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LRU Cache Capacity Calculator

DATA STRUCTURES · FREE DEVELOPER TOOL October 2026 11 min read

Estimates LRU hit rate and memory from working-set size and cache entries using a Zipf popularity model, and finds the capacity needed for a target hit rate. Free, instant and entirely client-side.

LRU hit rate depends on the request popularity distribution, not just the cache size.

Estimates LRU hit rate and memory from working-set size and cache entries using a Zipf popularity model, and finds the capacity needed for a target hit rate.

DATA STRUCTURESClient-sideWorked examples2026
Common hash-table load factor
0.75
Source: Java / .NET
Maximum nodes in a binary tree
2^h − 1
Source: Tree height h
Pointer on 64-bit systems
8 B
Source: LP64
Typical object header
16 B
Source: JVM / .NET

Quick answer

Estimates LRU hit rate and memory from working-set size and cache entries using a Zipf popularity model, and finds the capacity needed for a target hit rate. The calculator applies hit_ratio ≈ h(c, α) ÷ h(n, α), where h is the generalized harmonic sum; memory = entries × entry_bytes × (1 + overhead) and shows every intermediate value.

On this page
  1. 1Quick Answer
  2. 2Interactive Calculator
  3. 3LRU Cache Capacity Calculator: Calculation Breakdown
  4. 4Key Takeaways
  5. 5Computer Science & Data Structures Reference Numbers
  6. 6Implementation Approaches
  7. 7Reference Tables
  8. 8Formulas & How to Use
  9. 9Worked Examples
  10. 10Advanced Guide: LRU Cache Capacity Calculator
  11. 11Language Playbooks
  12. 12Glossary
  13. 13Mistakes & Pro Tips
  14. 14Use Cases
  15. 15Related Calculators
  16. 16Long-Tail Answers
  17. 17FAQs (29)
  18. 18References

Interactive Calculator

Enter a value — every result updates instantly, in your browser. Free forever, no sign-up.

Private Exact math

LRU Cache Capacity Calculator

Every input is evaluated locally in your browser with deterministic arithmetic.

Runs in your browser
Unique items in working set Distinct keys the cache could see.Cache capacity (entries) Maximum number of keys retained.Zipf skew (α) 1.0 is classic Zipf; higher values concentrate traffic on fewer keys.Bytes per cache entry Value plus key and per-entry metadata.Cache overhead (%) Hash table, LRU links and allocator overhead.Requests per second Used to convert hit rate into load.Target hit rate (%) Used to find the required capacity.

Live result

81.6% estimated hit rate

The cache covers 10% of the working set but captures an estimated 81.6% of requests under Zipf traffic.

LRU cacheZipf modelCapacity planning

Estimated hit rate

81.6%

Miss rate

18.4%

Cache memory

23.44 MB

Cache misses per second

920.1

LRU Cache Capacity Calculator: Calculation Breakdown

Every input, intermediate value and result row behind the estimate.

Calculated values and supporting details
Estimated hit rate
81.6%
Miss rate
18.4%
Cache memory
23.44 MB
Cache misses per second
920.1
Working-set coverage
10%
Entries for target hit rate
28,614
Memory for target hit rate
67.06 MB
Bytes per cached entry
2.4 KB

Step-by-step calculation

  1. 1Zipf hit ratio with 10,000 of 100,000 keys at α = 1: H(10000, α) ÷ H(100000, α) ≈ 81.6%.
  2. 2Memory: 10,000 entries × 2,048 B × 1.2 = 23.44 MB.
  3. 3Misses at 5,000 req/s: 5,000 × 18.4% = 920.1 per second.
  4. 4For a 90% hit target, about 28,614 entries are needed (67.06 MB); modelled hit rate there is 90%.

Key Takeaways

The 30-second version — the exact answers Google loves to feature.

  • 1LRU hit rate depends on the request popularity distribution, not just the cache size.
  • 2Zipf-like traffic means a small cache can capture a large share of requests.
  • 3Each cache entry usually costs more than its payload because of hash-table and linked-list metadata.
  • 4Formula: hit_ratio ≈ H(c, α) ÷ H(N, α), where H is the generalized harmonic sum; memory = entries × entry_bytes × (1 + overhead)
  • 5Default scenario: 81.6% estimated hit rate

Computer Science & Data Structures Reference Numbers

Useful benchmarks to sanity-check the output before you commit to a decision.

Common hash-table load factor
0.75
Source: Java / .NET
Maximum nodes in a binary tree
2^h − 1
Source: Tree height h
Pointer on 64-bit systems
8 B
Source: LP64
Typical object header
16 B
Source: JVM / .NET
Typical Bloom-filter false positive
1%
Source: Tunable
Instant estimates
100%
Source: No allocation performed

Implementation Approaches

Three practical ways teams use lru cache capacity calculator, with the precision and safeguards each one needs.

