聊聊flink的Table Formats

序

本文主要研究一下flink的Table Formats

实例

CSV Format

.withFormat(
  new Csv()
    .field("field1", Types.STRING)    // required: ordered format fields
    .field("field2", Types.TIMESTAMP)
    .fieldDelimiter(",")              // optional: string delimiter "," by default
    .lineDelimiter("\n")              // optional: string delimiter "\n" by default
    .quoteCharacter('"')              // optional: single character for string values, empty by default
    .commentPrefix('#')               // optional: string to indicate comments, empty by default
    .ignoreFirstLine()                // optional: ignore the first line, by default it is not skipped
    .ignoreParseErrors()              // optional: skip records with parse error instead of failing by default
)
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• flink内置支持csv format，无需添加额外依赖

JSON Format

.withFormat(
  new Json()
    .failOnMissingField(true)   // optional: flag whether to fail if a field is missing or not, false by default

    // required: define the schema either by using type information which parses numbers to corresponding types
    .schema(Type.ROW(...))

    // or by using a JSON schema which parses to DECIMAL and TIMESTAMP
    .jsonSchema(
      "{" +
      "  type: 'object'," +
      "  properties: {" +
      "    lon: {" +
      "      type: 'number'" +
      "    }," +
      "    rideTime: {" +
      "      type: 'string'," +
      "      format: 'date-time'" +
      "    }" +
      "  }" +
      "}"
    )

    // or use the table's schema
    .deriveSchema()
)
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• 可以使用schema或者jsonSchema或者deriveSchema来定义json format，需要额外添加flink-json依赖

Apache Avro Format

.withFormat(
  new Avro()

    // required: define the schema either by using an Avro specific record class
    .recordClass(User.class)

    // or by using an Avro schema
    .avroSchema(
      "{" +
      "  \"type\": \"record\"," +
      "  \"name\": \"test\"," +
      "  \"fields\" : [" +
      "    {\"name\": \"a\", \"type\": \"long\"}," +
      "    {\"name\": \"b\", \"type\": \"string\"}" +
      "  ]" +
      "}"
    )
)
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• 可以使用recordClass或者avroSchema来定义Avro schema，需要添加flink-avro依赖

ConnectTableDescriptor

flink-table_2.11-1.7.1-sources.jar!/org/apache/flink/table/descriptors/ConnectTableDescriptor.scala

abstract class ConnectTableDescriptor[D <: ConnectTableDescriptor[D]](
    private val tableEnv: TableEnvironment,
    private val connectorDescriptor: ConnectorDescriptor)
  extends TableDescriptor
  with SchematicDescriptor[D]
  with RegistrableDescriptor { this: D =>

  private var formatDescriptor: Option[FormatDescriptor] = None
  private var schemaDescriptor: Option[Schema] = None

  //......

  override def withFormat(format: FormatDescriptor): D = {
    formatDescriptor = Some(format)
    this
  }

  //......
}
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• StreamTableEnvironment的connect方法创建StreamTableDescriptor；StreamTableDescriptor继承了ConnectTableDescriptor；ConnectTableDescriptor提供了withFormat方法，返回FormatDescriptor

FormatDescriptor

flink-table-common-1.7.1-sources.jar!/org/apache/flink/table/descriptors/FormatDescriptor.java

@PublicEvolving
public abstract class FormatDescriptor extends DescriptorBase implements Descriptor {

	private String type;

	private int version;

	/**
	 * Constructs a {@link FormatDescriptor}.
	 *
	 * @param type string that identifies this format
	 * @param version property version for backwards compatibility
	 */
	public FormatDescriptor(String type, int version) {
		this.type = type;
		this.version = version;
	}

	@Override
	public final Map<String, String> toProperties() {
		final DescriptorProperties properties = new DescriptorProperties();
		properties.putString(FormatDescriptorValidator.FORMAT_TYPE, type);
		properties.putInt(FormatDescriptorValidator.FORMAT_PROPERTY_VERSION, version);
		properties.putProperties(toFormatProperties());
		return properties.asMap();
	}

	/**
	 * Converts this descriptor into a set of format properties. Usually prefixed with
	 * {@link FormatDescriptorValidator#FORMAT}.
	 */
	protected abstract Map<String, String> toFormatProperties();
}
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• FormatDescriptor是个抽象类，Csv、Json、Avro都是它的子类

Csv

flink-table_2.11-1.7.1-sources.jar!/org/apache/flink/table/descriptors/Csv.scala

class Csv extends FormatDescriptor(FORMAT_TYPE_VALUE, 1) {

  private var fieldDelim: Option[String] = None
  private var lineDelim: Option[String] = None
  private val schema: mutable.LinkedHashMap[String, String] =
    mutable.LinkedHashMap[String, String]()
  private var quoteCharacter: Option[Character] = None
  private var commentPrefix: Option[String] = None
  private var isIgnoreFirstLine: Option[Boolean] = None
  private var lenient: Option[Boolean] = None

  def fieldDelimiter(delim: String): Csv = {
    this.fieldDelim = Some(delim)
    this
  }

  def lineDelimiter(delim: String): Csv = {
    this.lineDelim = Some(delim)
    this
  }

  def schema(schema: TableSchema): Csv = {
    this.schema.clear()
    schema.getFieldNames.zip(schema.getFieldTypes).foreach { case (n, t) =>
      field(n, t)
    }
    this
  }

  def field(fieldName: String, fieldType: TypeInformation[_]): Csv = {
    field(fieldName, TypeStringUtils.writeTypeInfo(fieldType))
    this
  }

