Why SQL Joins Don't Work at Scale

I love SQL. I’ve been using it since 2009. It is a crazy powerful tool and I’ve used it for a million different things in different jobs.?

If you’ve worked with SQL, you’ve probably used joins. You know —LEFT JOIN, RIGHT JOIN, INNER JOIN (or just JOIN), FULL OUTER JOIN (not used often without a specific reason, like finding orphaned records).

Joins are essential for expressing complex relationships in relational databases. They let you combine data from multiple tables based on shared keys, building richer, more informative results. It’s a fundamental feature of the relational model, and is a big part of what made it so powerful in the first place.

However, data sizes are exploding and architectures are shifting toward distributed, “big data” approaches. In these environments, joins run into serious performance issues. The problem isn’t that joins are conceptually bad?—?it’s that the implementation methods break down at scale.

Quadratic Complexity

Standard join algorithms often have O(n2) complexity in both time and space. This is wildly different from simpler operations that scale linearly with the number of records.?

As your dataset grows, the cost of a join balloons. More records mean exponentially more comparisons to find matching keys, and a lot more memory (or temporary disk space) to hold intermediate results.

For small datasets, this may not matter. The joins feel instant and the computations feel trivial. But at millions or billions of rows, the cost becomes brutal. You run out of memory. You have to start writing intermediate stages to disk. Everything slows down, and the joy of relational expressiveness sours into operational pain. (This might be the dorkiest sentence I’ve ever written, sorry.)?

Poorly Scaling Algorithms

In traditional (single-server databases), joins might be implemented using sophisticated indexing or hashing techniques that work well enough on one machine. But when we move into distributed environments?—?think Hadoop, Spark, or modern cloud data warehouses?—?the classic, single-node join algorithms don’t automatically scale out.

Why? Because these algorithms assume a centralized environment. They rely on the idea that data can be efficiently moved and merged inside a single memory space. Distribute your data across dozens of machines, each holding shards of different tables, and a simple hash join turns into a data shuffling algorithm.

Denormalization?—?precomputing certain joins ahead of time?—?is a common strategy to limit join complexity in big data systems. But denormalization only helps if you know in advance which joins matter. It’s usually a workaround, not a general solution. And it increases complexity in other ways: you store more data, you lose normalization’s flexibility, and if your queries change, you might need to redo some denormalization work.

Parallel Join Algorithms in Common?Stacks

It’s not intrinsically impossible to parallelize joins. Looking at advanced systems, we can see that clever algorithms and data layouts can distribute and execute joins efficiently across multiple nodes.?

For example:

• Snowflake: Uses a cloud-native architecture with micro-partitioning and distributed processing to handle joins at scale, combining advanced query optimization with a flexible metadata layer.
• Google F1: Built on Spanner, it features globally distributed transactions and can execute certain joins efficiently across nodes thanks to a well-structured data model and advanced concurrency control.

BUT most big data stacks don’t implement these advanced strategies (they don’t have Google’s budget, probably). Instead, they rely on rudimentary techniques that force you to rethink or avoid joins altogether.

This leaves a gap: either you accept the massive overhead of naive distributed joins, or you contort your data model to sidestep them?—?often losing the relational clarity that made SQL powerful in the first place.

Building for?Scale

At HiiBo, we’re dealing with complex search and memory operations that could theoretically involve joins on large datasets. Whether we’re talking about searching across a user’s historical chat context or navigating a memory store with multiple attributes and references, naive join operations could cripple our performance at scale.

We’re not dealing with just a single “big data” pipeline. We’re building AI-driven search and memory features that must handle large volumes of structured and semi-structured data, and merge that data fluidly on command. The challenge is ensuring that these features remain fast and cost-effective no matter how large the dataset grows.

What Are We Doing Differently?

• Targeted Precomputation Rather than blindly denormalizing everything, we selectively precompute certain relationships, storing them in specialized indices. This helps us handle the queries we already know we need to answer quickly, without rewriting the entire data model or ballooning our storage. It’s not a universal join replacement?—?just enough to remove bottlenecks where we see them.
• Smarter Partitioning We’re mapping out how our data is distributed so we don’t have to shuffle entire tables across the cluster just to answer one query. By aligning partitions with the queries we most care about?—?like grouping memory records for a particular user?—?we reduce the chance that a single join operation requires fetching data from half the servers in the network.
• Minimal Join Semantics for Key Scenarios We’re not eliminating joins across the board. But we’re honest about when full relational joins are overkill. For instance, when we do memory or chat searches, sometimes we just need to link records by a common tag or user ID?—?this is a partial join that can be satisfied with simpler lookups or inverted indexes rather than a heavy cross-table relational join.
• Upfront Clarity on Data Flow We’re clarifying the path data takes from ingestion to querying. By maintaining incremental updates or small “materialized partial views,” we can limit how often we do big merges. This ensures that if a join-like operation is needed, it’s run on a manageable subset or an already aggregated set, not the entire raw dataset.
• Adaptive Iteration Our architecture includes feedback loops. If we detect certain query patterns consistently causing pseudo-join-like overhead, we adapt. We add indexes, restructure certain datasets, or precompute certain relationships as needed. The point is to be proactive and data-driven about tackling join-like complexity before it becomes a bottleneck.

Final Musings

SQL joins aren’t “bad” in theory. In fact they aren’t bad at all, they are f***ing great. They’re essential for relational queries.?

But as datasets scale and we move into distributed environments, naive join implementations face severe performance issues. The complexity is both computational and architectural, forcing systems to either denormalize data, accept massive overhead, or rely on expensive workarounds.

At HiiBo, we recognize these limitations and are building our chat and memory features with scaling in mind?—?eschewing naive join approaches and focusing on data structures and precomputation techniques that can handle complexity gracefully. Instead of complaining about joins and telling everyone to avoid them, we’re using domain knowledge, indexing strategies, and clever data layouts to deliver efficient, scalable querying without crippling complexity.

Ultimately, the future of large-scale, AI-driven systems lies not in discarding relational thinking, but in evolving beyond simplistic join algorithms that don’t align with modern, distributed realities.

About the Author Sam Hilsman is the CEO of CloudFruit?, HiiBo, and oneXerp. If you’re interested in what we’re building or want to become a developer ambassador for HiiBo, visit www.HiiBo.app/dev-ambassadors.