Merge branch 'release-sqlseattle' of https://github.com/MicrosoftDocs/sql-docs-pr into ssms-18

Author: stevestein

docs/advanced-analytics/java/extension-java.md

@@ -77,6 +77,8 @@ Install the JDK under the default /Program Files/ folder if you want to avoid ha
 > [!Note]
 > The authorization and isolation model for extensions has changed in this release. For more information, see [Differences in a SQL Server Machine 2019 Learning Services installation](../install/sql-machine-learning-services-ver15.md).
 
+<a name="perms-nonwindows"></a>
+
 ### Grant access to non-default JDK folder (Windows only)
 
 You can skip this step if you installed the JDK/JRE in the default folder. For a non-default folder installation, run the following PowerShell scripts to grant access to the **SQLRUsergroup** and SQL Server service accounts (in ALL_APPLICATION_PACKAGES) for accessing the JVM and the Java classpath.

docs/advanced-analytics/java/java-first-sample.md

@@ -12,7 +12,7 @@ manager: cgronlun
 monikerRange: ">=sql-server-ver15||=sqlallproducts-allversions"
 ---
 
-# SQL Server Java sample
+# SQL Server Java sample walkthrough
 
 This example demonstrates a Java class that receives two columns (ID and text) from SQL Server and returns two columns back to SQL Server (ID and ngram). For a given ID and string combination, the code generates permutations of ngrams (substrings), returning those permutations along with the original ID. The length of the ngram is defined by a parameter sent to the Java class.
 
@@ -24,7 +24,7 @@ This example demonstrates a Java class that receives two columns (ID and text) f
 
 + Java SE Development Kit (JDK) 1.10 on Windows, or JDK 1.8 on Linux.
 
-A Java IDE is helpful for creating and compiling classes. If you don't have one, we recommend [Visual Studio Code](https://code.visualstudio.com/download) with the [Java extension](https://code.visualstudio.com/docs/languages/java) (not related to the SQL Server Java extension).
+Command-line compilation using **javac** is sufficient for this tutorial. 
 
 ## 1 - Load sample data
 
@@ -165,7 +165,7 @@ public class InputRow {
 
 ## 4 - Class OutputRow.java
 
-The third and final class is called **OutputRow.java**. Copy the code into the class and save it in the same location as the others.
+The third and final class is called **OutputRow.java**. Copy the code and save as OutputRow.java in the same location as the others.
 
 ```java
 package pkg;
@@ -187,21 +187,23 @@ public class OutputRow {
 
 ## 5 - Compile
 
-Once you have your classes ready, run javac to compile them into ".class" files (`javac Ngram.java InputRow.java OutputRow.java). You should get three .class files for this sample (Ngram.class, InputRow.class, and OutputRow.class).
+Once you have your classes ready, run javac to compile them into ".class" files (`javac Ngram.java InputRow.java OutputRow.java`). You should get three .class files for this sample (Ngram.class, InputRow.class, and OutputRow.class).
 
-On the SQL Server computer, place these files in a subfolder called "pkg" in your classpath location. For example, on Linux, if the classpath location is called '/home/myclasspath/', then the .class files should be in '/home/myclasspath/pkg'. In this sample, the CLASSPATH provided in the sp_execute_external_script is '/home/myclasspath/' assuming Linux. 
+Place the compiled code into a subfolder called "pkg" in the classpath location. If you are working on a development workstation, this step is where you copy the files to the SQL Server computer.
 
-On Windows, set the value to a Windows folder path 'C:\myJavaCode' and then create a subfolder called "pkg" to contain the compiled classes. In this CTP, use a relatively shallow folder structure to simplify permissions.
+The classpath is the location of compiled code. For example, on Linux, if the classpath is '/home/myclasspath/', then the .class files should be in '/home/myclasspath/pkg'. In the example script in step 7, the CLASSPATH provided in the sp_execute_external_script is '/home/myclasspath/' (assuming Linux). 
 
-For instructions on how to set the classpath, see [Set CLASSPATH](howto-call-java-from-sql.md#set-classpath). 
+On Windows, we recommend using a relatively shallow folder structure, one or two levels deep, to simplify permissions. For example, your classpath might look like 'C:\myJavaCode' with a subfolder called '\pkg' containing the compiled classes. 
+
+For more information about classpath, see [Set CLASSPATH](howto-call-java-from-sql.md#set-classpath). 
 
 ### Using .jar files
 
-If you plan to package your classes and dependencies into .jar files, provide the full path to the .jar file in the sp_execute_external_script CLASSPATH parameter. For example, if the jar file is called 'ngram.jar', the CLASSPATH will be '/home/myclasspath/ngram.jar'
+If you plan to package your classes and dependencies into .jar files, provide the full path to the .jar file in the sp_execute_external_script CLASSPATH parameter. For example, if the jar file is called 'ngram.jar', the CLASSPATH will be '/home/myclasspath/ngram.jar' on Linux.
 
 ## 6 - Permissions
 
-Grant permissions on the compiled code so that SQL Server Launchpad service and AppContainers can execute it.
+Script execution only succeeds if the process identities have access to your code. 
 
 ### On Linux
 
@@ -220,19 +222,19 @@ Grant 'Read and Execute' permissions to **SQLRUserGroup** and the **All applicat
 5. Enter **SQLRUserGroup**, check the name, and then click OK to add the group.
 6. Enter **all application packages**, check the name, and then click OK to add. If the name doesn't resolve, revisit the Locations step. The SID is local to your machine.
 
-Make sure both security identities have 'Read and Execute' permissions on the folder and on the "pkg" subfolder.
+Make sure both security identities have 'Read and Execute' permissions on the folder and "pkg" subfolder.
 
 <a name="call-method"></a>
 
 ## 7 - Call *getNgrams()*
 
-To call the code from SQL Server, specify the Java method *getNgrams()* from the "script" parameter of sp_execute_external_script. This method belongs to a package called "pkg" and a class file called **Ngram.java**.
+To call the code from SQL Server, specify the Java method **getNgrams()** in the "script" parameter of sp_execute_external_script. This method belongs to a package called "pkg" and a class file called **Ngram.java**.
 
 This example passes the CLASSPATH parameter to provide the path to the Java files. It also uses "params" to pass a parameter to the Java class. Make sure that classpath does not exceed 30 characters. If it does, increase the value in the script below.
 
 + On Linux, run the following code in SQL Server Management Studio or another tool used for running Transact-SQL. 
 
-+ On Windows, change **@myClassPath** to N'C:\myJavaCode\' (assuming it's the parent folder of \pkg) before executing the query. 
++ On Windows, change **@myClassPath** to N'C:\myJavaCode\' (assuming it's the parent folder of \pkg) before executing the query in SQL Server Management Studio or another tool.
 
