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[HUDI-340]: made max events to read from kafka source configurable (#1039)

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This commit is contained in:
Pratyaksh Sharma
2019-11-26 16:04:02 +05:30
committed by leesf
parent a7e07cd910
commit 2a4cfb47c7
2 changed files with 96 additions and 10 deletions
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93
hudi-utilities/src/test/java/org/apache/hudi/utilities/sources/TestKafkaSource.java
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@@ -32,6 +32,7 @@ import org.apache.hudi.utilities.UtilitiesTestBase;
import org.apache.hudi.utilities.deltastreamer.SourceFormatAdapter;
import org.apache.hudi.utilities.schema.FilebasedSchemaProvider;
import org.apache.hudi.utilities.sources.helpers.KafkaOffsetGen.CheckpointUtils;
import org.apache.hudi.utilities.sources.helpers.KafkaOffsetGen.Config;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
@@ -78,18 +79,26 @@ public class TestKafkaSource extends UtilitiesTestBase {
testUtils.teardown();
}
@Test
public void testJsonKafkaSource() throws IOException {
// topic setup.
testUtils.createTopic(TEST_TOPIC_NAME, 2);
HoodieTestDataGenerator dataGenerator = new HoodieTestDataGenerator();
private TypedProperties createPropsForJsonSource(Long maxEventsToReadFromKafkaSource) {
TypedProperties props = new TypedProperties();
props.setProperty("hoodie.deltastreamer.source.kafka.topic", TEST_TOPIC_NAME);
props.setProperty("metadata.broker.list", testUtils.brokerAddress());
props.setProperty("auto.offset.reset", "smallest");
props.setProperty("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.setProperty("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.setProperty("hoodie.deltastreamer.kafka.source.maxEvents",
maxEventsToReadFromKafkaSource != null ? String.valueOf(maxEventsToReadFromKafkaSource) :
String.valueOf(Config.DEFAULT_MAX_EVENTS_FROM_KAFKA_SOURCE));
return props;
}
@Test
public void testJsonKafkaSource() throws IOException {
// topic setup.
testUtils.createTopic(TEST_TOPIC_NAME, 2);
HoodieTestDataGenerator dataGenerator = new HoodieTestDataGenerator();
TypedProperties props = createPropsForJsonSource(null);
Source jsonSource = new JsonKafkaSource(props, jsc, sparkSession, schemaProvider);
SourceFormatAdapter kafkaSource = new SourceFormatAdapter(jsonSource);
@@ -131,6 +140,78 @@ public class TestKafkaSource extends UtilitiesTestBase {
assertEquals(Option.empty(), fetch4AsRows.getBatch());
}
@Test
public void testJsonKafkaSourceWithDefaultUpperCap() throws IOException {
// topic setup.
testUtils.createTopic(TEST_TOPIC_NAME, 2);
HoodieTestDataGenerator dataGenerator = new HoodieTestDataGenerator();
TypedProperties props = createPropsForJsonSource(Long.MAX_VALUE);
Source jsonSource = new JsonKafkaSource(props, jsc, sparkSession, schemaProvider);
SourceFormatAdapter kafkaSource = new SourceFormatAdapter(jsonSource);
Config.DEFAULT_MAX_EVENTS_FROM_KAFKA_SOURCE = 500;
/*
1. Extract without any checkpoint => get all the data, respecting default upper cap since both sourceLimit and
maxEventsFromKafkaSourceProp are set to Long.MAX_VALUE
*/
testUtils.sendMessages(TEST_TOPIC_NAME, Helpers.jsonifyRecords(dataGenerator.generateInserts("000", 1000)));
InputBatch<JavaRDD<GenericRecord>> fetch1 = kafkaSource.fetchNewDataInAvroFormat(Option.empty(), Long.MAX_VALUE);
assertEquals(500, fetch1.getBatch().get().count());
// 2. Produce new data, extract new data based on sourceLimit
testUtils.sendMessages(TEST_TOPIC_NAME, Helpers.jsonifyRecords(dataGenerator.generateInserts("001", 1000)));
InputBatch<Dataset<Row>> fetch2 =
kafkaSource.fetchNewDataInRowFormat(Option.of(fetch1.getCheckpointForNextBatch()), 1500);
assertEquals(1500, fetch2.getBatch().get().count());
//reset the value back since it is a static variable
Config.DEFAULT_MAX_EVENTS_FROM_KAFKA_SOURCE = Config.defaultMaxEventsFromKafkaSource;
}
@Test
public void testJsonKafkaSourceWithConfigurableUpperCap() throws IOException {
// topic setup.
testUtils.createTopic(TEST_TOPIC_NAME, 2);
HoodieTestDataGenerator dataGenerator = new HoodieTestDataGenerator();
TypedProperties props = createPropsForJsonSource(500L);
Source jsonSource = new JsonKafkaSource(props, jsc, sparkSession, schemaProvider);
SourceFormatAdapter kafkaSource = new SourceFormatAdapter(jsonSource);
// 1. Extract without any checkpoint => get all the data, respecting sourceLimit
testUtils.sendMessages(TEST_TOPIC_NAME, Helpers.jsonifyRecords(dataGenerator.generateInserts("000", 1000)));
InputBatch<JavaRDD<GenericRecord>> fetch1 = kafkaSource.fetchNewDataInAvroFormat(Option.empty(), 900);
assertEquals(900, fetch1.getBatch().get().count());
// 2. Produce new data, extract new data based on upper cap
testUtils.sendMessages(TEST_TOPIC_NAME, Helpers.jsonifyRecords(dataGenerator.generateInserts("001", 1000)));
InputBatch<Dataset<Row>> fetch2 =
kafkaSource.fetchNewDataInRowFormat(Option.of(fetch1.getCheckpointForNextBatch()), Long.MAX_VALUE);
assertEquals(500, fetch2.getBatch().get().count());
//fetch data respecting source limit where upper cap > sourceLimit
InputBatch<JavaRDD<GenericRecord>> fetch3 =
kafkaSource.fetchNewDataInAvroFormat(Option.of(fetch1.getCheckpointForNextBatch()), 400);
assertEquals(400, fetch3.getBatch().get().count());
//fetch data respecting source limit where upper cap < sourceLimit
InputBatch<JavaRDD<GenericRecord>> fetch4 =
kafkaSource.fetchNewDataInAvroFormat(Option.of(fetch2.getCheckpointForNextBatch()), 600);
assertEquals(600, fetch4.getBatch().get().count());
// 3. Extract with previous checkpoint => gives same data back (idempotent)
InputBatch<JavaRDD<GenericRecord>> fetch5 =
kafkaSource.fetchNewDataInAvroFormat(Option.of(fetch1.getCheckpointForNextBatch()), Long.MAX_VALUE);
assertEquals(fetch2.getBatch().get().count(), fetch5.getBatch().get().count());
assertEquals(fetch2.getCheckpointForNextBatch(), fetch5.getCheckpointForNextBatch());
// 4. Extract with latest checkpoint => no new data returned
InputBatch<JavaRDD<GenericRecord>> fetch6 =
kafkaSource.fetchNewDataInAvroFormat(Option.of(fetch4.getCheckpointForNextBatch()), Long.MAX_VALUE);
assertEquals(Option.empty(), fetch6.getBatch());
}
private static HashMap<TopicAndPartition, LeaderOffset> makeOffsetMap(int[] partitions, long[] offsets) {
HashMap<TopicAndPartition, LeaderOffset> map = new HashMap<>();
for (int i = 0; i < partitions.length; i++) {