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HIVE-29544: Fix Vectorized Parquet reading Struct columns with all fields null #6408

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ayushtkn merged 4 commits into from
Apr 10, 2026
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HIVE-29544: Fix Vectorized Parquet reading Struct columns with all fields null #6408

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HIVE-29544: Fix Vectorized Parquet reading Struct columns with all fi…
ayushtkn Apr 4, 2026
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ayushtkn Apr 8, 2026
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Rename Variable
ayushtkn Apr 9, 2026
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Fix QFILE
ayushtkn Apr 9, 2026
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2 changes: 1 addition & 1 deletion iceberg/iceberg-handler/src/test/results/positive/iceberg_default_column.q.out
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Expand Up @@ -174,7 +174,7 @@ POSTHOOK: Output: hdfs://### HDFS PATH ###
3 {"x":100,"y":99} unknown 25 50000.0 true 2024-01-01 2024-01-01 10:00:00 100.00 general NULL
4 {"x":100,"y":99} NULL 25 50000.0 true 2024-01-01 2024-01-01 10:00:00 100.00 general NULL
5 {"x":100,"y":99} custom_name 30 50000.0 true 2024-01-01 2024-01-01 10:00:00 100.00 general NULL
6 NULL unknown 25 50000.0 true 2024-01-01 2024-01-01 10:00:00 100.00 general NULL
6 {"x":null,"y":null} unknown 25 50000.0 true 2024-01-01 2024-01-01 10:00:00 100.00 general NULL
7 NULL null NULL 50000.0 true 2024-01-01 2024-01-01 10:00:00 100.00 general NULL
8 NULL null NULL 50000.0 true 2024-01-01 2024-01-01 10:00:00 100.00 general {"name":"John","address":{"street":"Main St","city":"New York"}}
9 NULL null NULL 50000.0 true 2024-01-01 2024-01-01 10:00:00 100.00 general {"name":null,"address":{"street":null,"city":"Bangalore"}}
11 changes: 11 additions & 0 deletions ql/src/java/org/apache/hadoop/hive/ql/io/parquet/vector/BaseVectorizedColumnReader.java
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Expand Up @@ -87,6 +87,9 @@ public abstract class BaseVectorizedColumnReader implements VectorizedColumnRead
protected int definitionLevel;
protected int repetitionLevel;

protected int[] currentDefLevels;
protected int defLevelIndex = 0;

/**
* Repetition/Definition/Value readers.
*/
Expand Down Expand Up @@ -154,6 +157,9 @@ public BaseVectorizedColumnReader(
protected void readRepetitionAndDefinitionLevels() {
repetitionLevel = repetitionLevelColumn.nextInt();
definitionLevel = definitionLevelColumn.nextInt();
if (currentDefLevels != null && defLevelIndex < currentDefLevels.length) {
currentDefLevels[defLevelIndex++] = definitionLevel;
}
valuesRead++;
}

Expand Down Expand Up @@ -309,4 +315,9 @@ int nextInt() {
return 0;
}
}

@Override
public int[] getDefinitionLevels() {
return currentDefLevels;
}
}
4 changes: 4 additions & 0 deletions ql/src/java/org/apache/hadoop/hive/ql/io/parquet/vector/VectorizedColumnReader.java
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Expand Up @@ -36,4 +36,8 @@ void readBatch(
int total,
ColumnVector column,
TypeInfo columnType) throws IOException;

default int[] getDefinitionLevels() {
return null;
}
}
17 changes: 11 additions & 6 deletions ql/src/java/org/apache/hadoop/hive/ql/io/parquet/vector/VectorizedParquetRecordReader.java
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@abstractdog abstractdog Apr 8, 2026

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is the same problem applies to another complex types? better to have more test cases and maybe even rename the qfile to parquet_complex_..., maybe parquet_complex_types_null_vectorization.q to clearly distinguish from the already existing parquet_complex_types_vectorization.q

e.g.:
LIST:

NULL vs. [null, null] or [1, null]

or MAP ("same" as struct but not fixed schema)

NULL vs. { "a": 1, "b": null } and  { "a": 1, "b": null, "c": null }

or even nested struct to validate the logic on deeper recursive callpaths:

