qlong commented on code in PR #14297:
URL: https://github.com/apache/iceberg/pull/14297#discussion_r3169100035


##########
parquet/src/main/java/org/apache/iceberg/parquet/VariantShreddingAnalyzer.java:
##########
@@ -0,0 +1,532 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+package org.apache.iceberg.parquet;
+
+import java.math.BigDecimal;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import org.apache.iceberg.Schema;
+import org.apache.iceberg.relocated.com.google.common.collect.ImmutableMap;
+import org.apache.iceberg.relocated.com.google.common.collect.Lists;
+import org.apache.iceberg.relocated.com.google.common.collect.Maps;
+import org.apache.iceberg.relocated.com.google.common.collect.Sets;
+import org.apache.iceberg.types.Types.NestedField;
+import org.apache.iceberg.variants.PhysicalType;
+import org.apache.iceberg.variants.VariantArray;
+import org.apache.iceberg.variants.VariantObject;
+import org.apache.iceberg.variants.VariantPrimitive;
+import org.apache.iceberg.variants.VariantValue;
+import org.apache.parquet.schema.GroupType;
+import org.apache.parquet.schema.LogicalTypeAnnotation;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Type;
+import org.apache.parquet.schema.Types;
+
+/**
+ * Analyzes variant data across buffered rows to determine an optimal 
shredding schema.
+ *
+ * <p>Determinism contract: for a given set of variant values (regardless of 
row arrival order),
+ * this analyzer produces the same shredded schema. When the number of 
distinct fields at any level
+ * exceeds {@code MAX_INTERMEDIATE_FIELDS}, field tracking becomes 
insertion-order dependent and
+ * determinism is not guaranteed.
+ *
+ * <ul>
+ *   <li>Object fields use a TreeMap, so field ordering is alphabetical and 
deterministic.
+ *   <li>Type selection picks the most common type with explicit tie-break 
priority (see
+ *       TIE_BREAK_PRIORITY), not enum ordinal.
+ *   <li>Integer types (INT8/16/32/64) and decimal types (DECIMAL4/8/16) are 
each promoted to the
+ *       widest observed before competing with other types.
+ *   <li>Fields below {@code MIN_FIELD_FREQUENCY} are pruned. Above {@code 
MAX_SHREDDED_FIELDS}, the
+ *       most frequent are kept with alphabetical tie-breaking.
+ *   <li>Recursion into nested objects/arrays stops at {@code 
MAX_SHREDDING_DEPTH} (default 50).
+ *   <li>New struct fields are not tracked once a node reaches {@code 
MAX_INTERMEDIATE_FIELDS}
+ *       (default 1000) to bound memory during inference.
+ * </ul>
+ *
+ * <p>This contract holds within a single batch. Different batches with 
different distributions may
+ * produce different layouts; cross-batch stability requires schema pinning 
(not yet implemented).
+ *
+ * <p>Subclasses implement {@link #extractVariantValues} to convert 
engine-specific row types into
+ * {@link VariantValue} instances.
+ *
+ * @param <T> the engine-specific row type (e.g., Spark InternalRow, Flink 
RowData)
+ * @param <S> the engine-specific schema type (e.g., Spark StructType, Flink 
RowType)
+ */
+public abstract class VariantShreddingAnalyzer<T, S> {
+  private static final String TYPED_VALUE = "typed_value";
+  private static final String VALUE = "value";
+  private static final String ELEMENT = "element";
+  private static final double MIN_FIELD_FREQUENCY = 0.10;
+  private static final int MAX_SHREDDED_FIELDS = 300;
+  private static final int MAX_SHREDDING_DEPTH = 50;
+  private static final int MAX_INTERMEDIATE_FIELDS = 1000;
+
+  protected VariantShreddingAnalyzer() {}
+
+  /**
+   * Analyzes buffered variant values to determine the optimal shredding 
schema.
