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__test__
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LRUCache.js
LRUCache.js
LRUCacheOnMap.js
LRUCacheOnMap.js
README.md
README.md

README.md

Least Recently Used (LRU) Cache

A Least Recently Used (LRU) Cache organizes items in order of use, allowing you to quickly identify which item hasn't been used for the longest amount of time.

Picture a clothes rack, where clothes are always hung up on one side. To find the least-recently used item, look at the item on the other end of the rack.

The problem statement

Implement the LRUCache class:

  • LRUCache(int capacity) Initialize the LRU cache with positive size capacity.
  • int get(int key) Return the value of the key if the key exists, otherwise return undefined.
  • void set(int key, int value) Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache. If the number of keys exceeds the capacity from this operation, evict the least recently used key.

The functions get() and set() must each run in O(1) average time complexity.

Implementation

Version 1: Doubly Linked List + Hash Map

See the LRUCache implementation example in LRUCache.js. The solution uses a HashMap for fast O(1) (in average) cache items access, and a DoublyLinkedList for fast O(1) (in average) cache items promotions and eviction (to keep the maximum allowed cache capacity).

Linked List

Made with okso.app

You may also find more test-case examples of how the LRU Cache works in LRUCache.test.js file.

Version 2: Ordered Map

The first implementation that uses doubly linked list is good for learning purposes and for better understanding of how the average O(1) time complexity is achievable while doing set() and get().

However, the simpler approach might be to use a JavaScript Map object. The Map object holds key-value pairs and remembers the original insertion order of the keys. We can use this fact in order to keep the recently-used items in the "end" of the map by removing and re-adding items. The item at the beginning of the Map is the first one to be evicted if cache capacity overflows. The order of the items may checked by using the IterableIterator like map.keys().

See the LRUCacheOnMap implementation example in LRUCacheOnMap.js.

You may also find more test-case examples of how the LRU Cache works in LRUCacheOnMap.test.js file.

Complexities

Average
SpaceO(n)
Get itemO(1)
Set itemO(1)

References

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all \u003cpre\u003e\u003ccode\u003e blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks"); } } catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); } })(); (function(){ try { var __m = "github.com"; var __re = new RegExp('^' + "github\\.com" + '
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__test__
__test__
images
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LRUCache.js
LRUCache.js
LRUCacheOnMap.js
LRUCacheOnMap.js
README.md
README.md

README.md

Least Recently Used (LRU) Cache

A Least Recently Used (LRU) Cache organizes items in order of use, allowing you to quickly identify which item hasn't been used for the longest amount of time.

Picture a clothes rack, where clothes are always hung up on one side. To find the least-recently used item, look at the item on the other end of the rack.

The problem statement

Implement the LRUCache class:

  • LRUCache(int capacity) Initialize the LRU cache with positive size capacity.
  • int get(int key) Return the value of the key if the key exists, otherwise return undefined.
  • void set(int key, int value) Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache. If the number of keys exceeds the capacity from this operation, evict the least recently used key.

The functions get() and set() must each run in O(1) average time complexity.

Implementation

Version 1: Doubly Linked List + Hash Map

See the LRUCache implementation example in LRUCache.js. The solution uses a HashMap for fast O(1) (in average) cache items access, and a DoublyLinkedList for fast O(1) (in average) cache items promotions and eviction (to keep the maximum allowed cache capacity).

Linked List

Made with okso.app

You may also find more test-case examples of how the LRU Cache works in LRUCache.test.js file.

Version 2: Ordered Map

The first implementation that uses doubly linked list is good for learning purposes and for better understanding of how the average O(1) time complexity is achievable while doing set() and get().

However, the simpler approach might be to use a JavaScript Map object. The Map object holds key-value pairs and remembers the original insertion order of the keys. We can use this fact in order to keep the recently-used items in the "end" of the map by removing and re-adding items. The item at the beginning of the Map is the first one to be evicted if cache capacity overflows. The order of the items may checked by using the IterableIterator like map.keys().

See the LRUCacheOnMap implementation example in LRUCacheOnMap.js.

