org.apache.flink.streaming.api

Enum TimeCharacteristic

java.lang.Object

java.lang.Enum<TimeCharacteristic>

org.apache.flink.streaming.api.TimeCharacteristic

All Implemented Interfaces:

Serializable, Comparable<TimeCharacteristic>

@PublicEvolving
public enum TimeCharacteristic
extends Enum<TimeCharacteristic>

The time characteristic defines how the system determines time for time-dependent order and operations that depend on time (such as time windows).

Enum Constant Summary

Enum Constants

Enum Constant and Description
EventTime Event time means that the time of each individual element in the stream (also called event) is determined by the event's individual custom timestamp.
IngestionTime Ingestion time means that the time of each individual element in the stream is determined when the element enters the Flink streaming data flow.
ProcessingTime Processing time for operators means that the operator uses the system clock of the machine to determine the current time of the data stream.

Method Summary

Modifier and Type	Method and Description
static TimeCharacteristic	valueOf(String name) Returns the enum constant of this type with the specified name.
static TimeCharacteristic[]	values() Returns an array containing the constants of this enum type, in the order they are declared.

Methods inherited from class java.lang.Enum

clone, compareTo, equals, finalize, getDeclaringClass, hashCode, name, ordinal, toString, valueOf

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Enum Constant Detail

ProcessingTime

public static final TimeCharacteristic ProcessingTime

Processing time for operators means that the operator uses the system clock of the machine to determine the current time of the data stream. Processing-time windows trigger based on wall-clock time and include whatever elements happen to have arrived at the operator at that point in time.

Using processing time for window operations results in general in quite non-deterministic results, because the contents of the windows depends on the speed in which elements arrive. It is, however, the cheapest method of forming windows and the method that introduces the least latency.

IngestionTime

public static final TimeCharacteristic IngestionTime

Ingestion time means that the time of each individual element in the stream is determined when the element enters the Flink streaming data flow. Operations like windows group the elements based on that time, meaning that processing speed within the streaming dataflow does not affect windowing, but only the speed at which sources receive elements.

Ingestion time is often a good compromise between processing time and event time. It does not need any special manual form of watermark generation, and events are typically not too much out-or-order when they arrive at operators; in fact, out-of-orderness can only be introduced by streaming shuffles or split/join/union operations. The fact that elements are not very much out-of-order means that the latency increase is moderate, compared to event time.

EventTime

public static final TimeCharacteristic EventTime

Event time means that the time of each individual element in the stream (also called event) is determined by the event's individual custom timestamp. These timestamps either exist in the elements from before they entered the Flink streaming dataflow, or are user-assigned at the sources. The big implication of this is that it allows for elements to arrive in the sources and in all operators out of order, meaning that elements with earlier timestamps may arrive after elements with later timestamps.

Operators that window or order data with respect to event time must buffer data until they can be sure that all timestamps for a certain time interval have been received. This is handled by the so called "time watermarks".

Operations based on event time are very predictable - the result of windowing operations is typically identical no matter when the window is executed and how fast the streams operate. At the same time, the buffering and tracking of event time is also costlier than operating with processing time, and typically also introduces more latency. The amount of extra cost depends mostly on how much out of order the elements arrive, i.e., how long the time span between the arrival of early and late elements is. With respect to the "time watermarks", this means that the cost typically depends on how early or late the watermarks can be generated for their timestamp.

In relation to IngestionTime, the event time is similar, but refers the the event's original time, rather than the time assigned at the data source. Practically, that means that event time has generally more meaning, but also that it takes longer to determine that all elements for a certain time have arrived.

Method Detail

values

public static TimeCharacteristic[] values()

Returns an array containing the constants of this enum type, in the order they are declared. This method may be used to iterate over the constants as follows:

for (TimeCharacteristic c : TimeCharacteristic.values())
    System.out.println(c);

Returns:

an array containing the constants of this enum type, in the order they are declared

valueOf

public static TimeCharacteristic valueOf(String name)

Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an enum constant in this type. (Extraneous whitespace characters are not permitted.)

Parameters:

name - the name of the enum constant to be returned.

Returns:

the enum constant with the specified name

Throws:

IllegalArgumentException - if this enum type has no constant with the specified name

NullPointerException - if the argument is null