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| (___ | | | | ___ _ __ ___ _ __ | | ___ __ __ _ | |_ _ _
\___ \ | | | | / _ \ | '_ ` _ \ | '_ \ | | / _ \\ \/ /| || __|| | | |
____) || |__| || (_) || | | | | || |_) || |____| __/ > < | || |_ | |_| |
|_____/ \___\_\ \___/ |_| |_| |_|| .__/ |______|\___|/_/\_\|_| \__| \__, |
| | __/ |
Calculate complexity scores |_| for SQL queries |___/
SQompLexity is a metric that assigns a complexity score to SQL queries. It is specifically tailored to work with MySQL queries, but other dialects of SQL will likely work as well. It needs no knowledge of the database schema and quantifies each query in a vacuum.
npm i sqomplexityhttps://bert-w.github.io/sqomplexity/
import { Sqomplexity } from 'sqomplexity';
(async () => {
const sqomplexity = new Sqomplexity([
"SELECT * FROM users",
]);
console.log(
await sqomplexity.score()
);
// Result: [ 2.40625 ]
})();See examples/node.js for a full example.
Use the precompiled dist/sqomplexity.umd.js file:
<script src="sqomplexity.umd.js"></script>
<script>
(async() => {
// The UMD build exposes the `$sqomplexity` global constructor.
console.log(
await (new window.$sqomplexity('SELECT * FROM users')).score()
)
// Result: [ 7.876953 ]
})();
</script>See examples/browser.html for a full example.
Use the precompiled dist/sqomplexity.js containing all required code in a single file.
Options:
node sqomplexity.js --help
Arguments:
queries one or multiple SQL queries (space separated or quoted)
Options:
-V, --version output the version number
-f, --files assumes the given arguments/queries are filepaths, and it will read the contents from them.
Every file is expected to contain 1 query; if not, their complexity is summed
-b, --base64 assumes the given arguments/queries are base64 encoded
-s, --score output only the complexity score. -1 will be returned if an error occurs
-w, --weights <weights> takes a path to a json file that defines a custom set of weights
-a, --all returns all data including the AST
-p, --pretty-print output JSON with indentation and newlines (default: false)
-h, --help display help for commandSee examples/cli.sh for various examples.
The scoring of an SQL query is based on 2 major components, being:
Data complexity (see prefix D in the table below), also called Computational complexity, which takes into account elements like the amount of rows that a query operates on (relatively speaking), the computation paths a query may take, and the usage of table indexes (indices). All of these determine the computational cost of a certain component.
Cognitive complexity (see prefix C in the table below), which describes the mental effort and the concepts a person must understand in order to parse the query. This includes components like understanding of First-order logic, understanding of grouping, filtering and sorting (common SQL concepts), and Domain knowledge like the context of the query compared to its database schema.
| Code | Explanation |
|---|---|
| Indexing behavior | |
| D1-A | No possibility to affect the chosen index |
| D1-B | Low possibility to affect the chosen index |
| D1-C | High possibility to affect the chosen index |
| Running time | |
| D2-A | $O(0)$ (negligible) running time w.r.t. the number of rows |
| D2-B | $O(1)$ (constant) running time w.r.t. the number of rows |
| D2-C | $O(\log n)$ (logarithmic) running time w.r.t. the number of rows |
| D2-D | $O(n)$ (linear) running time w.r.t. the number of rows |
| D2-E | $O(n \log n)$ (linearithmic) running time w.r.t. the number of rows |
| D2-F | $O(x)$ (highly variable) running time w.r.t. the number of rows |
| Relational algebra | |
| C1 | Requires understanding of projection (selection of columns) |
| C2 | Requires understanding of selection (e.g. boolean logic like (in)equalities and comparisons) |
| C3 | Requires understanding of composition (multiple tables, column relations, set theory) |
| C4 | Requires understanding of grouping |
| C5 | Requires understanding of aggregation |
| Programming | |
| C6 | Requires understanding of data types (e.g. integers, decimals, booleans, dates, times) |
| C7 | Requires understanding of variable scopes |
| C8 | Requires understanding of nesting |
| Usage | |
| C9-A | One parameter |
| C9-B | Low amount of parameters |
| C9-C | High amount of parameters |
| C10 | Requires understanding of the database schema |
| C11 | Requires understanding of the RDBMS toolset (e.g. function support and differences) |
What follows is the assignment of each of these indicators to components of an SQL query. The table below shows the result of this process. The combination and presence of these indicators are combined into a final weighting for each component, namely Low, Medium or High.
