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Python 版 Hello World

本示例是一个“Hello World”应用，采用 Python 编写而成，旨在说明如何完成以下操作：

设置身份验证。
连接到 Bigtable 实例。
新建一个表。
将数据写入表中。
重新读取这些数据。
删除表。

Bigtable 的 Python 客户端库提供两个 API，asyncio 和同步 API。如果您的应用是异步的，请使用 asyncio。

设置身份验证

如需在本地开发环境中使用本页面上的 Python 示例，请安装并初始化 gcloud CLI，然后使用您的用户凭据设置应用默认凭据。

Install the Google Cloud CLI.
To initialize the gcloud CLI, run the following command:
```
gcloud init
```
If you're using a local shell, then create local authentication credentials for your user account:
```
gcloud auth application-default login
```
You don't need to do this if you're using Cloud Shell.

如需了解详情，请参阅 Set up authentication for a local development environment。

运行示例

本示例使用 Python 版 Cloud 客户端库的 Bigtable 软件包与 Bigtable 通信。Bigtable 软件包是新应用的最佳选择。如果您需要将现有 HBase 工作负载移至 Bigtable，请参阅使用 HappyBase 软件包的“hello world”示例。

要运行此示例程序，请按照 GitHub 上的示例说明执行操作。

将 Cloud 客户端库与 Bigtable 搭配使用

示例应用会连接到 Bigtable 并演示一些操作。

安装和导入客户端库

使用 PIP 将所需的 Python 软件包安装到 virtualenv 环境中。该示例包含一个需求文件，其中定义了所需的软件包。

google-cloud-bigtable==2.25.0
google-cloud-core==2.4.1

导入模块。

Asyncio

如需了解如何安装和使用 Bigtable 的客户端库，请参阅 Bigtable 客户端库。

如需向 Bigtable 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

from google.cloud import bigtable
from google.cloud.bigtable.data import row_filters

同步

如需了解如何安装和使用 Bigtable 的客户端库，请参阅 Bigtable 客户端库。

如需向 Bigtable 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

import datetime

from google.cloud import bigtable
from google.cloud.bigtable import column_family
from google.cloud.bigtable import row_filters

连接到 Bigtable

使用 bigtable.Client 连接到 Bigtable。

Asyncio

如需了解如何安装和使用 Bigtable 的客户端库，请参阅 Bigtable 客户端库。

如需向 Bigtable 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

client = bigtable.data.BigtableDataClientAsync(project=project_id)
table = client.get_table(instance_id, table_id)

同步

如需了解如何安装和使用 Bigtable 的客户端库，请参阅 Bigtable 客户端库。

如需向 Bigtable 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

# The client must be created with admin=True because it will create a
# table.
client = bigtable.Client(project=project_id, admin=True)
instance = client.instance(instance_id)

创建表

使用 Instance.table() 实例化表对象。创建列族并设置其垃圾回收政策，然后将列族传递到 Table.create() 以创建表。

print("Creating the {} table.".format(table_id))
table = instance.table(table_id)

print("Creating column family cf1 with Max Version GC rule...")
# Create a column family with GC policy : most recent N versions
# Define the GC policy to retain only the most recent 2 versions
max_versions_rule = bigtable.column_family.MaxVersionsGCRule(2)
column_family_id = "cf1"
column_families = {column_family_id: max_versions_rule}
if not table.exists():
    table.create(column_families=column_families)
else:
    print("Table {} already exists.".format(table_id))

将行写入表

循环遍历一系列问候语字符串，从而为该表创建一些新行。在每次迭代中，使用 Table.row() 来定义行并为其分配一个行键，调用 Row.set_cell() 来为当前单元设置值，并将新行附加到行数组中。最后，调用 Table.mutate_rows() 将行添加到表中。

Asyncio

如需了解如何安装和使用 Bigtable 的客户端库，请参阅 Bigtable 客户端库。

如需向 Bigtable 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

print("Writing some greetings to the table.")
greetings = ["Hello World!", "Hello Cloud Bigtable!", "Hello Python!"]
mutations = []
column = "greeting"
for i, value in enumerate(greetings):
    # Note: This example uses sequential numeric IDs for simplicity,
    # but this can result in poor performance in a production
    # application.  Since rows are stored in sorted order by key,
    # sequential keys can result in poor distribution of operations
    # across nodes.
    #
    # For more information about how to design a Bigtable schema for
    # the best performance, see the documentation:
    #
    #     https://cloud.google.com/bigtable/docs/schema-design
    row_key = "greeting{}".format(i).encode()
    row_mutation = bigtable.data.RowMutationEntry(
        row_key, bigtable.data.SetCell(column_family_id, column, value)
    )
    mutations.append(row_mutation)
await table.bulk_mutate_rows(mutations)

