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An In-Depth Guide to Threads in OpenAI Assistants API

By Mohammed Talib

In this blog, we will explore what chat completion models can and cannot do and then see how Assistants API addresses those limitations. We will also focus on threads and messages — how to create them, list them, retrieve them, modify them, and delete them. Additionally, we will add some Python code snippets and show possible outputs based on the script language. Limitations of Chat Completion Models No Memory Chat completion models do not have a memory concept. For example, if you ask: “What’s the capital of Japan?” The model might say: “The capital of Japan is Tokyo.” But when you ask again: “Tell me something about that city.” It often responds with: “I’m sorry but you didn’t specify which city you are referring to.” It does not understand what was discussed previously. That’s the main issue: there is no memory concept in chat completions. Poor at Computational Tasks Chat completion models are really bad at direct computational tasks. For instance, if you want to reverse the string “openaichatgpt,” it may generate the wrong output, like inserting extra characters or missing some letters. No Direct File Handling In chat completions, there is no way to process text files or Word documents directly. You have to convert those files to text, do chunking (divide documents into smaller chunks), create embeddings, and do vector searches yourself. Only then do you pass some relevant text chunks to the model as context. Synchronous Only Chat completion models are not asynchronous. You must ask a question and wait for it to finish. You cannot do something else while it’s processing without extra workarounds. Capabilities of the Assistants API Context Support With Threads In Assistants API, you can create a thread for each user. A thread is like a chat container where you can add many messages. It persists the conversation, so when the user logs in again, you can pass the same thread ID to retrieve what was discussed previously. This is very helpful. Code Interpreter There is also a code interpreter. Whenever you ask for some computational task, it runs Python code. It then uses that answer to expand or explain. This makes it very helpful for reversing strings, finding dates, or any Python-based operations. Retrieval With Files The Assistants API has retrieval support, letting you upload files and ask questions based on those files. The system handles the vector search process and then uses relevant chunks as context. You can upload up to 20 files in Assistants as context. This is very helpful for referencing company documents, reports, or data sets. Function Calling Function calling allows the model to tell you what function to call and what arguments to pass, so that you can get external data (like weather or sales from your own database). It does not call the function automatically; it indicates which function to call and with what parameters, and then you handle that externally. Asynchronous Workflows The Assistants API is asynchronous. You can run a request, and you don’t have to wait for it immediately. You can keep checking if it’s done after a few seconds. This is very helpful if you have multiple tasks or want to do other things in parallel. Focusing on Threads and Messages A thread is essentially a container that holds all messages in a conversation. OpenAI recommends creating one thread per user as soon as they start using your product. This thread can store any number of messages, so you do not have to manually manage the context window. Unlimited messages. You can add as many user queries and assistant responses as you want.Automatic context handling. The system uses truncation if the conversation grows beyond token limits.Metadata storage. You can store additional data in the thread’s metadata (for example, user feedback or premium status). Below are code snippets to demonstrate how to create, retrieve, modify, and delete threads. 1. Creating an Assistant First, you can create an assistant with instructions and tools. For example: Python from openai import OpenAI client = OpenAI() file_input = client.files.create(file=open("Location/to/the/path", "rb"), purpose = "assistants") file_input.model_dump() Python assistant = client.beta.assistants.create( name="data_science_tutor", instructions="This assistant is a data science tutor.", tools=[{"type":"code_interpreter", {"type":"retrieval"}], model="gpt-4-1106-preview", file_ids=[file_input.id] ) print(assistant.model_dump()) 2. Creating Threads A thread is like a container that holds the conversation. We can create one thread per user. Python thread = client.beta.threads.create() print(thread.model_dump()) id – a unique identifier that starts with thr-object – always "thread"metadata – an empty dictionary by default Why Create Separate Threads? OpenAI recommends creating one thread per user as soon as they start using your product. This structure ensures that the conversation context remains isolated for each user. 3. Retrieving a Thread Python retrieved_thread = client.beta.threads.retrieve(thread_id=thread.id) print(retrieved_thread.model_dump()) This returns a JSON object similar to what you get when you create a thread, including the id, object, and metadata fields. 4. Modifying a Thread You can update the thread’s metadata to store important flags or notes relevant to your application. For instance, you might track if the user is premium or if the conversation has been reviewed by a manager. Python updated_thread = client.beta.threads.update( thread_id=thread.id, metadata={"modified_today": True, "user_is_premium": True} ) print(updated_thread.model_dump()) modified_today – a custom Boolean to note whether you changed the thread todayuser_is_premium – a Boolean flag for user account tierconversation_topic – a string that labels this thread’s main subject Further Metadata Examples {"language_preference": "English"} – if the user prefers answers in English or another language{"escalated": true} – if the thread needs special attention from a support team{"feedback_rating": 4.5} – if you collect a rating for the conversation 5. Deleting a Thread When you no longer need a thread, or if a user deletes their account, you can remove the entire conversation container: Python delete_response = client.beta.threads.delete(thread_id=thread.id) print(delete_response.model_dump()) Once deleted, you can no longer retrieve this thread or any messages it contained. Working With Messages Previously, we focused on threads — the containers that hold conversations in the Assistants API. Now, let’s explore messages, which are the individual pieces of content (questions, responses, or system notes) you add to a thread. We’ll walk through creating messages, attaching files, listing and retrieving messages, and updating message metadata. We’ll also show Python code snippets illustrating these steps. Messages and Their Role in Threads What Are Messages? Messages are mostly text (like user queries or assistant answers), but they can also include file references. Each thread can have many messages, and every message is stored with an ID, a role (for example, "user" or "assistant"), optional file attachments, and other metadata. Opposite Index Order Unlike chat completions, where the first message in the list is the earliest, here, the first message you see in the array is actually the most recent. So, index 0 corresponds to the newest message in the thread. Annotations and File Attachments Messages can include annotations, for instance, if a retrieval step references certain files. When using a code interpreter, any new files generated may also appear as part of the message annotations. Create a Message in a Thread Messages are added to a thread. Each message can be a user message or an assistant message. Messages can also contain file references. Before adding messages, we need a thread. If you do not already have one: Python # Create a new thread new_thread = client.beta.threads.create() print(thread.model_dump()) # Shows the thread's detailspython Python # Create a new message in the thread message = client.beta.threads.messages.create( thread_id=thread.id, role="user", content="ELI5: What is a neural network?", file_ids=[file_input.id] # Passing one or more file IDs ) print(message.model_dump()) Here, you can see: Message ID – unique identifier starting with msgRole – user, indicating this is a user inputFile attachments – the file_ids list includes any referenced filesAnnotations – empty at creation, but can include details like file citations if retrieval is involvedMetadata – a placeholder for storing additional key-value pairs List Messages in a Thread To list messages in a thread, use the list method. The limit parameter determines how many recent messages to retrieve. Now, let’s try to list all the messages: You will see only the most recent messages. For instance, if we’ve added just one message, the output will look like: Python messages_list = client.beta.threads.messages.list( thread_id=thread.id, limit=5 ) for msg in messages_list.data: print(msg.id, msg.content) If there are multiple messages, the system works like a linked list: The first ID points to the newest message.The last ID points to the earliest message. Retrieve a Specific Message Python retrieved_msg = client.beta.threads.messages.retrieve( thread_id=new_thread.id, message_id=message.id ) print(retrieved_msg.model_dump()) Retrieve a Message File Now, let’s retrieve a message file: This provides the file’s metadata, including its creation timestamp. Python files_in_msg = client.beta.threads.messages.files.list( thread_id=new_thread.id, message_id=message.id ) print(files_in_msg.model_dump()) Modify a Message Python updated_msg = client.beta.threads.messages.update( thread_id=new_thread.id, message_id=message.id, metadata={"added_note": "Revised content"} ) print(updated_msg.model_dump()) Delete a Message Python deleted_msg = client.beta.threads.messages.delete( thread_id=new_thread.id, message_id=message.id ) print(deleted_msg.model_dump()) We have seen that chat completion models have no memory concept, are bad at computational tasks, cannot process files directly, and are not asynchronous. Meanwhile, Assistants API has context support with threads, code interpreter for computational tasks, retrieval for files, function calling for external data, and it also supports asynchronous usage. In this blog, we focused on how to create, list, retrieve, modify, and delete threads and messages. We also saw how to handle file references within messages. In the next session, we will learn more about runs, which connect threads and assistants to get actual outputs from the model. I hope this is helpful. Thank you for reading! Let’s connect on LinkedIn! Further Reading Where did multi-agent systems come from?Summarising Large Documents with GPT-4oHow does LlamaIndex compare to LangChain in terms of ease of use for beginnersPre-training vs. Fine-tuning [With code implementation]Costs of Hosting Open Source LLMs vs Closed Sourced (OpenAI)Embeddings: The Back Bone of LLMsHow to Use a Fine-Tuned Language Model for Summarization More

