JSON table BigQuerySink 的 bigquery.TableSchema 架构

Question

我有一个非常重要的 table 模式（涉及嵌套和重复的字段）以 JSON 格式（具有名称、类型、模式属性）定义并存储在文件中。它已成功用于使用 bq load 命令填充 bigquery table。

但是当我尝试使用 Dataflow Python SDK 和 BigQuerySink 做同样的事情时，schema 参数需要是 'name':'type' 元素的逗号分隔列表，或者一个 bigquery.TableSchema 对象。

有什么方便的方法可以将我的 JSON 架构转换为 bigquery.TableSchema，或者我是否必须将其转换为 name:value 列表？

Answer 1

目前您不能直接指定JSON架构。您必须将架构指定为包含逗号分隔字段列表的字符串或 bigquery.TableSchema 对象。

如果架构复杂且包含嵌套的 and/or 个重复字段，我们建议构建一个 bigquery.TableSchema 对象。

这是一个示例 bigquery.TableSchema 具有嵌套和重复字段的对象。

from apitools.clients import bigquery

table_schema = bigquery.TableSchema()

# ‘string’ field
field_schema = bigquery.TableFieldSchema()
field_schema.name = 'fullName'
field_schema.type = 'string'
field_schema.mode = 'required'
table_schema.fields.append(field_schema)

# ‘integer’ field
field_schema = bigquery.TableFieldSchema()
field_schema.name = 'age'
field_schema.type = 'integer'
field_schema.mode = 'nullable'
table_schema.fields.append(field_schema)

# nested field
field_schema = bigquery.TableFieldSchema()
field_schema.name = 'phoneNumber'
field_schema.type = 'record'
field_schema.mode = 'nullable'

area_code = bigquery.TableFieldSchema()
area_code.name = 'areaCode'
area_code.type = 'integer'
area_code.mode = 'nullable'
field_schema.fields.append(area_code)

number = bigquery.TableFieldSchema()
number.name = 'number'
number.type = 'integer'
number.mode = 'nullable'
field_schema.fields.append(number)
table_schema.fields.append(field_schema)

# repeated field
field_schema = bigquery.TableFieldSchema()
field_schema.name = 'children'
field_schema.type = 'string'
field_schema.mode = 'repeated'
table_schema.fields.append(field_schema)

Answer 2

我遇到了同样的问题。在我的例子中，我已经在 bigquery 中加载了一些 json，并自动生成了一个模式。

所以我能够使用以下命令获取自动生成的架构：

bq show --format prettyjson my-gcp-project:my-bq-table |jq .schema > my-bq-table.json

然后可以使用此代码段

将架构转换为 bigquery.TableSchema

from apache_beam.io.gcp.internal.clients import bigquery


def _get_field_schema(**kwargs):
    field_schema = bigquery.TableFieldSchema()
    field_schema.name = kwargs['name']
    field_schema.type = kwargs.get('type', 'STRING')
    field_schema.mode = kwargs.get('mode', 'NULLABLE')
    fields = kwargs.get('fields')
    if fields:
        for field in fields:
            field_schema.fields.append(_get_field_schema(**field))
    return field_schema


def _inject_fields(fields, table_schema):
    for field in fields:
        table_schema.fields.append(_get_field_schema(**field))


def parse_bq_json_schema(schema):
    table_schema = bigquery.TableSchema()
    _inject_fields(schema['fields'], table_schema)
    return table_schema

它将与 bigquery json 模式规范一起工作，如果你像我一样懒惰，你可以避免指定 type 和 mode 如果你对一个字段感到满意默认情况下可为空的字符串。

Answer 3

上面由 Andrea Pierleoni 发布的代码片段适用于 google-cloud-bigquery python 客户端的旧版本，例如 google-cloud-bigquery 的 0.25.0 版本恰好通过pip install apache-beam[gcp].

但是，BigQuery Python 客户端 API 在 google-cloud-bigquery 的最新版本中发生了巨大变化，例如在我当前使用的版本 1.8.0 中，bigquery.TableFieldSchema() 和 bigquery.TableSchema() 不起作用。

如果您使用的是更新版本的 google-cloud-bigquery 软件包，您可以按照以下方法获取所需的 SchemaField 列表（例如，需要创建 table）来自 JSON 文件。这是对 Andrea Pierleoni 上面发布的代码的改编（感谢！）

def _get_field_schema(field):
    name = field['name']
    field_type = field.get('type', 'STRING')
    mode = field.get('mode', 'NULLABLE')
    fields = field.get('fields', [])

    if fields:
        subschema = []
        for f in fields:
            fields_res = _get_field_schema(f)
            subschema.append(fields_res)
    else:
        subschema = []

    field_schema = bigquery.SchemaField(name=name, 
        field_type=field_type,
        mode=mode,
        fields=subschema
    )
    return field_schema


def parse_bq_json_schema(schema_filename):
    schema = []
    with open(schema_filename, 'r') as infile:
        jsonschema = json.load(infile)

    for field in jsonschema:
        schema.append(_get_field_schema(field))

    return schema

现在，假设您有一个 table 的 schema already defined in JSON. Say you had this particular "schema.json" file，然后使用上述辅助方法，您可以获得 Python 客户端所需的 SchemaField 表示像这样：

