Add specifications for gamification, generation, and R42 chat services
- Introduced gamification service spec detailing responsibilities, API surface, XP calculation, levels, streaks, badges, milestone cards, and heatmap data. - Added generation service spec outlining the process for generating micro learning content, including API endpoints, AI call configuration, prompt strategies, and error handling. - Created R42 chat service spec covering chatbot interactions, retrieval pipeline, prompt construction, response generation, and stateless design principles.
This commit is contained in:
RaymondVerhoef
1888
app/services/ingestion/package-lock.json
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1888
app/services/ingestion/package-lock.json
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@@ -2,6 +2,7 @@
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"name": "ingestion",
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"version": "0.1.0",
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"private": true,
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"type": "module",
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"scripts": {
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"dev": "tsx watch src/index.ts",
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"build": "tsc",
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@@ -10,21 +11,22 @@
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"migrate:qdrant": "tsx src/migrations/002_qdrant_setup.ts"
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},
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"dependencies": {
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"fastify": "^4",
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"@anthropic-ai/sdk": "^0.24",
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"openai": "^4",
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"@qdrant/js-client-rest": "^1.9",
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"pocketbase": "^0.21",
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"fastify": "^4",
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"openai": "^4",
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"pdf-parse": "^1.1",
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"pocketbase": "^0.21",
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"uuid": "^9",
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"zod": "^3"
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},
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"devDependencies": {
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"typescript": "^5",
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"tsx": "^4",
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"dotenv": "^16",
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"@types/node": "^20",
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"@types/pdf-parse": "^1.1",
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"@types/uuid": "^9"
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"@types/uuid": "^9",
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"dotenv": "^16",
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"pdfkit": "^0.18.0",
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"tsx": "^4",
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"typescript": "^5"
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}
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}
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16
app/services/ingestion/service.err
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16
app/services/ingestion/service.err
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@@ -0,0 +1,16 @@
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C:\Users\RaymondVerhoef\Gitea\learning-platform\app\services\ingestion\node_modules\.bin\tsx:2
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basedir=$(dirname "$(echo "$0" | sed -e 's,\\,/,g')")
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^^^^^^^
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SyntaxError: missing ) after argument list
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at wrapSafe (node:internal/modules/cjs/loader:1662:18)
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at Module._compile (node:internal/modules/cjs/loader:1704:20)
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at Object..js (node:internal/modules/cjs/loader:1895:10)
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at Module.load (node:internal/modules/cjs/loader:1465:32)
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at Function._load (node:internal/modules/cjs/loader:1282:12)
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at TracingChannel.traceSync (node:diagnostics_channel:322:14)
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at wrapModuleLoad (node:internal/modules/cjs/loader:235:24)
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at Function.executeUserEntryPoint [as runMain] (node:internal/modules/run_main:171:5)
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at node:internal/main/run_main_module:36:49
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Node.js v22.16.0
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@@ -46,7 +46,7 @@ const field = {
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}),
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json: (name: string): FieldDef => ({
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name, type: 'json', required: false, options: {},
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name, type: 'json', required: false, options: { maxSize: 2097152 },
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}),
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date: (name: string): FieldDef => ({
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@@ -146,18 +146,17 @@ async function run(): Promise<void> {
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const usersCol = await pb.collections.getFirstListItem<CollectionModel>('name="users"');
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ids.set('users', usersCol.id);
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const hasRole = usersCol.schema.some(s => s.name === 'role');
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if (!hasRole) {
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const existingFieldNames = new Set(usersCol.schema.map((s: { name: string }) => s.name));
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const fieldsToAdd: FieldDef[] = [];
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if (!existingFieldNames.has('role')) fieldsToAdd.push(field.select('role', ['admin', 'employee'], true));
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if (!existingFieldNames.has('display_name')) fieldsToAdd.push(field.text('display_name'));
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if (fieldsToAdd.length > 0) {
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const updateBody: Record<string, unknown> = {
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schema: [
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...usersCol.schema,
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field.select('role', ['admin', 'employee'], true),
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field.text('display_name'),
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field.file('avatar', ['image/jpeg', 'image/png', 'image/webp']),
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],
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schema: [...usersCol.schema, ...fieldsToAdd],
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};
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await pb.collections.update(usersCol.id, updateBody);
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console.log(' extended with role, display_name, avatar');
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console.log(` extended with: ${fieldsToAdd.map((f: FieldDef) => f.name).join(', ')}`);
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} else {
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console.log(' skip users (already extended)');
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}
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@@ -26,11 +26,16 @@ export async function embedAndStore(
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writtenTopics: WrittenTopic[],
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onProgress: (embedded: number) => void,
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): Promise<void> {
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// Build chunk → topic mapping
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// Build chunk → topic mapping.