+   *
+   * @param bufferedRows the buffered rows to analyze
+   * @param variantFieldIndex the index of the variant field in the rows
+   * @return the shredded schema type, or null if no shredding should be 
performed
+   */
+  public Type analyzeAndCreateSchema(List<T> bufferedRows, int 
variantFieldIndex) {
+    List<VariantValue> variantValues = extractVariantValues(bufferedRows, 
variantFieldIndex);
+    if (variantValues.isEmpty()) {
+      return null;
+    }
+
+    PathNode root = buildPathTree(variantValues);
+    PhysicalType rootType = root.info.getMostCommonType();
+    if (rootType == null) {
+      return null;
+    }
+
+    pruneInfrequentFields(root, root.info.observationCount);
+
+    return buildTypedValue(root, rootType);
+  }
+
+  protected abstract List<VariantValue> extractVariantValues(
+      List<T> bufferedRows, int variantFieldIndex);
+
+  /**
+   * Resolves a column name to its index in the engine-specific schema. 
Returns -1 if the column is
+   * not found.
+   */
+  protected abstract int resolveColumnIndex(S engineSchema, String columnName);
+
+  /**
+   * Analyzes all variant columns in the schema, resolving column indices via 
the engine-specific
+   * {@link #resolveColumnIndex} method.
+   *
+   * @param bufferedRows the buffered rows to analyze
+   * @param icebergSchema the Iceberg table schema
+   * @param engineSchema the engine-specific schema used to resolve column 
indices
+   * @return a map from Iceberg field ID to the shredded Parquet type for each 
variant column
+   */
+  public Map<Integer, Type> analyzeVariantColumns(
+      List<T> bufferedRows, Schema icebergSchema, S engineSchema) {
+    Map<Integer, Type> shreddedTypes = Maps.newHashMap();
+    for (NestedField col : icebergSchema.columns()) {
+      if (col.type().isVariantType()) {
+        int rowIndex = resolveColumnIndex(engineSchema, col.name());
+        if (rowIndex >= 0) {
+          Type typed = analyzeAndCreateSchema(bufferedRows, rowIndex);
+          if (typed != null) {
+            shreddedTypes.put(col.fieldId(), typed);
+          }
+        }
+      }
+    }
+
+    return shreddedTypes;
+  }
+
+  private static PathNode buildPathTree(List<VariantValue> variantValues) {
+    PathNode root = new PathNode(null);
+    root.info = new FieldInfo();
+
+    for (VariantValue value : variantValues) {
+      traverse(root, value, 0);
+    }
+
+    return root;
+  }
+
+  private static void pruneInfrequentFields(PathNode node, int totalRows) {
+    if (node.objectChildren.isEmpty() && node.arrayElement == null) {
+      return;
+    }
+
+    // Remove fields below frequency threshold
+    node.objectChildren
+        .entrySet()
+        .removeIf(
+            entry -> {
+              FieldInfo info = entry.getValue().info;
+              return info != null
+                  && ((double) info.observationCount / totalRows) < 
MIN_FIELD_FREQUENCY;
+            });
+
+    // Cap at MAX_SHREDDED_FIELDS, keep the most frequently observed
+    if (node.objectChildren.size() > MAX_SHREDDED_FIELDS) {
+      List<Map.Entry<String, PathNode>> sorted = 
Lists.newArrayList(node.objectChildren.entrySet());
+      sorted.sort(
+          (a, b) -> {
+            int cmp =
+                Integer.compare(
+                    b.getValue().info.observationCount, 
a.getValue().info.observationCount);
+            return cmp != 0 ? cmp : a.getKey().compareTo(b.getKey());
+          });
+      Set<String> keep = Sets.newHashSet();
+      for (int i = 0; i < MAX_SHREDDED_FIELDS; i++) {
+        keep.add(sorted.get(i).getKey());
+      }
+      node.objectChildren.entrySet().removeIf(entry -> 
!keep.contains(entry.getKey()));
+    }
+
+    // Recurse into remaining object children
+    for (PathNode child : node.objectChildren.values()) {
+      pruneInfrequentFields(child, totalRows);
+    }
+
+    // Recurse into array elements (arrays of objects need pruning too)
+    if (node.arrayElement != null) {
+      pruneInfrequentFields(node.arrayElement, totalRows);
+    }
+  }
+
+  private static void traverse(PathNode node, VariantValue value, int depth) {
+    if (value == null || value.type() == PhysicalType.NULL) {
+      return;
+    }
+
+    node.info.observe(value);
+
+    if (value.type() == PhysicalType.OBJECT && depth < MAX_SHREDDING_DEPTH) {
