leesf commented on a change in pull request #1761:
URL: https://github.com/apache/hudi/pull/1761#discussion_r446965016
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File path: docs/_docs/2_2_writing_data.md
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@@ -176,15 +176,49 @@ In some cases, you may want to migrate your existing
table into Hudi beforehand.
## Datasource Writer
-The `hudi-spark` module offers the DataSource API to write (and also read) any
data frame into a Hudi table.
-Following is how we can upsert a dataframe, while specifying the field names
that need to be used
-for `recordKey => _row_key`, `partitionPath => partition` and `precombineKey
=> timestamp`
+The `hudi-spark` module offers the DataSource API to write (and read) a Spark
DataFrame into a Hudi table. There are a number of options available:
+**`HoodieWriteConfig`**:
+
+**TABLE_NAME** (Required)<br>
+
+
+**`DataSourceWriteOptions`**:
+
+**RECORDKEY_FIELD_OPT_KEY** (Required): Primary key field(s). Nested fields
can be specified using the dot notation eg: `a.b.c`. When using multiple
columns as primary key use comma seperated notaion, eg: `"col1,col2,col3,etc"`.
Single or multiple columns as primary key specified by
`KEYGENERATOR_CLASS_OPT_KEY` property.<br>
+Default value: `"uuid"`<br>
+
+**PARTITIONPATH_FIELD_OPT_KEY** (Required): Columns to be used for
partitioning the table. To prevent partitioning, provide empty string as value
eg: `""`. Specify paritioning/no partitioning using
`HIVE_PARTITION_EXTRACTOR_CLASS_OPT_KEY`<br>
+Default value: `"partitionpath"`<br>
+
+**PRECOMBINE_FIELD_OPT_KEY** (Required): When two records have the same key
value, the record with the largest value from the field specified here will be
choosen.<br>
+Default value: `"ts"`<br>
+
+**OPERATION_OPT_KEY**: The [write operations](#write-operations) to use. Note:
this cannot change across writes.<br>
Review comment:
the flag method is not only applicable when using DeltaStreamer, but
also applicable when using HoodieWriteClient/Spark DataSource, in this way, the
ingested batch record would consist of both new insert records and delete
records, that means you would insert and delete records with one batch, but
when setting to `EmptyHoodieRecordPayload`, the ingested batch record is
considered to be all delete records.
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