Merge branch-24.08 into main

CHANGELOG.md

@@ -1,5 +1,5 @@
 # Change log
-Generated on 2024-08-12
+Generated on 2024-08-16
 
 ## Release 24.08
 
@@ -88,6 +88,8 @@ Generated on 2024-08-12
 ### PRs
 |||
 |:---|:---|
+|[#11335](https://github.com/NVIDIA/spark-rapids/pull/11335)|Fix Delta Lake truncation of min/max string values|
+|[#11304](https://github.com/NVIDIA/spark-rapids/pull/11304)|Update changelog for v24.08.0 release [skip ci]|
 |[#11303](https://github.com/NVIDIA/spark-rapids/pull/11303)|Update rapids JNI and private dependency to 24.08.0|
 |[#11296](https://github.com/NVIDIA/spark-rapids/pull/11296)|[DOC] update doc for 2408 release [skip CI]|
 |[#11309](https://github.com/NVIDIA/spark-rapids/pull/11309)|[Doc ]Update lore doc about the range [skip ci]|

delta-lake/common/src/main/scala/com/nvidia/spark/rapids/delta/GpuStatisticsCollection.scala

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
  *
  * This file was derived from StatisticsCollection.scala
  * in the Delta Lake project at https://github.com/delta-io/delta.
@@ -31,7 +31,7 @@ import com.nvidia.spark.rapids.delta.shims.{ShimDeltaColumnMapping, ShimDeltaUDF
 import org.apache.spark.sql.{Column, SparkSession}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
-import org.apache.spark.sql.functions.{count, lit, max, min, struct, substring, sum, when}
+import org.apache.spark.sql.functions.{count, lit, max, min, struct, sum, when}
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.vectorized.ColumnarBatch
 
@@ -87,7 +87,9 @@ trait GpuStatisticsCollection extends ShimUsesMetadataFields {
       collectStats(MIN, statCollectionSchema) {
         // Truncate string min values as necessary
         case (c, GpuSkippingEligibleDataType(StringType), true) =>
-          substring(min(c), 0, stringPrefixLength)
+          val udfTruncateMin = ShimDeltaUDF.stringStringUdf(
+            GpuStatisticsCollection.truncateMinStringAgg(prefixLength)_)
+          udfTruncateMin(min(c))
 
         // Collect all numeric min values
         case (c, GpuSkippingEligibleDataType(_), true) =>
@@ -203,25 +205,76 @@ trait GpuStatisticsCollection extends ShimUsesMetadataFields {
 }
 
 object GpuStatisticsCollection {
+  val ASCII_MAX_CHARACTER = '\u007F'
+
+  val UTF8_MAX_CHARACTER = new String(Character.toChars(Character.MAX_CODE_POINT))
+
+  def truncateMinStringAgg(prefixLen: Int)(input: String): String = {
+    if (input == null || input.length <= prefixLen) {
+      return input
+    }
+    if (prefixLen <= 0) {
+      return null
+    }
+    if (Character.isHighSurrogate(input.charAt(prefixLen - 1)) &&
+        Character.isLowSurrogate(input.charAt(prefixLen))) {
+      // If the character at prefixLen - 1 is a high surrogate and the next character is a low
+      // surrogate, we need to include the next character in the prefix to ensure that we don't
+      // truncate the string in the middle of a surrogate pair.
+      input.take(prefixLen + 1)
+    } else {
+      input.take(prefixLen)
+    }
+  }
+
   /**
-   * Helper method to truncate the input string `x` to the given `prefixLen` length, while also
-   * appending the unicode max character to the end of the truncated string. This ensures that any
-   * value in this column is less than or equal to the max.
+   * Helper method to truncate the input string `input` to the given `prefixLen` length, while also
+   * ensuring the any value in this column is less than or equal to the truncated max in UTF-8
+   * encoding.
    */
-  def truncateMaxStringAgg(prefixLen: Int)(x: String): String = {
-    if (x == null || x.length <= prefixLen) {
-      x
-    } else {
-      // Grab the prefix. We want to append `\ufffd` as a tie-breaker, but that is only safe
-      // if the character we truncated was smaller. Keep extending the prefix until that
-      // condition holds, or we run off the end of the string.
-      // scalastyle:off nonascii
-      val tieBreaker = '\ufffd'
-      x.take(prefixLen) + x.substring(prefixLen).takeWhile(_ >= tieBreaker) + tieBreaker
-      // scalastyle:off nonascii
+  def truncateMaxStringAgg(prefixLen: Int)(originalMax: String): String = {
+    // scalastyle:off nonascii
+    if (originalMax == null || originalMax.length <= prefixLen) {
+      return originalMax
     }
+    if (prefixLen <= 0) {
+      return null
+    }
+
+    // Grab the prefix. We want to append max Unicode code point `\uDBFF\uDFFF` as a tie-breaker,
+    // but that is only safe if the character we truncated was smaller in UTF-8 encoded binary
+    // comparison. Keep extending the prefix until that condition holds, or we run off the end of
+    // the string.
+    // We also try to use the ASCII max character `\u007F` as a tie-breaker if possible.
+    val maxLen = getExpansionLimit(prefixLen)
+    // Start with a valid prefix
+    var currLen = truncateMinStringAgg(prefixLen)(originalMax).length
+    while (currLen <= maxLen) {
+      if (currLen >= originalMax.length) {
+        // Return originalMax if we have reached the end of the string
+        return originalMax
+      } else if (currLen + 1 < originalMax.length &&
+        originalMax.substring(currLen, currLen + 2) == UTF8_MAX_CHARACTER) {
+        // Skip the UTF-8 max character. It occupies two characters in a Scala string.
+        currLen += 2
+      } else if (originalMax.charAt(currLen) < ASCII_MAX_CHARACTER) {
+        return originalMax.take(currLen) + ASCII_MAX_CHARACTER
+      } else {
+        return originalMax.take(currLen) + UTF8_MAX_CHARACTER
+      }
+    }
+
+    // Return null when the input string is too long to truncate.
+    null
+    // scalastyle:on nonascii
   }
 
