Restore Spark compatibility

Author: sergiimk

CHANGELOG.md

@@ -4,6 +4,10 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.7.1] - 2024-01-15
+### Changed
+- Reverted to use `int32` and `int64` for `op` and `offset` respectively to preserve compatibility with Spark engine until Spark's Parquet version is upgraded.
+
 ## [0.7.0] - 2024-01-10
 ### Changed
 - Migration to ODF changelog schema

Cargo.lock

@@ -1,6 +1,6 @@
 [package]
 name = "kamu-engine-datafusion"
-version = "0.7.0"
+version = "0.7.1"
 authors = ["Kamu Data Inc. <info@kamu.dev>"]
 license-file = "LICENSE.txt"
 edition = "2021"

Cargo.toml

@@ -127,8 +127,10 @@ impl Engine {
 
         // Get result's execution plan
         let df = ctx.table(Self::OUTPUT_VIEW_NAME).await.int_err()?;
+        tracing::info!(schema = ?df.schema(), "Raw result schema");
 
         let df = Self::normalize_raw_result(df, &request.vocab)?;
+        tracing::info!(schema = ?df.schema(), "Normalized result schema");
 
         Self::validate_raw_result(&df, &request.vocab)?;
 
@@ -321,11 +323,17 @@ impl Engine {
                     )
                     .alias(field.name())
                 }
-                // For compatibility with engines that cannot write correct Parquet logical types we
-                // allow plain INT32, but cast it to UINT8 here
-                DataType::Int32 if *field.name() == vocab.operation_type_column => {
+                // TODO: Normalize towards UInt8 after Spark is updated
+                // See: https://github.com/kamu-data/kamu-cli/issues/445
+                DataType::Int8
+                | DataType::UInt8
+                | DataType::Int16
+                | DataType::UInt16
+                | DataType::UInt32
+                    if *field.name() == vocab.operation_type_column =>
+                {
                     noop = false;
-                    cast(col(field.unqualified_column()), DataType::UInt8).alias(field.name())
+                    cast(col(field.unqualified_column()), DataType::Int32).alias(field.name())
                 }
                 _ => col(field.unqualified_column()),
             };
@@ -343,8 +351,6 @@ impl Engine {
         df: &DataFrame,
         vocab: &DatasetVocabulary,
     ) -> Result<(), ExecuteTransformError> {
-        tracing::info!(schema = ?df.schema(), "Computed raw result schema");
-
         let system_columns = [&vocab.offset_column, &vocab.system_time_column];
         for system_column in system_columns {
             if df.schema().has_column_with_unqualified_name(system_column) {
@@ -366,11 +372,13 @@ impl Engine {
             .first()
         {
             match op_col.data_type() {
-                DataType::UInt8 => {}
+                // TODO: Require UInt8 after Spark is updated
+                // See: https://github.com/kamu-data/kamu-cli/issues/445
+                DataType::Int32 => {}
                 typ => {
                     return Err(TransformResponseInvalidQuery {
                         message: format!(
-                            "Operation type column '{}' should be UInt8, but found: {}",
+                            "Operation type column '{}' should be Int32, but found: {}",
                             vocab.operation_type_column, typ
                         ),
                     }
@@ -475,11 +483,13 @@ impl Engine {
             }),
         )?;
 
+        // TODO: Cast to UInt64 after Spark is updated
+        // See: https://github.com/kamu-data/kamu-cli/issues/445
         let df = df.with_column(
             &vocab.offset_column,
             cast(
                 col(&vocab.offset_column as &str) + lit(start_offset as i64 - 1),
-                DataType::UInt64,
+                DataType::Int64,
             ),
         )?;
 