同步

如需了解如何安装和使用 Bigtable 的客户端库，请参阅 Bigtable 客户端库。

如需向 Bigtable 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

print("Writing some greetings to the table.")
greetings = ["Hello World!", "Hello Cloud Bigtable!", "Hello Python!"]
rows = []
column = "greeting".encode()
for i, value in enumerate(greetings):
    # Note: This example uses sequential numeric IDs for simplicity,
    # but this can result in poor performance in a production
    # application.  Since rows are stored in sorted order by key,
    # sequential keys can result in poor distribution of operations
    # across nodes.
    #
    # For more information about how to design a Bigtable schema for
    # the best performance, see the documentation:
    #
    #     https://cloud.google.com/bigtable/docs/schema-design
    row_key = "greeting{}".format(i).encode()
    row = table.direct_row(row_key)
    row.set_cell(
        column_family_id, column, value, timestamp=datetime.datetime.utcnow()
    )
    rows.append(row)
table.mutate_rows(rows)

创建过滤器

在读取您写入的数据之前，请使用 row_filters.CellsColumnLimitFilter() 创建过滤条件，以限制 Bigtable 返回的数据。此过滤条件指示 Bigtable 仅返回每列中的最新单元，即使表包含在垃圾回收期间尚未移除的旧单元也是如此。

Asyncio

如需了解如何安装和使用 Bigtable 的客户端库，请参阅 Bigtable 客户端库。

如需向 Bigtable 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

# Create a filter to only retrieve the most recent version of the cell
# for each column across entire row.
row_filter = bigtable.data.row_filters.CellsColumnLimitFilter(1)

同步

如需了解如何安装和使用 Bigtable 的客户端库，请参阅 Bigtable 客户端库。

如需向 Bigtable 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

row_filter = bigtable.row_filters.CellsColumnLimitFilter(1)

按行键读取行

调用表的 Table.read_row() 方法以通过特定行键引用行，传入行键和过滤条件，以获取该行中每个值的一个版本。

Asyncio

如需了解如何安装和使用 Bigtable 的客户端库，请参阅 Bigtable 客户端库。

如需向 Bigtable 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

print("Getting a single greeting by row key.")
key = "greeting0".encode()

row = await table.read_row(key, row_filter=row_filter)
cell = row.cells[0]
print(cell.value.decode("utf-8"))

同步

如需了解如何安装和使用 Bigtable 的客户端库，请参阅 Bigtable 客户端库。

如需向 Bigtable 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

print("Getting a single greeting by row key.")
key = "greeting0".encode()

row = table.read_row(key, row_filter)
cell = row.cells[column_family_id][column][0]
print(cell.value.decode("utf-8"))

扫描所有表行

使用 Table.read_rows() 从表中读取一系列行。

Asyncio

如需了解如何安装和使用 Bigtable 的客户端库，请参阅 Bigtable 客户端库。

如需向 Bigtable 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

print("Scanning for all greetings:")
query = bigtable.data.ReadRowsQuery(row_filter=row_filter)
async for row in await table.read_rows_stream(query):
    cell = row.cells[0]
    print(cell.value.decode("utf-8"))

同步

如需了解如何安装和使用 Bigtable 的客户端库，请参阅 Bigtable 客户端库。

如需向 Bigtable 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

print("Scanning for all greetings:")
partial_rows = table.read_rows(filter_=row_filter)

for row in partial_rows:
    cell = row.cells[column_family_id][column][0]
    print(cell.value.decode("utf-8"))

删除表

使用 Table.delete() 删除表。

print("Deleting the {} table.".format(table_id))
table.delete()

总结

以下为不包含注释的完整示例。

Asyncio

如需了解如何安装和使用 Bigtable 的客户端库，请参阅 Bigtable 客户端库。

如需向 Bigtable 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。



"""Demonstrates how to connect to Cloud Bigtable and run some basic operations with the async APIs

Prerequisites:

- Create a Cloud Bigtable instance.
  https://cloud.google.com/bigtable/docs/creating-instance
- Set your Google Application Default Credentials.
  https://developers.google.com/identity/protocols/application-default-credentials
"""

import argparse
import asyncio
from ..utils import wait_for_table

from google.cloud import bigtable
from google.cloud.bigtable.data import row_filters

row_filters


async def main(project_id, instance_id, table_id):
    client = bigtable.data.BigtableDataClientAsync(project=project_id)
    table = client.get_table(instance_id, table_id)

    from google.cloud.bigtable import column_family

    print("Creating the {} table.".format(table_id))
    admin_client = bigtable.Client(project=project_id, admin=True)
    admin_instance = admin_client.instance(instance_id)
    admin_table = admin_instance.table(table_id)

    print("Creating column family cf1 with Max Version GC rule...")
    max_versions_rule = column_family.MaxVersionsGCRule(2)
    column_family_id = "cf1"
    column_families = {column_family_id: max_versions_rule}
    if not admin_table.exists():
        admin_table.create(column_families=column_families)
    else:
        print("Table {} already exists.".format(table_id))

    try:
        wait_for_table(admin_table)
        print("Writing some greetings to the table.")
        greetings = ["Hello World!", "Hello Cloud Bigtable!", "Hello Python!"]
        mutations = []
        column = "greeting"
        for i, value in enumerate(greetings):
            row_key = "greeting{}".format(i).encode()
            row_mutation = bigtable.data.RowMutationEntry(
                row_key, bigtable.data.SetCell(column_family_id, column, value)
            )
            mutations.append(row_mutation)
        await table.bulk_mutate_rows(mutations)

        row_filter = bigtable.data.row_filters.CellsColumnLimitFilter(1)

        print("Getting a single greeting by row key.")
        key = "greeting0".encode()

        row = await table.read_row(key, row_filter=row_filter)
        cell = row.cells[0]
        print(cell.value.decode("utf-8"))

        print("Scanning for all greetings:")
        query = bigtable.data.ReadRowsQuery(row_filter=row_filter)
        async for row in await table.read_rows_stream(query):
            cell = row.cells[0]
            print(cell.value.decode("utf-8"))
    finally:
        print("Deleting the {} table.".format(table_id))
        admin_table.delete()


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument("project_id", help="Your Cloud Platform project ID.")
    parser.add_argument(
        "instance_id", help="ID of the Cloud Bigtable instance to connect to."
    )
    parser.add_argument(
        "--table", help="Table to create and destroy.", default="Hello-Bigtable"
    )

    args = parser.parse_args()
    asyncio.run(main(args.project_id, args.instance_id, args.table))

同步

如需了解如何安装和使用 Bigtable 的客户端库，请参阅 Bigtable 客户端库。

如需向 Bigtable 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。



"""Demonstrates how to connect to Cloud Bigtable and run some basic operations.

Prerequisites:

- Create a Cloud Bigtable instance.
  https://cloud.google.com/bigtable/docs/creating-instance
- Set your Google Application Default Credentials.
  https://developers.google.com/identity/protocols/application-default-credentials
"""

import argparse
from ..utils import wait_for_table

import datetime

from google.cloud import bigtable
from google.cloud.bigtable import column_family
from google.cloud.bigtable import row_filters


row_filters
column_family


def main(project_id, instance_id, table_id):
    client = bigtable.Client(project=project_id, admin=True)
    instance = client.instance(instance_id)

    print("Creating the {} table.".format(table_id))
    table = instance.table(table_id)

    print("Creating column family cf1 with Max Version GC rule...")
    max_versions_rule = bigtable.column_family.MaxVersionsGCRule(2)
    column_family_id = "cf1"
    column_families = {column_family_id: max_versions_rule}
    if not table.exists():
        table.create(column_families=column_families)
    else:
        print("Table {} already exists.".format(table_id))

    try:
        wait_for_table(table)

        print("Writing some greetings to the table.")
        greetings = ["Hello World!", "Hello Cloud Bigtable!", "Hello Python!"]
        rows = []
        column = "greeting".encode()
        for i, value in enumerate(greetings):
            row_key = "greeting{}".format(i).encode()
            row = table.direct_row(row_key)
            row.set_cell(
                column_family_id, column, value, timestamp=datetime.datetime.utcnow()
            )
            rows.append(row)
        table.mutate_rows(rows)

        row_filter = bigtable.row_filters.CellsColumnLimitFilter(1)

        print("Getting a single greeting by row key.")
        key = "greeting0".encode()

        row = table.read_row(key, row_filter)
        cell = row.cells[column_family_id][column][0]
        print(cell.value.decode("utf-8"))

        print("Scanning for all greetings:")
        partial_rows = table.read_rows(filter_=row_filter)

        for row in partial_rows:
            cell = row.cells[column_family_id][column][0]
            print(cell.value.decode("utf-8"))

    finally:
        print("Deleting the {} table.".format(table_id))
        table.delete()


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument("project_id", help="Your Cloud Platform project ID.")
    parser.add_argument(
        "instance_id", help="ID of the Cloud Bigtable instance to connect to."
    )
    parser.add_argument(
        "--table", help="Table to create and destroy.", default="Hello-Bigtable"
    )

    args = parser.parse_args()
    main(args.project_id, args.instance_id, args.table)