Designing AI Multi-Agent Systems in Java

By Luca Venturi

The year 2025 is the year of AI agents. For the purposes of this article, an AI agent is a system that can leverage AI to achieve a goal by following a series of steps, possibly reasoning on its results and making corrections. In practice, the steps that an agent follows can constitute a graph. We will build a reactive agent (meaning that it reacts to a stimulus, in our case, the input from a user) to help people find their perfect vacation. Our agent will find the best city in the specified country, considering the food, sea, and activity specified by the user. The agent will look like this: In the first phase, it will collect information in parallel, ranking the cities by a single characteristic. The last step will use this information to choose the best city. You could use a search engine to collect information, but we will use ChatGPT for all the steps, though we will use different models. You could write all the code by hand or use some library to help you simplify the code a bit. Today, we will use a new feature that I added to Fibry, my Actor System, to implement the graph and control the parallelism in great detail. Fibry is a simple and small Actor System that provides an easy way to leverage actors to simplify multi-threading code and does not have any dependency. Fibry also implements a Finite State Machine, so I decided to extend it to make it easier to write agents in Java. My inspiration has been LangGraph. As Fibry is about multi-threading, the new features allow plenty of flexibility in deciding the level of parallelism while keeping everything as simple as possible. You should use Fibry 3.0.2, for example: Plain Text compile group: 'eu.lucaventuri', name: 'fibry', version: '3.0.2' Defining the Prompts The first step is defining the prompts that we need for the LLM: Java public static class AiAgentVacations { private static final String promptFood = "You are a foodie from {country}. Please tell me the top 10 cities for food in {country}."; private static final String promptActivity = "You are from {country}, and know it inside out. Please tell me the top 10 cities in {country} where I can {goal}"; private static final String promptSea = "You are an expert traveler, and you {country} inside out. Please tell me the top 10 cities for sea vacations in {country}."; private static final String promptChoice = """ You enjoy traveling, eating good food and staying at the sea, but you also want to {activity}. Please analyze the following suggestions from your friends for a vacation in {country} and choose the best city to visit, offering the best mix of food and sea and where you can {activity}. Food suggestions: {food}. Activity suggestions: {activity}. Sea suggestions: {sea}. """; } Defining the States Normally, you would define four states, one for each step. However, since branching out and back is quite common, I added a feature to handle this with only a single state. As a result, we need only two states: CITIES, where we collect information, and CHOICE, where we choose the city. Plain Text enum VacationStates { CITIES, CHOICE } Defining the Context The different steps of the agent will collect information that needs to be stored somewhere; let’s call it context. Ideally, you would want every step to be independent and know as little as possible of the other, but achieving this in a simple way, with a low amount of code while keeping as much type safety as possible and maintaining thread safety, is not exactly straightforward. As a result, I choose to force the context to be a record, providing some functionality to update the values of the record (using reflection underneath) while we wait for JEP 468 (Derived Record Creation) to be implemented. Java public record VacationContext(String country, String goal, String food, String activity, String sea, String proposal) { public static VacationContext from(String country, String goal) { return new VacationContext(country, goal, null, null, null, null); } } Defining the Nodes Now, we can define the logic of the agent. We will allow the user to use two different LLM models, for example, a “normal” LLM for the search and a “reasoning” one for the choice step. This is where things become a bit trickier, as it is quite dense: Java AgentNode<VacationStates, VacationContext> nodeFood = state -> state.setAttribute("food", modelSearch.call("user", replaceField(promptFood, state.data(), "country"))); AgentNode<VacationStates, VacationContext> nodeActivity = state -> state.setAttribute("activity", modelSearch.call("user", replaceField(promptActivity, state.data(), "country"))); AgentNode<VacationStates, VacationContext> nodeSea = state -> state.setAttribute("sea", modelSearch.call("user", replaceField(promptSea, state.data(), "country"))); AgentNode<VacationStates, VacationContext> nodeChoice = state -> { var prompt = replaceAllFields(promptChoice, state.data()); System.out.println("***** CHOICE PROMPT: " + prompt); return state.setAttribute("proposal", modelThink.call("user", prompt)); }; As you might have guessed, modelSearch is the model used for search (e.g., ChatGPT 4o), and modelThink could be a “reasoning model” (e.g., ChatGPT o1). Fibry provides a simple LLM interface and a simple implementation for ChatGPT, exposed by the class ChatGpt. Please note that calling ChatPGT API requires an API key that you need to define using the “-DOPENAI_API_KEY=xxxx” JVM parameter. Different and more advanced use cases will require custom implementations or the usage of a library. There is also a small issue related to the philosophy of Fibry, as Fibry is meant not to have any dependencies, and this gets tricky with JSON. As a result, now Fibry can operate in two ways: If Jackson is detected, Fibry will use it with reflection to parse JSON.If Jackson is not detected, a very simple custom parser (that seems to work with ChatGPT output) is used. This is recommended only for quick tests, not for production.Alternatively, you can provide your own JSON processor implementation and call JsonUtils.setProcessor(), possibly checking JacksonProcessor for inspiration. The replaceField() and replaceAllFields() methods are defined by RecordUtils and are just convenience methods to replace text in the prompt, so that we can provide our data to the LLM.The setAttribute() function is used to set the value of an attribute in the state without you having to manually recreate the record or define a list of “withers” methods. There are other methods that you might use, like mergeAttribute(), addToList(), addToSet(), and addToMap(). Building the Agent Now that we have the logic, we need to describe the graph of dependencies between states and specify the parallelism we want to achieve. If you imagine a big multi-agent system in production, being able to express the parallelism required to maximize performance without exhausting resources, hitting rate limiters, or exceeding the parallelism allowed by external systems is a critical feature. This is where Fibry can help, making everything explicit but relatively easy to set up. Let’s start creating the agent builder: Plain Text var builder = AiAgent.<VacationStates, VacationContext>builder(true); The parameter autoGuards is used to put automatic guards on the states, which means that they are executed with an AND logic, and a state is executed only after all the incoming states have been processed. If the parameter is false, the state is called once for each incoming state. In the previous example, if the intention is to execute D once after A and once after C, then autoGuards should be false, while if you want it to be called only once after both have been executed, then autoGuards should be true. But let’s continue with the vacation agent. Plain Text builder.addState(VacationStates.CHOICE, null, 1, nodeChoice, null); Let’s start with the method addState(). It is used to specify that a certain state should be followed by another state and execute a certain logic. In addition, you can specify the parallelism (more on that soon) and the guards. In this case: The state is CHOICEThere is no default following state (e.g., this is a final state)The parallelism is 1There is no guard The next state is just a default because the node has the possibility to overwrite the next state, which means that the graph can dynamically change at runtime, and in particular, it can perform cycles, for example, if some steps need to be repeated to collect more or better information. This is an advanced use case. An unexpected concept might be the parallelism. This has no consequences in a single run of the agent, but it is meaningful in production at scale. In Fibry, every node is backed by an actor, which, from a practical point of view, is a thread with a list of messages to process. Every message is an execution step. So parallelism is the number of messages that can be executed at a single time. In practice: parallelism == 1 means there is only one thread managing the step, so only one execution at a time.parallelism > 1 means that there is a thread pool backing the actor, with the number of threads specified by the user. By default, it uses virtual threads.parallelism == 0 means that every message creates a new actor backed by a virtual thread, so the parallelism can be as high as necessary. Every step can be configured configured independently, which should allow you to configure performance and resource usage quite well. Please consider that if parallelism != 1, you might have multi-threading, as the thread confinement typically associated with actors is lost. This was a lot to digest. If it is clear, you can check state compression. State Compression As said earlier, it is quite common to have a few states that are related to each other, they need to be performed in parallel and join before moving to a common state. In this case, you do not need to define multiple states, but you can use only one: Plain Text builder.addStateParallel(VacationStates.CITIES, VacationStates.CHOICE, 1, List.of(nodeFood, nodeActivity, nodeSea), null); In this case, we see that the CITIES state is defined by three nodes, and addStateParallel() takes care of executing them in parallel and waits for the execution of all of them to be finished. In this case, the parallelism is applied to each node, so in this case, you will get three single-thread actors. Please note that if you do not use autoGuards, this basically allows you to mix OR and AND logic. In case you want to merge some nodes in the same state, but they need to be executed serially (e.g., because they need information generated by the previous node), the addStateSerial() method is also available. AIAgent creation is simple, but there are a few parameters to specify: The initial stateThe final state (which can be null)A flag to execute states in parallel when possible Plain Text var vacationAgent = builder.build(VacationStates.CITIES, null, true); Now we have an agent, and we can use it, calling process: Plain Text vacationsAgent.process(AiAgentVacations.VacationContext.from("Italy", "Dance Salsa and Bachata"), (state, info) -> System.out.println(state + ": " + info)); This version of process() takes two parameters: The initial state, which contains the information required by the agent to perform its actionsAn optional listener, for example, if you want to print the output of each step If you need to start the action and check its return value, later, you can use processAsync(). If you are interested in learning more about the parallelism options, I recommend you check the unit test TestAIAgent. It simulates an agent with nodes that sleep for a while and can help you see the impact of each choice: But I promised you a multi-agent, didn’t I? Extending to Multi-Agents The AIAgent that you just created is an actor, so it runs on its own thread (plus all the threads used by the nodes), and it also implements the Function interface, in case you need it. There is actually nothing special about a multi-agent; just one or more nodes of an agent ask another agent to perform an action. However, you can build a library of agents and combine them in the best way while simplifying the whole system. Let’s imagine that we want to leverage the output of our previous agent and use it to calculate how much that vacation would cost so the user can decide if it is affordable enough. Like a real Travel Agent! This is what we want to build: First, we need prompts to extract the destination and compute the cost. Java private static final String promptDestination = "Read the following text describing a destination for a vacation and extract the destination as a simple city and country, no preamble. Just the city and the country. {proposal}"; private static final String promptCost = "You are an expert travel agent. A customer asked you to estimate the cost of travelling from {startCity}, {startCountry} to {destination}, for {adults} adults and {kids} kids}"; We just need two states, one to research the cities, which is done by the previous agent, and one to calculate the cost. Plain Text enum TravelStates { SEARCH, CALCULATE } We also need a context, that should also hold the proposal from the previous agent. Plain Text public record TravelContext(String startCity, String startCountry, int adults, int kids, String destination, String cost, String proposal) { } Then we can define the agent logic, which requires as a parameter another agent. The first node calls the previous agent to get the proposal. Java var builder = AiAgent.<TravelStates, TravelContext>builder(false); AgentNode<TravelStates, TravelContext> nodeSearch = state -> { var vacationProposal = vacationsAgent.process(AiAgentVacations.VacationContext.from(country, goal), 1, TimeUnit.MINUTES, (st, info) -> System.out.print(debugSubAgentStates ? st + ": " + info : "")); return state.setAttribute("proposal", vacationProposal.proposal()) .setAttribute("destination", model.call(promptDestination.replaceAll("\\{proposal\\}", vacationProposal.proposal()))); }; The second node computes the cost: Plain Text AgentNode<TravelStates, TravelContext> nodeCalculateCost = state -> state.setAttribute("cost", model.call(replaceAllFields(promptCost, state.data()))); Then, we can define the graph and build the agent Java builder.addState(TravelStates.SEARCH, TravelStates.CALCULATE, 1, nodeSearch, null); builder.addState(TravelStates.CALCULATE, null, 1, nodeCalculateCost, null); var agent = builder.build(TravelStates.SEARCH, null, false); Now we can instantiate the two agents (I chose to use ChatGPT 4o and ChatGPT 01-mini) and use them: Java try (var vacationsAgent = AiAgentVacations.buildAgent(ChatGPT.GPT_MODEL_4O, ChatGPT.GPT_MODEL_O1_MINI)) { try (var travelAgent = AiAgentTravelAgency.buildAgent(ChatGPT.GPT_MODEL_4O, vacationsAgent, "Italy", "Dance Salsa and Bachata", true)) { var result = travelAgent.process(new AiAgentTravelAgency.TravelContext("Oslo", "Norway", 2, 2, null, null, null), (state, info) -> System.out.println(state + ": " + info)); System.out.println("*** Proposal: " + result.proposal()); System.out.println("\n\n\n*** Destination: " + result.destination()); System.out.println("\n\n\n*** Cost: " + result.cost()); } } Final Outputs If you wonder what the result is, here is the long output that you can get when stating that what you want to do is to dance Salsa and Bachata: Destination Plain Text Naples, Italy Proposal Plain Text Based on the comprehensive analysis of your friends' suggestions, **Naples** emerges as the ideal city for your vacation in Italy. Here's why Naples stands out as the best choice, offering an exceptional mix of excellent food, beautiful seaside experiences, and a vibrant salsa and bachata dance scene: ### **1. Vibrant Dance Scene** - **Dance Venues:** Naples boasts numerous venues and events dedicated to salsa and bachata, ensuring that you can immerse yourself in lively dance nights regularly. - **Passionate Culture:** The city's passionate and energetic atmosphere enhances the overall dance experience, making it a hotspot for Latin dance enthusiasts. ### **2. Culinary Excellence** - **Authentic Neapolitan Pizza:** As the birthplace of pizza, Naples offers some of the best and most authentic pizzerias in the world. - **Fresh Seafood:** Being a coastal city, Naples provides access to a wide variety of fresh seafood dishes, enhancing your culinary adventures. - **Delicious Pastries:** Don't miss out on local specialties like **sfogliatella**, a renowned Neapolitan pastry that is a must-try for any foodie. ### **3. Stunning Seaside Location** - **Bay of Naples:** Enjoy breathtaking views and activities along the Bay of Naples, including boat tours and picturesque sunsets. - **Proximity to Amalfi Coast:** Naples serves as a gateway to the famous Amalfi Coast, allowing you to explore stunning coastal towns like Amalfi, Positano, and Sorrento with ease. - **Beautiful Beaches:** Relax on the city's beautiful beaches or take short trips to nearby seaside destinations for a perfect blend of relaxation and exploration. ### **4. Cultural Richness** - **Historical Sites:** Explore Naples' rich history through its numerous museums, historic sites, and UNESCO World Heritage landmarks such as the Historic Centre of Naples. - **Vibrant Nightlife:** Beyond dancing, Naples offers a lively nightlife scene with a variety of bars, clubs, and entertainment options to suit all tastes. ### **5. Accessibility and Convenience** - **Transportation Hub:** Naples is well-connected by air, rail, and road, making it easy to travel to other parts of Italy and beyond. - **Accommodation Options:** From luxury hotels to charming boutique accommodations, Naples offers a wide range of lodging options to fit your preferences and budget. ### **Conclusion** Naples perfectly balances a thriving dance scene, exceptional culinary offerings, and beautiful seaside attractions. Its unique blend of culture, history, and vibrant nightlife makes it the best city in Italy to fulfill your desires for travel, good food, and lively dance experiences. Whether you're dancing the night away, savoring authentic pizza by the sea, or exploring nearby coastal gems, Naples promises an unforgettable vacation. ### **Additional Recommendations** - **Day Trips:** Consider visiting nearby attractions such as Pompeii, the Isle of Capri, and the stunning Amalfi Coast to enrich your travel experience. - **Local Experiences:** Engage with locals in dance classes or attend festivals to dive deeper into Naples' vibrant cultural scene. Enjoy your trip to Italy, and may Naples provide you with the perfect blend of everything you're looking for! Cost Plain Text To estimate the cost of traveling from Oslo, Norway, to Naples, Italy, for two adults and two kids, we need to consider several key components of the trip: flights, accommodations, local transportation, food, and activities. Here's a breakdown of potential costs: 1. **Flights**: - Round-trip flights from Oslo to Naples typically range from $100 to $300 per person, depending on the time of booking, the season, and the airline. Budget airlines might offer lower prices, while full-service carriers could be on the higher end. - For a family of four, the cost could range from $400 to $1,200. 2. **Accommodations**: - Hotels in Naples can vary significantly. Expect to pay approximately $70 to $150 per night for a mid-range hotel room that accommodates a family. Vacation rentals might offer more flexibility and potentially lower costs. - For a typical 5-night stay, this would range from $350 to $750. 3. **Local Transportation**: - Public transportation in Naples (buses, metro, trams) is affordable, and daily tickets cost around $4 per person. - Assume about $50 to $100 for the family's local transport for the entire trip, depending on usage. 4. **Food**: - Dining costs are highly variable. A budget for meals might be around $10-$20 per person per meal at casual restaurants, while dining at mid-range restaurants could cost $20-$40 per person. - A family of four could expect to spend around $50 to $100 per day, reaching a total of $250 to $500 for five days. 5. **Activities**: - Entry fees for attractions can vary. Some museums and archaeological sites charge around $10 to $20 per adult, with discounts for children. - Budget around $100 to $200 for family activities and entrance fees. 6. **Miscellaneous**: - Always allow a little extra for souvenirs, snacks, and unexpected expenses. A typical buffer might be $100 to $200. **Estimated Total Cost**: - **Low-end estimate**: $1,250 - **High-end estimate**: $2,950 These are general estimates and actual costs can vary based on when you travel, how far in advance you book, and your personal preferences for accommodation and activities. For the most accurate assessment, consider reaching out to airlines for current flight prices, hotels for room rates, and looking into specific attractions you wish to visit. That was a lot, and this is only the output of the two “reasoning” models! But the result is quite interesting. Naples is on my bucket list, and I am curious to see if the agent is correct! Let’s also check the intermediate results to see how it reached this conclusion, which seems reasonable to me. Intermediate Outputs If you are curious, there are intermediate results. Food Plain Text As a foodie exploring Italy, you're in for a treat, as the country boasts a rich culinary heritage with regional specialties. Here's a list of the top 10 cities in Italy renowned for their food: 1. **Bologna** - Often referred to as the gastronomic heart of Italy, Bologna is famous for its rich Bolognese sauce, tasty mortadella, and fresh tagliatelle. 2. **Naples** - The birthplace of pizza, Naples offers authentic Neapolitan pizza, as well as delicious seafood and pastries like sfogliatella. 3. **Florence** - Known for its Florentine steak, ribollita (a hearty bread and vegetable soup), and delicious wines from the surrounding Tuscany region. 4. **Rome** - Enjoy classic Roman dishes such as carbonara, cacio e pepe, and Roman-style artichokes in the bustling capital city. 5. **Milan** - A city that blends tradition and innovation, Milan offers risotto alla milanese, ossobuco, and an array of high-end dining experiences. 6. **Turin** - Known for its chocolate and coffee culture, as well as traditional dishes like bagna cauda and agnolotti. 7. **Palermo** - Sample the vibrant street food scene with arancini, panelle, and sfincione, as well as fresh local seafood in this Sicilian capital. 8. **Venice** - Famous for its seafood risotto, sarde in saor (sweet and sour sardines), and cicchetti (Venetian tapas) to enjoy with a glass of prosecco. 9. **Parma** - Home to the famous Parmigiano-Reggiano cheese and prosciutto di Parma, it’s a haven for lovers of cured meats and cheeses. 10. **Genoa** - Known for its pesto Genovese, focaccia, and variety of fresh seafood dishes, Genoa offers a unique taste of Ligurian cuisine. Each of these cities offers a distinct culinary experience influenced by local traditions and ingredients, making them must-visit destinations for any food enthusiast exploring Italy. Sea Plain Text Italy is renowned for its stunning coastline and beautiful seaside cities. Here are ten top cities and regions perfect for a sea vacation: 1. **Amalfi** - Nestled in the famous Amalfi Coast, this city is known for its dramatic cliffs, azure waters, and charming coastal villages. 2. **Positano** - Also on the Amalfi Coast, Positano is famous for its colorful buildings, steep streets, and picturesque pebble beachfronts. 3. **Sorrento** - Offering incredible views of the Bay of Naples, Sorrento serves as a gateway to the Amalfi Coast and provides a relaxing seaside atmosphere. 4. **Capri** - The island of Capri is known for its rugged landscape, upscale hotels, and the famous Blue Grotto, a spectacular sea cave. 5. **Portofino** - This quaint fishing village on the Italian Riviera is known for its picturesque harbor, pastel-colored houses, and luxurious coastal surroundings. 6. **Cinque Terre** - Comprising five stunning villages along the Ligurian coast, Cinque Terre is a UNESCO World Heritage site known for its dramatic seaside and hiking trails. 7. **Taormina** - Situated on a hill on the east coast of Sicily, Taormina offers sweeping views of the Ionian Sea and beautiful beaches like Isola Bella. 8. **Rimini** - Located on the Adriatic coast, Rimini is known for its long sandy beaches and vibrant nightlife, making it a favorite for beach-goers and party enthusiasts. 9. **Alghero** - A city on the northwest coast of Sardinia, Alghero is famous for its medieval architecture, stunning beaches, and Catalan culture. 10. **Lerici** - Near the Ligurian Sea, Lerici is part of the stunning Gulf of Poets and is known for its beautiful bay, historic castle, and crystal-clear waters. Each of these destinations offers a unique blend of beautiful beaches, cultural sites, and local cuisine, making Italy a fantastic choice for a sea vacation. Activity Plain Text Italy has a vibrant dance scene with many cities offering great opportunities to enjoy salsa and bachata. Here are ten cities where you can indulge in these lively dance styles: 1. **Rome** - The capital city has a bustling dance scene with numerous salsa clubs and events happening regularly. 2. **Milan** - Known for its nightlife, Milan offers various dance clubs and events catering to salsa and bachata enthusiasts. 3. **Florence** - A cultural hub, Florence has several dance studios and clubs where you can enjoy Latin dances. 4. **Naples** - Known for its passionate culture, Naples offers several venues and events for salsa and bachata lovers. 5. **Turin** - This northern city has a growing salsa community with events and social dances. 6. **Bologna** - Known for its lively student population, Bologna has a number of dance clubs and events for salsa and bachata. 7. **Venice** - While famous for its romantic canals, Venice also hosts various dance events throughout the year. 8. **Palermo** - In Sicily, Palermo has a vibrant Latin dance scene reflecting the island's festive culture. 9. **Verona** - Known for its romantic setting, Verona has several dance studios and clubs for salsa and bachata. 10. **Bari** - This coastal city in the south offers dance festivals and clubs perfect for salsa and bachata enthusiasts. These cities offer a mix of cultural experiences and lively dance floors, ensuring you can enjoy salsa and bachata across Italy. Interestingly enough, Naples does not top any of the lists, though the first four cities in the sea list are all close to Naples. Licensing Details Before closing the article, just two words on the license of Fibry. Fibry is no longer distributed as a pure MIT license. The main difference now is that if you want to build a system to generate code at scale for third parties (like a software engineer agent), you need a commercial license. Also, it is forbidden to include it in any datasets to train systems to generate code (e.g., ChatGPT should not be trained on the source code of Fibry). Anything else, you are good to go. I can provide commercial support and develop features on demand. Conclusion I hope you had fun and could get an idea of how to use Fibry to write AI agents. If you think that a multi-agent system needs to be distributed and run on multiple nodes, Fibry has got you covered! While we’ll save the details for another article, it’s worth noting that setting up Fibry actors in a distributed system is straightforward, and your agents are already actors: when you call process() or processAsync(), a message is sent to the underlying actor. In Fibry, sending and receiving messages over the network is abstracted away, so you don’t even need to modify your agent logic to enable distribution. This makes Fibry uniquely simple for scaling across nodes without rewriting core logic. Happy coding! More

