Warehouse And Query Performance

Why this skill matters for a Data Platform Engineer

Warehouse and query performance is about making analytical workloads fast, predictable, and cost-effective. As a Data Platform Engineer, you design how data is stored, queried, and governed across the platform. Solid performance practices reduce compute bills, stabilize dashboards and ML features, and keep SLAs reliable during peak hours.

• Speed: Lower end-to-end latency for dashboards, APIs, and ad-hoc analysis.
• Scale: Handle more users and larger datasets with the same compute.
• Cost: Cut waste via smart storage, caching, and workload isolation.
• Reliability: Fewer query timeouts and more predictable throughput.

What you'll be able to do

• Read execution plans and spot common bottlenecks (scans, skew, bad join orders).
• Choose the right table layout (partitioning, clustering, statistics) for each workload.
• Use caching and materialization to accelerate repeated queries safely.
• Configure concurrency controls and workload isolation to avoid noisy neighbors.
• Set cost guardrails, quotas, and query budgets without blocking productivity.
• Build practical monitoring dashboards and runbooks for incident response.

Who this is for

• Data Platform Engineers responsible for shared warehouses, lakehouses, or query services.
• Analytics Engineers and Data Engineers optimizing BI and ELT workloads.
• Team leads defining cost/performance SLOs for analytics.

Prerequisites

• Comfortable with SQL (SELECT, JOIN, GROUP BY, window functions).
• Basic understanding of columnar storage, partitions, and file formats (e.g., Parquet, ORC).
• Familiar with your platform’s compute model (virtual warehouses, pools, clusters, or serverless).

Learning path

1. Milestone 1: Query Engine Tuning Basics

   Learn to use EXPLAIN/PROFILE and identify scan hotspots, join types, and spill-to-disk events.

2. Milestone 2: Table Optimization & Statistics

   Design partitions, clustering/ordering keys, and ensure up-to-date statistics to support pruning and better join decisions.

3. Milestone 3: Caching & Materialization Strategy

   Apply result caching, summary tables, or materialized views for repeated access patterns and SLAs.

4. Milestone 4: Concurrency & Workload Management

   Isolate heavy ETL from BI, configure queues/pools/warehouses, and set fair-share policies.

5. Milestone 5: Cost Controls & Quotas

   Introduce budgets, max scan limits, and auto-suspend/scale policies to control spend safely.

6. Milestone 6: Monitoring & Troubleshooting

   Build dashboards (latency, scanned bytes, failures) and a runbook for slow-query triage.

7. Milestone 7: Governance for Shared Compute

   Standardize tags, priorities, and access to protect critical workloads in multi-tenant environments.

Worked examples

-- Example: Investigate plan
EXPLAIN
SELECT c.customer_id, SUM(s.amount) AS revenue
FROM sales s
JOIN customers c ON s.customer_id = c.customer_id
WHERE s.sale_date BETWEEN DATE '2025-01-01' AND DATE '2025-01-31'
GROUP BY c.customer_id;

-- Anti-pattern (may disable pruning)
WHERE DATE_TRUNC('month', sale_date) = DATE '2025-01-01'

-- Better: direct range on the partition column
WHERE sale_date >= DATE '2025-01-01' AND sale_date < DATE '2025-02-01'

-- Create summary table (pattern)
CREATE TABLE IF NOT EXISTS fact_sales_7d AS
SELECT DATE(sale_date) AS d, product_id, SUM(amount) AS revenue
FROM sales
WHERE sale_date >= CURRENT_DATE - INTERVAL '7' DAY
GROUP BY 1, 2;

-- Incremental refresh pattern
DELETE FROM fact_sales_7d WHERE d = CURRENT_DATE;  -- if supported
INSERT INTO fact_sales_7d
SELECT DATE(sale_date) AS d, product_id, SUM(amount) AS revenue
FROM sales
WHERE sale_date = CURRENT_DATE
GROUP BY 1, 2;