@@ -490,7 +500,9 @@ impl Engine {
         {
             df.with_column(
                 &vocab.operation_type_column,
-                lit(OperationType::Append as u8),
+                // TODO: Cast to u8 after Spark is updated
+                // See: https://github.com/kamu-data/kamu-cli/issues/445
+                lit(OperationType::Append as i32),
             )?
         } else {
             df
@@ -572,6 +584,6 @@ impl Engine {
             tracing::info!("Produced empty result",);
             let _ = std::fs::remove_file(path);
         }
-        Ok(num_records)
+        Ok(num_records as u64)
     }
 }

src/engine.rs

@@ -53,15 +53,17 @@ fn write_sample_data(path: impl AsRef<Path>, data: &[Record]) {
     use datafusion::arrow::datatypes::{DataType, Field, Schema, TimeUnit};
     use datafusion::arrow::record_batch::RecordBatch;
 
+    // TODO: Replace with UInt64 and UInt8 after Spark is updated
+    // See: https://github.com/kamu-data/kamu-cli/issues/445
     let schema = Arc::new(Schema::new(vec![
         Field::new(
             DatasetVocabulary::DEFAULT_OFFSET_COLUMN_NAME,
-            DataType::UInt64,
+            DataType::Int64,
             false,
         ),
         Field::new(
             DatasetVocabulary::DEFAULT_OPERATION_TYPE_COLUMN_NAME,
-            DataType::UInt8,
+            DataType::Int32,
             false,
         ),
         Field::new(
@@ -81,11 +83,11 @@ fn write_sample_data(path: impl AsRef<Path>, data: &[Record]) {
     let record_batch = RecordBatch::try_new(
         schema,
         vec![
-            Arc::new(array::UInt64Array::from(
-                data.iter().map(|r| r.offset).collect::<Vec<_>>(),
+            Arc::new(array::Int64Array::from(
+                data.iter().map(|r| r.offset as i64).collect::<Vec<_>>(),
             )),
-            Arc::new(array::UInt8Array::from(
-                data.iter().map(|r| r.op as u8).collect::<Vec<_>>(),
+            Arc::new(array::Int32Array::from(
+                data.iter().map(|r| r.op as i32).collect::<Vec<_>>(),
             )),
             Arc::new(
                 array::TimestampMillisecondArray::from(
@@ -315,8 +317,8 @@ async fn test_result_schema() {
         expected_schema: Some(indoc!(
             r#"
             message arrow_schema {
-              OPTIONAL INT64 offset (INTEGER(64,false));
-              REQUIRED INT32 op (INTEGER(8,false));
+              OPTIONAL INT64 offset;
+              REQUIRED INT32 op;
               REQUIRED INT64 system_time (TIMESTAMP(MILLIS,true));
               REQUIRED INT64 event_time (TIMESTAMP(MILLIS,true));
               REQUIRED BYTE_ARRAY city (STRING);
@@ -337,8 +339,8 @@ async fn test_result_optimal_parquet_encoding() {
         expected_schema: Some(indoc!(
             r#"
             message arrow_schema {
-              OPTIONAL INT64 offset (INTEGER(64,false));
-              REQUIRED INT32 op (INTEGER(8,false));
+              OPTIONAL INT64 offset;
+              REQUIRED INT32 op;
               REQUIRED INT64 system_time (TIMESTAMP(MILLIS,true));
               REQUIRED INT64 event_time (TIMESTAMP(MILLIS,true));
               REQUIRED BYTE_ARRAY city (STRING);
@@ -667,8 +669,8 @@ async fn test_event_time_coerced_to_millis() {
         expected_schema: Some(indoc!(
             r#"
             message arrow_schema {
-              OPTIONAL INT64 offset (INTEGER(64,false));
-              REQUIRED INT32 op (INTEGER(8,false));
+              OPTIONAL INT64 offset;
+              REQUIRED INT32 op;
               REQUIRED INT64 system_time (TIMESTAMP(MILLIS,true));
               REQUIRED INT64 event_time (TIMESTAMP(MILLIS,true));
               REQUIRED BYTE_ARRAY city (STRING);