Bumped to 2.4.2

Author: rjrudin

build.gradle

@@ -9,7 +9,7 @@ plugins {
 }
 
 group 'com.marklogic'
-version '2.4-SNAPSHOT'
+version '2.4.2'
 
 java {
   // To support reading RDF files, Apache Jena is used - but that requires Java 11.

docs/getting-started/jupyter.md

@@ -32,15 +32,15 @@ connector and also to initialize Spark:
 
 ```
 import os
-os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars "/path/to/marklogic-spark-connector-2.4.1.jar" pyspark-shell'
+os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars "/path/to/marklogic-spark-connector-2.4.2.jar" pyspark-shell'
 
 from pyspark.sql import SparkSession
 spark = SparkSession.builder.master("local[*]").appName('My Notebook').getOrCreate()
 spark.sparkContext.setLogLevel("WARN")
 spark
 ```
 
-The path of `/path/to/marklogic-spark-connector-2.4.1.jar` should be changed to match the location of the connector 
+The path of `/path/to/marklogic-spark-connector-2.4.2.jar` should be changed to match the location of the connector 
 jar on your filesystem. You are free to customize the `spark` variable in any manner you see fit as well. 
 
 Now that you have an initialized Spark session, you can run any of the examples found in the 

docs/getting-started/pyspark.md

@@ -29,7 +29,7 @@ shell by pressing `ctrl-D`.
 
 Run PySpark from the directory that you downloaded the connector to per the [setup instructions](setup.md):
 
-    pyspark --jars marklogic-spark-connector-2.4.1.jar
+    pyspark --jars marklogic-spark-connector-2.4.2.jar
 
 The `--jars` command line option is PySpark's method for utilizing Spark connectors. Each Spark environment should have
 a similar mechanism for including third party connectors; please see the documentation for your particular Spark

docs/getting-started/setup.md

@@ -31,10 +31,10 @@ have an instance of MarkLogic running, you can skip step 4 below, but ensure tha
 extracted directory contains valid connection properties for your instance of MarkLogic.
 
 1. From [this repository's Releases page](https://github.com/marklogic/marklogic-spark-connector/releases), select 
-   the latest release and download the `marklogic-spark-getting-started-2.4.1.zip` file.
+   the latest release and download the `marklogic-spark-getting-started-2.4.2.zip` file.
 2. Extract the contents of the downloaded zip file. 
 3. Open a terminal window and go to the directory created by extracting the zip file; the directory should have a 
-   name of "marklogic-spark-getting-started-2.4.1".
+   name of "marklogic-spark-getting-started-2.4.2".
 4. Run `docker-compose up -d` to start an instance of MarkLogic
 5. Ensure that the `./gradlew` file is executable; depending on your operating system, you may need to run
    `chmod 755 gradlew` to make the file executable.

examples/entity-aggregation/build.gradle

@@ -8,7 +8,7 @@ repositories {
 
 dependencies {
   implementation 'org.apache.spark:spark-sql_2.12:3.5.3'
-  implementation "com.marklogic:marklogic-spark-connector:2.4.1"
+  implementation "com.marklogic:marklogic-spark-connector:2.4.2"
   implementation "org.postgresql:postgresql:42.6.2"
 }
 

examples/getting-started/marklogic-spark-getting-started.ipynb

@@ -9,7 +9,7 @@
    "source": [
     "# Make the MarkLogic connector available to the underlying PySpark application.\n",
     "import os\n",
-    "os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars \"marklogic-spark-connector-2.4.1.jar\" pyspark-shell'\n",
+    "os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars \"marklogic-spark-connector-2.4.2.jar\" pyspark-shell'\n",
     "\n",
     "# Define the connection details for the getting-started example application.\n",
     "client_uri = \"spark-example-user:password@localhost:8003\"\n",

examples/java-dependency/build.gradle

@@ -8,7 +8,7 @@ repositories {
 
 dependencies {
   implementation 'org.apache.spark:spark-sql_2.12:3.5.3'
-  implementation 'com.marklogic:marklogic-spark-connector:2.4.1'
+  implementation 'com.marklogic:marklogic-spark-connector:2.4.2'
 }
 
 task runApp(type: JavaExec) {