@Test
public void testTargetView() throws IOException {
PartitionStrategy partitionStrategy =
new PartitionStrategy.Builder().hash("username", 2).build();
Dataset<Record> inputDataset =
repo.create(
"ns",
"in",
new DatasetDescriptor.Builder()
.schema(USER_SCHEMA)
.partitionStrategy(partitionStrategy)
.build());
Dataset<Record> outputDataset =
repo.create(
"ns",
"out",
new DatasetDescriptor.Builder()
.schema(USER_SCHEMA)
.partitionStrategy(partitionStrategy)
.build());
writeTestUsers(inputDataset, 10);
View<Record> inputView = inputDataset.with("username", "test-0");
Assert.assertEquals(1, datasetSize(inputView));
View<Record> outputView = outputDataset.with("username", "test-0");
Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
PCollection<GenericData.Record> data = pipeline.read(CrunchDatasets.asSource(inputView));
pipeline.write(data, CrunchDatasets.asTarget(outputView), Target.WriteMode.APPEND);
pipeline.run();
Assert.assertEquals(1, datasetSize(outputDataset));
}
@Test
public void testSignalReadyOutputView() {
Assume.assumeTrue(!Hadoop.isHadoop1());
Dataset<Record> inputDataset =
repo.create("ns", "in", new DatasetDescriptor.Builder().schema(USER_SCHEMA).build());
Dataset<Record> outputDataset =
repo.create("ns", "out", new DatasetDescriptor.Builder().schema(USER_SCHEMA).build());
writeTestUsers(inputDataset, 10);
View<Record> inputView = inputDataset.with("username", "test-8", "test-9");
View<Record> outputView = outputDataset.with("username", "test-8", "test-9");
Assert.assertEquals(2, datasetSize(inputView));
Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
PCollection<GenericData.Record> data = pipeline.read(CrunchDatasets.asSource(inputView));
pipeline.write(data, CrunchDatasets.asTarget(outputView), Target.WriteMode.APPEND);
pipeline.run();
Assert.assertEquals(2, datasetSize(outputView));
Assert.assertFalse(
"Output dataset should not be signaled ready", ((Signalable) outputDataset).isReady());
Assert.assertTrue("Output view should be signaled ready", ((Signalable) outputView).isReady());
}
@Test
public void testPartitionedSource() throws IOException {
PartitionStrategy partitionStrategy =
new PartitionStrategy.Builder().hash("username", 2).build();
Dataset<Record> inputDataset =
repo.create(
"ns",
"in",
new DatasetDescriptor.Builder()
.schema(USER_SCHEMA)
.partitionStrategy(partitionStrategy)
.build());
Dataset<Record> outputDataset =
repo.create(
"ns",
"out",
new DatasetDescriptor.Builder().schema(USER_SCHEMA).format(Formats.PARQUET).build());
writeTestUsers(inputDataset, 10);
PartitionKey key = new PartitionKey(0);
Dataset<Record> inputPart0 =
((PartitionedDataset<Record>) inputDataset).getPartition(key, false);
Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
PCollection<GenericData.Record> data = pipeline.read(CrunchDatasets.asSource(inputPart0));
pipeline.write(data, CrunchDatasets.asTarget(outputDataset), Target.WriteMode.APPEND);
pipeline.run();
Assert.assertEquals(5, datasetSize(outputDataset));
}
@Test
public void testUseReaderSchema() throws IOException {
// Create a schema with only a username, so we can test reading it
// with an enhanced record structure.
Schema oldRecordSchema =
SchemaBuilder.record("org.kitesdk.data.user.OldUserRecord")
.fields()
.requiredString("username")
.endRecord();
// create the dataset
Dataset<Record> in =
repo.create("ns", "in", new DatasetDescriptor.Builder().schema(oldRecordSchema).build());
Dataset<Record> out =
repo.create("ns", "out", new DatasetDescriptor.Builder().schema(oldRecordSchema).build());
Record oldUser = new Record(oldRecordSchema);
oldUser.put("username", "user");
DatasetWriter<Record> writer = in.newWriter();
try {
writer.write(oldUser);
} finally {
writer.close();
}
Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
// read data from updated dataset that has the new schema.
