class AvroToJsonFormatter[K, V] extends AbstractSchemaBasedRecordFormatter[K, V]
Formatter uses writer schema ids to assure test data represent raw events data, without schema evolution (which adjusts data to reader schema). Test data record contains data of ConsumerRecord and contains key and value schema ids (see [SchemaBasedSerializableConsumerRecord]).
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Instance Constructors
- new AvroToJsonFormatter(kafkaConfig: KafkaConfig, schemaRegistryClient: SchemaRegistryClient, keyMessageReader: AvroMessageReader, valueMessageReader: AvroMessageReader, deserializationSchema: KafkaDeserializationSchema[ConsumerRecord[K, V]], schemaIdFromMessageExtractor: SchemaIdFromMessageExtractor)(implicit arg0: ClassTag[K], arg1: ClassTag[V])
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- val deserializationSchema: KafkaDeserializationSchema[ConsumerRecord[K, V]]
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- AvroToJsonFormatter → AbstractSchemaBasedRecordFormatter
- def deserialize(record: ConsumerRecord[Array[Byte], Array[Byte]], valueSchemaIdOpt: Option[SchemaId]): ConsumerRecord[K, V]
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- def formatMessage(schemaIdOpt: Option[SchemaId], data: Any): Json
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- AvroToJsonFormatter → AbstractSchemaBasedRecordFormatter
- def formatRecord(record: ConsumerRecord[Array[Byte], Array[Byte]]): TestRecord
Step 1: Deserialize raw kafka event to record domain (e.g.
Step 1: Deserialize raw kafka event to record domain (e.g. GenericRecord). Step 2: Create Encoders that convert record to json Step 3: Encode event's data with schema id's with derived encoder.
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- AbstractSchemaBasedRecordFormatter → RecordFormatter
- def generateTestData(topics: NonEmptyList[ForSource], size: Int, kafkaConfig: KafkaConfig): TestData
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- RecordFormatter
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- val kafkaConfig: KafkaConfig
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- AvroToJsonFormatter → AbstractSchemaBasedRecordFormatter
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- final def notify(): Unit
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- def parseRecord(topic: ForSource, testRecord: TestRecord): ConsumerRecord[Array[Byte], Array[Byte]]
Step 1: Deserialize raw json bytes to SchemaBasedSerializableConsumerRecord[Json, Json] domain without interpreting key and value content.
Step 1: Deserialize raw json bytes to SchemaBasedSerializableConsumerRecord[Json, Json] domain without interpreting key and value content. Step 2: Create key and value json-to-record interpreter based on schema id's provided in json. Step 3: Use interpreter to create raw kafka ConsumerRecord
- Definition Classes
- AbstractSchemaBasedRecordFormatter → RecordFormatter
- def prepareGeneratedTestData(records: List[ConsumerRecord[Array[Byte], Array[Byte]]]): TestData
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- RecordFormatter
- def readRecordKeyMessage(schemaOpt: Option[ParsedSchema], topic: ForSource, jsonObj: Json): Array[Byte]
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- AvroToJsonFormatter → AbstractSchemaBasedRecordFormatter
- def readValueMessage(schemaOpt: Option[ParsedSchema], topic: ForSource, jsonObj: Json): Array[Byte]
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- AvroToJsonFormatter → AbstractSchemaBasedRecordFormatter
- val schemaIdFromMessageExtractor: SchemaIdFromMessageExtractor
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- AvroToJsonFormatter → AbstractSchemaBasedRecordFormatter
- val schemaRegistryClient: SchemaRegistryClient
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- AvroToJsonFormatter → AbstractSchemaBasedRecordFormatter
- final def synchronized[T0](arg0: => T0): T0
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- def toString(): String
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- Deprecated
(Since version 9)