import { describe, it } from 'node:test';
import { strict as assert } from 'node:assert';
/**
* Performance tests for pagination and large dataset tools
*
* Tests performance characteristics of:
* - Pagination logic and efficiency
* - Large dataset processing
* - Memory usage optimization
* - Response time validation
* - Batch processing efficiency
*/
describe('Performance Tests', () => {
describe('Pagination Performance', () => {
it('should validate efficient pagination logic', () => {
const TOTAL_ITEMS = 4579; // MCP test cases
const PAGE_SIZE = 100;
const EXPECTED_PAGES = Math.ceil(TOTAL_ITEMS / PAGE_SIZE);
// Simulate pagination state tracking
const paginationTracker = {
currentPage: 0,
totalPages: EXPECTED_PAGES,
itemsProcessed: 0,
startTime: Date.now()
};
// Simulate processing all pages
for (let page = 0; page < EXPECTED_PAGES; page++) {
const itemsInPage = Math.min(PAGE_SIZE, TOTAL_ITEMS - (page * PAGE_SIZE));
paginationTracker.itemsProcessed += itemsInPage;
paginationTracker.currentPage = page;
}
const processingTime = Date.now() - paginationTracker.startTime;
assert.equal(paginationTracker.itemsProcessed, TOTAL_ITEMS, 'should process all items');
assert.equal(paginationTracker.currentPage, EXPECTED_PAGES - 1, 'should reach last page');
assert.ok(processingTime < 100, 'pagination logic should be fast');
});
it('should validate token-based pagination efficiency', () => {
const mockTokenPagination = {
tokens: ['token1', 'token2', 'token3', 'token4', null], // null indicates end
pageSize: 100,
totalProcessed: 0,
requestCount: 0,
maxRequests: 50
};
// Simulate token-based pagination
let currentTokenIndex = 0;
while (currentTokenIndex < mockTokenPagination.tokens.length &&
mockTokenPagination.tokens[currentTokenIndex] !== null &&
mockTokenPagination.requestCount < mockTokenPagination.maxRequests) {
mockTokenPagination.totalProcessed += mockTokenPagination.pageSize;
mockTokenPagination.requestCount++;
currentTokenIndex++;
}
assert.ok(mockTokenPagination.requestCount <= mockTokenPagination.maxRequests,
'should not exceed max request limit');
assert.ok(mockTokenPagination.totalProcessed > 0, 'should process items');
assert.equal(mockTokenPagination.requestCount, 4, 'should make expected number of requests');
});
it('should validate pagination memory efficiency', () => {
const LARGE_DATASET_SIZE = 10000;
const BATCH_SIZE = 100;
const MEMORY_LIMIT_MB = 50;
// Simulate memory-efficient batch processing
const memoryUsageSimulation = {
currentBatchSize: 0,
maxBatchSize: BATCH_SIZE,
totalProcessed: 0,
estimatedMemoryMB: 0,
batchCount: 0
};
for (let i = 0; i < LARGE_DATASET_SIZE; i++) {
memoryUsageSimulation.currentBatchSize++;
memoryUsageSimulation.estimatedMemoryMB = (memoryUsageSimulation.currentBatchSize * 1024) / (1024 * 1024); // 1KB per item
if (memoryUsageSimulation.currentBatchSize >= BATCH_SIZE || i === LARGE_DATASET_SIZE - 1) {
// Process batch
memoryUsageSimulation.totalProcessed += memoryUsageSimulation.currentBatchSize;
memoryUsageSimulation.batchCount++;
memoryUsageSimulation.currentBatchSize = 0;
memoryUsageSimulation.estimatedMemoryMB = 0; // Reset after processing
}
}
assert.equal(memoryUsageSimulation.totalProcessed, LARGE_DATASET_SIZE, 'should process all items');
assert.ok(memoryUsageSimulation.estimatedMemoryMB < MEMORY_LIMIT_MB, 'should stay within memory limits');
assert.equal(memoryUsageSimulation.batchCount, Math.ceil(LARGE_DATASET_SIZE / BATCH_SIZE), 'should use correct number of batches');
});
it('should validate concurrent pagination handling', () => {
const CONCURRENT_REQUESTS = 5;
const ITEMS_PER_REQUEST = 100;
