[FLINK-34876][transform] Support UDF functions in transform

README.md

@@ -35,41 +35,46 @@ full database synchronization, sharding table synchronization, schema evolution
 2. [Download](https://github.com/apache/flink-cdc/releases) Flink CDC tar, unzip it and put jars of pipeline connector to Flink `lib` directory.
 3. Create a **YAML** file to describe the data source and data sink, the following example synchronizes all tables under MySQL app_db database to Doris :
   ```yaml
-    source:
-       type: mysql
-       name: MySQL Source
-       hostname: 127.0.0.1
-       port: 3306
-       username: admin
-       password: pass
-       tables: adb.\.*
-       server-id: 5401-5404
-
-    sink:
-      type: doris
-      name: Doris Sink
-      fenodes: 127.0.0.1:8030
-      username: root
-      password: pass
-
-    transform:
-      - source-table: adb.web_order01
-        projection: \*, UPPER(product_name) as product_name
-        filter: id > 10 AND order_id > 100
-        description: project fields and filter
-      - source-table: adb.web_order02
-        projection: \*, UPPER(product_name) as product_name
-        filter: id > 20 AND order_id > 200
-        description: project fields and filter  
-
-    route:
-      - source-table: adb.web_order\.*
-        sink-table: adb.ods_web_orders
-        description: sync sharding tables to one destination table
-
-    pipeline:
-       name: MySQL to Doris Pipeline
-       parallelism: 4
+   source:
+     type: mysql
+     hostname: localhost
+     port: 3306
+     username: root
+     password: 123456
+     tables: app_db.\.*
+
+   sink:
+     type: doris
+     fenodes: 127.0.0.1:8030
+     username: root
+     password: ""
+
+   transform:
+     - source-table: adb.web_order01
+       projection: \*, format('%S', product_name) as product_name
+       filter: addone(id) > 10 AND order_id > 100
+       description: project fields and filter
+     - source-table: adb.web_order02
+       projection: \*, format('%S', product_name) as product_name
+       filter: addone(id) > 20 AND order_id > 200
+       description: project fields and filter
+
+   route:
+     - source-table: app_db.orders
+       sink-table: ods_db.ods_orders
+     - source-table: app_db.shipments
+       sink-table: ods_db.ods_shipments
+     - source-table: app_db.products
+       sink-table: ods_db.ods_products
+
+   pipeline:
+     name: Sync MySQL Database to Doris
+     parallelism: 2
+     user-defined-function:
+       - name: addone
+         classpath: com.example.functions.AddOneFunctionClass
+       - name: format
+         classpath: com.example.functions.FormatFunctionClass
   ```
 4. Submit pipeline job using `flink-cdc.sh` script.
  ```shell

docs/content.zh/docs/core-concept/data-pipeline.md

@@ -79,15 +79,15 @@ We could use following yaml file to define a complicated Data Pipeline describin
      fenodes: 127.0.0.1:8030
      username: root
      password: ""
-  
+
    transform:
      - source-table: adb.web_order01
-       projection: \*, UPPER(product_name) as product_name
-       filter: id > 10 AND order_id > 100
+       projection: \*, format('%S', product_name) as product_name
+       filter: addone(id) > 10 AND order_id > 100
        description: project fields and filter
      - source-table: adb.web_order02
-       projection: \*, UPPER(product_name) as product_name
-       filter: id > 20 AND order_id > 200
+       projection: \*, format('%S', product_name) as product_name
+       filter: addone(id) > 20 AND order_id > 200
        description: project fields and filter
 
    route:
@@ -96,11 +96,16 @@ We could use following yaml file to define a complicated Data Pipeline describin
      - source-table: app_db.shipments
        sink-table: ods_db.ods_shipments
      - source-table: app_db.products
-       sink-table: ods_db.ods_products  
+       sink-table: ods_db.ods_products
 
    pipeline:
      name: Sync MySQL Database to Doris
      parallelism: 2
+     user-defined-function:
+       - name: addone
+         classpath: com.example.functions.AddOneFunctionClass
+       - name: format
+         classpath: com.example.functions.FormatFunctionClass
 ```
 
