public class View
extends java.lang.Object
PCollectionViews from PCollections (to read them as side inputs).
While a PCollection<ElemT> has many values of type ElemT per
window, a PCollectionView<ViewT> has a single value of type ViewT for each window. It can be thought of as a mapping from windows to values of type ViewT. The transforms here represent ways of converting the ElemT values in a window
into a ViewT for that window.
When a ParDo tranform is processing a main input element in a window w and a
PCollectionView is read via DoFn.ProcessContext.sideInput(org.apache.beam.sdk.values.PCollectionView<T>), the value of the view
for w is returned.
The SDK supports viewing a PCollection, per window, as a single value, a List,
an Iterable, a Map, or a multimap (iterable-valued Map).
For a PCollection that contains a single value of type T per window, such as
the output of Combine.globally(org.apache.beam.sdk.transforms.SerializableFunction<java.lang.Iterable<V>, V>), use asSingleton() to prepare it for use as a
side input:
PCollectionView<T> output = someOtherPCollection
.apply(Combine.globally(...))
.apply(View.<T>asSingleton());
For a small PCollection with windows that can fit entirely in memory, use asList() to prepare it for use as a List. When read as a side input, the entire
list for a window will be cached in memory.
PCollectionView<List<T>> output =
smallPCollection.apply(View.<T>asList());
If a PCollection of KV<K, V> is known to have a single value per window for
each key, then use asMap() to view it as a Map<K, V>:
PCollectionView<Map<K, V> output =
somePCollection.apply(View.<K, V>asMap());
Otherwise, to access a PCollection of KV<K, V> as a Map<K,
Iterable<V>> side input, use asMultimap():
PCollectionView<Map<K, Iterable<V>> output =
somePCollection.apply(View.<K, Iterable<V>>asMultimap());
To iterate over an entire window of a PCollection via side input, use asIterable():
PCollectionView<Iterable<T>> output =
somePCollection.apply(View.<T>asIterable());
Both asMultimap() and asMap() are useful for implementing lookup
based "joins" with the main input, when the side input is small enough to fit into memory.
For example, if you represent a page on a website via some Page object and have some
type UrlVisits logging that a URL was visited, you could convert these to more fully
structured PageVisit objects using a side input, something like the following:
PCollection<Page> pages = ... // pages fit into memory PCollection<UrlVisit> urlVisits = ... // very large collection final PCollectionView<Map<URL, Page>> urlToPageView = pages .apply(WithKeys.of( ... )) // extract the URL from the page .apply(View.<URL, Page>asMap()); PCollection<PageVisit> pageVisits = urlVisits .apply(ParDo.withSideInputs(urlToPageView) .of(new DoFn<UrlVisit, PageVisit>(){@Override void processElement(ProcessContext context) { UrlVisit urlVisit = context.element(); Map<URL, Page> urlToPage = context.sideInput(urlToPageView); Page page = urlToPage.get(urlVisit.getUrl()); c.output(new PageVisit(page, urlVisit.getVisitData())); }}));
See ParDo.SingleOutput.withSideInputs(org.apache.beam.sdk.values.PCollectionView<?>...) for details on how to access this variable
inside a ParDo over another PCollection.
| Modifier and Type | Class and Description |
|---|---|
static class |
View.AsIterable<T>
For internal use only; no backwards-compatibility guarantees.
|
static class |
View.AsList<T>
For internal use only; no backwards-compatibility guarantees.
|
static class |
View.AsMap<K,V>
For internal use only; no backwards-compatibility guarantees.
|
static class |
View.AsMultimap<K,V>
For internal use only; no backwards-compatibility guarantees.
|
static class |
View.AsSingleton<T>
For internal use only; no backwards-compatibility guarantees.
|
static class |
View.CreatePCollectionView<ElemT,ViewT>
For internal use only; no backwards-compatibility guarantees.
|
| Modifier and Type | Method and Description |
|---|---|
static <T> View.AsIterable<T> |
asIterable()
Returns a
View.AsIterable transform that takes a PCollection as input and
produces a PCollectionView mapping each window to an Iterable of the values in
that window. |
static <T> View.AsList<T> |
asList()
Returns a
View.AsList transform that takes a PCollection and returns a PCollectionView mapping each window to a List containing all of the elements in the
window. |
static <K,V> View.AsMap<K,V> |
asMap()
Returns a
View.AsMap transform that takes a PCollection<KV<K,
V>> as input and produces a PCollectionView mapping each window to a Map<K, V>. |
static <K,V> View.AsMultimap<K,V> |
asMultimap()
Returns a
View.AsMultimap transform that takes a PCollection<KV<K, V>> as input and produces a PCollectionView mapping each
window to its contents as a Map<K, Iterable<V>> for use as a side
input. |
static <T> View.AsSingleton<T> |
asSingleton()
Returns a
View.AsSingleton transform that takes a PCollection with a single value
per window as input and produces a PCollectionView that returns the value in the main
input window when read as a side input. |
public static <T> View.AsSingleton<T> asSingleton()
View.AsSingleton transform that takes a PCollection with a single value
per window as input and produces a PCollectionView that returns the value in the main
input window when read as a side input.
PCollection<InputT> input = ...
CombineFn<InputT, OutputT> yourCombineFn = ...
PCollectionView<OutputT> output = input
.apply(Combine.globally(yourCombineFn))
.apply(View.<OutputT>asSingleton());
If the input PCollection is empty, throws NoSuchElementException
in the consuming DoFn.
If the input PCollection contains more than one element, throws IllegalArgumentException in the consuming DoFn.
public static <T> View.AsList<T> asList()
View.AsList transform that takes a PCollection and returns a PCollectionView mapping each window to a List containing all of the elements in the
window.
Unlike with asIterable(), the resulting list is required to fit in memory.
public static <T> View.AsIterable<T> asIterable()
View.AsIterable transform that takes a PCollection as input and
produces a PCollectionView mapping each window to an Iterable of the values in
that window.
The values of the Iterable for a window are not required to fit in memory, but they
may also not be effectively cached. If it is known that every window fits in memory, and
stronger caching is desired, use asList().
public static <K,V> View.AsMap<K,V> asMap()
View.AsMap transform that takes a PCollection<KV<K,
V>> as input and produces a PCollectionView mapping each window to a Map<K, V>. It is required that each key of the input be associated with a single value,
per window. If this is not the case, precede this view with Combine.perKey, as in the
example below, or alternatively use asMultimap().
PCollection<KV<K, V>> input = ...
CombineFn<V, OutputT> yourCombineFn = ...
PCollectionView<Map<K, OutputT>> output = input
.apply(Combine.perKey(yourCombineFn))
.apply(View.<K, OutputT>asMap());
Currently, the resulting map is required to fit into memory.
public static <K,V> View.AsMultimap<K,V> asMultimap()
View.AsMultimap transform that takes a PCollection<KV<K, V>> as input and produces a PCollectionView mapping each
window to its contents as a Map<K, Iterable<V>> for use as a side
input. In contrast to asMap(), it is not required that the keys in the input
collection be unique.
PCollection<KV<K, V>> input = ... // maybe more than one occurrence of a some keys
PCollectionView<Map<K, Iterable<V>>> output = input.apply(View.<K, V>asMultimap());