# Time Attributes

Flink is able to process streaming data based on different notions of _time_.

*   _Processing time_ refers to the system time of the machine (also known as “wall-clock time”) that is executing the respective operation.
*   _Event time_ refers to the processing of streaming data based on timestamps which are attached to each row. The timestamps can encode when an event happened.
*   _Ingestion time_ is the time that events enter Flink; internally, it is treated similarly to event time.

For more information about time handling in Flink, see the introduction about [Event Time and Watermarks](//ci.apache.org/projects/flink/flink-docs-release-1.7/dev/event_time.html).

This pages explains how time attributes can be defined for time-based operations in Flink’s Table API & SQL.

## Introduction to Time Attributes

Time-based operations such as windows in both the [Table API](//ci.apache.org/projects/flink/flink-docs-release-1.7/dev/table/tableApi.html#group-windows) and [SQL](//ci.apache.org/projects/flink/flink-docs-release-1.7/dev/table/sql.html#group-windows) require information about the notion of time and its origin. Therefore, tables can offer _logical time attributes_ for indicating time and accessing corresponding timestamps in table programs.

Time attributes can be part of every table schema. They are defined when creating a table from a `DataStream` or are pre-defined when using a `TableSource`. Once a time attribute has been defined at the beginning, it can be referenced as a field and can be used in time-based operations.

As long as a time attribute is not modified and is simply forwarded from one part of the query to another, it remains a valid time attribute. Time attributes behave like regular timestamps and can be accessed for calculations. If a time attribute is used in a calculation, it will be materialized and becomes a regular timestamp. Regular timestamps do not cooperate with Flink’s time and watermarking system and thus can not be used for time-based operations anymore.

Table programs require that the corresponding time characteristic has been specified for the streaming environment:

<figure class="highlight">

```
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime); // default

// alternatively:
// env.setStreamTimeCharacteristic(TimeCharacteristic.IngestionTime);
// env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
```

</figure>

<figure class="highlight">

```
val env = StreamExecutionEnvironment.getExecutionEnvironment

env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime) // default 
// alternatively:
// env.setStreamTimeCharacteristic(TimeCharacteristic.IngestionTime) // env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
```

</figure>

## Processing time

Processing time allows a table program to produce results based on the time of the local machine. It is the simplest notion of time but does not provide determinism. It neither requires timestamp extraction nor watermark generation.

There are two ways to define a processing time attribute.

### During DataStream-to-Table Conversion

The processing time attribute is defined with the `.proctime` property during schema definition. The time attribute must only extend the physical schema by an additional logical field. Thus, it can only be defined at the end of the schema definition.

<figure class="highlight">

```
DataStream<Tuple2<String, String>> stream = ...;

// declare an additional logical field as a processing time attribute
Table table = tEnv.fromDataStream(stream, "Username, Data, UserActionTime.proctime");

WindowedTable windowedTable = table.window(Tumble.over("10.minutes").on("UserActionTime").as("userActionWindow"));
```

</figure>

<figure class="highlight">

```
val stream: DataStream[(String, String)] = ...

// declare an additional logical field as a processing time attribute val table = tEnv.fromDataStream(stream, 'UserActionTimestamp, 'Username, 'Data, 'UserActionTime.proctime)

val windowedTable = table.window(Tumble over 10.minutes on 'UserActionTime as 'userActionWindow)
```

</figure>

### Using a TableSource

The processing time attribute is defined by a `TableSource` that implements the `DefinedProctimeAttribute` interface. The logical time attribute is appended to the physical schema defined by the return type of the `TableSource`.

