2018 年 11 月November 2018

這些功能和 Azure Databricks 平臺改進已于2018年11月發行。These features and Azure Databricks platform improvements were released in November 2018.

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.

程式庫 UI Library UI

重要

此更新已于2018年12月7日還原。This update was reverted on December 7, 2018.

2018年11月27日-12 月4日:2.85 版November 27-December 4, 2018: Version 2.85

在此版本中,程式庫 UI 已大幅改進。In this release, the library UI has been significantly improved.

Azure Databricks UI 現在支援工作區程式庫和叢集附加的程式庫。The Azure Databricks UI now supports workspace libraries and cluster-attached libraries. 工作區中有工作區程式庫,而且可以附加至一或多個叢集。A workspace library exists in the Workspace and can be attached to one or more clusters. 叢集連結的程式庫是一個程式庫,只存在於其所連結的叢集內容中。A cluster-attached library is a library that exists only in the context of the cluster that it is attached to. 此外:In addition:

  • 您現在可以從上傳至物件儲存體的檔案建立程式庫。You can now create a library from a file uploaded to object storage.
  • 您現在可以從程式庫的 [詳細資料] 頁面和叢集的 [程式庫] 索引標籤附加和中斷程式庫。You can now attach and detach libraries from the library details page and a cluster’s Libraries tab.
  • 使用 API 安裝的程式庫現在會顯示在叢集的 [程式庫] 索引標籤中。Libraries installed using the API now display in a cluster’s Libraries tab.

已啟用自訂 Spark 堆積記憶體設定Custom Spark heap memory settings enabled

2018年11月27日-12 月4日:2.85 版November 27-December 4, 2018: Version 2.85

下列 Spark 記憶體設定現在會生效:The following Spark memory settings now take effect:

  • spark.executor.memory
  • spark.driver.memory

重要

  • Azure Databricks 有在每個節點上執行的服務,因此 Spark 可允許的最大記憶體小於雲端提供者所報告 VM 的記憶體容量。Azure Databricks has services running on each node so the maximum allowable memory for Spark is less than the memory capacity of the VM reported by the cloud provider. 如果您想要為 Spark 提供執行程式或驅動程式的最大堆積記憶體數量,請勿 spark.executor.memory 分別指定或 spark.driver.memoryIf you want to provide Spark with the maximum amount of heap memory for the executor or driver, don’t specify spark.executor.memory or spark.driver.memory respectively.
  • 某些先前無效但被忽略的叢集設定,可能會導致叢集失敗。Some cluster configurations that were previously invalid but ignored may result in cluster failures.

作業和閒置執行內容收回Jobs and idle execution context eviction

2018年11月27日-12 月4日:2.85 版November 27-December 4, 2018: Version 2.85

作業現在會自動收回閒置的執行內容。Jobs now auto-evict idle execution contexts. 請參閱 執行內容。See Execution contexts. 若要將自動收回降至最低,Azure Databricks 建議您針對作業和互動式工作負載使用不同的叢集。To minimize auto-eviction, Azure Databricks recommends that you use different clusters for jobs and interactive workloads.

適用於 Machine Learning 的 Databricks Runtime 5.0 (Beta) 版本Databricks Runtime 5.0 for Machine Learning (Beta) release

2018 年 11 月 19 日November 19, 2018

Databricks Runtime 5.0 ML (Beta) 提供適用于機器學習和資料科學的現成環境。Databricks Runtime 5.0 ML (Beta) provides a ready-to-go environment for machine learning and data science. 它包含多個熱門的程式庫,包括 TensorFlow、Keras 和 XGBoost。It contains multiple popular libraries, including TensorFlow, Keras, and XGBoost. 它也支援使用 Horovod 的分散式 TensorFlow 訓練。It also supports distributed TensorFlow training using Horovod. Databricks Runtime 5.0 ML 建置於 Databricks Runtime 5.0 之上。Databricks Runtime 5.0 ML is built on top of Databricks Runtime 5.0. Databricks Runtime 5.0 ML 包含下列新功能:Databricks Runtime 5.0 ML includes the following new features:

請參閱 Databricks Runtime 5.0 ML (Beta) 的完整版本資訊。See the complete release notes for Databricks Runtime 5.0 ML (Beta).

Databricks Runtime 5.0 版本Databricks Runtime 5.0 release

2018 年 11 月 8 日November 8, 2018

Databricks Runtime 5.0 現在已可供使用。Databricks Runtime 5.0 is now available. Databricks Runtime 5.0 包括 Apache Spark 2.4.0、新的 Delta Lake 以及結構化串流功能和升級,以及升級的 Python、R 和 JAVA 和 Scala 程式庫。Databricks Runtime 5.0 includes Apache Spark 2.4.0, new Delta Lake and Structured Streaming features and upgrades, and upgraded Python, R, and Java and Scala libraries. 如需詳細資訊,請參閱 Databricks Runtime 5.0 (不支援的) For details, see Databricks Runtime 5.0 (Unsupported).

在 Databricks Runtime 5.0 上,當叢集達到 (145) 的最大內容限制時,Azure Databricks 現在會收回閒置執行內容。On Databricks Runtime 5.0, Azure Databricks now evicts idle execution contexts once a cluster has reached the maximum context limit (145). 請參閱 執行內容。See Execution contexts.

可無限制載入第三方內容的 displayHTML 支援displayHTML support for unrestricted loading of third-party content

2018年11月6-13 日:版本2.84November 6-13, 2018: Version 2.84

先前 displayHTML iframe 沙箱缺少 允許的原始來源 屬性。Previously the displayHTML iframe sandbox was missing the allow-same-origin attribute. 這表示 iframe 具有 null 原點,這對 跨原始 XHR 要求、cookie 或存取內嵌 iframe 而言並不容易。This meant that the iframe had a null origin, which wasn’t friendly to cross-origin XHR requests, cookies, or accessing embedded iframes. 使用此版本時, displayHTML 會從新的網域提供 iframe, databricksusercontent.com 而 iframe 沙箱現在會包含 allow-same-origin 屬性。With this release, the displayHTML iframe is served from a new domain, databricksusercontent.com, and the iframe sandbox now includes the allow-same-origin attribute.

如果您的 displayHTML 已可供使用,就不需要變更您的使用方式。There is no need to change your usage of displayHTML if it’s already working for you.

databricksusercontent.com 必須可從瀏覽器存取。databricksusercontent.com will need to be accessible from your browser. 如果貴公司的網路目前封鎖了該網址,請要求 IT 將該網址列入允許清單中。If it is currently blocked by your corporate network, it will need to be whitelisted by IT.