-- Conceptual example (syntax varies by platform)
-- Create two pools: BI_POOL (low latency), ELT_POOL (throughput)
-- Route interactive queries to BI_POOL using query tags or roles
-- Route batch jobs to ELT_POOL and set max concurrency on BI_POOL
-- Example tag:
-- SET query_tag = 'BI';  -- then a routing rule sends BI to BI_POOL

-- Conceptual guardrails
-- Set a per-query scan limit for ad-hoc roles
-- SET max_scan_bytes = 200 * 1024 * 1024 * 1024;  -- 200 GiB (if supported)

-- Encourage cache reuse: stable queries benefit from result caching
-- e.g., avoid SELECT * and keep SQL text stable for cache hits

Drills and quick exercises

• Open a real slow query and run EXPLAIN/PROFILE. Note: scanned bytes/rows, join types, any spills, and stage with highest time.
• Rewrite a filter to enable partition pruning (avoid function-wrapping partition keys).
• Compare EXISTS vs IN vs LEFT JOIN + IS NOT NULL for semi-joins; measure performance on your data.
• Create a small clustered table variant and verify fewer micro-partitions are scanned for a targeted query.
• Build a 7-day summary table or materialized view; benchmark before vs after latency.
• Route a heavy job to a separate pool/warehouse; observe improvements for BI queries.
• Enable query tags and create a basic monthly cost report by tag or role.

Common mistakes and debugging tips

• Using SELECT * in dashboards. Tip: Select only needed columns to reduce I/O and improve cache hit rates.
• Function-wrapping partition columns. Tip: Filter by direct ranges on the partition column.
• Out-of-date statistics. Tip: Refresh/collect stats after large loads or repartitions.
• Skewed joins and aggregations. Tip: Use salting/repartitioning keys, or pre-aggregate to reduce skew.
• Over-materializing. Tip: Materialize only repeated, latency-critical paths; document refresh strategy.
• No workload isolation. Tip: Separate BI from ELT with pools/warehouses and priorities.
• Ignoring cost guardrails. Tip: Apply max scan limits and auto-suspend/scale to avoid runaway spend.

Mini project: Accelerate a weekly revenue dashboard

Goal: Bring dashboard query p95 latency under 5 seconds and reduce scanned bytes by 70%+ without changing visuals.

1. Baseline: Capture current p50/p95 latency, scanned bytes, and query plan for the main dashboard query.
2. Storage: Partition the fact table by date and cluster/order by (date, customer_id). Refresh stats.
3. Materialize: Create a 7-day rolling summary table or view consumed by the dashboard.
4. Concurrency: Route dashboard queries to a low-latency pool; move heavy ETL to a separate pool.
5. Guardrails: Set max scan limits for ad-hoc users and add query tags for the dashboard.
6. Monitoring: Build a simple dashboard showing query latency, scanned bytes, failures, and usage by tag.
7. Validate: Re-run the dashboard and compare metrics against the baseline.

Practical projects

• Self-serve BI accelerator: Catalog top 20 dashboard queries, add indexes/cluster keys, and materialize 5 summaries to cut median latency by half.
• Cost guardrail rollout: Implement max scan limits and monthly budgets with reports by team; reduce surprise spend by 30%.
• Skew buster: Detect and fix 3 skewed joins by introducing salting or pre-aggregation; document patterns in a runbook.

Performance monitoring runbook (quick start)

• When a slowdown happens: Identify if it is localized (one query family) or widespread (resource contention).
• Check plan deltas: Did the join order or distribution strategy change compared to last good plan?
• Scan anomalies: Look for a sudden rise in scanned bytes; confirm partitions and stats are intact.
• Resource view: Verify queue depth, active slots/threads, and any spills to disk.
• Rollback strategy: Temporarily route traffic to a known-good materialized summary while investigating.

Next steps

• Apply these patterns to your top 5 most expensive query families and measure improvements.
• Document a performance SLO (e.g., p95 < 5s for BI) and wire it into your monitoring.
• Extend governance: add query tags, budgets, and workload isolation to all production jobs.

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