Red Kubes is a Dutch start-up founded in 2019

Red Kubes

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Imagine you're tasked with implementing a geo-fencing feature for your service. At first glance, dropping in an IP GeoLocation API might seem straightforward, but often, project constraints or requirements complicate the simplest solutions. Let's explore our options. Local GeoIP Database Implementation This method involves embedding a geolocation database directly within your application. It offers fast response times as it resides in-memory and operates independently of external services. However, this approach can be resource-intensive and harder to manage. Let’s see it in more detail. Pros Speed: IP lookups in milliseconds or less.
Reliability: No reliance on third-party services reduces failure chances.
Scalability: No extra costs as usage grows. Cons Resource Intensive: Each app instance consumes significant memory.
More Dependencies: Requires packages for decoding and looking up GeoIP data.
Challenging Updates: Updates require manual intervention or another self-hosted service. Using External GeoIP APIs (Often Paid) Opting for a third-party API to handle geolocation queries reduces your application's resource load and ensures access to the most current data, but it comes with its own challenges: Pros Minimal Resource Use: Outsourcing GeoIP data and logic doesn't burden your app's resources.
Ease of Implementation: Usually involves consuming a standard HTTP/GraphQL or gRPC API endpoint.
Fewer Dependencies: Often only requires an HTTP client.
Updates without hassle: Third-party will handle updates Cons Slower Responses: Calls to third-party services can introduce significant latency.
Increased Unreliability: Greater likelihood of failures due to network issues or third-party downtimes.
Scaling Costs: Costs can escalate as usage increases. Introducing Atlas: Combining the Best of Both Worlds Faced with these challenges, I developed Atlas—a robust solution designed to synergize the benefits of both approaches. Atlas acts as a self-hosting HTTP service that automatically manages and serves the latest MaxMind geolocation databases, providing: Low Latency: Similar to local databases, Atlas offers rapid response times. (often in a few milliseconds) Reliability and Ease of Updates: It autonomously handles database updates, removing the typical maintenance burdens. Cost Efficiency: No additional costs with scale in terms of third-party fees. Dead-Simple API: A very simple API that lets you implement quicker and focus on what matters. Open and Free: Atlas is Open Source and freely available under Apache-2.0 License. By hosting Atlas closer to your application but operating like a remote API, it offers a middle ground that maximizes efficiency without the drawbacks of either method we have discussed. Why Rust? Safe, Performant, and Resource-Efficient Atlas is written in Rust, a language chosen for its unparalleled safety, performance, and resource efficiency. Rust's ownership model ensures memory safety without the need for a garbage collector, which is particularly advantageous during database updates—these operations consume minimal memory, avoiding the typical overhead associated with garbage-collected languages. This design choice not only boosts Atlas's performance but also enhances its reliability, making it an ideal solution for managing GeoIP data efficiently and safely. How to Deploy and Use Atlas? Atlas Docker images are automatically built and pushed to Docker Hub via GitHub Actions, making it easy for anyone to use. For detailed instructions on using these Docker images or building Atlas from source and configuration parameters, please refer to the Atlas README.md. Atlas features a straightforward API, documented with OpenAPI 3.0 Specs. You can access the API documentation from the public internet here: https://atlas-rs.fly.dev/swagger-ui/. Here’s an example of how you can use curl to query the demo instance of Atlas (hosted publicly thanks to Fly.io and the GeoLite2 MaxMind database license) to retrieve country information for an IP address 8.8.8.8: > curl https://atlas-rs.fly.dev/geoip/lookup/country/8.8.8.8 | jq
{
  "results": {
    "8.8.8.8": {
      "country": {
        "iso_code": "US",
        // additional fields...
      },
      // additional results...
    }
  },
  "database_build_epoch": 1715091859
} Please note that this demo instance is intended only for demonstration and documentation purposes. Join the Atlas Community and Contribute As a newly introduced open-source project, Atlas is in the early stages of building its community. I am excited about the potential for Atlas to evolve and thrive with contributions from developers like you. Whether you're interested in refining functionality, adding new features, or simply improving documentation, your contributions are welcome and greatly valued. How You Can Contribute Try Atlas: The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage.