CREATE TABLE test_parquet_nested_struct_nulls (
    id INT,
    st_prim STRUCT<x:INT, y:INT>,
    st_nested STRUCT<x:INT, y:STRUCT<v:INT, w:INT>>
) STORED AS PARQUET;

and nested data can contain NULL on different levels where you patch is actually hit I assume, e.g.:

NULL
{x: 1, y: NULL}
{x: 1, y: {v: 2, w: NULL}
{x: 1, y: {v: 2, w: 3}

I would appreciate a lot if this patch could validate all of these

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@ayushtkn ayushtkn Apr 8, 2026 •
edited
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The Struct value turning to Null, if sub fields are NULL was due to specific code in VectorizedStructReader

hive/ql/src/java/org/apache/hadoop/hive/ql/io/parquet/vector/VectorizedStructColumnReader.java

Lines 50 to 55 in 13f3208

for (int j = 0; j < vectors[i].isNull.length; j++) {
structColumnVector.isNull[j] =
(i == 0) ? vectors[i].isNull[j] : structColumnVector.isNull[j] && vectors[i].isNull[j];
}
structColumnVector.noNulls =
(i == 0) ? vectors[i].noNulls : structColumnVector.noNulls && vectors[i].noNulls;

It used to check, if all fields have null, it used to set null, that I fixed.

I checked the LIST & MAP. They have another bug :-) In LIST & MAP,

If you insert NULL in them it defaults to 0

eg.

CREATE TABLE test_parquet_array_nulls (
>     id INT,
>     arr_prim ARRAY<INT>
> ) STORED AS PARQUET;
> 
> INSERT INTO test_parquet_array_nulls VALUES
>     -- 1: Array exists, but all elements inside are NULL
>     (1, array(CAST(NULL AS INT), CAST(NULL AS INT))),
>     
>     -- 2: The Array itself is strictly NULL
>     (2, if(1=0, array(1, 2), null)),
>     
>     -- 3: Array exists, containing a mix of valid and NULL elements
>     (3, array(3, CAST(NULL AS INT))),
>     
>     -- 4: Array exists, all elements are valid
>     (4, array(4, 5));
> 
> SELECT * FROM test_parquet_array_nulls ORDER BY id;

It outputs

+------------------------------+------------------------------------+
| test_parquet_array_nulls.id  | test_parquet_array_nulls.arr_prim  |
+------------------------------+------------------------------------+
| 1                            | [0,0]                              |
| 2                            | NULL                               |
| 3                            | [3,0]                              |
| 4                            | [4,5]                              |
+------------------------------+------------------------------------+

Disabling vectorization gives correct

+------------------------------+------------------------------------+
| test_parquet_array_nulls.id  | test_parquet_array_nulls.arr_prim  |
+------------------------------+------------------------------------+
| 1                            | [null,null]                        |
| 2                            | NULL                               |
| 3                            | [3,null]                           |
| 4                            | [4,5]                              |
+------------------------------+------------------------------------+

This seems some different bug, like every NULL is treated as 0.

Will it be ok, if we chase this in a different ticket. I believe it is some where it is returning default value of int instead NULL, some check is wrong which I have to debug.

Regarding nested, vectorization is disabled so, that doesn't kick in:
https://issues.apache.org/jira/browse/HIVE-19016

For map we already have a test: https://github.com/apache/hive/blob/master/ql/src/test/queries/clientpositive/parquet_map_null_vectorization.q

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@ayushtkn ayushtkn Apr 8, 2026

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For the Map there is test but the output is wrong, it is testing for null but the output is having 0,
https://github.com/apache/hive/blame/13f3208c01fec2d20108302efc3fd033d1d76a19/ql/src/test/results/clientpositive/llap/parquet_map_null_vectorization.q.out#L153-L158

It should have been

+-----+---------------+
| id  |    intmap     |
+-----+---------------+
| 1   | {1:null,2:3}  |
| 2   | NULL          |
+-----+---------------+

The inserts were

insert into parquet_map_type_int SELECT 1, MAP(1, null, 2, 3)
insert into parquet_map_type_int (id) VALUES (2)

Let me know if you are ok chasing this separately

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@abstractdog abstractdog Apr 9, 2026

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thanks for clarifying the different cases
the issue with MAPs is indeed another one, it's fine to be handled separately

