mirror of
https://github.com/qdrant/landing_page.git
synced 2026-09-28 23:48:31 +02:00
256 lines
10 KiB
Markdown
256 lines
10 KiB
Markdown
---
|
|
title: Apache Spark
|
|
weight: 1400
|
|
aliases: [ ../integrations/spark/ ]
|
|
---
|
|
|
|
# Apache Spark
|
|
|
|
[Spark](https://spark.apache.org/) is a distributed computing framework designed for big data processing and analytics. The [Qdrant-Spark connector](https://github.com/qdrant/qdrant-spark) enables Qdrant to be a storage destination in Spark.
|
|
|
|
## 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 [GitHub releases page](https://github.com/qdrant/qdrant-spark/releases). These JAR files come with all the necessary dependencies.
|
|
|
|
### Building from Source
|
|
|
|
If you prefer to build the JAR from source, you'll need [JDK 8](https://www.azul.com/downloads/#zulu) 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
|
|
```
|
|
|
|
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 use with Java and Scala projects, the package can be found [here](https://central.sonatype.com/artifact/io.qdrant/spark).
|
|
|
|
## Usage
|
|
|
|
Below, we'll walk through the 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:
|
|
|
|
```python
|
|
from pyspark.sql import SparkSession
|
|
|
|
spark = SparkSession.builder.config(
|
|
"spark.jars",
|
|
"spark-VERSION.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-VERSION.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-VERSION.jar") // Specify the downloaded JAR file
|
|
.master("local[*]")
|
|
.appName("qdrant")
|
|
.getOrCreate();
|
|
}
|
|
}
|
|
```
|
|
|
|
### Loading data into Qdrant
|
|
|
|
<aside role="status">Before loading the data using this connector, a collection has to be <a href="/documentation/concepts/collections/#create-a-collection">created</a> in advance with the appropriate vector dimensions and configurations.</aside>
|
|
|
|
The connector supports ingesting multiple named/unnamed, dense/sparse vectors.
|
|
|
|
_Click each to expand._
|
|
|
|
<details>
|
|
<summary><b>Unnamed/Default vector</b></summary>
|
|
|
|
```python
|
|
<pyspark.sql.DataFrame>
|
|
.write
|
|
.format("io.qdrant.spark.Qdrant")
|
|
.option("qdrant_url", <QDRANT_GRPC_URL>)
|
|
.option("collection_name", <QDRANT_COLLECTION_NAME>)
|
|
.option("embedding_field", <EMBEDDING_FIELD_NAME>) # Expected to be a field of type ArrayType(FloatType)
|
|
.option("schema", <pyspark.sql.DataFrame>.schema.json())
|
|
.mode("append")
|
|
.save()
|
|
```
|
|
|
|
</details>
|
|
|
|
<details>
|
|
<summary><b>Named vector</b></summary>
|
|
|
|
```python
|
|
<pyspark.sql.DataFrame>
|
|
.write
|
|
.format("io.qdrant.spark.Qdrant")
|
|
.option("qdrant_url", <QDRANT_GRPC_URL>)
|
|
.option("collection_name", <QDRANT_COLLECTION_NAME>)
|
|
.option("embedding_field", <EMBEDDING_FIELD_NAME>) # Expected to be a field of type ArrayType(FloatType)
|
|
.option("vector_name", <VECTOR_NAME>)
|
|
.option("schema", <pyspark.sql.DataFrame>.schema.json())
|
|
.mode("append")
|
|
.save()
|
|
```
|
|
|
|
> #### NOTE
|
|
>
|
|
> The `embedding_field` and `vector_name` options are maintained for backward compatibility. It is recommended to use `vector_fields` and `vector_names` for named vectors as shown below.
|
|
|
|
</details>
|
|
|
|
<details>
|
|
<summary><b>Multiple named vectors</b></summary>
|
|
|
|
```python
|
|
<pyspark.sql.DataFrame>
|
|
.write
|
|
.format("io.qdrant.spark.Qdrant")
|
|
.option("qdrant_url", "<QDRANT_GRPC_URL>")
|
|
.option("collection_name", "<QDRANT_COLLECTION_NAME>")
|
|
.option("vector_fields", "<COLUMN_NAME>,<ANOTHER_COLUMN_NAME>")
|
|
.option("vector_names", "<VECTOR_NAME>,<ANOTHER_VECTOR_NAME>")
|
|
.option("schema", <pyspark.sql.DataFrame>.schema.json())
|
|
.mode("append")
|
|
.save()
|
|
```
|
|
|
|
</details>
|
|
|
|
<details>
|
|
<summary><b>Sparse vectors</b></summary>
|
|
|
|
```python
|
|
<pyspark.sql.DataFrame>
|
|
.write
|
|
.format("io.qdrant.spark.Qdrant")
|
|
.option("qdrant_url", "<QDRANT_GRPC_URL>")
|
|
.option("collection_name", "<QDRANT_COLLECTION_NAME>")
|
|
.option("sparse_vector_value_fields", "<COLUMN_NAME>")
|
|
.option("sparse_vector_index_fields", "<COLUMN_NAME>")
|
|
.option("sparse_vector_names", "<SPARSE_VECTOR_NAME>")
|
|
.option("schema", <pyspark.sql.DataFrame>.schema.json())
|
|
.mode("append")
|
|
.save()
