wypoon opened a new issue, #11221:
URL: https://github.com/apache/iceberg/issues/11221
### Apache Iceberg version
main (development)
### Query engine
Spark
### Please describe the bug 🐞
The bug is present in Iceberg 1.2 and later (and is in main).
A customer uses Impala to write Parquet data into an Iceberg table. We have
a sample Parquet file written by Impala. It contains a single decimal(38, 0)
column.
```
$ parquet-tools meta impala_test_data.parq
file: file:/home/systest/impala_test_data.parq
creator: impala version 4.0.0.2024.0.18.1-1 (build
1fe5a71a0498831cf13e5b30ca5e431d69da58bd)
file schema: schema
--------------------------------------------------------------------------------
id: OPTIONAL FIXED_LEN_BYTE_ARRAY L:DECIMAL(38,0) R:0 D:1
row group 1: RC:435942 TS:2755159 OFFSET:4
--------------------------------------------------------------------------------
id: FIXED_LEN_BYTE_ARRAY SNAPPY DO:4 FPO:243092
SZ:2755159/7058047/2.56 VC:435942 ENC:PLAIN_DICTIONARY,PLAIN,RLE ST:[min:
224798, max: 5431555, num_nulls: 0]
```
Spark is able to read the Parquet file correctly (note this is Spark's own
Parquet read path, not Spark Iceberg support):
```
scala> val df =
spark.read.parquet("/user/systest/decimal_test/impala_test_data.parq")
df: org.apache.spark.sql.DataFrame = [id: decimal(38,0)]
scala> df.count()
res0: Long = 435942
scala> df.show()
+-------+
| id|
+-------+
|3025050|
|1401270|
| 505425|
| 479647|
|5061822|
|4170450|
|3307794|
| 683409|
|3205921|
|3261299|
|1596856|
|5260644|
|4865400|
|4737157|
|4808919|
|4032370|
|5183774|
|4119261|
|1911171|
| 782928|
+-------+
only showing top 20 rows
```
However, when we create an Iceberg table, add the file to it (using the
Spark `add_files` procedure), and then query the table, we get
```
scala> spark.sql("select * from test_iceberg").show(80, false)
+-------+
|id |
+-------+
|3025050|
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|1401270|
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|505425 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|479647 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|5061822|
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
|0 |
+-------+
only showing top 80 rows
```
As we can see, the values do show up, but there are 15 zeros that show up
after each value.
This is using vectorized Parquet read, as
`read.parquet.vectorization.enabled` is true by default.
When I set it to false in table properties for the table and query it again,
the results are correct. Thus the bug is in the vectorized read path.
Note that when I write the DataFrame from reading the original Parquet file
back out into another Iceberg table (with the same schema), the file written by
Spark has a different encoding:
```
$ parquet-tools meta spark_iceberg_data.parquet
file: file:/home/systest/spark_iceberg_data.parquet
creator: parquet-mr version 1.13.1 (build
db4183109d5b734ec5930d870cdae161e408ddba)
extra: iceberg.schema =
{"type":"struct","schema-id":0,"fields":[{"id":1,"name":"id","required":false,"type":"decimal(38,
0)"}]}
file schema: table
--------------------------------------------------------------------------------
id: OPTIONAL FIXED_LEN_BYTE_ARRAY L:DECIMAL(38,0) R:0 D:1
row group 1: RC:435942 TS:6975886 OFFSET:4
--------------------------------------------------------------------------------
id: FIXED_LEN_BYTE_ARRAY SNAPPY DO:0 FPO:4 SZ:2685449/6975886/2.60
VC:435942 ENC:PLAIN,RLE,BIT_PACKED ST:[min: 224798, max: 5431555, num_nulls: 0]
```
### Willingness to contribute
- [ ] I can contribute a fix for this bug independently
- [X] I would be willing to contribute a fix for this bug with guidance from
the Iceberg community
- [ ] I cannot contribute a fix for this bug at this time
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