 ```sql
 DECLARE @myClassPath nvarchar(30)

docs/analytics-platform-system/toc.yml

@@ -1,13 +1,12 @@
-- name: About this release
+- name: What's new in APS
+  href: whats-new-analytics-platform-system.md
   items:
-  - name: What's new
-    items:
-    - name: APS CU7.1
-      href: whats-new-analytics-platform-system.md#h2-aps-cu7.1
-    - name: APS AU7
-      href: whats-new-analytics-platform-system.md#h2-aps-au7
-    - name: APS 2016
-      href: whats-new-analytics-platform-system.md#h2-aps-au6
+  - name: APS CU7.1
+    href: whats-new-analytics-platform-system.md#h2-aps-cu7.1?view=aps-pdw-2016-au7
+  - name: APS AU7
+    href: whats-new-analytics-platform-system.md#h2-aps-au7?view=aps-pdw-2016-au7
+  - name: APS 2016
+    href: whats-new-analytics-platform-system.md#h2-aps-au6?view=aps-pdw-2016
 - name: Architecture
   items:
   - name: Parallel Data Warehouse overview

docs/analytics-platform-system/whats-new-analytics-platform-system.md

@@ -16,6 +16,7 @@ See what’s new in the latest Appliance Updates for Microsoft® Analytics Platf
 ::: moniker range=">= aps-pdw-2016-au7 || = sqlallproducts-allversions"
 <a name="h2-aps-cu7.1"></a>
 ## APS CU7.1
+Release date - July 2018
 
 ### DBCC commands do not consume concurrency slots (behavior change)
 APS supports a subset of the T-SQL [DBCC commands](https://docs.microsoft.com/sql/t-sql/database-console-commands/dbcc-transact-sql) such as [DBCC DROPCLEANBUFFERS](https://docs.microsoft.com/sql/t-sql/database-console-commands/dbcc-dropcleanbuffers-transact-sql). Previously, these commands would consume a [concurrency slot](https://docs.microsoft.com/en-us/sql/analytics-platform-system/workload-management?view=aps-pdw-2016-au7#concurrency-slots) reducing the number of user loads/queries that could be executed. The `DBCC` commands are now run in a local queue that do not consume a user concurrency slot improving overall query execution performance.
@@ -34,7 +35,9 @@ We have upgraded to SQL Server 2016 SP2 CU2 with APS CU7.1. The upgrade fixes so
 
 <a name="h2-aps-au7"></a>
 ## APS AU7
-APS 2016 is a prerequisite to upgrade to AU7. The following are new in APS AU7:
+Release date - May 2018
+
+APS 2016 is a prerequisite to upgrade to AU7. The following are new features in APS AU7:
 
 ### Auto-create and auto-update statistics
 APS AU7 creates and updates statistics automatically, by default. To update statistics settings, administrators can use a new feature switch menu item in the [Configuration Manager](appliance-configuration.md#CMTasks). The [feature switch](appliance-feature-switch.md) controls the auto-create, auto-update, and asynchronous update behavior of statistics. You can also update statistics settings with the [ALTER DATABASE (Parallel Data Warehouse)](../t-sql/statements/alter-database-transact-sql.md?tabs=sqlpdw) statement.

docs/big-data-cluster/big-data-cluster-overview.md

@@ -27,13 +27,13 @@ By leveraging [SQL Server PolyBase](../relational-databases/polybase/polybase-gu
 
 ### Data lake
 
-A SQL Big Data Cluster includes a scalable HDFS [storage pool](concept-storage-pool.md). This can be used to directly store big data, potentially ingested from multiple external sources. Once in the Big Data Cluster, you can analyze and query the data and combine it with your high-value relational data.
+A SQL Big Data Cluster includes a scalable HDFS *storage pool*. This can be used to directly store big data, potentially ingested from multiple external sources. Once in the Big Data Cluster, you can analyze and query the data and combine it with your high-value relational data.
 