Report Issues: If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement.


Suggest Features: Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request.


Improve Documentation: As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable.


Spread the Word: If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Commitment to Open Source I am relatively new to Rust but bring many years of experience from other technology stacks. This project presents a great opportunity for more experienced Rust developers to contribute, ensuring that Atlas adheres to Rust's best practices and conventions since I know that it is not as good as it can be right now. I am committed to maintaining Atlas as a community-driven project and believe in open, collaborative development. I am eager to see how the community will shape Atlas's path forward and encourage you to join me in this journey, helping to build a tool that serves the needs of developers globally. In the end, I would like to give a special thanks to oschwald for creating the rust maxminddb crate which made Atlas possible. Future Directions As we continue to develop and improve Atlas, my vision is to create a robust, user-friendly minimal service. As of today, Atlas only supports MaxMind databases, but it would be beneficial to have an extensible implementation so more and more formats can be supported. Another aspect I would like to pay attention to is adding more endpoint types such as gRPC so that users can choose the best endpoint based on their requirements and needs. Thank you for taking the time to learn about Atlas. I am excited to see where we can take this project with your support and collaboration! Imagine you're tasked with implementing a geo-fencing feature for your service. At first glance, dropping in an IP GeoLocation API might seem straightforward, but often, project constraints or requirements complicate the simplest solutions. Let's explore our options. Local GeoIP Database Implementation Local GeoIP Database Implementation This method involves embedding a geolocation database directly within your application. It offers fast response times as it resides in-memory and operates independently of external services. However, this approach can be resource-intensive and harder to manage. Let’s see it in more detail. Pros Speed: IP lookups in milliseconds or less. Reliability: No reliance on third-party services reduces failure chances. Scalability: No extra costs as usage grows. Speed : IP lookups in milliseconds or less. Speed Reliability : No reliance on third-party services reduces failure chances. Reliability Scalability : No extra costs as usage grows. Scalability Cons Resource Intensive: Each app instance consumes significant memory. More Dependencies: Requires packages for decoding and looking up GeoIP data. Challenging Updates: Updates require manual intervention or another self-hosted service. Resource Intensive : Each app instance consumes significant memory. Resource Intensive More Dependencies : Requires packages for decoding and looking up GeoIP data. More Dependencies Challenging Updates : Updates require manual intervention or another self-hosted service. Challenging Updates Using External GeoIP APIs (Often Paid) Using External GeoIP APIs (Often Paid) Opting for a third-party API to handle geolocation queries reduces your application's resource load and ensures access to the most current data, but it comes with its own challenges: Pros Minimal Resource Use: Outsourcing GeoIP data and logic doesn't burden your app's resources. Ease of Implementation: Usually involves consuming a standard HTTP/GraphQL or gRPC API endpoint. Fewer Dependencies: Often only requires an HTTP client. Updates without hassle: Third-party will handle updates Minimal Resource Use : Outsourcing GeoIP data and logic doesn't burden your app's resources. Minimal Resource Use Ease of Implementation : Usually involves consuming a standard HTTP/GraphQL or gRPC API endpoint. Ease of Implementation Fewer Dependencies : Often only requires an HTTP client. Fewer Dependencies Updates without hassle : Third-party will handle updates Updates without hassle Cons Slower Responses: Calls to third-party services can introduce significant latency. Increased Unreliability: Greater likelihood of failures due to network issues or third-party downtimes. Scaling Costs: Costs can escalate as usage increases. Slower Responses : Calls to third-party services can introduce significant latency. Slower Responses Increased Unreliability : Greater likelihood of failures due to network issues or third-party downtimes. Increased Unreliability Scaling Costs : Costs can escalate as usage increases. Scaling Costs Introducing Atlas : Combining the Best of Both Worlds Introducing Atlas Faced with these challenges, I developed Atlas —a robust solution designed to synergize the benefits of both approaches. Atlas acts as a self-hosting HTTP service that automatically manages and serves the latest MaxMind geolocation databases, providing: Atlas Low Latency: Similar to local databases, Atlas offers rapid response times. (often in a few milliseconds) Low Latency : Similar to local databases, Atlas offers rapid response times. (often in a few milliseconds) Low Latency Reliability and Ease of Updates: It autonomously handles database updates, removing the typical maintenance burdens. Reliability and Ease of Updates : It autonomously handles database updates, removing the typical maintenance burdens. Reliability and Ease of Updates Cost Efficiency: No additional costs with scale in terms of third-party fees. Cost Efficiency : No additional costs with scale in terms of third-party fees. Cost Efficiency Dead-Simple API: A very simple API that lets you implement quicker and focus on what