Original file line number Diff line number Diff line change
Expand Up @@ -459,14 +459,14 @@ private void checkEndOfRowGroup() throws IOException {
columnReaders[i] =
buildVectorizedParquetReader(columnTypesList.get(colsToInclude.get(i)), types.get(i),
pages, requestedSchema.getColumns(), skipTimestampConversion, writerTimezone, skipProlepticConversion,
legacyConversionEnabled, 0);
legacyConversionEnabled, 0, 0);
}
}
} else {
for (int i = 0; i < types.size(); ++i) {
columnReaders[i] = buildVectorizedParquetReader(columnTypesList.get(i), types.get(i), pages,
requestedSchema.getColumns(), skipTimestampConversion, writerTimezone, skipProlepticConversion,
legacyConversionEnabled, 0);
legacyConversionEnabled, 0, 0);
}
}

Expand Down Expand Up @@ -522,7 +522,12 @@ private VectorizedColumnReader buildVectorizedParquetReader(
ZoneId writerTimezone,
boolean skipProlepticConversion,
boolean legacyConversionEnabled,
int depth) throws IOException {
int depth, int currentDefLevel) throws IOException {

int typeDefLevel = currentDefLevel;
if (type.isRepetition(Type.Repetition.OPTIONAL) || type.isRepetition(Type.Repetition.REPEATED)) {
typeDefLevel++;
}
List<ColumnDescriptor> descriptors =
getAllColumnDescriptorByType(depth, type, columnDescriptors);
// Support for schema evolution: if the column from the current
Expand All @@ -549,8 +554,8 @@ private VectorizedColumnReader buildVectorizedParquetReader(
List<Type> types = type.asGroupType().getFields();
for (int i = 0; i < fieldTypes.size(); i++) {
VectorizedColumnReader r =
buildVectorizedParquetReader(fieldTypes.get(i), types.get(i), pages, descriptors,
skipTimestampConversion, writerTimezone, skipProlepticConversion, legacyConversionEnabled, depth + 1);
buildVectorizedParquetReader(fieldTypes.get(i), types.get(i), pages, descriptors, skipTimestampConversion,
writerTimezone, skipProlepticConversion, legacyConversionEnabled, depth + 1, typeDefLevel);
if (r != null) {
fieldReaders.add(r);
} else {
Expand All @@ -559,7 +564,7 @@ private VectorizedColumnReader buildVectorizedParquetReader(
.getTypeName() + " and Parquet type" + types.get(i).toString());
}
}
return new VectorizedStructColumnReader(fieldReaders);
return new VectorizedStructColumnReader(fieldReaders, typeDefLevel);
case LIST:
checkListColumnSupport(((ListTypeInfo) typeInfo).getListElementTypeInfo());
if (columnDescriptors == null || columnDescriptors.isEmpty()) {
Expand Down
2 changes: 2 additions & 0 deletions ql/src/java/org/apache/hadoop/hive/ql/io/parquet/vector/VectorizedPrimitiveColumnReader.java
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Expand Up @@ -64,6 +64,8 @@ public void readBatch(
int total,
ColumnVector column,
TypeInfo columnType) throws IOException {
this.currentDefLevels = new int[total];
this.defLevelIndex = 0;
int rowId = 0;
while (total > 0) {
// Compute the number of values we want to read in this page.
Expand Down
29 changes: 23 additions & 6 deletions ql/src/java/org/apache/hadoop/hive/ql/io/parquet/vector/VectorizedStructColumnReader.java
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Expand Up @@ -29,9 +29,11 @@
public class VectorizedStructColumnReader implements VectorizedColumnReader {

private final List<VectorizedColumnReader> fieldReaders;
private final int structDefLevel;

public VectorizedStructColumnReader(List<VectorizedColumnReader> fieldReaders) {
public VectorizedStructColumnReader(List<VectorizedColumnReader> fieldReaders, int structDefLevel) {
this.fieldReaders = fieldReaders;
this.structDefLevel = structDefLevel;
}