const concurrentPagination = {
activeRequests: 0,
maxConcurrentRequests: CONCURRENT_REQUESTS,
completedRequests: 0,
totalItems: 0,
errors: 0,
startTime: Date.now()
};
// Simulate concurrent request processing (synchronous for testing)
const processRequest = (requestId: number) => {
if (concurrentPagination.activeRequests < concurrentPagination.maxConcurrentRequests) {
concurrentPagination.activeRequests++;
// Simulate immediate request completion (no async)
concurrentPagination.activeRequests--;
concurrentPagination.completedRequests++;
concurrentPagination.totalItems += ITEMS_PER_REQUEST;
return true;
}
return false;
};
// Queue multiple requests
let successfulRequests = 0;
for (let i = 0; i < 10; i++) {
if (processRequest(i)) {
successfulRequests++;
}
}
assert.ok(successfulRequests <= 10, 'should process requests');
assert.equal(concurrentPagination.activeRequests, 0, 'should complete all requests');
assert.equal(concurrentPagination.completedRequests, successfulRequests, 'should track completed requests');
});
});
describe('Large Dataset Processing', () => {
it('should validate efficient data structure usage', () => {
const LARGE_SUITE_COUNT = 1000;
const startTime = Date.now();
// Test Map vs Array performance for lookups
const suiteMap = new Map();
const suiteArray: any[] = [];
// Populate data structures
for (let i = 0; i < LARGE_SUITE_COUNT; i++) {
const suite = { id: i, title: `Suite ${i}`, parentSuiteId: i > 0 ? i - 1 : null };
suiteMap.set(i, suite);
suiteArray.push(suite);
}
const populationTime = Date.now() - startTime;
// Test lookup performance
const lookupStartTime = Date.now();
const targetId = Math.floor(LARGE_SUITE_COUNT / 2);
// Map lookup (O(1))
const mapResult = suiteMap.get(targetId);
const mapLookupTime = Date.now() - lookupStartTime;
// Array lookup (O(n))
const arrayLookupStartTime = Date.now();
const arrayResult = suiteArray.find(s => s.id === targetId);
const arrayLookupTime = Date.now() - arrayLookupStartTime;
assert.ok(mapResult, 'Map should find the item');
assert.ok(arrayResult, 'Array should find the item');
assert.ok(populationTime < 100, 'data structure population should be fast');
assert.ok(mapLookupTime <= arrayLookupTime, 'Map lookup should be faster than or equal to array lookup');
});
it('should validate memory-efficient string operations', () => {
const LARGE_STRING_COUNT = 1000;
const AVERAGE_STRING_LENGTH = 100;
// Test efficient string concatenation
const stringBuilderTest = {
arrayJoinMethod: () => {
const parts: string[] = [];
for (let i = 0; i < LARGE_STRING_COUNT; i++) {
parts.push('a'.repeat(AVERAGE_STRING_LENGTH));
}
return parts.join('');
},
directConcatenation: () => {
let result = '';
for (let i = 0; i < LARGE_STRING_COUNT; i++) {
result += 'a'.repeat(AVERAGE_STRING_LENGTH);
}
return result;
}
};
const arrayJoinStart = Date.now();
const arrayJoinResult = stringBuilderTest.arrayJoinMethod();
const arrayJoinTime = Date.now() - arrayJoinStart;
const directConcatStart = Date.now();
const directConcatResult = stringBuilderTest.directConcatenation();
const directConcatTime = Date.now() - directConcatStart;
assert.equal(arrayJoinResult.length, directConcatResult.length, 'both methods should produce same length');
assert.ok(arrayJoinTime < 1000, 'array join should complete in reasonable time');
assert.ok(directConcatTime < 1000, 'direct concatenation should complete in reasonable time');
// Note: Array join is typically more efficient for large concatenations
});
it('should validate efficient filtering and sorting', () => {
const LARGE_DATASET = Array.from({ length: 5000 }, (_, i) => ({
id: i,
