Apache Spark

This feature is in alpha

Expect rapid changes, limited features, and possible breaking updates. Share feedback as we refine the experience and expand access.

Apache Spark enables distributed analytical processing of large datasets stored in your analytics buckets. Use it for complex transformations, aggregations, and machine learning workflows.

Installation

First, ensure you have Spark installed. For Python-based workflows:

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pip install pyspark

For detailed Spark setup instructions, see the Apache Spark documentation.

Basic setup

Here's a complete example showing how to configure Spark with your Supabase analytics bucket:

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from pyspark.sql import SparkSession
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# Configuration - Update with your Supabase credentials
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PROJECT_REF = "your-project-ref"
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WAREHOUSE = "your-analytics-bucket-name"
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SERVICE_KEY = "your-service-key"
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# S3 credentials from Project Settings > Storage
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S3_ACCESS_KEY = "your-access-key"
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S3_SECRET_KEY = "your-secret-key"
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S3_REGION = "us-east-1"
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# Construct Supabase endpoints
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S3_ENDPOINT = f"https://{PROJECT_REF}.supabase.co/storage/v1/s3"
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CATALOG_URI = f"https://{PROJECT_REF}.supabase.co/storage/v1/iceberg"
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# Initialize Spark session with Iceberg configuration
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spark = SparkSession.builder \
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    .master("local[*]") \
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    .appName("SupabaseIceberg") \
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    .config("spark.driver.host", "127.0.0.1") \
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    .config("spark.driver.bindAddress", "127.0.0.1") \
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    .config(
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        'spark.jars.packages',
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        'org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.6.1,org.apache.iceberg:iceberg-aws-bundle:1.6.1'
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    ) \
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    .config("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions") \
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    .config("spark.sql.catalog.supabase", "org.apache.iceberg.spark.SparkCatalog") \
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    .config("spark.sql.catalog.supabase.type", "rest") \
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    .config("spark.sql.catalog.supabase.uri", CATALOG_URI) \
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    .config("spark.sql.catalog.supabase.warehouse", WAREHOUSE) \
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    .config("spark.sql.catalog.supabase.token", SERVICE_KEY) \
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    .config("spark.sql.catalog.supabase.s3.endpoint", S3_ENDPOINT) \
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    .config("spark.sql.catalog.supabase.s3.path-style-access", "true") \
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    .config("spark.sql.catalog.supabase.s3.access-key-id", S3_ACCESS_KEY) \
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    .config("spark.sql.catalog.supabase.s3.secret-access-key", S3_SECRET_KEY) \
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    .config("spark.sql.catalog.supabase.s3.remote-signing-enabled", "false") \
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    .config("spark.sql.defaultCatalog", "supabase") \
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    .getOrCreate()
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print("✓ Spark session initialized with Iceberg")

Creating tables

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# Create a namespace for organization
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spark.sql("CREATE NAMESPACE IF NOT EXISTS analytics")
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# Create a new Iceberg table
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spark.sql("""
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    CREATE TABLE IF NOT EXISTS analytics.events (
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        event_id BIGINT,
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        user_id BIGINT,
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        event_name STRING,
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        event_timestamp TIMESTAMP,
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        properties STRING
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    )
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    USING iceberg
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""")
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print("✓ Created table: analytics.events")

Writing data

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# Insert data into the table
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spark.sql("""
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    INSERT INTO analytics.events (event_id, user_id, event_name, event_timestamp, properties)
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    VALUES
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        (1, 101, 'login', TIMESTAMP '2024-01-15 10:30:00', '{"browser":"chrome"}'),
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        (2, 102, 'view_product', TIMESTAMP '2024-01-15 10:35:00', '{"product_id":"123"}'),
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        (3, 101, 'logout', TIMESTAMP '2024-01-15 10:40:00', '{}'),
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        (4, 103, 'purchase', TIMESTAMP '2024-01-15 10:45:00', '{"amount":99.99}')
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""")
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print("✓ Inserted 4 rows into analytics.events")

Reading data

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# Read the entire table
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result_df = spark.sql("SELECT * FROM analytics.events")
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result_df.show(truncate=False)
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# Apply filters
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filtered_df = spark.sql("""
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    SELECT event_id, user_id, event_name
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    FROM analytics.events
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    WHERE event_name = 'login'
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""")
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filtered_df.show()
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# Aggregations
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summary_df = spark.sql("""
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    SELECT
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        event_name,
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        COUNT(*) as event_count,
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        COUNT(DISTINCT user_id) as unique_users
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    FROM analytics.events
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    GROUP BY event_name
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    ORDER BY event_count DESC
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""")
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summary_df.show()

