Posted in Python onSeptember 25, 2021
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
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
# 定义 Schema
schema = 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.parquet
pq.write_table(table, 'plain.parquet')
import pandas as pd
import pyarrow as pa
import pyarrow . parquet as pq
# 定义 Schema
schema = 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.parquet
pq . 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
$ 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
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：
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
import pandas as pd
import pyarrow as pa
import 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_fields
schema = 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 pd
import pyarrow as pa
import 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_fields
schema = 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
$ 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
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：
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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用 Python 定义 Schema 并生成 Parquet 文件详情
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