SreeramGarlapati commented on a change in pull request #2660:
URL: https://github.com/apache/iceberg/pull/2660#discussion_r655709384
##########
File path: core/src/main/java/org/apache/iceberg/util/SnapshotUtil.java
##########
@@ -107,4 +120,19 @@ public static boolean ancestorOf(Table table, long
snapshotId, long ancestorSnap
return newFiles;
}
+
+ /**
+ * traverses through the table's snapshots and finds the {@link Table}'s
+ * Snapshot that is committed after the specified snapshotId
+ * @return null if passed in snapshot expired, else the snapshot after the
passed in snapshot
+ */
+ public static Snapshot snapshotAfter(Table table, long snapshotId) {
+ Snapshot previousSnapshot = table.snapshot(snapshotId);
+ Snapshot pointer = table.currentSnapshot();
Review comment:
done.
##########
File path: core/src/main/java/org/apache/iceberg/util/SnapshotUtil.java
##########
@@ -63,6 +63,19 @@ public static boolean ancestorOf(Table table, long
snapshotId, long ancestorSnap
return ancestorIds(table.currentSnapshot(), table::snapshot);
}
+ /**
+ * Find the oldest Snapshot of a {@link Table}.
Review comment:
fixed it.
##########
File path:
spark3/src/main/java/org/apache/iceberg/spark/source/SparkMicroBatchStream.java
##########
@@ -0,0 +1,252 @@
+/*
+ * 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.BufferedWriter;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.io.OutputStreamWriter;
+import java.io.UncheckedIOException;
+import java.nio.charset.StandardCharsets;
+import java.util.List;
+import org.apache.iceberg.CombinedScanTask;
+import org.apache.iceberg.DataOperations;
+import org.apache.iceberg.FileScanTask;
+import org.apache.iceberg.MicroBatches;
+import org.apache.iceberg.MicroBatches.MicroBatch;
+import org.apache.iceberg.Schema;
+import org.apache.iceberg.SchemaParser;
+import org.apache.iceberg.SerializableTable;
+import org.apache.iceberg.Snapshot;
+import org.apache.iceberg.Table;
+import org.apache.iceberg.io.CloseableIterable;
+import org.apache.iceberg.io.FileIO;
+import org.apache.iceberg.io.InputFile;
+import org.apache.iceberg.io.OutputFile;
+import org.apache.iceberg.relocated.com.google.common.base.Joiner;
+import org.apache.iceberg.relocated.com.google.common.base.Preconditions;
+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.Spark3Util;
+import org.apache.iceberg.spark.SparkReadOptions;
+import org.apache.iceberg.spark.source.SparkBatchScan.ReadTask;
+import org.apache.iceberg.spark.source.SparkBatchScan.ReaderFactory;
+import org.apache.iceberg.util.PropertyUtil;
+import org.apache.iceberg.util.SnapshotUtil;
+import org.apache.iceberg.util.TableScanUtil;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.broadcast.Broadcast;
+import org.apache.spark.sql.connector.read.InputPartition;
+import org.apache.spark.sql.connector.read.PartitionReaderFactory;
+import org.apache.spark.sql.connector.read.streaming.MicroBatchStream;
+import org.apache.spark.sql.connector.read.streaming.Offset;
+import org.apache.spark.sql.util.CaseInsensitiveStringMap;
+
+import static org.apache.iceberg.TableProperties.SPLIT_LOOKBACK;
+import static org.apache.iceberg.TableProperties.SPLIT_LOOKBACK_DEFAULT;
+import static org.apache.iceberg.TableProperties.SPLIT_OPEN_FILE_COST;
+import static org.apache.iceberg.TableProperties.SPLIT_OPEN_FILE_COST_DEFAULT;
+import static org.apache.iceberg.TableProperties.SPLIT_SIZE;
+import static org.apache.iceberg.TableProperties.SPLIT_SIZE_DEFAULT;
+
+public class SparkMicroBatchStream implements MicroBatchStream {
+ private static final Joiner SLASH = Joiner.on("/");
+
+ private final Table table;
+ private final boolean caseSensitive;
+ private final String expectedSchema;
+ private final Broadcast<Table> tableBroadcast;
+ private final Long splitSize;
+ private final Integer splitLookback;
+ private final Long splitOpenFileCost;
+ private final boolean localityPreferred;
+ private final StreamingOffset initialOffset;
+
+ SparkMicroBatchStream(JavaSparkContext sparkContext, Table table, boolean
caseSensitive,
+ Schema expectedSchema, CaseInsensitiveStringMap
options, String checkpointLocation) {
+ this.table = table;
+ this.caseSensitive = caseSensitive;
+ this.expectedSchema = SchemaParser.toJson(expectedSchema);
+ this.localityPreferred = Spark3Util.isLocalityEnabled(table.io(),
table.location(), options);
+ this.tableBroadcast =
sparkContext.broadcast(SerializableTable.copyOf(table));
+
+ long tableSplitSize = PropertyUtil.propertyAsLong(table.properties(),
SPLIT_SIZE, SPLIT_SIZE_DEFAULT);
+ this.splitSize = Spark3Util.propertyAsLong(options,
SparkReadOptions.SPLIT_SIZE, tableSplitSize);
+
+ int tableSplitLookback = PropertyUtil.propertyAsInt(table.properties(),