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CORE

Page Transactions: A New Approach to Test Automation

Guará is the Python implementation of the design pattern Page Transactions. It is more of a programming pattern than a tool. As a pattern, it can be bound to any driver other than Selenium, including the ones used for Linux, Windows, and Mobile automation. The intent of this pattern is to simplify test automation. It was inspired by Page Objects, App Actions, and Screenplay. Page Transactions focus on the operations (transactions) a user can perform on an application, such as Login, Logout, or Submit Forms. This initiative was born to improve the readability, maintainability, and flexibility of automation testing code without the need for new automation tools or the necessity of many abstractions to build human-readable statements. Another concern was to avoid binding the framework to specific automation tools, like Selenium, leaving the testers free to choose their preferred ones. No new plugin nor new knowledge is required to start using Guará with Helium, Dogtail, PRA Python, Playwright or whatever tool you like or need. It is worth to say again: Guará is the Python implementation of the design pattern Page Transactions. It is more of a programming pattern than a tool. Guará leverages the Command Pattern (GoF) to group user interactions, like pressing buttons or filling texts, into transactions. Although I’m calling it a framework, it is not a new tool. Instead of focusing on UI elements like buttons, check boxes, or text areas, it emphasizes the user journey. The complexity is abstracted into these transactions, making the test statements feel like plain English. Testers also have the flexibility to extend assertions to custom ones that are not provided by the framework. The Framework in Action This simple implementation mimics the user switching languages in a web page: Python import home from selenium import webdriver from guara.transaction import Application from guara import it, setup def test_language_switch(): app = Application(webdriver.Chrome()) # Open the application app.at(setup.OpenApp, url="https://example.com/") # Change language and assert app.at(home.ChangeToPortuguese).asserts(it.IsEqualTo, "Conteúdo em Português") app.at(home.ChangeToEnglish).asserts(it.IsEqualTo, "Content in English") # Close the application app.at(setup.CloseApp) Each user transaction is grouped into its own class (e.g., ChangeToPortuguese) which inherits from AbstractTransaction. The tester just has to override the do method, and the framework does the work. Python from guara.transaction import AbstractTransaction class ChangeToPortuguese(AbstractTransaction): def do(self, **kwargs): self._driver.find_element(By.CSS_SELECTOR, ".btn-pt").click() return self._driver.find_element(By.CSS_SELECTOR, ".content").text The tester can check the transactions and assertions in the logs after running the tests: Shell test_demo.py::test_language_switch 2025-01-24 21:07:10 INFO Transaction: setup.OpenApp 2025-01-24 21:07:10 INFO url: https://example.com/ 2025-01-24 21:07:14 INFO Transaction: home.ChangeToPortuguese 2025-01-24 21:07:14 INFO Assertion: IsEqualTo 2025-01-24 21:07:14 INFO Actual Data: Conteúdo em Português 2025-01-24 21:07:14 INFO Expected: Conteúdo em Português 2025-01-24 21:07:14 INFO Transaction: home.ChangeToEnglish 2025-01-24 21:07:14 INFO Assertion: IsEqualTo 2025-01-24 21:07:14 INFO Actual Data: Content in English 2025-01-24 21:07:14 INFO Expected: Content in English 2025-01-24 21:07:14 INFO Transaction: setup.CloseApp The tester can also use fixtures like set up and tear down to initiate and finish the tests. Remember, it is not a new tool, so you can use pytest or unit testing features without any problem. The Pattern Explained AbstractTransaction: This is the class from which all transactions inherit. The do method is implemented by each transaction. In this method, calls to WebDriver are placed. If the method returns something, like a string, the automation can use it for assertions. setup.OpenApp and setup.CloseApp are part of the framework and provide basic implementation to open and close the web application using Selenium Webdriver. IAssertion: This is the interface implemented by all assertion classes. The asserts method of each subclass contains the logic to perform validations. For example, the IsEqualTo subclass compares the result with the expected value provided by the tester.Testers can inherit from this interface to add new sub-classes of validations that the framework does not natively supportit is the module that contains the concrete assertions. Application: This is the runner of the automation. It executes the do method of each transaction and validates the result using the asserts method. The asserts method receives a reference to an IAssertion instance. It implements the Strategy Pattern (GoF) to allow its behavior to change at runtime.Another important component of the application is the result property. It holds the result of the transaction, which can be used by asserts or inspected by the test using the native built-in assert method. Why Use Guará? Each class represents a complete user transaction, improving code re-usability. Also, the code is written in plain English, making it easier for non-technical collaborators to review and contribute. Custom assertions can be created and shared by testers. Additionally, Guará can be integrated with any non-Selenium tool. Page Transactions can automate REST APIs, unit tests, desktop, and mobile tests. As a side effect of the Command Pattern, the framework can even be used in product development. Using Guará Setting up Guará is simple: 1. Install Guará with the command: Plain Text pip install guara 2. Build your transactions using the AbstractTransaction class. 3. Invoke the transactions using the application runner and its methods at and asserts. 3. Execute tests with detailed logging with Pytest: Python python -m pytest -o log_cli=1 --log-cli-level=INFO For more examples, check out the tutorial. Conclusion Guará is a new way testers can organize their code, making it simple to read, maintain, and integrate with any automation driver. It improves the collaboration between testers and non-technical members. Testers can also extend it by building and sharing new assertions. Try Guará today!

