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fix: enable full decimal to decimal support #1385

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fix: enable full decimal to decimal support #1385

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a2689a3
enable decimal to decimal and enable castoptions to be passed
himadripal Feb 6, 2025
99727b8
make decimal precision error check hard coded common in test for all …
himadripal Feb 11, 2025
42648f4
review comments and fix failing test.
himadripal Feb 14, 2025
4df3e68
review comments
himadripal Feb 16, 2025
5cce0c4
add special handling for decimal in doc generation, fix the assert as…
himadripal Feb 21, 2025
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1 change: 1 addition & 0 deletions docs/source/user-guide/compatibility.md
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Expand Up @@ -131,6 +131,7 @@ The following cast operations are generally compatible with Spark except for the
| decimal | long | |
| decimal | float | |
| decimal | double | |
| decimal | decimal | |
| decimal | string | There can be formatting differences in some case due to Spark using scientific notation where Comet does not |
| string | boolean | |
| string | byte | |
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9 changes: 8 additions & 1 deletion native/spark-expr/src/conversion_funcs/cast.rs
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Expand Up @@ -872,6 +872,13 @@ fn cast_array(
let array = array_with_timezone(array, cast_options.timezone.clone(), Some(to_type))?;
let from_type = array.data_type().clone();

let native_cast_options: CastOptions = CastOptions {
safe: !matches!(cast_options.eval_mode, EvalMode::Ansi), // take safe mode from cast_options passed
format_options: FormatOptions::new()
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I think one can use a default value defined for FormatOptions here

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@himadripal himadripal Feb 11, 2025 •
edited
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In the default CAST_OPTIONS which is replaced by this native_cast_options had two these set to

static TIMESTAMP_FORMAT: Option<&str> = Some("%Y-%m-%d %H:%M:%S%.f");
           
 timestamp_format: TIMESTAMP_FORMAT,
 timestamp_tz_format: TIMESTAMP_FORMAT,

If we change it to default, I checked FormatOptions::default() implementation set these

            timestamp_format: None,
            timestamp_tz_format: None,

Hence kept it as it is defined inside default CAST_OPTIONS for comet.

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Fair enough. (The format options are used only to make the cast of timestamp to string compatible with Spark, and are not needed anywhere else) but I guess it is a good idea to be consistent everywhere.

.with_timestamp_tz_format(TIMESTAMP_FORMAT)
.with_timestamp_format(TIMESTAMP_FORMAT),
};

let array = match &from_type {
Dictionary(key_type, value_type)
if key_type.as_ref() == &Int32
Expand Down Expand Up @@ -963,7 +970,7 @@ fn cast_array(
|| is_datafusion_spark_compatible(from_type, to_type, cast_options.allow_incompat) =>
{
// use DataFusion cast only when we know that it is compatible with Spark
Ok(cast_with_options(&array, to_type, &CAST_OPTIONS)?)
Ok(cast_with_options(&array, to_type, &native_cast_options)?)
}
_ => {
// we should never reach this code because the Scala code should be checking
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3 changes: 2 additions & 1 deletion spark/src/main/scala/org/apache/comet/GenerateDocs.scala
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Expand Up @@ -69,7 +69,8 @@ object GenerateDocs {
w.write("|-|-|-|\n".getBytes)
for (fromType <- CometCast.supportedTypes) {
for (toType <- CometCast.supportedTypes) {
if (Cast.canCast(fromType, toType) && fromType != toType) {
if (Cast.canCast(fromType, toType) && (fromType != toType || fromType.typeName
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@andygrove please check - I added this exception for decimal

.contains("decimal"))) {
val fromTypeName = fromType.typeName.replace("(10,2)", "")
val toTypeName = toType.typeName.replace("(10,2)", "")
CometCast.isSupported(fromType, toType, None, CometEvalMode.LEGACY) match {
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9 changes: 2 additions & 7 deletions spark/src/main/scala/org/apache/comet/expressions/CometCast.scala
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Expand Up @@ -70,13 +70,8 @@ object CometCast {
case _ =>
Unsupported
}
case (from: DecimalType, to: DecimalType) =>
if (to.precision < from.precision) {
// https://github.com/apache/datafusion/issues/13492
Incompatible(Some("Casting to smaller precision is not supported"))
} else {
Compatible()
}
case (_: DecimalType, _: DecimalType) =>
Compatible()
case (DataTypes.StringType, _) =>
canCastFromString(toType, timeZoneId, evalMode)
case (_, DataTypes.StringType) =>
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63 changes: 36 additions & 27 deletions spark/src/test/scala/org/apache/comet/CometCastSuite.scala
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Expand Up @@ -25,12 +25,12 @@ import scala.util.Random
import scala.util.matching.Regex