SPLIT_LOOKBACK, SPLIT_LOOKBACK_DEFAULT);
+ this.splitLookback = Spark3Util.propertyAsInt(options,
SparkReadOptions.LOOKBACK, tableSplitLookback);
+
+ long tableSplitOpenFileCost = PropertyUtil.propertyAsLong(
+ table.properties(), SPLIT_OPEN_FILE_COST,
SPLIT_OPEN_FILE_COST_DEFAULT);
+ this.splitOpenFileCost = Spark3Util.propertyAsLong(options,
SPLIT_OPEN_FILE_COST, tableSplitOpenFileCost);
+
+ InitialOffsetStore initialOffsetStore = new InitialOffsetStore(table,
checkpointLocation);
+ this.initialOffset = initialOffsetStore.initialOffset();
+ }
+
+ @Override
+ public Offset latestOffset() {
+ table.refresh();
+ Snapshot latestSnapshot = table.currentSnapshot();
+ if (latestSnapshot == null) {
+ return StreamingOffset.START_OFFSET;
+ }
+
+ return new StreamingOffset(latestSnapshot.snapshotId(),
Iterables.size(latestSnapshot.addedFiles()), false);
+ }
+
+ @Override
+ public InputPartition[] planInputPartitions(Offset start, Offset end) {
+ Preconditions.checkArgument(end instanceof StreamingOffset, "Invalid end
offset: %s is not a StreamingOffset", end);
+ Preconditions.checkArgument(
+ start instanceof StreamingOffset, "Invalid start offset: %s is not a
StreamingOffset", start);
+
+ if (end.equals(StreamingOffset.START_OFFSET)) {
+ return new InputPartition[0];
+ }
+
+ StreamingOffset endOffset = (StreamingOffset) end;
+ StreamingOffset startOffset = (StreamingOffset) start;
+
+ List<FileScanTask> fileScanTasks = planFiles(startOffset, endOffset);
+
+ CloseableIterable<FileScanTask> splitTasks = TableScanUtil.splitFiles(
+ CloseableIterable.withNoopClose(fileScanTasks),
+ splitSize);
+ List<CombinedScanTask> combinedScanTasks = Lists.newArrayList(
+ TableScanUtil.planTasks(splitTasks, splitSize, splitLookback,
splitOpenFileCost));
+ InputPartition[] readTasks = new InputPartition[combinedScanTasks.size()];
+
+ for (int i = 0; i < combinedScanTasks.size(); i++) {
+ readTasks[i] = new ReadTask(
+ combinedScanTasks.get(i), tableBroadcast, expectedSchema,
+ caseSensitive, localityPreferred);
+ }
+
+ return readTasks;
+ }
+
+ @Override
+ public PartitionReaderFactory createReaderFactory() {
+ return new ReaderFactory(0);
+ }
+
+ @Override
+ public Offset initialOffset() {
+ return initialOffset;
+ }
+
+ @Override
+ public Offset deserializeOffset(String json) {
+ return StreamingOffset.fromJson(json);
+ }
+
+ @Override
+ public void commit(Offset end) {
+ }
+
+ @Override
+ public void stop() {
+ }
+
+ private List<FileScanTask> planFiles(StreamingOffset startOffset,
StreamingOffset endOffset) {
+ List<FileScanTask> fileScanTasks = Lists.newArrayList();
+ MicroBatch latestMicroBatch = null;
+ StreamingOffset batchStartOffset =
StreamingOffset.START_OFFSET.equals(startOffset) ?
+ new StreamingOffset(SnapshotUtil.oldestSnapshot(table).snapshotId(),
0, false) :
+ startOffset;
+
+ do {
+ StreamingOffset currentOffset =
+ latestMicroBatch != null && latestMicroBatch.lastIndexOfSnapshot() ?
+ new
StreamingOffset(snapshotAfter(latestMicroBatch.snapshotId()), 0L, false) :
+ batchStartOffset;
+
+ latestMicroBatch =
MicroBatches.from(table.snapshot(currentOffset.snapshotId()), table.io())
+ .caseSensitive(caseSensitive)
+ .specsById(table.specs())
+ .generate(currentOffset.position(), Long.MAX_VALUE,
currentOffset.shouldScanAllFiles());
+
+ fileScanTasks.addAll(latestMicroBatch.tasks());
+ } while (latestMicroBatch.snapshotId() != endOffset.snapshotId());
+
+ return fileScanTasks;
+ }
+
+ private long snapshotAfter(long snapshotId) {
+ Snapshot snapshotAfter = SnapshotUtil.snapshotAfter(table, snapshotId);
+
+ Preconditions.checkState(snapshotAfter != null, "Cannot find next
snapshot: as Snapshot %s expired", snapshotId);
Review comment:
Hmm. This is an assertion on the `State` & `SnapshotUtil` has static
(stateless) methods.
I feel `snapshotAfter` should just return a `null` - if it cannot find one
and the specific usecases should decide whether to throw or not. Does this make
sense!?
##########
File path:
spark3/src/main/java/org/apache/iceberg/spark/source/StreamingOffset.java
##########
@@ -62,20 +63,23 @@ static StreamingOffset fromJson(String json) {
try {
JsonNode node = JsonUtil.mapper().readValue(json, JsonNode.class);
- // The version of StreamingOffset. The offset was created with a version
number
- // used to validate when deserializing from json string.
- int version = JsonUtil.getInt(VERSION, node);
- Preconditions.checkArgument(version == CURR_VERSION,
- "Cannot parse offset JSON: offset version %s is not supported",
version);
+ return fromJsonNode(node);
+ } catch (IOException e) {
+ throw new IllegalArgumentException(String.format("Failed to parse
StreamingOffset from JSON string %s", json), e);
Review comment:
fixed it.
