[fix](ES Catalog)Support parse single value for array column (#40614)

be/src/exec/es/es_scroll_parser.cpp

@@ -418,6 +418,159 @@ Status insert_int_value(const rapidjson::Value& col, PrimitiveType type,
     return parse_and_insert_data(col);
 }
 
+template <typename T>
+Status handle_value(const rapidjson::Value& col, PrimitiveType sub_type, bool pure_doc_value,
+                    T& val) {
+    RETURN_IF_ERROR(get_int_value<T>(col, sub_type, &val, pure_doc_value));
+    return Status::OK();
+}
+
+template <>
+Status handle_value<float>(const rapidjson::Value& col, PrimitiveType sub_type, bool pure_doc_value,
+                           float& val) {
+    RETURN_IF_ERROR(get_float_value<float>(col, sub_type, &val, pure_doc_value));
+    return Status::OK();
+}
+
+template <>
+Status handle_value<double>(const rapidjson::Value& col, PrimitiveType sub_type,
+                            bool pure_doc_value, double& val) {
+    RETURN_IF_ERROR(get_float_value<double>(col, sub_type, &val, pure_doc_value));
+    return Status::OK();
+}
+
+template <>
+Status handle_value<std::string>(const rapidjson::Value& col, PrimitiveType sub_type,
+                                 bool pure_doc_value, std::string& val) {
+    RETURN_ERROR_IF_COL_IS_ARRAY(col, sub_type, true);
+    if (!col.IsString()) {
+        val = json_value_to_string(col);
+    } else {
+        val = col.GetString();
+    }
+    return Status::OK();
+}
+
+template <>
+Status handle_value<bool>(const rapidjson::Value& col, PrimitiveType sub_type, bool pure_doc_value,
+                          bool& val) {
+    if (col.IsBool()) {
+        val = col.GetBool();
+        return Status::OK();
+    }
+
+    if (col.IsNumber()) {
+        val = col.GetInt();
+        return Status::OK();
+    }
+
+    bool is_nested_str = false;
+    if (pure_doc_value && col.IsArray() && !col.Empty() && col[0].IsBool()) {
+        val = col[0].GetBool();
+        return Status::OK();
+    } else if (pure_doc_value && col.IsArray() && !col.Empty() && col[0].IsString()) {
+        is_nested_str = true;
+    } else if (pure_doc_value && col.IsArray()) {
+        return Status::InternalError(ERROR_INVALID_COL_DATA, "BOOLEAN");
+    }
+
+    const rapidjson::Value& str_col = is_nested_str ? col[0] : col;
+    const std::string& str_val = str_col.GetString();
+    size_t val_size = str_col.GetStringLength();
+    StringParser::ParseResult result;
+    val = StringParser::string_to_bool(str_val.c_str(), val_size, &result);
+    RETURN_ERROR_IF_PARSING_FAILED(result, str_col, sub_type);
+    return Status::OK();
+}
+
+template <typename T>
+Status process_single_column(const rapidjson::Value& col, PrimitiveType sub_type,
+                             bool pure_doc_value, vectorized::Array& array) {
+    T val;
+    RETURN_IF_ERROR(handle_value<T>(col, sub_type, pure_doc_value, val));
+    array.push_back(val);
+    return Status::OK();
+}
+
+template <typename T>
+Status process_column_array(const rapidjson::Value& col, PrimitiveType sub_type,
+                            bool pure_doc_value, vectorized::Array& array) {
+    for (const auto& sub_col : col.GetArray()) {
+        RETURN_IF_ERROR(process_single_column<T>(sub_col, sub_type, pure_doc_value, array));
+    }
