pvary commented on code in PR #9179:
URL: https://github.com/apache/iceberg/pull/9179#discussion_r1413777549
##########
docs/flink-queries.md:
##########
@@ -277,6 +277,66 @@ DataStream<Row> stream = env.fromSource(source,
WatermarkStrategy.noWatermarks()
"Iceberg Source as Avro GenericRecord", new
GenericRecordAvroTypeInfo(avroSchema));
```
+### Emitting watermarks
+Emitting watermarks from the source itself could be beneficial for several
purposes, like harnessing the
+[Flink Watermark
Alignment](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/datastream/event-time/generating_watermarks/#watermark-alignment),
+or prevent triggering
[windows](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/datastream/operators/windows/)
+too early when reading multiple data files concurrently.
+
+Enable watermark generation for an `IcebergSource` by setting the
`watermarkColumn`.
+The supported column types are `timestamp`, `timestamptz` and `long`.
+Iceberg `timestamp` or `timestamptz` inherently contains the time precision.
So there is no need
+to specify the time unit. But `long` type column doesn't contain time unit
information. Use
+`watermarkTimeUnit` to configure the conversion for long columns.
+
+The watermarks are generated based on column metrics stored for data files and
emitted once per split.
+If multiple smaller files with different time ranges are combined into a
single split, it can increase
+the out-of-orderliness and extra data buffering in the Flink state. The main
purpose of watermark alignment
+is to reduce out-of-orderliness and excess data buffering in the Flink state.
Hence it is recommended to
+set `read.split.open-file-cost` to a very large value to prevent combining
multiple smaller files into a
+single split. Do not forget to consider the additional memory and CPU load
caused by having multiple
+splits in this case.
+
+By default, the column metrics are collected for the first 100 columns of the
table.
+Use [write properties](configuration.md#write-properties) starting with
`write.metadata.metrics` when needed.
+
+```java
+StreamExecutionEnvironment env =
StreamExecutionEnvironment.createLocalEnvironment();
+TableLoader tableLoader =
TableLoader.fromHadoopTable("hdfs://nn:8020/warehouse/path");
+
+// Ordered data file reads with windowing, using a timestamp column
Review Comment:
Changed the examples part... could you please check?
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