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Bumped to 2.5.1
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NOTICE.txt

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@@ -14,7 +14,7 @@ jackson-dataformat-xml 2.15.2 (Apache-2.0)
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jdom2 2.0.6.1 (Apache-2.0)
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jena-arq 4.10.0 (Apache-2.0)
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langchain4j 0.35.0 (Apache-2.0)
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marklogic-client-api 7.0.0 (Apache-2.0)
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marklogic-client-api 7.1.0 (Apache-2.0)
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okhttp 4.12.0 (Apache-2.0)
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Common Licenses
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Third-Party Components
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The following is a list of the third-party components used by the MarkLogic® Spark connector 2.5.0 (last updated December 17, 2024):
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The following is a list of the third-party components used by the MarkLogic® Spark connector 2.5.1 (last updated January 6, 2025):
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jackson-dataformat-xml 2.15.2 (Apache-2.0)
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https://repo1.maven.org/maven2/com/fasterxml/jackson/dataformat/jackson-dataformat-xml/
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https://repo1.maven.org/maven2/dev/langchain4j/langchain4j/
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For the full text of the Apache-2.0 license, see Apache License 2.0 (Apache-2.0)
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marklogic-client-api 7.0.0 (Apache-2.0)
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marklogic-client-api 7.1.0 (Apache-2.0)
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https://repo1.maven.org/maven2/com/marklogic/marklogic-client-api/
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For the full text of the Apache-2.0 license, see Apache License 2.0 (Apache-2.0)
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docs/getting-started/jupyter.md

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```
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import os
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os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars "/path/to/marklogic-spark-connector-2.5.0.jar" pyspark-shell'
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os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars "/path/to/marklogic-spark-connector-2.5.1.jar" pyspark-shell'
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from pyspark.sql import SparkSession
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spark = SparkSession.builder.master("local[*]").appName('My Notebook').getOrCreate()
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spark.sparkContext.setLogLevel("WARN")
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spark
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```
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The path of `/path/to/marklogic-spark-connector-2.5.0.jar` should be changed to match the location of the connector
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The path of `/path/to/marklogic-spark-connector-2.5.1.jar` should be changed to match the location of the connector
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jar on your filesystem. You are free to customize the `spark` variable in any manner you see fit as well.
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Now that you have an initialized Spark session, you can run any of the examples found in the

docs/getting-started/pyspark.md

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Run PySpark from the directory that you downloaded the connector to per the [setup instructions](setup.md):
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pyspark --jars marklogic-spark-connector-2.5.0.jar
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pyspark --jars marklogic-spark-connector-2.5.1.jar
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The `--jars` command line option is PySpark's method for utilizing Spark connectors. Each Spark environment should have
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a similar mechanism for including third party connectors; please see the documentation for your particular Spark

docs/getting-started/setup.md

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extracted directory contains valid connection properties for your instance of MarkLogic.
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1. From [this repository's Releases page](https://github.com/marklogic/marklogic-spark-connector/releases), select
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the latest release and download the `marklogic-spark-getting-started-2.5.0.zip` file.
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the latest release and download the `marklogic-spark-getting-started-2.5.1.zip` file.
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2. Extract the contents of the downloaded zip file.
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3. Open a terminal window and go to the directory created by extracting the zip file; the directory should have a
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name of "marklogic-spark-getting-started-2.5.0".
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name of "marklogic-spark-getting-started-2.5.1".
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4. Run `docker-compose up -d` to start an instance of MarkLogic
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5. Ensure that the `./gradlew` file is executable; depending on your operating system, you may need to run
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`chmod 755 gradlew` to make the file executable.

examples/entity-aggregation/build.gradle

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dependencies {
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implementation 'org.apache.spark:spark-sql_2.12:3.5.3'
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implementation "com.marklogic:marklogic-spark-connector:2.5.0"
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implementation "com.marklogic:marklogic-spark-connector:2.5.1"
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implementation "org.postgresql:postgresql:42.7.4"
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}
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examples/getting-started/marklogic-spark-getting-started.ipynb

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"source": [
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"# Make the MarkLogic connector available to the underlying PySpark application.\n",
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"import os\n",
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"os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars \"marklogic-spark-connector-2.5.0.jar\" pyspark-shell'\n",
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"os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars \"marklogic-spark-connector-2.5.1.jar\" pyspark-shell'\n",
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"\n",
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"# Define the connection details for the getting-started example application.\n",
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"client_uri = \"spark-example-user:password@localhost:8003\"\n",

examples/java-dependency/build.gradle

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dependencies {
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implementation 'org.apache.spark:spark-sql_2.12:3.5.3'
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implementation 'com.marklogic:marklogic-spark-connector:2.5.0'
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implementation 'com.marklogic:marklogic-spark-connector:2.5.1'
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}
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task runApp(type: JavaExec) {

marklogic-langchain4j/build.gradle

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dependencies {
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compileOnly "com.fasterxml.jackson.core:jackson-databind:2.17.2"
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api "com.marklogic:marklogic-client-api:7.1-SNAPSHOT"
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api "com.marklogic:marklogic-client-api:7.1.0"
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// Supports splitting documents.
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api "dev.langchain4j:langchain4j:0.35.0"

marklogic-spark-connector/build.gradle

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shadowDependencies project(":marklogic-spark-api")
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shadowDependencies("com.marklogic:marklogic-client-api:7.1-SNAPSHOT") {
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shadowDependencies("com.marklogic:marklogic-client-api:7.1.0") {
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// The Java Client uses Jackson 2.15.2; Scala 3.4.x does not yet support that and will throw the following error:
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// Scala module 2.14.2 requires Jackson Databind version >= 2.14.0 and < 2.15.0 - Found jackson-databind version 2.15.2
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// So the 4 Jackson modules are excluded to allow for Spark's to be used.

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