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From a straightforward browser scripting language, JavaScript has morphed into an ultra-flexible and versatile technology that powers everything from dynamic front-end UIs and back-end services (Node.js) to automation scripts and IoT devices (with libraries like Johnny-Five). Yet, that flexibility introduces a lot of complexity in writing efficient, performant code. Fortunately, JavaScript engines working to execute your code employ a number of optimization strategies during runtime to improve performance. For these optimizations to be most effective, though, you as the developer must understand how the engines practically work and adopt coding practices that kowtow to their internal processes. This article will, for the most part, stick to the basics of how the engines work and what kinds of practical, everyday coding strategies you can utilize to just get more oomph out of your engine. The Journey of JavaScript Code Your JavaScript code executes after it passes through three primary stages in the JavaScript runtime: AST Generation (Abstract Syntax Tree) The code is parsed and translated into an Abstract Syntax Tree (AST). This structure stands between the source code and the machine language. The engine processes the AST, so the format is crucial. Bytecode Compilation The AST is then compiled into bytecode, a lower-level intermediate code that is closer to machine code, but still independent of the platform. This bytecode is run by the JavaScript engine. JIT Optimization When the bytecode runs, the JavaScript engine continually optimizes the code using Just-In-Time (JIT) compilation. The JIT compiler collects data about the runtime (like types and usage patterns) and creates efficient machine code tailored to the environment where the code is running. These phases are critical to comprehend if you're going to write fast JavaScript. It's not enough just to write code that works; you need to structure it in a way that lets the engine optimize it effectively. Then it will run faster and perform better. The Importance of JIT Compilation JavaScript is a language with dynamic typing, which means types are determined at runtime rather than at compile-time. This allows for flexibility in the language, but it poses certain challenges for JavaScript engines. When the engine compiles JavaScript to machine code, it has very little information about the kinds of variables or the types of function arguments that were used in the code. With so little information, the engine cannot generate highly optimized machine code at the outset. This is where the JIT compiler comes in and does its work. JIT compilers are capable of watching the code execute and gathering information at runtime about what kinds of variables and types are being used, and they then use this information to optimize the code as it runs. By optimizing the code based on actual usage, the JIT compiler can produce highly efficient machine code for the hot paths in your code — i.e., the frequently executed parts. But not every part of the code is eligible for this kind of optimization. Writing Optimization-Friendly Code One of the most crucial aspects of JavaScript programming that can be efficiently handled by the JIT compiler is maintaining consistency in your code, especially where types are concerned. When you create functions that take arguments of different types or forms, it makes it hard for the JIT compiler to guess what you really meant and thus what kinds of optimizations it can apply. Type Consistency: A Key to Performance To showcase the significance of type consistency, let's evaluate a basic instance. Imagine you possess the following function: JavaScript function get_x(obj) { return obj.x; } At first glance, this function seems to work in a very straightforward manner — it just returns the x property of an object. However, JavaScript engines have to evaluate the location of the x property during execution. This is because x could be either a direct property of the object we're passing in or something that our engine has to check for along the prototype chain. The engine also has to check to see if the property exists at all, which augments any overhead costs tied to the function's execution. Now consider that we're calling this function with objects of varying shape: JavaScript get_x({x: 3, y: 4}); get_x({x: 5, z: 6}); Here, the get_x function gets objects with different property sets (x, y, z) that make it tough for the engine to optimize. Each time the function is called, the engine must check to determine where the x property exists and whether it’s valid. However, if you standardize the structure of the objects being passed, even if some properties are undefined, the engine can optimize the function: JavaScript get_x({x: 3, y: 4, z: undefined}); get_x({x: 5, y: undefined, z: 6}); Now, the function is consistently receiving objects that maintain the same shape. This consistency allows the engine to optimize property access more effectively. When the engine accesses properties, it can now predict with a greater degree of certainty that certain properties will be present. For example, it knows that an "x" property will always be present — even if the engine also knows that the "x" property is undefined a significant amount of the time. Measuring Optimization in Action Modern JavaScript engines offer a way to monitor the optimization of your code. You can, for instance, use Node.js to observe the optimization workings of the engine on your code. JavaScript function get_x(obj) { return obj.x + 4; } // Run a loop to trigger optimization for (let i = 1; i <= 1000000; i++) { get_x({x: 3, y: 4}); } Executing this code with Node.js using the --trace-deopt and --trace-opt flags enables you to glimpse how the engine optimizes the function. JavaScript node --trace-deopt --trace-opt index.js In the output, you might see something like this: JavaScript [marking 0x063d930d45d9 <JSFunction get_x (sfi = 0x27245c029f41)> for optimization to TURBOFAN, ConcurrencyMode::kConcurrent, reason: small function] This message shows that the get_x function has been set aside for optimization and will be compiled with the very fast TURBOFAN compiler. Understanding Deoptimization Understanding deoptimization — when the JIT compiler gives up an optimized code path — is just as important as understanding optimization. Deoptimization occurs when the engine's assumptions about the types or structures of data no longer hold true. For instance, if you change the function to manage another object shape: JavaScript function get_x(obj) { return obj.x + 4; } // Initial consistent calls for (let i = 1; i <= 1000000; i++) { get_x({x: 3, y: 4}); } // Breaking optimization with different object shape get_x({z: 4}); The output may contain a message about deoptimization: JavaScript [bailout (kind: deopt-eager, reason: wrong map): begin. deoptimizing 0x1239489435c1 <JSFunction get_x...>] This means that the engine had to give up its optimized code path because the new object shape wasn't what it was expecting. The function get_x is no longer working on objects that have a reliable structure, and so the engine has reverted to a version of the function that is not as fast. Maintaining Optimization With Consistent Object Shapes To prevent deoptimization and to help the engine maintain optimizations, it's crucial to keep object shapes consistent. For instance: JavaScript function get_x(obj) { return obj.x + 4; } // All calls use the same object shape for (let i = 1; i <= 1000000; i++) { get_x({x: 3, y: 4, z: undefined}); } get_x({x: undefined, y: undefined, z: 4}); Here, the get_x function always gets objects with the same shape. This lets the engine maintain its optimizations since there's no need to deoptimize when an object's shape is consistent. Best Practices for Optimizable Code To ensure that the JavaScript code is maximally efficient and optimized by the JIT compiler, follow these best practices. Consistent object shapes: Make sure that the function receives the same set of properties from the object, even if some of the properties are undefined.Type stability: Maintain consistency in types for function parameters and return values. Don't switch between primitive types (e.g., string, number) in the same function.Direct property access: Optimizing direct property access (like obj.x) is easier than optimizing dynamic property access (like obj["x"]), so it's best to avoid dynamic lookups when you can.Focus on hot code paths: Focus your optimization activities on the parts of your code that are run the most. Profiling tools can help you direct your optimization efforts to the areas of your code that will benefit the most from those efforts.Minimize type variability: Refrain from dynamically altering the type or shape of objects and arrays. The engine can make better assumptions and optimizations when structures remain static. Conclusion To write performant JavaScript, you need more than just a clean syntax; you need a solid understanding of how the JavaScript engine optimizes and executes your code. This means maintaining type consistency, using consistent object shapes, and knowing how the JIT compiler works. Right now, it is essential to remember that if you start trying to make your code perform well at every conceivable point, you will end up making it perform poorly at some points that matter and wasting your time in the process. The point, then, is not to write optimized code but rather to write code that is simple to understand and reason about, hitting the few places where it needs to be performant in a straight path from start to finish.

