---
title: Spark (PySpark)
description: Connect PySpark to R2 Data Catalog to read and write Iceberg tables.
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---

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# Spark (PySpark)

Below is an example of using [PySpark ↗](https://spark.apache.org/docs/latest/api/python/index.html) to connect to R2 Data Catalog.

## Prerequisites

* Sign up for a [Cloudflare account ↗](https://dash.cloudflare.com/sign-up/workers-and-pages).
* [Create an R2 bucket](https://edgetunnel-b2h.pages.dev/r2/buckets/create-buckets/) and [enable the data catalog](https://edgetunnel-b2h.pages.dev/r2/data-catalog/manage-catalogs/#enable-r2-data-catalog-on-a-bucket).
* [Create an R2 API token](https://edgetunnel-b2h.pages.dev/r2/api/tokens/) with both [R2 and data catalog permissions](https://edgetunnel-b2h.pages.dev/r2/api/tokens/#permissions).
* Install the [PySpark ↗](https://spark.apache.org/docs/latest/api/python/getting%5Fstarted/install.html) library.

## Example usage

**Python**

```py
from pyspark.sql import SparkSession


# Define catalog connection details (replace variables)
WAREHOUSE = "<WAREHOUSE>"
TOKEN = "<TOKEN>"
CATALOG_URI = "<CATALOG_URI>"


# Build Spark session with Iceberg configurations
spark = SparkSession.builder \
  .appName("R2DataCatalogExample") \
  .config('spark.jars.packages', 'org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.6.1,org.apache.iceberg:iceberg-aws-bundle:1.6.1') \
  .config("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions") \
  .config("spark.sql.catalog.my_catalog", "org.apache.iceberg.spark.SparkCatalog") \
  .config("spark.sql.catalog.my_catalog.type", "rest") \
  .config("spark.sql.catalog.my_catalog.uri", CATALOG_URI) \
  .config("spark.sql.catalog.my_catalog.warehouse", WAREHOUSE) \
  .config("spark.sql.catalog.my_catalog.token", TOKEN) \
  .config("spark.sql.catalog.my_catalog.header.X-Iceberg-Access-Delegation", "vended-credentials") \
  .config("spark.sql.catalog.my_catalog.s3.remote-signing-enabled", "false") \
  .config("spark.sql.defaultCatalog", "my_catalog") \
  .getOrCreate()
spark.sql("USE my_catalog")


# Create namespace if it does not exist
spark.sql("CREATE NAMESPACE IF NOT EXISTS default")


# Create a table in the namespace using Iceberg
spark.sql("""
    CREATE TABLE IF NOT EXISTS default.my_table (
        id BIGINT,
        name STRING
    )
    USING iceberg
""")


# Create a simple DataFrame
df = spark.createDataFrame(
    [(1, "Alice"), (2, "Bob"), (3, "Charlie")],
    ["id", "name"]
)


# Write the DataFrame to the Iceberg table
df.write \
    .format("iceberg") \
    .mode("append") \
    .save("default.my_table")


# Read the data back from the Iceberg table
result_df = spark.read \
    .format("iceberg") \
    .load("default.my_table")


result_df.show()
```

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