import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
@Test
public void testTableSchema() {
	KafkaTableSource.Builder b = getBuilder();
	configureBuilder(b);

	KafkaTableSource source = b.build();

	// check table schema
	TableSchema schema = source.getTableSchema();
	assertNotNull(schema);
	assertEquals(5, schema.getColumnNames().length);
	// check table fields
	assertEquals("field1", schema.getColumnNames()[0]);
	assertEquals("field2", schema.getColumnNames()[1]);
	assertEquals("time1", schema.getColumnNames()[2]);
	assertEquals("time2", schema.getColumnNames()[3]);
	assertEquals("field3", schema.getColumnNames()[4]);
	assertEquals(Types.LONG(), schema.getTypes()[0]);
	assertEquals(Types.STRING(), schema.getTypes()[1]);
	assertEquals(Types.SQL_TIMESTAMP(), schema.getTypes()[2]);
	assertEquals(Types.SQL_TIMESTAMP(), schema.getTypes()[3]);
	assertEquals(Types.DOUBLE(), schema.getTypes()[4]);
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
@Override
public TableSchema getTableSchema() {
	TypeInformation<?>[] types = new TypeInformation[] {
			Types.LONG,
			Types.BOOLEAN,
			Types.FLOAT,
			Types.FLOAT,
			Types.FLOAT,
			Types.FLOAT,
			Types.SHORT,
			Types.SQL_TIMESTAMP
	};

	String[] names = new String[]{
			"rideId",
			"isStart",
			"startLon",
			"startLat",
			"endLon",
			"endLat",
			"passengerCnt",
			"eventTime"
	};

	return new TableSchema(names, types);
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
private static ExternalCatalogTable mockExternalCatalogTable(Map<String, String> props) {
  ExternalCatalogTable table = mock(ExternalCatalogTable.class);
  TableSchema schema = new TableSchema(new String[] {"foo"}, new TypeInformation[] {INT_TYPE_INFO});
  doReturn(schema).when(table).schema();
  doReturn(props).when(table).properties();
  doReturn("kafka+json").when(table).tableType();
  return table;
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Creates a Kafka 0.9 {@link StreamTableSource}.
 *
 * @param topic                 Kafka topic to consume.
 * @param properties            Properties for the Kafka consumer.
 * @param deserializationSchema Deserialization schema to use for Kafka records.
 * @param typeInfo              Type information describing the result type. The field names are used
 *                              to parse the JSON file and so are the types.
 */
public Kafka09TableSource(
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema,
		TableSchema schema,
		TypeInformation<Row> typeInfo) {

	super(topic, properties, schema, typeInfo);

	this.deserializationSchema = deserializationSchema;
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Creates a Kafka 0.9 JSON {@link StreamTableSource}.
 *
 * @param topic       Kafka topic to consume.
 * @param properties  Properties for the Kafka consumer.
 * @param tableSchema The schema of the table.
 * @param jsonSchema  The schema of the JSON messages to decode from Kafka.
 */
public Kafka09JsonTableSource(
	String topic,
	Properties properties,
	TableSchema tableSchema,
	TableSchema jsonSchema) {

	super(topic, properties, tableSchema, jsonSchema);
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Creates a Kafka 0.9 Avro {@link StreamTableSource} using a given {@link SpecificRecord}.
 *
 * @param topic      Kafka topic to consume.
 * @param properties Properties for the Kafka consumer.
 * @param schema	 Schema of the produced table.
 * @param record     Avro specific record.
 */
public Kafka09AvroTableSource(
	String topic,
	Properties properties,
	TableSchema schema,
	Class<? extends SpecificRecordBase> record) {

	super(
		topic,
		properties,
		schema,
		record);
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Creates a Kafka 0.8 {@link StreamTableSource}.
 *
 * @param topic                 Kafka topic to consume.
 * @param properties            Properties for the Kafka consumer.
 * @param deserializationSchema Deserialization schema to use for Kafka records.
 * @param typeInfo              Type information describing the result type. The field names are used
 *                              to parse the JSON file and so are the types.
 */
public Kafka08TableSource(
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema,
		TableSchema schema,
		TypeInformation<Row> typeInfo) {

	super(topic, properties, schema, typeInfo);

	this.deserializationSchema = deserializationSchema;
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Creates a Kafka 0.8 Avro {@link StreamTableSource} using a given {@link SpecificRecord}.
 *
 * @param topic      Kafka topic to consume.
 * @param properties Properties for the Kafka consumer.
 * @param schema     Schema of the produced table.
 * @param record     Avro specific record.
 */
public Kafka08AvroTableSource(
	String topic,
	Properties properties,
	TableSchema schema,
	Class<? extends SpecificRecordBase> record) {

	super(
		topic,
		properties,
		schema,
		record);
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Creates a Kafka 0.8 JSON {@link StreamTableSource}.
 *
 * @param topic       Kafka topic to consume.
 * @param properties  Properties for the Kafka consumer.
 * @param tableSchema The schema of the table.
 * @param jsonSchema  The schema of the JSON messages to decode from Kafka.
 */
public Kafka08JsonTableSource(
	String topic,
	Properties properties,
	TableSchema tableSchema,
	TableSchema jsonSchema) {

	super(topic, properties, tableSchema, jsonSchema);
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Creates a Kafka 0.11 JSON {@link StreamTableSource}.
 *
 * @param topic       Kafka topic to consume.
 * @param properties  Properties for the Kafka consumer.
 * @param tableSchema The schema of the table.
 * @param jsonSchema  The schema of the JSON messages to decode from Kafka.
 */
public Kafka011JsonTableSource(
	String topic,
	Properties properties,
	TableSchema tableSchema,
	TableSchema jsonSchema) {

