Caching And Result Reuse

Why this matters

As an Analytics Engineer, you want dashboards to load fast and workloads to cost less. Effective caching and result reuse can turn repeated heavy queries into near-instant responses while reducing compute spend. This shows up in real tasks like speeding up stakeholder dashboards, optimizing incremental dbt models, and making ad-hoc analytics responsive during peak hours.

• Speed: Reused results can return in milliseconds instead of seconds/minutes.
• Cost: Fewer repeated computations means lower warehouse bills.
• Stability: Controlled freshness reduces load spikes and makes SLAs predictable.

Who this is for

• Analytics Engineers maintaining warehouses, models, and BI dashboards.
• Data/BI Developers who tune queries and data products for scale.
• Team leads aiming to reduce cost without sacrificing data freshness.

Prerequisites

• Basic SQL (SELECT, JOIN, WHERE, GROUP BY).
• Familiarity with your warehouse execution model (compute slots/warehouses).
• Understanding of data freshness requirements for your stakeholders.

Concept explained simply

Caching stores the result of a previous computation so the same request can be answered faster next time. Result reuse means the engine or a higher layer returns a previous result instead of executing the full query again.

Typical cache layers

• Warehouse result cache: The SQL engine returns a prior result if the query is the same and underlying data is unchanged.
• Materialized results: Precomputed tables or views refreshed on a schedule or on change.
• BI/dashboard cache: The BI tool caches rendered query results for a time window per filter set.
• Application cache: API/service level caching, often parameter-aware.

Mental model: The 3 keys of a cache hit

• Same request: The query text (and parameters) must be equivalent.
• Safe freshness: Underlying data hasn’t changed in a way that invalidates the cached result (or you accept staleness via TTL).
• Scope and settings: Session/account settings allow cache usage; some caches are per-user, per-role, or engine-wide.

Worked examples

1) Speed up a repeated dashboard query

Scenario: A dashboard panel runs the same aggregation every minute for many viewers.

1. Enable BI cache for 5–15 minutes per unique filter combination.
2. Normalize the SQL so the text is stable (parameterized filters, standardized formatting).
3. Ensure warehouse-level result reuse is allowed and not disabled by session settings.

Outcome: The first run computes; subsequent runs within the window return fast from cache.

2) Materialize a heavy join for stable reuse

Scenario: Daily metrics query joins large fact and multiple dims, taking 45 seconds.

1. Create a materialized result (table or materialized view) with the joined and aggregated data.
2. Refresh it daily after upstream loads finish.
3. Point dashboards to the materialized object; allow BI caching for 15 minutes.

Outcome: Dashboards query a smaller, pre-aggregated data set and return quickly.

3) Cache-busting when freshness matters

Scenario: A finance page must always show latest intraday revenue after load completes.

1. Leave warehouse result cache enabled for general use.
2. For this critical query, add a harmless parameter or setting that forces a fresh run right after the load completes.
3. Optionally shorten BI cache TTL for this specific panel (e.g., 2 minutes).

Outcome: Regular queries benefit from reuse; the finance panel bypasses stale results right when needed.

How to implement in practice

1. Map your hotspots: Identify top N slowest or most frequent queries/dashboards.
2. Choose the cache layer: Warehouse result cache for identical queries; materialized objects for heavy transforms; BI cache for repeated reads.
3. Normalize queries: Use consistent formatting, parameters, and stable SQL generation.
4. Set freshness rules: Pick TTLs; define when to bypass cache (after loads, month-end close).
5. Measure and iterate: Track runtime and cost before/after.

Exercises

Do these hands-on tasks. Then check your answers below or in the exercise solutions. Your progress is saved only if you are logged in; otherwise you can still complete everything for free.

Exercise 1 — Normalize for result reuse

Take this pair of queries and make them hit the same warehouse result cache by normalizing the SQL. Assume the underlying data does not change during the test.

-- Run 1
SELECT  product_id, SUM(revenue) as rev
FROM   analytics.fact_sales
WHERE  sale_date BETWEEN '2025-05-01' AND '2025-05-31'
GROUP BY product_id;

-- Run 2 (currently not matching)
SELECT product_id, SUM(revenue) AS revenue
FROM analytics.fact_sales
WHERE sale_date >= '2025-05-01' AND sale_date <= '2025-05-31'
GROUP BY product_id; 

• Unify aliases and expression forms.
• Standardize whitespace and order.
• Parameterize dates if your system supports it.

-- Normalized text (one consistent version)
SELECT product_id, SUM(revenue) AS rev
FROM analytics.fact_sales
WHERE sale_date BETWEEN '2025-05-01' AND '2025-05-31'
GROUP BY product_id;

Exercise 2 — Pick cache layers and TTLs

You own a dashboard with 3 tiles:

• Daily revenue by product (heavy join, updates once per day after 03:00).
• Last 7 days conversion funnel (moderate aggregation, viewed hourly).
• Real-time support tickets (must be within 2 minutes of latest ingest).

Design a caching plan: which layer to use for each tile, what TTL, and any cache-busting rules.

Common mistakes and how to self-check

• Changing SQL text accidentally: Different aliases, order, or whitespace can prevent reuse. Self-check: Keep a canonical query template.
• Ignoring freshness needs: Overly long TTL on time-sensitive tiles. Self-check: Tag tiles by freshness class (real-time, hourly, daily).
• One-size-fits-all cache: Using only BI cache or only warehouse cache. Self-check: Map cache to workload type (transform vs read-heavy).
• No invalidation plan: Post-load views serve stale results. Self-check: Tie cache-busting to load completion events.
• Assuming cache is free: Caches use memory/storage and can serve stale data. Self-check: Monitor cache hit rate and user freshness complaints.

Practical projects

• Dashboard acceleration: Pick one slow dashboard, implement BI cache + warehouse result reuse, and compare p95 load time before/after.
• Materialized layer: Build a materialized table for a heavy daily metric and schedule refresh after data load completion.
• Freshness-aware routing: For a critical tile, add a cache-busting parameter immediately after ingest completes; compare staleness vs latency.

Learning path

1. Learn warehouse result reuse conditions (query equality, data change rules, session settings).
2. Practice SQL normalization and parameterization for stable text.
3. Introduce materialized results for heavy patterns.
4. Tune BI cache TTLs by tile based on freshness needs.
5. Implement cache-busting tied to data loads; monitor hit rates and costs.

Next steps

• Instrument your top 5 queries with basic runtime and cache-hit tracking.
• Apply normalization to remove accidental query text drift.
• Propose a lightweight materialized layer for the heaviest join.

Mini challenge

Pick one KPI tile that users say is “slow.” Create two variants: one using raw tables, one using a materialized object plus BI cache. Compare p95 latency over a day. Decide whether to keep the optimization based on freshness tolerance and cost.

Quick Test

Anyone can take this test for free. Progress is saved only if you are logged in.