@Override
Expand All @@ -46,14 +48,29 @@ public void readBatch(
fieldReaders.get(i)
.readBatch(total, vectors[i], structTypeInfo.getAllStructFieldTypeInfos().get(i));
structColumnVector.isRepeating = structColumnVector.isRepeating && vectors[i].isRepeating;
}
int[] defLevels = getDefinitionLevels();

for (int j = 0; j < vectors[i].isNull.length; j++) {
structColumnVector.isNull[j] =
(i == 0) ? vectors[i].isNull[j] : structColumnVector.isNull[j] && vectors[i].isNull[j];
// Evaluate struct nullability using Parquet Definition Levels
if (defLevels != null) {
for (int j = 0; j < total; j++) {
if (defLevels[j] < structDefLevel) {
// The Definition Level boundary crossed the struct. The whole struct is null.
structColumnVector.isNull[j] = true;
structColumnVector.noNulls = false;
}
}
structColumnVector.noNulls =
(i == 0) ? vectors[i].noNulls : structColumnVector.noNulls && vectors[i].noNulls;
}
}

@Override
public int[] getDefinitionLevels() {
for (VectorizedColumnReader reader : fieldReaders) {
int[] defLevels = reader.getDefinitionLevels();
if (defLevels != null) {
return defLevels;
}
}
return null;
}
}
15 changes: 8 additions & 7 deletions ql/src/test/org/apache/hadoop/hive/ql/io/parquet/VectorizedColumnReaderTestBase.java
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Expand Up @@ -317,14 +317,15 @@ protected static void writeData(ParquetWriter<Group> writer, boolean isDictionar
g.addGroup("nsf").append("c", intVal).append("d", intVal);
g.append("e", doubleVal);

Group some_null_g = group.addGroup("struct_field_some_null");
if (i % 2 != 0) {
some_null_g.append("f", intVal);
}
if (i % 3 != 0) {
some_null_g.append("g", doubleVal);
if (i % 2 != 0 || i % 3 != 0) {
Group some_null_g = group.addGroup("struct_field_some_null");
if (i % 2 != 0) {
some_null_g.append("f", intVal);
}
if (i % 3 != 0) {
some_null_g.append("g", doubleVal);
}
}

Group mapGroup = group.addGroup("map_field");
if (i % 13 != 1) {
mapGroup.addGroup("map").append("key", binary).append("value", "abc");
Expand Down
31 changes: 31 additions & 0 deletions ql/src/test/queries/clientpositive/parquet_struct_with_null_vectorization.q
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@@ -0,0 +1,31 @@
-- SORT_QUERY_RESULTS
SET hive.vectorized.execution.enabled=true;
set hive.vectorized.execution.reduce.enabled=true;
SET hive.fetch.task.conversion=none;

CREATE TABLE test_parquet_struct_nulls (
id INT,
st_prim STRUCT<x:INT, y:INT>
) STORED AS PARQUET;

INSERT INTO test_parquet_struct_nulls VALUES
(1, named_struct('x', CAST(NULL AS INT), 'y', CAST(NULL AS INT))),
(2, if(1=0, named_struct('x', 1, 'y', 1), null)),
(3, named_struct('x', 3, 'y', CAST(NULL AS INT))),
(4, named_struct('x', 4, 'y', 4));

-- Test A: Full table scan to check JSON representation
SELECT * FROM test_parquet_struct_nulls;

-- Test B: Verify IS NULL evaluates correctly
SELECT id FROM test_parquet_struct_nulls WHERE st_prim IS NULL;

-- Test C: Verify IS NOT NULL evaluates correctly
SELECT id FROM test_parquet_struct_nulls WHERE st_prim IS NOT NULL;

-- Test D: Verify field-level null evaluation inside a valid struct
SELECT id FROM test_parquet_struct_nulls WHERE st_prim IS NOT NULL AND st_prim.x IS NULL;

-- Validate without vectorization
SET hive.vectorized.execution.enabled=true;
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@abstractdog abstractdog Apr 9, 2026

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SET hive.vectorized.execution.enabled=false;

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@ayushtkn ayushtkn Apr 9, 2026

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Fixed!

