# FlatMap 类
对集合中的每个 `DynamicFrame` 应用转换。结果不会拼凑成单个 `DynamicFrame`，而是作为一个集合保存。
## FlatMap 的示例
以下示例片段展示了当应用于 `FlatMap` 时，如何对动态帧集合使用 `ResolveChoice` 转换。用于输入的数据是位于占位符 Amazon S3 地址 `s3://bucket/path-for-data/sample.json` 的 JSON，包含以下数据。
### JSON 数据示例
```
[{
"firstname": "Arnav",
"lastname": "Desai",
"address": {
"street": "6 Anyroad Avenue",
"city": "London",
"state": "England",
"country": "UK"
},
"phone": 17235550101,
"affiliations": [
"General Anonymous Example Products",
"Example Independent Research",
"Government Department of Examples"
]
},
{
"firstname": "Mary",
"lastname": "Major",
"address": {
"street": "7821 Spot Place",
"city": "Centerville",
"state": "OK",
"country": "US"
},
"phone": 19185550023,
"affiliations": [
"Example Dot Com",
"Example Independent Research",
"Example.io"
]
},
{
"firstname": "Paulo",
"lastname": "Santos",
"address": {
"street": "123 Maple Street",
"city": "London",
"state": "Ontario",
"country": "CA"
},
"phone": 12175550181,
"affiliations": [
"General Anonymous Example Products",
"Example Dot Com"
]
}]
```
**Example 将 ResolveChoice 应用于 DynamicFrameCollection，将显示输出。**
```
#Read DynamicFrame
datasource = glueContext.create_dynamic_frame_from_options("s3", connection_options = {"paths":["s3://bucket/path/to/file/mysamplejson.json"]}, format="json")
datasource.printSchema()
datasource.show()
## Split to create a DynamicFrameCollection
split_frame=datasource.split_fields(["firstname","lastname","address"],"personal_info","business_info")
split_frame.keys()
print("---")
## Use FlatMap to run ResolveChoice
kwargs = {"choice": "cast:string"}
flat = FlatMap.apply(split_frame, ResolveChoice, frame_name="frame", transformation_ctx='tcx', **kwargs)
flat.keys()
##Select one of the DynamicFrames
personal_info = flat.select("personal_info")
personal_info.printSchema()
personal_info.show()
print("---")
business_info = flat.select("business_info")
business_info.printSchema()
business_info.show()
```
调用 `FlatMap.apply` 时，`frame_name` 参数**必须**为 `"frame"`。目前不接受其他值。
### 示例输出
```
root
|-- firstname: string
|-- lastname: string
|-- address: struct
| |-- street: string
| |-- city: string
| |-- state: string
| |-- country: string
|-- phone: long
|-- affiliations: array
| |-- element: string
---
{
"firstname": "Mary",
"lastname": "Major",
"address": {
"street": "7821 Spot Place",
"city": "Centerville",
"state": "OK",
"country": "US"
},
"phone": 19185550023,
"affiliations": [
"Example Dot Com",
"Example Independent Research",
"Example.io"
]
}
{
"firstname": "Paulo",
"lastname": "Santos",
"address": {
"street": "123 Maple Street",
"city": "London",
"state": "Ontario",
"country": "CA"
},
"phone": 12175550181,
"affiliations": [
"General Anonymous Example Products",
"Example Dot Com"
]
}
---
root
|-- firstname: string
|-- lastname: string
|-- address: struct
| |-- street: string
| |-- city: string
| |-- state: string
| |-- country: string
{
"firstname": "Mary",
"lastname": "Major",
"address": {
"street": "7821 Spot Place",
"city": "Centerville",
"state": "OK",
"country": "US"
}
}
{
"firstname": "Paulo",
"lastname": "Santos",
"address": {
"street": "123 Maple Street",
"city": "London",
"state": "Ontario",
"country": "CA"
}
}
---
root
|-- phone: long
|-- affiliations: array
| |-- element: string
{
"phone": 19185550023,
"affiliations": [
"Example Dot Com",
"Example Independent Research",
"Example.io"
]
}
{
"phone": 12175550181,
"affiliations": [
"General Anonymous Example Products",
"Example Dot Com"
]
}
```
## 方法
+ [\$1\$1call\$1\$1](#aws-glue-api-crawler-pyspark-transforms-flat-map-__call__)
+ [Apply](#aws-glue-api-crawler-pyspark-transforms-flat-map-apply)
+ [名称](#aws-glue-api-crawler-pyspark-transforms-flat-map-name)
+ [describeArgs](#aws-glue-api-crawler-pyspark-transforms-flat-map-describeArgs)
+ [describeReturn](#aws-glue-api-crawler-pyspark-transforms-flat-map-describeReturn)
+ [describeTransform](#aws-glue-api-crawler-pyspark-transforms-flat-map-describeTransform)
+ [describeErrors](#aws-glue-api-crawler-pyspark-transforms-flat-map-describeErrors)
+ [描述](#aws-glue-api-crawler-pyspark-transforms-flat-map-describe)
## frame\$1name \$1\$1call\$1\$1 (dfc，BaseTransform transformation\$1ctx = “”，\$1\$1，base\$1kwargs)，
向集合中的每个 `DynamicFrame` 应用转换，并展平结果。
+ `dfc` – 要展平的 `DynamicFrameCollection` (必需)。
+ `BaseTransform` – 派生自 `GlueTransform`、要应用于集合的每个成员的转换 (必需)。
+ `frame_name` – 要将集合元素传递到的参数名称 (必需)。
+ `transformation_ctx` – 用于标识状态信息的唯一字符串 (可选)。
+ `base_kwargs` – 要传递到基本转换的参数 (必需)。
通过将返回新 `DynamicFrameCollection` 创建的每个转换 `DynamicFrame` 在源 `DynamicFrameCollection`。
## apply(cls, \$1args, \$1\$1kwargs)
继承自 `GlueTransform` [apply](aws-glue-api-crawler-pyspark-transforms-GlueTransform.md#aws-glue-api-crawler-pyspark-transforms-GlueTransform-apply)。
## name(cls)
继承自 `GlueTransform` [名称](aws-glue-api-crawler-pyspark-transforms-GlueTransform.md#aws-glue-api-crawler-pyspark-transforms-GlueTransform-name)。
## describeArgs(cls)
继承自 `GlueTransform` [describeArgs](aws-glue-api-crawler-pyspark-transforms-GlueTransform.md#aws-glue-api-crawler-pyspark-transforms-GlueTransform-describeArgs)。
## describeReturn(cls)
继承自 `GlueTransform` [describeReturn](aws-glue-api-crawler-pyspark-transforms-GlueTransform.md#aws-glue-api-crawler-pyspark-transforms-GlueTransform-describeReturn)。
## describeTransform(cls)
继承自 `GlueTransform` [describeTransform](aws-glue-api-crawler-pyspark-transforms-GlueTransform.md#aws-glue-api-crawler-pyspark-transforms-GlueTransform-describeTransform)。
## describeErrors(cls)
继承自 `GlueTransform` [describeErrors](aws-glue-api-crawler-pyspark-transforms-GlueTransform.md#aws-glue-api-crawler-pyspark-transforms-GlueTransform-describeErrors)。
## describe(cls)
继承自 `GlueTransform` [describe](aws-glue-api-crawler-pyspark-transforms-GlueTransform.md#aws-glue-api-crawler-pyspark-transforms-GlueTransform-describe)。