When choosing a backend for your next Python project, the comparison between Django and FastAPI often comes up. Many developers who have spent years in Django’s “batteries-included” world eventually experiment with FastAPI for its modern, async-first approach. A Reddit thread titled “Django Architecture versus FastAPI” captures this exact debate: a long-term Django user moving to FastAPI for asynchronous advantages. I am a contributor to FastOpp, an open-source FastAPI starter package for AI web applications. It uses pre-built admin components to give FastAPI functionality comparable to Django for AI-first applications. This article organizes lessons from building FastOpp, focusing on community insights, into a learning path. It contrasts both frameworks’ philosophies, pinpoints when each is the right fit, and outlines practical steps for experimenting or migrating. Django’s Architecture and Strengths Django is a mature, full-stack web framework based on the Model-Template-View (MTV) pattern. It provides ORM, templating, forms, authentication, and an admin interface out of the box. The emphasis is on productivity: strong defaults and conventions help developers build complex apps quickly. Strengths Large ecosystem of reusable apps and plugins.Integrated admin UI, auth, and migrations.Strong community and extensive documentation. Trade-Offs Primarily synchronous. While Django introduced some async support, core components remain synchronous.More suited to monolithic applications and CRUD-heavy projects.Additional effort needed when building API-first or high-concurrency services. FastAPI’s Architecture and Strengths FastAPI, released in 2018, is a modern Python framework optimized for APIs. It is async-first, type-hint aware, and automatically generates OpenAPI/Swagger documentation. Its design favors explicitness: developers choose their own ORM, auth, and middleware. Strengths High performance with async/await and minimal overhead.Auto-validation and type safety via Pydantic.Dependency injection and auto-generated API documentation. Trade-Ofs Minimal “batteries” included. Auth, ORM, and background tasks require external libraries.More architectural decisions fall to the developer.Ecosystem is smaller compared to Django. Code Contrast: ChatMessage API in FastOpp-Style FastAPI vs. Django REST Framework To make this comparison tangible, let’s adapt an example inspired by FastOpp, an opinionated FastAPI starter stack for AI web applications. We’ll implement a simple ChatMessage API. FastAPI (FastOpp-style) Python # models.py from sqlalchemy import Column, Integer, String, Text, DateTime, func from sqlalchemy.orm import declarative_base Base = declarative_base() class ChatMessage(Base): __tablename__ = "chat_messages" id = Column(Integer, primary_key=True, autoincrement=True) role = Column(String(20), nullable=False) # "user" | "assistant" | "system" content = Column(Text, nullable=False) session_id = Column(String(64), index=True) created_at = Column(DateTime(timezone=True), server_default=func.now()) Python # schemas.py from pydantic import BaseModel, Field from typing import Optional class ChatMessageIn(BaseModel): role: str = Field(pattern="^(user|assistant|system)$") content: str session_id: Optional[str] = None class ChatMessageOut(ChatMessageIn): id: int Python # routes/chat.py from fastapi import APIRouter, Depends from sqlalchemy.orm import Session from typing import List from db import SessionLocal from models import ChatMessage from schemas import ChatMessageIn, ChatMessageOut router = APIRouter(prefix="/api/chat", tags=["chat"]) def get_db(): db = SessionLocal() try: yield db finally: db.close() @router.get("/messages", response_model=List[ChatMessageOut]) async def list_messages(session_id: str, db: Session = Depends(get_db)): return db.query(ChatMessage).filter(ChatMessage.session_id == session_id).order_by(ChatMessage.id.asc()).all() @router.post("/messages", response_model=ChatMessageOut, status_code=201) async def create_message(payload: ChatMessageIn, db: Session = Depends(get_db)): msg = ChatMessage(**payload.model_dump()) db.add(msg) db.commit() db.refresh(msg) return msg Observation: This is modular and explicit. Each layer — models, schemas, routes — is wired manually, but you gain async support, type safety, and auto-generated docs. Django REST Framework (DRF) Python # models.py from django.db import models class ChatMessage(models.Model): ROLE_CHOICES = [("user", "user"), ("assistant", "assistant"), ("system", "system")] role = models.CharField(max_length=20, choices=ROLE_CHOICES) content = models.TextField() session_id = models.CharField(max_length=64, db_index=True) created_at = models.DateTimeField(auto_now_add=True) Python # serializers.py from rest_framework import serializers from .models import ChatMessage class ChatMessageSerializer(serializers.ModelSerializer): class Meta: model = ChatMessage fields = ["id", "role", "content", "session_id", "created_at"] Python # views.py from rest_framework import viewsets from rest_framework.response import Response from rest_framework.decorators import action from .models import ChatMessage from .serializers import ChatMessageSerializer class ChatMessageViewSet(viewsets.ModelViewSet): queryset = ChatMessage.objects.all().order_by("id") serializer_class = ChatMessageSerializer @action(detail=False, methods=["get"]) def by_session(self, request): session_id = request.query_params.get("session_id") qs = self.queryset.filter(session_id=session_id) if session_id else self.queryset.none() serializer = self.get_serializer(qs, many=True) return Response(serializer.data) Python # urls.py from rest_framework.routers import DefaultRouter from .views import ChatMessageViewSet router = DefaultRouter() router.register(r"chat/messages", ChatMessageViewSet, basename="chatmessage") urlpatterns = router.urls Observation: DRF delivers a lot of functionality with fewer moving parts: serialization, routing, and even a browsable API are built in. But concurrency and async remain limited compared to FastAPI. Insights From the Reddit Discussion The Reddit conversation highlights real-world migration motives: The original poster had been using Django for 10 years but left due to asynchronous limitations.FastAPI is attractive for performance-critical, modern workloads and for its explicit, “less magic” approach.Commenters note that Django remains powerful for traditional applications, especially where built-in features like admin and templating matter.Hybrid approaches are common: use Django for monolithic needs and FastAPI for performance-critical endpoints. This reflects what many teams practice in production today. A Learning Path: From Django to FastAPI Here is a phased roadmap for developers who know Django but want to explore or integrate FastAPI. PhaseGoalActivities0. Strengthen Django knowledgeUnderstand Django internalsStudy request lifecycle, middleware, ORM, async support, channels.1. Build an API with Django REST Framework (DRF)Learn Django’s approach to APIsCRUD endpoints, serializers, viewsets, permissions.2. Prototype in FastAPIGet comfortable with idiomsWrite async endpoints, Pydantic models, background tasks, explore auto-docs.3. Compare directlyContrast patternsRebuild selected DRF endpoints in FastAPI; compare validation, performance, error handling.4. Hybrid experimentCombine frameworksRun FastAPI services alongside Django, e.g., for high-throughput endpoints.5. BenchmarkTest performance under loadConcurrency benchmarks, database pooling, caching, async vs sync results.6. Selective migrationMove critical partsIncrementally replace Django endpoints with FastAPI while monitoring regression risk. When to Choose Which Django (With DRF) CRUD-heavy apps with relational data models.Need for admin UI, auth, templating, or rapid prototyping.Teams preferring conventions over configuration. FastAPI API-first or microservice architectures.High-concurrency or I/O-bound workloads.Preference for type safety and minimal middleware. Hybrid Keep Django for established modules.Spin up FastAPI for latency-sensitive services like ML inference or real-time APIs.Migrate gradually as needs evolve. Common Pitfalls in Hybrid or Migration Logic duplication: Extract business logic into shared libraries to avoid drift.Data consistency: If both frameworks share a database, carefully manage transactions and migrations.Authentication split: Standardize on JWT, OAuth, or another central auth service.Operational overhead: Two runtimes mean double monitoring and deployment complexity.Premature optimization: Validate Django bottlenecks before migrating—extra complexity must be justified. Conclusion The Reddit thread “Django Architecture versus FastAPI” captures a broader reality: Django is still excellent for monolithic, feature-rich applications, while FastAPI excels at modern, async-driven APIs. Many teams combine both, letting each framework play to its strengths. Your path doesn’t need to be binary. Start by reinforcing Django fundamentals, then prototype in FastAPI. Compare patterns, test performance, and — if justified — adopt a hybrid approach. With careful planning, you gain flexibility without locking into a single paradigm.

By Jesse Casman CORE

Hey, DZone Community! We have an exciting year of research ahead for our beloved Trend Reports. And once again, we are asking for your insights and expertise (anonymously if you wish) — readers just like you drive the content we cover in our Trend Reports. Check out the details for our research survey below. Database Systems Research With databases powering nearly every modern application nowadays, how are developers and organizations utilizing, managing, and evolving these systems — across usage, architecture, operations, security, and emerging trends like AI and real-time analytics? Take our short research survey (~10 minutes) to contribute to our upcoming Trend Report. Oh, and did we mention that anyone who takes the survey could be one of the lucky four to win an e-gift card of their choosing? We're diving into key topics such as: The databases and query languages developers rely onExperiences and challenges with cloud migrationPractices and tools for data security and observabilityData processing architectures and the role of real-time analyticsEmerging approaches like vector and AI-assisted databases Join the Database Systems Research Over the coming month, we will compile and analyze data from hundreds of respondents; results and observations will be featured in the "Key Research Findings" of our upcoming Trend Report. Your responses help inform the narrative of our Trend Reports, so we truly cannot do this without you. Stay tuned for each report's launch and see how your insights align with the larger DZone Community. We thank you in advance for your help! —The DZone Content and Community team

