huaxingao commented on code in PR #6582:
URL: https://github.com/apache/iceberg/pull/6582#discussion_r1619092149


##########
spark/v3.3/spark/src/main/java/org/apache/iceberg/spark/procedures/DistinctCountProcedure.java:
##########
@@ -0,0 +1,191 @@
+/*
+ * 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.spark.procedures;
+
+import static java.lang.String.format;
+import static java.util.UUID.randomUUID;
+import static 
org.apache.hadoop.shaded.com.google.common.collect.ImmutableList.toImmutableList;
+
+import java.io.IOException;
+import java.io.UncheckedIOException;
+import java.nio.ByteBuffer;
+import org.apache.iceberg.GenericBlobMetadata;
+import org.apache.iceberg.GenericStatisticsFile;
+import org.apache.iceberg.HasTableOperations;
+import org.apache.iceberg.Table;
+import org.apache.iceberg.TableOperations;
+import org.apache.iceberg.io.FileIO;
+import org.apache.iceberg.io.OutputFile;
+import org.apache.iceberg.puffin.Blob;
+import org.apache.iceberg.puffin.Puffin;
+import org.apache.iceberg.puffin.PuffinWriter;
+import org.apache.iceberg.puffin.StandardBlobTypes;
+import org.apache.iceberg.relocated.com.google.common.collect.ImmutableList;
+import org.apache.iceberg.relocated.com.google.common.collect.ImmutableMap;
+import org.apache.iceberg.spark.source.SparkTable;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.catalyst.InternalRow;
+import org.apache.spark.sql.catalyst.util.ArrayData;
+import org.apache.spark.sql.connector.catalog.Identifier;
+import org.apache.spark.sql.connector.catalog.TableCatalog;
+import org.apache.spark.sql.connector.iceberg.catalog.ProcedureParameter;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.unsafe.types.UTF8String;
+import org.jetbrains.annotations.NotNull;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/**
+ * A procedure that gets approximate NDV (number of distinct value) for the 
requested columns
+ * and sets this to the table's StatisticsFile.
+ */
+public class DistinctCountProcedure extends BaseProcedure {
+  private static final Logger LOG = 
LoggerFactory.getLogger(DistinctCountProcedure.class);
+
+  private static final ProcedureParameter[] PARAMETERS =
+      new ProcedureParameter[] {
+        ProcedureParameter.required("table", DataTypes.StringType),
+        ProcedureParameter.optional("distinct_count_view", 
DataTypes.StringType),
+        ProcedureParameter.optional("columns", STRING_ARRAY),
+      };
+
+  private static final StructType OUTPUT_TYPE =
+      new StructType(
+          new StructField[] {
+            new StructField("view_name", DataTypes.StringType, false, 
Metadata.empty())
+          });
+
+  public static SparkProcedures.ProcedureBuilder builder() {
+    return new Builder<DistinctCountProcedure>() {
+      @Override
+      protected DistinctCountProcedure doBuild() {
+        return new DistinctCountProcedure(tableCatalog());
+      }
+    };
+  }
+
+  private DistinctCountProcedure(TableCatalog tableCatalog) {
+    super(tableCatalog);
+  }
+
+  @Override
+  public ProcedureParameter[] parameters() {
+    return PARAMETERS;
+  }
+
+  @Override
+  public StructType outputType() {
+    return OUTPUT_TYPE;
+  }
+
+  @Override
+  public InternalRow[] call(InternalRow args) {
+    String tableName = args.getString(0);
+    Identifier tableIdent = toIdentifier(tableName, PARAMETERS[0].name());
+    SparkTable sparkTable = loadSparkTable(tableIdent);
+    StructType schema = sparkTable.schema();
+    Table table = sparkTable.table();
+    ArrayData columns = args.getArray(2);
+    int columnSizes = columns.numElements();
+
+    long[] ndvs = new long[columnSizes];
+    int[] fieldId = new int[columnSizes];
+    String query = "SELECT ";
+    for (int i = 0; i < columnSizes; i++) {
+      String colName = columns.getUTF8String(i).toString();
+      query += "APPROX_COUNT_DISTINCT(" + colName + "), ";
+      fieldId[i] = schema.fieldIndex(colName);
+    }
+
+    query = query.substring(0, query.length() - 2) + " FROM " + tableName;
+    Dataset<Row> df = spark().sql(query);
+
+    for (int i = 0; i < columnSizes; i++) {
+      ndvs[i] = df.head().getLong(i);
+    }
+
+    TableOperations operations = ((HasTableOperations) table).operations();
+    FileIO fileIO = ((HasTableOperations) table).operations().io();
+    String path = operations.metadataFileLocation(format("%s.stats", 
randomUUID()));
+    OutputFile outputFile = fileIO.newOutputFile(path);
+
+    try (PuffinWriter writer =
+        Puffin.write(outputFile).createdBy("Spark 
DistinctCountProcedure").build()) {
+      for (int i = 0; i < columnSizes; i++) {
+        writer.add(
+            new Blob(
+                StandardBlobTypes.NDV_BLOB,
+                ImmutableList.of(fieldId[i]),
+                table.currentSnapshot().snapshotId(),
+                table.currentSnapshot().sequenceNumber(),
+                ByteBuffer.allocate(0),

Review Comment:
   I think Spark only adds support for HLL sketch, but we want to use Theta 
sketch.
   Karuppayya is continuing the work. Here is the new PR 
https://github.com/apache/iceberg/pull/10288



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