rdblue commented on a change in pull request #1221:
URL: https://github.com/apache/iceberg/pull/1221#discussion_r459167500
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File path:
spark2/src/test/java/org/apache/iceberg/spark/source/TestSparkSchema24.java
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@@ -19,5 +19,43 @@
package org.apache.iceberg.spark.source;
+import java.io.IOException;
+import java.util.List;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.iceberg.PartitionSpec;
+import org.apache.iceberg.SnapshotSummary;
+import org.apache.iceberg.Table;
+import org.apache.iceberg.hadoop.HadoopTables;
+import org.apache.iceberg.relocated.com.google.common.collect.Lists;
+import org.apache.iceberg.spark.SparkSchemaUtil;
+import org.apache.iceberg.util.PropertyUtil;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+import org.junit.Assert;
+import org.junit.Test;
+
public class TestSparkSchema24 extends TestSparkSchema {
+
+ @Test
+ public void testReaderUtils() throws IOException {
+ String tableLocation = temp.newFolder("iceberg-table").toString();
+ HadoopTables tables = new HadoopTables(CONF);
+ PartitionSpec spec = PartitionSpec.unpartitioned();
+ tables.create(SCHEMA, spec, null, tableLocation);
+ List<SimpleRecord> expectedRecords = Lists.newArrayList(
+ new SimpleRecord(1, "a")
+ );
+ Dataset<Row> originalDf = spark.createDataFrame(expectedRecords,
SimpleRecord.class);
+ originalDf.select("id", "data").write()
+ .format("iceberg")
+ .mode("append")
+ .save(tableLocation);
+
+ Table table = tables.load(tableLocation);
+ long totalRecords =
PropertyUtil.propertyAsLong(table.currentSnapshot().summary(),
+ SnapshotSummary.TOTAL_RECORDS_PROP, Long.MAX_VALUE);
+ Assert.assertEquals("totalRecords match", 1, totalRecords);
+ long tableSize =
ReaderUtils.approximateTableSize(SparkSchemaUtil.convert(table.schema()),
totalRecords);
+ Assert.assertEquals("table size matches with expected approximation", 24,
tableSize);
Review comment:
I don't think the method being tested warrants a test that creates a
table, writes data, etc. The method has 2 cases:
* `numRows` is `Long.MAX_VALUE` -> return `Long.MAX_VALUE`
* `numRows` is not -> multiply `numRows` by `sizeEstimate(dataType)`
All you need is 2 test cases: one for each possibility for `numRows`. The
second case should make sure the estimate is sane.
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