The Dataflow SDKs use a concept called Windowing to subdivide a
PCollection according to the timestamps of its
individual elements. Dataflow transforms that aggregate
multiple elements, such as GroupByKey and
Combine, work
implicitly on a per-window basis—that is, they process each PCollection as a
succession of multiple, finite windows, though the entire collection itself may be of unlimited
or infinite size.
The Dataflow SDKs use a related concept called Triggers to determine when to "close" each
finite window as unbounded data arrives. Using a trigger can help to refine the windowing
strategy for your PCollection to deal with late-arriving data or to provide early
results. See Triggers for more information.
Windowing Basics
Windowing is most useful with an unbounded PCollection,
which represents a continuously updating data set of unknown/unlimited size (e.g. streaming
data). Some Dataflow transforms, such as GroupByKey
and Combine, group multiple elements by a common key.
Ordinarily, that grouping operation groups all of the elements that have the same key in the
entire data set. With an unbounded data set, it is impossible to collect all of the
elements, since new elements are constantly being added.
In the Dataflow model, any PCollection can be subdivided into logical
windows. Each element in a PCollection gets assigned to one or more windows
according to the PCollection's windowing function, and each individual window
contains a finite number of elements. Grouping transforms then consider each
PCollection's elements on a per-window basis. GroupByKey, for example,
implicitly groups the elements of a PCollection by key and window. Dataflow
only groups data within the same window, and doesn't group data in other windows.
Windowing Constraints
Once you set the windowing function for a PCollection, the elements' windows are
used the next time you apply a grouping transform to that PCollection.
Dataflow performs the actual window grouping on an as-needed basis; if you set a windowing function
using the Window transform, each element is assigned to a window, but the windows are
not considered until you group the PCollection with GroupByKey or
Combine. This can have different effects on your pipeline.
Consider the example pipeline in Figure 1 below:
In the above pipeline, we create an unbounded PCollection by reading a set of
key/value pairs using PubsubIO, and then apply a windowing
function to that collection using the Window transform. We then apply a
ParDo to the collection, and then later group the result of that
ParDo using GroupByKey. The windowing function has no effect on
the ParDo transform, because the windows are not actually used until they're
needed for the GroupByKey.
Subsequent transforms, however, are applied to the result of the GroupByKey--that
is, data grouped by both key and window.
Using Windowing With Bounded PCollections
You can use windowing with fixed-size data sets in bounded
PCollections. Note, however, that windowing considers only the implicit timestamps
attached to each element of a PCollection, and data sources that create fixed data
sets (such as TextIO and BigQueryIO) assign the same
timestamp to every element. This means that all the elements are by default part of a single,
global window. Having all elements assigned to the same window will cause a pipeline to execute in
classic MapReduce batch style.
To use windowing with fixed data sets, you can assign your own
timestamps to each element. To assign timestamps to elements, you use a ParDo
transform with a DoFn that outputs each element with a new timestamp.
Using windowing with a bounded PCollection can affect how your pipeline processes
data. For example, consider the following pipeline:
In the above pipeline, we create a bounded PCollection by reading a set of
key/value pairs using TextIO. We then group the collection
using GroupByKey, and apply a ParDo transform to the grouped
PCollection. In this example, the GroupByKey creates a collection of
unique keys, and then ParDo gets applied exactly once per key.
Now, consider the same pipeline, but using a windowing function:
As before, the pipeline creates a bounded PCollection of key/value pairs. We then
set a windowing function for that
PCollection. The GroupByKey transform now groups the elements of the
PCollection by both key and window. The subsequent ParDo
transform gets applied multiple times per key, once for each window.
Windowing Functions
The Dataflow SDKs let you define different kinds of windows to divide the elements of your
PCollection. The SDK provides several windowing functions, including:
- Fixed Time Windows
- Sliding Time Windows
- Per-Session Windows
- Single Global Window
Note that each element can logically belong to more than one window, depending on the windowing function you use. Sliding time windowing, for example, creates overlapping windows wherein a single element can be assigned to multiple windows.
Fixed Time Windows
The most simple form of windowing is a fixed time window: given a timestamped
PCollection, which might be continuously updating, each window might capture
(for example) five minutes worth of elements.