+      traverseObject(node, value.asObject(), depth);
+    } else if (value.type() == PhysicalType.ARRAY && depth < 
MAX_SHREDDING_DEPTH) {
+      traverseArray(node, value.asArray(), depth);
+    }
+  }
+
+  private static void traverseObject(PathNode node, VariantObject obj, int 
depth) {
+    for (String fieldName : obj.fieldNames()) {
+      VariantValue fieldValue = obj.get(fieldName);
+      if (fieldValue != null) {
+        PathNode childNode = node.objectChildren.get(fieldName);
+        if (childNode == null) {
+          if (node.objectChildren.size() >= MAX_INTERMEDIATE_FIELDS) {
+            continue;
+          }
+          childNode = new PathNode(fieldName);
+          childNode.info = new FieldInfo();
+          node.objectChildren.put(fieldName, childNode);
+        }
+        traverse(childNode, fieldValue, depth + 1);
+      }
+    }
+  }
+
+  // observationCount inside arrays counts per-element, not per-row, so fields 
in long arrays
+  // have inflated frequency and resist pruning.
+  private static void traverseArray(PathNode node, VariantArray array, int 
depth) {
+    int numElements = array.numElements();
+    if (node.arrayElement == null) {
+      node.arrayElement = new PathNode(null);
+      node.arrayElement.info = new FieldInfo();
+    }
+    for (int i = 0; i < numElements; i++) {
+      VariantValue element = array.get(i);
+      if (element != null) {
+        traverse(node.arrayElement, element, depth + 1);
+      }
+    }
+  }
+
+  private static Type buildFieldGroup(PathNode node) {
+    PhysicalType commonType = node.info.getMostCommonType();
+    if (commonType == null) {
+      return null;
+    }
+
+    Type typedValue = buildTypedValue(node, commonType);
+    if (typedValue == null) {
+      return null;
+    }
+
+    return Types.buildGroup(Type.Repetition.REQUIRED)
+        .optional(PrimitiveType.PrimitiveTypeName.BINARY)
+        .named(VALUE)
+        .addField(typedValue)
+        .named(node.fieldName);
+  }
+
+  private static Type buildTypedValue(PathNode node, PhysicalType 
physicalType) {
+    return switch (physicalType) {
+      case ARRAY -> createArrayTypedValue(node);
+      case OBJECT -> createObjectTypedValue(node);
+      default -> createPrimitiveTypedValue(node.info, physicalType);
+    };
+  }
+
+  private static Type createObjectTypedValue(PathNode node) {
+    if (node.objectChildren.isEmpty()) {
+      return null;
+    }
+
+    Types.GroupBuilder<GroupType> builder = 
Types.buildGroup(Type.Repetition.OPTIONAL);
+    boolean hasFields = false;
+    for (PathNode child : node.objectChildren.values()) {
+      Type fieldType = buildFieldGroup(child);
+      if (fieldType != null) {
+        builder.addField(fieldType);
+        hasFields = true;
+      }
+    }
+
+    return hasFields ? builder.named(TYPED_VALUE) : null;
+  }
+
+  private static Type createArrayTypedValue(PathNode node) {
+    PathNode elementNode = node.arrayElement;
+    if (elementNode == null) {
+      return null;
+    }
+    PhysicalType elementType = elementNode.info.getMostCommonType();
+    if (elementType == null) {
+      return null;
+    }
+    Type elementTypedValue = buildTypedValue(elementNode, elementType);
+    if (elementTypedValue == null) {
+      return null;
+    }
+
+    GroupType elementGroup =
+        Types.buildGroup(Type.Repetition.REQUIRED)
+            .optional(PrimitiveType.PrimitiveTypeName.BINARY)
+            .named(VALUE)
+            .addField(elementTypedValue)
+            .named(ELEMENT);
+
+    return Types.optionalList().element(elementGroup).named(TYPED_VALUE);
+  }
+
+  private static class PathNode {
+    private final String fieldName;
+    private final Map<String, PathNode> objectChildren = Maps.newTreeMap();

Review Comment:
   Performance nit: this map is on the hot path, tree map requires string 
comparison. Maybe change it to hashmap since ordering is not important until 
after pruning  `createObjectTypedValue`?  We do sort there:
   
   ```
   // createObjectTypedValue: sort once here
   private static Type createObjectTypedValue(PathNode node) {
       List<PathNode> sorted = Lists.newArrayList(node.objectChildren.values());
       sorted.sort(Comparator.comparing(child -> child.fieldName));
       ...