By Douglas Cardoso

SmartXML: An Alternative to XPath for Complex XML Files

XML is one of the most widely used data formats, which in popularity can compete only with JSON. Still, very often, this format is used as an intermediate representation of data that needs to be transferred between two information systems. And like any intermediate representation the final storage point of XML is a database. Usually, XPath is used to parse XML because it represents a set of functions that allows you to extract data from an XML tree. However, not all XML files are formed correctly, which creates great difficulties when using XPath. Typical Problems When Working With XPath Differences in node naming. You may have an array of documents with a similar logical structure, but they may have differences in the way node names are spelled.Missing nodes. If bad XML generators are used on the server side, they may skip some nesting levels for some of the data in the resulting XML.Object or array? XPath does not allow you to explicitly specify whether the contents of a particular node are an object or an array of objects.Inability to extend syntax. XPath is just a node traversal tool with no syntax extension capability. In this article, I will discuss a tool called SmartXML that solves these problems and allows you to upload complex XML documents to a database. Project Structure SmartXML uses an intermediate representation when processing data — SmartDOM. Unlike traditional DOM, this structure controls the level of element hierarchy and can complete its nodes. SmartDOM consists of the declarative description itself and sets of rules for its transformation. Three Examples of Documents With a Divergent Structure Example 1 The document has a relatively correct structure. All sections have correct nesting. Plain Text <doc> <commonInfo> <supplyNumber>100480</supplyNumber> <supplyDate>2025-01-20</supplyDate> </commonInfo> <lots> <lot> <objects> <object> <name>apples</name> <price>3.25</price> <currency>USD</currency> </object> <object> <name>oranges</name> <price>3.50</price> <currency>USD</currency> </object> </objects> </lot> <lot> <objects> <object> <name>bananas</name> <price>2.50</price> <currency>EUR</currency> </object> <object> <name>strawberries</name> <price>5.00</price> <currency>USD</currency> </object> <object> <name>grapes</name> <price>3.75</price> <currency>USD</currency> </object> </objects> </lot> </lots> </doc> Example 2 The nesting of sections is broken. The object does not have a parent. Plain Text <doc> <commonInfo> <supplyNumber>100593</supplyNumber> <date>2025-01-21</date> </commonInfo> <lots> <lot> <object> <name>raspberry</name> <price>7.50</price> <currency>USD</currency> </object> </lot> </lots> </doc> Example 3 The nesting is correct, but the node names do not match the other sections. Plain Text <doc> <commonInfo> <supplyNumber>100601</supplyNumber> <date>2025-01-22</date> </commonInfo> <lots> <lot> <objects> <obj> <name>cherries</name> <price>3.20</price> <currency>EUR</currency> </obj> <obj> <name>blueberries</name> <price>4.50</price> <currency>USD</currency> </obj> <obj> <name>peaches</name> <price>2.80</price> <currency>USD</currency> </obj> </objects> </lot> </lots> </doc> As you can see, all three of these documents contain the same data but have different storage structures. Intermediate Data View The full structure of the SmartDOM view from data-templates.red: Plain Text #[ sample: #[ ; section name supply_sample: #[ ; subsection name supply_number: none supply_date: none delivery_items: [ item: [ name: none price: none currency: none ] ] ] ] ] Project Setup Create a project and set up a mapping between SmartDOM and XML tree nodes for each XML file. Now, we need to specify how XML nodes are mapped to SmartDOM. This can be done either in the interface on the Rules tab or in the configuration file grow-rules.red, making it look as follows: Plain Text sample: [ item: ["object" "obj"] ] For correct linking of tables, we also need to specify the name of the tag from the root element, which should be passed to the descendant nodes. Without this, it will be impossible to link two tables. Since we have a unique supply_number, it can be used as a similar key. To do this, let's add it to the injection-rules.red rule: Plain Text sample: [ inject-tag-to-every-children: [supply_number] enumerate-nodes: [] injection-tag-and-recipients: [] ] Now, it remains to create the necessary tables in the database and insert there the results of processing XML files: Plain Text PRAGMA foreign_keys = ON; CREATE TABLE supply_sample ( id INTEGER PRIMARY KEY AUTOINCREMENT, supply_number TEXT NOT NULL UNIQUE, supply_date TEXT NOT NULL ); CREATE TABLE delivery_items ( id INTEGER PRIMARY KEY AUTOINCREMENT, supply_number TEXT NOT NULL, name TEXT NOT NULL, price REAL NOT NULL, currency TEXT NOT NULL, FOREIGN KEY (supply_number) REFERENCES supply_sample(supply_number) ); Result The result of converting three XML files to SQL: Plain Text INSERT INTO supply_sample ("supply_number", "supply_date") VALUES ('100480', '2025-01-20'); INSERT INTO delivery_items ("supply_number", "name", "price", "currency") VALUES ('100480', 'apples', '3.25', 'USD'), ('100480', 'oranges', '3.50', 'USD'), ('100480', 'bananas', '2.50', 'EUR'), ('100480', 'strawberries', '5.00', 'USD'), ('100480', 'grapes', '3.75', 'USD'); -- INSERT INTO supply_sample ("supply_number", "supply_date") VALUES ('100593', '2025-01-21'); INSERT INTO delivery_items ("supply_number", "name", "price", "currency") VALUES ('100593', 'raspberry', '7.50', 'USD'); -- INSERT INTO supply_sample ("supply_number", "supply_date") VALUES ('100601', '2025-01-22'); INSERT INTO delivery_items ("supply_number", "name", "price", "currency") VALUES ('100601', 'cherries', '3.20', 'EUR'), ('100601', 'blueberries', '4.50', 'USD'), ('100601', 'peaches', '2.80', 'USD'); This is what the result looks like in tabular form: Conclusion So, we have demonstrated how you can parse and upload to a database quite complex XML files without writing program code. This solution can be useful for system analysts, as well as other people who often work with XML. In the case of parsing using popular programming languages such as Python, we would have to process each separate file with a separate script, which would require more code and time. You can learn more about the SmartXML project structure in the official documentation.

By Luca Sanders

Bridging Graphviz and Cytoscape.js for Interactive Graphs

Visualizing complex digraphs often requires balancing clarity with interactivity. Graphviz is a great tool for generating static graphs with optimal layouts, ensuring nodes and edges don't overlap. On the flip side, Cytoscape.js offers interactive graph visualizations but doesn't inherently prevent overlapping elements, which can clutter the display. This article describes a method to convert Graphviz digraphs into interactive Cytoscape.js graphs. This approach combines Graphviz's layout algorithms with Cytoscape.js's interactive capabilities, resulting in clear and navigable visualizations. By extracting Graphviz's calculated coordinates and bounding boxes and mapping them into Cytoscape.js's format, we can recreate the same precise layouts in an interactive environment. This technique leverages concepts from computational geometry and graph theory. Why This Matters Interactive graphs allow users to engage with data more effectively, exploring relationships and patterns that static images can't convey. By converting Graphviz layouts to Cytoscape.js, we retain the benefits of Graphviz's non-overlapping, well-organized structures while enabling dynamic interaction. This enhances presentation, making complex graphs easier to work with. Technical Steps Here's an overview of the process to convert a Graphviz digraph into a Cytoscape.js graph: 1. Convert Graphviz Output to DOT Format Graphviz can output graphs in DOT format, which contains detailed information about nodes, edges, and their positions. Python import pygraphviz def convert_gviz_image(gviz): graph_dot = pygraphviz.AGraph(str(gviz)) image_str = graph_dot.to_string() return image_str 2. Parse the DOT File and Extract Elements Using libraries like networkx and json_graph, we parse the DOT file to extract nodes and edges along with their attributes. Python import networkx from networkx.readwrite import json_graph def parse_dot_file(dot_string): graph_dot = pygraphviz.AGraph(dot_string) graph_netx = networkx.nx_agraph.from_agraph(graph_dot) graph_json = json_graph.node_link_data(graph_netx) return graph_json 3. Transform Coordinates for Cytoscape.js Graphviz and Cytoscape.js use different coordinate systems. We need to adjust the node positions accordingly, typically inverting the Y-axis to match Cytoscape.js's system. Python def transform_coordinates(node): (x, y) = map(float, node['pos'].split(',')) node['position'] = {'x': x, 'y': -y} return node 4. Calculate Edge Control Points For edges, especially those with curves, we calculate control points to replicate Graphviz's edge paths in Cytoscape.js. This involves computing the distance and weight of each control point relative to the source and target nodes. Edge Control Points Calculation Python def get_control_points(node_pos, edges): for edge in edges: if 'data' in edge: src = node_pos[edge['data']['source']] tgt = node_pos[edge['data']['target']] if src != tgt: cp = edge['data'].pop('controlPoints') control_points = cp.split(' ') d = '' w = '' for i in range(1, len(control_points) - 1): cPx = float(control_points[i].split(",")[0]) cPy = float(control_points[i].split(",")[1]) * -1 result_distance, result_weight = \ get_dist_weight(src['x'], src['y'], tgt['x'], tgt['y'], cPx, cPy) d += ' ' + result_distance w += ' ' + result_weight d, w = reduce_control_points(d[1:], w[1:]) edge['data']['point-distances'] = d edge['data']['point-weights'] = w return edges Python def convert_control_points(d, w): remove_list = [] d_tmp = d w_tmp = w for i in range(len(d)): d_tmp[i] = float(d_tmp[i]) w_tmp[i] = float(w_tmp[i]) if w_tmp[i] > 1 or w_tmp[i] < 0: remove_list.append(w_tmp[i]) d_tmp = [x for x, y in zip(d_tmp, w_tmp) if y not in remove_list] w_tmp = [x for x in w_tmp if x not in remove_list] d_check = [int(x) for x in d_tmp] if len(set(d_check)) == 1 and d_check[0] == 0: d_tmp = [0.0, 0.0, 0.0] w_tmp = [0.1, 0.5, 0.9] return d_tmp, w_tmp In the get_control_points function, we iterate over each edge, and if it connects different nodes, we process its control points: Extract control points: Split the control points string into a list.Calculate distances and weights: For each control point (excluding the first and last), calculate the distance (d) and weight (w) using the get_dist_weight function.Accumulate results: Append the calculated distances and weights to strings d and w.Simplify control points: Call reduce_control_points to simplify the control points for better performance and visualization.Update edge data: The calculated point-distances and point-weights are assigned back to the edge's data. The convert_control_points function ensures that control point weights are within the valid range (0 to 1). It filters out any weights that are outside this range and adjusts the distances accordingly. Distance and Weight Calculation Function The get_dist_weight function calculates the perpendicular distance from a control point to the straight line between the source and target nodes (d) and the relative position of the control point along that line (w): Python import math def get_dist_weight(sX, sY, tX, tY, PointX, PointY): if sX == tX: slope = float('inf') else: slope = (sY - tY) / (sX - tX) denom = math.sqrt(1 + slope**2) if slope != float('inf') else 1 d = (PointY - sY + (sX - PointX) * slope) / denom w = math.sqrt((PointY - sY)**2 + (PointX - sX)**2 - d**2) dist_AB = math.hypot(tX - sX, tY - sY) w = w / dist_AB if dist_AB != 0 else 0 delta1 = 1 if (tX - sX) * (PointY - sY) - (tY - sY) * (PointX - sX) >= 0 else -1 delta2 = 1 if (tX - sX) * (PointX - sX) + (tY - sY) * (PointY - sY) >= 0 else -1 d = abs(d) * delta1 w = w * delta2 return str(d), str(w) This function handles both vertical and horizontal lines and uses basic geometric principles to compute the distances and weights. Simplifying Control Points The reduce_control_points function reduces the number of control points to simplify the edge rendering in Cytoscape.js: Python def reduce_control_points(d, w): d_tmp = d.split(' ') w_tmp = w.split(' ') idx_list = [] d_tmp, w_tmp = convert_control_points(d_tmp, w_tmp) control_point_length = len(d_tmp) if control_point_length > 5: max_value = max(map(float, d_tmp), key=abs) max_idx = d_tmp.index(str(max_value)) temp_idx = max_idx // 2 idx_list = [temp_idx, max_idx, control_point_length - 1] elif control_point_length > 3: idx_list = [1, control_point_length - 2] else: return ' '.join(d_tmp), ' '.join(w_tmp) d_reduced = ' '.join(d_tmp[i] for i in sorted(set(idx_list))) w_reduced = ' '.join(w_tmp[i] for i in sorted(set(idx_list))) return d_reduced, w_reduced This function intelligently selects key control points to maintain the essential shape of the edge while reducing complexity. 5. Build Cytoscape.js Elements With nodes and edges prepared, construct the elements for Cytoscape.js, including the calculated control points. Python def build_cytoscape_elements(graph_json): elements = {'nodes': [], 'edges': []} for node in graph_json['nodes']: node = transform_coordinates(node) elements['nodes'].append({'data': node}) node_positions = {node['data']['id']: node['position'] for node in elements['nodes']} edges = graph_json['links'] edges = get_control_points(node_positions, edges) elements['edges'] = [{'data': edge['data']} for edge in edges] return elements 6. Apply Styling We can style nodes and edges based on attributes like frequency or performance metrics, adjusting colors, sizes, and labels for better visualization. Cytoscape.js offers extensive customization, allowing you to tailor the graph's appearance to highlight important aspects of your data. Conclusion This solution combines concepts from: Graph theory: Understanding graph structures, nodes, edges, and their relationships helps in accurately mapping elements between Graphviz and Cytoscape.js.Computational geometry: Calculating positions, distances, and transformations. Python programming: Utilizing libraries such as pygraphviz, networkx, and json_graph facilitates graph manipulation and data handling. By converting Graphviz digraphs to Cytoscape.js graphs, we achieve interactive visualizations that maintain the clarity of Graphviz's layouts. This approach can be extended to accommodate various types of graphs and data attributes. It's particularly useful in fields like bioinformatics, social network analysis, and any domain where understanding complex relationships is essential.