<figure class="highlight">

```
// define a table source with a processing attribute
public class UserActionSource implements StreamTableSource<Row>, DefinedProctimeAttribute {

	@Override
	public TypeInformation<Row> getReturnType() {
		String[] names = new String[] {"Username" , "Data"};
		TypeInformation[] types = new TypeInformation[] {Types.STRING(), Types.STRING()};
		return Types.ROW(names, types);
	}

	@Override
	public DataStream<Row> getDataStream(StreamExecutionEnvironment execEnv) {
		// create stream
		DataStream<Row> stream = ...;
		return stream;
	}

	@Override
	public String getProctimeAttribute() {
		// field with this name will be appended as a third field
		return "UserActionTime";
	}
}

// register table source
tEnv.registerTableSource("UserActions", new UserActionSource());

WindowedTable windowedTable = tEnv
	.scan("UserActions")
	.window(Tumble.over("10.minutes").on("UserActionTime").as("userActionWindow"));
```

</figure>

<figure class="highlight">

```
// define a table source with a processing attribute class UserActionSource extends StreamTableSource[Row] with DefinedProctimeAttribute {

	override def getReturnType = {
		val names = Array[String]("Username" , "Data")
		val types = Array[TypeInformation[_]](Types.STRING, Types.STRING)
		Types.ROW(names, types)
	}

	override def getDataStream(execEnv: StreamExecutionEnvironment): DataStream[Row] = {
		// create stream 		val stream = ...
		stream
	}

	override def getProctimeAttribute = {
		// field with this name will be appended as a third field 		"UserActionTime"
	}
}

// register table source tEnv.registerTableSource("UserActions", new UserActionSource)

val windowedTable = tEnv
	.scan("UserActions")
	.window(Tumble over 10.minutes on 'UserActionTime as 'userActionWindow)
```

</figure>

## Event time

Event time allows a table program to produce results based on the time that is contained in every record. This allows for consistent results even in case of out-of-order events or late events. It also ensures replayable results of the table program when reading records from persistent storage.

Additionally, event time allows for unified syntax for table programs in both batch and streaming environments. A time attribute in a streaming environment can be a regular field of a record in a batch environment.

In order to handle out-of-order events and distinguish between on-time and late events in streaming, Flink needs to extract timestamps from events and make some kind of progress in time (so-called [watermarks](//ci.apache.org/projects/flink/flink-docs-release-1.7/dev/event_time.html)).

An event time attribute can be defined either during DataStream-to-Table conversion or by using a TableSource.

### During DataStream-to-Table Conversion

The event time attribute is defined with the `.rowtime` property during schema definition. [Timestamps and watermarks](//ci.apache.org/projects/flink/flink-docs-release-1.7/dev/event_time.html) must have been assigned in the `DataStream` that is converted.

There are two ways of defining the time attribute when converting a `DataStream` into a `Table`. Depending on whether the specified `.rowtime` field name exists in the schema of the `DataStream` or not, the timestamp field is either

*   appended as a new field to the schema or
*   replaces an existing field.

In either case the event time timestamp field will hold the value of the `DataStream` event time timestamp.

<figure class="highlight">

```
// Option 1:

// extract timestamp and assign watermarks based on knowledge of the stream
DataStream<Tuple2<String, String>> stream = inputStream.assignTimestampsAndWatermarks(...);

// declare an additional logical field as an event time attribute
Table table = tEnv.fromDataStream(stream, "Username, Data, UserActionTime.rowtime");

// Option 2:

// extract timestamp from first field, and assign watermarks based on knowledge of the stream
DataStream<Tuple3<Long, String, String>> stream = inputStream.assignTimestampsAndWatermarks(...);

// the first field has been used for timestamp extraction, and is no longer necessary
// replace first field with a logical event time attribute
Table table = tEnv.fromDataStream(stream, "UserActionTime.rowtime, Username, Data");

// Usage:

WindowedTable windowedTable = table.window(Tumble.over("10.minutes").on("UserActionTime").as("userActionWindow"));
```

</figure>

<figure class="highlight">

```
// Option 1: 
// extract timestamp and assign watermarks based on knowledge of the stream val stream: DataStream[(String, String)] = inputStream.assignTimestampsAndWatermarks(...)

// declare an additional logical field as an event time attribute val table = tEnv.fromDataStream(stream, 'Username, 'Data, 'UserActionTime.rowtime)

// Option 2: 
// extract timestamp from first field, and assign watermarks based on knowledge of the stream val stream: DataStream[(Long, String, String)] = inputStream.assignTimestampsAndWatermarks(...)

// the first field has been used for timestamp extraction, and is no longer necessary
// replace first field with a logical event time attribute val table = tEnv.fromDataStream(stream, 'UserActionTime.rowtime, 'Username, 'Data)

// Usage: 
val windowedTable = table.window(Tumble over 10.minutes on 'UserActionTime as 'userActionWindow)
```

</figure>

### Using a TableSource

The event time attribute is defined by a `TableSource` that implements the `DefinedRowtimeAttributes` interface. The `getRowtimeAttributeDescriptors()` method returns a list of `RowtimeAttributeDescriptor` for describing the final name of a time attribute, a timestamp extractor to derive the values of the attribute, and the watermark strategy associated with the attribute.

