Standard SQL vs Legacy SQL in BigQuery: Which to Use?

When you start working with BigQuery, one of the first decisions you'll encounter is which SQL dialect to use. You might notice that BigQuery supports both Standard SQL and Legacy SQL, and wonder whether it matters which one you choose. The answer is yes, and choosing the wrong one can lead to queries that are harder to maintain, more prone to errors, and potentially less performant.

This choice affects everything from how you write joins to how you work with nested data structures. Many developers who are new to Google Cloud assume that both dialects are equally valid options, but that assumption can create technical debt that becomes harder to address as your data warehouse grows.

Understanding the Two SQL Dialects

BigQuery offers two distinct query languages: Standard SQL and Legacy SQL. Standard SQL adheres to the SQL 2011 standard, which means it works like the SQL you've likely used in other database systems. It's the modern, recommended approach for all new BigQuery projects in Google Cloud Platform.

Legacy SQL, on the other hand, was Google's original implementation for BigQuery. It has unique syntax and functions that diverge from conventional SQL standards. While Google maintains backward compatibility for existing projects that use Legacy SQL, all new features, optimizations, and development efforts focus exclusively on Standard SQL.

The critical thing to understand here is that this choice affects more than preference or style. Standard SQL represents where BigQuery is heading, while Legacy SQL exists primarily to support older code that was written before Standard SQL became available.

Why the Difference Matters: A Practical Example

Consider a common scenario at a freight logistics company that needs to join shipment data with customer information. The company wants to retrieve the tracking number from their shipments table and the customer name from their customers table, matching records where the customer ID is the same.

In Standard SQL, the query looks like this:

SELECT 
  shipments.tracking_number,
  customers.company_name
FROM 
  logistics.shipments
JOIN 
  logistics.customers
ON 
  shipments.customer_id = customers.customer_id;

In Legacy SQL, the same query would be written as:

SELECT 
  shipments.tracking_number,
  customers.company_name
FROM 
  logistics.shipments,
  logistics.customers
WHERE 
  shipments.customer_id = customers.customer_id;

Notice the fundamental difference here. Standard SQL uses explicit JOIN and ON keywords to clearly indicate that you're joining two tables and specify exactly how they connect. Legacy SQL uses implicit joins, listing tables separated by commas and burying the join condition in the WHERE clause alongside other filters.

This might seem like a minor syntactic variation, but the implications run deep. The explicit join syntax in Standard SQL makes your intent clear to both human readers and the BigQuery query optimizer. When someone reads your query six months later, they immediately understand the relationship between tables. The optimizer can also make better decisions about execution strategy because the join structure is unambiguous.

The Hidden Dangers of Implicit Joins

The implicit join syntax in Legacy SQL creates several real problems. First, it's remarkably easy to accidentally create a cross join, which matches every row in one table with every row in another. Imagine a telehealth platform with a patient appointments table containing 100,000 rows and a providers table with 500 doctors. If you forget to include the join condition in the WHERE clause, you suddenly have 50 million rows to process instead of the 100,000 you intended.

This kind of mistake happens more often than you might think. When joins are implicit and the join condition sits alongside other WHERE clause filters, it's easy to overlook or mistype the condition. With Standard SQL's explicit joins, the structure of your query forces you to think about the join condition separately from your filtering logic.

Second, implicit joins make debugging significantly harder. When a query returns unexpected results, you need to trace through the logic to understand what's happening. With Standard SQL, you can immediately see the join structure and verify that each relationship is correct. With Legacy SQL, you need to parse through the WHERE clause, mentally separating join conditions from filters, and reconstruct the implicit join structure in your head.

Working with Nested and Repeated Data

One of BigQuery's unique strengths within GCP is its ability to handle nested and repeated fields efficiently. This becomes particularly important when working with semi-structured data like JSON logs or event streams. A mobile game studio, for example, might have player data where each player has multiple game sessions, and each session contains multiple events.

Standard SQL provides strong support for querying these nested structures using array functions and dot notation. You can unnest arrays, filter within nested fields, and aggregate across repeated data with clear, readable syntax. This capability is essential for working with modern data structures where strict normalization isn't always practical or desirable.

Legacy SQL has more limited support for these features, and the syntax is often less intuitive. As Google Cloud continues to enhance BigQuery's capabilities around nested data, these improvements consistently target Standard SQL. If you build your queries in Legacy SQL, you're effectively cutting yourself off from these advancements.