##########
File path: core/src/main/java/org/apache/iceberg/util/SnapshotUtil.java
##########
@@ -63,6 +63,19 @@ public static boolean ancestorOf(Table table, long
snapshotId, long ancestorSnap
return ancestorIds(table.currentSnapshot(), table::snapshot);
}
+ /**
+ * Traverses the history of the table's state and finds the oldest Snapshot.
+ * @return null if the table is empty, else the oldest Snapshot.
Review comment:
yeah, that makes it clearer.. fixed it.
##########
File path: core/src/main/java/org/apache/iceberg/util/SnapshotUtil.java
##########
@@ -63,6 +63,19 @@ public static boolean ancestorOf(Table table, long
snapshotId, long ancestorSnap
return ancestorIds(table.currentSnapshot(), table::snapshot);
}
+ /**
+ * Traverses the history of the table's state and finds the oldest Snapshot.
Review comment:
fixed.
##########
File path:
spark3/src/main/java/org/apache/iceberg/spark/source/SparkMicroBatchStream.java
##########
@@ -0,0 +1,252 @@
+/*
+ * 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.BufferedWriter;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.io.OutputStreamWriter;
+import java.io.UncheckedIOException;
+import java.nio.charset.StandardCharsets;
+import java.util.List;
+import org.apache.iceberg.CombinedScanTask;
+import org.apache.iceberg.DataOperations;
+import org.apache.iceberg.FileScanTask;
+import org.apache.iceberg.MicroBatches;
+import org.apache.iceberg.MicroBatches.MicroBatch;
+import org.apache.iceberg.Schema;
+import org.apache.iceberg.SchemaParser;
+import org.apache.iceberg.SerializableTable;
+import org.apache.iceberg.Snapshot;
+import org.apache.iceberg.Table;
+import org.apache.iceberg.io.CloseableIterable;
+import org.apache.iceberg.io.FileIO;
+import org.apache.iceberg.io.InputFile;
+import org.apache.iceberg.io.OutputFile;
+import org.apache.iceberg.relocated.com.google.common.base.Joiner;
+import org.apache.iceberg.relocated.com.google.common.base.Preconditions;
+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.Spark3Util;
+import org.apache.iceberg.spark.SparkReadOptions;
+import org.apache.iceberg.spark.source.SparkBatchScan.ReadTask;
+import org.apache.iceberg.spark.source.SparkBatchScan.ReaderFactory;
+import org.apache.iceberg.util.PropertyUtil;
+import org.apache.iceberg.util.SnapshotUtil;
+import org.apache.iceberg.util.TableScanUtil;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.broadcast.Broadcast;
+import org.apache.spark.sql.connector.read.InputPartition;
+import org.apache.spark.sql.connector.read.PartitionReaderFactory;
+import org.apache.spark.sql.connector.read.streaming.MicroBatchStream;
+import org.apache.spark.sql.connector.read.streaming.Offset;
+import org.apache.spark.sql.util.CaseInsensitiveStringMap;
+
+import static org.apache.iceberg.TableProperties.SPLIT_LOOKBACK;
+import static org.apache.iceberg.TableProperties.SPLIT_LOOKBACK_DEFAULT;
+import static org.apache.iceberg.TableProperties.SPLIT_OPEN_FILE_COST;
+import static org.apache.iceberg.TableProperties.SPLIT_OPEN_FILE_COST_DEFAULT;
+import static org.apache.iceberg.TableProperties.SPLIT_SIZE;
+import static org.apache.iceberg.TableProperties.SPLIT_SIZE_DEFAULT;
+
+public class SparkMicroBatchStream implements MicroBatchStream {
+ private static final Joiner SLASH = Joiner.on("/");
+
+ private final Table table;
+ private final boolean caseSensitive;
+ private final String expectedSchema;
+ private final Broadcast<Table> tableBroadcast;
+ private final Long splitSize;
+ private final Integer splitLookback;
+ private final Long splitOpenFileCost;
+ private final boolean localityPreferred;
+ private final StreamingOffset initialOffset;
+
+ SparkMicroBatchStream(JavaSparkContext sparkContext, Table table, boolean
caseSensitive,
+ Schema expectedSchema, CaseInsensitiveStringMap
options, String checkpointLocation) {
+ this.table = table;
+ this.caseSensitive = caseSensitive;
+ this.expectedSchema = SchemaParser.toJson(expectedSchema);
+ this.localityPreferred = Spark3Util.isLocalityEnabled(table.io(),
table.location(), options);
+ this.tableBroadcast =
sparkContext.broadcast(SerializableTable.copyOf(table));
+
+ long tableSplitSize = PropertyUtil.propertyAsLong(table.properties(),
SPLIT_SIZE, SPLIT_SIZE_DEFAULT);
+ this.splitSize = Spark3Util.propertyAsLong(options,