Please make sure that the `DataStream` returned by the `getDataStream()` method is aligned with the defined time attribute. The timestamps of the `DataStream` (the ones which are assigned by a `TimestampAssigner`) are only considered if a `StreamRecordTimestamp` timestamp extractor is defined. Watermarks of a `DataStream` are only preserved if a `PreserveWatermarks` watermark strategy is defined. Otherwise, only the values of the `TableSource`’s rowtime attribute are relevant.

<figure class="highlight">

```
// define a table source with a rowtime attribute
public class UserActionSource implements StreamTableSource<Row>, DefinedRowtimeAttributes {

	@Override
	public TypeInformation<Row> getReturnType() {
		String[] names = new String[] {"Username", "Data", "UserActionTime"};
		TypeInformation[] types =
		    new TypeInformation[] {Types.STRING(), Types.STRING(), Types.LONG()};
		return Types.ROW(names, types);
	}

	@Override
	public DataStream<Row> getDataStream(StreamExecutionEnvironment execEnv) {
		// create stream
		// ...
		// assign watermarks based on the "UserActionTime" attribute
		DataStream<Row> stream = inputStream.assignTimestampsAndWatermarks(...);
		return stream;
	}

	@Override
	public List<RowtimeAttributeDescriptor> getRowtimeAttributeDescriptors() {
		// Mark the "UserActionTime" attribute as event-time attribute.
		// We create one attribute descriptor of "UserActionTime".
		RowtimeAttributeDescriptor rowtimeAttrDescr = new RowtimeAttributeDescriptor(
			"UserActionTime",
			new ExistingField("UserActionTime"),
			new AscendingTimestamps());
		List<RowtimeAttributeDescriptor> listRowtimeAttrDescr = Collections.singletonList(rowtimeAttrDescr);
		return listRowtimeAttrDescr;
	}
}

// register the table source
tEnv.registerTableSource("UserActions", new UserActionSource());

WindowedTable windowedTable = tEnv
	.scan("UserActions")
	.window(Tumble.over("10.minutes").on("UserActionTime").as("userActionWindow"));
```

</figure>

<figure class="highlight">

```
// define a table source with a rowtime attribute class UserActionSource extends StreamTableSource[Row] with DefinedRowtimeAttributes {

	override def getReturnType = {
		val names = Array[String]("Username" , "Data", "UserActionTime")
		val types = Array[TypeInformation[_]](Types.STRING, Types.STRING, Types.LONG)
		Types.ROW(names, types)
	}

	override def getDataStream(execEnv: StreamExecutionEnvironment): DataStream[Row] = {
		// create stream 		// ... 		// assign watermarks based on the "UserActionTime" attribute 		val stream = inputStream.assignTimestampsAndWatermarks(...)
		stream
	}

	override def getRowtimeAttributeDescriptors: util.List[RowtimeAttributeDescriptor] = {
		// Mark the "UserActionTime" attribute as event-time attribute. 		// We create one attribute descriptor of "UserActionTime". 		val rowtimeAttrDescr = new RowtimeAttributeDescriptor(
			"UserActionTime",
			new ExistingField("UserActionTime"),
			new AscendingTimestamps)
		val listRowtimeAttrDescr = Collections.singletonList(rowtimeAttrDescr)
		listRowtimeAttrDescr
	}
}

// register the table source tEnv.registerTableSource("UserActions", new UserActionSource)

val windowedTable = tEnv
	.scan("UserActions")
	.window(Tumble over 10.minutes on 'UserActionTime as 'userActionWindow)
```

</figure>