|
|
```
|
|
|
|
</details>
|
|
|
|
<details>
|
|
<summary><b>Multiple sparse vectors</b></summary>
|
|
|
|
```python
|
|
<pyspark.sql.DataFrame>
|
|
.write
|
|
.format("io.qdrant.spark.Qdrant")
|
|
.option("qdrant_url", "<QDRANT_GRPC_URL>")
|
|
.option("collection_name", "<QDRANT_COLLECTION_NAME>")
|
|
.option("sparse_vector_value_fields", "<COLUMN_NAME>,<ANOTHER_COLUMN_NAME>")
|
|
.option("sparse_vector_index_fields", "<COLUMN_NAME>,<ANOTHER_COLUMN_NAME>")
|
|
.option("sparse_vector_names", "<SPARSE_VECTOR_NAME>,<ANOTHER_SPARSE_VECTOR_NAME>")
|
|
.option("schema", <pyspark.sql.DataFrame>.schema.json())
|
|
.mode("append")
|
|
.save()
|
|
```
|
|
|
|
</details>
|
|
|
|
<details>
|
|
<summary><b>Combination of named dense and sparse vectors</b></summary>
|
|
|
|
```python
|
|
<pyspark.sql.DataFrame>
|
|
.write
|
|
.format("io.qdrant.spark.Qdrant")
|
|
.option("qdrant_url", "<QDRANT_GRPC_URL>")
|
|
.option("collection_name", "<QDRANT_COLLECTION_NAME>")
|
|
.option("vector_fields", "<COLUMN_NAME>,<ANOTHER_COLUMN_NAME>")
|
|
.option("vector_names", "<VECTOR_NAME>,<ANOTHER_VECTOR_NAME>")
|
|
.option("sparse_vector_value_fields", "<COLUMN_NAME>,<ANOTHER_COLUMN_NAME>")
|
|
.option("sparse_vector_index_fields", "<COLUMN_NAME>,<ANOTHER_COLUMN_NAME>")
|
|
.option("sparse_vector_names", "<SPARSE_VECTOR_NAME>,<ANOTHER_SPARSE_VECTOR_NAME>")
|
|
.option("schema", <pyspark.sql.DataFrame>.schema.json())
|
|
.mode("append")
|
|
.save()
|
|
```
|
|
|
|
</details>
|
|
|
|
<details>
|
|
<summary><b>No vectors - Entire dataframe is stored as payload</b></summary>
|
|
|
|
```python
|
|
<pyspark.sql.DataFrame>
|
|
.write
|
|
.format("io.qdrant.spark.Qdrant")
|
|
.option("qdrant_url", "<QDRANT_GRPC_URL>")
|
|
.option("collection_name", "<QDRANT_COLLECTION_NAME>")
|
|
.option("schema", <pyspark.sql.DataFrame>.schema.json())
|
|
.mode("append")
|
|
.save()
|
|
```
|
|
|
|
</details>
|
|
|
|
## Databricks
|
|
|
|
<aside role="status">
|
|
<p>Check out our <a href="/documentation/examples/databricks/" target="_blank">example</a> of using the Spark connector with Databricks.</p>
|
|
</aside>
|
|
|
|
You can use the `qdrant-spark` connector as a library in [Databricks](https://www.databricks.com/).
|
|
|
|
- Go to the `Libraries` section in your Databricks cluster dashboard.
|
|
- Select `Install New` to open the library installation modal.
|
|
- Search for `io.qdrant:spark:VERSION` in the Maven packages and click `Install`.
|
|
|
|
|
|
|
|
## Datatype Support
|
|
|
|
Qdrant supports all the Spark data types, and the appropriate data types are mapped based on the provided schema.
|
|
|
|
## Configuration Options
|
|
|
|
| Option | Description | Column DataType | Required |
|
|
| :--------------------------- | :------------------------------------------------------------------ | :---------------------------- | :------- |
|
|
| `qdrant_url` | GRPC URL of the Qdrant instance. Eg: <http://localhost:6334> | - | ✅ |
|
|
| `collection_name` | Name of the collection to write data into | - | ✅ |
|
|
| `schema` | JSON string of the dataframe schema | - | ✅ |
|
|
| `embedding_field` | Name of the column holding the embeddings | `ArrayType(FloatType)` | ❌ |
|
|
| `id_field` | Name of the column holding the point IDs. Default: Random UUID | `StringType` or `IntegerType` | ❌ |
|
|
| `batch_size` | Max size of the upload batch. Default: 64 | - | ❌ |
|
|
| `retries` | Number of upload retries. Default: 3 | - | ❌ |
|
|
| `api_key` | Qdrant API key for authentication | - | ❌ |
|
|
| `vector_name` | Name of the vector in the collection. | - | ❌ |
|
|
| `vector_fields` | Comma-separated names of columns holding the vectors. | `ArrayType(FloatType)` | ❌ |
|
|
| `vector_names` | Comma-separated names of vectors in the collection. | - | ❌ |
|
|
| `sparse_vector_index_fields` | Comma-separated names of columns holding the sparse vector indices. | `ArrayType(IntegerType)` | ❌ |
|
|
| `sparse_vector_value_fields` | Comma-separated names of columns holding the sparse vector values. | `ArrayType(FloatType)` | ❌ |
|
|
| `sparse_vector_names` | Comma-separated names of the sparse vectors in the collection. | - | ❌ |
|
|
| `shard_key_selector` | Comma-separated names of custom shard keys to use during upsert. | - | ❌ |
|
|
|
|
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!
|