According to a 2024 Gartner report, more than 92% of large enterprises now operate in multi-cloud environments. This reflects strategic priorities such as geographic scalability, high availability, regulatory compliance, and cost optimization. But with these benefits comes significant complexity. Each provider — AWS, GCP, Alibaba Cloud, and others — exposes its own APIs, semantics, and SDKs. As a result, development teams must reconcile divergent models for storage, databases, identity, and more. The outcome is often fragmented codebases filled with conditional logic, code forking, duplicated workflows, and costly rewrites when onboarding new providers. For large organizations, this slows delivery, increases operational risk, and erodes the developer experience. Ideally, enterprises would rely on a shared abstraction layer - one that standardizes semantics across providers while encapsulating provider-specific details. Previous efforts, such as Apache Jclouds, attempted to solve this for a subset of services, such as blobstore, compute, and load balancers, but struggled with a REST-based architecture that lagged behind evolving cloud features, ultimately landing in the Apache Attic. This created a gap in the Java ecosystem: the need for an actively maintained, extensible SDK with deep, provider-backed integration. MultiCloudJ, recently open-sourced by Salesforce, fills that gap. Built directly on official provider SDKs rather than raw REST APIs, it ensures compatibility with the latest features while offering cloud-neutral abstractions. With portable APIs, driver layers, and provider implementations, MultiCloudJ gives Java developers a modern, unified SDK for building cloud-agnostic applications. The Multi-Cloud Challenge Enterprises are rapidly adopting multi-cloud strategies to strengthen resilience, meet compliance needs, and avoid over-reliance on a single provider. The business motivations are clear: Resilience and availability: Enable failover, disaster recovery, and regional deployments for low latency.Compliance: Satisfy data sovereignty and regulatory requirements with regional providers.Cost optimization: Run workloads on the most cost-effective cloud infrastructure.Leverage: Reduce vendor lock-in and strengthen negotiation power with providers.Time-to-market: Accelerate rollouts by avoiding costly rewrites tied to a single provider’s SDK. Yet these benefits for the business often create challenges for developers. Each provider exposes unique SDKs and semantics, forcing teams to duplicate logic, maintain sprawling if/else, case switch branches, complete code forks, and manage inconsistent workflows. Onboarding a new provider typically requires expensive refactoring, while steep learning curves slow down delivery. In practice, the strategic promise of multi-cloud often translates into day-to-day friction for engineers, who spend more time reconciling SDK differences than building business value. The Need for a Unified Approach This growing gap between enterprise strategy and developer experience calls for a new abstraction: a way to standardize cloud access without sacrificing provider capabilities. By offering consistent interfaces across storage, databases, and messaging services, such an approach reduces duplication, simplifies onboarding, and allows teams to focus on business logic instead of cloud-specific rewrites. That need is what led to the creation of MultiCloudJ — an open-source Java SDK designed to make multi-cloud development practical and standardized. What Does MultiCloudJ Offer? MultiCloudJ is a modular Java SDK that exposes cloud-neutral interfaces for some of the most commonly used cloud services: Blob/Object storage: Backed by AWS S3, Google Cloud GCS, Alibaba OSSDocument stores: AWS DynamoDB, GCP Firestore, Alibaba TablestoreSecurity Token Service (STS): for credential delegationPub/Sub (message queues): coming soon.. By encapsulating native provider SDKs behind a uniform API, MultiCloudJ centralizes provider-specific complexity. Applications interact only with portable abstractions, while the SDK manages request translation to a specific provider, semantic differences, and provides a consistent response. Architecture Deep Dive MultiCloudJ follows a three-layer architecture: 1. Portable Layer (Public API) Developer-facing layer with portable methods (such upload(), download(), delete()) for blobstore. Java BucketClient client = BucketClient.builder("aws") .withBucket("exampleBucket") .withRegion("us-east-1"); UploadRequest req = new UploadRequest.Builder() .withKey("foo/bar.jpg") .build(); UploadResponse res = client.upload(req, "sample-content"); Switching to GCP or Alibaba requires changing only the cloud-specific values, such as the provider name from “aws” to “gcp” or Alibaba; bucket name, region name, and business logic remain unchanged. 2. Driver Layer (Abstraction and Coordination) Defines core operations and shared validation. Java public abstract class AbstractBlobStore { protected abstract UploadResponse doUpload(UploadRequest request); protected abstract void doDelete(String key); } This shields the public API from leaking provider details. 3. Provider Layer (Substrate Implementations) Implements cloud-specific logic using native SDKs. Java public class AWSBlobStore extends AbstractBlobStore { @Override protected UploadResponse doUpload(UploadRequest request) { PutObjectRequest putReq = PutObjectRequest.builder() .bucket(bucket) .key(request.getKey()) .build(); s3Client.putObject(putReq, RequestBody.fromString(request.getContent())); return new UploadResponse(...); } } Design Benefits Portability: Swap providers without rewriting application code.Unified, cloud-agnostic interfaces: Write once and interact with multiple clouds through consistent APIs.Extensibility: Onboard new providers or services without modifying existing code.Flexibility: Override defaults or inject custom implementations as needed.Uniform semantics: Normalize differences across providers to simplify development. Example – Object deletion: AWS S3 returns 200 OK when deleting a non-existent object, while Google Cloud Storage returns 404 Not Found. MultiCloudJ abstracts this by providing a consistent, predictable response.Example – Pagination: DynamoDB uses LastEvaluatedKey for continuation, while Firestore relies on the last document of a page. MultiCloudJ standardizes pagination into a uniform API.Reliability: Robust error handling and retry mechanisms ensure consistent, dependable operation. Note: MultiCloudJ strives for uniform semantics but does not replicate every provider-specific capability; there are always some rare exceptions. For example, global transactions are available in DynamoDB, Firestore, and CosmosDB, but not in Alibaba Tablestore. In such cases, the SDK throws explicit exceptions to signal unsupported features. Real-World Use Cases Global SaaS platforms: deploy to AWS in North America and Alibaba Cloud in AsiaHybrid cloud deployments: route workloads by business unit or geographyDisaster recovery: maintain a fallback provider for failoverCross-cloud replication: sync documents or blobs across providersMigration: shift workloads between providers with minimal application changes Getting Started for Developers Maven Dependency XML <dependency> <groupId>com.salesforce.multicloudj</groupId> <artifactId>docstore-client</artifactId> <version>0.2.2</version> </dependency> Switch docstore with blobstore, sts, or provider modules. Runtime Configuration Include provider-specific modules with Maven profiles: XML <dependency> <groupId>com.salesforce.multicloudj</groupId> <artifactId>docstore-gcp</artifactId> <version>0.2.2</version> </dependency> Example: Writing to a Document Store Java CollectionOptions opts = new CollectionOptions.CollectionOptionsBuilder() .withTableName("books") .withPartitionKey("title") .withSortKey("author") .build(); DocStoreClient client = DocStoreClient.builder("aws") .withRegion("us-west-2") .withCollectionOptions(opts) .build(); Book book = new Book("YellowBook", "Zoe", "Seattle", 3.99f, Map.of("Ch1", 5, "Ch2", 10), null); client.create(new Document(book)); Switching to Firestore or Tablestore requires only updating provider and resource configs. Lessons Learned in Building MultiCloudJ Conformance Testing Across Providers To ensure consistent behavior, all MultiCloudJ tests are written against abstract driver classes. This allows the same test suite to run across different providers, with each provider supplying its own configuration (credentials, resource names, etc.). This approach guarantees uniform behavior and quickly highlights any provider-specific deviations. Testing in CI Environments Running full integration tests in CI pipelines posed challenges, primarily because securely managing live credentials for multiple providers is risky. To solve this, the team used Wiremock as a proxy, recording and replaying provider responses. This enabled reliable CI testing without requiring actual provider credentials, while still validating real-world request/response flows. Handling Cloud-Specific Features MultiCloudJ generally avoids exposing features unique to a single provider, as doing so would compromise portability. However, in some cases, the team chose to implement provider-specific functionality at the SDK layer while keeping a consistent client-facing experience. For example, Google Cloud Storage lacks native multipart upload support, so MultiCloudJ implements it internally using composable objects — giving developers a seamless, uniform experience. Defining Meaningful Semantics Each provider has its own interpretation of common operations, making it critical to standardize semantics for developers. Some differences are simple, while others require more nuanced handling, such as pagination approaches for GCP Firestore, and no multi-part upload support in Google Cloud SDK natively. We need to design the common semantics very carefully to offer uniform behavior. Leverage the cloud provider SDK instead of reinventing the wheel. Leverage provider SDKs instead of reinventing the wheel. We considered reviving Apache jclouds, but building directly on raw REST APIs is impractical. Cloud providers’ SDKs already handle the heavy lifting - request signing, headers, authentication flows, TLS, error handling, and endpoint management. Recreating and maintaining all of that ourselves would be fragile, time-consuming, and unsustainable. Conclusion MultiCloudJ tackles one of the toughest challenges in enterprise cloud engineering: achieving portability without compromising capability. By abstracting core services through provider-backed APIs, it delivers a consistent developer experience and reduces the operational complexity of multi-cloud environments. For enterprises navigating compliance, cost, and resilience requirements, MultiCloudJ offers a practical and sustainable path forward. The project is open source under Salesforce’s GitHub organization and welcomes community contributions and feedback: github.com/salesforce/multicloudj.