You may also find more test-case examples of how the LRU Cache works in LRUCacheOnMap.test.js file.

Complexities

Average
SpaceO(n)
Get itemO(1)
Set itemO(1)

References

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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__test__
__test__
images
images
LRUCache.js
LRUCache.js
LRUCacheOnMap.js
LRUCacheOnMap.js
README.md
README.md

README.md

Least Recently Used (LRU) Cache

A Least Recently Used (LRU) Cache organizes items in order of use, allowing you to quickly identify which item hasn't been used for the longest amount of time.

Picture a clothes rack, where clothes are always hung up on one side. To find the least-recently used item, look at the item on the other end of the rack.

The problem statement

Implement the LRUCache class:

  • LRUCache(int capacity) Initialize the LRU cache with positive size capacity.
  • int get(int key) Return the value of the key if the key exists, otherwise return undefined.
  • void set(int key, int value) Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache. If the number of keys exceeds the capacity from this operation, evict the least recently used key.

The functions get() and set() must each run in O(1) average time complexity.

Implementation

Version 1: Doubly Linked List + Hash Map

See the LRUCache implementation example in LRUCache.js. The solution uses a HashMap for fast O(1) (in average) cache items access, and a DoublyLinkedList for fast O(1) (in average) cache items promotions and eviction (to keep the maximum allowed cache capacity).

Linked List

Made with okso.app

You may also find more test-case examples of how the LRU Cache works in LRUCache.test.js file.

Version 2: Ordered Map

The first implementation that uses doubly linked list is good for learning purposes and for better understanding of how the average O(1) time complexity is achievable while doing set() and get().

However, the simpler approach might be to use a JavaScript Map object. The Map object holds key-value pairs and remembers the original insertion order of the keys. We can use this fact in order to keep the recently-used items in the "end" of the map by removing and re-adding items. The item at the beginning of the Map is the first one to be evicted if cache capacity overflows. The order of the items may checked by using the IterableIterator like map.keys().

See the LRUCacheOnMap implementation example in LRUCacheOnMap.js.

You may also find more test-case examples of how the LRU Cache works in LRUCacheOnMap.test.js file.

Complexities

Average
SpaceO(n)
Get itemO(1)
Set itemO(1)

References

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length \u003e 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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__test__
__test__
images
images
LRUCache.js
LRUCache.js
LRUCacheOnMap.js
LRUCacheOnMap.js
README.md
README.md

README.md

Least Recently Used (LRU) Cache

A Least Recently Used (LRU) Cache organizes items in order of use, allowing you to quickly identify which item hasn't been used for the longest amount of time.

Picture a clothes rack, where clothes are always hung up on one side. To find the least-recently used item, look at the item on the other end of the rack.

The problem statement

Implement the LRUCache class:

  • LRUCache(int capacity) Initialize the LRU cache with positive size capacity.
  • int get(int key) Return the value of the key if the key exists, otherwise return undefined.
  • void set(int key, int value) Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache. If the number of keys exceeds the capacity from this operation, evict the least recently used key.

The functions get() and set() must each run in O(1) average time complexity.

Implementation

Version 1: Doubly Linked List + Hash Map

See the LRUCache implementation example in LRUCache.js. The solution uses a HashMap for fast O(1) (in average) cache items access, and a DoublyLinkedList for fast O(1) (in average) cache items promotions and eviction (to keep the maximum allowed cache capacity).

Linked List

Made with okso.app

You may also find more test-case examples of how the LRU Cache works in LRUCache.test.js file.

Version 2: Ordered Map

The first implementation that uses doubly linked list is good for learning purposes and for better understanding of how the average O(1) time complexity is achievable while doing set() and get().

However, the simpler approach might be to use a JavaScript Map object. The Map object holds key-value pairs and remembers the original insertion order of the keys. We can use this fact in order to keep the recently-used items in the "end" of the map by removing and re-adding items. The item at the beginning of the Map is the first one to be evicted if cache capacity overflows. The order of the items may checked by using the IterableIterator like map.keys().

See the LRUCacheOnMap implementation example in LRUCacheOnMap.js.

You may also find more test-case examples of how the LRU Cache works in LRUCacheOnMap.test.js file.

