How to integrate ParadeDB with Apache Spark in PostgreSQL

How do I integrate ParadeDB vectors with Apache Spark workloads?

Connect Apache Spark to a ParadeDB-enabled PostgreSQL instance, allowing distributed analytics and vector search in one workflow.

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Description

Example H2

Example H3

Why combine ParadeDB and Apache Spark?

Integrating ParadeDB’s vector search with Spark lets you run large-scale feature engineering, similarity search, and analytics without moving data from PostgreSQL. Spark handles distributed compute while ParadeDB stores vectors alongside transactional data.

What are the integration prerequisites?

• ParadeDB installed on PostgreSQL 15+.
• Apache Spark 3.4 with the PostgreSQL JDBC driver.
• Network access between Spark executors and the database.
• A vectors extension enabled (CREATE EXTENSION paradedb;).

How do I load ParadeDB vectors into Spark?

Use Spark’s built-in JDBC connector.Specify the table containing your vector column so Spark can treat it as a binary field for downstream processing.

val df = spark.read .format("jdbc") .option("url", "jdbc:postgresql://db:5432/shop") .option("dbtable", "products") .option("user", "sparkuser") .option("password", sys.env("PG_PASS")) .load()

How do I push down vector similarity filters?

Wrap your ParadeDB <-> operator in a subquery.Spark pushes the filter to PostgreSQL, returning only the closest matches.

SELECT * FROM ( SELECT id, name, price, embedding <-> ARRAY[0.12,0.88,0.55] AS distance FROM products ORDER BY distance LIMIT 50 ) AS t;

Can Spark write results back to ParadeDB?

Yes. After processing, use DataFrame.write with mode = "append" or "overwrite".Ensure the destination table has a compatible vector column type.

Example Scala write

processedDF.write .format("jdbc") .option("url", "jdbc:postgresql://db:5432/shop") .option("dbtable", "recommendations") .option("user", "sparkuser") .option("password", sys.env("PG_PASS")) .mode("append") .save()

Best practices for performance?

• Use small fetchSize (e.g., 1 000) to stream rows.
• Partition reads by numeric primary key to parallelize.
• Create ParadeDB ivfflat index on the vector column.
• Keep Spark executors close to the database to reduce latency.

What security settings are recommended?

• Use SSL in the JDBC URL (ssl=true&sslmode=require).
• Restrict firewall access to Spark’s IP range.
• Store credentials in environment variables or a secrets manager.

How do I automate the workflow?

Package the Scala/PySpark job, schedule it in Airflow, and parameterize the target vector and customer cohort.Keep connection properties in centralized configs.

Common mistakes and fixes

Using the wrong JDBC driver version

Ensure the driver major version matches the PostgreSQL server; mismatches cause authentication errors. Upgrade the JAR in Spark’s classpath.

Reading vectors as strings

If vectors appear as text, set stringtype=unspecified in the JDBC URL so the driver returns them as bytea, preserving dimensionality.

Key takeaway

With a few JDBC options and ParadeDB indexing, Spark can query billions of embeddings directly in PostgreSQL, unifying analytics and ML workflows.

Why How to integrate ParadeDB with Apache Spark in PostgreSQL is important

How to integrate ParadeDB with Apache Spark in PostgreSQL Example Usage


-- Recommend related products for the latest order
WITH latest_order AS (
  SELECT o.id, oi.product_id, p.embedding
  FROM orders o
  JOIN orderitems oi ON oi.order_id = o.id
  JOIN products p ON p.id = oi.product_id
  WHERE o.customer_id = 42
  ORDER BY o.order_date DESC
  LIMIT 1
), similar AS (
  SELECT p2.id, p2.name, p2.price,
         p2.embedding <-> lo.embedding AS distance
  FROM latest_order lo, products p2
  ORDER BY distance
  LIMIT 5
)
SELECT * FROM similar;

How to integrate ParadeDB with Apache Spark in PostgreSQL Syntax


-- Enable ParadeDB
CREATE EXTENSION IF NOT EXISTS paradedb;

-- Create a vector-enabled table
CREATE TABLE products (
  id SERIAL PRIMARY KEY,
  name TEXT NOT NULL,
  price NUMERIC(10,2),
  stock INT,
  embedding VECTOR(1536)
);

-- Spark JDBC read (Scala)
val df = spark.read
  .format("jdbc")
  .option("url", "jdbc:postgresql://db:5432/shop?ssl=true&sslmode=require")
  .option("dbtable", "products")
  .option("user", "sparkuser")
  .option("password", sys.env("PG_PASS"))
  .option("fetchsize", "1000")
  .load()

-- Push-down similarity search (Spark SQL)
val topK = spark.read
  .format("jdbc")
  .option("url", "jdbc:postgresql://db:5432/shop")
  .option("query", "SELECT id, name, price FROM (SELECT *, embedding <-> ARRAY[0.1,0.2,0.3] AS dist FROM products ORDER BY dist LIMIT 20) t")
  .option("user", "sparkuser")
  .option("password", sys.env("PG_PASS"))
  .load()

Common Mistakes

Ignoring PostgreSQL fetchSize, causing OutOfMemoryError. Fix: set JDBC fetchSize to stream rows in chunks.
Failing to create an ivfflat index before similarity queries, leading to table scans. Fix: CREATE INDEX idx_products_vector ON products USING ivfflat (embedding vector_cosine_ops) WITH (lists=100);