[jira] [Assigned] (SPARK-32885) Add DataStreamReader.table API

["Apache Spark (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-32885?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-32885:
------------------------------------

    Assignee: Apache Spark

> Add DataStreamReader.table API
> ------------------------------
>
>                 Key: SPARK-32885
>                 URL: https://issues.apache.org/jira/browse/SPARK-32885
>             Project: Spark
>          Issue Type: New Feature
>          Components: Structured Streaming
>    Affects Versions: 3.1.0
>            Reporter: Yuanjian Li
>            Assignee: Apache Spark
>            Priority: Major
>
> This ticket aims to add a new `table` API in DataStreamReader, which is similar to the table API in DataFrameReader. Users can directly use this API to get a Streaming DataFrame on a table. Below is a simple example:
> Application 1 for initializing and starting the streaming job:
> {code:java}
> val path = "/home/yuanjian.li/runtime/to_be_deleted"
> val tblName = "my_table"
> // Write some data to `my_table`
> spark.range(3).write.format("parquet").option("path", path).saveAsTable(tblName)
> // Read the table as a streaming source, write result to destination directory
> val table = spark.readStream.table(tblName)
> table.writeStream.format("parquet").option("checkpointLocation", "/home/yuanjian.li/runtime/to_be_deleted_ck").start("/home/yuanjian.li/runtime/to_be_deleted_2")
> {code}
> Application 2 for appending new data:
> {code:java}
> // Append new data into the path
> spark.range(5).write.format("parquet").option("path", "/home/yuanjian.li/runtime/to_be_deleted").mode("append").save(){code}
> Check result:
> {code:java}
> // The desitination directory should contains all written data
> spark.read.parquet("/home/yuanjian.li/runtime/to_be_deleted_2").show()
> {code}
>  



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