Advanced operations

Working with dataframes

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# Read as DataFrame
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events_df = spark.read.format("iceberg").load("analytics.events")
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# Apply Spark transformations
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from pyspark.sql.functions import count, col, year, month
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# Monthly event counts
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monthly_events = events_df \
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    .withColumn("month", month(col("event_timestamp"))) \
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    .withColumn("year", year(col("event_timestamp"))) \
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    .groupBy("year", "month", "event_name") \
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    .agg(count("event_id").alias("count")) \
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    .orderBy("year", "month")
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monthly_events.show()

Joining tables

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# Create another table
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spark.sql("""
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    CREATE TABLE IF NOT EXISTS analytics.users (
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        user_id BIGINT,
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        username STRING,
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        email STRING
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    )
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    USING iceberg
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""")
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spark.sql("""
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    INSERT INTO analytics.users VALUES
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        (101, 'alice', 'alice@example.com'),
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        (102, 'bob', 'bob@example.com'),
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        (103, 'charlie', 'charlie@example.com')
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""")
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# Join events with users
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joined_df = spark.sql("""
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    SELECT
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        e.event_id,
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        e.event_name,
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        u.username,
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        u.email,
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        e.event_timestamp
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    FROM analytics.events e
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    JOIN analytics.users u ON e.user_id = u.user_id
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    ORDER BY e.event_timestamp
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""")
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joined_df.show(truncate=False)

Exporting results

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# Export to Parquet
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spark.sql("""
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    SELECT event_name, COUNT(*) as count
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    FROM analytics.events
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    GROUP BY event_name
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""").write \
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    .mode("overwrite") \
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    .parquet("/tmp/event_summary.parquet")
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# Export to CSV
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spark.sql("""
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    SELECT *
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    FROM analytics.events
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    WHERE event_timestamp > TIMESTAMP '2024-01-15 10:30:00'
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""").write \
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    .mode("overwrite") \
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    .option("header", "true") \
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    .csv("/tmp/recent_events.csv")
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print("✓ Results exported")

Performance best practices

Partition tables - Partition large tables by date or region for faster queries
Select columns - Only select columns you need to reduce I/O
Use filters early - Apply WHERE clauses to reduce data processed
Cache frequently accessed tables - Use spark.catalog.cacheTable() for tables accessed multiple times
Cluster mode - Use cluster mode for production workloads instead of local mode

Complete example: Data processing pipeline

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from pyspark.sql import SparkSession
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from pyspark.sql.functions import col, year, month, count
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# Setup (see Basic Setup section above)
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spark = SparkSession.builder \
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    .master("local[*]") \
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    .appName("SupabaseAnalytics") \
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    .config("spark.sql.defaultCatalog", "supabase") \
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    # ... (add all config from Basic Setup)
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    .getOrCreate()
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# Step 1: Read raw events
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raw_events = spark.sql("SELECT * FROM analytics.events")
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# Step 2: Transform and aggregate
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monthly_summary = raw_events \
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    .withColumn("month", month(col("event_timestamp"))) \
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    .withColumn("year", year(col("event_timestamp"))) \
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    .groupBy("year", "month", "event_name") \
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    .agg(count("event_id").alias("total_events"))
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# Step 3: Save results
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monthly_summary.write \
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    .mode("overwrite") \
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    .option("path", "analytics.monthly_summary") \
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    .saveAsTable("analytics.monthly_summary")
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print("✓ Pipeline completed")
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monthly_summary.show()

Apache Spark

This feature is in alpha

Installation

Basic setup

Creating tables

Writing data

Reading data

Advanced operations

Working with dataframes

Joining tables

Exporting results

Performance best practices

Complete example: Data processing pipeline

Next steps

Is this helpful?

Apache Spark

This feature is in alpha

Installation#

Basic setup#

Creating tables#

Writing data#

Reading data#

Advanced operations#

Working with dataframes#

Joining tables#

Exporting results#

Performance best practices#

Complete example: Data processing pipeline#

Next steps#

Is this helpful?

Installation

Basic setup

Creating tables

Writing data

Reading data

Advanced operations

Working with dataframes

Joining tables

Exporting results

Performance best practices

Complete example: Data processing pipeline

Next steps