Recommended

Design-time sizing

Best for: Choosing a data structure before implementation

  • Method: Estimate n, key size, pointer width and load factor, then compare memory and operation complexity.
  • Precision: Account for allocator overhead, object headers and capacity growth factors.
  • Safeguards: Benchmark at realistic n; asymptotic advantages can disappear at small sizes.

Capacity planning

Best for: Caches, buffers and in-memory indexes

  • Method: Start from the memory budget, subtract overhead per entry, and derive the maximum number of records.
  • Precision: Use measured average entry size and peak concurrency.
  • Safeguards: Keep headroom for resizing, fragmentation and temporary copies.

Implementation review

Best for: Validating index math and memory estimates

  • Method: Check parent/child indices, wrap-around arithmetic and capacity thresholds against boundary cases.
  • Precision: Test index 0, the last index, an empty structure and a full structure.
  • Safeguards: Use assertions or property-based tests for wrap-around and resize logic.

Quick Reference Tables

Widths, ranges, boundary values and language types — the lookup tables you will come back to.

Data structure operation costs

StructureLookupInsertMemory overhead
ArrayO(1) by indexO(n) middleLow, contiguous
Linked listO(n)O(1) at known nodePointer per node
Hash tableO(1) averageO(1) averageBuckets + load factor
Balanced BSTO(log n)O(log n)Left/right pointers
HeapO(1) min/maxO(log n)Array-backed
TrieO(key length)O(key length)Node per prefix

Formulas & How to Use This Calculator

Core relationship

hit_ratio ≈ H(c, α) ÷ H(N, α), where H is the generalized harmonic sum; memory = entries × entry_bytes × (1 + overhead)

Unit convention

Inputs are unique items in working set, cache capacity (entries), zipf skew (α), bytes per cache entry, cache overhead (%), requests per second, target hit rate (%). Keep each unit consistent with the result rows; convert before comparing estimates that use different units or time windows.

Validation

1. Reproduce the headline from the displayed rows. 2. Compare the result with a measured, billed or benchmarked value. 3. Re-run with a boundary input and with a scaled input. 4. Record the assumptions, source and date next to the result.

How to use — step by step

  1. Open the LRU Cache Capacity Calculator and review the input defaults.
  2. Replace unique items in working set with your measured or planned value.
  3. Set cache capacity (entries) (Maximum number of keys retained.).
  4. Set zipf skew (α) (1.0 is classic Zipf; higher values concentrate traffic on fewer keys.).
  5. Set bytes per cache entry (Value plus key and per-entry metadata.).
  6. Read the headline result and compare it with the supporting rows in the breakdown.
  7. Test one boundary value and one scaled value to understand the sensitivity.
  8. Copy or record the result together with the units and assumptions used.

Worked Examples

Real numbers and raw bits, fully worked — copy the pattern for your own code or protocol.

Default scenario

Inputs

Unique items in working set = 100000; Cache capacity (entries) = 10000; Zipf skew (α) = 1.0; Bytes per cache entry = 2048; Cache overhead (%) = 20

Math

hit_ratio ≈ H(c, α) ÷ H(N, α), where H is the generalized harmonic sum; memory = entries × entry_bytes × (1 + overhead)

Result

81.6% estimated hit rate · Estimated hit rate: 81.6% · Miss rate: 18.4% · Cache memory: 23.44 MB

Scaled scenario

Inputs

Unique items in working set = 200000; Cache capacity (entries) = 15000; Zipf skew (α) = 1.0; Bytes per cache entry = 2048; Cache overhead (%) = 20

Math

hit_ratio ≈ H(c, α) ÷ H(N, α), where H is the generalized harmonic sum; memory = entries × entry_bytes × (1 + overhead)

Result

80.4% estimated hit rate · Estimated hit rate: 80.39% · Miss rate: 19.61% · Cache memory: 35.16 MB

Advanced Guide: LRU Cache Capacity Calculator

Beyond the calculator — how to use lru cache capacity calculator in real engineering, capacity and review decisions.

What the LRU Cache Capacity Calculator measures

Estimates LRU hit rate and memory from working-set size and cache entries using a Zipf popularity model, and finds the capacity needed for a target hit rate. It turns ordinary project, infrastructure or runtime inputs into a visible number you can defend in a design review. The calculator is deliberately transparent: the result is derived from hit_ratio ≈ h(c, α) ÷ h(n, α), where h is the generalized harmonic sum; memory = entries × entry_bytes × (1 + overhead) rather than from an opaque benchmark, so you can change one assumption and immediately see how the answer moves.