A previous article explains why PostgreSQL full-text search (FTS) is not a good candidate for implementing a general find functionality, where the user is expected to provide a pattern to be looked up and matched against the fields of one or multiple entities. Considering the previously explained technical challenges, it is clear that FTS is great for semantic and language-aware search, although it cannot cover raw searches in detail for various use cases. In the field of software development, it isn’t uncommon for us to need to accept trade-offs. Actually, almost every decision that we make when architecting and designing a product is a compromise. The balance is tilted depending on the purpose, requirements, and specifics of the developed product so that the solution and the value delivery are ensured, and the customers are helped to accomplish their needs. With this statement in mind, to achieve success, you need to compromise. This article aims to compare the two methods, FTS and pattern matching, in terms of execution performance, of course, under a set of clear preconditions (trade-offs) that are assumed. The purpose is obviously to increase the objectivity related to these methods’ applicability and help programmers choose easily when needed. Preconditions Perform a pattern search on three different entities by looking up in multiple tables’ columns.The entities of interest are: telecom invoices, inventory items for which the invoices are issued, and orders of such inventory items.Entities that compose the result of the operation are displayed together.A ‘starts with’ matching against the aimed columns is acceptable.The fields of interest are: invoice number and comments, inventory number and comments, and order number and comments. Performance Analysis Assuming the PostgreSQL Database Server is up and running, one can connect to it and explore the three entities of interest. SQL select count(*) from inventories; -- 2_821_800 records select count(*) from invoice; -- 55_911 records select count(*) from orders; -- 30_564 records One can observe that the total number of searched entities is around 3 million, which is a pretty good sample for an objective analysis. The purpose isn’t necessarily to provide the implementation in detail, but to observe the performance of several strategies and make a comparison. In order to be able to make the analysis without having to modify the existing tables, the following materialized view is created. SQL CREATE MATERIALIZED VIEW IF NOT EXISTS mv_fts_entities AS SELECT contractid AS id, 'INVENTORY' AS type, extrainfo AS number, ( setweight(to_tsvector('simple', coalesce(extrainfo, '')), 'A') || setweight(to_tsvector('simple', coalesce(itemcomments, '')), 'B') ) AS search_vector FROM inventories UNION ALL SELECT i.invoiceid AS id, 'INVOICE' AS type, i.invoicenum AS number, ( setweight(to_tsvector('simple', i.invoicenum), 'A') || setweight(to_tsvector('simple', coalesce(i.comments, '')), 'B') || setweight(to_tsvector('simple', a.account), 'A') ) AS search_vector FROM invoice i LEFT JOIN account a on a.id = i.accountid UNION ALL SELECT orderid AS id, 'ORDER' AS type, ordernumber AS numnber, ( setweight(to_tsvector('simple', ordernumber), 'A') || setweight(to_tsvector('simple', coalesce(comments, '')), 'B') ) AS search_vector FROM orders; A few clarifications: The materialized view reunites all the records contained in the three tables of interest.id represents the unique identifier of each entity.type allows identifying the entity — INVENTORY, INVOICE, or ORDER.number is the field we’re mainly interested performing the pattern search on — inventory, invoice and order number respectively.search_vector contains the tsvector representation of the columns of interest and it represents PostgreSQL’s text search vector type that denotes the searchable content.setweight() – depending on the considered column, different weights are set when computing the lexemes, for instance, in case of orders, we consider the match to have a higher priority on order number than on comments.coalesce() handles the null values gracefully.For invoice records, a match is attempted on the invoice account as well, although the column designates an attribute of a different entity. The materialized view creation takes about three seconds. If interested in refreshing the content so that the data is up to date, one can issue the command below. SQL REFRESH MATERIALIZED VIEW mv_fts_entities; The materialized view refresh takes around 10 seconds. Additionally, the following indexes are created to improve performance in both cases. A GIN index on the search_vectorcolumn to improve full-text search SQL CREATE INDEX mv_fts_entities_search_idx ON mv_fts_entities USING GIN (search_vector); A B-Tree index on the number column to improve the ‘starts with’ pattern searching SQL CREATE INDEX mv_fts_entities_number_idx ON mv_fts_entities(number); Both operations take about five seconds. With the above items in place, let’s examine the performance in each of the following scenarios. 1. Full-Text Search SQL EXPLAIN ANALYZE SELECT id, type, number, search_vector, ts_rank(search_vector, query) as rank FROM mv_fts_entities, to_tsquery('simple', '101:*') query WHERE search_vector @@ query ORDER BY rank DESC LIMIT 10; This returns the following results and query plan: Plain Text +------+---------+---------------------+---------------------------------------------+---------+ |id |type |number |search_vector |rank | +------+---------+---------------------+---------------------------------------------+---------+ |162400|INVENTORY|KBBC24100 101ATI |'101ati':2A 'kbbc24100':1A |0.6079271| |13162 |INVOICE |M566274 |'10130bafy0':2A 'm566274':1A |0.6079271| |4880 |INVOICE |M554853 |'10130bafy0':2A 'm554853':1A |0.6079271| |55713 |INVOICE |M628493 |'10130bbt0':2A 'm628493':1A |0.6079271| |52525 |INVOICE |M623623 |'10130bfml0':2A 'm623623':1A |0.6079271| |35131 |INVOICE |4233816-IVG |'10111020':3A '4233816':1A 'ivg':2A |0.6079271| |34326 |INVOICE |4233312-IVG |'10111020':3A '4233312':1A 'ivg':2A |0.6079271| |34082 |INVOICE |4232587IVG |'10111020':2A '4232587ivg':1A |0.6079271| |46370 |INVOICE |101912352160142303323|'101912352160142303323':1A '9897901309489':2A|0.6079271| |132670|INVENTORY|KBBC75705 101ATI |'101ati':2A 'kbbc75705':1A |0.6079271| +------+---------+---------------------+---------------------------------------------+---------+ +-------------------------------------------------------------------------------------------------------------------------------------------------+ |QUERY PLAN | +-------------------------------------------------------------------------------------------------------------------------------------------------+ |Limit (cost=173.72..173.75 rows=10 width=29) (actual time=1.120..1.122 rows=10 loops=1) | | -> Sort (cost=173.72..174.08 rows=145 width=29) (actual time=1.119..1.120 rows=10 loops=1) | | Sort Key: (ts_rank(mv_fts_entities.search_vector, '''101'':*'::tsquery)) DESC | | Sort Method: top-N heapsort Memory: 25kB | | -> Bitmap Heap Scan on mv_fts_entities (cost=9.93..170.59 rows=145 width=29) (actual time=0.259..0.967 rows=914 loops=1) | | Recheck Cond: (search_vector @@ '''101'':*'::tsquery) | | Heap Blocks: exact=476 | | -> Bitmap Index Scan on mv_fts_entities_search_idx (cost=0.00..9.89 rows=145 width=0) (actual time=0.213..0.213 rows=914 loops=1)| | Index Cond: (search_vector @@ '''101'':*'::tsquery) | |Planning Time: 0.199 ms | |Execution Time: 1.141 ms | +-------------------------------------------------------------------------------------------------------------------------------------------------+ Key points: The Bitmap Index Scan is used for matchingUses Top-N heapsort for ordering (ranking) the resultsThe search time is around 1 ms 2. Case-Sensitive Wildcard Pattern Search SQL EXPLAIN ANALYZE SELECT id, type, number FROM mv_fts_entities WHERE number LIKE '101%' LIMIT 10; This returns the following results and query plan: Plain Text +------+---------+-----------+ |id |type |number | +------+---------+-----------+ |532 |ORDER |TEM1101 | |15642 |ORDER |CCON101 | |310983|INVENTORY|7037618101 | |36445 |INVOICE |83551101 | |1532 |ORDER |TEM2101 | |16642 |ORDER |CCON1101 | |546667|INVENTORY|P0000010101| |13180 |INVOICE |28071101 | |2529 |ORDER |TEM3101 | |17642 |ORDER |CCON2101 | +------+---------+-----------+ +---------------------------------------------------------------------------------------------------------------------+ |QUERY PLAN | +---------------------------------------------------------------------------------------------------------------------+ |Limit (cost=0.00..2268.43 rows=10 width=25) (actual time=0.395..2.265 rows=10 loops=1) | | -> Seq Scan on mv_fts_entities (cost=0.00..66011.44 rows=291 width=25) (actual time=0.393..2.262 rows=10 loops=1)| | Filter: ((number)::text ~~ '101%'::text) | | Rows Removed by Filter: 35657 | |Planning Time: 0.063 ms | |Execution Time: 2.277 ms | +---------------------------------------------------------------------------------------------------------------------+ Key points: The sequential scan is usedThe result set is obviously different from the one at point 1The search time is slightly longer, about 2.2 ms 3. Case-Insensitive Wildcard Pattern Search SQL EXPLAIN ANALYZE SELECT id, type, number FROM mv_fts_entities WHERE number ILIKE '101%' LIMIT 10; This returns the following results and query plan: Plain Text +------+---------+----------------------------+ |id |type |number | +------+---------+----------------------------+ |46370 |INVOICE |101912352160142303323 | |455785|INVENTORY|101t1zflivnminwdcolivomigf | |455782|INVENTORY|101t1zfclevohizhopclevoh62 | |455783|INVENTORY|101t1zfclmmohkfhoohlrdoh87 | |455784|INVENTORY|101t1zfgbtpm11111h00grblmimn| |455786|INVENTORY|101t1zfnilsmimndc0nltpmid0 | |455787|INVENTORY|101t1zfpthrmibiho6pthrmimn | |36819 |INVOICE |101912352160142393323 | |32931 |INVOICE |101912352160142392823 | |8002 |INVOICE |101912352121 | +------+---------+----------------------------+ +----------------------------------------------------------------------------------------------------------------------+ |QUERY PLAN | +----------------------------------------------------------------------------------------------------------------------+ |Limit (cost=0.00..2268.43 rows=10 width=25) (actual time=1.939..11.356 rows=10 loops=1) | | -> Seq Scan on mv_fts_entities (cost=0.00..66011.44 rows=291 width=25) (actual time=1.938..11.353 rows=10 loops=1)| | Filter: ((number)::text ~~* '101%'::text) | | Rows Removed by Filter: 35657 | |Planning Time: 0.098 ms | |Execution Time: 11.369 ms | +----------------------------------------------------------------------------------------------------------------------+ Key points: The sequential scan is usedThe result set is obviously different from the one at point 1, but also from the one at point 2The search time is longer than the ones before, about 11.5 ms 4. Case-Insensitive Regular Expression Search SQL EXPLAIN ANALYZE SELECT id, type, number FROM mv_fts_entities WHERE number ~* '^101' LIMIT 10; This returns the following results and query plan: Plain Text +------+---------+----------------------------+ |id |type |number | +------+---------+----------------------------+ |46370 |INVOICE |101912352160142303323 | |455785|INVENTORY|101t1zflivnminwdcolivomigf | |455782|INVENTORY|101t1zfclevohizhopclevoh62 | |455783|INVENTORY|101t1zfclmmohkfhoohlrdoh87 | |455784|INVENTORY|101t1zfgbtpm11111h00grblmimn| |455786|INVENTORY|101t1zfnilsmimndc0nltpmid0 | |455787|INVENTORY|101t1zfpthrmibiho6pthrmimn | |36819 |INVOICE |101912352160142393323 | |32931 |INVOICE |101912352160142392823 | |8002 |INVOICE |101912352121 | +------+---------+----------------------------+ +---------------------------------------------------------------------------------------------------------------------+ |QUERY PLAN | +---------------------------------------------------------------------------------------------------------------------+ |Limit (cost=0.00..2268.43 rows=10 width=25) (actual time=0.943..6.265 rows=10 loops=1) | | -> Seq Scan on mv_fts_entities (cost=0.00..66011.44 rows=291 width=25) (actual time=0.942..6.262 rows=10 loops=1)| | Filter: ((number)::text ~* '^101'::text) | | Rows Removed by Filter: 35657 | |Planning Time: 0.088 ms | |Execution Time: 6.277 ms | +---------------------------------------------------------------------------------------------------------------------+ Key points: The sequential scan is used.The result set is obviously different from the one at point 1, but identical to the previous one.The search time is longer than the ones using the full-text search, but shorter than the pattern searching, about 6.2 ms. The results obtained in terms of execution time can be summarized as follows: FTS << Case Sens. Pattern Search < Case Insens. Regex Search < Case Insens. Pattern Search The experiment in this article and the results obtained make FTS a candidate worth considering even for pattern searching when its known limitations in the scenarios of interest are acceptable. Moreover, its configuration flexibility in terms of tsvector computation and its speed of execution make it superior in comparison to other solutions, of course, under the presented circumstances. Resources Pattern Searching and PostgreSQL Full-Text Search: Understanding the MismatchThe picture was taken at an immersive exhibition in Bucharest, Romania.

