Merge branch 'master' of https://github.com/Microsoft/sql-server-samples

uc-msft · uc-msft · commit 26801b966120 · 2019-08-08T16:21:20.000-07:00
diff --git a/samples/features/sql-big-data-cluster/README.md b/samples/features/sql-big-data-cluster/README.md
@@ -9,7 +9,7 @@ Installation instructions for SQL Server 2019 big data clusters can be found [he
 ## Executing the sample scripts
 The scripts should be executed in a specific order to test the various features. Execute the scripts from each folder in below order:
 
-1. __[spark/dataloading/transform-csv-files.ipynb](spark/dataloading/transform-csv-files.ipynb)__
+1. __[spark/data-loading/transform-csv-files.ipynb](spark/data-loading/transform-csv-files.ipynb)__
 1. __[data-virtualization/generic-odbc](data-virtualization/generic-odbc)__
 1. __[data-virtualization/hadoop](data-virtualization/hadoop)__
 1. __[data-virtualization/storage-pool](data-virtualization/storage-pool)__
diff --git a/samples/features/sql-big-data-cluster/app-deploy/RollDice/README.md b/samples/features/sql-big-data-cluster/app-deploy/RollDice/README.md
@@ -41,10 +41,10 @@ To run this sample, you need the following prerequisites.
 ## Run this sample
 
 1. Clone or download this sample on your computer.
-2. Log in to the SQL Server big data cluster using the command below using the IP address of the `mgmtproxy-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
+2. Log in to the SQL Server big data cluster using the command below using the IP address of the `controller-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
 
     ```bash
-    azdata login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
+    azdata login -e https://<ip-address-of-controller-svc-external>:30080 -u <user-name> -p <password>
     ```
 3. Deploy the application by running the following command, specifying the folder where your `spec.yaml` and `roll-dice.R` files are located:
     ```bash
diff --git a/samples/features/sql-big-data-cluster/app-deploy/SSIS/README.md b/samples/features/sql-big-data-cluster/app-deploy/SSIS/README.md
@@ -32,10 +32,10 @@ To run this sample, you need the following prerequisites.
 ## Run this sample
 
 1. Clone or download this sample on your computer.
-2. Log in to the SQL Server big data cluster using the command below using the IP address of the `mgmtproxy-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
+2. Log in to the SQL Server big data cluster using the command below using the IP address of the `controller-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
 
     ```bash
-    azdata login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
+    azdata login -e https://<ip-address-of-controller-svc-external>:30080 -u <user-name>
     ```
 3. Replace `[SA_PASSWORD]` in the `spec.yaml` file with the password for SQL user `sa`.
 4. Deploy the application by running the following command, specifying the folder where your `spec.yaml` and `back-up-db.dtsx` files are located:
diff --git a/samples/features/sql-big-data-cluster/app-deploy/addpy/README.md b/samples/features/sql-big-data-cluster/app-deploy/addpy/README.md
@@ -42,10 +42,10 @@ To run this sample, you need the following prerequisites.
 ## Run this sample
 
 1. Clone or download this sample on your computer.
-2. Log in to the SQL Server big data cluster using the command below using the IP address of the `mgmtproxy-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
+2. Log in to the SQL Server big data cluster using the command below using the IP address of the `controller-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
 
     ```bash
-    azdata login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
+    azdata login -e https://<ip-address-of-controller-svc-external>:30080 -u <user-name>
     ```
 3. Deploy the application by running the following command, specifying the folder where your `spec.yaml` and `add.py` files are located:
     ```bash
diff --git a/samples/features/sql-big-data-cluster/app-deploy/magic8ball/README.md b/samples/features/sql-big-data-cluster/app-deploy/magic8ball/README.md
@@ -41,10 +41,10 @@ To run this sample, you need the following prerequisites.
 ## Run this sample
 