// At this point, User class has the old schema
PCollection<NewUserRecord> data =
pipeline.read(CrunchDatasets.asSource(in.getUri(), NewUserRecord.class));
PCollection<NewUserRecord> processed =
data.parallelDo(new UserRecordIdentityFn(), Avros.records(NewUserRecord.class));
pipeline.write(processed, CrunchDatasets.asTarget(out));
DatasetReader reader = out.newReader();
Assert.assertTrue("Pipeline failed.", pipeline.run().succeeded());
try {
// there should be one record that is equal to our old user generic record.
Assert.assertEquals(oldUser, reader.next());
Assert.assertFalse(reader.hasNext());
} finally {
reader.close();
}
}
@Test
public void testWriteModeCheckpointToNotReadyOutput() throws Exception {
// identity partition so we can overwrite the output
PartitionStrategy partitionStrategy =
new PartitionStrategy.Builder().identity("username").build();
Dataset<Record> inputDataset =
repo.create(
"ns",
"in",
new DatasetDescriptor.Builder()
.schema(USER_SCHEMA)
.partitionStrategy(partitionStrategy)
.build());
Dataset<Record> outputDataset =
repo.create(
"ns",
"out",
new DatasetDescriptor.Builder()
.schema(USER_SCHEMA)
.partitionStrategy(partitionStrategy)
.build());
writeTestUsers(inputDataset, 1, 0);
// ensure output is newer than input on local filesystems with 1s granularity
Thread.sleep(1000);
runCheckpointPipeline(inputDataset, outputDataset);
checkTestUsers(outputDataset, 1);
// under hadoop1 the issues with LocalJobRunner (MAPREDUCE-2350) require that we
// manually ready the output dataset
if (Hadoop.isHadoop1()) {
((Signalable) outputDataset).signalReady();
} else {
// under hadoop2 the output will have been marked ready
Assert.assertTrue(
"output dataset should be ready after mapreduce", ((Signalable) outputDataset).isReady());
}
long lastModified = ((LastModifiedAccessor) outputDataset).getLastModified();
// ensure output is newer than input on local filesystems with 1s granularity
Thread.sleep(1000);
// now output to a view, this ensures that the view isn't ready
View<Record> outputView = outputDataset.with("username", "test-0");
// re-run without changing input and output should change since the view is not ready
runCheckpointPipeline(inputDataset, outputView);
checkTestUsers(outputDataset, 1);
Assert.assertTrue(((LastModifiedAccessor) outputView).getLastModified() > lastModified);
}
@Test(expected = CrunchRuntimeException.class)
public void testWriteModeDefaultFailsWithExisting() throws IOException {
Dataset<Record> inputDataset =
repo.create("ns", "in", new DatasetDescriptor.Builder().schema(USER_SCHEMA).build());
Dataset<Record> outputDataset =
repo.create("ns", "out", new DatasetDescriptor.Builder().schema(USER_SCHEMA).build());
writeTestUsers(inputDataset, 1, 0);
writeTestUsers(outputDataset, 1, 0);
Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
PCollection<GenericData.Record> data = pipeline.read(CrunchDatasets.asSource(inputDataset));
pipeline.write(data, CrunchDatasets.asTarget((View<Record>) outputDataset));
}
/**
* Create a {@link Dataset} for the given dataset or view URI. {@code create} returns an empty
* dataset. You can use {@code DatasetWriter} to populate your dataset.
*
* <p>URIs must begin with {@code dataset:} or {@code view:}. The remainder of the URI is
* implementation specific, depending on the dataset scheme. If the URI is a view URI, this method
* creates the underlying dataset and returns a view of it.