matters. Dead-Simple API : A very simple API that lets you implement quicker and focus on what matters. Dead-Simple API Open and Free: Atlas is Open Source and freely available under Apache-2.0 License. Open and Free: Atlas is Open Source and freely available under Apache-2.0 License. Open and Free : Atlas is Open Source and freely available under Apache-2.0 License. Open and Free By hosting Atlas closer to your application but operating like a remote API, it offers a middle ground that maximizes efficiency without the drawbacks of either method we have discussed. Why Rust? Safe, Performant, and Resource-Efficient Why Rust? Atlas is written in Rust, a language chosen for its unparalleled safety, performance, and resource efficiency. Rust's ownership model ensures memory safety without the need for a garbage collector, which is particularly advantageous during database updates—these operations consume minimal memory, avoiding the typical overhead associated with garbage-collected languages. This design choice not only boosts Atlas's performance but also enhances its reliability, making it an ideal solution for managing GeoIP data efficiently and safely. How to Deploy and Use Atlas? Atlas Docker images are automatically built and pushed to Docker Hub via GitHub Actions, making it easy for anyone to use. For detailed instructions on using these Docker images or building Atlas from source and configuration parameters, please refer to the Atlas README.md . Atlas README.md Atlas features a straightforward API, documented with OpenAPI 3.0 Specs. You can access the API documentation from the public internet here: https://atlas-rs.fly.dev/swagger-ui/ . https://atlas-rs.fly.dev/swagger-ui/ Here’s an example of how you can use curl to query the demo instance of Atlas (hosted publicly thanks to Fly.io and the GeoLite2 MaxMind database license) to retrieve country information for an IP address 8.8.8.8 : Fly.io 8.8.8.8 > curl https://atlas-rs.fly.dev/geoip/lookup/country/8.8.8.8 | jq
{
  "results": {
    "8.8.8.8": {
      "country": {
        "iso_code": "US",
        // additional fields...
      },
      // additional results...
    }
  },
  "database_build_epoch": 1715091859
} > curl https://atlas-rs.fly.dev/geoip/lookup/country/8.8.8.8 | jq
{
  "results": {
    "8.8.8.8": {
      "country": {
        "iso_code": "US",
        // additional fields...
      },
      // additional results...
    }
  },
  "database_build_epoch": 1715091859
} Please note that this demo instance is intended only for demonstration and documentation purposes. Please note that this demo instance is intended only for demonstration and documentation purposes. Join the Atlas Community and Contribute Join the Atlas Community and Contribute As a newly introduced open-source project, Atlas is in the early stages of building its community. I am excited about the potential for Atlas to evolve and thrive with contributions from developers like you. Whether you're interested in refining functionality, adding new features, or simply improving documentation, your contributions are welcome and greatly valued. How You Can Contribute Try Atlas: The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage. Report Issues: If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement. Suggest Features: Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request. Improve Documentation: As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable. Spread the Word: If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Try Atlas: The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage. Try Atlas : The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage. Try Atlas Report Issues: If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement. Report Issues : If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement. Report Issues Suggest Features: Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request. Suggest Features : Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request. Suggest Features Improve Documentation: As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable. Improve Documentation : As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable. Improve Documentation Spread the Word: If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Spread the Word : If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Spread the Word Commitment to Open Source I am relatively new to Rust but bring many years of experience from other technology stacks. This project presents a great opportunity for more experienced Rust developers to contribute, ensuring that Atlas adheres to Rust's best practices and conventions since I know that it is not as good as it can be right now. I am committed to maintaining Atlas as a community-driven project and believe in open, collaborative development. I am eager to see how the community will shape Atlas's path forward and encourage you to join me in this journey, helping to build a tool that serves the needs of developers globally. In the end, I would like to give a special thanks to oschwald for creating the rust maxminddb crate which made Atlas possible. special thanks to oschwald oschwald maxminddb Future Directions As we continue to develop and improve Atlas, my vision is to create a robust, user-friendly minimal service. As of today, Atlas only supports MaxMind databases, but it would be beneficial to have an extensible implementation so more and more formats can be supported. Another aspect I would like to pay attention to is adding more endpoint types such as gRPC so that users can choose the best endpoint based on their requirements and needs. Thank you for taking the time to learn about Atlas. I am excited to see where we can take this project with your support and collaboration!