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// The AI labels chunks as [CHUNK-<uuid>] so sourceChunkIds may carry that prefix;
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// strip it so lookups match the bare UUID used as the Qdrant point ID.
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const normalise = (id: string): string => id.replace(/^CHUNK-/i, '');
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const chunkTopicMap = new Map<string, string>();
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const chunkThemeMap = new Map<string, string>();
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for (const topic of writtenTopics) {
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for (const chunkId of topic.sourceChunkIds) {
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for (const rawId of topic.sourceChunkIds) {
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const chunkId = normalise(rawId);
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chunkTopicMap.set(chunkId, topic.id);
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chunkThemeMap.set(chunkId, topic.themeId);
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}
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@@ -94,7 +99,9 @@ export async function embedAndStore(
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// Update topics.qdrant_chunk_ids in PocketBase
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// -------------------------------------------------------------------------
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for (const topic of writtenTopics) {
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const qdrantIds = topic.sourceChunkIds.filter(id => chunkTopicMap.get(id) === topic.id);
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const qdrantIds = topic.sourceChunkIds
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.map(id => normalise(id))
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.filter(id => chunkTopicMap.get(id) === topic.id);
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if (qdrantIds.length > 0) {
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await updateTopicQdrantIds(topic.id, qdrantIds);
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}
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@@ -61,7 +61,7 @@ function buildUserPrompt(chunks: Chunk[], filename: string, format: DocumentForm
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async function callClaude(chunks: Chunk[], filename: string, format: DocumentFormat, strict: boolean): Promise<DraftKB> {
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const response = await anthropic.messages.create({
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model: MODELS.SONNET,
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max_tokens: 8000,
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max_tokens: 16000,
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temperature: 0,
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system: SYSTEM_PROMPT,
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messages: [{ role: 'user', content: buildUserPrompt(chunks, filename, format, strict) }],
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@@ -76,12 +76,14 @@ async function callClaude(chunks: Chunk[], filename: string, format: DocumentFor
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try {
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parsed = JSON.parse(textBlock.text);
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} catch {
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console.error(`[structure] JSON parse failed (strict=${strict}), response length=${textBlock.text.length}, stop_reason=${response.stop_reason}`);
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if (strict) throw new Error('structure_extraction_failed');
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return callClaude(chunks, filename, format, true);
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}
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const result = DraftKBSchema.safeParse(parsed);
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if (!result.success) {
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console.error(`[structure] Zod validation failed (strict=${strict}):`, JSON.stringify(result.error.issues.slice(0,3)));
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if (strict) throw new Error('structure_extraction_failed');
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return callClaude(chunks, filename, format, true);
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}
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@@ -82,6 +82,7 @@ export interface WrittenTopic {
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// ---------------------------------------------------------------------------
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export interface SourceChunkPayload {
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[key: string]: unknown;
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source_document_id: string;
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chunk_index: number;
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text: string;
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@@ -91,6 +92,7 @@ export interface SourceChunkPayload {
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}
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export interface TopicSummaryPayload {
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[key: string]: unknown;
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topic_id: string;
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theme_id: string;
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title: string;
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605
app/services/ingestion/test-files/large_doc.md
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605
app/services/ingestion/test-files/large_doc.md
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@@ -0,0 +1,605 @@
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# Comprehensive Guide to Enterprise Software Architecture
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## Introduction to Software Architecture
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Software architecture defines the high-level structure of a software system. It encompasses the decisions made about the organization of a system, the selection of structural elements and their interfaces, and the composition of these elements.
