SleekRank for data structure encyclopedia pages
Maintain a sheet of data structures with operations, time and space complexity, invariants, and reference implementations. SleekRank generates an indexable page at /cs/data-structures/{slug}/ for each row, with structured code samples and operation tables.
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Data structures are reference material with a fixed shape
Hash tables, red-black trees, B-trees, skip lists, tries, segment trees. Every data structure reference looks the same: a description, a list of operations with complexity, the invariants the structure maintains, a memory model, and a reference implementation in one or two languages. About 300 structures cover the field as taught in a graduate course.
A row per structure holds name, category (linear, tree, graph, hash, persistent), operations (a JSON array of objects with name, time, and space complexity), invariants (array of strings), memory_model, and implementations (object keyed by language). Each row becomes /cs/data-structures/red-black-tree/ with an operations table, an invariants block, and tabbed implementations.
The operations array is the workhorse. A list mapping renders one table row per operation, with columns for name, average and worst complexity. The invariants array becomes a bulleted list. Implementations render as tabbed code blocks via a selector that loops the implementations object. Update the sheet, every page reflects the change on next cache cycle.
Workflow
From structure sheet to reference page
Design the base structure page
Structure the source sheet
Wire selectors and mappings
Build the category clusters
Data in, pages out
Rows of structures, tables of operations
| slug | name | category | search_avg | insert_avg |
|---|---|---|---|---|
| red-black-tree | Red-black tree | Balanced BST | O(log n) | O(log n) |
| skip-list | Skip list | Probabilistic | O(log n) | O(log n) |
| b-tree | B-tree | Disk-based | O(log n) | O(log n) |
| disjoint-set-union | Disjoint set union | Set | O(alpha(n)) | O(alpha(n)) |
| segment-tree | Segment tree | Range query | O(log n) | O(log n) |
/cs/data-structures/{slug}/
- /cs/data-structures/red-black-tree/
- /cs/data-structures/skip-list/
- /cs/data-structures/b-tree/
- /cs/data-structures/disjoint-set-union/
- /cs/data-structures/segment-tree/
Comparison
Per-structure articles vs SleekRank pages
Hand-built reference posts
- Each structure is a long-form post with drifting operation-table conventions
- Operations and complexity edited per article, easy to miss when a fix is needed
- Reference code samples copied between articles and quietly rot over the years
- Related-structure links pinned by hand and incomplete as the corpus grows past 50
- No structured data tying operations to schema, so search engines see only prose
- New structures require an editor, a markup pass, and a manual link-add to siblings
SleekRank
-
One row per data structure renders
/cs/data-structures/{slug}/automatically - Operations stored as a JSON array drive a consistent operation table on every page
- Invariants array renders as a bulleted block, maintained centrally in the sheet
- Implementations object keyed by language renders code tabs through a selector
- Category and use-case columns drive automatic related-structure cross-links neatly
- Update an operation's complexity once and every linked context page reflects it
Features
What SleekRank gives you for Data structures one-per-page
Operations array as a table
Store operations as a JSON array of objects: name, time_avg, time_worst, space. A list mapping renders one tr per operation in a consistent operations table on every page. Adding a new operation is one array entry in the source sheet.
Invariants block
Invariants (every red node has black children, root is black, tree height is O(log n)) live as a string array. A list mapping renders them as a bulleted block with clear separation from the operations table. Refactor an invariant once across the corpus reliably.
Multi-language implementations
Implementations object keyed by language renders as tabbed code blocks. A reader switches between Python, C++, Rust, and Go without leaving the page. Each tab carries syntax highlighting and a copy button via the base template, not per-article markup work.
Use cases
Where data structure references live
University course companion sites
Mirror a graduate algorithms textbook chapter list as a sheet. Each row becomes a course-aligned reference page with consistent operations tables and invariant blocks, refreshed centrally as the syllabus evolves over multiple terms.
Interview prep platforms
Tie each structure to practice problems and complexity drills via a related-problem slug array. Each data structure page funnels users into practice paths with structured snippets driving better rich-result eligibility in search.
Engineering team wikis
Internal documentation for the structures a platform uses in production. Tie each entry to the services that depend on it via a usage column, surfacing cross-team dependencies the moment a structure's API is reviewed.
The bigger picture
Why structured CS references beat hand-written ones
Reference material lives or dies by consistency. A developer who looks up a data structure expects the same six sections in the same order every time: description, operations table, invariants, memory layout, reference implementation, related structures. When the corpus is hand-written across 300 structures by a rotating cast of contributors, some sections get six paragraphs and some get half a sentence, the operations table style drifts every twenty articles, and the related-structure links rot as the index grows.
SleekRank fixes all of that without an editorial guideline document. The template enforces structure because the structure is the template. Every row carries the same fields and every page renders the same blocks.
A reader scanning the operations table on a B-tree page sees exactly the layout they saw on the red-black tree page ten seconds ago. The cognitive cost of cross-referencing across a 300-page corpus collapses, and the editorial cost of keeping it current collapses with it. Adding a new data structure becomes a row of data, not an article assignment with style review attached.
The end result is a reference that actually competes on coverage and accuracy rather than on the heroism of whoever was willing to write 300 articles.
Questions
Common questions about SleekRank for Data structures one-per-page
Add an amortized column to each operation in the array (alongside avg and worst). The list mapping renders an extra column when present, and the template hides the column on rows that don't carry an amortized value. Disjoint set union's near-constant amortized complexity gets first-class display without changing the schema.
 Yes. Add a diagram_svg or diagram_url column. The base page includes a slot that renders inline SVG or an image based on which column is populated. Structures without a diagram simply skip the slot. The same approach handles animated demos via an embed_html column.
 Add a paradigm column with values like imperative, persistent, lock-free. The category column on its own handles the basic split, but paradigm provides finer slicing. Filtered list mappings can then render Other persistent structures on a finger tree page or Other lock-free structures on a Treiber stack page.
 Yes. Add a stdlib_mapping column with a JSON object: python -> dict, java -> HashMap, cpp -> std::unordered_map. A selector renders a table showing how each language's standard library spells the structure. Readers map from theory to the API they actually call in their day-to-day work.
 Each significant variant takes its own row and slug. A variants array on each row holds the slugs of cousins. The base template renders a See also block via list mapping. The B-tree page lists B+, B*, and 2-3-4 tree variants; each variant page links back to the parent without manual editing.
 Yes. The blog at /blog/ keeps its own template and editorial flow; the data-structure corpus lives under /cs/data-structures/ via SleekRank. The two link freely via internal anchors. Blog posts that deep-dive into a topic cite the canonical reference page; the reference page links the deep-dive in a Further reading block.
 Yes. Google Sheets handles concurrent edits well, and a draft column gated by a conditional mapping lets contributors stage changes without affecting live pages. A reviewer flips the column to published, the cache invalidates, and the page goes live. Standard editorial workflow without WordPress role complexity.
 Tables alone don't trigger a specific rich result, but pairing the operations data with a TechArticle and FAQPage schema (the FAQs you publish per page) covers two visible result types. Eligibility is always Google's call. The structured-data baseline that makes pages competitive is handled by SleekRank's mappings.
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