Java和Python实现 Avro 转换成Parquet格式，chema都是在 Avro 中定义的。这里要尝试的是如何定义Parquet的Schema, 然后据此填充数据并生成Parquet文件。
一、简单字段定义
1、定义 Schema 并生成 Parquet 文件
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import pandas as pdimport pyarrow as paimport pyarrow.parquet as pq# 定义 Schemaschema = pa.schema([ ('id', pa.int32()), ('email', pa.string())])# 准备数据ids = pa.array([1, 2], type = pa.int32())emails = pa.array(['first@example.com', 'second@example.com'], pa.string())# 生成 Parquet 数据batch = pa.RecordBatch.from_arrays( [ids, emails], schema = schema)table = pa.Table.from_batches([batch])# 写 Parquet 文件 plain.parquetpq.write_table(table, 'plain.parquet')import pandas as pdimport pyarrow as paimport pyarrow . parquet as pq# 定义 Schemaschema = pa . schema ( [ ( 'id' , pa . int32 ( ) ) , ( 'email' , pa . string ( ) )] )# 准备数据ids = pa . array ( [ 1 , 2 ] , type = pa . int32 ( ) )emails = pa . array ( [ 'first@example.com' , 'second@example.com' ] , pa . string ( ) )# 生成 Parquet 数据batch = pa . RecordBatch . from_arrays ( [ ids , emails ] , schema = schema)table = pa . Table . from_batches ( [ batch ] )# 写 Parquet 文件 plain.parquetpq . write_table ( table , 'plain.parquet' ) |
2、验证 Parquet 数据文件
我们可以用工具 parquet-tools 来查看 plain.parquet 文件的数据和 Schema
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$ parquet-tools schema plain.parquet message schema { optional int32 id; optional binary email (STRING); } $ parquet-tools cat --json plain.parquet {"id":1,"email":"first@example.com"} {"id":2,"email":"second@example.com"} |
没问题，与我们期望的一致。也可以用 pyarrow 代码来获取其中的 Schema 和数据
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schema = pq.read_schema('plain.parquet')print(schema)df = pd.read_parquet('plain.parquet')print(df.to_json())schema = pq . read_schema ( 'plain.parquet' )print ( schema )df = pd . read_parquet ( 'plain.parquet' )print ( df . to_json ( ) ) |
输出为：
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schema = pq.read_schema('plain.parquet')print(schema)df = pd.read_parquet('plain.parquet')print(df.to_json())schema = pq . read_schema ( 'plain.parquet' )print ( schema )df = pd . read_parquet ( 'plain.parquet' )print ( df . to_json ( ) ) |
二、含嵌套字段定义
下面的 Schema 定义加入一个嵌套对象，在 address 下分 email_address 和 post_address，Schema 定义及生成 Parquet 文件的代码如下
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import pandas as pdimport pyarrow as paimport pyarrow.parquet as pq# 内部字段address_fields = [ ('email_address', pa.string()), ('post_address', pa.string()),]# 定义 Parquet Schema，address 嵌套了 address_fieldsschema = pa.schema(j)# 准备数据ids = pa.array([1, 2], type = pa.int32())addresses = pa.array( [('first@example.com', 'city1'), ('second@example.com', 'city2')], pa.struct(address_fields))# 生成 Parquet 数据batch = pa.RecordBatch.from_arrays( [ids, addresses], schema = schema)table = pa.Table.from_batches([batch])# 写 Parquet 数据到文件pq.write_table(table, 'nested.parquet')import pandas as pdimport pyarrow as paimport pyarrow . parquet as pq# 内部字段address_fields = [ ( 'email_address' , pa . string ( ) ) , ( 'post_address' , pa . string ( ) ) ,]# 定义 Parquet Schema，address 嵌套了 address_fieldsschema = pa . schema ( j )# 准备数据ids = pa . array ( [ 1 , 2 ] , type = pa . int32 ( ) )addresses = pa . array ( [ ( 'first@example.com' , 'city1' ) , ( 'second@example.com' , 'city2' ) ] , pa . struct ( address_fields ))# 生成 Parquet 数据batch = pa . RecordBatch . from_arrays ( [ ids , addresses ] , schema = schema)table = pa . Table . from_batches ( [ batch ] )# 写 Parquet 数据到文件pq . write_table ( table , 'nested.parquet' ) |
1、验证 Parquet 数据文件
同样用 parquet-tools 来查看下 nested.parquet 文件
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$ parquet-tools schema nested.parquet message schema { optional int32 id; optional group address { optional binary email_address (STRING); optional binary post_address (STRING); } } $ parquet-tools cat --json nested.parquet {"id":1,"address":{"email_address":"first@example.com","post_address":"city1"}} {"id":2,"address":{"email_address":"second@example.com","post_address":"city2"}} |
用 parquet-tools 看到的 Schama 并没有 struct 的字样，但体现了它 address 与下级属性的嵌套关系。
用 pyarrow 代码来读取 nested.parquet 文件的 Schema 和数据是什么样子
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schema = pq.read_schema("nested.parquet")print(schema)df = pd.read_parquet('nested.parquet')print(df.to_json())schema = pq . read_schema ( "nested.parquet" )print ( schema )df = pd . read_parquet ( 'nested.parquet' )print ( df . to_json ( ) ) |
输出：
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id: int32 -- field metadata -- PARQUET:field_id: '1'address: struct<email_address: string, post_address: string> child 0, email_address: string -- field metadata -- PARQUET:field_id: '3' child 1, post_address: string -- field metadata -- PARQUET:field_id: '4' -- field metadata -- PARQUET:field_id: '2'{"id":{"0":1,"1":2},"address":{"0":{"email_address":"first@example.com","post_address":"city1"},"1":{"email_address":"second@example.com","post_address":"city2"}}}id : int32 -- field metadata -- PARQUET : field_id : '1'address : struct & lt ; email_address : string , post_address : string & gt ; child 0 , email_address : string -- field metadata -- PARQUET : field_id : '3' child 1 , post_address : string -- field metadata -- PARQUET : field_id : '4' -- field metadata -- PARQUET : field_id : '2'{ "id" : { "0" : 1 , "1" : 2 } , "address" : { "0" : { "email_address" : "first@example.com" , "post_address" : "city1" } , "1" : { "email_address" : "second@example.com" , "post_address" : "city2" } } } |
数据当然是一样的，有略微不同的是显示的 Schema 中, address 标识为 struct<email_address: string, post_address: string> , 明确的表明它是一个 struct 类型，而不是只展示嵌套层次。
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原文链接：https://www.tuicool.com/articles/mEfMZrM