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### Microservices Architecture Part 1
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Microservices is an architectural style that structures applications as a collection of small, independently deployable services. Each service runs in its own process and communicates through lightweight APIs.
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This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
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When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
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## Event-Driven Architecture Part 1
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Event-driven architecture is a software design pattern where components communicate through events. Producers publish events to an event broker, and consumers subscribe to receive relevant events asynchronously.
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This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
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When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
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## Domain-Driven Design Part 1
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Domain-driven design is an approach to software development that centers the development on programming a domain model that has a rich understanding of the processes and rules of a domain.
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This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
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When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
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### CQRS Pattern Part 1
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Command Query Responsibility Segregation separates read and write operations for a data store. The read and write sides can be scaled independently and use different models optimized for their specific purpose.
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This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
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When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
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## Event Sourcing Part 1
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Event sourcing stores the state of a business entity as a sequence of state-changing events. Instead of storing just the current state, the complete history of all events is stored.
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This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
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When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
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## Saga Pattern Part 1
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The Saga pattern manages distributed transactions in microservices. It breaks a long transaction into a sequence of smaller local transactions, each with compensating actions for rollback.
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This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
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When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
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### API Gateway Pattern Part 1
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An API gateway is a single entry point for client requests in a microservices architecture. It handles cross-cutting concerns like authentication, rate limiting, and request routing.
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This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
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When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
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## Service Mesh Part 1
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A service mesh is a dedicated infrastructure layer for handling service-to-service communication. It provides features like load balancing, service discovery, health checking, and observability.
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This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
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|
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When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
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## Hexagonal Architecture Part 1
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Hexagonal architecture, also known as Ports and Adapters, separates the application core from external concerns. The domain logic sits in the center, surrounded by ports and adapters.
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|
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This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
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|
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When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
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### Clean Architecture Part 1
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Clean architecture organizes code into concentric layers with dependencies pointing inward. The innermost layer contains enterprise business rules, followed by application rules, interface adapters, and frameworks.
|
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|
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This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
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## Repository Pattern Part 1
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The repository pattern provides an abstraction layer between the data access layer and the business logic. It centralizes data access logic and provides a consistent interface for querying data.
|
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|
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This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
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## Unit of Work Pattern Part 1
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The Unit of Work pattern maintains a list of objects affected by a business transaction. It coordinates the writing of changes and resolves concurrency problems.
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||||