+    return Status::OK();
+}
+
+template <typename T>
+Status process_column(const rapidjson::Value& col, PrimitiveType sub_type, bool pure_doc_value,
+                      vectorized::Array& array) {
+    if (!col.IsArray()) {
+        return process_single_column<T>(col, sub_type, pure_doc_value, array);
+    } else {
+        return process_column_array<T>(col, sub_type, pure_doc_value, array);
+    }
+}
+
+template <typename DateType, typename RT>
+Status process_date_column(const rapidjson::Value& col, PrimitiveType sub_type, bool pure_doc_value,
+                           vectorized::Array& array, const cctz::time_zone& time_zone) {
+    if (!col.IsArray()) {
+        RT data;
+        RETURN_IF_ERROR(
+                (get_date_int<DateType, RT>(col, sub_type, pure_doc_value, &data, time_zone)));
+        array.push_back(data);
+    } else {
+        for (const auto& sub_col : col.GetArray()) {
+            RT data;
+            RETURN_IF_ERROR((get_date_int<DateType, RT>(sub_col, sub_type, pure_doc_value, &data,
+                                                        time_zone)));
+            array.push_back(data);
+        }
+    }
+    return Status::OK();
+}
+
+Status ScrollParser::parse_column(const rapidjson::Value& col, PrimitiveType sub_type,
+                                  bool pure_doc_value, vectorized::Array& array,
+                                  const cctz::time_zone& time_zone) {
+    switch (sub_type) {
+    case TYPE_CHAR:
+    case TYPE_VARCHAR:
+    case TYPE_STRING:
+        return process_column<std::string>(col, sub_type, pure_doc_value, array);
+    case TYPE_TINYINT:
+        return process_column<int8_t>(col, sub_type, pure_doc_value, array);
+    case TYPE_SMALLINT:
+        return process_column<int16_t>(col, sub_type, pure_doc_value, array);
+    case TYPE_INT:
+        return process_column<int32>(col, sub_type, pure_doc_value, array);
+    case TYPE_BIGINT:
+        return process_column<int64_t>(col, sub_type, pure_doc_value, array);
+    case TYPE_LARGEINT:
+        return process_column<__int128>(col, sub_type, pure_doc_value, array);
+    case TYPE_FLOAT:
+        return process_column<float>(col, sub_type, pure_doc_value, array);
+    case TYPE_DOUBLE:
+        return process_column<double>(col, sub_type, pure_doc_value, array);
+    case TYPE_BOOLEAN:
+        return process_column<bool>(col, sub_type, pure_doc_value, array);
+    // date/datetime v2 is the default type for catalog table,
+    // see https://github.com/apache/doris/pull/16304
+    // No need to support date and datetime types.
+    case TYPE_DATEV2: {
+        return process_date_column<vectorized::DateV2Value<vectorized::DateV2ValueType>, uint32_t>(
+                col, sub_type, pure_doc_value, array, time_zone);
+    }
+    case TYPE_DATETIMEV2: {
+        return process_date_column<vectorized::DateV2Value<vectorized::DateTimeV2ValueType>,
+                                   uint64_t>(col, sub_type, pure_doc_value, array, time_zone);
+    }
+    default:
+        LOG(ERROR) << "Do not support Array type: " << sub_type;
+        return Status::InternalError("Unsupported type");
+    }
+}
+
 ScrollParser::ScrollParser(bool doc_value_mode) : _size(0), _line_index(0) {}
 
