2020 年 6 月June 2020

這些功能和 Azure Databricks 平臺改進已于2020年6月發行。These features and Azure Databricks platform improvements were released in June 2020.

注意

發行是暫存的。Releases are staged. 在初始發行日期之後,您的 Azure Databricks 帳戶可能不會更新到一周。Your Azure Databricks account may not be updated until up to a week after the initial release date.

Databricks Connect 現在支援 Databricks Runtime 6.6Databricks Connect now supports Databricks Runtime 6.6

2020年6月26日June 26, 2020

Databricks Connect 現在支援 Databricks Runtime 6.6。Databricks Connect now supports Databricks Runtime 6.6.

Databricks Runtime 7.0 ML GADatabricks Runtime 7.0 ML GA

2020年6月22日Jun 22, 2020

Databricks Runtime 7.0 ML 建置於 Databricks Runtime 7.0 之上,並包含下列新功能:Databricks Runtime 7.0 ML is built on top of Databricks Runtime 7.0 and includes the following new features:

  • 筆記本範圍的 Python 程式庫,以及由 conda 和 pip 命令管理的自訂環境。Notebook-scoped Python libraries and custom environments managed by conda and pip commands.
  • 主要 Python 套件的更新,包括 tensorflow、tensorboard、pytorch、xgboost、sparkdl 和 hyperopt。Updates for major Python packages including tensorflow, tensorboard, pytorch, xgboost, sparkdl, and hyperopt.
  • 新增的 Python 套件 lightgbm、nltk、petastorm 載入資料和 plotly。Newly added Python packages lightgbm, nltk, petastorm, and plotly.
  • RStudio 伺服器開放原始碼1.2。RStudio Server Open Source v1.2.

如需詳細資訊,請參閱完整的 Databricks Runtime 7.0 ML (不支援的) 版本資訊。For more information, see the complete Databricks Runtime 7.0 ML (Unsupported) release notes.

Databricks Runtime 7.0 GA,由 Apache Spark 3.0 提供技術支援Databricks Runtime 7.0 GA, powered by Apache Spark 3.0

2020年6月18日June 18, 2020

Databricks Runtime 7.0 是由 Apache Spark 3.0 提供技術支援,現在支援 Scala 2.12Databricks Runtime 7.0 is powered by Apache Spark 3.0 and now supports Scala 2.12.

Spark 3.0 帶來許多額外的功能和增強功能,包括:Spark 3.0 brings many additional features and improvements, including:

  • 彈性查詢執行是一種彈性的架構,可在 Spark SQL 中執行彈性執行,並支援在執行時間變更歸納器數目。Adaptive Query Execution, a flexible framework to do adaptive execution in Spark SQL and support changing the number of reducers at runtime.
  • 使用類型提示重新設計 pandas Udf。Redesigned pandas UDFs with type hints.
  • 結構化串流 web UI。Structured Streaming web UI.
  • 更妥善地與 ANSI SQL 標準相容。Better compatibility with ANSI SQL standards.
  • 聯結提示。Join hints.

Databricks Runtime 7.0 新增:Databricks Runtime 7.0 adds:

  • 改進 自動載入 器,以隨著在 ETL 期間抵達雲端 blob 存放區時,以累加的方式處理新的資料檔案。Improved Auto Loader for processing new data files incrementally as they arrive on a cloud blob store during ETL.
  • 改進了 複製到命令 ,以將資料載入 Delta Lake 的等冪重試。Improved COPY INTO command for loading data into Delta Lake with idempotent retries.
  • 許多增強功能、程式庫新增與升級,以及 bug 修正。Many improvements, library additions and upgrades, and bug fixes.

如需詳細資訊,請參閱完整的 Databricks Runtime 7.0 (不支援的) 版本資訊。For more information, see the complete Databricks Runtime 7.0 (Unsupported) release notes.