IP Geo-Location: Local Database vs. Remote API—Finding the Middle Ground With Atlas

is a popular technology for efficient and scalable data storage and retrieval. However, maintaining its performance and data integrity requires a crucial practice called reindexing. Indexing is the initial process of adding data to Elasticsearch, while reindexing is essential for maintaining data accuracy and optimizing search performance. Elasticsearch Whether you're a seasoned Elasticsearch user or just beginning your journey, understanding reindexing is important for maintaining an efficient Elasticsearch cluster. In this article, we'll delve into the essentials of Elasticsearch reindexing, answering when it's necessary, how to trigger it, and the best practices to get the most out of your Elasticsearch cluster. Understanding Elasticsearch Reindexing In Elasticsearch, reindexing helps maintain data integrity and increase performance. Put simply, it's the process of copying data from one index to another. While this might sound straightforward, if not done correctly it can cause issues such as slow data retrieval or even incorrect results Imagine your Elasticsearch indices as well-organized libraries. Over time, books might need to be updated, rearranged, or even replaced. Reindexing is akin to rearranging the library shelves or updating the books to keep everything in order. Without it, your library can become disorganized, leading to slower searches and potential inaccuracies in your data. This analogy underscores the importance of understanding reindexing in Elasticsearch. It's not just about copying data; it's about maintaining the integrity of your "library" for efficient searching and retrieval. Let’s take a look at and how to keep on top of it. when reindexing is required When is Reindexing Necessary? Reindexing becomes essential when changes occur in your Elasticsearch data models or mappings, or when you're seeking performance enhancements. In this section, we’ll look into these scenarios in more detail to understand the nuances around why reindexing is required. Structural Changes in Data Models Structural changes in data models refer to modifications in how data is structured within Elasticsearch. These changes can include things like adding or removing new fields or altering data types of existing fields. Introducing new fields often requires a reindex to ensure Elasticsearch knows how to efficiently search for data stored in that field. Modifying data types requires a new index altogether as you cannot change data types in place. Once the new mapping has been created for the modified data type then the data needs reindexing. These structural changes require reindexing due to Elasticsearch's schema-on-write approach. Elasticsearch indexes data as it is ingested, and any changes to the data structure can lead to inconsistencies between existing data and data written with the new schema. As a result, without reindexing, search queries may yield unexpected or inaccurate results due to the schema mismatch of data items. This can have an impact on both data accuracy and search performance. Mapping Updates or Changes Mappings serve as the blueprint for how data is indexed and queried in Elasticsearch. When these mappings are modified then reindexing is usually required. Mappings define the data types and properties of fields within Elasticsearch. Any change to these mappings affects how data is indexed, stored, and retrieved. For instance, altering a text field to a date field fundamentally changes how data is processed and queried. Elasticsearch enforces data consistency based on mapping definitions. Changes to mappings can lead to inconsistencies between existing data and the updated schema if the data is not reindexed. When mappings are modified, particularly if it involves changing data types or field properties, backfilling also becomes important. Backfilling is the process of retroactively populating or updating existing data to align it with a new schema or data structure. This means that the existing data can still be queried efficiently and accurately after the mapping change. Performance Enhancements and Index Optimizations Reindexing isn't just a routine maintenance task, it's a powerful tool for optimizing search performance within Elasticsearch. For example, reindexing allows you to modify the number of shards in an index. Adjusting the shard count, or resharding, can distribute data more evenly, preventing uneven workloads on specific nodes to improve search performance. Reindexing can also be used to consolidate indices together. Let's say you have multiple small indices that share the same data structure and are frequently queried together. Reindexing can consolidate them into a single, larger index. This reduces the overhead of managing numerous small indices which can in turn enhance search speed. Finally, reindexing can be used to improve routing. By reindexing and applying routing strategies effectively, you can route queries to specific shards, minimizing the number of shards that need to be searched. This targeted approach can significantly speed up search queries if your data is frequently searched by specific keys such as a user ID. Upgrading Your Cluster When upgrading from Elasticsearch version 6.X to 8.0 (current major version) and beyond, you may need to reindex any indices that were created in version 6. Elasticsearch's data structures and underlying mechanisms changed significantly between these versions requiring reindexing for compatibility and optimal performance. The reindexing process ensures that data aligns with the updated structure and new functionality to ensure you can migrate seamlessly from old to new. Elasticsearch recommends using their upgrade assistant to help with this process. How to Trigger a Reindexing Operation Reindexing in Elasticsearch is made possible through the Elasticsearch Reindex API. The Reindex API serves as the bridge between your existing index and the new index you want to create or modify. Its primary purpose is to enable the efficient transfer of data from one index to another, on top of this, you can also: Selectively copy documents from the source index to the target index. Apply complex data transformations, such as field renaming or type conversions. Filter data based on specific criteria. Control the indexing process with options like throttling and refresh intervals. Before using the Reindex API, ensure that the target index, where you want to move or transform your data, is created and properly configured. To trigger reindexing, you then need to formulate a POST request to the endpoint, specifying the source and target indices, as well as any desired transformations or filters. An example reindex POST request could look as follows. _reindex POST /_reindex