title: `Item ${i}`,
priority: ['LOW', 'MEDIUM', 'HIGH'][i % 3],
score: Math.random() * 100,
active: i % 2 === 0
}));
const filteringStart = Date.now();
// Chain multiple operations efficiently
const processedData = LARGE_DATASET
.filter(item => item.active) // Filter first to reduce dataset
.filter(item => item.priority === 'HIGH') // Then apply more specific filters
.sort((a, b) => b.score - a.score) // Sort by score descending
.slice(0, 100); // Take top 100
const filteringTime = Date.now() - filteringStart;
assert.ok(processedData.length <= 100, 'should limit results to 100');
assert.ok(processedData.every(item => item.active), 'all results should be active');
assert.ok(processedData.every(item => item.priority === 'HIGH'), 'all results should be high priority');
assert.ok(filteringTime < 100, 'filtering and sorting should be fast');
// Validate sorting
for (let i = 1; i < processedData.length; i++) {
assert.ok(processedData[i - 1].score >= processedData[i].score, 'should be sorted by score descending');
}
});
it('should validate efficient hierarchy processing', () => {
const HIERARCHY_SIZE = 1000;
const MAX_DEPTH = 10;
// Generate hierarchical data
const hierarchyData = Array.from({ length: HIERARCHY_SIZE }, (_, i) => ({
id: i + 1,
title: `Node ${i + 1}`,
parentId: i === 0 ? null : Math.floor((i + 1) / 2) // Binary tree structure
}));
const hierarchyProcessingStart = Date.now();
// Build hierarchy efficiently using Map for O(1) lookups
const nodeMap = new Map(hierarchyData.map(node => [node.id, { ...node, children: [] as any[] }]));
const roots: any[] = [];
hierarchyData.forEach(node => {
const nodeWithChildren = nodeMap.get(node.id);
if (nodeWithChildren) {
if (node.parentId === null) {
roots.push(nodeWithChildren);
} else {
const parent = nodeMap.get(node.parentId);
if (parent) {
parent.children.push(nodeWithChildren);
}
}
}
});
// Calculate depth efficiently
const calculateDepth = (node: any, currentDepth = 0): number => {
if (node.children.length === 0) return currentDepth;
return Math.max(...node.children.map((child: any) => calculateDepth(child, currentDepth + 1)));
};
const maxDepth = Math.max(...roots.map(root => calculateDepth(root)));
const hierarchyProcessingTime = Date.now() - hierarchyProcessingStart;
assert.ok(roots.length > 0, 'should have root nodes');
assert.ok(maxDepth <= MAX_DEPTH, 'should not exceed maximum depth');
assert.ok(hierarchyProcessingTime < 200, 'hierarchy processing should be efficient');
});
});
describe('Response Time Validation', () => {
it('should validate API response time expectations', () => {
const responseTimeTargets = {
listSuites: { target: 500, acceptable: 1000 }, // milliseconds
getSuite: { target: 300, acceptable: 600 },
getTestCase: { target: 400, acceptable: 800 },
validateTestCase: { target: 2000, acceptable: 5000 },
generateDraft: { target: 3000, acceptable: 10000 }
};
// Simulate response times
const mockResponseTimes = {
listSuites: 450,
getSuite: 280,
getTestCase: 380,
validateTestCase: 1800,
generateDraft: 2500
};
Object.entries(responseTimeTargets).forEach(([operation, targets]) => {
const actualTime = mockResponseTimes[operation as keyof typeof mockResponseTimes];
assert.ok(actualTime <= targets.acceptable,
`${operation} response time (${actualTime}ms) should be within acceptable limit (${targets.acceptable}ms)`);
if (actualTime <= targets.target) {
console.log(`✅ ${operation}: ${actualTime}ms (target: ${targets.target}ms)`);
} else {
console.log(`⚠️ ${operation}: ${actualTime}ms (exceeds target: ${targets.target}ms but within acceptable: ${targets.acceptable}ms)`);
}
});
});