The formula and its units

The core relationship is hit_ratio ≈ H(c, α) ÷ H(N, α), where H is the generalized harmonic sum; memory = entries × entry_bytes × (1 + overhead). Every field is labelled with its unit and expected range next to the input, and the breakdown repeats the intermediate values so a reviewer can verify the arithmetic. When you compare two results, make sure both were computed with the same units and the same time window; most apparent differences in computer science & data structures estimates are really unit or scope mismatches.

A worked check with the default inputs

With the default inputs (Unique items in working set = 100000; Cache capacity (entries) = 10000; Zipf skew (α) = 1.0; Bytes per cache entry = 2048; Cache overhead (%) = 20), the calculator reports 81.6% estimated hit rate. Change Unique items in working set and the headline moves to 80.4% estimated hit rate. That sensitivity is useful: if a small input change produces a large output change, the assumption deserves a source or a range rather than a single guess.

Inputs, assumptions and defaults

The inputs are Unique items in working set (Distinct keys the cache could see.); Cache capacity (entries) (Maximum number of keys retained.); Zipf skew (α) (1.0 is classic Zipf; higher values concentrate traffic on fewer keys.); Bytes per cache entry (Value plus key and per-entry metadata.); Cache overhead (%) (Hash table, LRU links and allocator overhead.); Requests per second (Used to convert hit rate into load.); Target hit rate (%) (Used to find the required capacity.). Defaults are realistic starting points, not recommendations for your system. Replace them with measured values wherever possible: production metrics, billing exports, compiler sizeof output, provider documentation or historical project data. Keep the assumptions beside the result so the estimate can be reproduced later.

Reading a low or high result

A low result usually means small scale, generous headroom or optimistic assumptions; a high result means the opposite. Neither is automatically good or bad. Ask which input dominates the change, then compare the result with a second method such as a benchmark, a bill, a query plan or a pilot. Agreement between two independent methods is much stronger evidence than a single formula.

Common mistakes in computer science & data structures

Sizing the cache by item count without multiplying by entry size and overhead. Assuming uniform key popularity. Ignoring scan traffic that evicts hot keys from an LRU cache. Document the units, the measurement window and the scope, and re-run the calculation when the workload or architecture changes.

How to validate the result

Validate with three checks: reproduce the headline from the displayed rows, compare the estimate with a measured production value, and test a boundary case (zero, one, the maximum or a doubled input). If the three checks disagree, the model or the input is wrong. Record the validation date and the data source so the next reviewer can repeat it.

Where this fits in Computer Science & Data Structures

This tool supports tree, heap, hash-table, graph, queue, stack, linked-list, Bloom-filter and cache capacity estimates. Use it during design, capacity review, incident follow-up or cost planning. Pair it with the related calculators to cover adjacent assumptions, and keep the output with the design decision so future changes can be compared against a known baseline.

Limitations and precision

No estimate is more accurate than its inputs. The calculator assumes steady-state behavior, consistent units and the defaults described in the field help. Real systems have bursty traffic, fragmentation, retries, background jobs and measurement error. Where the result informs a commitment or a budget, add a range and a contingency rather than treating the headline as a guarantee.

Related calculations to run next

The most useful companion tools are Hash Table Capacity Calculator, Hash Table Resize Threshold Calculator, Bloom Filter Memory Calculator, Trie Memory Estimator. Together they cover the neighbouring assumptions — for example scale, overhead, failure headroom and cost — so you can present a complete estimate rather than a single isolated number.

Language & Platform Playbooks

The same bit pattern behaves differently in every language. Here is how to adapt the conversion safely.

Java

HashMap uses a 0.75 load factor by default. ArrayList grows by 50%; LinkedList adds a node object per element. For lru cache capacity calculator, use the same units and time window described in the calculator so the result lines up with your platform data.

Python

dict and set are hash tables with compact entry arrays. Lists over-allocate to make append amortized O(1). For lru cache capacity calculator, use the same units and time window described in the calculator so the result lines up with your platform data.

C++

std::unordered_map uses node-based storage with a pointer per entry. std::vector is contiguous and usually more cache-friendly. For lru cache capacity calculator, use the same units and time window described in the calculator so the result lines up with your platform data.

Redis / databases

Use the memory model of the target engine: Redis has its own object headers, and database indexes add B-tree overhead per entry. For lru cache capacity calculator, use the same units and time window described in the calculator so the result lines up with your platform data.

