shardulm94 commented on a change in pull request #1271:
URL: https://github.com/apache/iceberg/pull/1271#discussion_r465199882
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File path:
spark/src/main/java/org/apache/iceberg/spark/data/SparkOrcValueReaders.java
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@@ -195,7 +196,12 @@ public Long nonNullRead(ColumnVector vector, int row) {
@Override
public Decimal nonNullRead(ColumnVector vector, int row) {
HiveDecimalWritable value = ((DecimalColumnVector) vector).vector[row];
- return new Decimal().set(value.serialize64(value.scale()),
value.precision(), value.scale());
+ BigDecimal decimal = new
BigDecimal(BigInteger.valueOf(value.serialize64(value.scale())), value.scale());
Review comment:
I believe `value.serialize64` returns the raw long value adjusted for
the requested scale (and since precision <= 18, it always fits in long), I
don't think it is tied to any precision. That being said, I am not very
familiar with using decimals, so maybe I am missing something. Can you give an
example of the case you are referring to?
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