適用於 Genomics GA 的 Databricks Runtime 7.0Databricks Runtime 7.0 for Genomics GA

2020年6月18日June 18, 2020

適用于 Genomics 的 Databricks Runtime 7.0 建置於 Databricks Runtime 7.0 之上,並包含下列程式庫變更:Databricks Runtime 7.0 for Genomics is built on top of Databricks Runtime 7.0 and includes the following library changes:

  • ADAM 程式庫已從版本0.30.0 更新為0.32.0。The ADAM library has been updated from version 0.30.0 to 0.32.0.
  • Hail 程式庫不包含在適用于 Genomics 的 Databricks Runtime 7.0 中,因為沒有任何版本是以 Apache Spark 3.0 為基礎。The Hail library is not included in Databricks Runtime 7.0 for Genomics, because there is no release based on Apache Spark 3.0.

如需詳細資訊,請參閱 Genomics 的完整 Databricks Runtime 7.0 (不支援的) 版本資訊。For more information, see the complete Databricks Runtime 7.0 for Genomics (Unsupported) release notes.

MLflow 模型的階段相依存取控制Stage-dependent access controls for MLflow models

2020年6月16-23:版本3.22June 16-23, 2020: Version 3.22

您現在可以將階段相依的存取控制指派給使用者或群組,讓他們能夠在暫存或生產階段管理在MLflow 模型登錄中註冊的MLflow 模型You can now assign stage-dependent access controls to users or groups, allowing them to manage MLflow Models registered in the MLflow Model Registry at the Staging or Production stage. 我們引進了兩個新的許可權等級, 可以管理預備版本 ,也 可以管理實際執行版本We introduced two new permission levels, Can Manage Staging Versions and Can Manage Production Versions. 具有這些許可權的使用者可以在層級允許的階段之間執行轉換。Users with these permissions can perform transitions between stages allowed for the level.

如需詳細資訊,請參閱 MLflow 模型許可權For details, see MLflow Model permissions.

筆記本現在支援停用自動滾動Notebooks now support disabling auto-scroll

2020年6月16-23:版本3.22June 16-23, 2020: Version 3.22

當您使用 shift + enter執行筆記本資料格時,如果看不到儲存格,預設的筆記本行為會自動滾動至下一個儲存格。When you run a notebook cell using shift+enter, the default notebook behavior is to auto-scroll to the next cell if the cell is not visible. 您現在可以停用  帳戶圖示 > 使用者設定 > 筆記本設定 的自動滾動。You can now disable auto-scroll in Account Icon > User Settings > Notebook Settings. 如果您停用自動滾動,則在 shift + enter 上,焦點會移至下一個儲存格,但筆記本不會滾動至該資料格。If you disable auto-scroll, on shift+enter the focus moves to the next cell, but the notebook does not scroll to that cell.

中繼存放區要在 2020 年 6 月 30 日變更的 IP 位址Metastore IP addresses to change on June 30, 2020

2020年6月11日June 11, 2020

Azure Databricks 的預設中繼存放區會使用適用於 MySQL 的 Azure 資料庫。The default metastore for Azure Databricks uses Azure Database for MySQL. 所有適用於 MySQL 的 Azure 資料庫 Azure Databricks 中繼存放區的 IP 位址都會在2020年6月30日變更。All Azure Database for MySQL IP addresses for Azure Databricks metastores are changing on June 30, 2020. 如果您在自己的虛擬網路中部署了 Azure Databricks 工作區,則該部署的路由表可能包含 Azure Databricks 中繼存放區 IP 位址,或使用包含該位址的存取清單來路由傳送至防火牆或 proxy 設備。If you have an Azure Databricks workspace deployed in your own virtual network, your route table for that deployment may include an Azure Databricks metastore IP address or route to a firewall or proxy appliance with an access list that includes that address. 如果是這種情況,您必須在2020年6月30日前,使用新的 MySQL Ip 來更新 Azure Databricks 路由表或防火牆,以避免中斷。If that is the case, you must update your Azure Databricks route tables or firewalls with new MySQL IPs before June 30, 2020 to avoid disruption. 如需詳細資訊,以及查閱中繼存放區 IP 位址,請參閱 使用新的 MySQL Ip 更新您的 Azure Databricks 路由表和防火牆For more information, and to look up metastore IP addresses, see Update your Azure Databricks route tables and firewalls with new MySQL IPs.

