Deploy pg_lakehouse with Simplyblock for Scalable Analytics

pg_lakehouse brings lakehouse functionality to PostgreSQL by allowing direct SQL queries on Parquet files. This eliminates the need for heavy ETL pipelines or duplicating datasets into relational tables. Instead, PostgreSQL users can run analytics on semi-structured data stored in object storage—using familiar SQL syntax and tooling.

This flexibility, however, introduces new performance and persistence challenges. Large Parquet reads, concurrent scans, and hybrid transactional-analytical workflows can overwhelm traditional storage. Simplyblock provides NVMe-over-TCP volumes and zone-independent storage that keeps PostgreSQL workloads responsive, scalable, and fault-tolerant.

🚀 Use simplyblock with pg_lakehouse for Scalable SQL on Parquet
Simplyblock helps PostgreSQL deliver fast, reliable analytics over external lake data.
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Optimizing Lakehouse Query Performance Starts with Storage

Unlike traditional PostgreSQL deployments, pg_lakehouse relies heavily on external data sources—typically object storage services like S3 or GCS. Query performance depends on how efficiently PostgreSQL can read, cache, and manage these large files. When reads are slow or temporary space is constrained, query latency spikes.

With simplyblock, you get high-throughput storage for caching, staging, and local operations. This is essential when PostgreSQL needs to create temp tables, manage execution plans, or run joins across foreign Parquet files. And because simplyblock’s volumes are zone-independent, performance remains stable even in distributed cloud setups or Kubernetes-based environments.

Step 1: Provision Simplyblock Volume for pg_lakehouse

Provision a simplyblock volume that PostgreSQL can use for metadata, temp data, or local cache:

sbctl pool create pg-lakehouse-pool /dev/nvme0n1

sbctl volume add pg-lakehouse-cache 200G pg-lakehouse-pool

sbctl volume connect pg-lakehouse-cache

Format and mount the volume:

mkfs.ext4 /dev/nvme0n1

mkdir -p /var/lib/postgresql/15/main

mount /dev/nvme0n1 /var/lib/postgresql/15/main

Persist the mount:

/dev/nvme0n1 /var/lib/postgresql/15/main ext4 defaults 0 0

This setup gives PostgreSQL low-latency, high-IOPS access to staging areas and local operations. All managed via the simplyblock CLI. 

To learn how pg_lakehouse integrates PostgreSQL with lakehouse functionality for scalable analytics, check pg_lakehouse’s integration with PostgreSQL.

Step 2: Configure PostgreSQL with pg_lakehouse

Install and enable the pg_lakehouse extension:

CREATE EXTENSION IF NOT EXISTS lakehouse;

Register an external Parquet file as a foreign table:

CREATE FOREIGN TABLE logs_parquet (

  user_id INT,

  event_type TEXT,

  timestamp TIMESTAMP

)

SERVER lakehouse

OPTIONS (

  filename ‘/mnt/data/logs_2023.parquet’

);

This allows PostgreSQL to query the file directly without ETL. Local caching and intermediate operations benefit from the fast volume mounted in Step 1—ideal for database performance optimization and real-time analytics.

Step 3: Scale Lakehouse Storage Without Interruptions

If your cache or staging area needs grow, scale the volume without downtime:

sbctl volume resize pg-lakehouse-cache 400G

resize2fs /dev/nvme0n1

This ensures queries don’t fail due to limited space and keeps data pipelines running. It’s especially useful in Kubernetes-based PostgreSQL deployments where workloads may grow dynamically.

Step 4: Run Multi-Zone PostgreSQL Analytics Without Storage Risk

In multi-zone Kubernetes clusters or cloud environments, PostgreSQL pods can be rescheduled between zones. Standard storage volumes often break under these conditions.

Simplyblock volumes are zone-resilient, meaning the volume stays attached regardless of where PostgreSQL runs. This is key to maintaining high availability and performance across distributed environments, especially during scaling, failover, or rolling updates.

For more on high availability in PostgreSQL, refer to the PostgreSQL High Availability Guide.

Step 5: Replicate Storage for Safer Workflows

To protect temporary data and improve fault tolerance, replicate the volume across zones:

sbctl volume replicate pg-lakehouse-cache –zones=zone-a,zone-b

This adds storage-level durability to PostgreSQL’s own replication and high-availability mechanisms. The process is managed through simplyblock’s operations and management toolkit, which integrates with any CI/CD or automation setup.

For more details on replication strategies, check the PostgreSQL Replication.

Build Faster, More Reliable Lakehouse Workflows with Simplyblock

pg_lakehouse brings the flexibility of data lakes to PostgreSQL, but it also demands a storage layer that can keep up with large reads, cache-heavy operations, and parallel analytics workloads. Simplyblock provides the performance foundation needed to make it production-ready.

With fast NVMe-over-TCP volumes, multi-zone support, and simple volume scaling, simplyblock helps PostgreSQL run analytics over Parquet efficiently and reliably. It’s a great fit for hybrid stacks, data engineering pipelines, and cloud-native analytics platforms.

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