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3ac8430e
编写于
2月 13, 2020
作者:
A
Aljoscha Krettek
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[FLINK-15999][doc] Fix formatting and links in timely-stream-processing.md
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docs/concepts/timely-stream-processing.md
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@@ -29,38 +29,47 @@ under the License.
## Time
When referring to time in a streaming program (for example to define windows),
one can refer to different notions
of time:
When referring to time in a streaming program (for example to define windows),
o
ne can refer to different notions o
f time:
-
**Event Time**
is the time when an event was created. It is usually described by a timestamp in the events,
for example attached by the producing sensor, or the producing service. Flink accesses event timestamps
via
[
timestamp assigners
](
{{
site.baseurl }}/dev/event_timestamps_watermarks.html).
-
**Event Time**
is the time when an event was created. It is usually
described by a timestamp in the events, for example attached by the
producing sensor, or the producing service. Flink accesses event timestamps
via
[
timestamp assigners
](
{{
site.baseurl
}}/dev/event_timestamps_watermarks.html).
-
**Ingestion time**
is the time when an event enters the Flink dataflow at the source operator.
-
**Ingestion time**
is the time when an event enters the Flink dataflow at
the source operator.
-
**Processing Time**
is the local time at each operator that performs a time-based operation.
-
**Processing Time**
is the local time at each operator that performs a
time-based operation.
<img
src=
"{{ site.baseurl }}/fig/event_ingestion_processing_time.svg"
alt=
"Event Time, Ingestion Time, and Processing Time"
class=
"offset"
width=
"80%"
/>
More details on how to handle time are in the
[
event time docs
](
{{
site.baseurl }}/dev/event_time.html).
More details on how to handle time are in the
[
event time docs
](
{{
site.baseurl
}}{% link dev/event_time.md %}).
{% top %}
## Windows
Aggregating events (e.g., counts, sums) works differently on streams than in batch processing.
For example, it is impossible to count all elements in a stream,
because streams are in general infinite (unbounded). Instead, aggregates on streams (counts, sums, etc),
are scoped by
**windows**
, such as
*"count over the last 5 minutes"*
, or
*"sum of the last 100 elements"*
.
Aggregating events (e.g., counts, sums) works differently on streams than in
batch processing. For example, it is impossible to count all elements in a
stream, because streams are in general infinite (unbounded). Instead,
aggregates on streams (counts, sums, etc), are scoped by
**windows**
, such as
*"count over the last 5 minutes"*
, or
*"sum of the last 100 elements"*
.
Windows can be
*time driven*
(example: every 30 seconds) or
*data driven*
(example: every 100 elements).
One typically distinguishes different types of windows, such as
*tumbling windows*
(no overlap),
*sliding windows*
(with overlap), and
*session windows*
(punctuated by a gap of inactivity).
Windows can be
*time driven*
(example: every 30 seconds) or
*data driven*
(example: every 100 elements). One typically distinguishes different types of
windows, such as
*tumbling windows*
(no overlap),
*sliding windows*
(with
overlap), and
*session windows*
(punctuated by a gap of inactivity).
<img
src=
"{{ site.baseurl }}/fig/windows.svg"
alt=
"Time- and Count Windows"
class=
"offset"
width=
"80%"
/>
More window examples can be found in this
[
blog post
](
https://flink.apache.org/news/2015/12/04/Introducing-windows.html
)
.
More details are in the
[
window docs
](
../dev/stream/operators/windows.html
)
.
More window examples can be found in this
[
blog
post
](
https://flink.apache.org/news/2015/12/04/Introducing-windows.html
)
. More
details are in the
[
window docs
](
{{
site.baseurl }}{% link
dev/stream/operators/windows.md %}).
{% top %}
...
...
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