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https://issues.apache.org/jira/browse/BEAM-4461?focusedWorklogId=206397&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-206397
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ASF GitHub Bot logged work on BEAM-4461:
----------------------------------------
Author: ASF GitHub Bot
Created on: 01/Mar/19 11:42
Start Date: 01/Mar/19 11:42
Worklog Time Spent: 10m
Work Description: kanterov commented on pull request #7353: [BEAM-4461]
Support inner and outer style joins in CoGroup.
URL: https://github.com/apache/beam/pull/7353#discussion_r261573017
##########
File path:
sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/transforms/CoGroup.java
##########
@@ -107,156 +106,251 @@
* those fields match. In this case, fields must be specified for every input
PCollection. For
* example:
*
- * <pre>{@code PCollection<KV<Row, Row>> joined = PCollectionTuple
- * .of(input1Tag, input1)
- * .and(input2Tag, input2)
+ * <pre>{@code PCollection<KV<Row, Row>> joined
+ * = PCollectionTuple.of("input1Tag", input1, "input2Tag", input2)
* .apply(CoGroup
- * .byFieldNames(input1Tag, "referringUser"))
- * .byFieldNames(input2Tag, "user"));
+ * .join("input1Tag", By.fieldNames("referringUser")))
+ * .join("input2Tag", By.fieldNames("user")));
* }</pre>
+ *
+ * <p>Traditional (SQL) joins are cross-product joins. All rows that match the
join condition are
+ * combined into individual rows and returned; in fact any SQL inner joins is
a subset of the
+ * cross-product of two tables. This transform also supports the same
functionality using the {@link
+ * Inner#crossProductJoin()} method.
+ *
+ * <p>For example, consider the SQL join: SELECT * FROM input1 INNER JOIN
input2 ON input1.user =
+ * input2.user
+ *
+ * <p>You could express this with:
+ *
+ * <pre>{@code
+ * PCollection<Row> joined = PCollectionTuple.of("input1", input1, "input2",
input2)
+ * .apply(CoGroup.join(By.fieldNames("user")).crossProductJoin();
+ * }</pre>
+ *
+ * <p>The schema of the output PCollection contains a nested message for each
of input1 and input2.
+ * Like above, you could use the {@link Convert} transform to convert it to
the following POJO:
+ *
+ * <pre>{@code
+ * {@literal @}DefaultSchema(JavaFieldSchema.class)
+ * public class JoinedValue {
+ * public Input1Type input1;
+ * public Input2Type input2;
+ * }
+ * }</pre>
+ *
+ * <p>The {@link Unnest} transform can then be used to flatten all the
subfields into one single
+ * top-level row containing all the fields in both Input1 and Input2; this
will often be combined
+ * with a {@link Select} transform to select out the fields of interest, as
the key fields will be
+ * identical between input1 and input2.
+ *
+ * <p>This transform also supports outer-join semantics. By default, all input
PCollections must
+ * participate fully in the join, providing inner-join semantics. This means
that the join will only
+ * produce values for "Bob" if all inputs have values for "Bob;" if even a
single input does not
+ * have a value for "Bob," an inner-join will produce no value. However, if
you mark that input as
+ * having outer-join participation then the join will contain values for
"Bob," as long as at least
+ * one input has a "Bob" value; null values will be added for inputs that have
no "Bob" values. To
+ * continue the SQL example:
+ *
+ * <p>SELECT * FROM input1 LEFT OUTER JOIN input2 ON input1.user = input2.user
+ *
+ * <p>Is equivalent to:
+ *
+ * <pre>{@code
+ * PCollection<Row> joined = PCollectionTuple.of("input1", input1, "input2",
input2)
+ * .apply(CoGroup.join("input1",
By.fieldNames("user").withOuterJoinParticipation())
+ * .join("input2", By.fieldNames("user"))
+ * .crossProductJoin();
+ * }</pre>
+ *
+ * <p>SELECT * FROM input1 RIGHT OUTER JOIN input2 ON input1.user = input2.user
+ *
+ * <p>Is equivalent to:
+ *
+ * <pre>{@code
+ * PCollection<Row> joined = PCollectionTuple.of("input1", input1, "input2",
input2)
+ * .apply(CoGroup.join("input1", By.fieldNames("user"))
+ * .join("input2",
By.fieldNames("user").withOuterJoinParticipation())
+ * .crossProductJoin();
+ * }</pre>
+ *
+ * <p>and SELECT * FROM input1 FULL OUTER JOIN input2 ON input1.user =
input2.user
+ *
+ * <p>Is equivalent to:
+ *
+ * <pre>{@code
+ * PCollection<Row> joined = PCollectionTuple.of("input1", input1, "input2",
input2)
+ * .apply(CoGroup.join("input1",
By.fieldNames("user").withOuterJoinParticipation())
+ * .join("input2",
By.fieldNames("user").withOuterJoinParticipation())
+ * .crossProductJoin();
+ * }</pre>
+ *
+ * <p>While the above examples use two inputs to mimic SQL's left and right
join semantics, the
+ * {@link CoGroup} transform supports any number of inputs, and outer-join
participation can be
+ * specified on any subset of them.
+ *
+ * <p>Do note that cross-product joins while simpler and easier to program,
can cause
*/
public class CoGroup {
- /**
- * Join by the following field names.
- *
- * <p>The same field names are used in all input PCollections.
- */
- public static Inner byFieldNames(String... fieldNames) {
- return
byFieldAccessDescriptor(FieldAccessDescriptor.withFieldNames(fieldNames));
- }
+ private static final List NULL_LIST;
- /**
- * Join by the following field ids.
- *
- * <p>The same field ids are used in all input PCollections.
- */
- public static Inner byFieldIds(Integer... fieldIds) {
- return
byFieldAccessDescriptor(FieldAccessDescriptor.withFieldIds(fieldIds));
+ static {
+ NULL_LIST = Lists.newArrayList();
+ NULL_LIST.add(null);
Review comment:
I'm not sure it's going to do safe publishing because list is modified after
final value is assigned. What about `NULL_LIST = Arrays.asList((Object) null)`
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Issue Time Tracking
-------------------
Worklog Id: (was: 206397)
Time Spent: 24h 20m (was: 24h 10m)
> Create a library of useful transforms that use schemas
> ------------------------------------------------------
>
> Key: BEAM-4461
> URL: https://issues.apache.org/jira/browse/BEAM-4461
> Project: Beam
> Issue Type: Sub-task
> Components: sdk-java-core
> Reporter: Reuven Lax
> Assignee: Reuven Lax
> Priority: Major
> Labels: triaged
> Time Spent: 24h 20m
> Remaining Estimate: 0h
>
> e.g. JoinBy(fields). Project, Filter, etc.
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