nvliyuan · wjxiz1992 · Jul 5, 2024 · Jul 1, 2024 · Jul 1, 2024 · Jul 2, 2024
diff --git a/docs/additional-functionality/advanced_configs.md b/docs/additional-functionality/advanced_configs.md
@@ -136,6 +136,8 @@ Name | Description | Default Value | Applicable at
 <a name="sql.json.read.decimal.enabled"></a>spark.rapids.sql.json.read.decimal.enabled|When reading a quoted string as a decimal Spark supports reading non-ascii unicode digits, and the RAPIDS Accelerator does not.|true|Runtime
 <a name="sql.json.read.double.enabled"></a>spark.rapids.sql.json.read.double.enabled|JSON reading is not 100% compatible when reading doubles.|true|Runtime
 <a name="sql.json.read.float.enabled"></a>spark.rapids.sql.json.read.float.enabled|JSON reading is not 100% compatible when reading floats.|true|Runtime
+<a name="sql.lore.dumpPath"></a>spark.rapids.sql.lore.dumpPath|The path to dump the LORE nodes' input data. This must be set if spark.rapids.sql.lore.idsToDump has been set. The data of each LORE node will be dumped to a subfolder with name 'loreId-<LORE id>' under this path. For more details, please refer to [the LORE documentation](../dev/lore.md).|None|Runtime
+<a name="sql.lore.idsToDump"></a>spark.rapids.sql.lore.idsToDump|Specify the LORE ids of operators to dump. The format is a comma separated list of LORE ids. For example: "1[0]" will dump partition 0 of input of gpu operator with lore id 1. For more details, please refer to [the LORE documentation](../dev/lore.md). If this is not set, no data will be dumped.|None|Runtime
 <a name="sql.mode"></a>spark.rapids.sql.mode|Set the mode for the Rapids Accelerator. The supported modes are explainOnly and executeOnGPU. This config can not be changed at runtime, you must restart the application for it to take affect. The default mode is executeOnGPU, which means the RAPIDS Accelerator plugin convert the Spark operations and execute them on the GPU when possible. The explainOnly mode allows running queries on the CPU and the RAPIDS Accelerator will evaluate the queries as if it was going to run on the GPU. The explanations of what would have run on the GPU and why are output in log messages. When using explainOnly mode, the default explain output is ALL, this can be changed by setting spark.rapids.sql.explain. See that config for more details.|executeongpu|Startup
 <a name="sql.optimizer.joinReorder.enabled"></a>spark.rapids.sql.optimizer.joinReorder.enabled|When enabled, joins may be reordered for improved query performance|true|Runtime
 <a name="sql.python.gpu.enabled"></a>spark.rapids.sql.python.gpu.enabled|This is an experimental feature and is likely to change in the future. Enable (true) or disable (false) support for scheduling Python Pandas UDFs with GPU resources. When enabled, pandas UDFs are assumed to share the same GPU that the RAPIDs accelerator uses and will honor the python GPU configs|false|Runtime
@@ -404,6 +406,7 @@ Name | SQL Function(s) | Description | Default Value | Notes
 <a name="sql.expression.Last"></a>spark.rapids.sql.expression.Last|`last_value`, `last`|last aggregate operator|true|None|
 <a name="sql.expression.Max"></a>spark.rapids.sql.expression.Max|`max`|Max aggregate operator|true|None|
 <a name="sql.expression.Min"></a>spark.rapids.sql.expression.Min|`min`|Min aggregate operator|true|None|
+<a name="sql.expression.MinBy"></a>spark.rapids.sql.expression.MinBy|`min_by`|MinBy aggregate operator. It may produce different results than CPU when multiple rows in a group have same minimum value in the ordering column and different associated values in the value column.|true|None|
 <a name="sql.expression.Percentile"></a>spark.rapids.sql.expression.Percentile|`percentile`|Aggregation computing exact percentile|true|None|
 <a name="sql.expression.PivotFirst"></a>spark.rapids.sql.expression.PivotFirst| |PivotFirst operator|true|None|
 <a name="sql.expression.StddevPop"></a>spark.rapids.sql.expression.StddevPop|`stddev_pop`|Aggregation computing population standard deviation|true|None|

diff --git a/docs/dev/lore.md b/docs/dev/lore.md
@@ -0,0 +1,70 @@
+---
+layout: page
+title: The Local Replay Framework
+nav_order: 13
+parent: Developer Overview
+---
+
+# Local Replay Framework
+
+## Overview
+
+LORE (the local replay framework) is a tool that allows developer to replay the execution of a 
+gpu operator in local environment, so that developer could debug and profile the operator for 
+performance analysis. In high level it works as follows:
+
+1. Each gpu operator will be assigned a LORE id, which is a unique identifier for the operator. 
+   This id is guaranteed to be unique within the same query, and guaranteed to be same when two 
+   sql executions have same sql, same configuration, and same data. 
+2. In the first run of the query, developer could found the LORE id of the operator they are 
+   interested in by checking spark ui, where LORE id usually appears in the arguments of operator.