	super(topic, properties, tableSchema, jsonSchema);
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Creates a Kafka 0.11 {@link StreamTableSource}.
 *
 * @param topic                 Kafka topic to consume.
 * @param properties            Properties for the Kafka consumer.
 * @param deserializationSchema Deserialization schema to use for Kafka records.
 * @param typeInfo              Type information describing the result type. The field names are used
 *                              to parse the JSON file and so are the types.
 */
public Kafka011TableSource(
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema,
		TableSchema schema,
		TypeInformation<Row> typeInfo) {

	super(topic, properties, schema, typeInfo);

	this.deserializationSchema = deserializationSchema;
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Creates a Kafka 0.11 Avro {@link StreamTableSource} using a given {@link SpecificRecord}.
 *
 * @param topic      Kafka topic to consume.
 * @param properties Properties for the Kafka consumer.
 * @param schema     Schema of the produced table.
 * @param record     Avro specific record.
 */
public Kafka011AvroTableSource(
	String topic,
	Properties properties,
	TableSchema schema,
	Class<? extends SpecificRecordBase> record) {

	super(
		topic,
		properties,
		schema,
		record);
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Creates a Kafka 0.10 Avro {@link StreamTableSource} using a given {@link SpecificRecord}.
 *
 * @param topic      Kafka topic to consume.
 * @param properties Properties for the Kafka consumer.
 * @param schema     Schema of the produced table.
 * @param record     Avro specific record.
 */
public Kafka010AvroTableSource(
	String topic,
	Properties properties,
	TableSchema schema,
	Class<? extends SpecificRecordBase> record) {

	super(
		topic,
		properties,
		schema,
		record);
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Creates a Kafka 0.10 JSON {@link StreamTableSource}.
 *
 * @param topic       Kafka topic to consume.
 * @param properties  Properties for the Kafka consumer.
 * @param tableSchema The schema of the table.
 * @param jsonSchema  The schema of the JSON messages to decode from Kafka.
 */
public Kafka010JsonTableSource(
	String topic,
	Properties properties,
	TableSchema tableSchema,
	TableSchema jsonSchema) {

	super(topic, properties, tableSchema, jsonSchema);
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Creates a Kafka 0.10 {@link StreamTableSource}.
 *
 * @param topic                 Kafka topic to consume.
 * @param properties            Properties for the Kafka consumer.
 * @param deserializationSchema Deserialization schema to use for Kafka records.
 * @param typeInfo              Type information describing the result type. The field names are used
 *                              to parse the JSON file and so are the types.
 */
public Kafka010TableSource(
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema,
		TableSchema schema,
		TypeInformation<Row> typeInfo) {

	super(topic, properties, schema, typeInfo);

	this.deserializationSchema = deserializationSchema;
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
@Override
public TableSchema getTableSchema() {
	if (this.tableSchema == null) {
		return new TableSchema(getFieldNames(), getFieldTypes());
	} else {
		return this.tableSchema;
	}
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Creates a generic Kafka {@link StreamTableSource}.
 *
 * @param topic                 Kafka topic to consume.
 * @param properties            Properties for the Kafka consumer.
 * @param schema                Schema of the produced table.
 * @param returnType            Type information of the produced physical DataStream.
 */
protected KafkaTableSource(
		String topic,
		Properties properties,
		TableSchema schema,
		TypeInformation<Row> returnType) {

	this.topic = Preconditions.checkNotNull(topic, "Topic must not be null.");
	this.properties = Preconditions.checkNotNull(properties, "Properties must not be null.");
	this.schema = Preconditions.checkNotNull(schema, "Schema must not be null.");
	this.returnType = Preconditions.checkNotNull(returnType, "Type information must not be null.");
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Sets the schema of the produced table.
 *
 * @param schema The schema of the produced table.
 * @return The builder.
 */
public B withSchema(TableSchema schema) {
	Preconditions.checkNotNull(schema, "Schema must not be null.");
	Preconditions.checkArgument(this.schema == null, "Schema has already been set.");
	this.schema = schema;
	return builder();
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Creates a generic Kafka Avro {@link StreamTableSource} using a given {@link SpecificRecord}.
 *
 * @param topic            Kafka topic to consume.
 * @param properties       Properties for the Kafka consumer.
 * @param schema           Schema of the produced table.
 * @param avroRecordClass  Class of the Avro record that is read from the Kafka topic.
 */
protected KafkaAvroTableSource(
	String topic,
	Properties properties,
	TableSchema schema,
	Class<? extends SpecificRecordBase> avroRecordClass) {

	super(
		topic,
		properties,
		schema,
		convertToRowTypeInformation(avroRecordClass));

	this.avroRecordClass = avroRecordClass;
}
 

import org.apache.flink.table.api.TableSchema; //导入依赖的package包/类
/**
 * Creates a generic Kafka JSON {@link StreamTableSource}.
 *
 * @param topic       Kafka topic to consume.
 * @param properties  Properties for the Kafka consumer.
 * @param tableSchema The schema of the table.
 * @param jsonSchema  The schema of the JSON messages to decode from Kafka.
 */
protected KafkaJsonTableSource(
	String topic,
	Properties properties,
	TableSchema tableSchema,
	TableSchema jsonSchema) {

	super(
		topic,
		properties,
		tableSchema,
		jsonSchemaToReturnType(jsonSchema));

	this.jsonSchema = jsonSchema;
}