By Puneet Malhotra

Why You Don’t Need That New JavaScript Library

Libraries can rise to stardom in months, only to crash and fade into obscurity within months. We’ve all seen this happen in the software development world, and my own journey has been filled with “must-have” JavaScript libraries, each claiming to be more revolutionary than the one before. But over the years, I’ve come to realize that the tools we need have been with us all along, and in this article, I’ll explain why it’s worth sticking to the fundamentals, how new libraries can become liabilities, and why stable, proven solutions usually serve us best in the long run. The Allure of New Libraries I’ll be the first to admit that I’ve been seduced by shiny new libraries before. Back in 2018, I led a team overhaul of our front-end architecture. We added a number of trendy state management tools and UI component frameworks, certain that they would streamline our workflow. Our package.json ballooned with dependencies, each seemingly indispensable. At first, it felt like we were riding a wave of innovation. Then, about six months in, a pattern emerged. A few libraries became outdated; some were abandoned by their maintainers. Every time we audited our dependencies, it seemed we were juggling security patches and version conflicts far more often than we shipped new features. The headache of maintenance made one thing crystal clear: every new dependency is a promise you make to maintain and update someone else’s code. The True Cost of Dependencies When we adopt a new library, we’re not just adding functionality; we’re also taking on significant risks. Here are just some of the hidden costs that frequently go overlooked: Maintenance Overhead New libraries don’t just drop into your project and remain stable forever. They require patching for security vulnerabilities, updating for compatibility with other tools, and diligence when major releases introduce breaking changes. If you’re not on top of these updates, you risk shipping insecure or buggy code to production. Version Conflicts Even robust tools like npm and yarn can’t guarantee complete harmony among your dependencies. One library might require a specific version of a package that conflicts with another library’s requirements. Resolving these inconsistencies can be a maddening, time-consuming process. Performance Implications The size of the bundle increases a lot because of front-end libraries. One specialized library may add tens or hundreds of kilobytes to your final JavaScript payload, making it heavier, which means slower load times and worse user experiences. Security Vulnerabilities In one audit for a client recently, 60% of their app’s vulnerabilities came from third-party packages, often many layers deep in the dependency tree. Sometimes, to patch one library, multiple interdependent packages need to be updated, which is rarely an easy process. A colleague and I once had a need for a date picker for a project. The hip thing to do would have been to install some feature-rich library and quickly drop it in. Instead, we polyfilled our own lightweight date picker in vanilla JavaScript, using the native Date object. It was a fraction of the size, had zero external dependencies, and was completely ours to modify. That tiny decision spared us from possible library update headaches, conflicts, or abandonment issues months later. The Power of Vanilla JavaScript Modern JavaScript is almost unrecognizable from what it was ten years ago. Many features that previously required libraries like Lodash or Moment are now part of the language — or can be replicated with a few lines of code. For example: JavaScript // Instead of installing Lodash to remove duplicates: const uniqueItems = [...new Set(items)]; // Instead of using a library for deep cloning: const clonedObject = structuredClone(complexObject); A deep familiarity with the standard library can frequently replace entire suites of utility functions. These days, JavaScript’s built-in methods handle most common tasks elegantly, making large chunks of external code unnecessary. When to Use External Libraries None of this is to say you should never install a third-party package. The key lies in discernment — knowing when a problem is big enough or specialized enough to benefit from a well-tested, well-maintained library. For instance: Critical complexity: Frameworks like React have proven their mettle for managing complex UI states in large-scale applications.Time-to-market: Sometimes, a short-term deliverable calls for a robust, out-of-the-box solution, and it makes sense to bring in a trusted library rather than build everything from scratch.Community and maintenance: Popular libraries with long track records and active contributor communities — like D3.js for data visualization — can be safer bets, especially if they’re solving well-understood problems. The key is to evaluate the cost-benefit ratio: Can this be done with native APIs or a small custom script?Do I trust this library’s maintainer track record?Is it solving a core problem or offering only minor convenience?Will my team actually use enough of its features to justify the extra weight? Strategies for Avoiding Unnecessary Dependencies To keep your projects lean and maintainable, here are a few best practices: 1. Evaluate Built-In Methods First You’d be surprised how many tasks modern JavaScript can handle without third-party code. Spend time exploring the newer ES features, such as array methods, Map/Set, async/await, and the Intl API for localization. 2. Document Your Choices If you do bring in a new library, record your reasoning in a few sentences. State the problem it solves, the alternatives you considered, and any trade-offs. Future maintainers (including your future self) will appreciate the context if questions arise later. 3. Regular Dependency Audits Re-scan your package.json every quarter or so. Is this library still maintained? Are you really using their features? Do a small cleanup of the project for removing dead weights that would reduce the potential for security flaws. 4. Aggressive Dependency vs. DevDependency Separation Throw build tooling, testing frameworks, other non-production packages into your devDependencies. Keep your production dependency listing lean in terms of just the things that you really need to function at runtime. The Case for Core Libraries A team I recently worked with had some advanced charting and visualization requirements. Although a newer charting library promised flashy animations and out-of-the-box UI components, we decided to use D3.js, a stalwart in the data visualization space. The maturity of the library, thorough documentation, and huge community made it a stable foundation for our custom charts. By building directly on top of D3’s fundamentals, we had full control over our final visualizations, avoiding the limitations of less established abstractions. That mindset — paying off in performance, maintainability, and peace of mind for embracing a core, proven library rather than chasing every new offering — means instead of spending time adapting our data to a proprietary system or debugging half-baked features, we have to focus on real product needs, confident that D3 would remain stable and well-supported. Performance Gains Libraries aren’t just maintenance overhead, they affect your app’s performance too. In one recent project, we reduced the initial bundle size by 60% simply by removing niche libraries and replacing them with native code. The numbers told the story. Load time dropped from 3.2s to 1.4s.Time to interact improved by nearly half.Memory usage fell by roughly 30%. These results didn’t come from advanced optimizations but from the simpler act of removing unnecessary dependencies. In an age of ever-growing user expectations, the performance benefits alone can justify a more minimal approach. Building for the Long Term Software is never static. Today’s must-have library may turn out to be tomorrow’s orphaned repository. Reliable, stable code tends to come from developers who favor well-understood, minimal solutions over ones that rely too heavily on external, fast-moving packages. Take authentication, for example: with the hundreds of packages that exist to handle user login flows, rolling a simple system with few dependencies may result in something easier to audit, more transparent, and less subject to churn from external libraries. The code might be a bit more verbose, but it’s also explicit, predictable, and directly under your control. Teaching and Team Growth One of the underrated benefits of using fewer libraries is how it fosters stronger problem-solving skills within your team. Having to implement features themselves forces the developers to have a deep understanding of core concepts-which pays dividends when debugging, performance tuning, or even evaluating new technologies in the future. Relying too much on abstractions from someone else can stunt that growth and transform capable coders into “framework operators.” Conclusion The next time you think about installing yet another trending package, reflect on whether it solves your pressing need or just for novelty. As experience has drummed into my head, each new dependency is for life. This is the way to ensure that one gets light solutions that are secure and easier to maintain by using built-in capabilities, well-tried libraries, and a deep understanding of fundamentals. Ultimately, “boring” but reliable libraries — and sometimes just vanilla JavaScript — tend to stand the test of time better than flashy newcomers. Balancing innovation with pragmatism is the hallmark of a seasoned developer. In an era of endless frameworks and packages, recognizing when you can simply reach for the tools you already have may be the most valuable skill of all.

By Denis Ermakov

Scrape Amazon Product Reviews With Python

Amazon is a well-known e-commerce platform with a large amount of data available in various formats on the web. This data can be invaluable for gaining business insights, particularly by analyzing product reviews to understand the quality of products provided by different vendors. In this guide, we will look into web scraping steps to extract Amazon reviews of a particular product and save them in Excel or CSV format. Since manually copying information online can be tedious, we’ll focus on scraping reviews from Amazon. This hands-on experience will enhance our practical understanding of web scraping techniques. Prerequisite Before we start, make sure you have Python installed in your system. You can do that from this link. The process is very simple — just install it like you would install any other application. Now that everything is set, let’s proceed. How to Scrape Amazon Reviews Using Python Install Anaconda through this link. Be sure to follow the default settings during installation. For more guidance, you can watch this video: We can use various IDEs, but to keep it beginner-friendly, let’s start with Jupyter Notebook in Anaconda. You can watch the video linked above to understand and get familiar with the software. Steps for Web Scraping Amazon Reviews Create a New Notebook and save it. Step 1: Import Necessary Modules Let’s start importing all the modules needed using the following code: Python import requests from bs4 import BeautifulSoup import pandas as pd Step 2: Define Headers To avoid getting your IP blocked, define custom headers. Note that you can replace the User-agent value with your user agent, which you can find by searching "my user agent" on Google. Python custom_headers = { "Accept-language": "en-GB,en;q=0.9", "User-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.1 Safari/605.1.15", } Step 3: Fetch Webpage Create a Python function to fetch the webpage, check for errors, and return a BeautifulSoup object for further processing. Python # Function to fetch the webpage and return a BeautifulSoup object def fetch_webpage(url): response = requests.get(url, headers=headers) if response.status_code != 200: print("Error in fetching webpage") exit(-1) page_soup = BeautifulSoup(response.text, "lxml") return page_soup Step 4: Extract Reviews Inspect Element to find the element and attribute from which we want to extract data. Let's create another function to select the div and attribute and set it to extract_reviews variable. It identifies review-related elements on a webpage but doesn’t yet extract the actual review content. You would need to add code to extract the relevant information from these elements (e.g., review text, ratings, etc.). Python # Function to extract reviews from the webpage def extract_reviews(page_soup): review_blocks = page_soup.select('div[data-hook="review"]') reviews_list = [] Step 5: Process Review Data The code below processes each review element, extracts the customer’s name (if available), and stores it in the customer variable. If no customer information is found, customer remains none. Python for review in review_blocks: author_element = review.select_one('span.a-profile-name') customer = author_element.text if author_element else None rating_element = review.select_one('i.review-rating') customer_rating = rating_element.text.replace("out of 5 stars", "") if rating_element else None title_element = review.select_one('a[data-hook="review-title"]') review_title = title_element.text.split('stars\n', 1)[-1].strip() if title_element else None content_element = review.select_one('span[data-hook="review-body"]') review_content = content_element.text.strip() if content_element else None date_element = review.select_one('span[data-hook="review-date"]') review_date = date_element.text.replace("Reviewed in the United States on ", "").strip() if date_element else None image_element = review.select_one('img.review-image-tile') image_url = image_element.attrs["src"] if image_element else None Step 6: Process Scraped Reviews The purpose of this function is to process scraped reviews. It takes various parameters related to a review (such as customer, customer_rating, review_title, review_content, review_date, and image URL), and the function returns the list of processed reviews. Python review_data = { "customer": customer, "customer_rating": customer_rating, "review_title": review_title, "review_content": review_content, "review_date": review_date, "image_url": image_url } reviews_list.append(review_data) return reviews_list Step 7: Initialize Review URL Now, let's initialize a search_url variable with an Amazon product review page URL. Python def main(): review_page_url = "https://www.amazon.com/BERIBES-Cancelling-Transparent-Soft-Earpads-Charging-Black/product- reviews/B0CDC4X65Q/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews" page_soup = fetch_webpage(review_page_url) scraped_reviews = extract_reviews(page_soup) Step 8: Verify Scraped Data Now, let’s print (“Scraped Data:”, data) scraped review data (stored in the data variable) to the console for verification purposes. Python # Print the scraped data to verify print("Scraped Data:", scraped_reviews) Step 9: Create a DataFrame Next, create a DataFrame from the data, which will help organize data into tabular form. Python # create a DataFrame and export it to a CSV file reviews_df = pd.DataFrame(data=scraped_reviews) Step 10: Export DataFrame to CSV Now, export the DataFrame to a CSV file in the current working directory. Python reviews_df.to_csv("reviews.csv", index=False) print("CSV file has been created.") Step 11: Ensure Standalone Execution The code construct below acts as a protective measure. It ensures that certain code runs only when the script is directly executed as a standalone program rather than being imported as a module by another script. Python # Ensuring the script runs only when executed directly if __name__ == '__main__': main() Result Why Scrape Amazon Product Reviews? Scraping Amazon product reviews can provide valuable insights for businesses. Here’s why they do it: Feedback Collection Every business needs feedback to understand customer requirements and implement changes to improve product quality. Scraping reviews allows businesses to gather large volumes of customer feedback quickly and efficiently. Sentiment Analysis Analyzing the sentiments expressed in reviews can help identify positive and negative aspects of products, leading to informed business decisions. Competitor Analysis Scraping allows businesses to monitor competitors’ pricing and product features, helping them stay competitive in the market. Business Expansion Opportunities By understanding customer needs and preferences, businesses can identify opportunities for expanding their product lines or entering new markets. Manually copying and pasting content is time-consuming and error-prone. This is where web scraping comes in. Using Python to scrape Amazon reviews can automate the process, reduce manual errors, and provide accurate data. Benefits of Scraping Amazon Reviews Efficiency: Automate data extraction to save time and resources.Accuracy: Reduce human errors with automated scripts.Large data volume: Collect extensive data for comprehensive analysis.Informed decision-making: Use customer feedback to make data-driven business decisions. Conclusion Now that we’ve covered how to scrape Amazon reviews using Python, you can apply the same techniques to other websites by inspecting their elements. Here are some key points to remember: Understanding HTML Familiarize yourself with the HTML structure. Knowing how elements are nested and how to navigate the Document Object Model (DOM) is crucial for finding the data you want to scrape. CSS Selectors Learn how to use CSS selectors to accurately target and extract specific elements from a webpage. Python Basics Understand Python programming, especially how to use libraries like requests for making HTTP requests and BeautifulSoup for parsing HTML content. Inspecting Elements Practice using browser developer tools (right-click on a webpage and select “Inspect” or press Ctrl+Shift+I) to examine the HTML structure. This helps you find the tags and attributes that hold the data you want to scrape. Error Handling Add error handling to your code to deal with possible issues, like network errors or changes in the webpage structure. Legal and Ethical Considerations Always check a website’s robots.txt file and terms of service to ensure compliance with legal and ethical rules of web scraping. By mastering these areas, you’ll be able to confidently scrape data from various websites, allowing you to gather valuable insights and perform detailed analyses.

