StarRocks Query Fix: No More Empty Results With RAND()

Hey everyone! Ever been scratching your head, wondering why your StarRocks query involving RAND() just isn't giving you any results, even when you know there's data in your tables? Well, folks, you're not alone! Today, we're diving deep into a fascinating bug that's been causing some serious headaches for users trying to do sampling or other non-deterministic operations in StarRocks. We're talking about the tricky business of incorrect predicate pushdown for non-deterministic functions, specifically RAND(). This issue, while subtle, can lead to your perfectly valid queries returning empty datasets, completely throwing off your data analysis and insights. Understanding this problem and its proposed solution is key to writing robust and reliable queries in StarRocks, ensuring that your data operations behave exactly as you intend. So, let's unpack why this happens, what it means for your queries, and how StarRocks is getting smarter to fix it, making sure you get accurate results every single time.

Understanding the StarRocks Predicate Pushdown Problem

Alright, let's kick things off by really understanding the core of the problem: predicate pushdown in StarRocks when it meets non-deterministic functions. Normally, predicate pushdown is a superhero feature in database optimizers, designed to make your queries lightning fast. It basically means the database engine tries to apply filters (predicates) as early as possible in the query execution plan, often pushing them down to the lowest-level nodes, like table scans. Why is this awesome, you ask? Because filtering data at the source means less data has to be read, processed, transferred, and ultimately joined, leading to much more efficient queries. It's like telling your grocery shopper exactly what you need before they even leave the house, rather than sorting through everything at home. However, like any superpower, if applied incorrectly, it can cause more harm than good, especially when dealing with functions that don't always give you the same result – our non-deterministic functions. In this particular StarRocks scenario, the optimizer's eagerness to push predicates down is causing a major misstep when those predicates involve functions like RAND(). Instead of sampling your combined, intermediate results from a CTE or view, it's attempting to sample individual base tables before they're even joined. This subtle but critical misapplication of an optimization technique is the root cause of those frustrating empty result sets, turning an efficiency gain into a correctness nightmare. It’s crucial for us to grasp this distinction: good optimization helps, bad optimization hurts, especially when it fundamentally changes the meaning of your query.

The Power and Pitfalls of Predicate Pushdown

So, what exactly is predicate pushdown? In simple terms, it's a fundamental optimization technique used by almost every modern database query optimizer, including StarRocks. Its main goal is to improve query performance by reducing the amount of data that needs to be processed. Imagine you have a huge table, and you only care about rows where status = 'active' and region = 'Europe'. Instead of reading all the data from disk, then bringing it into memory, and then filtering, the optimizer tries to push these filters down to the data access layer. This means the storage engine itself (or the scan nodes in a distributed system like StarRocks) can discard irrelevant rows before they even make it up the execution stack. The benefits are massive: dramatically reduced I/O, less network traffic in distributed systems, smaller intermediate result sets for joins, and overall faster query execution times. For typical, deterministic filters (like WHERE age > 30), this is almost always a win. It's a cornerstone of high-performance analytical databases, ensuring that only necessary data is ever processed. Without it, even simple queries on large datasets would grind to a halt. It makes complex joins feasible and allows StarRocks to handle massive amounts of data with impressive speed. However, this powerful optimization has its limits. The pitfall arises when the predicate being pushed down isn't as straightforward as age > 30. When you introduce functions that don't produce a consistent output every time they're evaluated – non-deterministic functions – the rules of the game change entirely. The optimizer needs to be smart enough to recognize these special cases and know when not to push the predicate, or at least, where to stop pushing it. If it pushes a non-deterministic filter too far down, it can alter the semantic meaning of the query, leading to logically incorrect results, which is exactly what we're seeing in StarRocks with RAND(). This is why carefully designed optimizer rules are critical: they need to balance performance gains with the absolute necessity of returning correct query results. It's a delicate dance between speed and accuracy, and sometimes, even the best dancers trip up.

Why Non-Deterministic Functions are Different

Now, let's talk about what makes non-deterministic functions so special and why they need a different approach from the optimizer. Guys, functions like RAND(), NOW(), UUID(), or CURRENT_TIMESTAMP() are non-deterministic because their output isn't solely dependent on their input arguments; it also depends on external factors like the current time, a random seed, or the specific invocation context. Every time you call RAND(), you get a different random number. NOW() gives you the current timestamp, which is, by its very nature, always changing. UUID() generates a universally unique identifier, different with each call. This is in stark contrast to deterministic functions like ABS(-5) (always returns 5), CONCAT('hello', 'world') (always returns 'helloworld'), or simple arithmetic operations (2 + 2, always 4). A deterministic function will always produce the same output for the same set of inputs. The problem arises because the database optimizer is built to assume, for the most part, that functions are deterministic or at least behave predictably. It expects that applying a filter WHERE some_function(column) = value will consistently filter rows. But with RAND(), for instance, the condition RAND() < 0.001 might evaluate to true for a row one moment and false the next, if it's evaluated independently multiple times. If an optimizer pushes RAND() < p down to individual Scan nodes for base tables before they're joined, each Scan node will generate its own independent random number for each row. This means the probability of a specific row from t1 passing its RAND() filter and a specific row from t2 (that would otherwise join with t1) also passing its own independent RAND() filter becomes incredibly low. It's like trying to win two different lotteries simultaneously with entirely separate tickets. The chance of both events happening for corresponding rows that would form a valid join tuple approaches zero very quickly, especially with small sampling probabilities. This behavior fundamentally violates the user's intent, which is to sample the final, joined result set of a CTE or view, not the individual components before they're even connected. The core issue is that the RAND() condition needs to be applied once per logical row of the intended intermediate result, not multiple times across disparate data sources. This semantic difference is crucial, and that's why non-deterministic functions require careful handling to preserve query correctness.

Diving Deep into the Incorrect RAND() Pushdown in StarRocks

Let's zero in on the exact mechanism of the RAND() pushdown bug in StarRocks, because understanding the