A fixed time window represents the time interval in the data stream that defines a bundle of
data for processing. Consider a window that operates at five-minute intervals: all of the elements
in your unbounded PCollection with timestamp values between 0:00:00 and 0:04:59
belong to the first window, elements with timestamp values between 0:05:00 and 0:09:59 belong to
the second window, and so on.
Sliding Time Windows
A sliding time window also uses time intervals in the data stream to define bundles of data; however, with sliding time windowing, the windows overlap. Each window might capture five minutes worth of data, but a new window starts every ten seconds. The frequency with which sliding windows begin is called the period. Therefore, our example would have a window size of five minutes and a period of ten seconds.
Because multiple windows overlap, most elements in a data set will belong to more than one window. This kind of Windowing is useful for taking running averages of data; using sliding time windows, you can compute a running average of the past five minutes' worth of data, updated every ten seconds, in our example.
Session Windows
A session window function defines windows around areas of concentration in the data. Session windowing is useful for data that is irregularly distributed with respect to time; for example, a data stream representing user mouse activity may have long periods of idle time interspersed with high concentrations of clicks. Session windowing groups the high concentrations of data into separate windows and filters out the idle sections of the data stream.
Note that session windowing applies on a per-key basis; that is, grouping into sessions only takes into account data that has the same key. Each key in your data collection will therefore be grouped into disjoint windows of differing sizes.
The simplest kind of session windowing specifies a minimum gap duration. All data arriving below a minimum threshold of time delay is grouped into the same window. If data arrives after the minimum specified gap duration time, this initiates the start of a new window.
Single Global Window
By default, all data in a PCollection is assigned to a single global window.
If your data set is of a fixed size, you can leave the global
window default for your PCollection. If the elements of your
PCollection all belong to a single global window, your pipeline will execute much
like a batch processing job (as in MapReduce-based processing).
Other Windowing Functions
The Dataflow SDKs provide more windowing functions beyond fixed, sliding, session, and global windows, such as Calendar-based windows.
Java
See the package com.google.cloud.dataflow.sdk.transforms.windowing for a complete list of the available windowing functions in the Dataflow SDK for Java.
Setting Your PCollection's Windowing Function
You can set the windowing function for a PCollection by applying the
Window transform. When you apply the Window transform, you must provide
a WindowFn. The WindowFn determines the windowing function your
PCollection will use for subsequent grouping transforms, such as a fixed or
sliding time window.
The Dataflow SDKs provide pre-defined WindownFns for the basic
windowing functions, or you can define your own WindowFn
in advanced cases.
Technically, like all transforms, Window takes an input
PCollection and outputs a new PCollection with each element assigned
to one or more logical, finite windows.
Setting Fixed-Time Windows
The following example code shows how to apply Window to divide a
PCollection into fixed windows, each one minute in length:
Java
PCollection<String> items = ...;
PCollection<String> fixed_windowed_items = items.apply(
Window.<String>into(FixedWindows.of(Duration.standardMinutes(1))));
Setting Sliding Time Windows
The following example code shows how to apply Window to divide a
PCollection into sliding time windows. Each window is 30 minutes in length, and a
new window begins every five seconds:
Java
PCollection<String> items = ...;
PCollection<String> sliding_windowed_items = items.apply(
Window.<String>into(SlidingWindows.of(Duration.standardMinutes(30)).every(Duration.standardSeconds(5))));
Setting Session Windows
The following example
code shows how to apply Window to divide a PCollection into session
windows, where each session must be separated by a time gap of at least 10 minutes:
Java
PCollection<String> items = ...;
PCollection<String> session_windowed_items = items.apply(
Window.<String>into(Sessions.withGapDuration(Duration.standardMinutes(10))));
Note that the sessions are per-key—each key in the collection will have its own session groupings depending on the data distribution.
Setting a Single Global Window
If your PCollection is bounded (the size is fixed), you can assign all the elements
to a single global window. The following example code shows how to set a single global window for
a PCollection:
To set a single global window for your PCollection, pass
new GlobalWindows() as the WindowFn when you apply the
Window transform. The following example code shows how to apply Window
to assign a PCollection into a single global window:
Java
PCollection<String> items = ...;
PCollection<String> batch_items = items.apply(
Window.<String>into(new GlobalWindows()));
Time Skew, Data Lag, and Late Data
In any data processing system, there is a certain amount of lag between the time a data event occurs (the "event time", determined by the timestamp on the data element itself) and the time the actual data element gets processed at any stage in your pipeline (the "processing time", determined by the clock on the system processing the element).
