lurnagao-dahua commented on PR #15211:
URL: https://github.com/apache/iceberg/pull/15211#issuecomment-3852643080
Benchmark
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package org.apache.iceberg.spark.source.parquet.vectorized;
import java.io.IOException;
import java.util.Map;
import org.apache.hadoop.conf.Configuration;
import org.apache.iceberg.PartitionSpec;
import org.apache.iceberg.Schema;
import org.apache.iceberg.Table;
import org.apache.iceberg.TableProperties;
import org.apache.iceberg.hadoop.HadoopTables;
import org.apache.iceberg.spark.source.IcebergSourceBenchmark;
import org.apache.iceberg.types.Types;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.openjdk.jmh.annotations.Benchmark;
import org.openjdk.jmh.annotations.Setup;
import org.openjdk.jmh.annotations.TearDown;
import org.openjdk.jmh.annotations.Threads;
import static org.apache.iceberg.types.Types.NestedField.optional;
import static org.apache.spark.sql.functions.col;
import static org.apache.spark.sql.functions.current_date;
import static org.apache.spark.sql.functions.date_add;
import static org.apache.spark.sql.functions.expr;
public class VectorizedPageSkippingParquetDataBenchmark extends
IcebergSourceBenchmark {
static final long NUM_ROWS = 10_000_000;
@Setup
public void setupBenchmark() {
setupSpark();
appendData();
}
@TearDown
public void tearDownBenchmark() throws IOException {
tearDownSpark();
cleanupFiles();
}
@Override
protected Configuration initHadoopConf() {
return new Configuration();
}
@Override
protected Table initTable() {
// bigDecimalCol is big enough to be encoded as fix len binary (9 bytes),
// decimalCol is small enough to be encoded as a 64-bit int
Schema schema =
new Schema(
optional(1, "longCol", Types.LongType.get()),
optional(2, "intCol", Types.IntegerType.get()),
optional(3, "floatCol", Types.FloatType.get()),
optional(4, "doubleCol", Types.DoubleType.get()),
optional(5, "bigDecimalCol", Types.DecimalType.of(20, 5)),
optional(6, "decimalCol", Types.DecimalType.of(18, 5)),
optional(7, "dateCol", Types.DateType.get()),
optional(8, "timestampCol", Types.TimestampType.withZone()),
optional(9, "stringCol", Types.StringType.get()));
PartitionSpec partitionSpec = PartitionSpec.unpartitioned();
HadoopTables tables = new HadoopTables(hadoopConf());
Map<String, String> properties = parquetWriteProps();
return tables.create(schema, partitionSpec, properties,
newTableLocation());
}
Map<String, String> parquetWriteProps() {
Map<String, String> properties = Maps.newHashMap();
properties.put(TableProperties.METADATA_COMPRESSION, "gzip");
properties.put(TableProperties.PARQUET_DICT_SIZE_BYTES, "1");
return properties;
}
void appendData() {
Dataset<Row> df =
spark()
.range(NUM_ROWS)
.withColumn("longCol", col("id"))
.drop("id")
.withColumn("intCol", expr("CAST(longCol AS INT)"))
.withColumn("floatCol", expr("CAST(longCol AS FLOAT)"))
.withColumn("doubleCol", expr("CAST(longCol AS DOUBLE)"))
.withColumn("bigDecimalCol", expr("CAST(longCol AS DECIMAL(20,
5))"))
.withColumn("decimalCol", expr("CAST(longCol AS DECIMAL(18,
5))"))
.withColumn("dateCol", date_add(current_date(), 1))
.withColumn("timestampCol", expr("TO_TIMESTAMP(dateCol)"))
.withColumn("stringCol", expr("CAST(longCol AS STRING)"));
appendAsFile(df);
}
@Benchmark
@Threads(1)
public void readLong10PercentBenchmark() {
runBenchmark("longCol < " + (NUM_ROWS * 10 / 100));
}
@Benchmark
@Threads(1)
public void readLong50PercentBenchmark() {
runBenchmark("longCol < " + (NUM_ROWS * 50 / 100));
}
@Benchmark
@Threads(1)
public void readLong90PercentBenchmark() {
runBenchmark("longCol < " + (NUM_ROWS * 90 / 100));
}
@Benchmark
@Threads(1)
public void readString10PercentBenchmark() {
// Approximately 10% of matching conditions
runBenchmark("stringCol < '2'");
}
@Benchmark
@Threads(1)
public void readString5PercentBenchmark() {
// Approximately 50% of matching conditions
runBenchmark("stringCol < '5'");
}
@Benchmark
@Threads(1)
public void readString90PercentBenchmark() {
// Approximately 90% of matching conditions
runBenchmark("stringCol < '9'");
}
public void runBenchmark(String filter) {
withTableProperties(
tablePropsWithVectorizationEnabled(5000),
() -> {
String tableLocation = table().location();
Dataset<Row> df =
spark().read().format("iceberg").load(tableLocation).select("*").filter(filter);
materialize(df);
});
}
private static Map<String, String> tablePropsWithVectorizationEnabled(int
batchSize) {
Map<String, String> tableProperties = Maps.newHashMap();
tableProperties.put(TableProperties.PARQUET_VECTORIZATION_ENABLED,
"true");
tableProperties.put(TableProperties.PARQUET_BATCH_SIZE,
String.valueOf(batchSize));
return tableProperties;
}
}
`
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