By Juveria dalvi

The Energy Efficiency of JVMs and the Role of GraalVM

As the world becomes increasingly conscious of energy consumption and its environmental impact, software development is joining the movement to go green. Surprisingly, even the choice of runtime environments and how code is executed can affect energy consumption. This brings us to the world of Java Virtual Machines (JVMs), an integral part of running Java applications, and the rising star in the JVM world, GraalVM. In this article, we will explore how code performance and energy efficiency intersect in the JVM ecosystem and why GraalVM stands out in this domain. Understanding Energy and Performance in the JVM Context To grasp why energy efficiency matters in JVMs, we first need to understand what JVMs do. A JVM is the engine that powers Java applications, converting platform-independent Java bytecode into machine-specific instructions. While this flexibility is a major strength, it also means JVMs carry some overhead, especially compared to compiled languages like C++. Now, energy consumption in software isn't just about the hardware running hotter or consuming more electricity. It's tied to the performance of the software itself. When code is slow or inefficient, it takes longer to execute, which directly correlates with more CPU cycles, increased power draw, and greater energy usage. This connection between performance and energy efficiency is at the heart of what makes optimizing JVMs so critical. Studies like those by Leeds Beckett University (2023) and HAL Open Science (2021) reveal how JVM optimizations and configurations significantly impact energy use. As newer JVMs improve performance through better garbage collection, just-in-time (JIT) compilation, and other optimizations, they reduce not just runtime but also energy costs. Yet, even within these advancements, there’s a standout contender reshaping how we think about energy-efficient Java: GraalVM. What Makes GraalVM Different? GraalVM is a high-performance runtime designed to improve the efficiency of applications written in Java and other JVM-compatible languages. Unlike traditional JVM implementations, GraalVM incorporates advanced optimization techniques that make it unique in both speed and energy usage. Its native image capability allows applications to be compiled ahead-of-time (AOT) into standalone executables. Traditional JVMs rely heavily on JIT compilation, which compiles bytecode into machine code at runtime. While this approach allows for adaptive optimizations (learning and optimizing as the program runs), it introduces a delay in startup time and consumes energy during execution. GraalVM’s AOT compilation eliminates this runtime overhead by pre-compiling the code, significantly reducing the startup time and resource consumption. Furthermore, GraalVM supports polyglot programming, which enables developers to mix languages like JavaScript, Python, and Ruby in a single application. This reduces the need for multiple runtime environments, simplifying deployment and cutting down on the energy costs associated with maintaining diverse infrastructures. Energy Efficiency in Numbers The question many might ask is: does GraalVM truly make a difference in energy terms? The combined studies offer some clarity. For example, Leeds Beckett University (2023) and HAL Open Science (2021) benchmarked GraalVM against traditional JVMs like OpenJDK, Amazon Corretto, and Azul Zulu, using diverse workloads. Both studies showed that GraalVM, particularly in its native image configuration, consumed less energy and completed tasks faster across the majority of scenarios. Interestingly, the energy consumption gains are not linear across all benchmarks. While GraalVM excelled in data-parallel tasks like Alternating Least Squares (ALS), it underperformed in certain highly parallel tasks like Avrora. This suggests that the workload type significantly influences the runtime's energy efficiency. Moreover, the researchers observed that while newer JVMs like HotSpot 15 generally offered better energy performance than older versions like HotSpot 8, GraalVM consistently stood out. Even when compared to JVMs optimized for long-running tasks, GraalVM delivered lower energy costs due to its AOT compilation, which minimized runtime overhead. Insights from JVM Configuration Studies Beyond runtime optimizations, how you configure a JVM can have profound effects on energy consumption. Both studies emphasized the role of garbage collection (GC) and JIT compiler settings. For instance, HAL Open Science found that default GC settings were energy-efficient in only half of the experiments. Alternative GC strategies, such as ParallelGC and SerialGC, sometimes outperformed default configurations like G1GC. Similarly, tweaking JIT compilation levels could improve energy efficiency, but such adjustments often required detailed performance evaluations. One of the most striking observations was the variability in energy savings based on application characteristics. For data-heavy tasks like H2 database simulations, energy savings were most pronounced when using GraalVM’s default configurations. However, for highly concurrent applications like Reactors, specific configurations of JIT threads delivered significant improvements. Carbon Footprint Reduction The environmental implications of these energy savings are immense. Using standardized energy-to-carbon conversion factors, the studies highlighted that GraalVM reduced carbon dioxide emissions more effectively than traditional JVMs. These reductions were particularly significant in cloud environments, where optimizing runtime efficiency lowered operational costs and reduced the carbon footprint of large-scale deployments. Broader Implications for Software Development The findings from Leeds Beckett University (2023) and HAL Open Science (2021) are clear: energy efficiency is no longer just about hardware; it’s about making smarter software choices. By adopting greener JVMs like GraalVM, developers can contribute directly to sustainability goals without compromising on performance. However, the road to greener software isn’t just about choosing a runtime. It involves understanding the nuances of workload types, runtime configurations, and application behaviors. Tools like J-Referral, introduced in HAL Open Science’s study, can help developers select the most energy-efficient JVM configurations for their specific needs, simplifying the path to sustainable computing. Conclusion The correlation between code performance and energy efficiency is clear: faster, optimized software consumes less energy. JVMs have long been at the heart of this discussion, and while traditional JVMs continue to evolve, GraalVM offers a leap forward. By combining high performance, energy efficiency, and versatility, it stands out as a powerful tool for modern developers looking to build applications that are not only fast but also environmentally conscious. With studies confirming its efficiency across a broad range of scenarios, GraalVM represents a shift in how we think about software sustainability. The journey to greener software begins with choices like these — choices that balance performance, cost, and environmental responsibility. References Vergilio, TG and Do Ha, L and Kor, A-LG (2023) Comparative Performance and Energy Efficiency Analysis of JVM Variants and GraalVM in Java Applications.Zakaria Ournani, Mohammed Chakib Belgaid, Romain Rouvoy, Pierre Rust, Joel Penhoat. Evaluating the Impact of Java Virtual Machines on Energy Consumption.