SparkReadOptions.SPLIT_SIZE, tableSplitSize);
+
+ int tableSplitLookback = PropertyUtil.propertyAsInt(table.properties(),
SPLIT_LOOKBACK, SPLIT_LOOKBACK_DEFAULT);
+ this.splitLookback = Spark3Util.propertyAsInt(options,
SparkReadOptions.LOOKBACK, tableSplitLookback);
+
+ long tableSplitOpenFileCost = PropertyUtil.propertyAsLong(
+ table.properties(), SPLIT_OPEN_FILE_COST,
SPLIT_OPEN_FILE_COST_DEFAULT);
+ this.splitOpenFileCost = Spark3Util.propertyAsLong(options,
SPLIT_OPEN_FILE_COST, tableSplitOpenFileCost);
+
+ InitialOffsetStore initialOffsetStore = new InitialOffsetStore(table,
checkpointLocation);
+ this.initialOffset = initialOffsetStore.initialOffset();
+ }
+
+ @Override
+ public Offset latestOffset() {
+ table.refresh();
+ Snapshot latestSnapshot = table.currentSnapshot();
+ if (latestSnapshot == null) {
+ return StreamingOffset.START_OFFSET;
+ }
+
+ return new StreamingOffset(latestSnapshot.snapshotId(),
Iterables.size(latestSnapshot.addedFiles()), false);
+ }
+
+ @Override
+ public InputPartition[] planInputPartitions(Offset start, Offset end) {
+ Preconditions.checkArgument(end instanceof StreamingOffset, "Invalid end
offset: %s is not a StreamingOffset", end);
+ Preconditions.checkArgument(
+ start instanceof StreamingOffset, "Invalid start offset: %s is not a
StreamingOffset", start);
+
+ if (end.equals(StreamingOffset.START_OFFSET)) {
+ return new InputPartition[0];
+ }
+
+ StreamingOffset endOffset = (StreamingOffset) end;
+ StreamingOffset startOffset = (StreamingOffset) start;
+
+ List<FileScanTask> fileScanTasks = planFiles(startOffset, endOffset);
+
+ CloseableIterable<FileScanTask> splitTasks = TableScanUtil.splitFiles(
+ CloseableIterable.withNoopClose(fileScanTasks),
+ splitSize);
+ List<CombinedScanTask> combinedScanTasks = Lists.newArrayList(
+ TableScanUtil.planTasks(splitTasks, splitSize, splitLookback,
splitOpenFileCost));
+ InputPartition[] readTasks = new InputPartition[combinedScanTasks.size()];
+
+ for (int i = 0; i < combinedScanTasks.size(); i++) {
+ readTasks[i] = new ReadTask(
+ combinedScanTasks.get(i), tableBroadcast, expectedSchema,
+ caseSensitive, localityPreferred);
+ }
+
+ return readTasks;
+ }
+
+ @Override
+ public PartitionReaderFactory createReaderFactory() {
+ return new ReaderFactory(0);
+ }
+
+ @Override
+ public Offset initialOffset() {
+ return initialOffset;
+ }
+
+ @Override
+ public Offset deserializeOffset(String json) {
+ return StreamingOffset.fromJson(json);
+ }
+
+ @Override
+ public void commit(Offset end) {
+ }
+
+ @Override
+ public void stop() {
+ }
+
+ private List<FileScanTask> planFiles(StreamingOffset startOffset,
StreamingOffset endOffset) {
+ List<FileScanTask> fileScanTasks = Lists.newArrayList();
+ MicroBatch latestMicroBatch = null;
+ StreamingOffset batchStartOffset =
StreamingOffset.START_OFFSET.equals(startOffset) ?
+ new StreamingOffset(SnapshotUtil.oldestSnapshot(table).snapshotId(),
0, false) :
+ startOffset;
+
+ do {
+ StreamingOffset currentOffset =
+ latestMicroBatch != null && latestMicroBatch.lastIndexOfSnapshot() ?
+ new
StreamingOffset(snapshotAfter(latestMicroBatch.snapshotId()), 0L, false) :
+ batchStartOffset;
+
+ latestMicroBatch =
MicroBatches.from(table.snapshot(currentOffset.snapshotId()), table.io())
+ .caseSensitive(caseSensitive)
+ .specsById(table.specs())
+ .generate(currentOffset.position(), Long.MAX_VALUE,
currentOffset.shouldScanAllFiles());
+
+ fileScanTasks.addAll(latestMicroBatch.tasks());
+ } while (latestMicroBatch.snapshotId() != endOffset.snapshotId());
+
+ return fileScanTasks;
+ }
+
+ private long snapshotAfter(long snapshotId) {
+ Snapshot snapshotAfter = SnapshotUtil.snapshotAfter(table, snapshotId);
+
+ Preconditions.checkState(snapshotAfter != null, "Cannot find next
snapshot: as Snapshot %s expired", snapshotId);
Review comment:
moved.
##########
File path: core/src/main/java/org/apache/iceberg/util/SnapshotUtil.java
##########
@@ -107,4 +120,19 @@ public static boolean ancestorOf(Table table, long
snapshotId, long ancestorSnap
return newFiles;
}
+
+ /**
+ * traverses through the table's snapshots and finds the {@link Table}'s
+ * Snapshot that is committed after the specified snapshotId
+ * @return null if passed in snapshot expired, else the snapshot after the
passed in snapshot
Review comment:
fixed this.