Glossary: Key Terms Explained

Load factor
The ratio of stored entries to available buckets in a hash table.
Trie
A tree keyed by characters or prefixes, sharing common prefixes.
Adjacency list
A graph representation storing a list of neighbours per vertex.
Circular buffer
A fixed-size buffer that wraps around when it reaches the end.
Bloom filter
A probabilistic set that can report false positives but never false negatives.

Common Mistakes & Pro Tips

Mistakes that break conversions

  • Sizing the cache by item count without multiplying by entry size and overhead.
  • Assuming uniform key popularity.
  • Ignoring scan traffic that evicts hot keys from an LRU cache.
  • Mixing units or time windows when comparing two lru cache capacity calculator results.
  • Treating the default values as a recommendation instead of a starting point.
  • Reporting a single number without the assumptions, range or measurement date behind it.

Pro tips for production code

  • Use segmented LRU or admission policies such as TinyLFU to protect hot keys from scans.
  • Validate the model against a production hit-rate metric before purchasing memory.
  • Measure the dominant input before refining the model; the largest uncertainty deserves the most attention.
  • Keep a short record of the inputs, result and date so future comparisons are meaningful.
  • Validate the estimate with a second method — a benchmark, a bill or production telemetry.
  • Pair this result with the related calculators (Hash Table Capacity Calculator, Hash Table Resize Threshold Calculator, Bloom Filter Memory Calculator) for a complete picture.

Who Should Use This Calculator?

  • ▸ Sizing a Redis or in-process LRU cache.
  • ▸ Estimating the memory needed for a target hit rate.
  • ▸ Comparing cache capacity with origin load.
  • ▸ Capacity review: turn unique items in working set into a defensible planning number.
  • ▸ Design review: compare two architectures or configurations with the same assumptions.
  • ▸ Incident follow-up: quantify the gap between the estimate and observed behaviour.

Related Programming Calculators

Keep going — every tool below links directly into this workflow.

People Also Search For

Tap a long-tail question to jump to the matching FAQ answer.

Frequently Asked Questions (29)

Long-tail answers: each question targets a real Google search about lru cache capacity calculator — click to expand.

Showing 29 of 29 answers.