 # Pipeline Configurations

docs/content.zh/docs/core-concept/transform.md

@@ -266,6 +266,75 @@ transform:
     description: classification mapping example
 ```
 
+## User-defined Functions
+
+User-defined functions (UDFs) can be used in transform rules.
+
+Classes could be used as a UDF if:
+
+* implements `org.apache.flink.cdc.common.udf.UserDefinedFunction` interface
+* has a public constructor with no parameters
+* has at least one public method named `eval`
+
+It may also:
+
+* overrides `getReturnType` method to indicate its return CDC type
+* overrides `open` and `close` method to do some initialization and cleanup work
+
+For example, this is a valid UDF class:
+
+```java
+public class AddOneFunctionClass implements UserDefinedFunction {
+    
+    public Object eval(Integer num) {
+        return num + 1;
+    }
+    
+    @Override
+    public DataType getReturnType() {
+        return DataTypes.INT();
+    }
+    
+    @Override
+    public void open() throws Exception {
+        // ...
+    }
+
+    @Override
+    public void close() throws Exception {
+        // ...
+    }
+}
+```
+
+To ease the migration from Flink SQL to Flink CDC, a Flink `ScalarFunction` could also be used as a transform UDF, with some limitations:
+
+* `ScalarFunction` with parameters is not supported.
+* Flink-style type hint in `ScalarFunction` will be ignored.
+* `open` / `close` lifecycle hooks will not be invoked.
+
+UDF classes could be registered by adding a `user-defined-function` block:
+
+```yaml
+pipeline:
+  user-defined-function:
+    - name: addone
+      classpath: org.apache.flink.cdc.udf.examples.java.AddOneFunctionClass
+    - name: format
+      classpath: org.apache.flink.cdc.udf.examples.java.FormatFunctionClass
+```
+
+Notice that given classpath must be fully-qualified, and corresponding `jar` files must be included in Flink `/lib` folder, or be passed with `flink-cdc.sh --jar` option.
+
+After being correctly registered, UDFs could be used in both `projection` and `filter` expressions, just like built-in functions:
+
+```yaml
+transform:
+  - source-table: db.\.*
+    projection: "*, inc(inc(inc(id))) as inc_id, format(id, 'id -> %d') as formatted_id"
+    filter: inc(id) < 100
+```
+
 # Known limitations
 * Currently, transform doesn't work with route rules. It will be supported in future versions.
 * Computed columns cannot reference trimmed columns that do not present in final projection results. This will be fixed in future versions.

docs/content/docs/core-concept/data-pipeline.md

@@ -97,17 +97,33 @@ We could use following yaml file to define a complicated Data Pipeline describin
      fenodes: 127.0.0.1:8030
      username: root
      password: ""
+
+   transform:
+     - source-table: adb.web_order01
+       projection: \*, format('%S', product_name) as product_name
+       filter: addone(id) > 10 AND order_id > 100
+       description: project fields and filter
+     - source-table: adb.web_order02
+       projection: \*, format('%S', product_name) as product_name
+       filter: addone(id) > 20 AND order_id > 200
+       description: project fields and filter
+
    route:
      - source-table: app_db.orders
        sink-table: ods_db.ods_orders
      - source-table: app_db.shipments
        sink-table: ods_db.ods_shipments
      - source-table: app_db.products
-       sink-table: ods_db.ods_products  
+       sink-table: ods_db.ods_products
 
    pipeline:
      name: Sync MySQL Database to Doris
      parallelism: 2
+     user-defined-function:
+       - name: addone
+         classpath: com.example.functions.AddOneFunctionClass
+       - name: format
+         classpath: com.example.functions.FormatFunctionClass
 ```
 