 ScrollParser::~ScrollParser() = default;
@@ -684,129 +837,7 @@ Status ScrollParser::fill_columns(const TupleDescriptor* tuple_desc,
         case TYPE_ARRAY: {
             vectorized::Array array;
             const auto& sub_type = tuple_desc->slots()[i]->type().children[0].type;
-            RETURN_ERROR_IF_COL_IS_ARRAY(col, type, false);
-            for (const auto& sub_col : col.GetArray()) {
-                switch (sub_type) {
-                case TYPE_CHAR:
-                case TYPE_VARCHAR:
-                case TYPE_STRING: {
-                    std::string val;
-                    RETURN_ERROR_IF_COL_IS_ARRAY(sub_col, sub_type, true);
-                    if (!sub_col.IsString()) {
-                        val = json_value_to_string(sub_col);
-                    } else {
-                        val = sub_col.GetString();
-                    }
-                    array.push_back(val);
-                    break;
-                }
-                case TYPE_TINYINT: {
-                    int8_t val;
-                    RETURN_IF_ERROR(get_int_value<int8_t>(sub_col, sub_type, &val, pure_doc_value));
-                    array.push_back(val);
-                    break;
-                }
-                case TYPE_SMALLINT: {
-                    int16_t val;
-                    RETURN_IF_ERROR(
-                            get_int_value<int16_t>(sub_col, sub_type, &val, pure_doc_value));
-                    array.push_back(val);
-                    break;
-                }
-                case TYPE_INT: {
-                    int32 val;
-                    RETURN_IF_ERROR(get_int_value<int32>(sub_col, sub_type, &val, pure_doc_value));
-                    array.push_back(val);
-                    break;
-                }
-                case TYPE_BIGINT: {
-                    int64_t val;
-                    RETURN_IF_ERROR(
-                            get_int_value<int64_t>(sub_col, sub_type, &val, pure_doc_value));
-                    array.push_back(val);
-                    break;
-                }
-                case TYPE_LARGEINT: {
-                    __int128 val;
-                    RETURN_IF_ERROR(
-                            get_int_value<__int128>(sub_col, sub_type, &val, pure_doc_value));
-                    array.push_back(val);
-                    break;
-                }
-                case TYPE_FLOAT: {
-                    float val {};
-                    RETURN_IF_ERROR(
-                            get_float_value<float>(sub_col, sub_type, &val, pure_doc_value));
-                    array.push_back(val);
-                    break;
-                }
-                case TYPE_DOUBLE: {
-                    double val {};
-                    RETURN_IF_ERROR(
-                            get_float_value<double>(sub_col, sub_type, &val, pure_doc_value));
-                    array.push_back(val);
-                    break;
-                }
-                case TYPE_BOOLEAN: {
-                    if (sub_col.IsBool()) {
-                        array.push_back(sub_col.GetBool());
-                        break;
-                    }
-
-                    if (sub_col.IsNumber()) {
-                        array.push_back(sub_col.GetInt());
-                        break;
-                    }
-
-                    bool is_nested_str = false;
-                    if (pure_doc_value && sub_col.IsArray() && !sub_col.Empty() &&
-                        sub_col[0].IsBool()) {
-                        array.push_back(sub_col[0].GetBool());
-                        break;
-                    } else if (pure_doc_value && sub_col.IsArray() && !sub_col.Empty() &&
-                               sub_col[0].IsString()) {
-                        is_nested_str = true;
-                    } else if (pure_doc_value && sub_col.IsArray()) {
-                        return Status::InternalError(ERROR_INVALID_COL_DATA, "BOOLEAN");
-                    }
-
-                    const rapidjson::Value& str_col = is_nested_str ? sub_col[0] : sub_col;
-
-                    const std::string& val = str_col.GetString();
-                    size_t val_size = str_col.GetStringLength();
-                    StringParser::ParseResult result;
-                    bool b = StringParser::string_to_bool(val.c_str(), val_size, &result);
-                    RETURN_ERROR_IF_PARSING_FAILED(result, str_col, type);
-                    array.push_back(b);
-                    break;
-                }
-                // date/datetime v2 is the default type for catalog table,
-                // see https://github.com/apache/doris/pull/16304
-                // No need to support date and datetime types.
-                case TYPE_DATEV2: {
-                    uint32_t data;
-                    RETURN_IF_ERROR(
-                            (get_date_int<vectorized::DateV2Value<vectorized::DateV2ValueType>,
-                                          uint32_t>(sub_col, sub_type, pure_doc_value, &data,
-                                                    time_zone)));
-                    array.push_back(data);
-                    break;
-                }
-                case TYPE_DATETIMEV2: {
-                    uint64_t data;
-                    RETURN_IF_ERROR(
-                            (get_date_int<vectorized::DateV2Value<vectorized::DateTimeV2ValueType>,
-                                          uint64_t>(sub_col, sub_type, pure_doc_value, &data,
-                                                    time_zone)));
-                    array.push_back(data);
-                    break;
-                }
-                default: {
-                    LOG(ERROR) << "Do not support Array type: " << sub_type;
-                    break;
-                }
-                }
-            }
+            RETURN_IF_ERROR(parse_column(col, sub_type, pure_doc_value, array, time_zone));
             col_ptr->insert(array);
             break;
         }

