SreeramGarlapati commented on a change in pull request #2660: URL: https://github.com/apache/iceberg/pull/2660#discussion_r658377920
########## File path: spark3/src/test/java/org/apache/iceberg/spark/source/TestStructuredStreamingRead3.java ########## @@ -0,0 +1,435 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.iceberg.spark.source; + +import java.io.File; +import java.util.Collections; +import java.util.List; +import java.util.Map; +import java.util.concurrent.TimeoutException; +import org.apache.hadoop.conf.Configuration; +import org.apache.iceberg.AssertHelpers; +import org.apache.iceberg.BaseTable; +import org.apache.iceberg.DataOperations; +import org.apache.iceberg.DeleteFile; +import org.apache.iceberg.Files; +import org.apache.iceberg.Schema; +import org.apache.iceberg.Table; +import org.apache.iceberg.TableMetadata; +import org.apache.iceberg.TableOperations; +import org.apache.iceberg.TestHelpers; +import org.apache.iceberg.data.FileHelpers; +import org.apache.iceberg.data.GenericRecord; +import org.apache.iceberg.data.Record; +import org.apache.iceberg.expressions.Expressions; +import org.apache.iceberg.relocated.com.google.common.collect.Iterables; +import org.apache.iceberg.relocated.com.google.common.collect.Lists; +import org.apache.iceberg.spark.SparkCatalogTestBase; +import org.apache.iceberg.types.Types; +import org.apache.spark.sql.Dataset; +import org.apache.spark.sql.Encoders; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.SparkSession; +import org.apache.spark.sql.streaming.DataStreamWriter; +import org.apache.spark.sql.streaming.OutputMode; +import org.apache.spark.sql.streaming.StreamingQuery; +import org.apache.spark.sql.streaming.StreamingQueryException; +import org.apache.spark.sql.streaming.Trigger; +import org.assertj.core.api.Assertions; +import org.junit.After; +import org.junit.AfterClass; +import org.junit.Assert; +import org.junit.Before; +import org.junit.BeforeClass; +import org.junit.Rule; +import org.junit.Test; +import org.junit.rules.ExpectedException; +import org.junit.rules.TemporaryFolder; +import org.junit.runner.RunWith; +import org.junit.runners.Parameterized; + +import static org.apache.iceberg.expressions.Expressions.ref; +import static org.apache.iceberg.types.Types.NestedField.optional; + +@RunWith(Parameterized.class) +public final class TestStructuredStreamingRead3 extends SparkCatalogTestBase { + public TestStructuredStreamingRead3( + String catalogName, String implementation, Map<String, String> config) { + super(catalogName, implementation, config); + } + + private Table table; + private String tableIdentifier; + + private static final Configuration CONF = new Configuration(); + private static final Schema SCHEMA = new Schema( + optional(1, "id", Types.IntegerType.get()), + optional(2, "data", Types.StringType.get()) + ); + + /** + * test data to be used by multiple writes + * each write creates a snapshot and writes a list of records + */ + private static final List<List<SimpleRecord>> TEST_DATA_MULTIPLE_SNAPSHOTS = Lists.newArrayList( + Lists.newArrayList( + new SimpleRecord(1, "one"), + new SimpleRecord(2, "two"), + new SimpleRecord(3, "three")), + Lists.newArrayList( + new SimpleRecord(4, "four"), + new SimpleRecord(5, "five")), + Lists.newArrayList( + new SimpleRecord(6, "six"), + new SimpleRecord(7, "seven"))); + + /** + * test data - to be used for multiple write batches + * each batch inturn will have multiple snapshots + */ + private static final List<List<List<SimpleRecord>>> TEST_DATA_MULTIPLE_WRITES_MULTIPLE_SNAPSHOTS = Lists.newArrayList( + Lists.newArrayList( + Lists.newArrayList( + new SimpleRecord(1, "one"), + new SimpleRecord(2, "two"), + new