 ![Data lake](media/big-data-cluster-overview/data-lake.png)
 
 ### Scale-out data mart
 
-SQL Big Data Clusters provides scale-out compute and storage to improve the performance of analyzing any data. Data from a variety of sources can be ingested and distributed across [data pool](concept-data-pool.md) nodes for further analysis.
+SQL Big Data Clusters provides scale-out compute and storage to improve the performance of analyzing any data. Data from a variety of sources can be ingested and distributed across *data pool* nodes for further analysis.
 
 ![Data mart](media/big-data-cluster-overview/data-mart.png)
 
@@ -60,7 +60,7 @@ You can use Azure Data Studio to perform a variety of tasks on the Big Data Clus
 
 ## <a id="architecture"></a> Architecture
 
-A SQL Big Data Cluster is a cluster of Linux nodes orchestrated by [Kubernetes](https://kubernetes.io/docs/concepts/). 
+A SQL Big Data Cluster is a cluster of Linux nodes orchestrated by [Kubernetes](https://kubernetes.io/docs/concepts/).
 
 ### Kubernetes concepts
 
@@ -82,29 +82,17 @@ Nodes in the cluster are arranged into three logical planes: the control plane,
 
 ### <a id="controlplane"></a> Control plane
 
-The control plane provides management and security for the cluster. It contains the Kubernetes master, the [SQL Server master instance](concept-master-instance.md), and other cluster-level services such as the Hive Metastore and Spark Driver.
+The control plane provides management and security for the cluster. It contains the Kubernetes master, the *SQL Server master instance*, and other cluster-level services such as the Hive Metastore and Spark Driver.
 
 ### <a id="computeplane"></a> Compute plane
 
-The compute plane provides computational resources to the cluster. It contains nodes running SQL Server on Linux pods. The pods in the compute plane are divided into [compute pools](concept-compute-pool.md) for specific processing tasks. A compute pool can act as a [PolyBase](../relational-databases/polybase/polybase-guide.md) scale-out group for distributed queries over different data sources—such as HDFS, Oracle, MongoDB, or Teradata.
+The compute plane provides computational resources to the cluster. It contains nodes running SQL Server on Linux pods. The pods in the compute plane are divided into *compute pools* for specific processing tasks. A compute pool can act as a [PolyBase](../relational-databases/polybase/polybase-guide.md) scale-out group for distributed queries over different data sources—such as HDFS, Oracle, MongoDB, or Teradata.
 
 ### <a id="dataplane"></a> Data plane
 
-The data plane is used for data persistence and caching. It contains the SQL data pool, and storage nodes.  The SQL [data pool](concept-data-pool.md) consists of one or more nodes running SQL Server on Linux. It is used to ingest data from SQL queries or Spark jobs. SQL Big Data Cluster data marts are persisted in the data pool. The [storage pool](concept-storage-pool.md) consists of storage nodes comprised of SQL Server on Linux, Spark, and HDFS. All the storage nodes in a SQL Big Data cluster are members of an HDFS cluster.
-
-## Get started
-
-SQL Big Data Clusters is first available as a limited public preview through the SQL Server 2019
-Early Adoption Program. To request access, register [here](https://aka.ms/eapsignup), and specify your interest to try Big Data Clusters. Microsoft will triage all requests and respond as soon as possible.
+The data plane is used for data persistence and caching. It contains the SQL data pool, and storage nodes.  The SQL data pool consists of one or more nodes running SQL Server on Linux. It is used to ingest data from SQL queries or Spark jobs. SQL Big Data Cluster data marts are persisted in the data pool. The storage pool consists of storage nodes comprised of SQL Server on Linux, Spark, and HDFS. All the storage nodes in a SQL Big Data cluster are members of an HDFS cluster.
 
 ## Next steps
 
-Learn more about SQL Server Big Data Clusters in the following articles:
-
-[HDFS](concept-hdfs.md)
-[Spark](concept-spark.md)
-[Controller](concept-controller.md)
-[Master instance](concept-master-instance.md)
-[Compute pool](concept-compute-pool.md)
-[Data pool](concept-data-pool.md)
-[Storage pool](concept-storage-pool.md)
+SQL Big Data Clusters is first available as a limited public preview through the SQL Server 2019
+Early Adoption Program. To request access, register [here](https://aka.ms/eapsignup), and specify your interest to try Big Data Clusters. Microsoft will triage all requests and respond as soon as possible.