it('should validate timeout handling', () => {
const timeoutScenarios = [
{ operation: 'quick_request', timeout: 5000, expectedTime: 2000, shouldTimeout: false },
{ operation: 'slow_request', timeout: 3000, expectedTime: 5000, shouldTimeout: true },
{ operation: 'normal_request', timeout: 10000, expectedTime: 1500, shouldTimeout: false }
];
timeoutScenarios.forEach(scenario => {
const wouldTimeout = scenario.expectedTime > scenario.timeout;
assert.equal(wouldTimeout, scenario.shouldTimeout,
`${scenario.operation} timeout behavior should match expectation`);
if (!wouldTimeout) {
assert.ok(scenario.expectedTime < scenario.timeout,
`${scenario.operation} should complete before timeout`);
}
});
});
it('should validate batch processing performance', () => {
const TOTAL_ITEMS = 4579;
const BATCH_SIZES = [50, 100, 200, 500];
const SIMULATED_REQUEST_TIME = 500; // milliseconds per request
const batchPerformanceResults = BATCH_SIZES.map(batchSize => {
const batchCount = Math.ceil(TOTAL_ITEMS / batchSize);
const totalTime = batchCount * SIMULATED_REQUEST_TIME;
const itemsPerSecond = TOTAL_ITEMS / (totalTime / 1000);
return {
batchSize,
batchCount,
totalTime,
itemsPerSecond,
efficiency: itemsPerSecond / batchSize // Items per second per batch size
};
});
// Find optimal batch size
const optimalBatch = batchPerformanceResults.reduce((best, current) =>
current.efficiency > best.efficiency ? current : best
);
assert.ok(optimalBatch.batchSize >= 50, 'optimal batch size should be reasonable');
assert.ok(optimalBatch.batchSize <= 500, 'optimal batch size should not be excessive');
assert.ok(optimalBatch.totalTime < 60000, 'total processing time should be under 1 minute');
console.log(`📊 Optimal batch size: ${optimalBatch.batchSize} (${optimalBatch.itemsPerSecond.toFixed(1)} items/sec)`);
});
});
describe('Memory Usage Optimization', () => {
it('should validate memory-efficient data processing', () => {
const LARGE_DATASET_SIZE = 10000;
const MEMORY_THRESHOLD_MB = 100;
// Simulate memory usage tracking
const memoryTracker = {
currentUsageMB: 0,
peakUsageMB: 0,
itemsProcessed: 0,
gcCollections: 0
};
// Simulate processing with memory monitoring
for (let i = 0; i < LARGE_DATASET_SIZE; i++) {
// Simulate item processing (1KB per item)
memoryTracker.currentUsageMB += 0.001;
memoryTracker.itemsProcessed++;
// Update peak usage
if (memoryTracker.currentUsageMB > memoryTracker.peakUsageMB) {
memoryTracker.peakUsageMB = memoryTracker.currentUsageMB;
}
// Simulate garbage collection every 1000 items
if (i % 1000 === 0 && i > 0) {
memoryTracker.currentUsageMB *= 0.7; // Simulate 30% memory reclaim
memoryTracker.gcCollections++;
}
}
assert.equal(memoryTracker.itemsProcessed, LARGE_DATASET_SIZE, 'should process all items');
assert.ok(memoryTracker.peakUsageMB < MEMORY_THRESHOLD_MB, 'peak memory usage should be within threshold');
assert.ok(memoryTracker.gcCollections > 0, 'should trigger garbage collection');
});
it('should validate streaming data processing', () => {
const STREAM_CHUNK_SIZE = 100;
const TOTAL_CHUNKS = 50;
const streamProcessor = {
chunksProcessed: 0,
currentChunkSize: 0,
maxChunkSize: STREAM_CHUNK_SIZE,
totalItemsProcessed: 0,
memoryEfficient: true
};
// Simulate streaming processing
for (let chunk = 0; chunk < TOTAL_CHUNKS; chunk++) {
streamProcessor.currentChunkSize = STREAM_CHUNK_SIZE;
// Process chunk
for (let item = 0; item < STREAM_CHUNK_SIZE; item++) {
streamProcessor.totalItemsProcessed++;
}
// Clear chunk from memory
streamProcessor.currentChunkSize = 0;
streamProcessor.chunksProcessed++;
}
assert.equal(streamProcessor.chunksProcessed, TOTAL_CHUNKS, 'should process all chunks');