 # Pipeline Configurations

docs/content/docs/core-concept/transform.md

@@ -266,6 +266,75 @@ transform:
     description: classification mapping example
 ```
 
+## User-defined Functions
+
+User-defined functions (UDFs) can be used in transform rules.
+
+Classes could be used as a UDF if:
+
+* implements `org.apache.flink.cdc.common.udf.UserDefinedFunction` interface
+* has a public constructor with no parameters
+* has at least one public method named `eval`
+
+It may also:
+
+* overrides `getReturnType` method to indicate its return CDC type
+* overrides `open` and `close` method to do some initialization and cleanup work
+
+For example, this is a valid UDF class:
+
+```java
+public class AddOneFunctionClass implements UserDefinedFunction {
+    
+    public Object eval(Integer num) {
+        return num + 1;
+    }
+    
+    @Override
+    public DataType getReturnType() {
+        return DataTypes.INT();
+    }
+    
+    @Override
+    public void open() throws Exception {
+        // ...
+    }
+
+    @Override
+    public void close() throws Exception {
+        // ...
+    }
+}
+```
+
+To ease the migration from Flink SQL to Flink CDC, a Flink `ScalarFunction` could also be used as a transform UDF, with some limitations:
+
+* `ScalarFunction` with parameters is not supported. 
+* Flink-style type hint in `ScalarFunction` will be ignored.
+* `open` / `close` lifecycle hooks will not be invoked.
+
+UDF classes could be registered by adding a `user-defined-function` block:
+
+```yaml
+pipeline:
+  user-defined-function:
+    - name: addone
+      classpath: org.apache.flink.cdc.udf.examples.java.AddOneFunctionClass
+    - name: format
+      classpath: org.apache.flink.cdc.udf.examples.java.FormatFunctionClass
+```
+
+Notice that given classpath must be fully-qualified, and corresponding `jar` files must be included in Flink `/lib` folder, or be passed with `flink-cdc.sh --jar` option.
+
+After being correctly registered, UDFs could be used in both `projection` and `filter` expressions, just like built-in functions:
+
+```yaml
+transform:
+  - source-table: db.\.*
+    projection: "*, inc(inc(inc(id))) as inc_id, format(id, 'id -> %d') as formatted_id"
+    filter: inc(id) < 100
+```
+
 # Known limitations
 * Currently, transform doesn't work with route rules. It will be supported in future versions.
 * Computed columns cannot reference trimmed columns that do not present in final projection results. This will be fixed in future versions.

flink-cdc-cli/src/main/java/org/apache/flink/cdc/cli/parser/YamlPipelineDefinitionParser.java

@@ -24,10 +24,12 @@
 import org.apache.flink.cdc.composer.definition.SinkDef;
 import org.apache.flink.cdc.composer.definition.SourceDef;
 import org.apache.flink.cdc.composer.definition.TransformDef;
+import org.apache.flink.cdc.composer.definition.UdfDef;
 
 import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.core.type.TypeReference;
 import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.JsonNode;
 import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.ObjectMapper;
+import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.node.ObjectNode;
 import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.dataformat.yaml.YAMLFactory;
 
 import java.nio.file.Path;
@@ -64,6 +66,11 @@ public class YamlPipelineDefinitionParser implements PipelineDefinitionParser {
     private static final String TRANSFORM_FILTER_KEY = "filter";
     private static final String TRANSFORM_DESCRIPTION_KEY = "description";
 
+    // UDF related keys
+    private static final String UDF_KEY = "user-defined-function";
+    private static final String UDF_FUNCTION_NAME_KEY = "name";
+    private static final String UDF_CLASSPATH_KEY = "classpath";
+
     public static final String TRANSFORM_PRIMARY_KEY_KEY = "primary-keys";
 
     public static final String TRANSFORM_PARTITION_KEY_KEY = "partition-keys";
@@ -107,6 +114,11 @@ public PipelineDef parse(Path pipelineDefPath, Configuration globalPipelineConfi
         Optional.ofNullable(root.get(ROUTE_KEY))
                 .ifPresent(node -> node.forEach(route -> routeDefs.add(toRouteDef(route))));
 
+        // UDFs are optional
+        List<UdfDef> udfDefs = new ArrayList<>();
+        Optional.ofNullable(((ObjectNode) root.get(PIPELINE_KEY)).remove(UDF_KEY))
+                .ifPresent(node -> node.forEach(udf -> udfDefs.add(toUdfDef(udf))));
+
         // Pipeline configs are optional
         Configuration userPipelineConfig = toPipelineConfig(root.get(PIPELINE_KEY));
 
@@ -115,7 +127,8 @@ public PipelineDef parse(Path pipelineDefPath, Configuration globalPipelineConfi
         pipelineConfig.addAll(globalPipelineConfig);
         pipelineConfig.addAll(userPipelineConfig);
 
-        return new PipelineDef(sourceDef, sinkDef, routeDefs, transformDefs, pipelineConfig);
+        return new PipelineDef(
+                sourceDef, sinkDef, routeDefs, transformDefs, udfDefs, pipelineConfig);
     }
 
     private SourceDef toSourceDef(JsonNode sourceNode) {
@@ -176,6 +189,23 @@ private RouteDef toRouteDef(JsonNode routeNode) {
         return new RouteDef(sourceTable, sinkTable, replaceSymbol, description);
     }
 
+    private UdfDef toUdfDef(JsonNode udfNode) {
+        String functionName =
+                checkNotNull(
+                                udfNode.get(UDF_FUNCTION_NAME_KEY),
+                                "Missing required field \"%s\" in UDF configuration",
+                                UDF_FUNCTION_NAME_KEY)
+                        .asText();
+        String classPath =
+                checkNotNull(
+                                udfNode.get(UDF_CLASSPATH_KEY),
+                                "Missing required field \"%s\" in UDF configuration",
+                                UDF_CLASSPATH_KEY)
+                        .asText();
+
+        return new UdfDef(functionName, classPath);
+    }
+
     private TransformDef toTransformDef(JsonNode transformNode) {
         String sourceTable =
                 checkNotNull(