landing_page/qdrant-landing/content/documentation/frameworks/spark.md

title, weight, aliases

title	weight	aliases
Apache Spark	1400	../integrations/spark/

Apache Spark

Spark 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 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. 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 and Maven installed on your system. Once you have the prerequisites in place, navigate to the project's root directory and run the following command:

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.

<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:

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()

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()

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

To load data into Qdrant, you'll need to create a collection with the appropriate vector dimensions and configurations in advance.

Here's how you can use the Qdrant-Spark Connector to upsert data:

<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()

<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()

<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. The Apache Spark guide is available here. Happy data processing!