be/src/exec/es/es_scroll_parser.h

@@ -47,6 +47,8 @@ class ScrollParser {
     int get_size() const;
 
 private:
+    Status parse_column(const rapidjson::Value& col, PrimitiveType sub_type, bool pure_doc_value,
+                        vectorized::Array& array, const cctz::time_zone& time_zone);
     std::string _scroll_id;
     int _size;
     rapidjson::SizeType _line_index;

docker/thirdparties/docker-compose/elasticsearch/scripts/data/composite_type_array_bulk.json

@@ -1,10 +1,10 @@
-{"name": "Andy", "sports": "soccer"}
-{"name": "Betty", "sports": "pingpong ball"}
-{"name": "Cindy", "sports": "武术"}
-{"name": "David", "sports": ["volleyball"]}
-{"name": "Emily", "sports": ["baseball", "golf", "hockey"]}
-{"name": "Frank", "sports": ["rugby", "cricket", "boxing"]}
-{"name": "Grace", "sports": ["table tennis", "badminton", "athletics"]}
-{"name": "Henry", "sports": ["archery", "fencing", "weightlifting"]}
-{"name": "Ivy", "sports": ["judo", "karate", "taekwondo"]}
-{"name": "Jack", "sports": ["wrestling", "gymnastics", "surfing"]}
+{"name": "Andy", "sports": "soccer", "scores": 100}
+{"name": "Betty", "sports": "pingpong ball", "scores": 90}
+{"name": "Cindy", "sports": "武术", "scores": 89}
+{"name": "David", "sports": ["volleyball"], "scores": [77]}
+{"name": "Emily", "sports": ["baseball", "golf", "hockey"], "scores": [56, 78, 99]}
+{"name": "Frank", "sports": ["rugby", "cricket", "boxing"], "scores": [45, 67, 88]}
+{"name": "Grace", "sports": ["table tennis", "badminton", "athletics"], "scores": [34, 56, 78]}
+{"name": "Henry", "sports": ["archery", "fencing", "weightlifting"], "scores": [23, 45, 67]}
+{"name": "Ivy", "sports": ["judo", "karate", "taekwondo"], "scores": [12, 34, 56]}
+{"name": "Jack", "sports": ["wrestling", "gymnastics", "surfing"], "scores": [1, 23, 45]}

docker/thirdparties/docker-compose/elasticsearch/scripts/index/array_meta_composite_type_array.json

@@ -2,7 +2,8 @@
   "_meta": {
     "doris":{
       "array_fields":[
-        "sports"
+        "sports",
+        "scores"
       ]
     }
   }

docker/thirdparties/docker-compose/elasticsearch/scripts/index/es6_composite_type_array.json

@@ -7,7 +7,8 @@
     "doc": {
       "properties": {
         "name": { "type": "keyword" },
-        "sports": { "type": "keyword", "doc_values": false}
+        "sports": { "type": "keyword", "doc_values": false},
+        "scores": { "type": "integer", "doc_values": false}
       }
     }
   }

docker/thirdparties/docker-compose/elasticsearch/scripts/index/es7_composite_type_array.json

@@ -6,7 +6,8 @@
   "mappings": {
     "properties": {
       "name": { "type": "keyword" },
-      "sports": { "type": "keyword", "doc_values": false}
+      "sports": { "type": "keyword", "doc_values": false},
+      "scores": { "type": "integer", "doc_values": false}
     }
   }
 }