Practical criteria and examples for selecting MySQL instead of Google BigQuery for transactional, low-latency relational workloads.
Pick MySQL when you need millisecond-latency OLTP, frequent single-row updates, and full transactional semantics. BigQuery shines for petabyte-scale analytics but adds cost and latency for daily app traffic.
High-write ecommerce sites, microservices storing user sessions, and applications requiring ACID guarantees fit MySQL better. MySQL keeps indexes in memory and uses row-level locking, enabling rapid INSERT/UPDATE on Orders
or OrderItems
.
MySQL pricing is instance-based: pay for the provisioned CPU/RAM regardless of queries. BigQuery is query-based: you pay per scanned data and storage tiers. For constant small queries, MySQL is cheaper.
Horizontal sharding, read replicas, and proxy layers let MySQL scale to tens of TBs. While not petabyte-native, proper partitioning of Orders
by date and archiving old rows maintains performance.
Use MySQL for realtime dashboards by maintaining summary tables (e.g., daily sales_totals
) and leveraging indexes. BigQuery’s batch nature adds delay unless you stream data continuously, incurring extra cost.
1) Normalize core tables (Customers
, Orders
) but keep hot aggregates denormalized.
2) Add composite indexes such as (customer_id, order_date)
on Orders
.
3) Enable slow-query log and tune buffer pool size to 70-80% of RAM.
SELECT c.id, c.name, SUM(oi.quantity * p.price) AS lifetime_value
FROM Customers c
JOIN Orders o ON o.customer_id = c.id
JOIN OrderItems oi ON oi.order_id = o.id
JOIN Products p ON p.id = oi.product_id
GROUP BY c.id, c.name
ORDER BY lifetime_value DESC
LIMIT 10;
SELECT c.id, c.name,
SUM(oi.quantity * p.price) AS lifetime_value
FROM `project.dataset.Customers` c
JOIN `project.dataset.Orders` o ON o.customer_id = c.id
JOIN `project.dataset.OrderItems` oi ON oi.order_id = o.id
JOIN `project.dataset.Products` p ON p.id = oi.product_id
GROUP BY c.id, c.name
ORDER BY lifetime_value DESC
LIMIT 10;
Keep column names and data types compatible with ANSI SQL, avoid engine-specific functions, and export data regularly to Cloud Storage so a future BigQuery load is painless.
MySQL excels when you need sub-second freshness. BigQuery can serve near-real-time but requires streaming inserts and caching layers, increasing complexity.
Yes. Use export jobs or change-data-capture to replicate data into BigQuery once analytics volumes grow beyond MySQL’s sweet spot.