  def field(fieldName: String, fieldType: String): Csv = {
    if (schema.contains(fieldName)) {
      throw new ValidationException(s"Duplicate field name $fieldName.")
    }
    schema += (fieldName -> fieldType)
    this
  }

  def quoteCharacter(quote: Character): Csv = {
    this.quoteCharacter = Option(quote)
    this
  }

  def commentPrefix(prefix: String): Csv = {
    this.commentPrefix = Option(prefix)
    this
  }

  def ignoreFirstLine(): Csv = {
    this.isIgnoreFirstLine = Some(true)
    this
  }

  def ignoreParseErrors(): Csv = {
    this.lenient = Some(true)
    this
  }

  override protected def toFormatProperties: util.Map[String, String] = {
    val properties = new DescriptorProperties()

    fieldDelim.foreach(properties.putString(FORMAT_FIELD_DELIMITER, _))
    lineDelim.foreach(properties.putString(FORMAT_LINE_DELIMITER, _))

    val subKeys = util.Arrays.asList(
      DescriptorProperties.TABLE_SCHEMA_NAME,
      DescriptorProperties.TABLE_SCHEMA_TYPE)

    val subValues = schema.map(e => util.Arrays.asList(e._1, e._2)).toList.asJava

    properties.putIndexedFixedProperties(
      FORMAT_FIELDS,
      subKeys,
      subValues)
    quoteCharacter.foreach(properties.putCharacter(FORMAT_QUOTE_CHARACTER, _))
    commentPrefix.foreach(properties.putString(FORMAT_COMMENT_PREFIX, _))
    isIgnoreFirstLine.foreach(properties.putBoolean(FORMAT_IGNORE_FIRST_LINE, _))
    lenient.foreach(properties.putBoolean(FORMAT_IGNORE_PARSE_ERRORS, _))

    properties.asMap()
  }
}
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• Csv提供了field、fieldDelimiter、lineDelimiter、quoteCharacter、commentPrefix、ignoreFirstLine、ignoreParseErrors等方法

Json

flink-json-1.7.1-sources.jar!/org/apache/flink/table/descriptors/Json.java

public class Json extends FormatDescriptor {

	private Boolean failOnMissingField;
	private Boolean deriveSchema;
	private String jsonSchema;
	private String schema;

	public Json() {
		super(FORMAT_TYPE_VALUE, 1);
	}

	public Json failOnMissingField(boolean failOnMissingField) {
		this.failOnMissingField = failOnMissingField;
		return this;
	}

	public Json jsonSchema(String jsonSchema) {
		Preconditions.checkNotNull(jsonSchema);
		this.jsonSchema = jsonSchema;
		this.schema = null;
		this.deriveSchema = null;
		return this;
	}

	public Json schema(TypeInformation<Row> schemaType) {
		Preconditions.checkNotNull(schemaType);
		this.schema = TypeStringUtils.writeTypeInfo(schemaType);
		this.jsonSchema = null;
		this.deriveSchema = null;
		return this;
	}

	public Json deriveSchema() {
		this.deriveSchema = true;
		this.schema = null;
		this.jsonSchema = null;
		return this;
	}

	@Override
	protected Map<String, String> toFormatProperties() {
		final DescriptorProperties properties = new DescriptorProperties();

		if (deriveSchema != null) {
			properties.putBoolean(FORMAT_DERIVE_SCHEMA, deriveSchema);
		}

		if (jsonSchema != null) {
			properties.putString(FORMAT_JSON_SCHEMA, jsonSchema);
		}

		if (schema != null) {
			properties.putString(FORMAT_SCHEMA, schema);
		}

		if (failOnMissingField != null) {
			properties.putBoolean(FORMAT_FAIL_ON_MISSING_FIELD, failOnMissingField);
		}

		return properties.asMap();
	}
}
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• Json提供了schema、jsonSchema、deriveSchema三种方式来定义json format

Avro

flink-avro-1.7.1-sources.jar!/org/apache/flink/table/descriptors/Avro.java

public class Avro extends FormatDescriptor {

	private Class<? extends SpecificRecord> recordClass;
	private String avroSchema;

	public Avro() {
		super(AvroValidator.FORMAT_TYPE_VALUE, 1);
	}

	public Avro recordClass(Class<? extends SpecificRecord> recordClass) {
		Preconditions.checkNotNull(recordClass);
		this.recordClass = recordClass;
		return this;
	}

	public Avro avroSchema(String avroSchema) {
		Preconditions.checkNotNull(avroSchema);
		this.avroSchema = avroSchema;
		return this;
	}

	@Override
	protected Map<String, String> toFormatProperties() {
		final DescriptorProperties properties = new DescriptorProperties();

		if (null != recordClass) {
			properties.putClass(AvroValidator.FORMAT_RECORD_CLASS, recordClass);
		}
		if (null != avroSchema) {
			properties.putString(AvroValidator.FORMAT_AVRO_SCHEMA, avroSchema);
		}

		return properties.asMap();
	}
}
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• Avro提供了recordClass、avroSchema两种方式来定义avro format

小结

• StreamTableEnvironment的connect方法创建StreamTableDescriptor；StreamTableDescriptor继承了ConnectTableDescriptor
• ConnectTableDescriptor提供了withFormat方法，返回FormatDescriptor；FormatDescriptor是个抽象类，Csv、Json、Avro都是它的子类
• Csv提供了field、fieldDelimiter、lineDelimiter、quoteCharacter、commentPrefix、ignoreFirstLine、ignoreParseErrors等方法；Json提供了schema、jsonSchema、deriveSchema三种方式来定义json format；Avro提供了recordClass、avroSchema两种方式来定义avro format

doc

• Table Formats