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+---
+title: Apache Spark
+weight: 1400
+---
+
+# Apache Spark
+
+[Spark](https://spark.apache.org/) is a leading distributed computing framework that empowers you to work with massive datasets efficiently. When it comes to leveraging the power of Spark for your data processing needs, the [Qdrant-Spark Connector](https://github.com/qdrant/qdrant-spark) is to be considered. This connector enables Qdrant to serve as a storage destination in Spark, offering a seamless bridge between the two.
+
+## Installation
+
+You can set up the Qdrant-Spark Connector in a few different ways, depending on your preferences and requirements.
+
+### GitHub Releases
+
+The simplest way to get started is by downloading pre-packaged JAR file releases from the [Qdrant-Spark GitHub releases page](https://github.com/qdrant/qdrant-spark/releases). These JAR files come with all the necessary dependencies to get you going.
+
+### Building from Source
+
+If you prefer to build the JAR from source, you'll need [JDK 17](https://www.oracle.com/java/technologies/javase/jdk17-archive-downloads.html) and [Maven](https://maven.apache.org/) installed on your system. Once you have the prerequisites in place, navigate to the project's root directory and run the following command:
+
+```bash
+mvn package -P assembly
+```
+This command will compile the source code and generate a fat JAR, which will be stored in the `target` directory by default.
+
+### Maven Central
+
+For Java and Scala projects, you can also obtain the Qdrant-Spark Connector from [Maven Central](https://central.sonatype.com/artifact/io.qdrant/spark).
+
+```xml
+<dependency>
+    <groupId>io.qdrant</groupId>
+    <artifactId>spark</artifactId>
+    <version>1.6</version>
+</dependency>
+```
+
+## Getting Started
+
+After successfully installing the Qdrant-Spark Connector, you can start integrating Qdrant with your Spark applications. Below, we'll walk through the basic steps of creating a Spark session with Qdrant support and loading data into Qdrant.
+
+### Creating a single-node Spark session with Qdrant Support
+
+To begin, import the necessary libraries and create a Spark session with Qdrant support. Here's how:
+
+```python
+from pyspark.sql import SparkSession
+
+spark = SparkSession.builder.config(
+        "spark.jars",
+        "spark-1.0-assembly.jar",  # Specify the downloaded JAR file
+    )
+    .master("local[*]")
+    .appName("qdrant")
+    .getOrCreate()
+```
+
+```scala
+import org.apache.spark.sql.SparkSession
+
+val spark = SparkSession.builder
+  .config("spark.jars", "spark-1.0-assembly.jar") // Specify the downloaded JAR file
+  .master("local[*]")
+  .appName("qdrant")
+  .getOrCreate()
+```
+
+```java
+import org.apache.spark.sql.SparkSession;
+
+public class QdrantSparkJavaExample {
+    public static void main(String[] args) {
+        SparkSession spark = SparkSession.builder()
+                .config("spark.jars", "spark-1.0-assembly.jar") // Specify the downloaded JAR file
+                .master("local[*]")
+                .appName("qdrant")
+                .getOrCreate();
+        ...
+    }
+}
+```
+
+### Loading Data into Qdrant
+
+<aside role="status">To load data into Qdrant, you'll need to create a collection with the appropriate vector dimensions and configurations in advance.</aside>
+
+Here's how you can use the Qdrant-Spark Connector to upsert data:
+
+```python
+<YourDataFrame>
+    .write
+    .format("io.qdrant.spark.Qdrant")
+    .option("qdrant_url", <QDRANT_URL>)  # REST URL of the Qdrant instance
+    .option("collection_name", <QDRANT_COLLECTION_NAME>)  # Name of the collection to write data into
+    .option("embedding_field", <EMBEDDING_FIELD_NAME>)  # Name of the field holding the embeddings
+    .option("schema", <YourDataFrame>.schema.json())  # JSON string of the dataframe schema
+    .mode("append")
+    .save()
+```
+
+```scala
+<YourDataFrame>
+    .write
+    .format("io.qdrant.spark.Qdrant")
+    .option("qdrant_url", QDRANT_URL) // REST URL of the Qdrant instance
+    .option("collection_name", QDRANT_COLLECTION_NAME) // Name of the collection to write data into
+    .option("embedding_field", EMBEDDING_FIELD_NAME) // Name of the field holding the embeddings
+    .option("schema", <YourDataFrame>.schema.json()) // JSON string of the dataframe schema
+    .mode("append")
+    .save()
+
+```
+
+```java
+<YourDataFrame>
+    .write()
+    .format("io.qdrant.spark.Qdrant")
+    .option("qdrant_url", QDRANT_URL) // REST URL of the Qdrant instance
+    .option("collection_name", QDRANT_COLLECTION_NAME) // Name of the collection to write data into
+    .option("embedding_field", EMBEDDING_FIELD_NAME) // Name of the field holding the embeddings
+    .option("schema", <YourDataFrame>.schema().json()) // JSON string of the dataframe schema
+    .mode("append")
+    .save();
+```
+
+## Datatype Support
+
+Qdrant supports all the Spark data types, and the appropriate data types are mapped based on the provided schema.
+
+## Options and Spark Types
+
+The Qdrant-Spark Connector provides a range of options to fine-tune your data integration process. Here's a quick reference:
+
+| Option            | Description                                                                  | DataType               | Required |
+| :---------------- | :--------------------------------------------------------------------------- | :--------------------- | :------- |
+| `qdrant_url`      | REST URL of the Qdrant instance                                              | `StringType`           | ✅       |
+| `collection_name` | Name of the collection to write data into                                    | `StringType`           | ✅       |
+| `embedding_field` | Name of the field holding the embeddings                                     | `ArrayType(FloatType)` | ✅       |
+| `schema`          | JSON string of the dataframe schema                                          | `StringType`           | ✅       |
+| `mode`            | Write mode of the dataframe                                                  | `StringType`           | ✅       |
+| `id_field`        | Name of the field holding the point IDs. Default: A random UUID is generated | `StringType`           | ❌       |
+| `batch_size`      | Max size of the upload batch. Default: 100                                   | `IntType`              | ❌       |
+| `retries`         | Number of upload retries. Default: 3                                         | `IntType`              | ❌       |
+| `api_key`         | Qdrant API key for authenticated requests. Default: null                     | `StringType`           | ❌       |
+
+For more information, be sure to check out the [Qdrant-Spark GitHub repository](https://github.com/qdrant/qdrant-spark). The Apache Spark guide is available [here](https://spark.apache.org/docs/latest/quick-start.html). Happy data processing!
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