SimpleRecord(3, "three")), + Lists.newArrayList( + new SimpleRecord(4, "four"), + new SimpleRecord(5, "five"))), + Lists.newArrayList( + Lists.newArrayList( + new SimpleRecord(6, "six"), + new SimpleRecord(7, "seven")), + Lists.newArrayList( + new SimpleRecord(8, "eight"), + new SimpleRecord(9, "nine"))), + Lists.newArrayList( + Lists.newArrayList( + new SimpleRecord(10, "ten"), + new SimpleRecord(11, "eleven"), + new SimpleRecord(12, "twelve")), + Lists.newArrayList( + new SimpleRecord(13, "thirteen"), + new SimpleRecord(14, "fourteen")), + Lists.newArrayList( + new SimpleRecord(15, "fifteen"), + new SimpleRecord(16, "sixteen")))); + + private static SparkSession spark = null; + + @Rule + public TemporaryFolder temp = new TemporaryFolder(); + + @Rule + public ExpectedException exceptionRule = ExpectedException.none(); + + @BeforeClass + public static void startSpark() { + TestStructuredStreamingRead3.spark = SparkSession.builder() + .master("local[2]") + .config("spark.sql.shuffle.partitions", 4) + .getOrCreate(); + } + + @AfterClass + public static void stopSpark() { + SparkSession currentSpark = TestStructuredStreamingRead3.spark; + TestStructuredStreamingRead3.spark = null; + currentSpark.stop(); + } + + @Before + public void setupTable() { + sql("CREATE TABLE %s " + + "(id INT, data STRING) " + + "USING iceberg " + + "PARTITIONED BY (bucket(3, id))", tableName); + this.table = validationCatalog.loadTable(tableIdent); + this.tableIdentifier = tableName; + } + + @After + public void stopStreams() throws TimeoutException { + for (StreamingQuery query : spark.streams().active()) { + query.stop(); + } + } + + @After + public void removeTables() { + sql("DROP TABLE IF EXISTS %s", tableName); + } + + @SuppressWarnings("unchecked") + @Test + public void testReadStreamOnIcebergTableWithMultipleSnapshots() throws Exception { + List<List<SimpleRecord>> expected = TEST_DATA_MULTIPLE_SNAPSHOTS; + appendDataAsMultipleSnapshots(expected, this.tableIdentifier); + + table.refresh(); + + Dataset<Row> df = spark.readStream() + .format("iceberg") + .load(this.tableIdentifier); + List<SimpleRecord> actual = processAvailable(df); + + Assertions.assertThat(actual).containsExactlyInAnyOrderElementsOf(Iterables.concat(expected)); + } + + @SuppressWarnings("unchecked") + @Test + public void testResumingStreamReadFromCheckpoint() throws Exception { + File writerCheckpointFolder = temp.newFolder("writer-checkpoint-folder"); + File writerCheckpoint = new File(writerCheckpointFolder, "writer-checkpoint"); + final String tempView = "microBatchView"; + + Dataset<Row> df = spark.readStream() + .format("iceberg") + .load(tableIdentifier); + + // Trigger.Once with the combination of StreamingQuery.awaitTermination, which succeeds after this code + // will result in stopping the stream. + // This is how Stream STOP and RESUME is simulated in this Test Case. + DataStreamWriter<Row> singleBatchWriter = df.writeStream() + .trigger(Trigger.Once()) + .option("checkpointLocation", writerCheckpoint.toString()) + .foreachBatch((batchDF, batchId) -> { + batchDF.createOrReplaceGlobalTempView(tempView); + }); + + String globalTempView = "global_temp." + tempView; + + List<SimpleRecord> processStreamOnEmptyIcebergTable = processMicroBatch(singleBatchWriter, globalTempView); + Assert.assertEquals(Collections.emptyList(), processStreamOnEmptyIcebergTable); + + for (List<List<SimpleRecord>> expectedCheckpoint : TEST_DATA_MULTIPLE_WRITES_MULTIPLE_SNAPSHOTS) { + appendDataAsMultipleSnapshots(expectedCheckpoint, tableIdentifier); + table.refresh(); + + List<SimpleRecord> actualDataInCurrentMicroBatch = processMicroBatch(singleBatchWriter, globalTempView); + Assertions.assertThat(actualDataInCurrentMicroBatch) + .containsExactlyInAnyOrderElementsOf(Iterables.concat(expectedCheckpoint)); + } + } + + @SuppressWarnings("unchecked") + @Test + public void testParquetOrcAvroDataInOneTable() throws Exception { + List<SimpleRecord> parquetFileRecords = Lists.newArrayList( + new SimpleRecord(1, "one"), + new SimpleRecord(2, "two"), + new SimpleRecord(3, "three")); + + List<SimpleRecord> orcFileRecords = Lists.newArrayList( + new SimpleRecord(4, "four"), + new SimpleRecord(5, "five")); + + List<SimpleRecord> avroFileRecords = Lists.newArrayList( + new SimpleRecord(6, "six"), + new SimpleRecord(7, "seven")); + + appendData(parquetFileRecords, tableIdentifier, "parquet"); + appendData(orcFileRecords, tableIdentifier, "orc"); + appendData(avroFileRecords, tableIdentifier, "avro"); + + table.refresh(); + + Dataset<Row> ds = spark.readStream() + .format("iceberg") + .load(tableIdentifier); + Assertions.assertThat(processAvailable(ds)) + .containsExactlyInAnyOrderElementsOf(Iterables.concat(parquetFileRecords, orcFileRecords, avroFileRecords)); + } + + @SuppressWarnings("unchecked") + @Test + public void testReadStreamFromEmptyTable() throws Exception { + table.refresh(); + + Dataset<Row> df = spark.readStream() + .format("iceberg") + .load(tableIdentifier); + + List<SimpleRecord> actual = processAvailable(df); + Assert.assertEquals(Collections.emptyList(), actual); + } + + @SuppressWarnings("unchecked") + @Test + public void testReadStreamWithSnapshotTypeOverwriteErrorsOut() throws Exception { + // upgrade table to version 2 - to facilitate creation of Snapshot of type OVERWRITE. + TableOperations ops = ((BaseTable) table).operations(); + TableMetadata meta = ops.current(); + ops.commit(meta, meta.upgradeToFormatVersion(2)); + + // fill table with some initial data + List<List<SimpleRecord>> dataAcrossSnapshots = TEST_DATA_MULTIPLE_SNAPSHOTS; + appendDataAsMultipleSnapshots(dataAcrossSnapshots, tableIdentifier); + + Schema deleteRowSchema = table.schema().select("data"); + Record dataDelete = GenericRecord.create(deleteRowSchema); + List<Record> dataDeletes = Lists.newArrayList( + dataDelete.copy("data", "one") // id = 1 + ); + + DeleteFile eqDeletes = FileHelpers.writeDeleteFile( + table, Files.localOutput(temp.newFile()), TestHelpers.Row.of(0), dataDeletes, deleteRowSchema); + + table.newRowDelta() + .addDeletes(eqDeletes) + .commit(); + + // check pre-condition - that the above Delete file write - actually resulted in snapshot of type OVERWRITE + Assert.assertEquals(DataOperations.OVERWRITE, table.currentSnapshot().operation()); + + Dataset<Row> df = spark.readStream() + .format("iceberg") + .load(tableIdentifier); + StreamingQuery streamingQuery = df.writeStream() + .format("memory") + .queryName("testtablewithoverwrites") + .outputMode(OutputMode.Append()) + .start(); + + AssertHelpers.assertThrowsCause( + "Streaming should fail with IllegalStateException, as the snapshot is not of type APPEND", + IllegalStateException.class, + "Invalid Snapshot operation", + () -> streamingQuery.processAllAvailable() + ); + } + + @SuppressWarnings("unchecked") + @Test + public void testReadStreamWithSnapshotTypeReplaceErrorsOut() throws Exception { + // fill table with some data + List<List<SimpleRecord>> dataAcrossSnapshots = TEST_DATA_MULTIPLE_SNAPSHOTS; + appendDataAsMultipleSnapshots(dataAcrossSnapshots, tableIdentifier); + + table.refresh(); + + // this should create a snapshot with type Replace. + table.rewriteManifests() + .clusterBy(f -> 1) + .commit(); Review comment: yes, this is on the top of my **What's Next** list. A spark option to `ignore-replace`. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
