pvary commented on code in PR #18709:
URL: https://github.com/apache/iceberg/pull/18709#discussion_r4535408858
##########
spark/v4.2/spark/src/test/java/org/apache/iceberg/spark/source/TestSparkReadMetrics.java:
##########
@@ -44,6 +61,180 @@ public class TestSparkReadMetrics extends
TestBaseWithCatalog {
@AfterEach
public void removeTables() {
sql("DROP TABLE IF EXISTS %s", tableName);
+ sql("DROP TABLE IF EXISTS dim");
+ }
+
+ @TestTemplate
+ void effectiveSplitTargetSize() throws NoSuchTableException, IOException {
+ long tableSplitSize = 128L * 1024 * 1024;
+ long optionSplitSize = 32L * 1024 * 1024;
+ int parallelism = 1000;
+ sql(
+ "CREATE TABLE %s (id BIGINT) USING iceberg TBLPROPERTIES
('read.split.target-size'='%s')",
+ tableName, tableSplitSize);
+ spark.range(100).coalesce(1).writeTo(tableName).append();
+
+ long scanSize = 0;
+ try (CloseableIterable<FileScanTask> tasks =
+ validationCatalog.loadTable(tableIdent).newScan().planFiles()) {
+ for (FileScanTask task : tasks) {
+ scanSize += task.sizeBytes();
+ }
+ }
+ long adaptiveSplitSize = TableScanUtil.adjustSplitSize(scanSize,
parallelism, tableSplitSize);
+ assertThat(adaptiveSplitSize).isNotEqualTo(tableSplitSize);
+
+ // adaptive sizing lowers the table target for a small scan
+ withSQLConf(
+ ImmutableMap.of(
+ SparkSQLProperties.READ_ADAPTIVE_SPLIT_SIZE_ENABLED,
+ "true",
+ SparkSQLProperties.READ_ADAPTIVE_SPLIT_SIZE_PARALLELISM,
+ String.valueOf(parallelism)),
+ () -> {
+ Dataset<Row> df = spark.sql(String.format("SELECT * FROM %s",
tableName));
+ df.collect();
+ SparkPlan scan =
df.queryExecution().executedPlan().collectLeaves().head();
+
assertThat(scan.metrics().get("effectiveSplitTargetSize").get().value())
+ .isEqualTo(adaptiveSplitSize);
+ });
+
+ // without adaptive sizing, the table property is the target
+ withSQLConf(
+ ImmutableMap.of(SparkSQLProperties.READ_ADAPTIVE_SPLIT_SIZE_ENABLED,
"false"),
+ () -> {
+ Dataset<Row> df = spark.sql(String.format("SELECT * FROM %s",
tableName));
+ df.collect();
+ SparkPlan scan =
df.queryExecution().executedPlan().collectLeaves().head();
+
assertThat(scan.metrics().get("effectiveSplitTargetSize").get().value())
+ .isEqualTo(tableSplitSize);
+ });
+
+ // an explicit read option is used as is, even with adaptive sizing enabled
+ withSQLConf(
+ ImmutableMap.of(SparkSQLProperties.READ_ADAPTIVE_SPLIT_SIZE_ENABLED,
"true"),
+ () -> {
+ Dataset<Row> df =
+ spark
+ .read()
+ .format("iceberg")
+ .option(SparkReadOptions.SPLIT_SIZE,
String.valueOf(optionSplitSize))
+ .load(tableName);
+ df.collect();
+ SparkPlan scan =
df.queryExecution().executedPlan().collectLeaves().head();
+
assertThat(scan.metrics().get("effectiveSplitTargetSize").get().value())
+ .isEqualTo(optionSplitSize);
+ });
+ }
+
+ @TestTemplate
+ void effectiveSplitTargetSizeWithoutSnapshot() {
+ sql("CREATE TABLE %s (id BIGINT) USING iceberg", tableName);
+
+ // no task groups are planned for a table without a snapshot
+ Dataset<Row> df = spark.sql(String.format("SELECT * FROM %s", tableName));
+ df.collect();
+ SparkPlan scan = df.queryExecution().executedPlan().collectLeaves().head();
+
assertThat(scan.metrics().get("effectiveSplitTargetSize").get().value()).isZero();
+ }
+
+ @TestTemplate
+ void effectiveSplitTargetSizeWithRuntimeFilter() throws
NoSuchTableException, IOException {
Review Comment:
Both values check the value on 16 MB here, so this test passes even if the
metric keeps the stale pre-filter value. Should we check that it actually
changes after the runtime filter re-plans? For example, use large fake data
files so the value is 64 MB before the filter and 32 MB after.
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