Performance Tuning: How to Reduce Query Latency by 70% Using Partitioning and Predicate Pushdown

Query performance is often dominated by how much data is scanned. In this walkthrough we'll apply practical techniques—partitioning, predicate pushdown, columnar formats, and clustering—to reduce both latency and cost. These techniques are widely applicable across Athena, BigQuery, Synapse, and other systems.

Understanding the Problem

Consider a daily report that reads an events table with 10 TB of raw data. If the query is not selective, it scans terabytes every run. Effective layout reduces scanned bytes and improves planner efficiency.

Step 1: Choose Columnar Formats

Switching from CSV/JSON to Parquet or ORC cuts I/O dramatically. Columnar formats enable reading only the columns referenced by the query.

Step 2: Partitioning Strategy

Partition by the most common filter, often date. For a events table, consider partitioning by event_date and optionally by a high-cardinality field like country code when queries often filter on it.

Step 3: Predicate Pushdown

Predicate pushdown ensures filters are applied as close to storage as possible. Write queries that use WHERE clauses referencing partition columns and indexed columns. Avoid functions on partition columns that block pushdown (e.g., avoid DATE(event_ts) = '2025-12-10' — instead use event_ts BETWEEN ...).

Step 4: Clustering / Sorting Within Partitions

Clustering (also called sorting or bucketing) organizes rows so the planner can skip more files when filtering. Use clustering on frequent join keys or high-selectivity fields.

Step 5: Materialized Aggregates

Precompute daily aggregates for metrics used by dashboards. Materialized views or scheduled ETL jobs can serve repeated requests without scanning raw data.

Example Walkthrough

Starting point: 10 TB events table, dashboard queries filter to last 7 days but the SQL uses a function on the timestamp column. After applying steps above:

1. Convert raw files to Parquet with snappy compression.
2. Partition by event_date and write daily Parquet files.
3. Rewrite queries to use event_date BETWEEN '2025-12-01' AND '2025-12-07'.
4. Create a materialized view of daily aggregates for dashboard metrics.

Result: scanned bytes drop from 1.2 TB per query to 120 GB (90% reduction). Observed query latency reduction: median from 12s to 3.5s (≈70% improvement).

Practical Tips

• Avoid small files—coalesce small objects into megabyte-scale files per partition to reduce metadata overhead.
• Prefer predicate-friendly data types—use native timestamp types instead of strings.
• Monitor file-level statistics and tune partitioning if skews appear.

When to Use Denormalization

Denormalize when joins are the performance bottleneck and the data update rate is manageable. Materialized denormalized tables are often the fastest for dashboard workloads.

Measuring Success

Track these KPIs:

• Bytes scanned per query
• Median and 95th percentile latency
• Cost per query or per dashboard refresh

Closing

Small structural changes in data layout and query patterns yield big wins. Focus on partitioning, columnar formats, predicate pushdown, and precomputation to reduce cost and improve user experience for analytics consumers.