Introduction: Why This Question Matters
Imagine running a query against a table with 10 million rows of event logs. Without optimization, BigQuery will scan the entire dataset, resulting in increased costs and slower query results. Now imagine you only needed data for March 2025 and a specific customer_id. Wouldn’t it be better if BigQuery could skip irrelevant data automatically?
This is exactly what PARTITION BY and CLUSTER BY do. They are two of BigQuery’s most powerful optimization features, yet many analysts and engineers confuse them. Some think clustering and partitioning are interchangeable, others wonder if they should use both and many underestimate the impact these techniques can have on query speed and cost efficiency.
In this guide, we will break down the difference between CLUSTER BY and PARTITION BY in BigQuery, explore real-world use case, and show you how to combine them for maximum performance. By the end, you’ll know exactly when and how to apply each strategy to supercharge your BigQuery workloads.
What is BigQuery? A Quick Refresher
Google BigQuery is a serverless, fully managed data warehouse designed for large-scale analytics. With BigQuery, you can run SQL queries on petabytes of data without managing servers or indexes.
Key features that matter for today’s topic include:
- Separation of storage and compute for flexible scaling
- Pay-per-query model where you are billed for the amount of data scanned
- Automatic parallelization of queries across distributed infrastructure
- Native support for machine learning, streaming ingestion and geospatial analysis
Because of the pay-per-scan model, query efficiency directly translates into cost savings. This is where PARTITION BY and CLUSTER BY come into play.
PARTITION BY in BigQuery: Dividing Data into Chunks
Partitioning breaks a table into smaller, manageable segments called partitions. When you run a query, BigQuery only scans the relevant partitions instead of the entire table.
How Partitioning Works
Partitioning is typically done on:
- Date or timestamp columns (e.g., order_date, event_time)
- Integer ranges (e.g., customer_id BETWEEN 1000 AND 2000)
- Ingestion time (BigQuery automatically assigns data to partitions based on load time)
Think of a partitioned table as a filing cabinet with drawers for each date or range. If you only need January’s data, BigQuery opens just that drawer.
Example: Partitioning a Table by Date
CREATE TABLE orders_partitioned
PARTITION BY DATE(order_date) AS
SELECT * FROM orders;
Querying a single month:
SELECT *
FROM orders_partitioned
WHERE order_date BETWEEN '2025-01-01' AND '2025-01-31';
Instead of scanning the full dataset, BigQuery only touches January’s partition.
Benefits of Partitioning
- Query cost savings: Only scans relevant partitions
- Faster queries for time-based analysis
- Manageability: Easier to archive or delete old partitions
Limitations of Partitioning
- Usually limited to one partitioning column
- Too many small partitions can increase storage costs
- Queries filtering on non-partitioned columns won’t benefit
CLUSTER BY in BigQuery: Organizing Rows Inside Partitions
Clustering improves performance by sorting data within a table (or partition) based on one or more columns. It does not split data into separate tables like partitioning but ensures rows with similar values are stored close together.
How Clustering Works
When data is clustered, BigQuery maintains metadata about value ranges in each storage block. Queries that filter on the clustered columns can then skip irrelevant blocks.
Think of clustering as alphabetizing files inside a drawer. The drawer (partition) holds all data for January and clustering ensures customers with the same customer_id are stored together.
Example: Clustering by Customer ID
CREATE TABLE orders_clustered
PARTITION BY DATE(order_date)
CLUSTER BY customer_id AS
SELECT * FROM orders;
Query:
SELECT *
FROM orders_clustered
WHERE order_date BETWEEN '2025-01-01' AND '2025-01-31'
AND customer_id = 101;
BigQuery scans only the January drawer and quickly jumps to the block containing customer_id = 101.