How accurate is the Zipf hit-rate model?
It is a planning approximation for workloads whose popularity roughly follows a power law. Real traces have temporal locality, scans and one-hit wonders, so measure the hit rate in production or with a trace simulation before committing memory.
Why does a small LRU cache get such a high hit rate?
Under Zipf traffic, the most popular 10% of keys can account for most requests. LRU keeps recently used popular keys, so even a small cache captures a large share of hits. The model shows the ceiling; real LRU eviction may perform somewhat worse.
What does the LRU Cache Capacity Calculator calculate?
Estimates LRU hit rate and memory from working-set size and cache entries using a Zipf popularity model, and finds the capacity needed for a target hit rate. The output includes the headline value, the intermediate rows and the formula, so you can audit the result rather than accepting a black-box number.
What formula does the LRU Cache Capacity Calculator use?
It uses hit_ratio ≈ H(c, α) ÷ H(N, α), where H is the generalized harmonic sum; memory = entries × entry_bytes × (1 + overhead). The calculator evaluates the formula with the values shown in the input panel and displays each intermediate term in the breakdown.
What should I enter for unique items in working set?
Distinct keys the cache could see. The default is 100000; replace it with a measured value whenever one is available, and keep the same unit for every comparison.
What should I enter for cache capacity (entries)?
Maximum number of keys retained. The default is 10000; replace it with a measured value whenever one is available, and keep the same unit for every comparison.
What should I enter for zipf skew (α)?
1.0 is classic Zipf; higher values concentrate traffic on fewer keys. The default is 1.0; replace it with a measured value whenever one is available, and keep the same unit for every comparison.
What should I enter for bytes per cache entry?
Value plus key and per-entry metadata. The default is 2048; replace it with a measured value whenever one is available, and keep the same unit for every comparison.
What should I enter for cache overhead (%)?
Hash table, LRU links and allocator overhead. The default is 20; replace it with a measured value whenever one is available, and keep the same unit for every comparison.
What should I enter for requests per second?
Used to convert hit rate into load. The default is 5000; replace it with a measured value whenever one is available, and keep the same unit for every comparison.
What should I enter for target hit rate (%)?
Used to find the required capacity. The default is 90; replace it with a measured value whenever one is available, and keep the same unit for every comparison.
How do I interpret the LRU Cache Capacity Calculator result?
The headline is 81.6% estimated hit rate. The cache covers 10% of the working set but captures an estimated 81.6% of requests under Zipf traffic. A single number is less useful than the trend and the assumptions behind it.
What units does the LRU Cache Capacity Calculator use?
The inputs are Unique items in working set, Cache capacity (entries), Zipf skew (α), Bytes per cache entry, Cache overhead (%), Requests per second, Target hit rate (%). Units are shown on every field and repeated in the result rows. Do not mix decimal and binary units or seconds and milliseconds without converting first.
What are common mistakes when calculating lru cache capacity calculator?
Sizing the cache by item count without multiplying by entry size and overhead. Assuming uniform key popularity. Ignoring scan traffic that evicts hot keys from an LRU cache. Re-run the calculation whenever the workload, architecture or pricing changes.
When should I use the LRU Cache Capacity Calculator?
Sizing a Redis or in-process LRU cache. Estimating the memory needed for a target hit rate. Comparing cache capacity with origin load. It is a client-side planning tool, not a substitute for production monitoring or a benchmark.
How can I validate the LRU Cache Capacity Calculator result?
Reproduce the headline from the displayed rows, compare it with a measured or billed value, and test a boundary input such as zero, one or double the normal value. If the checks disagree, correct the input or the model before using the result.
What is a worked example of the LRU Cache Capacity Calculator?
For Unique items in working set = 100000; Cache capacity (entries) = 10000; Zipf skew (α) = 1.0; Bytes per cache entry = 2048; Cache overhead (%) = 20, the result is 81.6% estimated hit rate (Estimated hit rate: 81.6%; Miss rate: 18.4%).
What related calculators should I use with the LRU Cache Capacity Calculator?
Use Hash Table Capacity Calculator, Hash Table Resize Threshold Calculator, Bloom Filter Memory Calculator, Trie Memory Estimator to cover the adjacent assumptions. Running several related estimates is more reliable than over-tuning one number.
Is the LRU Cache Capacity Calculator free and private?
Yes. The calculator runs entirely in your browser, requires no sign-up and sends no inputs to a server. That makes it safe for approximate capacity planning, although production secrets and customer data should never be pasted into any web page.
Can I use the LRU Cache Capacity Calculator for production planning?
Use it as a first-pass model and confirm the result with production measurements, provider documentation or a staging benchmark. Add headroom for uncertainty, and record the date and data source so the estimate can be reviewed later.
How sensitive is the LRU Cache Capacity Calculator to unique items in working set?
With the default value the result is 81.6% estimated hit rate; when unique items in working set is changed to 200000, the result becomes 80.4% estimated hit rate. Use this comparison to identify which input deserves the most careful measurement.
What load factor should a hash table use?
0.75 is a common default because it balances memory and lookup cost. Lower load factors reduce collisions but waste memory; higher factors save memory but increase probing and resize cost. Measure with your actual key distribution.
How much memory does a linked list use per element?
Each node stores the value plus one or two pointers. On a 64-bit system, a singly linked list node with an 8-byte value and an 8-byte pointer occupies at least 16 bytes, plus allocator overhead (often 8–16 bytes per allocation).
What is the maximum number of nodes in a binary tree of height h?
A binary tree with height h (root at level 0) has at most 2^(h+1) − 1 nodes. A complete binary tree with n nodes has height floor(log2 n), and a perfect tree of height h has 2^(h+1) − 1 nodes.
What does a typical LRU Cache Capacity Calculator output look like?
The default scenario shows 81.6% estimated hit rate with rows for estimated hit rate, miss rate, cache memory. The exact wording of each row is shown in the technical breakdown above.
How precise is the LRU Cache Capacity Calculator?
The arithmetic is exact for the values entered, but the result is only as good as the inputs. Engineering estimates typically carry ±10–30% uncertainty, and capacity models can be further off when traffic is bursty. Report a range and the main assumptions rather than a single deceptively precise number.
Does the LRU Cache Capacity Calculator include overhead?
It includes the overhead represented by its input fields: unique items in working set, cache capacity (entries), zipf skew (α), bytes per cache entry, cache overhead (%), requests per second, target hit rate (%). If your system has additional overhead — retries, replication, fragmentation, taxes or contingency — add it explicitly and document the adjustment.
How often should I re-run the LRU Cache Capacity Calculator?
Re-run it when the workload changes materially, when a provider or platform changes its limits or pricing, and at least once per planning cycle. Keep each result with the date and inputs so you can compare the trend instead of only the latest snapshot.
What is the best first step after using the LRU Cache Capacity Calculator?
Write down the three assumptions that most affect the result, then verify the largest one with a measurement. For computer science & data structures, that usually means checking a production metric, a bill or a documented limit before changing configuration.

Resources & Authoritative References

Further reading

Authoritative sources

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