By Graziano Casto

Control Your Services With OTEL, Jaeger, and Prometheus

Let's discuss an important question: how do we monitor our services if something goes wrong? On the one hand, we have Prometheus with alerts and Kibana for dashboards and other helpful features. We also know how to gather logs — the ELK stack is our go-to solution. However, simple logging isn’t always enough: it doesn’t provide a holistic view of a request’s journey across the entire ecosystem of components. You can find more info about ELK here. But what if we want to visualize requests? What if we need to correlate requests traveling between systems? This applies to both microservices and monoliths — it doesn’t matter how many services we have; what matters is how we manage their latency. Indeed, each user request might pass through a whole chain of independent services, databases, message queues, and external APIs. In such a complex environment, it becomes extremely difficult to pinpoint exactly where delays occur, identify which part of the chain acts as a performance bottleneck, and quickly find the root cause of failures when they happen. To address these challenges effectively, we need a centralized, consistent system to collect telemetry data — traces, metrics, and logs. This is where OpenTelemetry and Jaeger come to the rescue. Let's Look at the Basics There are two main terms we have to understand: Trace ID A Trace ID is a 16-byte identifier, often represented as a 32-character hexadecimal string. It’s automatically generated at the start of a trace and stays the same across all spans created by a particular request. This makes it easy to see how a request travels through different services or components in a system. Span ID Every individual operation within a trace gets its own Span ID, which is typically a randomly generated 64-bit value. Spans share the same Trace ID, but each one has a unique Span ID, so you can pinpoint exactly which part of the workflow each span represents (like a database query or a call to another microservice). How Are They Related? Trace ID and Span ID complement each other. When a request is initiated, a Trace ID is generated and passed to all involved services. Each service, in turn, creates a span with a unique Span ID linked to the Trace ID, enabling you to visualize the full lifecycle of the request from start to finish. Okay, so why not just use Jaeger? Why do we need OpenTelemetry (OTEL) and all its specifications? That’s a great question! Let’s break it down step by step. Find more about Jaeger here. TL;DR Jaeger is a system for storing and visualizing distributed traces. It collects, stores, searches, and displays data showing how requests “travel” through your services.OpenTelemetry (OTEL) is a standard (and a set of libraries) for collecting telemetry data (traces, metrics, logs) from your applications and infrastructure. It isn’t tied to any single visualization tool or backend. Put simply: OTEL is like a “universal language” and set of libraries for telemetry collection.Jaeger is a backend and UI for viewing and analyzing distributed traces. Why Do We Need OTEL if We Already Have Jaeger? 1. A Single Standard for Collection In the past, there were projects like OpenTracing and OpenCensus. OpenTelemetry unifies these approaches to collecting metrics and traces into one universal standard. 2. Easy Integration You write your code in Go (or another language), add OTEL libraries for auto-injecting interceptors and spans, and that’s it. Afterward, it doesn’t matter where you want to send that data—Jaeger, Tempo, Zipkin, Datadog, a custom backend—OpenTelemetry takes care of the plumbing. You just swap out the exporter. 3. Not Just Traces OpenTelemetry covers traces, but it also handles metrics and logs. You end up with a single toolset for all your telemetry needs, not just tracing. 4. Jaeger as a Backend Jaeger is an excellent choice if you’re primarily interested in distributed tracing visualization. But it doesn’t provide the cross-language instrumentation by default. OpenTelemetry, on the other hand, gives you a standardized way to collect data, and then you decide where to send it (including Jaeger). In practice, they often work together: Your application uses OpenTelemetry → communicates via OTLP protocol → goes to the OpenTelemetry Collector (HTTP or grpc) → exports to Jaeger for visualization. Tech Part System Design (A Little Bit) Let's quickly sketch out a couple of services that will do the following: Purchase Service – processes a payment and records it in MongoDBCDC with Debezium – listens for changes in the MongoDB table and sends them to KafkaPurchase Processor – consumes the message from Kafka and calls the Auth Service to look up the user_id for validationAuth Service – a simple user service In summary: 3 Go servicesKafkaCDC (Debezium)MongoDB Code Part Let’s start with the infrastructure. To tie everything together into one system, we’ll create a large Docker Compose file. We’ll begin by setting up telemetry. Note: All the code is available via a link at the end of the article, including the infrastructure. YAML services: jaeger: image: jaegertracing/all-in-one:1.52 ports: - "6831:6831/udp" # UDP port for the Jaeger agent - "16686:16686" # Web UI - "14268:14268" # HTTP port for spans networks: - internal prometheus: image: prom/prometheus:latest volumes: - ./prometheus.yml:/etc/prometheus/prometheus.yml:ro ports: - "9090:9090" depends_on: - kafka - jaeger - otel-collector command: --config.file=/etc/prometheus/prometheus.yml networks: - internal otel-collector: image: otel/opentelemetry-collector-contrib:0.91.0 command: ['--config=/etc/otel-collector.yaml'] ports: - "4317:4317" # OTLP gRPC receiver volumes: - ./otel-collector.yaml:/etc/otel-collector.yaml depends_on: - jaeger networks: - internal We’ll also configure the collector — the component that gathers telemetry. Here, we choose gRPC for data transfer, which means communication will happen over HTTP/2: YAML receivers: # Add the OTLP receiver listening on port 4317. otlp: protocols: grpc: endpoint: "0.0.0.0:4317" processors: batch: # https://github.com/open-telemetry/opentelemetry-collector/tree/main/processor/memorylimiterprocessor memory_limiter: check_interval: 1s limit_percentage: 80 spike_limit_percentage: 15 extensions: health_check: {} exporters: otlp: endpoint: "jaeger:4317" tls: insecure: true prometheus: endpoint: 0.0.0.0:9090 debug: verbosity: detailed service: extensions: [health_check] pipelines: traces: receivers: [otlp] processors: [memory_limiter, batch] exporters: [otlp] metrics: receivers: [otlp] processors: [memory_limiter, batch] exporters: [prometheus] Make sure to adjust any addresses as needed, and you’re done with the base configuration. We already know OpenTelemetry (OTEL) uses two key concepts — Trace ID and Span ID — that help track and monitor requests in distributed systems. Implementing the Code Now, let’s look at how to get this working in your Go code. We need the following imports: Go "go.opentelemetry.io/otel" "go.opentelemetry.io/otel/exporters/otlp/otlptrace" "go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracegrpc" "go.opentelemetry.io/otel/sdk/resource" "go.opentelemetry.io/otel/sdk/trace" semconv "go.opentelemetry.io/otel/semconv/v1.17.0" Then, we add a function to initialize our tracer in main() when the application starts: Go func InitTracer(ctx context.Context) func() { exp, err := otlptrace.New( ctx, otlptracegrpc.NewClient( otlptracegrpc.WithEndpoint(endpoint), otlptracegrpc.WithInsecure(), ), ) if err != nil { log.Fatalf("failed to create OTLP trace exporter: %v", err) } res, err := resource.New(ctx, resource.WithAttributes( semconv.ServiceNameKey.String("auth-service"), semconv.ServiceVersionKey.String("1.0.0"), semconv.DeploymentEnvironmentKey.String("stg"), ), ) if err != nil { log.Fatalf("failed to create resource: %v", err) } tp := trace.NewTracerProvider( trace.WithBatcher(exp), trace.WithResource(res), ) otel.SetTracerProvider(tp) return func() { err := tp.Shutdown(ctx) if err != nil { log.Printf("error shutting down tracer provider: %v", err) } } } With tracing set up, we just need to place spans in the code to track calls. For example, if we want to measure database calls (since that’s usually the first place we look for performance issues), we can write something like this: Go tracer := otel.Tracer("auth-service") ctx, span := tracer.Start(ctx, "GetUserInfo") defer span.End() tracedLogger := logging.AddTraceContextToLogger(ctx) tracedLogger.Info("find user info", zap.String("operation", "find user"), zap.String("username", username), ) user, err := s.userRepo.GetUserInfo(ctx, username) if err != nil { s.logger.Error(errNotFound) span.RecordError(err) span.SetStatus(otelCodes.Error, "Failed to fetch user info") return nil, status.Errorf(grpcCodes.NotFound, errNotFound, err) } span.SetStatus(otelCodes.Ok, "User info retrieved successfully") We have tracing at the service layer — great! But we can go even deeper, instrumenting the database layer: Go func (r *UserRepository) GetUserInfo(ctx context.Context, username string) (*models.User, error) { tracer := otel.Tracer("auth-service") ctx, span := tracer.Start(ctx, "UserRepository.GetUserInfo", trace.WithAttributes( attribute.String("db.statement", query), attribute.String("db.user", username), ), ) defer span.End() var user models.User // Some code that queries the DB... // err := doDatabaseCall() if err != nil { span.RecordError(err) span.SetStatus(codes.Error, "Failed to execute query") return nil, fmt.Errorf("failed to fetch user info: %w", err) } span.SetStatus(codes.Ok, "Query executed successfully") return &user, nil } Now, we have a complete view of the request journey. Head to the Jaeger UI, query for the last 20 traces under auth-service, and you’ll see all the spans and how they connect in one place. Now, everything is visible. If you need it, you can include the entire query in the tags. However, keep in mind that you shouldn’t overload your telemetry — add data deliberately. I’m simply demonstrating what’s possible, but including the full query, this way isn’t something I’d generally recommend. gRPC client-server If you want to see a trace that spans two gRPC services, it’s quite straightforward. All you need is to add the out-of-the-box interceptors from the library. For example, on the server side: Go server := grpc.NewServer( grpc.StatsHandler(otelgrpc.NewServerHandler()), ) pb.RegisterAuthServiceServer(server, authService) On the client side, the code is just as short: Go shutdown := tracing.InitTracer(ctx) defer shutdown() conn, err := grpc.Dial( "auth-service:50051", grpc.WithInsecure(), grpc.WithStatsHandler(otelgrpc.NewClientHandler()), ) if err != nil { logger.Fatal("error", zap.Error(err)) } That’s it! Ensure your exporters are configured correctly, and you’ll see a single Trace ID logged across these services when the client calls the server. Handling CDC Events and Tracing Want to handle events from the CDC as well? One simple approach is to embed the Trace ID in the object that MongoDB stores. That way, when Debezium captures the change and sends it to Kafka, the Trace ID is already part of the record. For instance, if you’re using MongoDB, you can do something like this: Go func (r *mongoPurchaseRepo) SavePurchase(ctx context.Context, purchase entity.Purchase) error { span := r.handleTracing(ctx, purchase) defer span.End() // Insert the record into MongoDB, including the current span's Trace ID _, err := r.collection.InsertOne(ctx, bson.M{ "_id": purchase.ID, "user_id": purchase.UserID, "username": purchase.Username, "amount": purchase.Amount, "currency": purchase.Currency, "payment_method": purchase.PaymentMethod, // ... "trace_id": span.SpanContext().TraceID().String(), }) return err } Debezium then picks up this object (including trace_id) and sends it to Kafka. On the consumer side, you simply parse the incoming message, extract the trace_id, and merge it into your tracing context: Go // If we find a Trace ID in the payload, attach it to the context newCtx := ctx if traceID != "" { log.Printf("Found Trace ID: %s", traceID) newCtx = context.WithValue(ctx, "trace-id", traceID) } // Create a new span tracer := otel.Tracer("purchase-processor") newCtx, span := tracer.Start(newCtx, "handler.processPayload") defer span.End() if traceID != "" { span.SetAttributes( attribute.String("trace.id", traceID), ) } // Parse the "after" field into a Purchase struct... var purchase model.Purchase if err := mapstructure.Decode(afterDoc, &purchase); err != nil { log.Printf("Failed to map 'after' payload to Purchase struct: %v", err) return err } Go // If we find a Trace ID in the payload, attach it to the context newCtx := ctx if traceID != "" { log.Printf("Found Trace ID: %s", traceID) newCtx = context.WithValue(ctx, "trace-id", traceID) } // Create a new span tracer := otel.Tracer("purchase-processor") newCtx, span := tracer.Start(newCtx, "handler.processPayload") defer span.End() if traceID != "" { span.SetAttributes( attribute.String("trace.id", traceID), ) } // Parse the "after" field into a Purchase struct... var purchase model.Purchase if err := mapstructure.Decode(afterDoc, &purchase); err != nil { log.Printf("Failed to map 'after' payload to Purchase struct: %v", err) return err } Alternative: Using Kafka Headers Sometimes, it’s easier to store the Trace ID in Kafka headers rather than in the payload itself. For CDC workflows, this might not be available out of the box — Debezium can limit what’s added to headers. But if you control the producer side (or if you’re using a standard Kafka producer), you can do something like this with Sarama: Injecting a Trace ID into Headers Go // saramaHeadersCarrier is a helper to set/get headers in a Sarama message. type saramaHeadersCarrier *[]sarama.RecordHeader func (c saramaHeadersCarrier) Get(key string) string { for _, h := range *c { if string(h.Key) == key { return string(h.Value) } } return "" } func (c saramaHeadersCarrier) Set(key string, value string) { *c = append(*c, sarama.RecordHeader{ Key: []byte(key), Value: []byte(value), }) } // Before sending a message to Kafka: func produceMessageWithTraceID(ctx context.Context, producer sarama.SyncProducer, topic string, value []byte) error { span := trace.SpanFromContext(ctx) traceID := span.SpanContext().TraceID().String() headers := make([]sarama.RecordHeader, 0) carrier := saramaHeadersCarrier(&headers) carrier.Set("trace-id", traceID) msg := &sarama.ProducerMessage{ Topic: topic, Value: sarama.ByteEncoder(value), Headers: headers, } _, _, err := producer.SendMessage(msg) return err } Extracting a Trace ID on the Consumer Side Go for message := range claim.Messages() { // Extract the trace ID from headers var traceID string for _, hdr := range message.Headers { if string(hdr.Key) == "trace-id" { traceID = string(hdr.Value) } } // Now continue your normal tracing workflow if traceID != "" { log.Printf("Found Trace ID in headers: %s", traceID) // Attach it to the context or create a new span with this info } } Depending on your use case and how your CDC pipeline is set up, you can choose the approach that works best: Embed the Trace ID in the database record so it flows naturally via CDC.Use Kafka headers if you have more control over the producer side or you want to avoid inflating the message payload. Either way, you can keep your traces consistent across multiple services—even when events are asynchronously processed via Kafka and Debezium. Conclusion Using OpenTelemetry and Jaeger provides detailed request traces, helping you pinpoint where and why delays occur in distributed systems. Adding Prometheus completes the picture with metrics — key indicators of performance and stability. Together, these tools form a comprehensive observability stack, enabling faster issue detection and resolution, performance optimization, and overall system reliability. I can say that this approach significantly speeds up troubleshooting in a microservices environment and is one of the first things we implement in our projects. Links Infra codeOTEL Getting StartedOTEL SQLOTEL CollectorGo gRPCGo ReflectionKafkaDebezium MongoDB Connector DocsUnwrap MongoDB SMT Example

By Ilia Ivankin

Exploring the Purpose of Pytest Fixtures: A Practical Guide

To set the groundwork for this article, let's first understand what Pytest is. Pytest is a popular testing framework for Python that simplifies the process of writing scalable and maintainable test cases. It supports fixtures, parameterized testing, and detailed test reporting, making it a powerful choice for both unit and functional testing. Pytest's simplicity and flexibility have made it a go-to framework for developers and testers alike. How to Install Pytest Pytest requires: Python 3.8+ or PyPy3. 1. Run the following command in your command line: Plain Text pip install -U pytest OR pip3 install -U pytest You can refer to the image below for your reference: Check that you have installed the correct version: Plain Text $ pytest --version You can refer to the image below for your reference: Purpose of Fixtures Fixtures in Pytest provide a robust way to manage setup and teardown logic for tests. They are particularly useful for initializing resources, mocking external services, or performing setup steps that are shared across multiple tests. By using fixtures, you can avoid repetitive code and ensure a consistent testing environment. Common Use Cases Initializing web drivers for browser interactionsNavigating to a specific URL before running testsClean up resources after tests are completed Example of Using Pytest Fixtures in Selenium Test Scenario We want to verify the login functionality of the Sauce Labs Demo website. The steps include: Open a browser and navigate to the login page.Perform login operations.Verify successful login.Close the browser after the test. Code Implementation Plain Text import pytest from selenium import webdriver from selenium.webdriver.common.by import By @pytest.fixture(scope="module") def browser(): # Setup: Initialize the WebDriver driver = webdriver.Chrome() # Replace with your WebDriver (Chrome, Firefox, etc.) driver.get("https://www.saucedemo.com/") # Navigate to the login page yield driver # Provide the WebDriver instance to the tests # Teardown: Close the browser after tests driver.quit() def test_login_success(browser): # Use the WebDriver instance provided by the fixture browser.find_element(By.ID, "user-name").send_keys("standard_user") browser.find_element(By.ID, "password").send_keys("secret_sauce") browser.find_element(By.ID, "login-button").click() # Verify successful login by checking the presence of a product page element assert "Products" in browser.page_source Explanation Fixture Definition Plain Text @pytest.fixture(scope="module") def browser(): driver = webdriver.Chrome() driver.get("https://www.saucedemo.com/") yield driver driver.quit() @pytest.fixture(scope="module"): Defines a fixture named browser with a scope of "module," meaning it will be set up once per module and shared among all tests in that moduleSetup: Initializes the WebDriver and navigates to the Sauce Labs Demo login pageYield: Provides the WebDriver instance to the test functionsTeardown: Closes the browser after all tests are completed Using the Fixture Plain Text def test_login_success(browser): browser.find_element(By.ID, "user-name").send_keys("standard_user") browser.find_element(By.ID, "password").send_keys("secret_sauce") browser.find_element(By.ID, "login-button").click() assert "Products" in browser.page_source The test_login_success function uses the browser fixture.It interacts with the login page by sending credentials and clicking the login button.Finally, it asserts the login's success by verifying the presence of the "Products" page element. How to Use a Parameterized Fixture in Pytest A parameterized fixture in Pytest allows you to run the same test function multiple times with different input values provided by the fixture. This is done by setting the params argument in the @pytest.fixture decorator. Here’s an example of a parameterized fixture: Plain Text import pytest # Define a parameterized fixture @pytest.fixture(params=["chrome", "firefox", "safari"]) def browser(request): # The 'request' object gives access to the parameter value return request.param # Use the parameterized fixture in a test def test_browser_launch(browser): print(f"Launching browser: {browser}") # Simulate browser testing logic assert browser in ["chrome", "firefox", "safari"] Explanation 1. Definition The @pytest.fixture decorator uses the params argument to provide a list of values (["chrome", "firefox", "safari"]).For each value in the list, Pytest will call the fixture once and pass the current value to the test function. 2. Usage In the test test_browser_launch, the fixture browser is passed as an argument.The request.param in the fixture gives the current parameter value being used. 3. Test Execution Pytest will run the test_browser_launchtest three times, once for each browser: browser = "chrome"browser = "firefox"browser = "safari" Output When you run the test: Plain Text pytest -s test_file.py You’ll see: Plain Text Launching browser: chrome Launching browser: firefox Launching browser: safari This approach is particularly useful for testing the same logic or feature across multiple configurations, inputs, or environments. How to Pass Data Dynamically into a Fixture In Python, you can use a fixture to pass data dynamically to your test cases with the help of the Pytest framework. A fixture in Pytest allows you to set up data or resources that can be shared across multiple test cases. You can create dynamic test data inside the fixture and pass it to your test functions by including it as a parameter in the test. Here's how you can use a fixture to pass dynamic data to your test cases: Example Plain Text import pytest import time # Define a fixture that generates dynamic data @pytest.fixture def dynamic_data(): # You can generate dynamic data based on various factors, such as timestamps or random values return "Dynamic Data " + str(time.time()) # Use the fixture in your test case def test_using_dynamic_data(dynamic_data): print(dynamic_data) # This prints dynamically generated data assert "Dynamic Data" in dynamic_data # Your test logic using dynamic data Explanation 1. Fixture Creation The @pytest.fixture decorator is used to create a fixture. The fixture function (dynamic_data) generates dynamic data (in this case, appending the current timestamp to the string). 2. Using Fixture in Test In the test function (test_using_dynamic_data), the fixture is passed as a parameter. Pytest automatically detects that the test function needs dynamic_data, so it calls the fixture and provides the generated data to the test. 3. Dynamic Data Each time the test runs, the fixture generates fresh, dynamic data (based on the current timestamp), making the test run with different data. Benefits of Using Fixtures Code reusability. Define setup and teardown logic once and reuse it across multiple tests.Readability. Keep test logic focused on assertions rather than setup/teardown details.Consistency. Ensure each test starts with a clean state. Closing Thoughts Pytest fixtures are a powerful tool for managing test resources efficiently. Adopting fixtures lets you write clean, reusable, and maintainable test code. Try implementing them in your test automation projects, starting with simple examples like the one above.

