In a gbase database cluster, many slow queries are not caused by poorly written SQL, but by unbalanced data distribution that overloads certain nodes. The problem is often subtle — tests may pass, yet production slows down dramatically when skewed data arrives. This guide provides a systematic approach to identifying, diagnosing, and fixing data skew.

1. What Is Data Skew?

Data skew occurs when data that should be evenly spread across nodes instead concentrates on a few nodes, turning them into bottlenecks. Common causes include poorly chosen distribution keys with hot values, low‑cardinality columns, partition schemes that don't match real write patterns, and mismatch between join key distribution and the underlying storage layout. The result: a few nodes do most of the work, and the overall response time is dictated by the slowest node.

2. Common Symptoms

1. Same SQL, varying execution times — fast in some runs, suddenly slow when certain business data enters.
2. Large resource imbalance across nodes — a few nodes at 100% CPU while others are mostly idle, with spiking disk or network I/O.
3. Heavy redistribution in execution plans, and the redistributed data still looks uneven.
4. Significant data volume differences per node — if the largest node holds more than 3× the data of the smallest node for the same table, distribution is likely problematic.

3. Diagnostic Workflow: Static First, Then Dynamic

1. Examine table design — check the distribution key, possible hot values, whether the partition key matches query predicates, and whether a low‑cardinality column was used for distribution.
2. Check data volume per node — query system views to compare row counts or storage per node and calculate the skew ratio.
3. Analyse hot values — count frequency of distribution key values to identify the top heavy hitters.

SELECT dist_key, COUNT(*) AS cnt
FROM fact_order
GROUP BY dist_key
ORDER BY cnt DESC
LIMIT 20;

1. Inspect the execution plan for data movement — look for excessive redistribution, oversized broadcast tables, and intermediate result inflation.

4. Real‑World Case: Order Details Joined with Customer Tags

Fact table fact_order_detail (tens of millions of new rows daily) joined with dim_customer_tag. Original runtime degraded from 9 seconds to 48 seconds. Investigation revealed: the fact table was not distributed by customer_id; the last week's data was heavily concentrated on a few highly active customers; the join and aggregation stages repeatedly redistributed on customer_id, causing hot nodes to stay above 95% CPU while idle nodes sat below 30%.

5. Optimization Methods

5.1 Choose a Better Distribution Key

Prioritise columns with high cardinality, high access frequency, alignment with core join conditions, and low risk of hot values. Remember: high cardinality alone isn't enough — it must serve the dominant query patterns.

5.2 Reduce Redistribution in Large Joins

• Align the join key with the distribution key where possible.
• Apply filters early to shrink the dataset before joining.
• Materialise frequently used intermediate results and distribute them optimally.
• Avoid unnecessary global sorts on huge result sets.

5.3 Targeted Handling of Hot Values

• Split hot customers, organisations, or channels into separate processing paths.
• Pre‑aggregate hot data during ETL.
• Use multi‑level aggregation to reduce the impact of granular hot spots.
• Split a large query into "hot" and "non‑hot" parts, then combine results.

5.4 Leverage Partitioning to Reduce Scan Scope

Ensure time‑based partitions align with query filters, eliminate scans on irrelevant partitions, archive cold data, and verify that partition pruning actually works.

6. Quantify the Improvement

Always measure before and after with concrete numbers: the ratio of rows scanned on the busiest vs. quietest node, peak CPU differences, total execution time, and data exchange volume. For example, the case above saw the max/min scan ratio drop from 4.8 to 1.6, runtime from 48s to 11s, and intermediate data exchange fall by 62%.

7. Practical Recommendations

• Evaluate distribution key hot‑spot risks during table design.
• Schedule regular distribution health checks for core fact tables.
• When investigating slow queries, always look at node‑level load imbalance — not just the query plan.
• Build targeted strategies for known hot‑spot business entities.
• Integrate skew analysis into routine inspection and capacity planning.

In a gbase database, the power of parallel processing depends on even data distribution and minimal unnecessary data movement. When you see "the same SQL gets slower and slower, and node load is wildly uneven," start with data skew. It's almost always more effective than tweaking SQL syntax alone.