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https://issues.apache.org/jira/browse/NIFI-1706?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16336812#comment-16336812
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ASF GitHub Bot commented on NIFI-1706:
--------------------------------------
Github user ijokarumawak commented on a diff in the pull request:
https://github.com/apache/nifi/pull/2162#discussion_r163441205
--- Diff:
nifi-nar-bundles/nifi-standard-bundle/nifi-standard-processors/src/main/java/org/apache/nifi/processors/standard/QueryDatabaseTable.java
---
@@ -366,10 +401,22 @@ public void onTrigger(final ProcessContext context,
final ProcessSessionFactory
protected String getQuery(DatabaseAdapter dbAdapter, String tableName,
String columnNames, List<String> maxValColumnNames,
String customWhereClause, Map<String,
String> stateMap) {
+
+ return getQuery(dbAdapter, tableName, "", columnNames,
maxValColumnNames, customWhereClause, stateMap);
--- End diff --
`null` may be preferable over the empty string object for `sqlQuery`.
> Extend QueryDatabaseTable to support arbitrary queries
> ------------------------------------------------------
>
> Key: NIFI-1706
> URL: https://issues.apache.org/jira/browse/NIFI-1706
> Project: Apache NiFi
> Issue Type: Improvement
> Components: Core Framework
> Affects Versions: 1.4.0
> Reporter: Paul Bormans
> Assignee: Peter Wicks
> Priority: Major
> Labels: features
>
> The QueryDatabaseTable is able to observe a configured database table for new
> rows and yield these into the flowfile. The model of an rdbms however is
> often (if not always) normalized so you would need to join various tables in
> order to "flatten" the data into useful events for a processing pipeline as
> can be build with nifi or various tools within the hadoop ecosystem.
> The request is to extend the processor to specify an arbitrary sql query
> instead of specifying the table name + columns.
> In addition (this may be another issue?) it is desired to limit the number of
> rows returned per run. Not just because of bandwidth issue's from the nifi
> pipeline onwards but mainly because huge databases may not be able to return
> so many records within a reasonable time.
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