+  /**
+   * Calculates the upper character limit when constructing a maximum is not possible with only
+   * prefixLen chars.
+   */
+  private def getExpansionLimit(prefixLen: Int): Int = 2 * prefixLen
+
   def batchStatsToRow(
       schema: StructType,
       explodedDataSchema: Map[Seq[String], Int],

integration_tests/src/main/python/delta_lake_utils.py

@@ -123,6 +123,10 @@ def json_to_sort_key(j):
     jsons.sort(key=json_to_sort_key)
     return jsons
 
+def read_delta_logs(spark, path):
+    log_data = spark.sparkContext.wholeTextFiles(path).collect()
+    return dict([(os.path.basename(x), _decode_jsons(y)) for x, y in log_data])
+
 def assert_gpu_and_cpu_delta_logs_equivalent(spark, data_path):
     cpu_log_data = spark.sparkContext.wholeTextFiles(data_path + "/CPU/_delta_log/*").collect()
     gpu_log_data = spark.sparkContext.wholeTextFiles(data_path + "/GPU/_delta_log/*").collect()

integration_tests/src/main/python/delta_lake_write_test.py

@@ -12,9 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import json
 import pyspark.sql.functions as f
 import pytest
-import sys
 
 from asserts import *
 from data_gen import *
@@ -628,27 +628,45 @@ def gen_bad_data(spark):
 @allow_non_gpu(*delta_meta_allow)
 @delta_lake
 @ignore_order
-@pytest.mark.parametrize("num_cols", [-1, 0, 1, 2, 3 ], ids=idfn)
 @pytest.mark.skipif(is_before_spark_320(), reason="Delta Lake writes are not supported before Spark 3.2.x")
-def test_delta_write_stat_column_limits(num_cols, spark_tmp_path):
+def test_delta_write_stat_column_limits(spark_tmp_path):
     data_path = spark_tmp_path + "/DELTA_DATA"
     confs = copy_and_update(delta_writes_enabled_conf, {"spark.databricks.io.skipping.stringPrefixLength": 8})
-    strgen = StringGen() \
-        .with_special_case((chr(sys.maxunicode) * 7) + "abc") \
-        .with_special_case((chr(sys.maxunicode) * 8) + "abc") \
-        .with_special_case((chr(sys.maxunicode) * 16) + "abc") \
-        .with_special_case(('\U0000FFFD' * 7) + "abc") \
-        .with_special_case(('\U0000FFFD' * 8) + "abc") \
-        .with_special_case(('\U0000FFFD' * 16) + "abc")
-    gens = [("a", StructGen([("x", strgen), ("y", StructGen([("z", strgen)]))])),
-            ("b", binary_gen),
-            ("c", strgen)]
+    # maximum unicode codepoint and maximum ascii character
+    umax, amax = chr(1114111), chr(0x7f)
+    expected_min = {"a": "abcdefgh", "b": "abcdefg�", "c": "abcdefgh",
+                    "d": "abcdefgh", "e": umax * 4, "f": umax * 4}
+    # no max expected for column f since it cannot be truncated to 8 characters and remain
+    # larger than the original value
+    expected_max = {"a": "bcdefghi", "b": "bcdefgh�", "c": "bcdefghi" + amax,
+                    "d": "bcdefghi" + umax, "e": umax * 8}
+    def write_table(spark, path):
+        df = spark.createDataFrame([
+            ("bcdefghi", "abcdefg�", "bcdefghijk", "abcdefgh�", umax * 4, umax * 9),
+            ("abcdefgh", "bcdefgh�", "abcdefghij", "bcdefghi�", umax * 8, umax * 9)],
+            "a string, b string, c string, d string, e string, f string")
+        df.repartition(1).write.format("delta").save(path)
+    def verify_stat_limits(spark):
+        log_data = read_delta_logs(spark, data_path + "/GPU/_delta_log/*.json")
+        assert len(log_data) == 1, "GPU should generate exactly one Delta log"
+        json_objs = list(log_data.values())[0]
+        json_adds = [x["add"] for x in json_objs if "add" in x]
+        assert len(json_adds) == 1, "GPU should only generate a single add in Delta log"
+        stats = json.loads(json_adds[0]["stats"])
+        actual_min = stats["minValues"]
+        assert expected_min == actual_min, \
+            f"minValues mismatch, expected: {expected_min} actual: {actual_min}"
+        actual_max = stats["maxValues"]
+        assert expected_max == actual_max, \
+                f"maxValues stats mismatch, expected: {expected_max} actual: {actual_max}"
     assert_gpu_and_cpu_writes_are_equal_collect(
-        lambda spark, path: gen_df(spark, gens).coalesce(1).write.format("delta").save(path),
-        lambda spark, path: spark.read.format("delta").load(path),
+        write_table,
+        read_delta_path,
         data_path,
         conf=confs)
-    with_cpu_session(lambda spark: assert_gpu_and_cpu_delta_logs_equivalent(spark, data_path))
+    # Many Delta Lake versions are missing the fix from https://github.com/delta-io/delta/pull/3430
+    # so instead of a full delta log compare with the CPU, focus on the reported statistics on GPU.
+    with_cpu_session(verify_stat_limits)
 
 @allow_non_gpu("CreateTableExec", *delta_meta_allow)
 @delta_lake