In a perfect system, the event time for each data element and the processing time would be equal, or at least have a consistent delta. However, in any real-world computing system, data generation and delivery are subject to any number of temporal limitations. In large or distributed systems, such as a distributed collection of web front-ends generating customer orders or log files, there are no guarantees that data events will appear in your pipeline in the same order that they were generated in various places on the web.
For example, let's say we have a PCollection that's using fixed-time windowing, with
windows that are five minutes long. For each window, Dataflow must collect all the data with an
event time timestamp in the given window range (between 0:00 and 4:59 in the first window,
for instance). Data with timestamps outside that range (data from 5:00 or later) belong to a
different window.
However, data isn't always guaranteed to arrive in a pipeline in correct time order, or to always arrive at predictable intervals. Dataflow tracks a watermark, which is the system's notion of when all data in a certain window can be expected to have arrived in the pipeline. Data that arrives with a timestamp after the watermark is considered late data.
From our example, suppose we have a simple watermark that assumes approximately 30s of lag time between the data timestamps (the event time) and the time the data appears in the pipeline (the processing time), then Dataflow would close the first window at 5:30. If a data record arrives at 5:34, but with a timestamp that would put it in the 0:00-4:59 window (say, 3:38), then that record is late data.
Note: For simplicity, we've assumed that we're using a very straightforward
watermark that estimates the lag time/time skew. In practice, your PCollection's data
source determines the watermark, and watermarks can be more precise or complex.
Managing Time Skew and Late Data
You can allow late data by invoking the .withAllowedLateness operation when
you set your PCollection's windowing strategy. The following code example
demonstrates a windowing strategy that will allow late data up to two days after the
end of a window.
Java
PCollection<String> items = ...;
PCollection<String> fixed_windowed_items = items.apply(
Window.<String>into(FixedWindows.of(Duration.standardMinutes(1)))
.withAllowedLateness(Duration.standardDays(2)));
When you set .withAllowedLateness on a PCollection, that allowed
lateness propagates forward to any subsequent PCollection derived from the
first PCollection you applied allowed lateness to. If you want to change the allowed
lateness later in your pipeline, you must do so explicitly by applying
Window.withAllowedLateness() again.
You can also use Dataflow's Triggers API to help you
refine the windowing strategy for a PCollection. You can use triggers to determine
exactly when each individual window aggregates and reports its results, including how the window
emits late elements.
Note: Dataflow's default windowing and trigger strategies discard late
data. If you want to ensure that your pipeline handles instances of late data, you'll need to
explicitly set .withAllowedLateness when you set your PCollection's
windowing strategy and set triggers for your PCollections accordingly.
Adding Timestamps To a PCollection's Elements
You can assign new timestamps to the elements of a PCollection by applying a
ParDo transform that outputs new elements with timestamps
that you set. Assigning timestamps can be useful if you want to use Dataflow's windowing
features, but your data set comes from a source without implicit timestamps (such as a
file from TextIO).
This is a good pattern to follow when your data set includes timestamp data, but the timestamps
are not generated by the Dataflow data source. An example might be if your pipeline reads
log records from an input file, and each log record includes a timestamp field; since your
pipeline reads the records in from a file, the file source doesn't assign timestamps
automatically. You can parse the timestamp field from each record and use a ParDo
transform to attach the timestamps to each element in your PCollection.
Java
To assign timestamps, your ParDo transform needs to use a DoFn that
outputs elements using ProcessContext.outputWithTimestamp (rather than the usual
ProcessContext.output used to emit elements to the main output collection). The
following example code shows a ParDo with a DoFn that outputs elements
with new timestamps:
PCollection<LogEntry> unstampedLogs = ...;
PCollection<LogEntry> stampedLogs =
unstampedLogs.apply(ParDo.of(new DoFn<LogEntry, LogEntry>() {
public void processElement(ProcessContext c) {
// Extract the timestamp from log entry we're currently processing.
Instant logTimeStamp = extractTimeStampFromLogEntry(c.element());
// Use outputWithTimestamp to emit the log entry with timestamp attached.
c.outputWithTimestamp(c.element(), logTimeStamp);
}
}));
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