##########
File path:
spark3/src/test/java/org/apache/iceberg/spark/source/TestStructuredStreamingRead3.java
##########
@@ -0,0 +1,502 @@
+/*
+ * 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.io.IOException;
+import java.util.Collections;
+import java.util.List;
+import java.util.concurrent.TimeoutException;
+import java.util.stream.Collectors;
+import java.util.stream.Stream;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.iceberg.BaseTable;
+import org.apache.iceberg.DataOperations;
+import org.apache.iceberg.DeleteFile;
+import org.apache.iceberg.Files;
+import org.apache.iceberg.PartitionSpec;
+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.hadoop.HadoopTables;
+import org.apache.iceberg.relocated.com.google.common.collect.Lists;
+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.junit.AfterClass;
+import org.junit.Assert;
+import org.junit.BeforeClass;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.ExpectedException;
+import org.junit.rules.TemporaryFolder;
+
+import static org.apache.iceberg.types.Types.NestedField.optional;
+
+public final class TestStructuredStreamingRead3 {
+ 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())
+ );
+ 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();
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testReadStreamOnIcebergTableWithMultipleSnapshots() throws
IOException, TimeoutException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+
+ HadoopTables tables = new HadoopTables(CONF);
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).bucket("id",
3).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+
+ List<List<SimpleRecord>> expected = getTestDataForMultipleSnapshots();
+ appendData(expected, location);
+
+ table.refresh();
+
+ try {
+ Dataset<Row> df = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+ List<SimpleRecord> actual = processStreamTillEnd(df);
+
+ Assert.assertEquals(
+ expected.stream().flatMap(List::stream).collect(Collectors.toList()),
+ actual);
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testResumingStreamReadFromCheckpoint() throws IOException,
TimeoutException, StreamingQueryException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+ File writerCheckpoint = new File(parent, "writer-checkpoint");
+
+ HadoopTables tables = new HadoopTables(CONF);
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).bucket("id",
3).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+ final String tempView = "microBatchView";
+
+ try {
+ Dataset<Row> df = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+ 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 :
getTestDataForMultipleWritesWithMultipleSnapshots()) {
+ appendData(expectedCheckpoint, location);
+ table.refresh();
+
+ List<SimpleRecord> actualDataInCurrentMicroBatch =
processMicroBatch(singleBatchWriter, globalTempView);
+ Assert.assertEquals(
+
expectedCheckpoint.stream().flatMap(List::stream).collect(Collectors.toList()),
+ actualDataInCurrentMicroBatch);
+ }
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testParquetOrcAvroDataInOneTable() throws IOException,
TimeoutException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+
+ HadoopTables tables = new HadoopTables(CONF);
+
+ // use partition buckets - so that single list<SimpleRecord> can generate
multiple files per write
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).bucket("id",
3).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+
+ 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, location, "parquet");
+ appendData(orcFileRecords, location, "orc");
+ appendData(avroFileRecords, location, "avro");
+
+ table.refresh();
+
+
+ try {
+ Dataset<Row> ds = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+ List<SimpleRecord> actual = processStreamTillEnd(ds);
+ List<SimpleRecord> expected = Stream.concat(Stream.concat(
+ parquetFileRecords.stream(),
+ orcFileRecords.stream()),
+ avroFileRecords.stream())
+ .collect(Collectors.toList());
+ Assert.assertEquals(expected, actual);
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testReadStreamFromEmptyTable() throws IOException,
TimeoutException {
Review comment:
@rdblue - the specific case you asked is already covered as part of
`testResumingStreamReadFromCheckpoint`..
##########
File path:
spark3/src/test/java/org/apache/iceberg/spark/source/TestStructuredStreamingRead3.java
##########
@@ -0,0 +1,502 @@
+/*
+ * 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.io.IOException;
+import java.util.Collections;
+import java.util.List;
+import java.util.concurrent.TimeoutException;
+import java.util.stream.Collectors;
+import java.util.stream.Stream;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.iceberg.BaseTable;
+import org.apache.iceberg.DataOperations;
+import org.apache.iceberg.DeleteFile;
+import org.apache.iceberg.Files;
+import org.apache.iceberg.PartitionSpec;
+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.hadoop.HadoopTables;
+import org.apache.iceberg.relocated.com.google.common.collect.Lists;
+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.junit.AfterClass;
+import org.junit.Assert;
+import org.junit.BeforeClass;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.ExpectedException;
+import org.junit.rules.TemporaryFolder;
+
+import static org.apache.iceberg.types.Types.NestedField.optional;
+
+public final class TestStructuredStreamingRead3 {
+ 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())
+ );
+ 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();
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testReadStreamOnIcebergTableWithMultipleSnapshots() throws
IOException, TimeoutException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+
+ HadoopTables tables = new HadoopTables(CONF);
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).bucket("id",
3).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+
+ List<List<SimpleRecord>> expected = getTestDataForMultipleSnapshots();
+ appendData(expected, location);
+
+ table.refresh();
+
+ try {
+ Dataset<Row> df = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+ List<SimpleRecord> actual = processStreamTillEnd(df);
+
+ Assert.assertEquals(
+ expected.stream().flatMap(List::stream).collect(Collectors.toList()),
+ actual);
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testResumingStreamReadFromCheckpoint() throws IOException,
TimeoutException, StreamingQueryException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+ File writerCheckpoint = new File(parent, "writer-checkpoint");
+
+ HadoopTables tables = new HadoopTables(CONF);
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).bucket("id",
3).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+ final String tempView = "microBatchView";
+
+ try {
+ Dataset<Row> df = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+ 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 :
getTestDataForMultipleWritesWithMultipleSnapshots()) {
+ appendData(expectedCheckpoint, location);
+ table.refresh();
+
+ List<SimpleRecord> actualDataInCurrentMicroBatch =
processMicroBatch(singleBatchWriter, globalTempView);
+ Assert.assertEquals(
+
expectedCheckpoint.stream().flatMap(List::stream).collect(Collectors.toList()),
+ actualDataInCurrentMicroBatch);
+ }
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testParquetOrcAvroDataInOneTable() throws IOException,
TimeoutException {
Review comment:
a single table - with multiple file formats is a very critical scenario
- for our usecase & hence the coverage.