SELECT * FROM test_parquet_struct_nulls;
85 changes: 85 additions & 0 deletions ql/src/test/results/clientpositive/llap/parquet_struct_with_null_vectorization.q.out
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@@ -0,0 +1,85 @@
PREHOOK: query: CREATE TABLE test_parquet_struct_nulls (
id INT,
st_prim STRUCT<x:INT, y:INT>
) STORED AS PARQUET
PREHOOK: type: CREATETABLE
PREHOOK: Output: database:default
PREHOOK: Output: default@test_parquet_struct_nulls
POSTHOOK: query: CREATE TABLE test_parquet_struct_nulls (
id INT,
st_prim STRUCT<x:INT, y:INT>
) STORED AS PARQUET
POSTHOOK: type: CREATETABLE
POSTHOOK: Output: database:default
POSTHOOK: Output: default@test_parquet_struct_nulls
PREHOOK: query: INSERT INTO test_parquet_struct_nulls VALUES
(1, named_struct('x', CAST(NULL AS INT), 'y', CAST(NULL AS INT))),
(2, if(1=0, named_struct('x', 1, 'y', 1), null)),
(3, named_struct('x', 3, 'y', CAST(NULL AS INT))),
(4, named_struct('x', 4, 'y', 4))
PREHOOK: type: QUERY
PREHOOK: Input: _dummy_database@_dummy_table
PREHOOK: Output: default@test_parquet_struct_nulls
POSTHOOK: query: INSERT INTO test_parquet_struct_nulls VALUES
(1, named_struct('x', CAST(NULL AS INT), 'y', CAST(NULL AS INT))),
(2, if(1=0, named_struct('x', 1, 'y', 1), null)),
(3, named_struct('x', 3, 'y', CAST(NULL AS INT))),
(4, named_struct('x', 4, 'y', 4))
POSTHOOK: type: QUERY
POSTHOOK: Input: _dummy_database@_dummy_table
POSTHOOK: Output: default@test_parquet_struct_nulls
POSTHOOK: Lineage: test_parquet_struct_nulls.id SCRIPT []
POSTHOOK: Lineage: test_parquet_struct_nulls.st_prim SCRIPT []
PREHOOK: query: SELECT * FROM test_parquet_struct_nulls
PREHOOK: type: QUERY
PREHOOK: Input: default@test_parquet_struct_nulls
#### A masked pattern was here ####
POSTHOOK: query: SELECT * FROM test_parquet_struct_nulls
POSTHOOK: type: QUERY
POSTHOOK: Input: default@test_parquet_struct_nulls
#### A masked pattern was here ####
1 {"x":null,"y":null}
2 NULL
3 {"x":3,"y":null}
4 {"x":4,"y":4}
PREHOOK: query: SELECT id FROM test_parquet_struct_nulls WHERE st_prim IS NULL
PREHOOK: type: QUERY
PREHOOK: Input: default@test_parquet_struct_nulls
#### A masked pattern was here ####
POSTHOOK: query: SELECT id FROM test_parquet_struct_nulls WHERE st_prim IS NULL
POSTHOOK: type: QUERY
POSTHOOK: Input: default@test_parquet_struct_nulls
#### A masked pattern was here ####
2
PREHOOK: query: SELECT id FROM test_parquet_struct_nulls WHERE st_prim IS NOT NULL
PREHOOK: type: QUERY
PREHOOK: Input: default@test_parquet_struct_nulls
#### A masked pattern was here ####
POSTHOOK: query: SELECT id FROM test_parquet_struct_nulls WHERE st_prim IS NOT NULL
POSTHOOK: type: QUERY
POSTHOOK: Input: default@test_parquet_struct_nulls
#### A masked pattern was here ####
1
3
4
PREHOOK: query: SELECT id FROM test_parquet_struct_nulls WHERE st_prim IS NOT NULL AND st_prim.x IS NULL
PREHOOK: type: QUERY
PREHOOK: Input: default@test_parquet_struct_nulls
#### A masked pattern was here ####
POSTHOOK: query: SELECT id FROM test_parquet_struct_nulls WHERE st_prim IS NOT NULL AND st_prim.x IS NULL
POSTHOOK: type: QUERY
POSTHOOK: Input: default@test_parquet_struct_nulls
#### A masked pattern was here ####
1
PREHOOK: query: SELECT * FROM test_parquet_struct_nulls
PREHOOK: type: QUERY
PREHOOK: Input: default@test_parquet_struct_nulls
#### A masked pattern was here ####
POSTHOOK: query: SELECT * FROM test_parquet_struct_nulls
POSTHOOK: type: QUERY
POSTHOOK: Input: default@test_parquet_struct_nulls
#### A masked pattern was here ####
1 {"x":null,"y":null}
2 NULL
3 {"x":3,"y":null}
4 {"x":4,"y":4}
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