Understand when PostgreSQL is a better fit than Google BigQuery for your workloads and how to act on that decision.
PostgreSQL excels at high-frequency inserts, row-level updates, and millisecond-level transactions. An ecommerce site must instantly record each new order and adjust Products.stock
; Postgres commits these changes synchronously, while BigQuery batches them, adding latency.
BigQuery charges per scanned byte and per streamed row. Constantly reading the full Orders
table for dashboards can incur surprise bills. PostgreSQL’s cost is fixed to compute and storage, so predictable workloads remain inexpensive.
Petabyte-scale, append-only analytics with infrequent updates fit BigQuery. Running nightly sales aggregates across years of OrderItems
data leverages its columnar engine. For mixed read/write or near-real-time analytics, pair Postgres for source-of-truth with BigQuery fed by batch jobs.
Remove BigQuery-only functions (e.g., STRUCT
, UNNEST
) and replace with equivalent Postgres JSON or lateral joins. Verify timestamp semantics; BigQuery’s DATETIME
is timezone-less, while Postgres TIMESTAMPTZ
stores offsets.
-- BigQuery
SELECT customer_id, SUM(total_amount) AS revenue
FROM `project.dataset.Orders`
GROUP BY customer_id;
-- PostgreSQL
SELECT customer_id, SUM(total_amount) AS revenue
FROM Orders
GROUP BY customer_id;
Keep OLTP tables (Customers
, Orders
, Products
) in Postgres. Use logical replication or Fivetran to stream changes into BigQuery for heavy analytics. Add covering indexes in Postgres to maintain sub-second API responses.
Ignoring index design. Assuming Postgres will scan efficiently like BigQuery’s columnar storage leads to slow queries. Index Orders(order_date)
and OrderItems(order_id)
.
Over-normalizing analytics tables. BigQuery handles wide denormalized tables well; Postgres may hit join bottlenecks. Aggregate data into materialized views for reporting workloads.
For steady OLTP and predictable analytics, Postgres’s fixed compute pricing is often cheaper. BigQuery becomes costly when queries scan large tables frequently.
Yes, with proper indexing and materialized views you can maintain sub-second dashboards over recent data. For multi-terabyte history, offload to BigQuery.
No. Use Foreign Data Wrappers or sync data to BigQuery for federation while keeping primary writes in Postgres.