By Sidharth Shukla

Enhancing API Integration Efficiency With a Mock Client

Due to the rapid growth of the API industry, client-server interaction must be seamless for everyone. Dependence on live APIs during development, however, may cause delays, particularly if the APIs are currently being built or undergoing frequent changes. A mock client can be helpful for developers aiming for efficiency and reliability. In this article, we focus on the concept of mock clients, their significance, and how to use them for development. What Is a Mock Client? A mock client is a simulated client that interacts with an API based on its defined specifications without sending actual requests to a live server. It mimics the behavior of the API, allowing developers to test and develop client-side code in isolation. This approach is invaluable during the development and testing phases, as it eliminates dependencies on the actual API implementation, leading to more streamlined and error-free code integration. Benefits of Having Mock Clients The mock clients are not just a technical enhancement for any code generator tool, it’s a strategic move to support development efficiency and software quality. Here’s why mock clients are beneficial: 1. Early API Integration Testing Mock clients allow developers to begin integrating and testing their code against the API even before the API is fully developed. This early testing makes sure that client-side functionality is validated upfront, reducing the risk of encountering significant issues later in the development cycle. This leads to having software quality process leverage. 2. Rapid Prototyping and Iteration Developers can quickly prototype features and iterate based on immediate feedback using mock clients. This agility is beneficial in today’s dynamic development environments, where requirements can change rapidly. 3. Enhanced Reliability Reduces dependencies on live APIs, minimizing issues caused by API downtime or instability during development. Demo of Using a Mock Client Note: Here, I have used Ballerina Swan Lake 2201.10.3 for demonstration. For this demonstration, I chose the Ballerina language to implement my app. The Ballerina language has the Ballerina OpenAPI tool to generate a mock client based on my OpenAPI contract. The Ballerina OpenAPI Tool has a primary mode that allows us to generate client code from a given OpenAPI specification. Additionally, the mock client is another sub-mode that can be executed within the client mode. If your OpenAPI contract includes examples for operation responses, the mock client generation code can be executed using the OAS contract. Step 1 First, I’m going to create a Ballerina package and a module for the mock client using this Organize Ballerina code guide. Create your Ballerina package using the bal new demo-mock command.Create a module for the mock client using the bal add mclient command. Step 2 With this Ballerina OpenAPI tool, you can generate mock clients if your OpenAPI contract includes examples of particular operation responses. Then, I use this OpenAPI contract to generate the mock client. This contract has examples with responses. OpenAPI Contract YAML openapi: 3.0.1 info: title: Convert version: 0.1.0 servers: - url: "{server}:{port}/convert" variables: server: default: http://localhost port: default: "9000" paths: /rate/{fromCurrency}/{toCurrency}: get: operationId: getRate parameters: - in: path name: fromCurrency schema: type: string required: true - in: path name: toCurrency schema: type: string required: true responses: "200": description: Created content: application/json: schema: type: object examples: json1: value: toAmount: 60000 fromCurrency: EUR toCurrency: LKR fromAmount: 200 timestamp: 2024-07-14 "202": description: Accepted "400": description: BadRequest content: application/json: schema: $ref: '#/components/schemas/ErrorPayload' components: schemas: ErrorPayload: required: - message - method - path - reason - status - timestamp type: object properties: timestamp: type: string status: type: integer format: int64 reason: type: string message: type: string path: type: string method: type: string Then, execute the mock client generation command inside the created module mclient: Plain Text bal openapi -i <yaml> --mode client --mock Example for Generated Ballerina Mock Client IDL // AUTO-GENERATED FILE. DO NOT MODIFY. // This file is auto-generated by the Ballerina OpenAPI tool. public isolated client class Client { # Gets invoked to initialize the `connector`. # # + config - The configurations to be used when initializing the `connector` # + serviceUrl - URL of the target service # + return - An error if connector initialization failed public isolated function init(ConnectionConfig config = {}, string serviceUrl = "http://localhost:9000/convert") returns error? { return; } # + headers - Headers to be sent with the request # + return - Created resource isolated function get rate/[string fromCurrency]/[string toCurrency](map<string|string[]> headers = {}) returns record {}|error { return {"fromCurrency": "EUR", "toCurrency": "LKR", "fromAmount": 200, "toAmount": 60000, "timestamp": "2024-07-14"}; } } Step 3 Now, you can use the generated sample mock client for the app implementation, as I used in the main.bal file. main.bal File IDL import ballerina/io; import demo_mock.mclient as mc; public function main() returns error? { mc:Client mclient = check new(); record {} mappingResult = check mclient->/rate/["EUR"]/["LKR"](); io:println(mappingResult); // remain logic can be address here } More mock client samples can be found here. Conclusion Whether you’re building complex client-side applications or managing API integrations, using mock clients can transform your development experience to be seamless. As APIs continue to evolve and play an increasingly central role in software ecosystems, tools like OpenAPI’s mock client generation are essential for staying ahead in the competitive landscape. Thank you for reading!

By Sumudu Nissanka

Jackson vs Gson: Edge Cases in JSON Parsing for Java Apps

JSON (Javascript Object Notation) is a collection of key-value pairs that can be easily parsed and generated by applications. It is a subset of JavaScript Programming Language Standard ECMA-262. The parsing of JSON is required in most applications, such as restful APIs or applications that need data serialization. In the Java ecosystem, the two most popular libraries for handling JSON data are Jackson and Gson. Both are used widely and offer unique advantages. This article uses edge-case examples to explore the features of both libraries on different parameters. Brief Overview of Jackson and Gson Jackson Jackson was developed by FasterXML and is used in enterprise applications and frameworks such as Spring Boot. It offers parsing, serialization, and deserialization of JSON data. The following features make this library popular among developers: Jackson is the default JSON processing library in Spring Boot, which eliminates manual configuration in most cases.It facilitates JSON deserialization into generic types using TypeReference or JavaType.It provides different annotations to customize serialization and deserialization behavior. For example, @JsonProperty(name) makes the mapping between the incoming key and the actual Java POJO field seamless.It provides extensive and robust support for bidirectional Databinding (JSON to POJO and vice versa), streaming API (API reads JSON into POJO), and Tree model parsing (an in-memory map of JSON objects).The Jackson library offers high performance due to minimizing memory overhead and optimizing serialization/deserialization (from JSON to POJO and vice versa).Jackson supports additional modules such as XML, YAML processing, and Kotlin, scala-specific enhancements.Annotations such as @JsonTypeInfo and @JsonSubTypes handle polymorphic types.It handles missing or additional fields in JSON data due to its backward and forward compatibility.Jackson provides support for immutable objects and classes with constructors, including those using builder patterns.The ObjectMapper class is thread-safe and, therefore, enables efficient use in multithreaded applications. Gson Gson was developed by Google and designed for converting JSON to Java objects (POJO) and vice versa. It is simple and ideal to use for smaller applications that need quick implementations. The open-source library offers the following key features: Gson has minimal external dependencies; therefore, it is easy to integrate.It supports nested objects and complex data types such as lists, maps, and custom classes.It can deserialize JSON into generic collections like List<T>, Map<K,V> using TypeToken.Gson Library’s JsonSerializer and JsonDeserializer interfaces allow customized implementation.The null values are excluded in the JSON output by default, and if required, null values can be included in the output.Annotations @SerializedName maps JSON keys to Java fields with different names.The Gson objects are thread-safe and, therefore, can be used in multithreaded applications.Class GsonBuilder can apply custom naming policies for fields. For example, FieldNamingPolicy.IDENTITY is the default policy, meaning the field name is unchanged. Edge Cases Considered in This Comparison FeatureJacksonGSON Extra Fields Ignored by default, configurable. Ignored by default. Null values Supports @JsonInclude. Requires .serializeNulls(). Circular References Supported using @JsonIdentityInfo. Not supported directly. Data Handling Supports Java 8 Date API with modules. Requires custom-type adapters. Polymorphism Built-in with @JsonTypeInfo. Needs custom deserialization logic. The input JSON considered for comparison with Jackson and Gson libraries is present on GitHub. The model class representation of JSON is on GitHub. Jackson Implementation The above JSON is converted to a Java object using the Jackson libraries below: XML  <dependency> <groupId>com.fasterxml.jackson.core</groupId> <artifactId>jackson-databind</artifactId> <version>2.18.2</version> </dependency> <dependency> <groupId>com.fasterxml.jackson.datatype</groupId> <artifactId>jackson-datatype-jsr310</artifactId> <version>2.18.2</version> </dependency>  JSON Parsing main class using Jackson library: Java public class JacksonJsonMain { public static void main(String[] args) throws IOException { ObjectMapper mapper = new ObjectMapper(); //Jackson Support for LocalDate using jackson-datatype-jsr310 mapper.registerModule(new JavaTimeModule()); //Configuration to ignore extra fields mapper.configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false); // Deserialize the JSON EmployeeModelData employeeModelData = mapper.readValue(json, EmployeeModelData.class); Employee employee=employeeModelData.getEmployee(); // display Json fields System.out.println("Jackson Library parsing output"); System.out.println("Employee Name: " + employee.getName()); System.out.println("Department Name: " + employee.getDepartment().getName()); System.out.println("Skills: " + employee.getSkills()); System.out.println("Team Members Count: " + employeeModelData.getTeamMembers().size()); } } The output of the above class is as follows: Gson Implementation The Gson dependency used to convert the above JSON to a Java object is below: XML  <dependency> <groupId>com.google.code.gson</groupId> <artifactId>gson</artifactId> <version>2.11.0</version> </dependency>  JSON parsing using GSON library main class: Java public class GsonJsonMain { public static void main(String[] args) { Gson gson = new GsonBuilder() .registerTypeAdapter(LocalDate.class, new LocalDateAdapter()) // Register LocalDate adapter .serializeNulls() // Handle null values .setPrettyPrinting() // Pretty print JSON .create(); // Deserialize the JSON EmployeeModelData data = gson.fromJson(json, EmployeeModelData.class); // Print Employee information System.out.println("GSON Library parsing output"); System.out.println("Employee Name: " + data.getEmployee().getName()); System.out.println("Department Name: " + data.getEmployee().getDepartment().getName()); System.out.println("Skills: " + data.getEmployee().getSkills()); System.out.println("Team Members Count: " + data.getTeamMembers().size()); } } The output of the above main class is as follows: Which One Should I Choose? Jackson offers high performance; therefore, it must be used when projects involve complex data structures or large datasets, whereas Gson must be used when there are smaller datasets and the data structure is simple. Conclusion Both libraries can handle the above dataset effectively and are excellent while processing JSON parsing in JAVA. The comparison mentioned above helps one to choose the right library based on project requirements. The code snippets mentioned above are available in the GitHub repository. A detailed comparison between Jackson and Gson is available on Baeldung. The Jackson official Documentation offers in-depth information on Jackson’s features and configuration. Similarly, Gson Official documentation provides a detailed implementation guide.

By Sulakshana Singh

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