{
  "source": {
    "index": "source_index"
  },
  "dest": {
    "index": "target_index"
  },
  "script": {
    "source": "ctx._source.new_field = 'transformed value'"
  },
  "query": {
    "term": {
      "category.keyword": "example"
    }
  }
} Once your request is built you can send the request to Elasticsearch, initiating the reindexing process. Elasticsearch will start copying data from the source index to the target index, following your defined instructions. Once the reindexing is complete, thoroughly test the data in the target index to ensure it aligns with your expectations. For example, you can compare the field mappings between the source and target indices to confirm that fields were mapped correctly during reindexing. You could also retrieve a sample of documents from both the source and target indices and compare them to verify the data was reindexed accurately. Best Practices for Reindexing When reindexing within Elasticsearch, you should look to follow these best practices to ensure the reindexing procedure is smooth with no data loss and little impact on existing cluster operations. Prioritize Data Backup Before initiating any reindexing activity, it is important to back up your cluster. This precautionary step acts as a safety net, offering a way to revert to the original state should any unexpected issues arise during the reindexing process. The source index should still exist after reindexing, however, it's a fundamental principle to always have a reliable copy of your data before making significant changes. Conduct Reindexing in a Controlled Environment First To mitigate potential risks and challenges during reindexing, it is advisable to perform the operation in a pre-production environment first. By doing so, you can identify and address any unforeseen issues without affecting the production system. Once the procedure has been completed and verified in the pre-production environment, it can then safely be run in production. Monitor Resource Usage It is important to monitor system resources during reindexing to prevent strain on your infrastructure. Reindexing can be resource-intensive, especially for larger datasets. Keeping a close eye on CPU, memory, disk usage, and network activity can help optimize resource allocation, ensuring the process runs efficiently without causing performance bottlenecks. To check resource usage you can use the node stats API. GET /_nodes/stats This will return a response that looks as follows. {
  "_nodes": {
    "total": 2,
    "successful": 2,
    "failed": 0
  },
  "cluster_name": "my_cluster",
  "nodes": {
    "node_id1": {
      "name": "node_name1",
      "process": {
        "cpu": {
          "percent": 30,
        }
      },
      "jvm": {
        "mem": {
          "heap_used_percent": 40.3,
          "heap_used_in_bytes": 123456789,
          "heap_max_in_bytes": 256000000
        }
      }
    },
    "node_id2": {
      "name": "node_name2",
      "process": {
        "cpu": {
          "percent": 50,
        }
      },
      "jvm": {
        "mem": {
          "heap_used_percent": 60.8,
          "heap_used_in_bytes": 210987654,
          "heap_max_in_bytes": 256000000
        }
      }
    }
  }
} If you find reindexing is too intensive, you can throttle the process by setting the parameter when submitting the reindex request. This will add a sleep between batches for the number of seconds set by the parameter, to provide a cooldown period between batches. requests_per_second Verify and Validate Results Once the reindexing is complete you should verify the data in the target index to ensure it looks as expected. This validation process should encompass a variety of tests including document counts, field mappings, and search queries. Alternative Solutions Elasticsearch has undoubtedly established itself as a prominent solution in the NoSQL search and analytics space. However, it's worth exploring alternative solutions that offer unique approaches to data indexing and querying, particularly one like Rockset. Rockset is a to Elasticsearch and offers a different perspective on indexing and querying data. Unlike Elasticsearch's schema-on-write approach, Rockset allows schemaless ingestion. Data can be ingested and queried without the need for upfront schema definition, offering more flexibility in handling ever-evolving datasets without the need for reindexing. cloud-native alternative In the area of index management, Rockset benefits from its model where a row index, a column index, and a search index are all created automatically for the data as it is ingested. This contrasts with Elasticsearch, where indexes are created by users and structural changes often necessitate time-consuming reindexing procedures. converged indexing While Elasticsearch remains a robust solution for various use cases, exploring alternatives like Rockset may be useful, especially if you find reindexing in Elasticsearch becoming a frequent activity. Conclusion Reindexing is a fundamental process in Elasticsearch and is important for maintaining the efficiency and accuracy of search results as data structures evolve. If you find that reindexing is becoming a constant time burden for your team it might be worth exploring alternative solutions like Rockset. Rockset offers a more streamlined index management process that enables developers to concentrate on more value-add activities.

