rdblue commented on a change in pull request #3749:
URL: https://github.com/apache/iceberg/pull/3749#discussion_r776492592
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File path: site/docs/spark-structured-streaming.md
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@@ -24,7 +24,21 @@ As of Spark 3.0, DataFrame reads and writes are supported.
| Feature support | Spark 3.0| Spark 2.4 |
Notes |
|--------------------------------------------------|----------|------------|------------------------------------------------|
-| [DataFrame write](#writing-with-streaming-query) | ✔ | ✔ |
|
+| [DataFrame write](#writing-with-streaming-query) | ✔ | ✔ |
|
+
+## Streaming Reads
+
+Iceberg supports processing incremental data in spark structured streaming
jobs which starts from a historical timestamp:
+
+```scala
+val df = spark.readStream
+ .format("iceberg")
+ .option(SparkReadOptions.STREAM_FROM_TIMESTAMP,
Long.toString(streamStartTimestamp))
+ .load("database.table_name")
+```
+
+!!! Note
+ Iceberg only supports reading data from APPEND snapshots. DELETE\OVERWRITE
snapshots cannot be processed and will cause an exception. To ignore delete
snapshots, add `streaming-skip-delete-snapshots=true` to option.
Review comment:
* There is no need to capitalize append, delete, and overwrite
* "DELETE\OVERWRITE" should be "Delete or overwrite" -- try to stick to
plain language rather than shortcuts
* The last two sentences conflict with one another because the first says
that a delete will cause an exception. Instead, say that overwrite snapshots
will cause an exception and address delete handling in a separate sentence: "By
default, delete snapshots will cause an exception, but deletes may be ignored
by setting ..."
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