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+# Columnar Expression Developer Guide
+
+This is a short guide on adding new columnar expressions in Gazelle.
+
+Currently, the columnar expressions in Gazelle are implemented based on Arrow/gandiva. Developer needs to
+implement a columnar expression class in Gazelle scala code and also add some logic to replace Spark expression
+with the implemented columnar expression. And the native code is implemented in Arrow/gandiva for expression's
+core functionality.
+
+Before native code development, we should check whether the desired function is already implemented
+in gandiva. If so, we can directly use it or just make a few code changes to meet the compatibility with Spark.
+
+Take `regexp_extract` as example.
+
+## Arrow/gandiva Native Code
+
+### functions need to use external C++ libs
+See [arrow/pull/97](https://github.com/oap-project/arrow/pull/97).
+
+Since C++ lib google/RE2 is leveraged, we implemented the core function in a gandiva function holder.
+
+In `extract_holder.h`, we need declare the below functions other than some other necessary functions.
+```
+static Status Make(const FunctionNode &node, std::shared_ptr<ExtractHolder> *holder);
+```
+This function is used to construct `ExtractHolder` and check the legality of input if needed. It will
+be called by `function_holder_registry.h` to register the function holder.
+
+In `gdv_function_stubs.cc`，we need to implement a function called `gdv_fn_regexp_extract_utf8_utf8_int32`,
+which will invoke overloaded `operator()` in `ExtractHolder`. The `operator()` is the core function to do
+the extract work.
+
+We need also register `regexp_extract` in `function_registry_string.cc` (for functions handling strings).
+This exposed function name will be used to create function tree in Gazelle scala code. In this case,
+function holder is required, so we should specify `NativeFunction::kNeedsFunctionHolder` in the registry.
+
+### functions does not need to use external C++ libs
+see [arrow/pull/103](https://github.com/oap-project/arrow/pull/103)
+The `pmod` function uses standard C libs only so it can be precompiled in Gandiva. The idea is similar
+with adding functions using external libs:
+- adding the function implemenation in precompile/xxx.c
+- register the function pointer in the function registry
+Here's also one [detailed guide](https://www.dremio.com/blog/adding-a-user-define-function-to-gandiva/) from Dremio
+
+
+For unit test, please refer to `extract_holder_test.cc`. Here is the compile steps.
+```
+cd arrow/cpp/release-build (create by yourself if not exists)
+cmake -DARROW_DEPENDENCY_SOURCE=BUNDLED -DARROW_CSV=ON -DARROW_GANDIVA_JAVA=ON -DARROW_GANDIVA=ON
+-DARROW_PARQUET=ON -DARROW_HDFS=ON -DARROW_BOOST_USE_SHARED=ON -DARROW_JNI=ON -DARROW_WITH_SNAPPY=ON
+-DARROW_FILESYSTEM=ON -DARROW_JSON=ON -DARROW_WITH_PROTOBUF=ON -DARROW_DATASET=ON -DARROW_IPC=ON
+-DARROW_WITH_LZ4=ON -DARROW_JEMALLOC=OFF -DARROW_BUILD_TESTS=ON ..
+make -j
+./release/gandiva-internals-test
+```
+## Gazelle Scala Code
+
+See [gazelle_plugin/pull/847](https://github.com/oap-project/gazelle_plugin/pull/847).
+
+In scala code, `ColumnarRegExpExtract` is created to replace Spark's `RegExpExtract`.
+
+We can add `buildCheck` to check the input types. For legal types currently not supported in this implementation,
+we can throw an `UnsupportedOperationException` to let the expression fallback. 
+
+In `doColumnarCodeGen`, arrow function node is constructed with gandiva function name `regexp_extract` specified.
+
+The `supportColumnarCodegen` function is used to check if columnar wholestage codegen support is added, please set
+to `false` if it's not implemented.
+
+At last, in `replaceWithColumnarExpression` of `ColumnarExpressionConverter.scala`，we need to replace Spark's
+expression to the implemented columnar expression.