>>> res_schema = parse_bq_json_schema("schema.json")

>>> print(res_schema)

[SchemaField(u'event_id', u'INTEGER', u'REQUIRED', None, ()), SchemaField(u'event_name', u'STRING', u'REQUIRED', None, ()), SchemaField(u'event_types', u'STRING', u'REPEATED', None, ()), SchemaField(u'product_code', u'STRING', u'REQUIRED', None, ()), SchemaField(u'product_sub_code', u'STRING', u'REPEATED', None, ()), SchemaField(u'source', u'RECORD', u'REQUIRED', None, (SchemaField(u'internal', u'RECORD', u'NULLABLE', None, (SchemaField(u'name', u'STRING', u'REQUIRED', None, ()), SchemaField(u'timestamp', u'TIMESTAMP', u'REQUIRED', None, ()))), SchemaField(u'external', u'RECORD', u'NULLABLE', None, (SchemaField(u'name', u'STRING', u'REQUIRED', None, ()), SchemaField(u'timestamp', u'TIMESTAMP', u'REQUIRED', None, ()))))), SchemaField(u'timestamp', u'TIMESTAMP', u'REQUIRED', None, ()), SchemaField(u'user_key', u'RECORD', u'REQUIRED', None, (SchemaField(u'device_id', u'STRING', u'NULLABLE', None, ()), SchemaField(u'cookie_id', u'STRING', u'NULLABLE', None, ()), SchemaField(u'profile_id', u'STRING', u'NULLABLE', None, ()), SchemaField(u'best_id', u'STRING', u'REQUIRED', None, ()))), SchemaField(u'message_id', u'STRING', u'REQUIRED', None, ()), SchemaField(u'message_type', u'STRING', u'REQUIRED', None, ()), SchemaField(u'tracking_id', u'STRING', u'NULLABLE', None, ()), SchemaField(u'funnel_stage', u'STRING', u'NULLABLE', None, ()), SchemaField(u'location', u'RECORD', u'NULLABLE', None, (SchemaField(u'latitude', u'FLOAT', u'REQUIRED', None, ()), SchemaField(u'longitude', u'FLOAT', u'REQUIRED', None, ()), SchemaField(u'geo_region_id', u'INTEGER', u'NULLABLE', None, ()))), SchemaField(u'campaign_id', u'STRING', u'NULLABLE', None, ()), SchemaField(u'topic', u'STRING', u'REQUIRED', None, ())]

现在到 create a table having the above schema using the Python SDK，你会做：

dataset_ref = bqclient.dataset('YOUR_DATASET')
table_ref = dataset_ref.table('YOUR_TABLE')
table = bigquery.Table(table_ref, schema=res_schema)

您可以选择像这样设置基于时间的分区（如果需要）：

table.time_partitioning = bigquery.TimePartitioning(
    type_=bigquery.TimePartitioningType.DAY,
    field='timestamp'  # name of column to use for partitioning
)

这最终创建了 table:

table = bqclient.create_table(table)

print('Created table {}, partitioned on column {}'.format(
    table.table_id, table.time_partitioning.field))

Answer 4

这是一个可以帮助您的简单程序。

import json
from apache_beam.io.gcp.internal.clients import bigquery


def bq_schema(json_schema):
    table_schema = bigquery.TableSchema()
    with open(json_schema) as json_file:
        data = json.load(json_file)
        for p in data:
            field = bigquery.TableFieldSchema()
            field.name = p['name']
            field.type = p['type']
            field.mode = p['mode']
            table_schema.fields.append(field)
    return table_schema

Answer 5

如今，您可以使用 built-in parse_table_schema_from_json 功能：

from apache_beam.io.gcp.bigquery_tools import parse_table_schema_from_json

with open('schema.json') as f:
    schema_string = f.read()
table_schema = parse_table_schema_from_json(schema_string)

Answer 6

BigQuery 库中有一个内置转换器函数：

from google.cloud import bigquery
...
client = bigquery.Client()
client.schema_from_json('path/to/schema.json`)

JSON table BigQuerySink 的 bigquery.TableSchema 架构

JSON table schema to bigquery.TableSchema for BigQuerySink

python

google-cloud-dataflow