| Component | Data Complexity | By | Cognitive Complexity | By |
|---|---|---|---|---|
| Clause:SELECT | Low | D1-A, D2-D | Low | C1, C6, C9-B, C10 |
| Clause:FROM | Medium | D1-B, D2-D | Low | C3, C7, C9-A, C10 |
| Clause:JOIN | Medium | D1-C, D2-F | Medium | C2, C3, C7, C9-B, C10 |
| Clause:WHERE | High | D1-C, D2-C/D | Medium | C2, C6, C9-B, C10 |
| Clause:GROUP BY | High | D1-C, D2-D/E | High | C2, C4, C5, C9-B, C10 |
| Clause:HAVING | Medium | D1-A, D2-D | High | C2, C4, C5, C9-C, C10 |
| Clause:ORDER BY | Low | D1-C, D2-D/E | Medium | C6, C9-B, C10 |
| Clause:LIMIT | Low | D1-A, D2-B | Low | C9-A |
| Clause:OFFSET | Low | D1-A, D2-B | Low | C9-A |
| Expression:Table | Medium | D1-B, D2-A | Medium | C9-A, C10 |
| Expression:Column | Medium | D1-B, D2-A | Medium | C6, C9-A, C10 |
| Expression:String | Low | D1-A, D2-A | Low | C6, C9-A |
| Expression:Number | Low | D1-A, D2-A | Low | C6, C9-A |
| Expression:Null | Low | D1-A, D2-A | Low | C6, C9-A |
| Expression:Star | Low | D1-A, D2-A | Low | C1, C9-A |
| Expression:Unary | Low | D1-A, D2-A | Medium | C2, C6, C9-A |
| Expression:Binary | Low | D1-A, D2-A | Medium | C2, C6, C9-B |
| Expression:Function | High | D1-B, D2-D | Medium | C6, C9-A, C11 |
| Expression:List | Low | D1-C, D2-A | Low | C6, C9-C |
| Expression:Agg-Function | High | D1-B, D2-F | High | C4, C5, C9-A, C10, C11 |
| Operator | Low | D1-C, D2-A | Medium | C2, C6, C9-B |
| Emergent:Cycle | Medium | D1-B, D2-F | High | C2, C3, C9-C, C10 |
| Emergent:Mixed-Style | None | D1-A, D2-A | Medium | C9-C |
| Emergent:Subquery | High | D1-C, D2-F | High | C1, C2, C3, C7, C8, C9-C, C10 |
| Emergent:Variety | None | D1-A, D2-A | Medium | C9-C |
Each query that passes through SQompLexity is parsed into an Abstract Syntax Tree (AST), which provides the backbone of the algorithm that sums up the weights. Each query is traversed fully (including subqueries), and the scores are summed to result in a final SQompLexity score for any given SQL query.
The numerical weights for each of groups are like so:
| Category | Numerical Score |
|---|---|
| Data Complexity | 50% |
| Cognitive Complexity | 50% |
| Low | 1.0 |
| Medium | 1.25 |
| High | 1.5 |
The equal contribution of both Data Complexity and Cognitive Complexity is arbitrary, and research could still be done to develop a distribution that more fairly approaches a general sense of complexity.
Similarly, the weights of Low, Medium and High are set to some sensible defaults. It is necessary though for all weights to be greater than or equal to 1, since multiplication may take place during the algorithm.
This is a product of my master's thesis on complexity progression and correlations on Stack Overflow. For this study, I have developed an SQL complexity metric to be used on question and answer data from Stack Overflow.