+3. In the second run of the query, developer needs to configure the LORE ids of the operators they 
+   are interested in, and LORE will dump the input data of the operator to given path.
+4. Developer could copy the dumped data to local environment, and replay the operator in local 
+   environment.
+
+## Configuration
+
+By default, LORE id will always be generated for operators, but user could disable this behavior 
+by setting `spark.rapids.sql.lore.tag.enabled` to `false`. 
+
+To tell LORE the LORE ids of the operators you are interested in, you need to set 
+`spark.rapids.sql.lore.idsToDump`. For example, you could set it to "1[*], 2[*], 3[*]" to tell 
+LORE to dump all partitions of input data of operators with id 1, 2, or 3. You can also only dump 
+some partition of the operator's input by appending partition numbers to lore ids. For example, 
+"1[0 4-6 7], 2[*]" tell LORE to dump operator with LORE id 1, but only dump partition 0, 4, 5, 6, 
+and 7. But for operator with LORE id 2, it will dump all partitions. 
+
+You also need to set `spark.rapids.sql.lore.dumpPath` to tell LORE where to dump the data, the 
+value of which should point to a directory. All dumped data of a query will live in this 
+directory. A typical directory hierarchy would look like this:
+
+```console
++ loreId-10/
+  - plan.meta
+  + input-0/
+    - rdd.meta
+    + partition-0/
+      - partition.meta
+      - batch-0.parquet
+      - batch-1.parquet
+    + partition-1/
+      - partition.meta
+      - batch-0.parquet
+  + input-1/
+    - rdd.meta
+    + partition-0/
+      - partition.meta
+      - batch-0.parquet
+      - batch-1.parquet
+
++ loreId-15/
+  - plan.meta
+  + input-0/
+    - rdd.meta
+    + partition-0/
+      - partition.meta
+      - batch-0.parquet
+```
+
+
diff --git a/docs/supported_ops.md b/docs/supported_ops.md
@@ -18090,6 +18090,138 @@ are limited.
 <th>UDT</th>
 </tr>
 <tr>
+<td rowSpan="6">MinBy</td>
+<td rowSpan="6">`min_by`</td>
+<td rowSpan="6">MinBy aggregate operator. It may produce different results than CPU when multiple rows in a group have same minimum value in the ordering column and different associated values in the value column.</td>
+<td rowSpan="6">None</td>
+<td rowSpan="3">aggregation</td>
+<td>value</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td><em>PS<br/>UTC is only supported TZ for TIMESTAMP</em></td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td><b>NS</b></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types CALENDAR, UDT</em></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types CALENDAR, UDT</em></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types CALENDAR, UDT</em></td>
+<td><b>NS</b></td>
+</tr>
+<tr>
+<td>ordering</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td><em>PS<br/>UTC is only supported TZ for TIMESTAMP</em></td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td><b>NS</b></td>
+<td><b>NS</b></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, UDT</em></td>
+<td> </td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, UDT</em></td>
+<td><b>NS</b></td>
+</tr>
+<tr>
+<td>result</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td><em>PS<br/>UTC is only supported TZ for TIMESTAMP</em></td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td><b>NS</b></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types CALENDAR, UDT</em></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types CALENDAR, UDT</em></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types CALENDAR, UDT</em></td>
+<td><b>NS</b></td>
+</tr>
+<tr>
+<td rowSpan="3">reduction</td>
+<td>value</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td><em>PS<br/>UTC is only supported TZ for TIMESTAMP</em></td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td><b>NS</b></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types CALENDAR, UDT</em></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types CALENDAR, UDT</em></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types CALENDAR, UDT</em></td>
+<td><b>NS</b></td>
+</tr>
+<tr>
+<td>ordering</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td><em>PS<br/>UTC is only supported TZ for TIMESTAMP</em></td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td><b>NS</b></td>
+<td><b>NS</b></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, UDT</em></td>
+<td> </td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, UDT</em></td>
+<td><b>NS</b></td>
+</tr>
+<tr>
+<td>result</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td><em>PS<br/>UTC is only supported TZ for TIMESTAMP</em></td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td>S</td>
+<td><b>NS</b></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types CALENDAR, UDT</em></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types CALENDAR, UDT</em></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types CALENDAR, UDT</em></td>
+<td><b>NS</b></td>
+</tr>
+<tr>
 <td rowSpan="8">Percentile</td>
 <td rowSpan="8">`percentile`</td>
 <td rowSpan="8">Aggregation computing exact percentile</td>
@@ -18396,6 +18528,32 @@ are limited.