import org.apache.hadoop.fs.Path
import org.apache.spark.sql.{CometTestBase, DataFrame, SaveMode}
import org.apache.spark.sql.{CometTestBase, DataFrame, Row, SaveMode}
import org.apache.spark.sql.catalyst.expressions.Cast
import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanHelper
import org.apache.spark.sql.functions.col
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types.{DataType, DataTypes, DecimalType}
import org.apache.spark.sql.types.{DataType, DataTypes, DecimalType, StructField, StructType}

import org.apache.comet.expressions.{CometCast, CometEvalMode, Compatible}

Expand Down Expand Up @@ -909,12 +909,15 @@ class CometCastSuite extends CometTestBase with AdaptiveSparkPlanHelper {
}

test("cast between decimals with different precision and scale") {
// cast between default Decimal(38, 18) to Decimal(6,2)
val values = Seq(BigDecimal("12345.6789"), BigDecimal("9876.5432"), BigDecimal("123.4567"))
val df = withNulls(values)
.toDF("b")
.withColumn("a", col("b").cast(DecimalType(6, 2)))
checkSparkAnswer(df)
val rowData = Seq(
Row(BigDecimal("12345.6789")),
Row(BigDecimal("9876.5432")),
Row(BigDecimal("123.4567")))
val df = spark.createDataFrame(
spark.sparkContext.parallelize(rowData),
StructType(Seq(StructField("a", DataTypes.createDecimalType(10, 4)))))

castTest(df, DecimalType(6, 2))
}

test("cast between decimals with higher precision than source") {
Expand Down Expand Up @@ -1126,27 +1129,33 @@ class CometCastSuite extends CometTestBase with AdaptiveSparkPlanHelper {
val cometMessage =
if (cometException.getCause != null) cometException.getCause.getMessage
else cometException.getMessage
if (CometSparkSessionExtensions.isSpark40Plus) {
// for Spark 4 we expect to sparkException carries the message
assert(
sparkException.getMessage
.replace(".WITH_SUGGESTION] ", "]")
.startsWith(cometMessage))
} else if (CometSparkSessionExtensions.isSpark34Plus) {
// for Spark 3.4 we expect to reproduce the error message exactly
assert(cometMessage == sparkMessage)
// for comet decimal conversion throws ArrowError(string) from arrow - across spark versions the message dont match.
if (sparkMessage.contains("cannot be represented as")) {
cometMessage.contains("cannot be represented as") || cometMessage.contains(
"too large to store")
} else {
Comment on lines +1132 to 1136
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I think we still need to remove this new if block and update the test cases below.
This new block may still pass with cometMessage.contains("cannot be represented as") that seems to be an indication of Spark cast instead of native cast

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@himadripal himadripal Feb 21, 2025 •
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when I removed the branch from the top, the test fails for double to decimal conversion with allow-incompatible flag, I think that is still using spark cast. Hence I had to put it back.

// for Spark 3.3 we just need to strip the prefix from the Comet message
// before comparing
val cometMessageModified = cometMessage
.replace("[CAST_INVALID_INPUT] ", "")
.replace("[CAST_OVERFLOW] ", "")
.replace("[NUMERIC_VALUE_OUT_OF_RANGE] ", "")

if (sparkMessage.contains("cannot be represented as")) {
assert(cometMessage.contains("cannot be represented as"))
if (CometSparkSessionExtensions.isSpark40Plus) {
// for Spark 4 we expect to sparkException carries the message
assert(
sparkException.getMessage
.replace(".WITH_SUGGESTION] ", "]")
.startsWith(cometMessage))
} else if (CometSparkSessionExtensions.isSpark34Plus) {
// for Spark 3.4 we expect to reproduce the error message exactly
assert(cometMessage == sparkMessage)
} else {
assert(cometMessageModified == sparkMessage)
// for Spark 3.3 we just need to strip the prefix from the Comet message
// before comparing
val cometMessageModified = cometMessage
.replace("[CAST_INVALID_INPUT] ", "")
.replace("[CAST_OVERFLOW] ", "")
.replace("[NUMERIC_VALUE_OUT_OF_RANGE] ", "")

if (sparkMessage.contains("cannot be represented as")) {
assert(cometMessage.contains("too large to store"))
} else {
assert(cometMessageModified == sparkMessage)
}
}
}
}
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