|
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This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
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### Factory Pattern Part 1
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|
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The factory pattern provides an interface for creating objects in a superclass while allowing subclasses to alter the type of objects created. It promotes loose coupling between object creation and usage.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Observer Pattern Part 1
|
||||
|
||||
The observer pattern defines a one-to-many dependency between objects. When one object changes state, all its dependents are notified and updated automatically.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Strategy Pattern Part 1
|
||||
|
||||
The strategy pattern defines a family of algorithms, encapsulates each one, and makes them interchangeable. It lets the algorithm vary independently from clients that use it.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Decorator Pattern Part 1
|
||||
|
||||
The decorator pattern attaches additional responsibilities to an object dynamically. Decorators provide a flexible alternative to subclassing for extending functionality.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Circuit Breaker Pattern Part 1
|
||||
|
||||
The circuit breaker pattern prevents cascading failures in distributed systems. It wraps calls to external services and monitors for failures, opening the circuit when a threshold is exceeded.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Bulkhead Pattern Part 1
|
||||
|
||||
The bulkhead pattern isolates elements of an application into pools so that if one fails, the others will continue to function. It limits the impact of a failure by containing it within a section.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Retry Pattern Part 1
|
||||
|
||||
The retry pattern enables an application to retry a failed operation. It handles transient failures that might occur when connecting to a service or network resource.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Throttling Pattern Part 1
|
||||
|
||||
The throttling pattern controls the consumption of resources used by an instance of an application. It allows resources to be fairly distributed and prevents any single instance from consuming too many resources.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Cache-Aside Pattern Part 1
|
||||
|
||||
The cache-aside pattern loads data into the cache on demand from the data store. It improves performance by keeping frequently accessed data in cache while allowing the data store to be the source of truth.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Sharding Pattern Part 1
|
||||
|
||||
The sharding pattern divides a data store into horizontal partitions called shards. Each shard can be maintained on a separate server, allowing the load to be distributed across multiple machines.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Data Lake Architecture Part 1
|
||||
|
||||
A data lake is a centralized repository that stores all structured and unstructured data at any scale. It enables you to store data as-is without first having to structure it.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Lambda Architecture Part 1
|
||||
|
||||
Lambda architecture is a data-processing design that handles massive quantities of data. It uses both batch and real-time processing methods, providing a balance between latency and throughput.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Kappa Architecture Part 1
|
||||
|
||||
Kappa architecture is a simplification of the lambda architecture. It removes the batch layer and processes everything as a stream, using a replayable log as the source of truth.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Microservices Architecture Part 2
|
||||
|
||||
Microservices is an architectural style that structures applications as a collection of small, independently deployable services. Each service runs in its own process and communicates through lightweight APIs.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Event-Driven Architecture Part 2
|
||||
|
||||
Event-driven architecture is a software design pattern where components communicate through events. Producers publish events to an event broker, and consumers subscribe to receive relevant events asynchronously.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Domain-Driven Design Part 2
|
||||
|
||||
Domain-driven design is an approach to software development that centers the development on programming a domain model that has a rich understanding of the processes and rules of a domain.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## CQRS Pattern Part 2
|
||||
|
||||
Command Query Responsibility Segregation separates read and write operations for a data store. The read and write sides can be scaled independently and use different models optimized for their specific purpose.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Event Sourcing Part 2
|
||||
|
||||
Event sourcing stores the state of a business entity as a sequence of state-changing events. Instead of storing just the current state, the complete history of all events is stored.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Saga Pattern Part 2
|
||||
|
||||
The Saga pattern manages distributed transactions in microservices. It breaks a long transaction into a sequence of smaller local transactions, each with compensating actions for rollback.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## API Gateway Pattern Part 2
|
||||
|
||||
An API gateway is a single entry point for client requests in a microservices architecture. It handles cross-cutting concerns like authentication, rate limiting, and request routing.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Service Mesh Part 2
|
||||
|
||||
A service mesh is a dedicated infrastructure layer for handling service-to-service communication. It provides features like load balancing, service discovery, health checking, and observability.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Hexagonal Architecture Part 2
|
||||
|
||||
Hexagonal architecture, also known as Ports and Adapters, separates the application core from external concerns. The domain logic sits in the center, surrounded by ports and adapters.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Clean Architecture Part 2
