2020 年 8 月August 2020

这些功能和 Azure Databricks 平台的改进已于 2020 年 8 月发布。These features and Azure Databricks platform improvements were released in August 2020.

备注

发布分阶段进行。Releases are staged. 在初始发布日期后,可能最长需要等待一周,你的 Azure Databricks 帐户才会更新。Your Azure Databricks account may not be updated until up to a week after the initial release date.

重要

仅面向加拿大中部和印度中部区域的客户发布了版本 3.26。Version 3.26 has been released to customers in the Canada Central and Central India regions only. 在发布 3.27 的同时,所有其他区域都将获得 3.26 版本的功能。All other regions will get the 3.26 features at the same time that 3.27 is released.

令牌管理 API 已正式发布,管理员可使用管理控制台向用户授予和撤销对令牌的访问权限iToken Management API is GA and admins can use the Admin Console to grant and revoke user access to tokens

2020 年 8 月 26 日 - 9 月 1 日:版本 3.27August 26 - September 1, 2020: Version 3.27

令牌管理现已正式发布。Token management is now generally available. Azure Databricks 管理员可以使用令牌管理 API 和管理员控制台来管理其用户的 Azure Databricks 个人访问令牌。Azure Databricks administrators can use the Token Management API and the Admin Console to manage their users’ Azure Databricks personal access tokens. 作为管理员,你可以:As an admin, you can:

  • 监视和撤销用户的个人访问令牌。Monitor and revoke users’ personal access tokens.
  • 控制工作区中未来令牌的生存期。Control the lifetime of future tokens in your workspace.
  • 通过权限 API 或管理员控制台来控制哪些用户可以创建和使用令牌。Control which users can create and use tokens via the Permissions API or in the Admin Console.

在公共预览版向正式发布版转换的期间,令牌管理 API 参数 created_by 更改为 created_by_id,并添加了一个新参数 created_by_usernameIn the transition from Public Preview to GA, the Token Management API parameter created_by was changed to created_by_id, and a new parameter, created_by_username was added.

有关详细信息,请参阅管理个人访问令牌For more information, see Manage personal access tokens.

Shiny 应用的消息大小已增加Message size limits for Shiny apps increased

2020 年 8 月 26 日 - 9 月 1 日:版本 3.27August 26 - September 1, 2020: Version 3.27

Shiny 应用的最大应用程序大小已从 10 MB 增加到 20 MB。The maximum application size for Shiny apps has been increased from 10 MB to 20 MB. 如果应用程序的总大小超过此限制,请参阅 Shiny FAQ 获取相关建议。If your application’s total size exceeds this limit, refer to the Shiny FAQ for recommendations.

改进了有关在本地模式设置群集的说明Improved instructions for setting up a cluster in local mode

2020 年 8 月 26 日 - 9 月 1 日:版本 3.27August 26 - September 1, 2020: Version 3.27

在群集 UI 中:In the cluster UI:

  • 如果创建的群集不具有任何辅助角色,会出现一个工具提示,建议使用本地模式并显示关联的配置设置 (spark.master local[*])。If you create a cluster with 0 workers, a tool tip appears recommending that you use local mode and showing the associated configuration setting (spark.master local[*]).
  • 无法再为群集设置 spark.master local[*],除非该群集不具有任何辅助角色。You can no longer set spark.master local[*] for a cluster, unless the cluster has 0 workers.

查看与运行关联的笔记本版本View version of notebook associated with a run

2020 年 8 月 26 日 - 9 月 1 日:版本 3.27August 26 - September 1, 2020: Version 3.27

现在可以从“试验”边栏中显示与运行相关联的笔记本版本。From the Experiments sidebar, you can now display the version of a notebook associated with a run. 有关详细信息,请参阅查看笔记本试验For details, see View notebook experiment.

Databricks Runtime 7.2 正式版Databricks Runtime 7.2 GA

2020 年 8 月 20 日August 20, 2020

在 Databricks Runtime 7.1 的基础上,Databricks Runtime 7.2 引入了许多额外的功能和改进,包括:Databricks Runtime 7.2 brings many additional features and improvements over Databricks Runtime 7.1, including:

  • 自动加载程序已正式发布:自动加载程序是一种有效的方法,可将大量文件以增量方式引入 Delta Lake。Auto Loader is generally available: Auto Loader is an efficient method for incrementally ingesting a large number of files into Delta Lake. 它现已正式发布,并添加了以下功能:It is now GA and adds the following features:
    • 目录列表模式选项:除了现有的文件通知模式外,自动加载程序还添加了新的目录列表模式,用于确定何时有新文件。Directory listing mode option: Auto Loader adds a new directory listing mode, in addition to the existing file notification mode, for determining when there are new files.
    • 云资源管理 API:现在你可以使用我们的 Scala API 来管理由自动加载程序创建的云资源。Cloud resource management API: You can now use our Scala API to manage cloud resources created by Auto Loader. 你可以使用此 API 列出通知服务并删除特定的通知服务。You can list notification services and tear down specific notification services using this API.
    • 速率限制选项:现在你可以使用 cloudFiles.maxBytesPerTrigger 选项来限制每个微批中处理的数据量。Rate limiting option: You can now use the cloudFiles.maxBytesPerTrigger option to limit the amount of data processed in each microbatch.
    • 选项验证:自动加载程序现在会验证你提供的选项。validationOption validation: Auto Loader now validates the options you provide.validation 将失败。will fail. 若要跳过选项验证,请将 cloudFiles.validateOptions 设置为 falseTo skip option validation, set cloudFiles.validateOptions to false.
  • 通过克隆高效复制 Delta 表Efficiently copy a Delta table with clone.
  • 改进:Improvements:
    • Snowflake 连接器已升级到版本 2.8.1,其中包括 Spark 3.0 支持。Snowflake connector has been upgraded to version 2.8.1, which includes Spark 3.0 support.
    • 凭据传递身份验证改进Credential passthrough improvements
    • TensorBoard 改进TensorBoard improvements
    • 升级了 Python 和 R 库Upgraded Python and R libraries

有关详细信息,请参阅完整的 Databricks Runtime 7.2 发行说明。For details, see the complete Databricks Runtime 7.2 release notes.

Databricks Runtime 7.2 ML 正式版Databricks Runtime 7.2 ML GA

2020 年 8 月 20 日August 20, 2020

用于机器学习的 Databricks Runtime 7.2 基于 Databricks Runtime 7.2 构建,并引入了已改进的全新 Python 和系统库。Databricks Runtime 7.2 for Machine Learning is built on top of Databricks Runtime 7.2 and brings new and improved Python and system libraries. 有关详细信息,请参阅完整的 Databricks Runtime 7.2 ML 发行说明。For details, see the complete Databricks Runtime 7.2 ML release notes.

Databricks Runtime 7.2 Genomics 正式版Databricks Runtime 7.2 Genomics GA

2020 年 8 月 20 日August 20, 2020

用于基因组学的 Databricks Runtime 7.2 基于 Databricks Runtime 7.2 构建,并极大地加快了将文本 numpy 1D 和 2D 浮动类型的 n 维数组转换为 Java 数组的速度。Databricks Runtime 7.2 for Genomics is built on top of Databricks Runtime 7.2 and significantly speeds up the conversion of literal numpy 1D and 2D float-typed ndarrays to Java arrays. Glow 基因组范围的关联研究文档反映使用情况。The Glow genome-wide association study documentation reflects the usage.

有关详细信息,请参阅完整的用于基因组学的 Databricks Runtime 7.2 发行说明。For details, see the complete Databricks Runtime 7.2 for Genomics release notes.

权限 API(公共预览版)Permissions API (Public Preview)

2020 年 8 月 18 日August 18, 2020

Databricks 很高兴地宣布推出权限 API 的公共预览版,你可以使用它来管理以下内容的权限:Databricks is pleased to announce the public preview of the Permissions API, which lets you manage permissions for:

  • 令牌Tokens
  • 群集Clusters
  • Pools
  • 作业Jobs
  • 笔记本Notebooks
  • 文件夹(目录)Folders (directories)
  • MLflow 注册模型MLflow registered models

有关详细信息,请参阅权限 APIFor more information, see Permissions API.

Databricks Connect 7.1 (GA)Databricks Connect 7.1 (GA)

2020 年 8 月 12 日August 12, 2020

Databricks Connect 现在支持 Databricks Runtime 7.1。Databricks Connect now supports Databricks Runtime 7.1.

在 Databricks Runtime 7.1 中,Databricks 建议始终使用 Databricks Connect 的最新版本。In Databricks Runtime 7.1, Databricks recommends that you always use the most recent version of Databricks Connect.

群集库的可重复安装顺序Repeatable installation order for cluster libraries

2020 年 8 月 12 日至 25 日:版本 3.26August 12-25, 2020: Version 3.26

在运行 Databricks Runtime 7.2 或更高版本的群集上,Azure Databricks 现在按照安装所有群集库的顺序对其进行处理。On a cluster running Databricks Runtime 7.2 or above, Azure Databricks now processes all cluster libraries in the order that they were installed.

从 MLflow 注册模型页创建模型(公共预览版)Create model from MLflow registered models page (Public Preview)

2020 年 8 月 12 日至 25 日:版本 3.26August 12-25, 2020: Version 3.26

现在可以从 MLflow 注册模型页中创建新模型。You can now create a new model from the MLflow registered models page. 有关详细信息,请参阅在模型注册表中注册模型For details, see Register a model in the Model Registry.

Databricks 容器服务支持 GPU 图像Databricks Container Services supports GPU images

2020 年 8 月 12 日至 25 日:版本 3.26August 12-25, 2020: Version 3.26

现在可以在具有 GPU 的群集上使用 Databricks 容器服务来通过自定义库创建可移植的深度学习环境。You can now use Databricks Container Services on clusters with GPUs to create portable deep learning environments with customized libraries.

有关详细信息,请参阅 GPU 群集上的 Databricks 容器服务For details, see Databricks Container Services on GPU clusters.