I found an issue with my initial implementation with this test case.
##########
File path: core/src/main/java/org/apache/iceberg/util/SnapshotUtil.java
##########
@@ -107,4 +121,26 @@ public static boolean ancestorOf(Table table, long
snapshotId, long ancestorSnap
return newFiles;
}
+
+ /**
+ * Traverses the history of the table's current snapshot and finds the
snapshot with the given snapshot id as its
+ * parent.
+ * @return null if the passed in snapshot is not present in the table, else
the snapshot for which the given snapshot
+ * is the parent
+ * @throws IllegalArgumentException when the given SnapshotId is not found
in the table
Review comment:
bad habit :). fixed.
##########
File path:
spark3/src/test/java/org/apache/iceberg/spark/source/TestStructuredStreamingRead3.java
##########
@@ -0,0 +1,502 @@
+/*
+ * 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.io.IOException;
+import java.util.Collections;
+import java.util.List;
+import java.util.concurrent.TimeoutException;
+import java.util.stream.Collectors;
+import java.util.stream.Stream;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.iceberg.BaseTable;
+import org.apache.iceberg.DataOperations;
+import org.apache.iceberg.DeleteFile;
+import org.apache.iceberg.Files;
+import org.apache.iceberg.PartitionSpec;
+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.hadoop.HadoopTables;
+import org.apache.iceberg.relocated.com.google.common.collect.Lists;
+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.junit.AfterClass;
+import org.junit.Assert;
+import org.junit.BeforeClass;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.ExpectedException;
+import org.junit.rules.TemporaryFolder;
+
+import static org.apache.iceberg.types.Types.NestedField.optional;
+
+public final class TestStructuredStreamingRead3 {
+ 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())
+ );
+ 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();
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testReadStreamOnIcebergTableWithMultipleSnapshots() throws
IOException, TimeoutException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+
+ HadoopTables tables = new HadoopTables(CONF);
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).bucket("id",
3).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+
+ List<List<SimpleRecord>> expected = getTestDataForMultipleSnapshots();
+ appendData(expected, location);
+
+ table.refresh();
+
+ try {
+ Dataset<Row> df = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+ List<SimpleRecord> actual = processStreamTillEnd(df);
+
+ Assert.assertEquals(
+ expected.stream().flatMap(List::stream).collect(Collectors.toList()),
+ actual);
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testResumingStreamReadFromCheckpoint() throws IOException,
TimeoutException, StreamingQueryException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+ File writerCheckpoint = new File(parent, "writer-checkpoint");
+
+ HadoopTables tables = new HadoopTables(CONF);
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).bucket("id",
3).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+ final String tempView = "microBatchView";
+
+ try {
+ Dataset<Row> df = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+ 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 :
getTestDataForMultipleWritesWithMultipleSnapshots()) {
+ appendData(expectedCheckpoint, location);
+ table.refresh();
+
+ List<SimpleRecord> actualDataInCurrentMicroBatch =
processMicroBatch(singleBatchWriter, globalTempView);
+ Assert.assertEquals(
+
expectedCheckpoint.stream().flatMap(List::stream).collect(Collectors.toList()),
+ actualDataInCurrentMicroBatch);
+ }
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testParquetOrcAvroDataInOneTable() throws IOException,
TimeoutException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+
+ HadoopTables tables = new HadoopTables(CONF);
+
+ // use partition buckets - so that single list<SimpleRecord> can generate
multiple files per write
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).bucket("id",
3).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+
+ 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, location, "parquet");
+ appendData(orcFileRecords, location, "orc");
+ appendData(avroFileRecords, location, "avro");
+
+ table.refresh();
+
+
+ try {
+ Dataset<Row> ds = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+ List<SimpleRecord> actual = processStreamTillEnd(ds);
+ List<SimpleRecord> expected = Stream.concat(Stream.concat(
+ parquetFileRecords.stream(),
+ orcFileRecords.stream()),
+ avroFileRecords.stream())
+ .collect(Collectors.toList());
+ Assert.assertEquals(expected, actual);
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testReadStreamFromEmptyTable() throws IOException,
TimeoutException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+
+ HadoopTables tables = new HadoopTables(CONF);
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).bucket("id",
3).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+
+ table.refresh();
+
+ try {
+ Dataset<Row> df = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+
+ List<SimpleRecord> actual = processStreamTillEnd(df);
+ Assert.assertEquals(Collections.emptyList(), actual);
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testReadStreamWithSnapshotTypeOverwriteErrorsOut() throws
IOException, TimeoutException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+
+ HadoopTables tables = new HadoopTables(CONF);
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).bucket("id",
3).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+
+ // upgrade table to verison 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 data
+ List<List<SimpleRecord>> dataAcrossSnapshots =
getTestDataForMultipleSnapshots();
+ appendData(dataAcrossSnapshots, location);
+
+ table.refresh();
+
+ 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());
+
+ try {
+ Dataset<Row> df = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+ StreamingQuery streamingQuery = df.writeStream()
+ .format("memory")
+ .queryName("testtablewithoverwrites")
+ .outputMode(OutputMode.Append())
+ .start();
+
+ try {
+ streamingQuery.processAllAvailable();
+ Assert.assertTrue(false); // should be unreachable
+ } catch (Exception exception) {
+ Assert.assertTrue(exception instanceof StreamingQueryException);
+ Assert.assertTrue(((StreamingQueryException) exception).cause()
instanceof IllegalStateException);
+ }
+
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testReadStreamWithSnapshotTypeReplaceErrorsOut() throws
IOException, TimeoutException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+
+ HadoopTables tables = new HadoopTables(CONF);
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+
+ // fill table with some data
+ List<List<SimpleRecord>> dataAcrossSnapshots =
getTestDataForMultipleSnapshots();
+ appendData(dataAcrossSnapshots, location);
+
+ table.refresh();
+
+ // this should create a snapshot with type Replace.
