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Posted to issues@iceberg.apache.org by GitBox <gi...@apache.org> on 2021/12/16 01:47:11 UTC
[GitHub] [iceberg] hililiwei commented on a change in pull request #3749: Spark: We should probably say why we cannot process the snapshot in SparkMicroBatchStream
hililiwei commented on a change in pull request #3749:
URL: https://github.com/apache/iceberg/pull/3749#discussion_r770165074
##########
File path: site/docs/spark-structured-streaming.md
##########
@@ -26,6 +26,28 @@ As of Spark 3.0, DataFrame reads and writes are supported.
|--------------------------------------------------|----------|------------|------------------------------------------------|
| [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 spark:SparkSession = ...
+val tableIdentifier: String = ...
+
+val df = spark.readStream
+ .format("iceberg")
+ .option(SparkReadOptions.STREAM_FROM_TIMESTAMP, Long.toString(streamStartTimestamp))
+ .load(tableIdentifier)
+```
+
+The `tableIdentifier` can be:
+
+* The fully-qualified path to a HDFS table, like `hdfs://nn:8020/path/to/table`
+* A table name if the table is tracked by a catalog, like `database.table_name`
+
+!!! Note
+ Iceberg only supports read data from snapshot whose type of Data Operations is APPEND\REPLACE\DELETE. In particular if some of your snapshots are of DELETE type, you need to add 'streaming-skip-delete-snapshots' option to skip it, otherwise the task will fail.
Review comment:
I just want to show this:
https://iceberg.apache.org/#maintenance/
##########
File path: site/docs/spark-structured-streaming.md
##########
@@ -26,6 +26,28 @@ As of Spark 3.0, DataFrame reads and writes are supported.
|--------------------------------------------------|----------|------------|------------------------------------------------|
| [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 spark:SparkSession = ...
+val tableIdentifier: String = ...
+
+val df = spark.readStream
+ .format("iceberg")
+ .option(SparkReadOptions.STREAM_FROM_TIMESTAMP, Long.toString(streamStartTimestamp))
+ .load(tableIdentifier)
+```
+
+The `tableIdentifier` can be:
+
+* The fully-qualified path to a HDFS table, like `hdfs://nn:8020/path/to/table`
+* A table name if the table is tracked by a catalog, like `database.table_name`
+
+!!! Note
+ Iceberg only supports read data from snapshot whose type of Data Operations is APPEND\REPLACE\DELETE. In particular if some of your snapshots are of DELETE type, you need to add 'streaming-skip-delete-snapshots' option to skip it, otherwise the task will fail.
Review comment:
I just want to show like this:
https://iceberg.apache.org/#maintenance/
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