 1. Clone or download this sample on your computer.
-2. Log in to the SQL Server big data cluster using the command below using the IP address of the `mgmtproxy-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
+2. Log in to the SQL Server big data cluster using the command below using the IP address of the `controller-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
 
     ```bash
-    azdata login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
+    azdata login -e https://<ip-address-of-controller-svc-external>:30080 -u <user-name>
     ```
 3. Deploy the application by running the following command, specifying the folder where your `spec.yaml` and `magic8ball.py` files are located:
     ```bash
diff --git a/samples/features/sql-big-data-cluster/app-deploy/mleap/README.md b/samples/features/sql-big-data-cluster/app-deploy/mleap/README.md
@@ -38,10 +38,10 @@ To run this sample, you need the following prerequisites.
 ## Run this sample
 
 1. Clone or download this sample on your computer.
-2. Log in to the SQL Server big data cluster using the command below using the IP address of the `mgmtproxy-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
+2. Log in to the SQL Server big data cluster using the command below using the IP address of the `controller-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
 
    ```bash
-   azdata login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
+   azdata login -e https://<ip-address-of-controller-svc-external>:30080 -u <user-name>
    ```
 3. This example uses a TensorFlow Machine Learning Model that uses public US Census data predict income. [More details and information on the example are here](https://docs.microsoft.com/en-us/sql/big-data-cluster/train-and-create-machinelearning-models-with-spark?view=sqlallproducts-allversions). The application you will be deploying as part of this sample is a Random Forest Model that was built in Spark and has been [serialized as an MLeap bundle](https://docs.microsoft.com/en-us/sql/big-data-cluster/export-model-with-spark-mleap?view=sqlallproducts-allversions).
 
diff --git a/samples/features/sql-big-data-cluster/app-deploy/sentiment-analysis/README.md b/samples/features/sql-big-data-cluster/app-deploy/sentiment-analysis/README.md
@@ -42,10 +42,10 @@ To run this sample, you need the following prerequisites.
 ## Run this sample
 
 1. Clone or download this sample on your computer.
-2. Log in to the SQL Server big data cluster using the command below using the IP address of the `mgmtproxy-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
+2. Log in to the SQL Server big data cluster using the command below using the IP address of the `controller-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
 
     ```bash
-    azdata login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
+    azdata login -e https://<ip-address-of-controller-svc-external>:30080 -u <user-name>
     ```
 3. Deploy the application by running the following command, specifying the folder where your `spec.yaml`, `sentiment.rds` and `sentiment.R` files are located:
     ```bash
diff --git a/samples/features/sql-big-data-cluster/app-deploy/sumofsq/README.md b/samples/features/sql-big-data-cluster/app-deploy/sumofsq/README.md
@@ -42,10 +42,10 @@ To run this sample, you need the following prerequisites.
 ## Run this sample
 
 1. Clone or download this sample on your computer.
-2. Log in to the SQL Server big data cluster using the command below using the IP address of the `mgmtproxy-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
+2. Log in to the SQL Server big data cluster using the command below using the IP address of the `controller-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
 
     ```bash
-    azdata login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
+    azdata login -e https://<ip-address-of-controller-svc-external>:30080 -u <user-name>
     ```
 3. Deploy the application by running the following command, specifying the folder where your `spec.yaml` and `sum_of_squares.R` files are located:
     ```bash
diff --git a/samples/features/sql-big-data-cluster/data-virtualization/hadoop/README.md b/samples/features/sql-big-data-cluster/data-virtualization/hadoop/README.md
@@ -12,10 +12,10 @@ In SQL Server 2019 big data cluster, the storage pool consists of HDFS data node
 
 1. Connect to SQL Server Master instance.
 
-1. Run the [../../spark/dataloading/transform-csv-files.ipynb](../../spark/dataloading/transform-csv-files.ipynb/) notebook to generate the sample parquet file(s).
+1. Run the [../../spark/data-loading/transform-csv-files.ipynb](../../spark/data-loading/transform-csv-files.ipynb/) notebook to generate the sample parquet file(s).
 