|
||||
|
||||
Clean architecture organizes code into concentric layers with dependencies pointing inward. The innermost layer contains enterprise business rules, followed by application rules, interface adapters, and frameworks.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Repository Pattern Part 2
|
||||
|
||||
The repository pattern provides an abstraction layer between the data access layer and the business logic. It centralizes data access logic and provides a consistent interface for querying data.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Unit of Work Pattern Part 2
|
||||
|
||||
The Unit of Work pattern maintains a list of objects affected by a business transaction. It coordinates the writing of changes and resolves concurrency problems.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Factory Pattern Part 2
|
||||
|
||||
The factory pattern provides an interface for creating objects in a superclass while allowing subclasses to alter the type of objects created. It promotes loose coupling between object creation and usage.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Observer Pattern Part 2
|
||||
|
||||
The observer pattern defines a one-to-many dependency between objects. When one object changes state, all its dependents are notified and updated automatically.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Strategy Pattern Part 2
|
||||
|
||||
The strategy pattern defines a family of algorithms, encapsulates each one, and makes them interchangeable. It lets the algorithm vary independently from clients that use it.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Decorator Pattern Part 2
|
||||
|
||||
The decorator pattern attaches additional responsibilities to an object dynamically. Decorators provide a flexible alternative to subclassing for extending functionality.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Circuit Breaker Pattern Part 2
|
||||
|
||||
The circuit breaker pattern prevents cascading failures in distributed systems. It wraps calls to external services and monitors for failures, opening the circuit when a threshold is exceeded.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Bulkhead Pattern Part 2
|
||||
|
||||
The bulkhead pattern isolates elements of an application into pools so that if one fails, the others will continue to function. It limits the impact of a failure by containing it within a section.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Retry Pattern Part 2
|
||||
|
||||
The retry pattern enables an application to retry a failed operation. It handles transient failures that might occur when connecting to a service or network resource.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Throttling Pattern Part 2
|
||||
|
||||
The throttling pattern controls the consumption of resources used by an instance of an application. It allows resources to be fairly distributed and prevents any single instance from consuming too many resources.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Cache-Aside Pattern Part 2
|
||||
|
||||
The cache-aside pattern loads data into the cache on demand from the data store. It improves performance by keeping frequently accessed data in cache while allowing the data store to be the source of truth.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Sharding Pattern Part 2
|
||||
|
||||
The sharding pattern divides a data store into horizontal partitions called shards. Each shard can be maintained on a separate server, allowing the load to be distributed across multiple machines.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Data Lake Architecture Part 2
|
||||
|
||||
A data lake is a centralized repository that stores all structured and unstructured data at any scale. It enables you to store data as-is without first having to structure it.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Lambda Architecture Part 2
|
||||
|
||||
Lambda architecture is a data-processing design that handles massive quantities of data. It uses both batch and real-time processing methods, providing a balance between latency and throughput.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Kappa Architecture Part 2
|
||||
|
||||
Kappa architecture is a simplification of the lambda architecture. It removes the batch layer and processes everything as a stream, using a replayable log as the source of truth.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Microservices Architecture Part 3
|
||||
|
||||
Microservices is an architectural style that structures applications as a collection of small, independently deployable services. Each service runs in its own process and communicates through lightweight APIs.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Event-Driven Architecture Part 3
|
||||
|
||||
Event-driven architecture is a software design pattern where components communicate through events. Producers publish events to an event broker, and consumers subscribe to receive relevant events asynchronously.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Domain-Driven Design Part 3
|
||||
|
||||
Domain-driven design is an approach to software development that centers the development on programming a domain model that has a rich understanding of the processes and rules of a domain.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## CQRS Pattern Part 3
|
||||
|
||||
Command Query Responsibility Segregation separates read and write operations for a data store. The read and write sides can be scaled independently and use different models optimized for their specific purpose.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Event Sourcing Part 3
|
||||
|
||||
Event sourcing stores the state of a business entity as a sequence of state-changing events. Instead of storing just the current state, the complete history of all events is stored.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Saga Pattern Part 3
|
||||
|
||||
The Saga pattern manages distributed transactions in microservices. It breaks a long transaction into a sequence of smaller local transactions, each with compensating actions for rollback.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## API Gateway Pattern Part 3
|
||||
|
||||
An API gateway is a single entry point for client requests in a microservices architecture. It handles cross-cutting concerns like authentication, rate limiting, and request routing.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Service Mesh Part 3
