stats() {
Statistics {
num_rows: Some(statistics.num_records as usize),
total_byte_size: Some(action.size as usize),
column_statistics: Some(
self.schema()
.unwrap()
.get_fields()
.iter()
.map(|field| ColumnStatistics {
null_count: statistics
.null_count
.get(field.get_name())
.and_then(|f| f.as_value().map(|v| v as usize)),
max_value: statistics
.max_values
.get(field.get_name())
.and_then(|f| to_scalar_value(f.as_value()?)),
min_value: statistics
.min_values
.get(field.get_name())
.and_then(|f| to_scalar_value(f.as_value()?)),
distinct_count: None, // TODO: distinct
})
.collect(),
),
is_exact: true,
}
} else {
Statistics::default()
};
Ok(ParquetPartition::new(
vec![PartitionedFile {
path: fname,
statistics,
}],
idx,
metrics.clone(),
))
})
.collect::<datafusion::error::Result<_>>()?;
let predicate_builder = combine_filters(filters).and_then(|predicate_expr| {
PruningPredicate::try_new(&predicate_expr, schema.clone()).ok()
});
Ok(Arc::new(ParquetExec::new(
partitions,
schema,
projection.clone(),
self.datafusion_table_statistics(),
ExecutionPlanMetricsSet::new(),
predicate_builder,
batch_size,
limit,
)))
}
fn as_any(&self) -> &dyn Any {
self
}
}
fn to_scalar_value(stat_val: &serde_json::Value) -> Option<datafusion::scalar::ScalarValue> {
if stat_val.is_number() {
if let Some(val) = stat_val.as_i64() {
Some(ScalarValue::from(val))
} else if let Some(val) = stat_val.as_u64() {
Some(ScalarValue::from(val))
} else {
stat_val.as_f64().map(ScalarValue::from)
}
} else {
None
}
}
fn correct_scalar_value_type(
value: datafusion::scalar::ScalarValue,
field_dt: &ArrowDataType,
) -> Option<datafusion::scalar::ScalarValue> {
match field_dt {
ArrowDataType::Int64 => {
let raw_value = i64::try_from(value).unwrap();
Some(ScalarValue::from(raw_value))
}
ArrowDataType::Int32 => {
let raw_value = i64::try_from(value).unwrap() as i32;
Some(ScalarValue::from(raw_value))
}
ArrowDataType::Int16 => {
let raw_value = i64::try_from(value).unwrap() as i16;
Some(ScalarValue::from(raw_value))
}
ArrowDataType::Int8 => {
let raw_value = i64::try_from(value).unwrap() as i8;
Some(ScalarValue::from(raw_value))
}
ArrowDataType::Float32 => {
let raw_value = f64::try_from(value).unwrap() as f32;
Some(ScalarValue::from(raw_value))
}
ArrowDataType::Float64 => {
let raw_value = f64::try_from(value).unwrap();
Some(ScalarValue::from(raw_value))
}
ArrowDataType::Decimal(_, _) => {
let raw_value = f64::try_from(value).unwrap();
Some(ScalarValue::from(raw_value))
}
ArrowDataType::Date32 => {
let raw_value = i64::try_from(value).unwrap() as i32;
Some(ScalarValue::Date32(Some(raw_value)))
}
ArrowDataType::Date64 => {
let raw_value = i64::try_from(value).unwrap();
Some(ScalarValue::Date64(Some(raw_value)))
}
ArrowDataType::Timestamp(TimeUnit::Nanosecond, None) => {
let raw_value = i64::try_from(value).unwrap();
Some(ScalarValue::TimestampNanosecond(Some(raw_value)))
}
ArrowDataType::Timestamp(TimeUnit::Microsecond, None) => {
let raw_value = i64::try_from(value).unwrap();
Some(ScalarValue::TimestampMicrosecond(Some(raw_value)))
}
ArrowDataType::Timestamp(TimeUnit::Millisecond, None) => {
let raw_value = i64::try_from(value).unwrap();
Some(ScalarValue::TimestampMillisecond(Some(raw_value)))
}
_ => {
log::error!(
"Scalar value of arrow type unimplemented for {:?} and {:?}",
value,
field_dt
);
None
}
}
}
fn left_larger_than_right(
left: datafusion::scalar::ScalarValue,
right: datafusion::scalar::ScalarValue,
) -> bool {
match left {
ScalarValue::Float64(Some(v)) => {
let f_right = f64::try_from(right).unwrap();
v > f_right
}
ScalarValue::Float32(Some(v)) => {
let f_right = f32::try_from(right).unwrap();
v > f_right
}
ScalarValue::Int8(Some(v)) => {
let i_right = i8::try_from(right).unwrap();
v > i_right
}
ScalarValue::Int16(Some(v)) => {
let i_right = i16::try_from(right).unwrap();
v > i_right
}
ScalarValue::Int32(Some(v)) => {
let i_right = i32::try_from(right).unwrap();
v > i_right
}
ScalarValue::Int64(Some(v)) => {
let i_right = i64::try_from(right).unwrap();
v > i_right
}
_ => unimplemented!(
"Scalar value comparison unimplemented for {:?} and {:?}",
left,
right
),
}
}