 1. Execute the [web-clickstreams-hdfs-orc.sql](web-clickstreams-hdfs-orc.sql). This script demonstrates how to read ORC file(s) stored in HDFS.
 
 1. Execute the [product-reviews-hdfs-orc.sql](product-reviews-hdfs-orc.sql). This script demonstrates how to read ORC file(s) stored in HDFS.
 
-1. Execute the [inventory-hdfs-rcfile.sql](inventory-hdfs-rcfile.sql). This script demonstrates how to export data from SQL Server into HDFS using PolyBase v1 syntax. This script will export data from SQL Server into RCFILE format.
+1. Execute the [inventory-hdfs-rcfile.sql](inventory-hdfs-rcfile.sql). This script demonstrates how to export data from SQL Server into HDFS using PolyBase v1 syntax. This script will export data from SQL Server into RCFILE format.
diff --git a/samples/features/sql-big-data-cluster/data-virtualization/storage-pool/README.md b/samples/features/sql-big-data-cluster/data-virtualization/storage-pool/README.md
@@ -12,7 +12,7 @@ In SQL Server 2019 big data cluster, the storage pool consists of HDFS data node
 
 1. Connect to SQL Server Master instance.
 
-1. Run the [../../spark/dataloading/transform-csv-files.ipynb](../../spark/dataloading/transform-csv-files.ipynb/) notebook to generate the sample parquet file(s).
+1. Run the [../../spark/data-loading/transform-csv-files.ipynb](../../spark/data-loading/transform-csv-files.ipynb/) notebook to generate the sample parquet file(s).
 
 1. Execute the [web-clickstreams-hdfs-csv.sql](web-clickstreams-hdfs-csv.sql). This script demonstrates how to read CSV file(s) stored in HDFS.
 
diff --git a/samples/features/sql-big-data-cluster/machine-learning/sql/README.md b/samples/features/sql-big-data-cluster/machine-learning/sql/README.md
@@ -12,6 +12,6 @@ This folder contains the R samples.
 
 This folder contains the Python samples.
 
-[java](java)
+[java](https://github.com/microsoft/sql-server-language-extensions/tree/master/language-extensions/java/samples/regex)
 
 This folder contains the Java samples.
diff --git a/samples/features/sql-big-data-cluster/spark/README.md b/samples/features/sql-big-data-cluster/spark/README.md
@@ -4,13 +4,13 @@ SQL Server Big Data cluster bundles Spark and HDFS together with SQL server. Azu
 
 ## Contents
 
-[PySpark Hello World](dataloading/hello_PySpark.ipynb)
+[PySpark Hello World](data-loading/hello_PySpark.ipynb)
 
-[Scala Hello World ](dataloading/hello_Scala.ipynb)
+[Scala Hello World ](data-loading/hello_Scala.ipynb)
 
-[SparkR Hello World ](dataloading/hello_sparkR.ipynb)
+[SparkR Hello World ](data-loading/hello_sparkR.ipynb)
 
-[DataLoading   - Transforming CSV to Parquet](dataloading/transform-csv-files.ipynb/)
+[Data Loading   - Transforming CSV to Parquet](data-loading/transform-csv-files.ipynb/)
 
 [Data Transfer - Spark to SQL using Spark JDBC connector](data-virtualization/spark_to_sql_jdbc.ipynb/)
 
@@ -20,7 +20,7 @@ SQL Server Big Data cluster bundles Spark and HDFS together with SQL server. Azu
 
 [Install - Install 3rd party packages](config-install/installpackage_Spark.ipynb/)
 
-[Restful-Access - Access Spark in BDC via restful Livy APIs](restful-api-accessn/accessing_spark_via_livy.ipynb/)
+[Restful-Access - Access Spark in BDC via restful Livy APIs](restful-api-access/accessing_spark_via_livy.ipynb/)
 
 ## Instructions on how to run in Azure Data Studio
 