|
||||
|
||||
A service mesh is a dedicated infrastructure layer for handling service-to-service communication. It provides features like load balancing, service discovery, health checking, and observability.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Hexagonal Architecture Part 3
|
||||
|
||||
Hexagonal architecture, also known as Ports and Adapters, separates the application core from external concerns. The domain logic sits in the center, surrounded by ports and adapters.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Clean Architecture Part 3
|
||||
|
||||
Clean architecture organizes code into concentric layers with dependencies pointing inward. The innermost layer contains enterprise business rules, followed by application rules, interface adapters, and frameworks.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Repository Pattern Part 3
|
||||
|
||||
The repository pattern provides an abstraction layer between the data access layer and the business logic. It centralizes data access logic and provides a consistent interface for querying data.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Unit of Work Pattern Part 3
|
||||
|
||||
The Unit of Work pattern maintains a list of objects affected by a business transaction. It coordinates the writing of changes and resolves concurrency problems.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Factory Pattern Part 3
|
||||
|
||||
The factory pattern provides an interface for creating objects in a superclass while allowing subclasses to alter the type of objects created. It promotes loose coupling between object creation and usage.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Observer Pattern Part 3
|
||||
|
||||
The observer pattern defines a one-to-many dependency between objects. When one object changes state, all its dependents are notified and updated automatically.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Strategy Pattern Part 3
|
||||
|
||||
The strategy pattern defines a family of algorithms, encapsulates each one, and makes them interchangeable. It lets the algorithm vary independently from clients that use it.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Decorator Pattern Part 3
|
||||
|
||||
The decorator pattern attaches additional responsibilities to an object dynamically. Decorators provide a flexible alternative to subclassing for extending functionality.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Circuit Breaker Pattern Part 3
|
||||
|
||||
The circuit breaker pattern prevents cascading failures in distributed systems. It wraps calls to external services and monitors for failures, opening the circuit when a threshold is exceeded.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Bulkhead Pattern Part 3
|
||||
|
||||
The bulkhead pattern isolates elements of an application into pools so that if one fails, the others will continue to function. It limits the impact of a failure by containing it within a section.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Retry Pattern Part 3
|
||||
|
||||
The retry pattern enables an application to retry a failed operation. It handles transient failures that might occur when connecting to a service or network resource.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Throttling Pattern Part 3
|
||||
|
||||
The throttling pattern controls the consumption of resources used by an instance of an application. It allows resources to be fairly distributed and prevents any single instance from consuming too many resources.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Cache-Aside Pattern Part 3
|
||||
|
||||
The cache-aside pattern loads data into the cache on demand from the data store. It improves performance by keeping frequently accessed data in cache while allowing the data store to be the source of truth.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Sharding Pattern Part 3
|
||||
|
||||
The sharding pattern divides a data store into horizontal partitions called shards. Each shard can be maintained on a separate server, allowing the load to be distributed across multiple machines.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
### Data Lake Architecture Part 3
|
||||
|
||||
A data lake is a centralized repository that stores all structured and unstructured data at any scale. It enables you to store data as-is without first having to structure it.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Lambda Architecture Part 3
|
||||
|
||||
Lambda architecture is a data-processing design that handles massive quantities of data. It uses both batch and real-time processing methods, providing a balance between latency and throughput.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
## Kappa Architecture Part 3
|
||||
|
||||
Kappa architecture is a simplification of the lambda architecture. It removes the batch layer and processes everything as a stream, using a replayable log as the source of truth.
|
||||
|
||||
This concept is fundamental to building scalable distributed systems. Organizations that adopt this pattern report significant improvements in system reliability and maintainability. The implementation requires careful consideration of the trade-offs involved.
|
||||
|
||||
When implementing this pattern, teams must consider the operational overhead, the learning curve, and the impact on the existing codebase. Success depends on having the right tooling, monitoring, and team expertise in place.
|
||||
|
||||
2
app/services/ingestion/test-files/malformed.pdf
Normal file
2
app/services/ingestion/test-files/malformed.pdf
Normal file
@@ -0,0 +1,2 @@
|
||||
%PDF-1.4
|
||||
This is not a valid PDF file. It contains garbage content that pdf-parse cannot handle.
|
||||
6
app/services/ingestion/test-files/payload_md.json
Normal file
6
app/services/ingestion/test-files/payload_md.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"documentId": "i9ue488qb30owl4",
|
||||
"filename": "sample.md",
|
||||
"format": "md",
|
||||
"filePath": "C:\\Users\\RaymondVerhoef\\Gitea\\learning-platform\\app\\services\\ingestion\\test-files\\sample.md"
|
||||
}
|
||||
72
app/services/ingestion/test-files/sample.md
Normal file
72
app/services/ingestion/test-files/sample.md
Normal file
@@ -0,0 +1,72 @@
|
||||
# Introduction to TypeScript
|
||||
|
||||
TypeScript is a strongly typed programming language that builds on JavaScript, giving you better tooling at any scale.
