[GitHub] [spark] hvanhovell commented on a diff in pull request #40352: [SPARK-42664][CONNECT] Support `bloomFilter` function for `DataFrameStatFunctions`

["hvanhovell (via GitHub)" <gi...@apache.org>]

hvanhovell commented on code in PR #40352:
URL: https://github.com/apache/spark/pull/40352#discussion_r1160389178


##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala:
##########
@@ -584,6 +585,97 @@ final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, roo
     }
     CountMinSketch.readFrom(ds.head())
   }
+
+  /**
+   * Builds a Bloom filter over a specified column.
+   *
+   * @param colName
+   *   name of the column over which the filter is built
+   * @param expectedNumItems
+   *   expected number of items which will be put into the filter.
+   * @param fpp
+   *   expected false positive probability of the filter.
+   * @since 3.4.0
+   */
+  def bloomFilter(colName: String, expectedNumItems: Long, fpp: Double): BloomFilter = {
+    buildBloomFilter(Column(colName), expectedNumItems, -1L, fpp)
+  }
+
+  /**
+   * Builds a Bloom filter over a specified column.
+   *
+   * @param col
+   *   the column over which the filter is built
+   * @param expectedNumItems
+   *   expected number of items which will be put into the filter.
+   * @param fpp
+   *   expected false positive probability of the filter.
+   * @since 3.4.0
+   */
+  def bloomFilter(col: Column, expectedNumItems: Long, fpp: Double): BloomFilter = {
+    buildBloomFilter(col, expectedNumItems, -1L, fpp)
+  }
+
+  /**
+   * Builds a Bloom filter over a specified column.
+   *
+   * @param colName
+   *   name of the column over which the filter is built
+   * @param expectedNumItems
+   *   expected number of items which will be put into the filter.
+   * @param numBits
+   *   expected number of bits of the filter.
+   * @since 3.4.0
+   */
+  def bloomFilter(colName: String, expectedNumItems: Long, numBits: Long): BloomFilter = {
+    buildBloomFilter(Column(colName), expectedNumItems, numBits, Double.NaN)
+  }
+
+  /**
+   * Builds a Bloom filter over a specified column.
+   *
+   * @param col
+   *   the column over which the filter is built
+   * @param expectedNumItems
+   *   expected number of items which will be put into the filter.
+   * @param numBits
+   *   expected number of bits of the filter.
+   * @since 3.4.0
+   */
+  def bloomFilter(col: Column, expectedNumItems: Long, numBits: Long): BloomFilter = {
+    buildBloomFilter(col, expectedNumItems, numBits, Double.NaN)
+  }
+
+  private def buildBloomFilter(
+      col: Column,
+      expectedNumItems: Long,
+      numBits: Long,
+      fpp: Double): BloomFilter = {
+
+    val dataType = sparkSession

Review Comment:
   Can we use the `TypeOf` expression for this instead? Alternatively we can try to figure this out in the planner.



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