assert.equal(streamProcessor.totalItemsProcessed, TOTAL_CHUNKS * STREAM_CHUNK_SIZE, 'should process all items');
assert.equal(streamProcessor.currentChunkSize, 0, 'should clear chunk memory after processing');
assert.ok(streamProcessor.memoryEfficient, 'should maintain memory efficiency');
});
});
describe('Scalability Validation', () => {
it('should validate performance scaling with data size', () => {
const dataSizes = [100, 500, 1000, 5000, 10000];
const performanceResults: any[] = [];
dataSizes.forEach(size => {
const startTime = Date.now();
// Simulate data processing
const data = Array.from({ length: size }, (_, i) => ({ id: i, value: Math.random() }));
const processed = data
.filter(item => item.value > 0.5)
.map(item => ({ ...item, processed: true }))
.sort((a, b) => a.value - b.value);
const processingTime = Date.now() - startTime;
const itemsPerMs = size / processingTime;
performanceResults.push({
dataSize: size,
processingTime,
itemsPerMs,
processedCount: processed.length
});
});
// Validate that performance doesn't degrade exponentially
for (let i = 1; i < performanceResults.length; i++) {
const current = performanceResults[i];
const previous = performanceResults[i - 1];
const sizeRatio = current.dataSize / previous.dataSize;
const timeRatio = current.processingTime / previous.processingTime;
// Time should not increase more than 5x the data size increase (allow for variance)
// Skip if processing time is very small (< 10ms) as measurements are unreliable
if (current.processingTime >= 10 && previous.processingTime >= 10) {
assert.ok(timeRatio <= sizeRatio * 5,
`Processing time should scale reasonably with data size (${current.dataSize} items: ${current.processingTime}ms, ratio: ${timeRatio.toFixed(2)} vs size ratio: ${sizeRatio.toFixed(2)})`);
} else {
// For small processing times, just ensure it's not exponentially bad
// Handle the case where previous processing time is 0
if (previous.processingTime === 0) {
assert.ok(current.processingTime <= 50,
`Processing time should remain reasonable for small datasets (${current.dataSize} items: ${current.processingTime}ms)`);
} else {
assert.ok(current.processingTime <= previous.processingTime * 10,
`Processing time should not grow exponentially (${current.dataSize} items: ${current.processingTime}ms vs ${previous.dataSize} items: ${previous.processingTime}ms)`);
}
}
}
console.log('📊 Performance scaling:');
performanceResults.forEach(result => {
console.log(` ${result.dataSize} items: ${result.processingTime}ms (${result.itemsPerMs.toFixed(2)} items/ms)`);
});
});
it('should validate concurrent processing limits', () => {
const CONCURRENT_LIMITS = [1, 2, 5, 10, 20];
const TASK_COUNT = 100;
const concurrencyResults = CONCURRENT_LIMITS.map(limit => {
let completedTasks = 0;
let maxActiveTasks = 0;
// Simulate synchronous concurrent task processing
while (completedTasks < TASK_COUNT) {
const tasksToProcess = Math.min(limit, TASK_COUNT - completedTasks);
maxActiveTasks = Math.max(maxActiveTasks, tasksToProcess);
completedTasks += tasksToProcess;
}
const theoreticalMinTime = Math.ceil(TASK_COUNT / limit) * 10; // 10ms per task
return {
concurrencyLimit: limit,
completedTasks,
theoreticalMinTime,
maxActiveTasks,
efficiency: limit / Math.max(limit, 1) // Simple efficiency metric
};
});
// Validate that all tasks were completed
concurrencyResults.forEach(result => {
assert.equal(result.completedTasks, TASK_COUNT, 'should complete all tasks');
assert.ok(result.maxActiveTasks <= result.concurrencyLimit,
'should not exceed concurrency limit');
});
});
});
});