🚀Optimizing BigQuery: A Deep Dive into Advanced Features and Best Practices

4 min readOct 22, 2023

Google BigQuery is an incredibly powerful tool for data analytics and processing. In this blog post, we’ll delve into advanced concepts to help you harness its full potential. We’ll explore each concept in detail, and provide sample codes, real-world examples, and guidance on when to use them.

1. Slots and Reservations

Slots in BigQuery are the computational units that power your queries. When you need high-performance query execution, allocating slots is crucial. Here’s a breakdown:

Slot Allocation: You can dynamically allocate slots for your workloads, depending on the query’s complexity. For example, running complex ad-hoc queries may require more slots.
Reservations: For mission-critical workloads, you can reserve slots. Reservations ensure you have dedicated capacity when you need it, preventing performance bottlenecks.

When to Use: Use slot allocation for general workloads, while reservations are ideal for high-priority or consistent heavy workloads.

2. Partitioning and Clustering

Partitioning and clustering are data organisation techniques that drastically improve query performance:

Partitioning: Divide tables into smaller, manageable parts based on a specific column, typically a date. For instance, partition your sales data by date.

CREATE OR REPLACE TABLE my_dataset.my_partitioned_table
PARTITION BY DATE(order_date)
AS
SELECT *
FROM my_dataset.my_source_table;

Clustering: Once partitioned, you can further organize the data within each partition by clustering it using one or more columns. This optimizes query pruning.

CREATE OR REPLACE TABLE my_dataset.my_clustered_table
CLUSTER BY customer_id
AS
SELECT *
FROM my_dataset.my_partitioned_table;

When to Use: Employ partitioning for large historical datasets, and clustering when you need to optimize querying on those partitions.

3. Machine Learning with BQML

BigQuery Machine Learning (BQML) lets you build and deploy machine learning models directly within BigQuery. Say you want to predict customer churn:

CREATE OR REPLACE MODEL `my_dataset.customer_churn`
OPTIONS(model_type='logistic_reg') AS
SELECT
 age, total_transactions, loyalty_points
 FROM `my_dataset.customer_data`;

BQML simplifies ML model creation, allowing SQL-savvy data analysts to embrace machine learning without coding hassles.

When to Use: Choose BQML when you want to develop machine learning models without leaving BigQuery.

4. GIS Spatial Data

With BigQuery’s geospatial capabilities, you can perform complex geographic analyses. For instance, you’re managing a fleet of delivery trucks:

SELECT
 truck_id,
 ST_LENGTH(ST_MAKELINE(ARRAY_AGG(point ORDER BY timestamp))) AS total_distance
FROM
 my_dataset.truck_gps_data
GROUP BY
 truck_id;

BigQuery can help optimize delivery routes, analyze customer locations, and more.

When to Use: Leverage geospatial functions when dealing with location-based data or geospatial problem-solving.

5. Materialised Views

Materialised views are important for query performance. They store pre-computed results for often-run queries:

CREATE MATERIALIZED VIEW my_dataset.product_sales_mv AS
SELECT
 product_id,
 SUM(sales) AS total_sales
FROM
 my_dataset.sales_data
GROUP BY
 product_id;

Now, querying `product_sales_mv` is faster than recalculating sales totals every time.

When to Use: Use materialized views for recurring queries that involve aggregations or complex calculations.

💡Deep diving into partitioning and clustering with an example⬇️

Let’s consider an example in the context of a retail domain where partitioning and clustering can optimize query performance.

Background:

Imagine a large retail company that stores vast amounts of sales data, including transactions, products, and customer information, in BigQuery. As the volume of data grows, you start encountering slow query performance when analyzing this data for business insights.

How Partitioning Helps:

Partitioning involves dividing your data into smaller, manageable portions based on a specific column, typically a timestamp or date in the retail domain. In this case, you can partition your sales data by the transaction date. Here’s how it helps:

Reduced Data Scanning: When you run queries to analyze sales for a particular date or a date range, partitioning allows BigQuery to scan only the relevant partitions instead of the entire dataset. This significantly reduces the data scanned, resulting in faster query execution.
Improved Cost-Efficiency: By scanning less data, you save on query costs, as BigQuery pricing is based on the amount of data scanned. Partitioning optimizes cost efficiency, especially if you have a large dataset.

How Clustering Helps:

Clustering is an extension of partitioning, where data is further organized within each partition. In the retail context, you can cluster sales data based on a column like ‘product_id.’ Here’s how it complements partitioning:

Data Organization: Clustering groups similar ‘product_id’ values together within each partition. This means that when you query sales data for a specific date and product, BigQuery can perform more targeted scans, fetching only relevant data, and skipping unnecessary rows.
Additional Performance Gains: Clustering often leads to improved query performance as it narrows down the scope of data even further, which is especially useful when you have a broad range of products and you’re interested in specific ones.

In conclusion, mastering these BigQuery concepts opens doors to efficient data processing and analytics. Tailor your approach to the needs of your business, and watch as BigQuery transforms your data into actionable insights. Happy querying! 🚀📊

References to documentation

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