{"action":"created","comment":{"url":"https://api.github.com/repos/confluentinc/ksql/pulls/comments/224199706","pull_request_review_id":163508994,"id":224199706,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIyNDE5OTcwNg==","diff_hunk":"@@ -0,0 +1,196 @@\n+/*\n+ * Copyright 2018 Confluent Inc.\n+ *\n+ * Licensed under the Apache License, Version 2.0 (the \"License\");\n+ * you may not use this file except in compliance with the License.\n+ * You may obtain a copy of the License at\n+ *\n+ * http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+package io.confluent.ksql.structured;\n+\n+import static java.util.Collections.emptyMap;\n+import static org.easymock.EasyMock.anyObject;\n+import static org.easymock.EasyMock.verify;\n+import static org.hamcrest.MatcherAssert.assertThat;\n+import static org.hamcrest.Matchers.is;\n+import static org.hamcrest.Matchers.sameInstance;\n+\n+import com.google.common.collect.ImmutableMap;\n+import io.confluent.kafka.schemaregistry.client.SchemaRegistryClient;\n+import io.confluent.ksql.GenericRow;\n+import io.confluent.ksql.function.FunctionRegistry;\n+import io.confluent.ksql.function.KsqlAggregateFunction;\n+import io.confluent.ksql.parser.tree.KsqlWindowExpression;\n+import io.confluent.ksql.parser.tree.WindowExpression;\n+import io.confluent.ksql.util.KsqlConfig;\n+import java.util.List;\n+import java.util.Map;\n+import org.apache.kafka.common.serialization.Serde;\n+import org.apache.kafka.connect.data.Field;\n+import org.apache.kafka.connect.data.Schema;\n+import org.apache.kafka.streams.kstream.Initializer;\n+import org.apache.kafka.streams.kstream.KGroupedStream;\n+import org.apache.kafka.streams.kstream.KTable;\n+import org.apache.kafka.streams.kstream.ValueMapper;\n+import org.apache.kafka.streams.kstream.ValueMapperWithKey;\n+import org.easymock.EasyMock;\n+import org.easymock.EasyMockRunner;\n+import org.easymock.Mock;\n+import org.easymock.MockType;\n+import org.junit.Before;\n+import org.junit.Test;\n+import org.junit.runner.RunWith;\n+\n+@SuppressWarnings(\"unchecked\")\n+@RunWith(EasyMockRunner.class)\n+public class SchemaKGroupedStreamTest {\n+\n+  @Mock(MockType.NICE)\n+  private Schema schema;\n+  @Mock(MockType.NICE)\n+  private KGroupedStream groupedStream;\n+  @Mock(MockType.NICE)\n+  private Field keyField;\n+  @Mock(MockType.NICE)\n+  private List<SchemaKStream> sourceStreams;\n+  @Mock(MockType.NICE)\n+  private KsqlConfig config;\n+  @Mock(MockType.NICE)\n+  private FunctionRegistry funcRegistry;\n+  @Mock(MockType.NICE)\n+  private SchemaRegistryClient srClient;\n+  @Mock(MockType.NICE)\n+  private Initializer initializer;\n+  @Mock(MockType.NICE)\n+  private Serde<GenericRow> topicValueSerDe;\n+  @Mock(MockType.NICE)\n+  private KsqlAggregateFunction windowStartFunc;\n+  @Mock(MockType.NICE)\n+  private KsqlAggregateFunction windowEndFunc;\n+  @Mock(MockType.NICE)\n+  private KsqlAggregateFunction otherFunc;\n+  @Mock(MockType.NICE)\n+  private KTable table;\n+  @Mock(MockType.NICE)\n+  private KTable table2;\n+  @Mock(MockType.NICE)\n+  private WindowExpression windowExp;\n+  @Mock(MockType.NICE)\n+  private KsqlWindowExpression ksqlWindowExp;\n+\n+  private SchemaKGroupedStream schemaGroupedStream;\n+\n+  @Before\n+  public void setUp() {\n+    schemaGroupedStream = new SchemaKGroupedStream(\n+        schema, groupedStream, keyField, sourceStreams, config, funcRegistry, srClient);\n+\n+    EasyMock.expect(windowStartFunc.getFunctionName()).andReturn(\"WindowStart\").anyTimes();\n+    EasyMock.expect(windowEndFunc.getFunctionName()).andReturn(\"WindowEnd\").anyTimes();\n+    EasyMock.expect(otherFunc.getFunctionName()).andReturn(\"NotWindowStartFunc\").anyTimes();\n+    EasyMock.expect(windowExp.getKsqlWindowExpression()).andReturn(ksqlWindowExp).anyTimes();\n+    EasyMock.replay(windowStartFunc, windowEndFunc, otherFunc, windowExp);\n+  }\n+\n+  @Test\n+  public void shouldNoUseSelectMapperForNonWindowed() {","path":"ksql-engine/src/test/java/io/confluent/ksql/structured/SchemaKGroupedStreamTest.java","position":104,"original_position":104,"commit_id":"63d14b3da9e055950af07695859f4541a547bf5e","original_commit_id":"63d14b3da9e055950af07695859f4541a547bf5e","user":{"login":"rodesai","id":2610028,"node_id":"MDQ6VXNlcjI2MTAwMjg=","avatar_url":"https://avatars0.githubusercontent.com/u/2610028?v=4","gravatar_id":"","url":"https://api.github.com/users/rodesai","html_url":"https://github.com/rodesai","followers_url":"https://api.github.com/users/rodesai/followers","following_url":"https://api.github.com/users/rodesai/following{/other_user}","gists_url":"https://api.github.com/users/rodesai/gists{/gist_id}","starred_url":"https://api.github.com/users/rodesai/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/rodesai/subscriptions","organizations_url":"https://api.github.com/users/rodesai/orgs","repos_url":"https://api.github.com/users/rodesai/repos","events_url":"https://api.github.com/users/rodesai/events{/privacy}","received_events_url":"https://api.github.com/users/rodesai/received_events","type":"User","site_admin":false},"body":"nit: shouldNotUse","created_at":"2018-10-10T18:51:04Z","updated_at":"2018-10-10T18:52:49Z","html_url":"https://github.com/confluentinc/ksql/pull/1993#discussion_r224199706","pull_request_url":"https://api.github.com/repos/confluentinc/ksql/pulls/1993","author_association":"CONTRIBUTOR","_links":{"self":{"href":"https://api.github.com/repos/confluentinc/ksql/pulls/comments/224199706"},"html":{"href":"https://github.com/confluentinc/ksql/pull/1993#discussion_r224199706"},"pull_request":{"href":"https://api.github.com/repos/confluentinc/ksql/pulls/1993"}}},"pull_request":{"url":"https://api.github.com/repos/confluentinc/ksql/pulls/1993","id":220045002,"node_id":"MDExOlB1bGxSZXF1ZXN0MjIwMDQ1MDAy","html_url":"https://github.com/confluentinc/ksql/pull/1993","diff_url":"https://github.com/confluentinc/ksql/pull/1993.diff","patch_url":"https://github.com/confluentinc/ksql/pull/1993.patch","issue_url":"https://api.github.com/repos/confluentinc/ksql/issues/1993","number":1993,"state":"open","locked":false,"title":"Add WindowStart and WindowEnd UDFs","user":{"login":"big-andy-coates","id":8012398,"node_id":"MDQ6VXNlcjgwMTIzOTg=","avatar_url":"https://avatars0.githubusercontent.com/u/8012398?v=4","gravatar_id":"","url":"https://api.github.com/users/big-andy-coates","html_url":"https://github.com/big-andy-coates","followers_url":"https://api.github.com/users/big-andy-coates/followers","following_url":"https://api.github.com/users/big-andy-coates/following{/other_user}","gists_url":"https://api.github.com/users/big-andy-coates/gists{/gist_id}","starred_url":"https://api.github.com/users/big-andy-coates/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/big-andy-coates/subscriptions","organizations_url":"https://api.github.com/users/big-andy-coates/orgs","repos_url":"https://api.github.com/users/big-andy-coates/repos","events_url":"https://api.github.com/users/big-andy-coates/events{/privacy}","received_events_url":"https://api.github.com/users/big-andy-coates/received_events","type":"User","site_admin":false},"body":"### Description \r\nFixes #1674 \r\n\r\nRequires #2024 to be merged before it can be completed. Still to do (These will be done in a follow up PR):\r\n- [ ] UDF variant to cover non-aggregate uses.\r\n- [ ] Ensure straight queries work too.\r\n- [ ] documentation!\r\n\r\n\r\nThis PR adds two new UDAFs to allow access to the bounds of a windowed key: `WindowStart()`  and `WindowEnd()`. These can be used to access the window bounds of the current window e.g.