[GitHub] [pinot] GSharayu commented on a diff in pull request #10394: Pinot Spark Connector for Spark3

["GSharayu (via GitHub)" <gi...@apache.org>]

GSharayu commented on code in PR #10394:
URL: https://github.com/apache/pinot/pull/10394#discussion_r1131253107


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pinot-connectors/pinot-spark-3-connector/documentation/read_model.md:
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+# Read Model
+
+Connector can scan offline, hybrid and realtime tables. Base two options <`table` and `tableType`> parameters have to given like below;
+- For offline table: `table: tbl`, `tableType: OFFLINE or offline`
+- For realtime table `table: tbl`, `tableType: REALTIME or realtime`
+- For hybrid table `table: tbl`, `tableType: HYBRID or hybrid`
+
+An example scan;
+
+```scala
+val df = spark.read
+      .format("pinot")
+      .option("table", "airlineStats")
+      .option("tableType", "offline")
+      .load()
+```
+
+Custom schema can be specified directly. If schema is not specified, connector read table schema from Pinot controller, and then convert to the Spark schema. 
+
+### Architecture
+
+Connector reads data from `Pinot Servers` directly. For this operation, firstly, connector creates query with given filters(if filter push down is enabled) and columns, then finds routing table for created query. It creates pinot splits that contains **ONE PINOT SERVER and ONE OR MORE SEGMENT per spark partition**, based on the routing table and `segmentsPerSplit`(detailed explain is defined below). Lastly, each partition read data from specified pinot server in parallel.
+
+![Spark-Pinot Connector Architecture](images/spark-pinot-connector-executor-server-interaction.jpg)
+
+Each Spark partition open connection with Pinot server, and read data. For example, assume that routing table informations for specified query is like that:
+
+```
+- realtime ->
+   - realtimeServer1 -> (segment1, segment2, segment3)
+   - realtimeServer2 -> (segment4)
+- offline ->
+   - offlineServer10 -> (segment10, segment20)
+```
+
+If `segmentsPerSplit` is equal to 3, there will be created 3 Spark partition like below;
+
+| Spark Partition  | Queried Pinot Server/Segments |
+| ------------- | ------------- |
+| partition1  | realtimeServer1 / segment1, segment2, segment3  |
+| partition2  | realtimeServer2 / segment4  |
+| partition3  | offlineServer10 / segment10, segment20 |
+
+If `segmentsPerSplit` is equal to 1, there will be created 6 Spark partition;
+
+| Spark Partition  | Queried Pinot Server/Segments |
+| ------------- | ------------- |
+| partition1  | realtimeServer1 / segment1 |
+| partition2  | realtimeServer1 / segment2  |
+| partition3  | realtimeServer1 / segment3 |
+| partition4  | realtimeServer2 / segment4 |
+| partition5  | offlineServer10 / segment10 |
+| partition6  | offlineServer10 / segment20 |
+
+If `segmentsPerSplit` value is too low, that means more parallelism. But this also mean that a lot of connection will be opened with Pinot servers, and will increase QPS on the Pinot servers. 
+
+If `segmetnsPerSplit` value is too high, that means less parallelism. Each Pinot server will scan more segments per request.  

Review Comment:
   (nit) typo segmentsPerSplit



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