Cannot read decimal values whose physical types are INT32 and INT64

[Avcu]

Describe the bug, including details regarding any error messages, version, and platform.

Issue

I am saving a parquet file with spark where one of the columns is decimal. Physical type of this column becomes INT32 and INT64 based on its precision. Then, when I read the parquet file with AvroParquetReader, I see logical type being long with the wrong value. For example, if original value is 23.4 then read value is 234.

Spark side

If I enable spark.sql.parquet.writeLegacyFormat for the Spark (ex Jira: SPARK-20297), I see that Spark does not use INT32/INT64 as physical type and then I can successfully read the parquet file. However, this is not the default option and also based on the decimal documentation of this repo, INT32/INT64 should be viable options.

How to reproduce

1. Writing with Spark (version: 3.3.0)

df_temp = spark.createDataFrame([
    (120.321, "Alex"), (24.45, "John")],
    schema=["salary", "name"]
)

df_temp.createOrReplaceTempView("companyTable")
df = spark.sql("SELECT *, CAST(salary as DECIMAL(10,1)) as decimal_salary FROM companyTable")
df.show()
df.write.parquet("my_path")

+-------+----+--------------+
| salary|name|decimal_salary|
+-------+----+--------------+
|120.321|Alex|         120.3|
|  24.45|John|          24.5|
+-------+----+--------------+

2. Confirming the schema

Running the parquet-tools:
parquet-tools inspect github_example.parquet

############ file meta data ############
created_by: parquet-mr version 1.12.2 (build ${buildNumber})
num_columns: 3
num_rows: 1
num_row_groups: 1
format_version: 1.0
serialized_size: 757


############ Columns ############
salary
name
decimal_salary

############ Column(salary) ############
name: salary
path: salary
max_definition_level: 1
max_repetition_level: 0
physical_type: DOUBLE
logical_type: None
converted_type (legacy): NONE
compression: SNAPPY (space_saved: -5%)

############ Column(name) ############
name: name
path: name
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8
compression: SNAPPY (space_saved: -5%)

############ Column(decimal_salary) ############
name: decimal_salary
path: decimal_salary
max_definition_level: 1
max_repetition_level: 0
physical_type: INT64
logical_type: Decimal(precision=10, scale=1)
converted_type (legacy): DECIMAL
compression: SNAPPY (space_saved: -5%)

3. Reading with AvroParquetReader

    public static void main(String[] args) {
        String filePath = "my_path";

        // Check if file exists
        File file = new File(filePath);
        if(!file.exists() || file.isDirectory()) {
            System.err.println("Invalid file path");
            return;
        }

        GenericData genericData = new GenericData();
        genericData.addLogicalTypeConversion(new Conversions.DecimalConversion());

        try {
            Path path = new Path(filePath);
            ParquetReader<GenericRecord> reader = AvroParquetReader
                    .<GenericRecord>builder(HadoopInputFile.fromPath(path, new Configuration()))
                    .withDataModel(genericData)
                    .build();

            GenericRecord record;

            while ((record = reader.read()) != null) {
                // Process the record
                System.out.println(record.toString());
                System.out.println(record.getSchema());
            }
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

{"salary": 120.321, "name": "Alex", "decimal_salary": 1203}
{"type":"record","name":"spark_schema","fields":[{"name":"salary","type":["null","double"],"default":null},{"name":"name","type":["null","string"],"default":null},{"name":"decimal_salary","type":["null","long"],"default":null}]}

Dependencies

    <dependencies>
        <dependency>
            <groupId>org.apache.parquet</groupId>
            <artifactId>parquet-common</artifactId>
            <version>1.15.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.parquet</groupId>
            <artifactId>parquet-encoding</artifactId>
            <version>1.15.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.parquet</groupId>
            <artifactId>parquet-column</artifactId>
            <version>1.15.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.parquet</groupId>
            <artifactId>parquet-hadoop</artifactId>
            <version>1.15.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.parquet</groupId>
            <artifactId>parquet-avro</artifactId>
            <version>1.15.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>3.4.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-core</artifactId>
            <version>3.4.1</version>
        </dependency>
    </dependencies>

Artifacts

github_example.parquet.zip

Component(s)

Avro

[ConeyLiu]

It looks like ParquetAvroReader doesn't handle decimal logical types.

[wgtmac]

@ConeyLiu IIUC, parquet-cli (which uses ParquetAvroReader) might also hit this issue?

[ConeyLiu]

Yes, it should have the same problem. I searched the code in ParquetAvroReader, and there is not any process for decimal.