How to Choose MySQL over BigQuery

When should I use MySQL instead of BigQuery?

Practical criteria and examples for selecting MySQL instead of Google BigQuery for transactional, low-latency relational workloads.

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Description

Example H2

Example H3

Why would I pick MySQL instead of BigQuery?

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.

What workloads favor MySQL?

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.

How does pricing differ?

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.

Can MySQL scale like BigQuery?

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.

What about real-time analytics?

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.

Best practices for MySQL over BigQuery

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.

Code example: same question, two engines

MySQL query

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;

BigQuery equivalent

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;

How do I migrate later if needs change?

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.

Why How to Choose MySQL over BigQuery is important

How to Choose MySQL over BigQuery Example Usage


-- MySQL: fetch last 20 orders for a customer
SELECT id, order_date, total_amount
FROM Orders
WHERE customer_id = 42
ORDER BY order_date DESC
LIMIT 20;

How to Choose MySQL over BigQuery Syntax


-- MySQL connection string
mysql -u ecommerce_user -p -h db.prod.internal ecommerce

-- BigQuery CLI query
bq query --use_legacy_sql=false \
'WITH daily AS (
   SELECT DATE(order_date) AS d, SUM(total_amount) AS sales
   FROM Orders GROUP BY d)
SELECT * FROM daily WHERE sales > 10000;'

-- MySQL example: add index for fast order lookups
ALTER TABLE Orders
  ADD INDEX idx_customer_date (customer_id, order_date);

-- BigQuery example: partition table by day
CREATE TABLE `project.dataset.Orders` PARTITION BY DATE(order_date) AS
SELECT * FROM external_source;

Common Mistakes

Assuming BigQuery is always cheaper. Query-based pricing can explode with chatty dashboards; small, frequent queries stay cost-efficient on a fixed-price MySQL instance.
Treating MySQL as unscalable. Sharding, read replicas, and partitioning let MySQL handle tens of billions of rows when designed correctly.

Frequently Asked Questions (FAQs)

Is MySQL or BigQuery better for real-time dashboards?

MySQL excels when you need sub-second freshness. BigQuery can serve near-real-time but requires streaming inserts and caching layers, increasing complexity.