Benefits of Clustering
- Improves query performance for high-cardinality columns
- Reduces scan size for queries with WHERE, ORDER BY and GROUP BY
- Can be applied on multiple columns
Limitations of Clustering
- Performance benefits depend on data distribution
- No predictable query cost estimate before execution
- Clustering effectiveness degrades over time as data grows (may require re-clustering)
Side-by-Side Breakdown of PARTITION BY vs CLUSTER BY in BigQuery
| FEATURES |
PARTITION BY |
CLUSTER BY |
| Definition |
Splits a table into physical segments (partitions) |
Organizes data within partitions by sorting rows |
| Best For |
Time-series data, logical ranges |
High-cardinality columns are frequently used in filters |
| Data Organization |
Independent partitions |
Sorted storage blocks within partitions |
| Query Optimization |
Skips entire partitions |
Skips irrelevant blocks inside partitions |
| Column Support |
Usually 1 column (date, int, or ingestion time) |
Multiple columns supported |
| Storage Cost Impact |
May increase with too many small partitions |
Minimal additional storage cost |
| Scalability |
Scales well for large datasets with natural segmentation |
Scales well for filtering large datasets |
| Primary Benefit |
Lower query costs by scanning fewer partitions |
Faster queries by reducing scans within partitions |
Using PARTITION BY and CLUSTER BY Together
The real magic happens when you combine both.
Example: Partitioning by Date + Clustering by Customer ID
CREATE TABLE call_logs
PARTITION BY DATE(call_date)
CLUSTER BY workspace_id AS
SELECT * FROM raw_call_data;
This design achieves two layers of optimization:
- Partition pruning: Queries only scan relevant date ranges
- Block pruning: Within each date, BigQuery skips irrelevant blocks for workspace_id
Result: maximum speed, minimum cost.
When to Use Partitioning vs Clustering
Use Partitioning When:
- Your queries mostly filter by date or time ranges
- You want predictable cost savings from partition pruning
- Your dataset grows in predictable segments (daily logs, monthly sales)
Use Clustering When:
- Queries filter by high-cardinality columns like user_id or transaction_id
- You often use ORDER BY or GROUP BY on certain columns
- Your data has large variety within partitions
Use Both Together When:
- You filter on both time and entity columns
- You want cost savings from partitions and speed from clustering
- You manage massive datasets where both strategies are necessary
Best Practices for Partitioning and Clustering in BigQuery
- Choose the right partitioning column: Most often a date column
- Avoid over-partitioning: Too many partitions increase costs
- Cluster on columns frequently used in filters
- Monitor clustering quality: Re-cluster if performance drops
- Test with real queries: Optimize based on actual workload patterns
Conclusion:
BigQuery gives us two tools that make all the difference:
- Partitioning helps you save money by skipping entire chunks of data you don’t need
- Clustering helps you move faster by keeping related rows close together
- Used together, they unlock the best balance of speed, cost and scale
If you’re working with serious amounts of data, knowing when to partition and when to cluster is not just a nice-to-have. It’s the line between queries that feel slow and expensive and queries that return insights almost instantly.
So here’s the next step: take a look at your tables. Ask yourself, can I partition by date or another natural split? Can I cluster on the fields my team filters on the most? Chances are, just a few changes will give you faster queries and smaller bills.
In the end, partitioning and clustering are less about technical tricks and more about designing BigQuery tables that actually work with your data, not against it. Once you start using them together, you’ll wonder how you ever managed without them.
Frequently Asked Questions
What is the difference between PARTITION BY and CLUSTER BY in BigQuery?
In BigQuery, PARTITION BY divides a table into smaller segments (often by date), while CLUSTER BY organizes rows inside those segments. Partitioning reduces query costs by skipping entire partitions and clustering improves query performance by reducing the data scanned within each partition.
When should I use PARTITION BY in BigQuery?
Use PARTITION BY when working with time-based or range-based data. It’s ideal for event logs, transactions or sales data filtered by date or timestamp. Partitioning is one of the most effective techniques for BigQuery cost optimization.
When should I use CLUSTER BY in BigQuery?
Use CLUSTER BY when queries filter or group by high-cardinality columns like user_id, transaction_id or region. Clustering improves SQL performance in BigQuery by storing related rows close together for faster scans inside partitions.
Can I combine PARTITION BY and CLUSTER BY in BigQuery?
Yes. Combining both is a best practice for large datasets. For example, you can partition by date and cluster by customer_id, which ensures BigQuery scans only the relevant partitions and retrieves rows faster within each partition.
Does clustering or partitioning reduce BigQuery costs more effectively?
Partitioning typically delivers greater cost savings because it avoids scanning entire partitions. Clustering mainly enhances speed by minimizing scans within partitions. For the best results in BigQuery cost reduction and performance, use them together.
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