1️⃣ Partitioning (Reduce Data Scanned = Lower Cost)

Partitioning splits a table into smaller pieces so queries scan only relevant partitions instead of the entire table.

? Types of Partitioning

A. Time-based Partitioning (Most Common)

Partition by:

• DATE

• TIMESTAMP

• DATETIME

Example:

Best practices:

• Always filter with _PARTITIONTIME or partition column

• Use REQUIRED partition filter for safety

B. Integer Range Partitioning

Useful for numeric ranges like customer IDs.

✅ Partitioning Benefits

• Reduces scanned data → lowers cost

• Improves performance

• Enables partition pruning

? Common Mistakes

• Not filtering on partition column

• Applying functions on partition column (prevents pruning)

Bad:

Good:

2️⃣ Clustering (Improve Performance Within Partitions)

Clustering sorts data within partitions by specific columns.

Example:

? When to Use Clustering

Use when:

• Frequent filtering on high-cardinality columns

• Queries filter on non-partition columns

• Tables are large (> 1GB per partition)

✅ Clustering Benefits

• Improves query speed

• Reduces data scanned further

• Helps with selective joins and filters

? Partition vs Clustering

3️⃣ Slot Management (Control Compute Power)

To understand cost & performance deeply, you must understand slots.

? What is a Slot?

In BigQuery, a slot is:

A virtual CPU used to execute a unit of work in parallel.

Think of slots like workers processing query tasks.

More slots → more parallelism → faster queries.

BigQuery automatically assigns slots in:

• On-demand pricing

• Reservations (flat-rate pricing)

4️⃣ Slot vs Slot Contention

✅ What Are Slots?

Slots are:

• Compute capacity units

• Used to execute query stages

• Allocated dynamically

If a query gets 200 slots → it runs highly parallel.

❌ What Is Slot Contention?

Slot contention happens when:

Multiple queries compete for limited slots.

Symptoms:

• Queries queued

• Longer wait times

• Slower performance

Example:

• You have 500 reserved slots

• 10 heavy queries start simultaneously

• Each wants 300 slots

• BigQuery must distribute → contention happens

? Difference Summary

5️⃣ How to Reduce Slot Contention

✅ 1. Use Reservations (Flat-rate pricing)

Allocate slots to:

• Different teams

• Different workloads (ETL vs BI)

✅ 2. Workload Management

Separate:

• Interactive queries

• Batch processing

• Data pipelines

✅ 3. Optimize Queries

• Avoid SELECT *

• Use partition pruning

• Reduce shuffle-heavy joins

• Pre-aggregate data

✅ 4. Monitor Slot Usage

Use:

• INFORMATION_SCHEMA views

• BigQuery Admin panel

• Slot utilization metrics

6️⃣ Advanced Optimization Tips

? Avoid Excessive Shuffle

Large joins cause shuffle → slot heavy.

Solutions:

• Pre-aggregate

• Use approximate functions

• Partition join keys

? Materialized Views

Precompute expensive aggregations.

? Use BI Engine (if dashboard heavy)

? Denormalize Strategically

BigQuery prefers fewer joins.

7️⃣ Cost Optimization Strategy Summary

? Real-World Strategy Example

For a 10TB event table:

• Partition by event_date

• Cluster by user_id

• Require partition filter

• Create reservation for ETL

• Separate BI reservation

• Pre-aggregate daily stats

Result:

• 70–90% cost reduction

• 3–5x faster dashboards

• No slot contention during peak

? Final Takeaway

• Partitioning reduces cost dramatically

• Clustering improves speed

• Slots control parallel compute

• Slot contention slows everything down

• Workload isolation is key in enterprise environments