Caching BigQuery Results to Reduce Costs

Google BigQuery bills by the number of bytes scanned. If you repeatedly run similar or identical queries, you can end up paying to scan the same data over and over again. Fortunately, BigQuery offers multiple layers of caching that let you reuse previous query results and pay only for queries that truly need fresh data. In this guide you’ll learn how each caching mechanism works, when it is (and isn’t) free, and how to integrate caching into your daily workflow—whether you run queries from the BigQuery console, a CI pipeline, or a modern SQL editor like Galaxy.

Why BigQuery Caching Matters

For interactive analytics, BigQuery’s on-demand pricing model is brilliant—no infrastructure to manage and you pay only for what you scan. But that model can backfire when:

• Analysts hit “Run” dozens of times while iterating on a query.
• BI dashboards issue the same queries every refresh cycle.
• Data engineers schedule development jobs that re-read unchanged historical partitions nightly.

Even at $5 per TB, costs add up quickly at scale. Caching turns repeated, identical work into $0 scans, often dropping query cost and latency to nearly nothing. The trick is understanding which cache applies to which use-case and how to structure SQL so that BigQuery can reuse results instead of re-scanning storage.

Layers of Caching in BigQuery

1. Automatic Query Result Cache

Every successful SELECT statement (that isn’t CREATE TABLE AS, scripts, or DML) automatically lands in a 24-hour result cache. If a subsequent query text matches byte-for-byte and its referenced data hasn’t changed, BigQuery serves results from cache in milliseconds and bills $0. Highlights:

• Works for 24 hours, or until any underlying table is modified.
• Ignores -- comments and whitespace but not semantically equivalent rewrites.
• Requires identical SQL project qualifiers (project.dataset.table).
• Can be bypassed with OPTION (disable_result_cache = true).

2. Persisted Results via CREATE TABLE AS SELECT (CTAS)

A quick way to freeze intermediate results is to materialize them into a permanent or temporary table:

CREATE OR REPLACE TABLE sandbox.daily_user_stats AS
SELECT user_id, COUNT(*) AS sessions
FROM `prod.raw_sessions`
WHERE _PARTITIONDATE = '2024-06-05'
GROUP BY 1;

Because the data is stored in BigQuery Storage, subsequent queries of sandbox.daily_user_stats can leverage partition pruning or clustering, greatly reducing future scan volume.

3. Materialized Views

Materialized views (MV) compute and store query results incrementally. When downstream queries touch an MV, BigQuery will read only rows that changed since the MV’s last refresh, charging for delta bytes rather than the full underlying table. Key facts:

• Supports simple aggregations (SUM, COUNT, MIN, MAX) and deterministic functions.
• Refreshes automatically every 30 minutes by default (or manual).
• Charges storage for MV itself (~half cost of active storage).
• Significant cost reduction for massive, append-only fact tables.

4. Table Snapshots

Snapshots create a read-only, point-in-time view of a table without duplicating storage blocks. If you need to preserve results for slower-changing data (e.g., month-end reports), snapshots provide cheap retention and zero scan of historical partitions.

5. Cached Remote Source (BigLake)

When querying external tables in object storage through BigLake, BigQuery caches remote files for 24 hours. This cuts both latency and per-TB scan cost for repetitive reads on data lakes.

Best Practices for Maximizing Cache Hits

Write Deterministic SQL

Use fully qualified table names, avoid SELECT * if column changes are common, and keep UDFs deterministic so that BigQuery can trust the cache.

Parameterize with DECLARE or Galaxy Variables

If your SQL editor supports variable substitution (e.g., Galaxy’s ${date} variables or BigQuery scripting DECLARE cutoff_date DATE;) you avoid hand-editing literals, which prevents accidental white-space/format tweaks that break cache matching.

Version Control Your Queries

Store complex queries in Git and reuse the same file path via CI pipelines or Galaxy Collections. Identical text === cache hits.

Adopt Partitioning & Clustering

Caching cannot help when underlying data changes each run. Proper partitioning avoids re-scanning cold partitions altogether.

Create Materialized Views for Hot Aggregations

Dashboards often compute the same SUM/COUNT across wide tables. Moving that logic into an MV slashes both query time and monthly bills.

Validate with EXPLAIN and INFORMATION_SCHEMA.JOBS

The column cache_hit in region-us.INFORMATION_SCHEMA.JOBS_BY_PROJECT tells you if BigQuery served a query from cache. In Galaxy, execution metadata appears in the right-side Job Details pane so you can confirm cache usage at a glance.

End-to-End Example

Suppose your data engineer iterates on a customer LTV query from Galaxy:

-- Calculate total spend per customer
SELECT customer_id,
SUM(order_total) AS lifetime_value
FROM `shop.orders`
WHERE order_date BETWEEN '2019-01-01' AND '2024-06-01'
GROUP BY customer_id;

While you tweak GROUP BYs, the result cache keeps each re-run free unless you change the literal dates or include additional columns. Once validated, create a materialized view:

CREATE MATERIALIZED VIEW shop.mv_customer_ltv AS
SELECT customer_id,
SUM(order_total) AS lifetime_value
FROM `shop.orders`
GROUP BY customer_id;

Your BI tool can simply SELECT * FROM shop.mv_customer_ltv and pay for delta updates only.

Monitoring & Cost Attribution

To quantify savings, join INFORMATION_SCHEMA.JOBS with the cache_hit flag and total_bytes_processed. A sudden spike of non-cached jobs often signals schema changes, string concatenation issues, or code-generation tools that vary whitespace.

How Galaxy Fits In

Galaxy’s desktop SQL editor keeps the full query text—comments included—under version control, meaning you’re far less likely to break cache by accidentally reformatting. Its AI Copilot can also suggest converting expensive subqueries into materialized views. Meanwhile, the Run History pane surfaces which executions were cache hits so you can immediately spot missed opportunities during development.

Common Misconceptions

“Adding a LIMIT clause makes it cheaper.”

BigQuery still scans the full underlying data to evaluate WHERE filters before applying LIMIT. Use partitioning or materialized views instead.

“Views are cached automatically.”

Standard views are virtual. Each reference triggers a scan of the base tables unless the entire view text and dependencies are unchanged and within 24 hours.

“DISABLE_CACHE prevents stale data issues.”

Yes, but it also forfeits a free scan. Prefer deterministic SQL and rely on BigQuery’s automatic invalidation when tables mutate.

Key Takeaways

• The result cache is free, automatic, and lasts 24 hours.
• Materialized views persist aggregated results and refresh incrementally.
• Partitioning and clustering complement caching by reducing scan size on fresh data.
• Tools like Galaxy help you keep query text stable for consistent cache hits.
• Always verify savings via cache_hit in INFORMATION_SCHEMA.JOBS.