[HUDI-3161][RFC-47] Add Call Produce Command for Spark SQL (#4607)

rfc/README.md

@@ -70,3 +70,4 @@ The list of all RFCs can be found here.
 | 44 | [Hudi Connector for Presto](./rfc-44/rfc-44.md) | `UNDER REVIEW` |
 | 45 | [Asynchronous Metadata Indexing](./rfc-45/rfc-45.md) | `UNDER REVIEW` |
 | 46 | [Optimizing Record Payload Handling](./rfc-46/rfc-46.md) | `UNDER REVIEW` |
+| 47 | [Add Call Produce Command for Spark SQL](./rfc-47/rfc-47.md) | `UNDER REVIEW` |

rfc/rfc-46/rfc-46.md

@@ -156,4 +156,4 @@ This refactoring will not be modifying any existing Hudi semantics other than th
 logical correctness of the many flows that will be affected by the refactoring we will rely on the existing set of test-suites.
 
 Nevertheless, we will run corresponding set of benchmarks stressing the flows being affected by the refactoring to validate
-that there are considerable performance advantage of abandoning conversion into intermediate representation completely.
+that there are considerable performance advantage of abandoning conversion into intermediate representation completely.

rfc/rfc-47/rfc-47.md

@@ -0,0 +1,111 @@
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+
+# RFC-[47]: Add RFC for Add Call Produce Command for Spark SQL
+
+
+## Proposers
+- @forwardxu
+
+## Approvers
+ - @vinoth @Danny @Raymond 
+
+## Status
+
+JIRA: [https://issues.apache.org/jira/browse/HUDI-3161](https://issues.apache.org/jira/browse/HUDI-3161)
+
+## Abstract
+
+We intend to implement Call Procedure Command for Spark SQL.
+
+## Background
+The Call Procedure Command can solve the problems that DDL and DML functions cannot handle. What I can think of are the following 4 aspects:
+- Commit management
+- Metadata table management
+- Table migration
+- Optimization table(Such as Table services like clustering)
+
+## Implementation
+
+### Extended SQL for CALL Command Syntax
+#### Named arguments
+All procedure arguments are named. When passing arguments by name, arguments can be in any order and any optional argument can be omitted.
+```
+CALL system.procedure_name(arg_name_2 => arg_2, arg_name_1 => arg_1, ... arg_name_n => arg_n)
+```
+#### Positional arguments
+When passing arguments by position, the arguments may be omitted if they are optional.
+```
+CALL system.procedure_name(arg_1, arg_2, ... arg_n)
+```
+*note:* The system here has no practical meaning, the complete procedure name is system.procedure_name.
+
+![](process.png)
+
+As shown in the figure above, The execution process of Call Command consists of two parts, SQL Parser and Procedure Run.
+
+### parse
+In the sql parse stage, we will inject a HoodieSqlParser to spark sql which will parse our extended `CALL` syntax to LogicalPlan. If the HoodieSqlParser failed to parse the sql statement, Spark will route it to Spark SQL parser. So we just need to implement our extended syntax in `HoodieSqlParser`.
+
+### resolution
+In the resolution stage, some Hudi resolution rules will be injected to Spark SQL to resolve our extended LogicalPlan to the resolve plan which is a command plan for `CALL`.
+
+### procedure#call
+The `procedure#call` method will translate the logical plan to Hudi's API call. For example, the `ShowCommitsProcedure` will be translated to Hudi API for showing Hudi timeline's commits.
+
+### examples
+Example 1:
+
+```
+call show_commits_metadata(table => 'test_hudi_table');
+```
+
+|    commit_time   | action |	 partition  |               	file_id              |	previous_commit |	num_writes | num_inserts | num_deletes | num_update_writes | total_errors | total_log_blocks | total_corrupt_logblocks | total_rollback_blocks | total_log_records | total_updated_records_compacted | total_bytes_written|
+|----------------- |  ----  |  ------------ |----------------------------------------| ---------------- |------------- |-------------|-------------|------------------ |------------- |----------------  |-------------------------|---------------------- |-------------------|-------------------------------  |------------------- |
+|20220109225319449 | commit | dt=2021-05-03	| d0073a12-085d-4f49-83e9-402947e7e90a-0 | null             | 	1          | 	1        | 	0          | 0                 | 0            | 0				 | 0					   | 0                     | 0                 | 0                               | 435349             |
+|20220109225311742 | commit	| dt=2021-05-02	| b3b32bac-8a44-4c4d-b433-0cb1bf620f23-0 | 20220109214830592| 	1          | 	1        | 	0	       | 0	               | 0            | 0				 | 0					   | 0                     | 0                 | 0                               | 435340             |
+|20220109225301429 | commit	| dt=2021-05-01	| 0d7298b3-6b55-4cff-8d7d-b0772358b78a-0 | 20220109214830592| 	1          | 	1        | 	0          | 0                 | 0	          | 0				 | 0					   | 0                     | 0                 | 0                               | 435340             |
+|20220109214830592 | commit	| dt=2021-05-01	| 0d7298b3-6b55-4cff-8d7d-b0772358b78a-0 | 20220109191631015| 	0          | 	0        | 	1	       | 0	               | 0            | 0				 | 0					   | 0                     | 0                 | 0                               | 432653             |
+|20220109214830592 | commit	| dt=2021-05-02	| b3b32bac-8a44-4c4d-b433-0cb1bf620f23-0 | 20220109191648181| 	0          | 	0        | 	1	       | 0	               | 0	          | 0				 | 0					   | 0                     | 0                 | 0                               | 432653             |
+|20220109191648181 | commit	| dt=2021-05-02	| b3b32bac-8a44-4c4d-b433-0cb1bf620f23-0 | null             | 	1          | 	1        | 	0          | 0                 | 0            | 0				 | 0					   | 0                     | 0                 | 0                               | 435341             |
+|20220109191631015 | commit	| dt=2021-05-01	| 0d7298b3-6b55-4cff-8d7d-b0772358b78a-0 | null             | 	1          | 	1        | 	0          | 0                 | 0            | 0				 | 0					   | 0                     | 0                 | 0                               | 435341             |
+
+Time taken: 0.844 seconds, Fetched 7 row(s)
+
+Example 2:
+
+```
+call rollback_to_instant(table => 'test_hudi_table', instant_time => '20220109225319449');
+```
+
+| rollback_result |
+| :---------------|
+|    true         |
+
+Time taken: 5.038 seconds, Fetched 1 row(s)
+
+### Spark SQL permissions and security
+For security, please refer to the description of [Spark Security](https://spark.apache.org/docs/latest/security.html).
+
+## Rollout/Adoption Plan
+This is a new feature can use Spark SQL works And does not depend on the Spark version.
+
+## Test Plan
+
+- [☑️️] Unit tests for this feature
+- [✖️] Product integration tests
+- [✖️] Benchmark snapshot query for large tables