*
* @param uri a {@code Dataset} or {@code View} URI
* @param type a Java class that represents an entity in the dataset
* @param <E> the type used for readers and writers created by this {@code Dataset}
* @param <V> the type of {@code Dataset} or {@code View} expected
* @return a newly created {@code Dataset} responsible for the given URI
* @throws NullPointerException if {@code uri}, {@code descriptor}, or {@code type} is {@code
* null}
* @throws IllegalArgumentException if {@code uri} is not a dataset or view URI
* @throws DatasetExistsException if a {@code Dataset} for the given URI already exists
* @throws IncompatibleSchemaException if the schema is not compatible with existing datasets with
* shared storage (for example, in the same HBase table)
*/
@SuppressWarnings("unchecked")
public static <E, V extends View<E>> V create(
URI uri, DatasetDescriptor descriptor, Class<E> type) {
boolean isView = URIBuilder.VIEW_SCHEME.equals(uri.getScheme());
Preconditions.checkArgument(
isView || URIBuilder.DATASET_SCHEME.equals(uri.getScheme()),
"Not a dataset or view URI: " + uri);
Preconditions.checkNotNull(
type,
"The entity type can't be null, use Object.class to have the type"
+ " determined by the schema.");
Pair<DatasetRepository, Map<String, String>> pair =
Registration.lookupDatasetUri(URI.create(uri.getRawSchemeSpecificPart()));
DatasetRepository repo = pair.first();
Map<String, String> uriOptions = pair.second();
if (descriptor.getLocation() == null && uriOptions.containsKey("location")) {
descriptor =
new DatasetDescriptor.Builder(descriptor).location(uriOptions.get("location")).build();
}
Dataset<E> dataset =
repo.create(
uriOptions.get(URIBuilder.NAMESPACE_OPTION),
uriOptions.get(URIBuilder.DATASET_NAME_OPTION),
descriptor,
type);
if (isView) {
return Datasets.<E, V>view(dataset, uriOptions);
} else {
return (V) dataset;
}
}
@Test
public void testGeneric() throws IOException {
Dataset<Record> inputDataset =
repo.create("ns", "in", new DatasetDescriptor.Builder().schema(USER_SCHEMA).build());
Dataset<Record> outputDataset =
repo.create("ns", "out", new DatasetDescriptor.Builder().schema(USER_SCHEMA).build());
// write two files, each of 5 records
writeTestUsers(inputDataset, 5, 0);
writeTestUsers(inputDataset, 5, 5);
Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
PCollection<GenericData.Record> data = pipeline.read(CrunchDatasets.asSource(inputDataset));
pipeline.write(data, CrunchDatasets.asTarget(outputDataset), Target.WriteMode.APPEND);
pipeline.run();
checkTestUsers(outputDataset, 10);
}
@Test
public void testWriteModeOverwrite() throws IOException {
Dataset<Record> inputDataset =
repo.create("ns", "in", new DatasetDescriptor.Builder().schema(USER_SCHEMA).build());
Dataset<Record> outputDataset =
repo.create("ns", "out", new DatasetDescriptor.Builder().schema(USER_SCHEMA).build());
writeTestUsers(inputDataset, 1, 0);
writeTestUsers(outputDataset, 1, 1);
Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
PCollection<GenericData.Record> data = pipeline.read(CrunchDatasets.asSource(inputDataset));
pipeline.write(
data, CrunchDatasets.asTarget((View<Record>) outputDataset), Target.WriteMode.OVERWRITE);
pipeline.run();
checkTestUsers(outputDataset, 1);
}
@Test
public void testWriteModeCheckpoint() throws Exception {
Dataset<Record> inputDataset =
repo.create("ns", "in", new DatasetDescriptor.Builder().schema(USER_SCHEMA).build());
Dataset<Record> outputDataset =
repo.create("ns", "out", new DatasetDescriptor.Builder().schema(USER_SCHEMA).build());
writeTestUsers(inputDataset, 1, 0);
Thread.sleep(
1000); // ensure output is newer than input on local filesystems with 1s granularity
runCheckpointPipeline(inputDataset, outputDataset);
// under hadoop1 the issues with LocalJobRunner (MAPREDUCE-2350) require that we
// manually ready the output dataset
if (Hadoop.isHadoop1()) {
((Signalable) outputDataset).signalReady();
}
checkTestUsers(outputDataset, 1);
long lastModified = ((LastModifiedAccessor) outputDataset).getLastModified();
// re-run without changing input and output should not change
runCheckpointPipeline(inputDataset, outputDataset);
checkTestUsers(outputDataset, 1);
Assert.assertEquals(lastModified, ((LastModifiedAccessor) outputDataset).getLastModified());
// re-write input then re-run and output should be re-written
Thread.sleep(1000); // ensure new input is newer than output
repo.delete("ns", "in");
inputDataset =
repo.create("ns", "in", new DatasetDescriptor.Builder().schema(USER_SCHEMA).build());
writeTestUsers(inputDataset, 1, 0);
runCheckpointPipeline(inputDataset, outputDataset);
checkTestUsers(outputDataset, 1);
Assert.assertTrue(((LastModifiedAccessor) outputDataset).getLastModified() > lastModified);
}