Skip to content

[LI][Spark][Avro] read avro union using decoder instead of directly returning v… #94

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Mar 16, 2022
Merged

[LI][Spark][Avro] read avro union using decoder instead of directly returning v… #94

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
040502f
[LI][Spark] read avro union using decoder instead of directly returni…
rzhang10 Mar 16, 2022
36febfc
Add a comment for the schema
rzhang10 Mar 16, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 2 additions & 1 deletion spark/src/main/java/org/apache/iceberg/spark/data/SparkValueReaders.java
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -320,7 +320,8 @@ public InternalRow read(Decoder decoder, Object reuse) throws IOException {
int index = decoder.readIndex();
if (index == nullIndex) {
// if it is a null data, directly return null as the whole union result
return null;
// we know for sure it is a null so the casting will always work.
return (InternalRow) readers[nullIndex].read(decoder, reuse);
}

// otherwise, we need to return an InternalRow as a struct data
Expand Down
140 changes: 140 additions & 0 deletions spark/src/test/java/org/apache/iceberg/spark/data/TestSparkAvroUnions.java
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,7 @@
import org.apache.iceberg.avro.Avro;
import org.apache.iceberg.avro.AvroIterable;
import org.apache.iceberg.avro.AvroSchemaUtil;
import org.apache.iceberg.relocated.com.google.common.collect.ImmutableList;
import org.apache.iceberg.relocated.com.google.common.collect.Lists;
import org.apache.spark.sql.catalyst.InternalRow;
import org.junit.Assert;
Expand Down Expand Up @@ -275,4 +276,143 @@ public void testDeeplyNestedUnionSchema2() throws IOException {
Assert.assertEquals(1, rows.get(0).getArray(0).getStruct(0, 3).getStruct(1, 1).getInt(0));
}
}

@Test
public void testDeeplyNestedUnionSchema3() throws IOException {
/*
* the printed write schema:
* {
"type": "record",
"name": "root",
"fields": [
{
"name": "value",
"type": [
{
"type": "record",
"name": "r1",
"fields": [
{
"name": "ff1",
"type": "long"
},
{
"name": "ff2",
"type": {
"type": "record",
"name": "r2",
"fields": [
{
"name": "fff1",
"type": [
"null",
"string",
"int"
],
"default": null
}
]
}
},
{
"name": "ff3",
"type": {
"type": "array",
"items": "string"
},
"default": []
}
]
},
"null"
]
}
]
}
* */
org.apache.avro.Schema writeSchema = SchemaBuilder
Copy link

@funcheetah funcheetah Mar 16, 2022

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Do you think it makes sense to add a comment for the string format schema? It is hard to read the actual schema this test is testing against.

Copy link
Member Author

@rzhang10 rzhang10 Mar 16, 2022

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Sure, I will add a pretty printed json string version of the schema as a comment.

.record("root")
.fields()
.name("value")
.type()
.unionOf()
.record("r1")
.fields()
.name("ff1")
.type()
.longType()
.noDefault()
.name("ff2")
.type()
.record("r2")
.fields()
.name("fff1")
.type()
.unionOf()
.nullType()
.and()
.stringType()
.and()
.intType()
.endUnion()
.nullDefault()
.endRecord()
.noDefault()
.name("ff3")
.type()
.array()
.items()
.stringType()
.arrayDefault(ImmutableList.of())
.endRecord()
.and()
.nullType()
.endUnion()
.noDefault()
.endRecord();

GenericData.Record record1 = new GenericData.Record(writeSchema);
GenericData.Record record11 = new GenericData.Record(writeSchema.getField("value").schema().getTypes().get(0));
GenericData.Record record111 =
new GenericData.Record(writeSchema.getField("value").schema().getTypes().get(0).getField("ff2").schema());
// record111.put("fff1", 1);
record11.put("ff1", 99);
record11.put("ff2", record111);
record11.put("ff3", ImmutableList.of());
record1.put("value", record11);

GenericData.Record record2 = new GenericData.Record(writeSchema);
GenericData.Record record22 = new GenericData.Record(writeSchema.getField("value").schema().getTypes().get(0));
GenericData.Record record222 =
new GenericData.Record(writeSchema.getField("value").schema().getTypes().get(0).getField("ff2").schema());
record222.put("fff1", 1);
record22.put("ff1", 99);
record22.put("ff2", record222);
record22.put("ff3", ImmutableList.of("foo"));
record2.put("value", record22);

File testFile = temp.newFile();
Assert.assertTrue("Delete should succeed", testFile.delete());

try (DataFileWriter<GenericData.Record> writer = new DataFileWriter<>(new GenericDatumWriter<>())) {
writer.create(writeSchema, testFile);
writer.append(record1);
writer.append(record2);
}

List<GenericData.Record> expected = ImmutableList.of(record1, record2);

org.apache.iceberg.Schema readIcebergSchema = AvroSchemaUtil.toIceberg(writeSchema);
// read written rows with evolved schema
List<InternalRow> rows;
try (AvroIterable<InternalRow> reader = Avro.read(Files.localInput(testFile))
.createReaderFunc(SparkAvroReader::new)
.project(readIcebergSchema)
.build()) {
rows = Lists.newArrayList(reader);
}

// making sure the rows can be read successfully
Assert.assertEquals(2, rows.size());
}
}