   }
   
   ```



##########
parquet/src/main/java/org/apache/iceberg/parquet/VariantShreddingAnalyzer.java:
##########
@@ -0,0 +1,532 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+package org.apache.iceberg.parquet;
+
+import java.math.BigDecimal;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import org.apache.iceberg.Schema;
+import org.apache.iceberg.relocated.com.google.common.collect.ImmutableMap;
+import org.apache.iceberg.relocated.com.google.common.collect.Lists;
+import org.apache.iceberg.relocated.com.google.common.collect.Maps;
+import org.apache.iceberg.relocated.com.google.common.collect.Sets;
+import org.apache.iceberg.types.Types.NestedField;
+import org.apache.iceberg.variants.PhysicalType;
+import org.apache.iceberg.variants.VariantArray;
+import org.apache.iceberg.variants.VariantObject;
+import org.apache.iceberg.variants.VariantPrimitive;
+import org.apache.iceberg.variants.VariantValue;
+import org.apache.parquet.schema.GroupType;
+import org.apache.parquet.schema.LogicalTypeAnnotation;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Type;
+import org.apache.parquet.schema.Types;
+
+/**
+ * Analyzes variant data across buffered rows to determine an optimal 
shredding schema.
+ *
+ * <p>Determinism contract: for a given set of variant values (regardless of 
row arrival order),
+ * this analyzer produces the same shredded schema. When the number of 
distinct fields at any level
+ * exceeds {@code MAX_INTERMEDIATE_FIELDS}, field tracking becomes 
insertion-order dependent and
+ * determinism is not guaranteed.
+ *
+ * <ul>
+ *   <li>Object fields use a TreeMap, so field ordering is alphabetical and 
deterministic.
+ *   <li>Type selection picks the most common type with explicit tie-break 
priority (see
+ *       TIE_BREAK_PRIORITY), not enum ordinal.
+ *   <li>Integer types (INT8/16/32/64) and decimal types (DECIMAL4/8/16) are 
each promoted to the
+ *       widest observed before competing with other types.
+ *   <li>Fields below {@code MIN_FIELD_FREQUENCY} are pruned. Above {@code 
MAX_SHREDDED_FIELDS}, the
+ *       most frequent are kept with alphabetical tie-breaking.
+ *   <li>Recursion into nested objects/arrays stops at {@code 
MAX_SHREDDING_DEPTH} (default 50).
+ *   <li>New struct fields are not tracked once a node reaches {@code 
MAX_INTERMEDIATE_FIELDS}
+ *       (default 1000) to bound memory during inference.
+ * </ul>
+ *
+ * <p>This contract holds within a single batch. Different batches with 
different distributions may
+ * produce different layouts; cross-batch stability requires schema pinning 
(not yet implemented).
+ *
+ * <p>Subclasses implement {@link #extractVariantValues} to convert 
engine-specific row types into
+ * {@link VariantValue} instances.
+ *
+ * @param <T> the engine-specific row type (e.g., Spark InternalRow, Flink 
RowData)
+ * @param <S> the engine-specific schema type (e.g., Spark StructType, Flink 
RowType)
+ */
+public abstract class VariantShreddingAnalyzer<T, S> {
+  private static final String TYPED_VALUE = "typed_value";
+  private static final String VALUE = "value";
+  private static final String ELEMENT = "element";
+  private static final double MIN_FIELD_FREQUENCY = 0.10;
+  private static final int MAX_SHREDDED_FIELDS = 300;

Review Comment:
   Maybe we can make those shredding params configurable in the future, or 
after more performance testing?    



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