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https://issues.apache.org/jira/browse/BEAM-6133?focusedWorklogId=169934&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-169934
]
ASF GitHub Bot logged work on BEAM-6133:
----------------------------------------
Author: ASF GitHub Bot
Created on: 27/Nov/18 18:25
Start Date: 27/Nov/18 18:25
Worklog Time Spent: 10m
Work Description: apilloud commented on a change in pull request #7141:
[BEAM-6133] [SQL] Add support for TableMacro UDF
URL: https://github.com/apache/beam/pull/7141#discussion_r236784012
##########
File path:
sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamCalciteTable.java
##########
@@ -40,17 +41,21 @@
import org.apache.calcite.schema.TranslatableTable;
/** Adapter from {@link BeamSqlTable} to a calcite Table. */
-class BeamCalciteTable extends AbstractQueryableTable
+public class BeamCalciteTable extends AbstractQueryableTable
implements ModifiableTable, TranslatableTable {
private final BeamSqlTable beamTable;
private final Map<String, String> pipelineOptions;
- public BeamCalciteTable(BeamSqlTable beamTable, Map<String, String>
pipelineOptions) {
+ BeamCalciteTable(BeamSqlTable beamTable, Map<String, String>
pipelineOptions) {
super(Object[].class);
this.beamTable = beamTable;
this.pipelineOptions = pipelineOptions;
}
+ public static BeamCalciteTable of(BeamSqlTable table) {
+ return new BeamCalciteTable(table, Maps.newHashMap());
Review comment:
Instead of `Maps.newHashMap()` how about `ImmutableMap.of()`.
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Issue Time Tracking
-------------------
Worklog Id: (was: 169934)
Time Spent: 50m (was: 40m)
> [SQL] Add support for TableMacro UDF
> ------------------------------------
>
> Key: BEAM-6133
> URL: https://issues.apache.org/jira/browse/BEAM-6133
> Project: Beam
> Issue Type: New Feature
> Components: dsl-sql
> Reporter: Gleb Kanterov
> Assignee: Gleb Kanterov
> Priority: Major
> Time Spent: 50m
> Remaining Estimate: 0h
>
> Now we support only ScalarFunction UDFs. In Calcite, there are other kinds of
> UDFs. With TableMacro UDFs users can connect external data sources in a
> similar way as in TableProvider, but without specifying a schema, or
> enumerating a list of existing tables in advance.
> An example use case is connecting external metadata service and querying
> range of partitions.
> {code}
> SELECT COUNT(*) FROM table(my_udf('dataset', start = '2017-01-01', end =
> '2018-01-01'))
> {code}
> Where the implementation of `my_udf` will connect to this service, get file
> locations for a range of partitions, and translate to PTransform reading it.
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