Microservices at Adobe Adobe’s transformation from desktop applications to cloud offerings triggered an explosion of microservices. Be it Acrobat, Photoshop, or Adobe Experience Cloud, they are all powered by suites of microservices mainly written in Java. With so many microservices created, every developer had to go through the same painful processes, i.e., security, compliance, scalability, resiliency, etc., to create a production-grade microservice. That was the genesis of Adobe Service Runtime. What Is ASR? ASR or Adobe Service Runtime is an implementation of the Microservice Chassis Pattern. More than 80% of Adobe’s microservices use ASR foundational libraries. It offers cross-cutting concerns that every production-grade microservice is expected to have. Highlights of the cross-cutting concerns included in ASR libraries: Foundational libraries for Java and Python These libraries offer log masking, request stitching, breadcrumb trail, exception handling, async invocation, resiliency, etc.À la carte libsASR connector libs/SDKs to talk to internal Adobe servicesBlessed base containers are the security-blessed containers that accelerate container adoption for applications in any language.Bootstrapping code and infrastructure templates for fast-tracking getting started.Opinionated build system — i.e., how to build a Java application, run tests, launch debug setups, and package into containers.Secure defaults for configurables to ensure teams get started with baselines that have been tested to work. Having cross-cutting concerns as a single chassis helps organizations produce production-grade microservices at scale, just like an automobile manufacturer’s production line. Why ASR? Large organizations often have heavy compliance, security, resiliency, and scalability requirements. ASR provides a collection of foundational libraries, components, tools, and best practices (12 factor). This enables rapid development of four 9s-capable, innovative, and secure services. It also enables a container-first deployment system. Value Proposition We did a study on internal teams to derive the value proposition of ASR. Category Task With ASR Without ASR Velocity Picking frameworks and libraries, getting them to work, setting up project structure and build system, and resolving dependency and build issues so you can start focusing on business logic. Less than 1 hour 1 - 2 weeks Velocity Implementing capabilities like log masking, req stitching, etc. All capabilities are available 'out of the box'. 4-6 weeks Security Legal and security reviews of core code and libraries (not including business logic) 2-3 days 3-6 weeks Community A strong community that empowers decentralized decision-making on feature priorities for service teams. Common framework makes it easy to share code and developers between projects. Diverse frameworks make it hard to share code across projects. Using ASR saved Developers time and improved security posture by not reinventing the wheel. Benchmarks RPS We did some benchmarking to see if ASR has any overhead over vanilla applications. We ran a ten-minute Gatling script to simulate 500 users, for example. AppRequests/second (RPS)ASR % overheadResponse times (p95)ASR % overhead Non-ASR 21678.506n/a 46 n/a ASR 23969.3837% 48 4% ASR Filters Some cross-cutting concerns are offered as Filters, which can add some overhead. Our baseline comparison is the mean requests/sec of 20474.225. Sections below show the performance change with individual filters disabled. ASR logging filter The cost of disabling this is that the ASR service won't log incoming requests and outgoing responses Performance: mean requests/sec 21260.143, a 3.8% improvementASR exception filter The cost of disabling this is that stack traces can escape in exceptions, an ASSET violationPerformance: Mean requests/sec 20988.308, a 2.5% improvementASR request ID filter The cost of disabling this is that the ASR service won't have a unique request ID per request for tracking.Performance: mean requests/sec 21354.042, a 4.3% improvementASR request response filter The cost of disabling this is that the ASR service won't automatically validate the Authorization header in the incoming request (if com.adobe.asr.enable-authorization-header-validation is set to true)Performance: mean requests/sec 20896.923, a 2% improvement The benchmarks reveal that using ASR adds minimal overhead when compared to the functionalities it offers. Security CVE scans often uncover millions of vulnerabilities across codebases in large organizations. If Adobe developers had to manually fix each one, they would spend most of their time on patching rather than building features. By providing secure defaults and hardened components, ASR serves as a foundational library that reduces vulnerability exposure and saves developers valuable time. CVEs The Log4J incident is a testament to the success of ASR. When the CVE was published, users of ASR had to upgrade to just one version of ASR. Non-ASR repos had to scramble to migrate their libs off of Log4j. This clearly demonstrated the significant multiplier impact ASR has created within the company. Sensitive Data in Logs Log masking is another popular feature that is often recreated across the orgs. ASR comes with a modular log masking library that masks sensitive information. Logs that contain credit card, SSN, or any Adobe-defined sensitive info by default are automatically masked. Developers can also extend it to customize masking for additional use cases. This ensures consistent protection of PII across all applications. ASR Connectors and Resiliency ASR has connectors, which can be used to consume APIs exposed by other services inside Adobe. ASR connectors are application environment aware, i.e, a connector will automatically pick the right root URL of the service based on the app environment. For example, if the Application is running in the stage environment, the identity connector will use the identity stage URL; when the application is running in the prod environment, the identity connector will use the prod URL. This is possible due to the AutoConfiguration that ASR provides for all the connectors. One of the challenges with microservices is that different SLAs are honored by services. Your service might have a higher standard, and you must often tolerate other services. By using ASR connectors, microservices get fault-tolerant communication out of the box. ASR connectors leverage Resilience4j to achieve this. Every connector comes with Resiliency features like bulkhead threadpool, circuit breakers, retries, exponential backoff, etc. By using ASR connectors, the posture of a microservice is greatly enhanced. There are guardrails in the thread pool that ensure there is no avalanche of threads. By using retries by default, the stress on Adobe's network is greatly reduced when the availability of the dependent service is degraded. This is a classic example of how pushing the cross-cutting concerns down to a common layer unlocks a lot of value and reduces redundancies. ASR Adoption at Adobe Almost every Java service at Adobe uses at least one of ASR’s libraries. The full suite of ASR is used by 80% or roughly 7000+ services at Adobe and continues to grow. With the growing need to make products more agentic, we see a strong need for libraries that support such use cases. ASR can be a powerful multiplier in enabling harm and bias guardrails, which are highly relevant to both the company and the industry today. Keep Calm and Shift Down! Inspired by shift left, shift down is a paradigm in platform engineering. A lot of cross-cutting concerns must be managed and provided by the platform out of the box. The users of the platform can focus on their functionalities without having to worry about the baseline standards set by Adobe. ASR enables shift down philosophy at scale. Security teams and executives keep calm due to the centralization of best practices and the at-scale adoption of ASR. Developers are at ease due to overhead being handled at the foundational layer. Every company interested in developer productivity and operational excellence should adopt a shift-down strategy like ASR. Over the years, the ROI keeps compounding and helps companies move fast on paved roads that power the developer journeys.

Software engineers often find themselves writing the same patterns of code again and again. Unit tests, API endpoints, error handling wrappers, and configuration files — these are essential parts of building robust applications, but they are also repetitive and time-consuming. Enter GitHub Copilot. Many engineers already use Copilot as a “pair programmer” to autocomplete functions and suggest code snippets. But Copilot becomes far more powerful when you combine it with structured templates. Templates allow you to guide Copilot, enforce best practices, and generate high-quality, standardized code quickly. This article explores how to create and use Copilot templates, why they work so well, and how they can speed up engineering workflows without sacrificing quality. Why Copilot Templates Matter Copilot is a pattern-matching engine at heart. It learns from context and expands on what it sees. If you start writing tests in a consistent format, Copilot will continue writing more tests that follow the same structure. If you scaffold an API handler in a clear template, Copilot can fill in the missing logic. It adheres to the company coding standards and best practices and creates consistency in the Code base. A diagram of the Copilot template work flow Think of Copilot templates as productivity multipliers: Reduce Repetition – Instead of writing the same boilerplate, Copilot expands the template.Consistency Across Teams – Everyone follows the same structure automatically.Faster Onboarding – New engineers can start delivering faster with clear patterns.Encourage Best Practices – Templates encode good habits (error handling, parameter validation, logging). In short: Copilot amplifies what you give it. If you feed it sloppy or inconsistent patterns, you’ll get messy output. But if you provide strong templates, you’ll get cleaner, more reliable suggestions. Test Generation with Templates Testing is one of the best use cases for Copilot templates. Writing exhaustive test cases manually is tedious, but Copilot can generate variations quickly once it sees the format. Example: Jest / Vitest Template JavaScript describe("<function>", () => { it("should <expected behavior>", () => { const result = <function>(<inputs>); expect(result).toBe(<expected>); }); it("should throw error for <invalid case>", () => { expect(() => <function>(<bad_input>)).toThrow(); }); }); After you write one or two cases, Copilot suggests more test cases automatically. You can even add a comment like // more edge cases, and Copilot will generate them. Example: Pytest Parametrized Tests Python import pytest from my_module import add_numbers @pytest.mark.parametrize("a,b,expected", [ (1, 2, 3), (0, 0, 0), (-1, 1, 0), ]) def test_add_numbers(a, b, expected): assert add_numbers(a, b) == expected Copilot recognizes the structure and suggests additional (a, b, expected) values, covering more edge cases without extra typing. Now, instead of writing 10 separate tests by hand, you can seed the pattern and let Copilot fill in the rest. JSDoc & Docstring Templates Copilot works best when it understands intent. Adding structured comments like JSDoc or Python docstrings gives Copilot the context it needs to suggest meaningful code and tests. JavaScript /** * @function calculateTax * @description Calculate tax on income * @param {number} income - annual income * @param {string} state - two-letter state code * @returns {number} tax amount * @throws {Error} if invalid input */ Now, when you write tests for calculateTax, Copilot already knows: Inputs (income, state) Expected return type (number) Edge cases (invalid input) That context leads to smarter test suggestions, reducing the time you spend thinking about trivial cases. API Endpoint Templates API development is another area where Copilot templates shine. Most API endpoints follow a similar structure: accept input, validate, call a service, return JSON, and handle errors. Here’s a reusable Express.js template: JavaScript // Express.js route template app.<method>("/<route>", async (req, res) => { try { const { <params> } = req.body; const result = await <service>.<method>(<params>); res.json({ success: true, data: result }); } catch (err) { res.status(500).json({ error: err.message }); } }); Start writing app.post("/users"... and Copilot will expand into a complete handler, reusing the same structure for other routes. This doesn’t just save keystrokes—it ensures your APIs are consistent across the codebase. VSCode Snippet + Copilot Combo You can take templates one step further by combining VSCode snippets with Copilot. Snippets give you a skeleton, and Copilot fills in the details. Example snippet for Jest tests (snippets.code-snippets): JavaScript { "Jest Test Case": { "prefix": "jesttest", "body": [ "it('should ${1:description}', () => {", " const result = ${2:functionUnderTest}(${3:inputs});", " expect(result).toBe(${4:expected});", "});" ] } } Now, when you type jesttest, VSCode inserts the skeleton, and Copilot suggests the rest of the test logic. This combo is extremely powerful for repetitive work like writing dozens of test cases, setting up CRUD endpoints, or scaffolding new services. Best Practices for Engineers Using Copilot templates effectively requires some discipline. Here are five best practices: Seed with Clarity – The clearer your first example, the better Copilot’s suggestions. Use Structured Comments – JSDoc, docstrings, or checklists act as hidden prompts. Think in Patterns, Not One-offs – Copilot learns from repeated structures. Review Everything – Copilot is fast, but it’s not infallible. Validate logic before committing. Standardize as a Team – Share templates in a team repo so everyone benefits from consistency. 6. A Real Workflow Example Imagine you’re building a financial app with dozens of utility functions. Write a function with a docstring: Python def calculate_interest(principal: float, rate: float, years: int) -> float: """Calculate compound interest. principal: initial amount rate: annual interest rate as decimal years: number of years returns: final amount """ Write the first test case: Python def test_calculate_interest_basic(): assert calculate_interest(1000, 0.05, 2) == 1102.5 Add a comment # more edge cases. Copilot will suggest tests for: Zero years Negative rate Very large numbers What would take 30 minutes of manual test writing now takes 5 minutes. Final Thoughts GitHub Copilot is more than just an autocomplete tool—it’s a pattern amplifier. Templates transform Copilot from a helpful assistant into a productivity engine that can enforce standards, reduce boilerplate, and speed up development. For individual engineers, templates mean faster iteration and fewer repetitive tasks. For teams, they mean consistency, reliability, and onboarding efficiency. If you want to get the most out of Copilot: Start with 3–5 reusable templates (tests, APIs, utility functions). Encourage your team to use them consistently. Expand over time as new patterns emerge. By combining the creativity of engineers with the power of Copilot, you can focus more on solving business problems and less on writing the same code over and over.