Internet Explorer 11 支援將於 8 月 15 日結束Internet Explorer 11 support ends on August 15

2020 年 6 月 9 日June 9, 2020

為了與產業趨勢保持一致,並確保客戶的穩定且一致的使用者體驗,Azure Databricks 將于2020年8月15日結束對 Internet Explorer 11 的支援。In keeping with industry trends and to ensure a stable and consistent user experience for our customers, Azure Databricks will end support for Internet Explorer 11 on August 15, 2020.

Databricks Runtime 6.2 系列支援結束Databricks Runtime 6.2 series support ends

2020年6月3日June 3, 2020

支援 Databricks Runtime 6.2、適用于 Machine Learning 的 Databricks Runtime 6.2,以及 Genomics 于6月3日結束的 Databricks Runtime 6.2。Support for Databricks Runtime 6.2, Databricks Runtime 6.2 for Machine Learning, and Databricks Runtime 6.2 for Genomics ended on June 3. 請參閱 Databricks 執行時間支援生命週期See Databricks runtime support lifecycle.

使用叢集原則來簡化及控制叢集建立 (公開預覽)Simplify and control cluster creation using cluster policies (Public Preview)

2020年6月2-9:版本3.21June 2-9, 2020: Version 3.21

叢集原則是系統管理員定義、可重複使用的叢集範本,可對叢集屬性強制執行規則,進而確保使用者建立符合這些規則的叢集。Cluster policies are admin-defined, reusable cluster templates that enforce rules on cluster attributes and thus ensure that users create clusters that conform to those rules. 作為 Azure Databricks 系統管理員,您現在可以建立叢集原則,並授與使用者原則許可權。As an Azure Databricks admin, you can now create cluster policies and give users policy permissions. 藉由這麼做,您可以更充分掌控所建立的資源,為使用者提供執行其工作所需的彈性層級,並大幅簡化叢集建立體驗。By doing that, you have more control over the resources created, give users the level of flexibility they need to do their work, and considerably simplify the cluster creation experience.

如需詳細資訊,請參閱 管理叢集原則For details, see Manage cluster policies.

SCIM Me 端點現在會傳回符合 SCIM 規範的回應SCIM Me endpoint now returns SCIM compliant response

2020年6月2-9:版本3.21June 2-9, 2020: Version 3.21

SCIM Me 端點現在會傳回與端點相同的資訊 /users/{id} ,包括群組和權利等資訊。The SCIM Me endpoint now returns the same information as the /users/{id} endpoint, including information such as groups and entitlements.

請參閱 SCIM API (Me) See SCIM API (Me).

使用 IP 存取清單來限制 Azure Databricks 的存取 (公開預覽)Restrict access to Azure Databricks using IP access lists (Public Preview)

2020 年 6 月 1 日June 1, 2020

現在可以設定 Azure Databricks 工作區,讓使用者只能透過安全周邊的現有公司網路連接到服務。Azure Databricks workspaces can now be configured so that users connect to the service only through existing corporate networks with a secure perimeter. Azure Databricks 系統管理員可以使用 IP 存取清單 API 來定義一組核准的 IP 位址,包括允許和封鎖清單。Azure Databricks admins can use the IP Access List API to define a set of approved IP addresses, including allow and block lists. 對 web 應用程式和 REST Api 的所有連入存取都需要使用者從授權 IP 位址進行連線,以保證無法從像咖啡廳或機場等公用網路存取工作區,除非您的使用者使用 VPN。All incoming access to the web application and REST APIs requires that the user connect from an authorized IP address, guaranteeing that workspaces cannot be accessed from a public network like a coffee shop or an airport unless your users use VPN.

這項功能需要 Azure Databricks Premium 方案This feature requires the Azure Databricks Premium Plan.

如需詳細資訊,請參閱 IP 存取清單For more information, see IP access lists.