|
||||
|
||||
## Type System
|
||||
|
||||
TypeScript adds optional static typing and class-based object-oriented programming to the language. Types provide a way to describe the shape of an object, providing better documentation, and allowing TypeScript to validate that your code is working correctly.
|
||||
|
||||
### Basic Types
|
||||
|
||||
TypeScript supports several basic types including `string`, `number`, `boolean`, `array`, and `tuple`. These types allow you to add type annotations to your variables and function parameters.
|
||||
|
||||
## Interfaces
|
||||
|
||||
Interfaces define the shape of an object in TypeScript. They are a powerful way to define contracts within your code as well as contracts with code outside of your project.
|
||||
|
||||
```typescript
|
||||
interface User {
|
||||
name: string;
|
||||
age: number;
|
||||
email?: string;
|
||||
}
|
||||
```
|
||||
|
||||
An interface can define optional properties using the `?` operator. This is useful when some properties may or may not be present.
|
||||
|
||||
## Classes
|
||||
|
||||
TypeScript supports full class-based object-oriented programming with inheritance, interfaces, and access modifiers. Classes provide a clean and reusable way to create objects.
|
||||
|
||||
```typescript
|
||||
class Animal {
|
||||
private name: string;
|
||||
|
||||
constructor(name: string) {
|
||||
this.name = name;
|
||||
}
|
||||
|
||||
public move(distance: number = 0): void {
|
||||
console.log(`${this.name} moved ${distance}m.`);
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Access modifiers (`public`, `private`, `protected`) control visibility of class members.
|
||||
|
||||
## Generics
|
||||
|
||||
Generics provide a way to make components work with any data type and not restrict to one data type. They allow users to consume these components and use their own types.
|
||||
|
||||
```typescript
|
||||
function identity<T>(arg: T): T {
|
||||
return arg;
|
||||
}
|
||||
```
|
||||
|
||||
Generics are particularly useful for building reusable data structures and functions that work across multiple types.
|
||||
|
||||
## Enums
|
||||
|
||||
Enumerations allow you to define a set of named constants. TypeScript provides both numeric and string-based enums.
|
||||
|
||||
```typescript
|
||||
enum Direction {
|
||||
Up = "UP",
|
||||
Down = "DOWN",
|
||||
Left = "LEFT",
|
||||
Right = "RIGHT",
|
||||
}
|
||||
```
|
||||
|
||||
String enums are more readable than numeric enums because they provide meaningful string values at runtime.
|
||||
55
app/services/ingestion/test-files/sample.pdf
Normal file
55
app/services/ingestion/test-files/sample.pdf
Normal file
@@ -0,0 +1,55 @@
|
||||
%PDF-1.4
|
||||
1 0 obj
|
||||
<</Type /Catalog /Pages 2 0 R>>
|
||||
endobj
|
||||
2 0 obj
|
||||
<</Type /Pages /Kids [3 0 R] /Count 1>>
|
||||
endobj
|
||||
3 0 obj
|
||||
<</Type /Page /Parent 2 0 R /MediaBox [0 0 612 792] /Resources <</Font <</F1 4 0 R>>>> /Contents 5 0 R>>
|
||||
endobj
|
||||
4 0 obj
|
||||
<</Type /Font /Subtype /Type1 /BaseFont /Helvetica>>
|
||||
endobj
|
||||
5 0 obj
|
||||
<</Length 582>>
|
||||
stream
|
||||
BT
|
||||
/F1 14 Tf
|
||||
50 750 Td
|
||||
(Introduction to Docker Containers) Tj
|
||||
0 -25 Td
|
||||
/F1 12 Tf
|
||||
(Docker is a platform for developing shipping and running applications.) Tj
|
||||
0 -18 Td
|
||||
(Containers are lightweight portable units that include all dependencies.) Tj
|
||||
0 -25 Td
|
||||
/F1 14 Tf
|
||||
(Container Images) Tj
|
||||
0 -25 Td
|
||||
/F1 12 Tf
|
||||
(A Docker image is a read-only template used to create containers.) Tj
|
||||
0 -18 Td
|
||||
(Images are built from a Dockerfile with build instructions.) Tj
|
||||
0 -25 Td
|
||||
/F1 14 Tf
|
||||
(Docker Compose) Tj
|
||||
0 -25 Td
|
||||
/F1 12 Tf
|
||||
(Docker Compose defines multi-container applications in compose.yml.) Tj
|
||||
ET
|
||||
endstream
|