AI coding assistants have become increasingly capable in understanding not only code but also project-specific documentation. In this experiment, we tested Context7, a new MCP server, to see how well it could work with a large-scale, real-world documentation set — ZK Framework’s developer documentation. For readers unfamiliar with ZK, it’s a Java-based web framework that allows developers to build rich web applications with minimal JavaScript by providing server-side components. Because ZK documentation covers both UI and server-side concepts, it serves as a good benchmark for evaluating how AI models handle complex, domain-specific technical text. Our Approach We ran a controlled experiment to see if the Context7 MCP server could improve AI-generated answers to ZK Framework questions. Our setup was straightforward: using the same AI model (Claude Code) with two conditions — with and without Context7 MCP server — across ten common ZK questions. Executive Summary Expected Result Context7 should help by providing up-to-date documentation. Actual Result Without Context7: 73.8% (PASS)With Context7: 59% (FAIL)Performance decreased by about 15 percentage points when using Context7 Why This Matters Context7 is popular in the AI coding communityUnderstanding when it works (and doesn’t) helps developers choose the right toolsHighlights the importance of how documentation retrieval systems interact with AI models Background: What Is Context7? Context7 is a Model Context Protocol (MCP) server by Upstash that automatically retrieves documentation for over 33,000 libraries. Used by AI coding assistants like Cursor and Claude Code, it promises “up-to-date, version-specific documentation.” How Context7 Works (based on their official documentation): Dynamically retrieves documentation from source repositories when triggeredDetects the specific library mentioned in promptsFetches version-specific documentation and code examplesInjects relevant documentation directly into the AI model’s context What Context7 provides: Up-to-date code snippets and examplesVersion-specific documentationFocused on providing current information to prevent AI hallucinations Important note: The exact internal architecture (how Context7 parses, indexes, and retrieves documentation) is not publicly documented. Based on community research and reverse engineering efforts (see this Reddit discussion), Context7 appears to use some form of semantic search and snippet extraction, but the specific implementation details remain proprietary. Critical note: ZK documentation already works well with Claude Code directly. Our test focused specifically on Context7’s retrieval quality. The Experiment Design Creating the Test First, I used Claude Code to create 10 common ZK Framework questions and their correct answers by searching and reading the official ZK documentation directly. This became our answer key. Control Group (Baseline — Without Context7): AI: Claude Code (Claude Sonnet 4.5)Documentation: None - answers based on the AI model’s own knowledgeMethod: Claude answers questions without accessing any documentation Test Group (With Context7): AI: Claude Code (same model)Documentation: Via Context7 MCP server onlyMethod: Context7 retrieves ZK documentation snippets. Claude answers using those snippets combined with its own knowledgeNote: Claude Code did NOT search ZK documentation directly in this test The 10 Questions How to upgrade ZK from version 8 to version 10?What is MVVM in ZK, and how do I enable it?How do I display a list of data in a Listbox component?How do I load my own zk-label.properties for internationalization?What is the difference between MVC and MVVM in ZK?What is ID Space in ZK, and how does it work?How do I create a macro component in ZK?How do I enable sorting in a Listbox?How does ZK prevent CSRF (Cross-Site Request Forgery) attacks?How do I handle events between components in ZK? Evaluation Method Use Claude code to give scores based on the answer key from the actual ZK documentationScore each answer 0 ~ 100 based on: Technical accuracyCompletenessCorrectness of examplesActionability The Results: Context7 Did Not Improve Accuracy Overall Scores MetricWithout Context7With Context7ChangeTotal Score738/1000 (73.8%)590/1000 (59%)-148 (-15%)Result PASS FAILCertification failedQuestions worse-8 out of 1080% degradationQuestions same-2 out of 1020% neutralQuestions better-0 out of 100% improvement Not a single question improved with Context7. Question-by-Question Results QuestionWithout Context7With Context7ChangeQ1: Upgrade 8→1055%35%-20 Q2: MVVM85%85%0Q3: Listbox data70%70%0Q4: i18n properties45%25%-20 Q5: MVC vs MVVM90%80%-10Q6: ID Space75%55%-20 Q7: Macro component80%75%-5Q8: Listbox sorting88%65%-23 Q9: CSRF security65%20%-45 Q10: Event handling85%80%-5 What This Means for ZK App Developers Should You Use Context7 With ZK? For These Questions: ❌ Not Recommended How to upgrade ZK?How does security work?Configuration setupArchitectural concepts For These Questions: ✅ Might Help Quick syntax lookupsComponent creation examplesSimple API references Better Approaches for ZK Use Claude Code directly (without Context7) Currently gives better resultsCan search the full documentationUnderstands complete guidesSearch the ZK documentation yourself Visit the official docs at zkoss.orgUse the site search for specific topicsRead complete guides for complex topicsAsk in the ZK Community ForumGet human expertise for complex questions The Silver Lining This test validates that ZK documentation is comprehensive and reliableAI models CAN already answer ZK-related questions well when given direct access to documentationThe issue lies in how documentation is retrieved, not in its quality Conclusion Key message: This experiment wasn’t about showing Context7 in a negative light — it was about understanding how AI tools interact with different types of documentation. Context7 performs well for API-style references, but when it comes to frameworks like ZK that emphasize architecture and conceptual guidance, it did not improve the accuracy. Takeaway for Community Test tools before adoptingCompare with baselineShare findings transparentlyChoose tools that match your needs What We Learned Even popular tools can vary greatly depending on the framework’s documentation style.Documentation retrieval and context matter as much as content quality.Evaluating tools objectively helps developers choose the right fit for their workflow. AI-assisted development is becoming a normal part of many developers’ workflows. Understanding when AI tools help — and when they don’t — allows developers to make better decisions, avoid misleading outputs, and use these tools more effectively. At the same time, insights like these also help framework and tool maintainers make their documentation more accessible to both humans and AI systems. Note: AI tooling evolves rapidly. The results here reflect the state of Context7 and related systems as of October 2025. Future improvements may significantly change how well these integrations perform. Reference ZK Framework Official Documentation Repository Discussion Have you tried using AI tools with framework documentation — like Context7, MCP servers, or IDE assistants? What worked well, and what didn’t? Share your thoughts in the comments — your experience can help other developers make sense of how AI fits into real-world coding workflows.

