docs/dev/table/streaming/time

title: “Time Attributes” nav-parent_id: streaming_tableapi nav-pos: 2

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]({{ site.baseurl }}/dev/event_time.html).

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

This will be replaced by the TOC {:toc}

Introduction to Time Attributes

Time-based operations such as windows in both the [Table API]({{ site.baseurl }}/dev/table/tableApi.html#group-windows) and [SQL]({{ site.baseurl }}/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 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:

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

// alternatively: // env.setStreamTimeCharacteristic(TimeCharacteristic.IngestionTime); // env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); {% endhighlight %}

env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime) // default

// alternatively: // env.setStreamTimeCharacteristic(TimeCharacteristic.IngestionTime) // env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime) {% endhighlight %}

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.

// 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”)); {% endhighlight %}

// 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) {% endhighlight %}

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.

@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”)); {% endhighlight %}

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) {% endhighlight %}

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]({{ site.baseurl }}/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]({{ site.baseurl }}/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.

// 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”)); {% endhighlight %}

// 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) {% endhighlight %}

Using a TableSource

The event time attribute is defined by a TableSource that implements the DefinedRowtimeAttribute interface. The getRowtimeAttribute() method returns the name of an existing field that carries the event time attribute of the table and is of type LONG or TIMESTAMP.

Moreover, the DataStream returned by the getDataStream() method must have watermarks assigned that are aligned with the defined time attribute. Please note that the timestamps of the DataStream (the ones which are assigned by a TimestampAssigner) are ignored. Only the values of the TableSource's rowtime attribute are relevant.

@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 String getRowtimeAttribute() {
	// Mark the "UserActionTime" attribute as event-time attribute.
	return "UserActionTime";
}

}

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

WindowedTable windowedTable = tEnv .scan(“UserActions”) .window(Tumble.over(“10.minutes”).on(“UserActionTime”).as(“userActionWindow”)); {% endhighlight %}

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 getRowtimeAttribute = {
	// Mark the "UserActionTime" attribute as event-time attribute.
	"UserActionTime"
}

}

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

val windowedTable = tEnv .scan(“UserActions”) .window(Tumble over 10.minutes on 'UserActionTime as 'userActionWindow) {% endhighlight %}

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