||||
endobj
|
||||
xref
|
||||
0 6
|
||||
0000000000 65535 f
|
||||
0000000009 00000 n
|
||||
0000000056 00000 n
|
||||
0000000111 00000 n
|
||||
0000000231 00000 n
|
||||
0000000299 00000 n
|
||||
trailer
|
||||
<</Size 6 /Root 1 0 R>>
|
||||
startxref
|
||||
930
|
||||
%%EOF
|
||||
31
app/services/ingestion/test-files/sample.txt
Normal file
31
app/services/ingestion/test-files/sample.txt
Normal file
@@ -0,0 +1,31 @@
|
||||
Introduction to Software Architecture Patterns
|
||||
|
||||
Software architecture patterns are reusable solutions to commonly occurring problems in software architecture within a given context. They are similar to design patterns but with a broader scope.
|
||||
|
||||
The most common architecture patterns include layered architecture, event-driven architecture, microservices, and service-oriented architecture. Each pattern has its own strengths and weaknesses that make it more or less appropriate for different use cases.
|
||||
|
||||
Layered Architecture
|
||||
|
||||
The layered architecture pattern organizes code into layers of functionality. The most common is the n-tier architecture with presentation, business logic, and data access layers. Each layer has a specific role and responsibility.
|
||||
|
||||
The presentation layer handles all user interface and browser communication logic. The business logic layer executes specific business rules associated with the request. The data access layer handles all aspects of the database interaction.
|
||||
|
||||
Benefits of the layered approach include separation of concerns, ease of testing, and good maintainability. Each layer can be developed and maintained independently, reducing the complexity of the system.
|
||||
|
||||
Event-Driven Architecture
|
||||
|
||||
Event-driven architecture is a software design pattern in which decoupled applications can asynchronously publish and subscribe to events via an event broker. This pattern promotes loose coupling and scalability.
|
||||
|
||||
Events represent things that have happened in the system. An event producer publishes an event to an event broker. Event consumers subscribe to event types and receive notifications when events are published.
|
||||
|
||||
Message queues like RabbitMQ and Apache Kafka are commonly used as event brokers. They provide durable, reliable messaging between producers and consumers, even when consumers are temporarily offline.
|
||||
|
||||
Microservices Architecture
|
||||
|
||||
Microservices is an architectural style that structures an application as a collection of small, independently deployable services. Each service is focused on a specific business capability.
|
||||
|
||||
Services communicate over well-defined APIs, usually HTTP REST or message queues. Each service can be deployed, scaled, and updated independently. This allows teams to work on different services without affecting the others.
|
||||
|
||||
Container orchestration platforms like Kubernetes are commonly used to manage microservices deployments. They handle service discovery, load balancing, and automatic scaling.
|
||||
|
||||
The challenges of microservices include distributed system complexity, data consistency, and operational overhead. Teams need to invest in DevOps tooling and practices to manage microservices effectively.
|
||||
BIN
app/services/ingestion/test-files/system_test.pdf
Normal file
BIN
app/services/ingestion/test-files/system_test.pdf
Normal file
Binary file not shown.
6
app/services/ingestion/test-files/test_payload.json
Normal file
6
app/services/ingestion/test-files/test_payload.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"documentId": "gjntkc43rs6s3e3",
|
||||
"filename": "sample.md",
|
||||
"format": "md",
|
||||
"filePath": "C:\\Users\\RaymondVerhoef\\Gitea\\learning-platform\\app\\services\\ingestion\\test-files\\sample.md"
|
||||
}
|
||||
Reference in New Issue
Block a user