+ table.rewriteManifests()
+ .clusterBy(f -> 1)
+ .commit();
+
+ // check pre-condition
+ Assert.assertEquals(DataOperations.REPLACE,
table.currentSnapshot().operation());
+
+ try {
+ Dataset<Row> df = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+ StreamingQuery streamingQuery = df.writeStream()
+ .format("memory")
+ .queryName("testtablewithreplace")
+ .outputMode(OutputMode.Append())
+ .start();
+
+ try {
+ streamingQuery.processAllAvailable();
+ Assert.assertTrue(false); // should be unreachable
+ } catch (Exception exception) {
+ Assert.assertTrue(exception instanceof StreamingQueryException);
+ Assert.assertTrue(((StreamingQueryException) exception).cause()
instanceof IllegalStateException);
+ }
+
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testReadStreamWithSnapshotTypeDeleteErrorsOut() throws
IOException, TimeoutException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+
+ HadoopTables tables = new HadoopTables(CONF);
+ PartitionSpec spec =
PartitionSpec.builderFor(SCHEMA).identity("id").build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+
+ // fill table with some data
+ List<List<SimpleRecord>> dataAcrossSnapshots =
getTestDataForMultipleSnapshots();
+ appendData(dataAcrossSnapshots, location);
+
+ table.refresh();
+
+ // this should create a snapshot with type delete.
+ table.newDelete()
+ .deleteFromRowFilter(Expressions.equal("id", 4))
+ .commit();
+
+ // check pre-condition - that the above newDelete operation on table
resulted in Snapshot of Type DELETE.
+ Assert.assertEquals(DataOperations.DELETE,
table.currentSnapshot().operation());
+
+ try {
+ Dataset<Row> df = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+ StreamingQuery streamingQuery = df.writeStream()
+ .format("memory")
+ .queryName("testtablewithdelete")
+ .outputMode(OutputMode.Append())
+ .start();
+
+ try {
+ streamingQuery.processAllAvailable();
+ Assert.assertTrue(false); // should be unreachable
+ } catch (Exception exception) {
+ Assert.assertTrue(exception instanceof StreamingQueryException);
+ Assert.assertTrue(((StreamingQueryException) exception).cause()
instanceof IllegalStateException);
+ }
+
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ private static List<SimpleRecord> processMicroBatch(DataStreamWriter<Row>
singleBatchWriter, String viewName)
+ throws TimeoutException, StreamingQueryException {
+ StreamingQuery streamingQuery = singleBatchWriter.start();
+ streamingQuery.awaitTermination();
+
+ return spark.sql(String.format("select * from %s", viewName))
+ .as(Encoders.bean(SimpleRecord.class))
+ .collectAsList();
+ }
+
+ /**
+ * get test data - a list of records per snapshot
+ */
+ private static List<List<SimpleRecord>> getTestDataForMultipleSnapshots() {
+ return 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"))
+ );
+ }
+
+ /**
+ * appends each list as a Snapshot on the iceberg table at the given
location.
+ * accepts a list of lists - each list representing data per snapshot.
+ */
+ private static void appendData(List<List<SimpleRecord>> data, File location)
{
+ // generate multiple snapshots
+ for (List<SimpleRecord> l : data) {
+ appendData(l, location, "parquet");
+ }
+ }
+
+ private static void appendData(List<SimpleRecord> data, File location,
String fileFormat) {
+ Dataset<Row> df = spark.createDataFrame(data, SimpleRecord.class);
+ df.select("id", "data").write()
+ .format("iceberg")
+ .option("write-format", fileFormat)
+ .mode("append")
+ .save(location.toString());
+ }
+
+ private static List<SimpleRecord> processStreamTillEnd(Dataset<Row> df)
throws TimeoutException {
+ StreamingQuery streamingQuery = df.writeStream()
+ .format("memory")
+ .queryName("test12")
+ .outputMode(OutputMode.Append())
+ .start();
+ streamingQuery.processAllAvailable();
+ return spark.sql("select * from test12")
+ .as(Encoders.bean(SimpleRecord.class))
+ .collectAsList();
+ }
+
+ /**
+ * gets test data - to be used for multiple write batches
+ * each batch inturn will have multiple snapshots
+ */
+ private static List<List<List<SimpleRecord>>>
getTestDataForMultipleWritesWithMultipleSnapshots() {
Review comment:
changed to `static final`.
i like to keep them as methods in general - so that if we need to generate
data - we can do so. here, i don't feel the need.
##########
File path:
spark3/src/test/java/org/apache/iceberg/spark/source/TestStructuredStreamingRead3.java
##########
@@ -0,0 +1,502 @@
+/*
+ * 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.io.IOException;
+import java.util.Collections;
+import java.util.List;
+import java.util.concurrent.TimeoutException;
+import java.util.stream.Collectors;
+import java.util.stream.Stream;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.iceberg.BaseTable;
+import org.apache.iceberg.DataOperations;
+import org.apache.iceberg.DeleteFile;
+import org.apache.iceberg.Files;
+import org.apache.iceberg.PartitionSpec;
+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.hadoop.HadoopTables;
+import org.apache.iceberg.relocated.com.google.common.collect.Lists;
+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.junit.AfterClass;
+import org.junit.Assert;
+import org.junit.BeforeClass;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.ExpectedException;
+import org.junit.rules.TemporaryFolder;
+
+import static org.apache.iceberg.types.Types.NestedField.optional;
+
+public final class TestStructuredStreamingRead3 {
+ private static final Configuration CONF = new Configuration();
+ private static final Schema SCHEMA = new Schema(
+ optional(1, "id", Types.IntegerType.get()),
Review comment:
fixed.