By Hawk Chen CORE

Control Diagram The Architecture: Core Components Query vault tableController proceduresTrigger point (can be an external or internal trigger) Details of Core Components 1. Query Vault Table This table serves as the heart of this strategy. We can securely store all the queries that load data into the cloud data warehouse in this Vault table. This table contains the following fields: Job_Group: This is a logical segregation of queries between jobs. For example, if the Employee table gets its data from different sources and has multiple queries, then all these queries come under one ‘Job Group.’ Similarly, for the other tables, if there are multiple queries, they come under a separate ‘Job Group.’ We are distinguishing one set of queries from the others to help the controlling section identify the jobs effectively.Destination: This field represents the final table where we load the data into. For example, there could be multiple staging tables before we load into the final target table (Employee table example). We need to mention the final Target table (Employee in this case), which provides visibility on the final table (be it a fact or dimension) and helps the controlling section to further segregate things one level down, in case there is more than one table, if we intend to load.Job_Group_Order_Number: This field helps in constraint-based loading in a particular job group. If we have multiple target tables, under the same job group, and they need to be loaded in a certain order, then while making entries in the Query vault table, we need to insert the entries in the order that we want them to be executed. Sorting on this field will help in executing the jobs in the correct and desired order during orchestration.Action_Tag: This is a descriptive field predominantly for the summarized understanding of the entry in the Vault table. This doesn’t hold any significance, but it enhances the understanding of the users. This can be removed if needed.Query_Mode: This field specifies the action executed by the query. For example, if we are inserting data into a table, then this field contains ‘Insert.’ Certain Logics in the controlling sections are based on the Action Tag field.Query_Text: This field contains the actual query that needs to be executed. Please refer to the ‘Rules for Query Text field’ sub-section under the ‘Important Notes of Usage’ section for important details.Run_Order: When we have multiple queries that load into one final table, there must be some order of query execution that we should follow to load the data correctly. This field will provide the controlling section with the order in which the queries need to be executed. Start with 1 and increment by 1. Please refer to the ‘rules for Run Order field’ sub-section under the ‘Important Notes of Usage’ section for important details.Log_Data_Lifespan_in_Days: The log table holds the statistics for both current and history executions. If we mentioned 10 in this field, it means we are directing the log table to hold only 10 days of execution history. This field helps with backtracking of executions and failures. This becomes extremely important when we try to build an operational dashboard to monitor the health metrics of jobs.Run_Plan: This field can contain only two values. One is ‘Normal,’ which indicates the ‘Job Group’ will execute in a Normal schedule, like once a day, week, or month; the other is ‘Out of Sequence,’ which indicates that the job needs special runs. Its main purpose is to serve as a descriptive field that informs the user whether a job runs on a normal schedule or OOS.Execution_Switch: We often encounter situations where we want to skip execution of a query (or set of queries) during execution due to restarts or incremental loads, etc. This switch gives the user control to select which query needs to be executed in not-so-regular situations. If this field says ‘Yes’, it means that the entry in the vault table must be considered for execution. If this field says ‘No,’ then the execution of the query that belongs to this record will be skipped.Job_Status: The default value for this would be ‘Not Started.’ Once the job group is submitted for execution, the ‘job status’ will change to ‘In Progress,’ and if the query is successfully executed, ‘Job status’ will be changed to ‘Success.’ If the job fails for any reason, this field will serve as a marker for the controlling section to restart from the failed point/query, instead of restarting from the first query. 2. Controller Procedures Workflow Controller The workflow controller is a stored procedure written in JavaScript (Can be written using PLSQL too) to control the execution, especially in a situation where we are targeting to load more than one target table or want to coordinate the dependencies between two data models. Execution statement: call schema_name. workflow_controller_stored_proc_name (Job_Group, Schedule) ‘Job Group’ and Schedule are the parameters that a user needs to pass manually, based on which either the query selection for the execution will be done, or parameters will be created for an iterative stored proc to execute multiple job groups. Task Controller The task controller is a stored procedure intended to control the execution of queries within a Job group and Destination. This will be helpful, especially when we have dependencies between different modules or models, and the loads must be performed in a specific sequence. All such modules or models must be clubbed under one job group. We do not have to explicitly specify or make a call to the task controller, as the call happens automatically, internally, and iteratively until all the combinations of Job Group and Destination are exhausted. Execution statement: call schema_name.task_controller_stored_proc_name (Job_Group, destination) 3. Trigger Controller Trigger controller refers to a tool from which we invoke the workflow controller. This can be an ETL tool (Informatica PowerCenter, IDMC) or a feature like Snowflake task in Snowflake database, or any other tool that can invoke a stored procedure in a database. Let’s consider both Informatica Intelligent Data Management Cloud (IDMC) and Snowflake for explanation purposes. Informatica IDMC Create a simple mapping with a dummy source and target.Create a mapping task on top of the mapping.Define a parameter (say $$Workflow_Controller) and use that parameter in the pre-SQL section.Mention the parameter file name and location in the mapping task.Prepare the parameter file by assigning a procedure call statement to a pre-SQL parameter.The purpose of creating parameters is to reuse a single mapping for multiple jobs. Snowflake Tasks Create a task with the desired name. The definition of this task contains the call statement to the stored proc.Enable the task as it's suspended by default.Ensure the role with which you are executing the task has permissions for warehouse, task, and stored proc. How This Orchestration Works A call will be made to the workflow controller from the trigger controller based on the schedule of the job or a manual trigger.This call contains two important parameters: Job Group and Schedule. This step creates multiple call statements, or a single call statement, based on the number of values in the ‘destination’ field for a job group.These call statements generated in step 2 will be passed to the Task controller in the order they are generated. ‘Job_Group_Order_Number’ will help in generating the call statements in the desired order.For each call statement passed to the task controller, Query selection for execution will be done based on the parameters provided and the ‘execution switch’ field. The ‘Run Order’ field will help in executing the queries in the right order inside a ‘job group’ and destination.Workflow controller ensures that the task controller is iterating through all the queries under a ‘job group’ and destination. It also ensures that the task controller loops through all the call statements generated.During the execution of queries for a ‘Job group’ and destination, the log information is stored in a temporary table, which is then pushed to the log table. Log Table The audit logging feature is completely automated and does not require any manual intervention. The log table comes in handy to identify the reason for query failures, the number of records got inserted/updated / deleted, the Amount of time a job has taken to complete the run, etc. The structure of the log table is as follows. Start time: Indicates the query kick-off time.End time: indicates the query execution completion time.Run time: The time taken for the query to complete.Status: Run status of the query – completed or failedRecords inserted: Number of records inserted by the queryRecords updated: Number of records updated by the queryRecords deleted: Number of records deleted by the queryError: The reason why the query has failed Features Smart Restart If a query fails during execution due to some issue, and when you restart the job after fixing the issue, the job will restart from the query that failed last time instead of restarting from query 1. The intermittent table, which is used to store the audit logs temporarily, helps in identifying the failure point. This is the default functionality. There would be cases where we do not want to restart from the failure point, instead run from the first query of that job group and destination. In this case, the entries in the intermittent table, which stores the audit logs temporarily, need to be flushed to override the default functionality. Manual Control We might come across cases where we do not want to execute all the queries under a ‘Job Group’ and Destination fields all the time. We sometimes might want to skip queries in between. In such cases, flipping the execution switch to ‘No’ would skip that query execution. Query Change Detection If a query is changed in between executions, the strategy detects the change and restarts the execution from query 1 for a particular ‘job group’ and destination values. This feature will be helpful when a query fails due to duplicates/data issues (due to code) or syntax issues. In these cases, a query update is mandatory, and we don’t need to worry about flushing the intermittent table to restart the job. Automatic Log History Deletion Log data will get accumulated over a period and will slow down the process. This strategy will auto-clean the log data based on the number given under the ‘Log_Data_Lifespan_in_Days’ field. If the value is 30, then the log clean-up happens every 30 days, keeping the log table lighter. Important Notes for Usage Rules for Query Text field: Double hyphens, which we generally use to comment out a piece of code in the query, must be avoided, as there is a chance those hyphens might comment out the rest of the code in addition to the piece of code.If we are against formatting the query into a single line, we can use a backslash (\) at the end of each line.Enclose the query in double dollar signs to avoid any misreads when encountering special characters.Make sure the objects on which the queries operate must have access to the user running this execution strategy. These are called the caller’s rights. Rules for ‘Run Order’ Field: Do not provide the same number for a particular ‘Job Group’ and Destination value. If we provide the same number for this combination, the order in which the query is executed is not guaranteed. Rules for Parallel Execution: Multiple individual ‘Job Groups’ can be run in parallel by creating separate jobs in the trigger controller section.This strategy does not support parallel execution by default, but that can be achieved by assigning a different job group to the queries.Inter Job Group dependency can be set while making entries by using the ‘job_group_order_num’ field. Final Notes This approach comes in handy, especially in the ELT ecosystem, where we replicate the data into our data platform first and then process it. If This Strategy helps us to make use of the full potential of the cloud platform, as the complete execution happens within the cloud native data warehouse. Enhancements: We can add a field in the Vault table to provide the size of the cluster if we are expecting to handle huge amounts of data.A dashboard can be created on top of the Log table to capture job-level stats.