Performance and Optimization Considerations

The BigQuery query optimizer has been built and refined with Standard SQL as the primary target. When you write queries using Standard SQL, you benefit from years of optimization work that focuses specifically on that dialect. The optimizer understands the explicit join syntax and can make intelligent decisions about join order, partition pruning, and resource allocation.

Legacy SQL still works, but it doesn't receive the same optimization attention. Google's engineering resources go toward making Standard SQL faster and more efficient. Over time, this gap will likely widen. A video streaming service processing billions of viewing events per day will see this difference compound as data volumes grow.

There's also a practical consideration around performance debugging. When you need to understand why a query is slow, the query execution plan in BigQuery provides insights based on how the optimizer interpreted your SQL. These explanations are clearer and more actionable when working with Standard SQL because the execution plan aligns with modern optimization techniques.

The Migration Path and Backward Compatibility

If you have existing queries written in Legacy SQL, you might wonder about the migration effort. BigQuery allows you to specify which dialect to use on a per-query basis, so you can mix both dialects during a transition period. Each query can include a prefix or setting that indicates whether it's Standard or Legacy SQL.

However, maintaining a mixed environment creates its own challenges. Team members need to understand both dialects, which increases cognitive load and the chance of errors. Documentation becomes more complex because you need to note which dialect each saved query or view uses. And when troubleshooting, you need to consider dialect-specific behavior.

The better approach is to treat Legacy SQL as technical debt that needs systematic remediation. For new work, use Standard SQL exclusively. For existing queries, prioritize migrating the ones that run frequently or that others depend on. Many queries translate fairly directly, though some Legacy SQL features require rethinking your approach in Standard SQL.

Accessing BigQuery with Different Tools

Regardless of which SQL dialect you choose, BigQuery offers several ways to execute your queries within Google Cloud. The Cloud Console provides a web-based interface where you can write queries, explore datasets, and visualize results. This is particularly useful when you're exploring data or working on new queries interactively.

For automation and scripting, the bq command-line tool lets you execute queries from a terminal. A data engineer at a hospital network might use this to schedule regular data exports or to integrate BigQuery operations into deployment pipelines.

BigQuery also provides client libraries for languages including Python, Java, Node.js, Go, PHP, Ruby, and C#. These libraries allow you to embed BigQuery functionality directly into your applications. A subscription box service might use the Python client library to run queries that generate personalized product recommendations based on customer behavior data stored in BigQuery.

Regardless of which access method you use, Standard SQL works consistently across all of them. The dialect you choose is independent of how you connect to BigQuery, which means you can write your queries once and execute them through any interface.

What You Should Do

The guidance here is straightforward: use Standard SQL for all new work in BigQuery. There's no compelling reason to start with Legacy SQL, and doing so creates unnecessary complications down the road.

When writing queries, embrace explicit join syntax. Make the structure of your data relationships clear and unambiguous. This practice pays dividends when you need to modify queries, when others need to understand your work, or when you're debugging unexpected results.

If you're working with a GCP project that has existing Legacy SQL queries, create a migration plan. Start with the most frequently executed queries or those that other queries depend on. Document the dialect of each query during the transition period to avoid confusion.

For teams new to BigQuery, establish Standard SQL as the default from day one. Include it in your coding standards and query templates. This consistency makes it easier for team members to learn and for the team to maintain a coherent codebase.

Building Toward Deeper Understanding

The choice between Standard SQL and Legacy SQL reflects a broader pattern in Google Cloud Platform: services evolve, and Google provides migration paths while maintaining backward compatibility. Understanding this pattern helps you make better decisions about when to adopt new features and when to modernize existing code.

Standard SQL is the foundation for everything BigQuery will become. As machine learning integration deepens, as query capabilities expand, and as performance improvements continue, they'll build on Standard SQL. Choosing Legacy SQL means choosing to stand still while the platform moves forward.

This same principle applies across GCP services. When Google introduces new features or approaches, there's usually a thoughtful reason behind the change. Taking the time to understand these reasons helps you anticipate future developments and make choices that age well.

The skills you build writing clear, explicit Standard SQL queries transfer beyond BigQuery. They make you a better SQL developer generally because you're working with widely recognized standards rather than proprietary syntax. When you move between BigQuery and other data warehouses in Google Cloud or elsewhere, that knowledge remains relevant.

If you're preparing to deepen your BigQuery expertise and want comprehensive guidance on data engineering patterns in Google Cloud, readers looking for structured exam preparation can check out the Professional Data Engineer course. Understanding these foundational decisions about query dialects sets the stage for more advanced data engineering work on the platform.