Complexities

Average
SpaceO(n)
Get itemO(1)
Set itemO(1)

References

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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__test__
__test__
images
images
LRUCache.js
LRUCache.js
LRUCacheOnMap.js
LRUCacheOnMap.js
README.md
README.md

README.md

Least Recently Used (LRU) Cache

A Least Recently Used (LRU) Cache organizes items in order of use, allowing you to quickly identify which item hasn't been used for the longest amount of time.

Picture a clothes rack, where clothes are always hung up on one side. To find the least-recently used item, look at the item on the other end of the rack.

The problem statement

Implement the LRUCache class:

  • LRUCache(int capacity) Initialize the LRU cache with positive size capacity.
  • int get(int key) Return the value of the key if the key exists, otherwise return undefined.
  • void set(int key, int value) Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache. If the number of keys exceeds the capacity from this operation, evict the least recently used key.

The functions get() and set() must each run in O(1) average time complexity.

Implementation

Version 1: Doubly Linked List + Hash Map

See the LRUCache implementation example in LRUCache.js. The solution uses a HashMap for fast O(1) (in average) cache items access, and a DoublyLinkedList for fast O(1) (in average) cache items promotions and eviction (to keep the maximum allowed cache capacity).

Linked List

Made with okso.app

You may also find more test-case examples of how the LRU Cache works in LRUCache.test.js file.

Version 2: Ordered Map

The first implementation that uses doubly linked list is good for learning purposes and for better understanding of how the average O(1) time complexity is achievable while doing set() and get().

However, the simpler approach might be to use a JavaScript Map object. The Map object holds key-value pairs and remembers the original insertion order of the keys. We can use this fact in order to keep the recently-used items in the "end" of the map by removing and re-adding items. The item at the beginning of the Map is the first one to be evicted if cache capacity overflows. The order of the items may checked by using the IterableIterator like map.keys().

See the LRUCacheOnMap implementation example in LRUCacheOnMap.js.

You may also find more test-case examples of how the LRU Cache works in LRUCacheOnMap.test.js file.

Complexities

Average
SpaceO(n)
Get itemO(1)
Set itemO(1)

References

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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__test__
__test__
images
images
LRUCache.js
LRUCache.js
LRUCacheOnMap.js
LRUCacheOnMap.js
README.md
README.md

README.md

Least Recently Used (LRU) Cache

A Least Recently Used (LRU) Cache organizes items in order of use, allowing you to quickly identify which item hasn't been used for the longest amount of time.

Picture a clothes rack, where clothes are always hung up on one side. To find the least-recently used item, look at the item on the other end of the rack.

The problem statement

Implement the LRUCache class:

  • LRUCache(int capacity) Initialize the LRU cache with positive size capacity.
  • int get(int key) Return the value of the key if the key exists, otherwise return undefined.
  • void set(int key, int value) Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache. If the number of keys exceeds the capacity from this operation, evict the least recently used key.

The functions get() and set() must each run in O(1) average time complexity.

Implementation

Version 1: Doubly Linked List + Hash Map

See the LRUCache implementation example in LRUCache.js. The solution uses a HashMap for fast O(1) (in average) cache items access, and a DoublyLinkedList for fast O(1) (in average) cache items promotions and eviction (to keep the maximum allowed cache capacity).

Linked List

Made with okso.app

You may also find more test-case examples of how the LRU Cache works in LRUCache.test.js file.

Version 2: Ordered Map

The first implementation that uses doubly linked list is good for learning purposes and for better understanding of how the average O(1) time complexity is achievable while doing set() and get().

However, the simpler approach might be to use a JavaScript Map object. The Map object holds key-value pairs and remembers the original insertion order of the keys. We can use this fact in order to keep the recently-used items in the "end" of the map by removing and re-adding items. The item at the beginning of the Map is the first one to be evicted if cache capacity overflows. The order of the items may checked by using the IterableIterator like map.keys().

See the LRUCacheOnMap implementation example in LRUCacheOnMap.js.

You may also find more test-case examples of how the LRU Cache works in LRUCacheOnMap.test.js file.