 <td><b>NS</b></td>
 </tr>
 <tr>
+<th>Expression</th>
+<th>SQL Functions(s)</th>
+<th>Description</th>
+<th>Notes</th>
+<th>Context</th>
+<th>Param/Output</th>
+<th>BOOLEAN</th>
+<th>BYTE</th>
+<th>SHORT</th>
+<th>INT</th>
+<th>LONG</th>
+<th>FLOAT</th>
+<th>DOUBLE</th>
+<th>DATE</th>
+<th>TIMESTAMP</th>
+<th>STRING</th>
+<th>DECIMAL</th>
+<th>NULL</th>
+<th>BINARY</th>
+<th>CALENDAR</th>
+<th>ARRAY</th>
+<th>MAP</th>
+<th>STRUCT</th>
+<th>UDT</th>
+</tr>
+<tr>
 <td rowSpan="6">StddevPop</td>
 <td rowSpan="6">`stddev_pop`</td>
 <td rowSpan="6">Aggregation computing population standard deviation</td>
@@ -18529,32 +18687,6 @@ are limited.
 <td> </td>
 </tr>
 <tr>
-<th>Expression</th>
-<th>SQL Functions(s)</th>
-<th>Description</th>
-<th>Notes</th>
-<th>Context</th>
-<th>Param/Output</th>
-<th>BOOLEAN</th>
-<th>BYTE</th>
-<th>SHORT</th>
-<th>INT</th>
-<th>LONG</th>
-<th>FLOAT</th>
-<th>DOUBLE</th>
-<th>DATE</th>
-<th>TIMESTAMP</th>
-<th>STRING</th>
-<th>DECIMAL</th>
-<th>NULL</th>
-<th>BINARY</th>
-<th>CALENDAR</th>
-<th>ARRAY</th>
-<th>MAP</th>
-<th>STRUCT</th>
-<th>UDT</th>
-</tr>
-<tr>
 <td rowSpan="6">StddevSamp</td>
 <td rowSpan="6">`std`, `stddev_samp`, `stddev`</td>
 <td rowSpan="6">Aggregation computing sample standard deviation</td>
@@ -18821,6 +18953,32 @@ are limited.
 <td> </td>
 </tr>
 <tr>
+<th>Expression</th>
+<th>SQL Functions(s)</th>
+<th>Description</th>
+<th>Notes</th>
+<th>Context</th>
+<th>Param/Output</th>
+<th>BOOLEAN</th>
+<th>BYTE</th>
+<th>SHORT</th>
+<th>INT</th>
+<th>LONG</th>
+<th>FLOAT</th>
+<th>DOUBLE</th>
+<th>DATE</th>
+<th>TIMESTAMP</th>
+<th>STRING</th>
+<th>DECIMAL</th>
+<th>NULL</th>
+<th>BINARY</th>
+<th>CALENDAR</th>
+<th>ARRAY</th>
+<th>MAP</th>
+<th>STRUCT</th>
+<th>UDT</th>
+</tr>
+<tr>
 <td rowSpan="6">VariancePop</td>
 <td rowSpan="6">`var_pop`</td>
 <td rowSpan="6">Aggregation computing population variance</td>
@@ -18954,32 +19112,6 @@ are limited.
 <td> </td>
 </tr>
 <tr>
-<th>Expression</th>
-<th>SQL Functions(s)</th>
-<th>Description</th>
-<th>Notes</th>
-<th>Context</th>
-<th>Param/Output</th>
-<th>BOOLEAN</th>
-<th>BYTE</th>
-<th>SHORT</th>
-<th>INT</th>
-<th>LONG</th>
-<th>FLOAT</th>
-<th>DOUBLE</th>
-<th>DATE</th>
-<th>TIMESTAMP</th>
-<th>STRING</th>
-<th>DECIMAL</th>
-<th>NULL</th>
-<th>BINARY</th>
-<th>CALENDAR</th>
-<th>ARRAY</th>
-<th>MAP</th>
-<th>STRUCT</th>
-<th>UDT</th>
-</tr>
-<tr>
 <td rowSpan="6">VarianceSamp</td>
 <td rowSpan="6">`var_samp`, `variance`</td>
 <td rowSpan="6">Aggregation computing sample variance</td>
@@ -19186,6 +19318,32 @@ are limited.
 <td><b>NS</b></td>
 </tr>
 <tr>
+<th>Expression</th>
+<th>SQL Functions(s)</th>
+<th>Description</th>
+<th>Notes</th>
+<th>Context</th>
+<th>Param/Output</th>
+<th>BOOLEAN</th>
+<th>BYTE</th>
+<th>SHORT</th>
+<th>INT</th>
+<th>LONG</th>
+<th>FLOAT</th>
+<th>DOUBLE</th>
+<th>DATE</th>
+<th>TIMESTAMP</th>
+<th>STRING</th>
+<th>DECIMAL</th>
+<th>NULL</th>
+<th>BINARY</th>
+<th>CALENDAR</th>
+<th>ARRAY</th>
+<th>MAP</th>
+<th>STRUCT</th>
+<th>UDT</th>
+</tr>
+<tr>
 <td rowSpan="2">HiveGenericUDF</td>
 <td rowSpan="2"> </td>
 <td rowSpan="2">Hive Generic UDF, the UDF can choose to implement a RAPIDS accelerated interface to get better performance</td>