\r\n\r\n```\r\n-- Given an existing stream such as:\r\nCREATE STREAM TEST (ID bigint) WITH (kafka_topic='test_topic', value_format='DELIMITED', key='ID');\r\n\r\n-- You can now write something like:\r\nCREATE STREAM S1 as SELECT id, WindowStart(), WindowEnd() FROM test WINDOW TUMBLING (SIZE 30 SECONDS) group by id;\r\n\r\n-- It also supports computation on these values too, e.g.\r\nCREATE STREAM S2 as SELECT id, WindowStart() - 1000, WindowEnd() / 2 FROM test WINDOW TUMBLING (SIZE 30 SECONDS) group by id;\r\n```\r\n\r\n#### Background\r\nBefore v5.0 the message coming out from `WINDOW`ed queries would have a timestamp equal to the start of the window. This was a bug in streams, which has been fixed, meaning that for v.50 the messages from the same queries came out with a timestamp equal to the timestamp of the message that caused the record to be output, i.e. _not_ equal to the start of the window. This change broke our own clickstream demo and possibly other customer use-cases.\r\n\r\nOur own clickstream demo was broken because we use a Connect SMT to extract the records timestamp and make it available, (as the window start time), in ElasticSearch.\r\n\r\n##### Possible fixes\r\nIt was proposed that we could fix the regression by:\r\n\r\n1. Adding a `ROWWINDOW` implicit column to windowed queries, e.g. `SELECT id, ROWWINDOW->start FROM test WINDOW TUMBLING (SIZE 5 SECONDS) GROUP BY id;`\r\n1. Adding a `WindowStart` `WindowEnd` methods, e.g. `SELECT id, WindowStart() FROM test WINDOW TUMBLING (SIZE 5 SECONDS) GROUP BY id;`\r\n1. Adding a SMT for Connect to use to extract the window start from the key.\r\n\r\nOf these, I've looked to implement the `WindowStart` approach. Why? I'll tell you:\r\n\r\n1. `ROWWINDOW` doesn't make conceptual sense as it is _not_ a feature of the source data. The other implicit columns related to the source stream. For example, consider a query like `SELECT id, ROWKEY, ROWWINDOW->start FROM test WINDOW ... `. In this case `ROWKEY` is the key of row from the `test` source.  But `ROWWINDOW` would _not_ be from `test`, it would be the window details of the _output_, not the _intput_.   So I think this would make `ROWWINDOW` very confusing / counterintuitive. \r\n\r\n   To put this another way, selecting `ROWTIME` and `ROWKEY` would be selecting fields on the input schema.  Where as `ROWWINDOW` would be on the output schema. You can have sources that might or might not already be windowed.  So if you have a source that is already windowed, a user might understandably expect `ROWWINDOW` to be the window information of the input, not the output.\r\n\r\n1. Adding a SMT is tricky. We can't easily add a KSQL specific SMT to Apache Connect. We can have one implemented in the KSQL repo that's packaged to a jar that would need to be dropped into Connect's class path to work - but this sucks in terms of UX. \r\n\r\nThat leaves `WindowStart()`... now this probably also falls into the same category as `ROWWINDOW` in that normally the `select` statement is selecting data from the source, where as this is selecting from the output. However, for some reason it seems to fit better - and its also easier to implement. Though I'm still open to other suggestions!\r\n\r\n#### Solution\r\nI've implemented `WindowStart` and `WindowEnd` UDAFs. However, because this is selecting from the output, it can't actually be implemented using just UDAF. So the method itself has no implementation. It's just a placeholder so that `WindowSelectMapper` can detect where in the output row the window bounds should be put.  This is necessary because the `Windowed` key is only available post the `aggregate` step, i.e. in the second `SelectMapper`.\r\n\r\n### Testing done \r\nManual + Json based test. \r\n\r\n### Reviewer checklist\r\n- [ ] Ensure docs are updated if necessary. (eg. if a user visible feature is being added or changed).\r\n- [ ] Ensure relevant issues are linked (description should include text like \"Fixes #<issue 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