Complexities

Average
SpaceO(n)
Get itemO(1)
Set itemO(1)

References

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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__test__
__test__
images
images
LRUCache.js
LRUCache.js
LRUCacheOnMap.js
LRUCacheOnMap.js
README.md
README.md

README.md

Least Recently Used (LRU) Cache

A Least Recently Used (LRU) Cache organizes items in order of use, allowing you to quickly identify which item hasn't been used for the longest amount of time.

Picture a clothes rack, where clothes are always hung up on one side. To find the least-recently used item, look at the item on the other end of the rack.

The problem statement

Implement the LRUCache class:

  • LRUCache(int capacity) Initialize the LRU cache with positive size capacity.
  • int get(int key) Return the value of the key if the key exists, otherwise return undefined.
  • void set(int key, int value) Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache. If the number of keys exceeds the capacity from this operation, evict the least recently used key.

The functions get() and set() must each run in O(1) average time complexity.

Implementation

Version 1: Doubly Linked List + Hash Map

See the LRUCache implementation example in LRUCache.js. The solution uses a HashMap for fast O(1) (in average) cache items access, and a DoublyLinkedList for fast O(1) (in average) cache items promotions and eviction (to keep the maximum allowed cache capacity).

Linked List

Made with okso.app

You may also find more test-case examples of how the LRU Cache works in LRUCache.test.js file.

Version 2: Ordered Map

The first implementation that uses doubly linked list is good for learning purposes and for better understanding of how the average O(1) time complexity is achievable while doing set() and get().

However, the simpler approach might be to use a JavaScript Map object. The Map object holds key-value pairs and remembers the original insertion order of the keys. We can use this fact in order to keep the recently-used items in the "end" of the map by removing and re-adding items. The item at the beginning of the Map is the first one to be evicted if cache capacity overflows. The order of the items may checked by using the IterableIterator like map.keys().

See the LRUCacheOnMap implementation example in LRUCacheOnMap.js.

You may also find more test-case examples of how the LRU Cache works in LRUCacheOnMap.test.js file.

Complexities

Average
SpaceO(n)
Get itemO(1)
Set itemO(1)

References

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
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LRUCache.js
LRUCache.js
LRUCacheOnMap.js
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README.md

README.md

Least Recently Used (LRU) Cache

A Least Recently Used (LRU) Cache organizes items in order of use, allowing you to quickly identify which item hasn't been used for the longest amount of time.

Picture a clothes rack, where clothes are always hung up on one side. To find the least-recently used item, look at the item on the other end of the rack.

The problem statement

Implement the LRUCache class:

  • LRUCache(int capacity) Initialize the LRU cache with positive size capacity.
  • int get(int key) Return the value of the key if the key exists, otherwise return undefined.
  • void set(int key, int value) Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache. If the number of keys exceeds the capacity from this operation, evict the least recently used key.

The functions get() and set() must each run in O(1) average time complexity.

Implementation

Version 1: Doubly Linked List + Hash Map

See the LRUCache implementation example in LRUCache.js. The solution uses a HashMap for fast O(1) (in average) cache items access, and a DoublyLinkedList for fast O(1) (in average) cache items promotions and eviction (to keep the maximum allowed cache capacity).

Linked List

Made with okso.app

You may also find more test-case examples of how the LRU Cache works in LRUCache.test.js file.

Version 2: Ordered Map

The first implementation that uses doubly linked list is good for learning purposes and for better understanding of how the average O(1) time complexity is achievable while doing set() and get().

However, the simpler approach might be to use a JavaScript Map object. The Map object holds key-value pairs and remembers the original insertion order of the keys. We can use this fact in order to keep the recently-used items in the "end" of the map by removing and re-adding items. The item at the beginning of the Map is the first one to be evicted if cache capacity overflows. The order of the items may checked by using the IterableIterator like map.keys().

See the LRUCacheOnMap implementation example in LRUCacheOnMap.js.

You may also find more test-case examples of how the LRU Cache works in LRUCacheOnMap.test.js file.

Complexities

Average
SpaceO(n)
Get itemO(1)
Set itemO(1)

References