##########
File path:
spark3/src/test/java/org/apache/iceberg/spark/source/TestStructuredStreamingRead3.java
##########
@@ -0,0 +1,502 @@
+/*
+ * 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.io.IOException;
+import java.util.Collections;
+import java.util.List;
+import java.util.concurrent.TimeoutException;
+import java.util.stream.Collectors;
+import java.util.stream.Stream;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.iceberg.BaseTable;
+import org.apache.iceberg.DataOperations;
+import org.apache.iceberg.DeleteFile;
+import org.apache.iceberg.Files;
+import org.apache.iceberg.PartitionSpec;
+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.hadoop.HadoopTables;
+import org.apache.iceberg.relocated.com.google.common.collect.Lists;
+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.junit.AfterClass;
+import org.junit.Assert;
+import org.junit.BeforeClass;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.ExpectedException;
+import org.junit.rules.TemporaryFolder;
+
+import static org.apache.iceberg.types.Types.NestedField.optional;
+
+public final class TestStructuredStreamingRead3 {
+ 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())
+ );
+ 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();
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testReadStreamOnIcebergTableWithMultipleSnapshots() throws
IOException, TimeoutException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+
+ HadoopTables tables = new HadoopTables(CONF);
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).bucket("id",
3).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+
+ List<List<SimpleRecord>> expected = getTestDataForMultipleSnapshots();
+ appendData(expected, location);
+
+ table.refresh();
+
+ try {
+ Dataset<Row> df = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+ List<SimpleRecord> actual = processStreamTillEnd(df);
+
+ Assert.assertEquals(
+ expected.stream().flatMap(List::stream).collect(Collectors.toList()),
+ actual);
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testResumingStreamReadFromCheckpoint() throws IOException,
TimeoutException, StreamingQueryException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+ File writerCheckpoint = new File(parent, "writer-checkpoint");
+
+ HadoopTables tables = new HadoopTables(CONF);
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).bucket("id",
3).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+ final String tempView = "microBatchView";
+
+ try {
+ Dataset<Row> df = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+ 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 :
getTestDataForMultipleWritesWithMultipleSnapshots()) {
+ appendData(expectedCheckpoint, location);
+ table.refresh();
+
+ List<SimpleRecord> actualDataInCurrentMicroBatch =
processMicroBatch(singleBatchWriter, globalTempView);
+ Assert.assertEquals(
+
expectedCheckpoint.stream().flatMap(List::stream).collect(Collectors.toList()),
+ actualDataInCurrentMicroBatch);
+ }
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testParquetOrcAvroDataInOneTable() throws IOException,
TimeoutException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+
+ HadoopTables tables = new HadoopTables(CONF);
+
+ // use partition buckets - so that single list<SimpleRecord> can generate
multiple files per write
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).bucket("id",
3).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+
+ 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, location, "parquet");
+ appendData(orcFileRecords, location, "orc");
+ appendData(avroFileRecords, location, "avro");
+
+ table.refresh();
+
+
+ try {
+ Dataset<Row> ds = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+ List<SimpleRecord> actual = processStreamTillEnd(ds);
+ List<SimpleRecord> expected = Stream.concat(Stream.concat(
+ parquetFileRecords.stream(),
+ orcFileRecords.stream()),
+ avroFileRecords.stream())
+ .collect(Collectors.toList());
+ Assert.assertEquals(expected, actual);
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testReadStreamFromEmptyTable() throws IOException,
TimeoutException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+
+ HadoopTables tables = new HadoopTables(CONF);
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).bucket("id",
3).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+
+ table.refresh();
+
+ try {
+ Dataset<Row> df = spark.readStream()
+ .format("iceberg")
+ .load(location.toString());
+
+ List<SimpleRecord> actual = processStreamTillEnd(df);
+ Assert.assertEquals(Collections.emptyList(), actual);
+ } finally {
+ for (StreamingQuery query : spark.streams().active()) {
+ query.stop();
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Test
+ public void testReadStreamWithSnapshotTypeOverwriteErrorsOut() throws
IOException, TimeoutException {
+ File parent = temp.newFolder("parent");
+ File location = new File(parent, "test-table");
+
+ HadoopTables tables = new HadoopTables(CONF);
+ PartitionSpec spec = PartitionSpec.builderFor(SCHEMA).bucket("id",
3).build();
+ Table table = tables.create(SCHEMA, spec, location.toString());
+
+ // upgrade table to verison 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 data
+ List<List<SimpleRecord>> dataAcrossSnapshots =
getTestDataForMultipleSnapshots();
+ appendData(dataAcrossSnapshots, location);
+
+ table.refresh();
+
+ Schema deleteRowSchema = table.schema().select("data");
+ Record dataDelete = GenericRecord.create(deleteRowSchema);
+ List<Record> dataDeletes = Lists.newArrayList(
Review comment:
@rdblue - i wanted to do that.
do you know how can I run Sql - when I am operating on FileLocation based
tables?
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