So you've been thrown into the world of Azure Data Factory (ADF) and you're wondering what the heck you've gotten yourself into? Been there. Let me break it down for you in terms that actually make sense. What Is This Thing Anyway? Think of Azure Data Factory as the ultimate data moving service. It's like having a really smart conveyor belt system that can grab data from basically anywhere, do stuff to it, and dump it somewhere else. Need to pull customer data from your SQL database, clean it up, and shove it into a data lake? ADF's got you covered. The best part? You don't need to write a ton of customer code or manage servers. Microsoft handles the heavy lifting while you focus on the "what" instead of the "how". Pipelines: Your Data Workflows Here's where things get interesting. A pipeline in ADF is basically a workflow, a series of steps that your data goes through. Picture it like a recipe: Get the ingredients (extract data)Prep them (transform data)Cook them (process data)Serve them (load to destination) Each step in your pipeline is called an "activity." You might have a Copy Activity to move data from Point A to Point B, a Data Flow activity to clean and transform it, or a stored procedure activity to run some customer SQL logic. The Stuff That Actually Matters Copy Activities This is your bread and butter. Copy Activities are probably 80% of what you will use. They're simple but incredibly powerful. You tell it where to get data, where to put it, and any basic transformations you want along the way. The connector library is huge — SQL Server, Oracle, MongoDB, REST APIs, flat files, you name it. I've used it to pull data from some seriously weird legacy systems that I thought were impossible to integrate with. Data Flows When you need to do more than just copy data around, Data Flows are your friend. Think of them as visual ETL (Extract, Transform, and Load) processes. You drag and drop transformations like joins, aggregations, and filtering without writing SQL or code. The learning curve is a bit steep at first, but once you get it, you can build complex data transformations pretty quickly. Plus, it generates Spark code under the hood, so it scales well. Triggers Nothing happens in ADF unless something kicks it off. Triggers are how you schedule your pipelines or make them respond to events. You've got your basic schedule triggers (run every day at 2 AM), tumbling window triggers (process data in chunks), and event-based triggers (run when a file lands in blob storage). The event-based ones are particularly handy for building real-time data processing. The Reality Check Let's be honest about what you're getting into: The Good Stuff No infrastructure to manageScales automaticallyIntegrates with everything Microsoft(and most non-Microsoft stuff)Visual interface and non-developers can understandBuilt-in monitoring and logging The Pain Points Debugging can be a nightmare when things go wrongThe visual designer sometimes feels clunkyPricing can get expensive if you're not carefulLimited when you need really custom logicVersion control is... not great Tips From The Trenches Start Small: Don't try to build your entire data architecture in one massive pipeline. Break things into smaller, manageable chunks. Trust me on this one. Use Parameters: Everything should be parameterized. Source paths, destination tables, date ranges, make it all configurable. Monitor Everything: Set up alerts for failed pipeline runs. There's nothing worse than finding out your critical data load failed three days ago. Test in Lower Environments: ADF doesn't have a great local development story, so having a proper dev/test environment is crucial. Learn the Expression Language: ADF has its own expression language for dynamic content. It's weird at first, but once you get comfortable with it, you can do some pretty cool stuff. When to Use ADF (And When Not To) The Azure Data Factory is perfect for: Moving data between Azure servicesBuilding traditional ETL pipelinesIntegrating cloud and on-premises systemsWhen you need something that business users can understand And maybe not so much for: Real-time streaming (though it can handle near real time)Complex business logic (stick to simpler transformations)When you need millisecond latencyIf you're not already in the Azure ecosystem Real Example: Processing Daily Customer Data Files Let me walk you through a real project I built, processing daily customer data CSV files that get dropped into blob storage and loading them into a SQL database for reporting. The Scenario Every morning at 6 AM, our system dumps a CSV file with yesterday's customer data into an Azure Storage account. We need to: Validate that the file exists and has dataClean and transform the dataLoad it into our reporting databaseArchive the processed fileSend notifications if anything fails Setting Up the Linked Services First, you need to define your connections. Here's what the JSON looks like for connecting to blob storage: JSON { "name": "BlobStorageLinkedService", "type": "Microsoft.DataFactory/factories/linkedservices", "properties": { "type": "AzureBlobStorage", "typeProperties": { "connectionString": { "type": "AzureKeyVaultSecret", "store": { "referenceName": "KeyVaultLinkedService", "type": "LinkedServiceReference" }, "secretName": "storage-connection-string" } } } } And here's the SQL Database connection: JSON { "name": "SqlDatabaseLinkedService", "type": "Microsoft.DataFactory/factories/linkedservices", "properties": { "type": "AzureSqlDatabase", "typeProperties": { "connectionString": { "type": "AzureKeyVaultSecret", "store": { "referenceName": "KeyVaultLinkedService", "type": "LinkedServiceReference" }, "secretName": "sqldb-connection-string" } } } } The Main Pipeline Here's the pipeline that orchestrates everything. JSON { "name": "ProcessDailyCustomer", "properties": { "parameters": { "ProcessDate": { "type": "string", "defaultValue": "@formatDateTime(utcNow(), 'yyyy-MM-dd')" } }, "activities": [ { "name": "CheckFileExists", "type": "GetMetadata", "typeProperties": { "dataset": { "referenceName": "CustomerFileDataset", "type": "DatasetReference", "parameters": { "fileName": "@concat('sales_', pipeline().parameters.ProcessDate, '.csv')" } }, "fieldList": ["exists", "itemName", "size"] } }, { "name": "ProcessCustomerData", "type": "ExecuteDataFlow", "dependsOn": [ { "activity": "CheckFileExists", "dependencyConditions": ["Succeeded"] } ], "policy": { "timeout": "0.12:00:00", "retry": 2 }, "typeProperties": { "dataflow": { "referenceName": "TransformCustomerData", "type": "DataFlowReference", "parameters": { "processDate": { "value": "@pipeline().parameters.ProcessDate", "type": "Expression" } } } } }, { "name": "ArchiveProcessedFile", "type": "Copy", "dependsOn": [ { "activity": "ProcessCustomerData", "dependencyConditions": ["Succeeded"] } ], "typeProperties": { "source": { "type": "DelimitedTextSource" }, "sink": { "type": "DelimitedTextSink" } } } ] } } The Data Flow for Transformations This is where the actual data processing happens: JSON { "name": "TransformCustomerData", "properties": { "type": "MappingDataFlow", "typeProperties": { "sources": [ { "dataset": { "referenceName": "CustomerFileDataset", "type": "DatasetReference" }, "name": "CustomerSource" } ], "sinks": [ { "dataset": { "referenceName": "CustomerTableDataset", "type": "DatasetReference" }, "name": "CustomerDB" } ], "transformations": [ { "name": "FilterValidRecords", "description": "Remove records with missing required fields" }, { "name": "CalculateAge", "description": "Calculate age from date of birth and add customer fields" }, { "name": "LookupContactInfo", "description": "Validate email format and standardize phone numbers" } ], "script": "source(output(\n\t\tCustomerID as string,\n\t\tFirstName as string, \n\t\tLastName as string,\n\t\tEmail as string,\n\t\tPhone as string,\n\t\tDateOfBirth as date, \n\t\tAddress as string,\n\t\tCity as string,\n\t\tState as string,\n\t\tZipCode as string, \n\t\tRegistrationDate as timestamp\n\t),\n\tallowSchemaDrift: true,\n\tvalidateSchema: false) ~> CustomerSource\n\nCustomerSource filter(!isNull(CustomerID) && !isNull(Email) && !isNull(FirstName) && !isNull(LastName)) ~> FilterValidCustomers\n\nFilterValidCustomers derive(CleanEmail = lower(trim(Email)),\n\t\tCleanPhone = regexReplace(Phone, '[^0-9]', ''), \n\t\tIsValidEmail = contains(Email, '@') && contains(Email, '.')) ~> StandardizeContactInfo \n\nStandardizeContactInfo derive(Age = toInteger(daysBetween(DateOfBirth, currentDate()) / 365), \n\t\tFullName = concat(FirstName, ' ', LastName), \n\t\tCustomerTenure = toInteger(daysBetween(RegistrationDate, currentTimestamp()) / 365), \n\t\tAgeGroup = case(\n\t\t\tAge < 25, 'Young Adult',\n\t\t\tAge < 45, 'Adult', \n\t\t\tAge < 65, 'Middle Age',\n\t\t\t'Senior'\n\t\t)) ~> EnrichCustomerProfile\n\nEnrichCustomerProfile derive(FullAddress = concat(Address, ', ', City, ', ', State, ' ', ZipCode), \n\t\tTenureSegment = case(\n\t\t\tCustomerTenure < 1, 'New',\n\t\t\tCustomerTenure < 3, 'Growing',\n\t\t\tCustomerTenure < 5, 'Established',\n\t\t\t'Loyal'\n\t\t), \n\t\tProcessedDate = currentTimestamp()) ~> DeriveCustomerMetrics\n\nDeriveCustomerMetrics sink(allowSchemaDrift: true,\n\tvalidateSchema: false,\n\tinput(\n\t\tCustomerID as string, \n\t\tFirstName as string,\n\t\tLastName as string,\n\t\tFullName as string,\n\t\tEmail as string, \n\t\tCleanEmail as string,\n\t\tPhone as string,\n\t\tCleanPhone as string, \n\t\tIsValidEmail as boolean,\n\t\tDateOfBirth as date,\n\t\tAge as integer, \n\t\tAgeGroup as string,\n\t\tAddress as string,\n\t\tCity as string,\n\t\tState as string, \n\t\tZipCode as string,\n\t\tFullAddress as string,\n\t\tRegistrationDate as timestamp, \n\t\tCustomerTenure as integer,\n\t\tTenureSegment as string,\n\t\tProcessedDate as timestamp\n\t), \n\tskipDuplicateMapInputs: true,\n\tskipDuplicateMapOutputs: true) ~> CustomerDB" } } } Setting Up the Trigger Here's how to set up a daily trigger: JSON { "name": "DailyCustomerProcessTrigger", "properties": { "type": "ScheduleTrigger", "typeProperties": { "recurrence": { "frequency": "Day", "interval": 1, "startTime": "2024-01-01T07:00:00Z", "timeZone": "UTC", "schedule": { "hours": [7], "minutes": [0] } } }, "pipelines": [ { "pipelineReference": { "referenceName": "ProcessDailyCustomer", "type": "PipelineReference" } } ] } } The Real World Gotchas File naming conventions: Make sure your file naming is consistent. I learned this the hard way when someone changed the date format and broke everything. Error handling: Always add proper error handling. In the example above, I'd add email notifications for failures. JSON { "name": "SendFailureEmail", "type": "WebActivity", "dependsOn": [ { "activity": "ProcessSalesData", "dependencyConditions": ["Failed"] } ], "typeProperties": { "url": "https://your-logic-app-webhook-url", "method": "POST", "body": { "subject": "Daily Sales Processing Failed", "message": "@concat('Pipeline failed for date: ', pipeline().parameters.ProcessDate)", "priority": "High" } } } Testing: Create a separate pipeline for testing with smaller datasets. Don't test with production data it never ends well. What This Actually Looks Like in Practice When you deploy this, here's what happens every morning: Trigger fires at 7 AM.Pipeline checks if the customer file exists.If found, the data flow processes ~50,000 records in about 3 minutes.Data gets loaded into the SQL database.Original file gets moved to the archive folder.You get a success notification (or failure alert if something breaks). Getting Started Now here's what I'd actually recommend for getting started Build a simple copy pipeline first: Move some data from A to B. Get familiar with the interface.Add some basic transformations: Try a lookup or a conditional split in a data flow.Set up monitoring and alerts: Learn how to read the run history and set up notifications.Play with triggers: Start with a simple schedule, then try event-based triggers.Dive into parameters and variables: This is where ADF really starts to shine. The Bottom Line Azure Data Factory isn't perfect, but it's pretty solid for most data integration scenarios. It saves you from writing a lot of boilerplate code and managing infrastructure. The visual interface makes it easier to explain to stakeholders what your data pipelines are doing. Just remember: start simple, parameterize everything, and don't try to force ADF to do things it wasn't designed for. It's a tool, not a magic wand. And hey, when it works, you will look like a data integration wizard. When it doesn't... well, that's what StackOverflow is for.