Understanding Elasticsearch Reindexing: When to Reindex, Best Practices and Alternatives

ইলাস্টিকসার্চ রিইন্ডেক্সিং বোঝা: কখন পুনঃসূচীকরণ করতে হবে, সর্বোত্তম অনুশীলন এবং বিকল্প

Neuindizierung von Elasticsearch verstehen: Wann eine Neuindizierung erfolgen sollte, Best Practices und Alternativen

Comprensión de la reindexación de Elasticsearch: cuándo reindexar, mejores prácticas y alternativas

Comprendre la réindexation Elasticsearch : quand réindexer, bonnes pratiques et alternatives

इलास्टिकसर्च रीइंडेक्सिंग को समझना: कब रीइंडेक्स करना है, सर्वोत्तम अभ्यास और विकल्प

Elasticsearch の再インデックスを理解する: 再インデックスを行うタイミング、ベストプラクティス、代替手段

Elasticsearch 재인덱싱 이해: 재인덱싱 시기, 모범 사례 및 대안

Compreendendo a reindexação do Elasticsearch: quando reindexar, práticas recomendadas e alternativas

Понимание переиндексации Elasticsearch: когда проводить переиндексацию, лучшие практики и альтернативы

Elasticsearch Yeniden İndekslemeyi Anlamak: Ne Zaman Yeniden İndekslenmeli, En İyi Uygulamalar ve Alternatifler

Tìm hiểu về lập chỉ mục lại Elaticsearch: Khi nào nên lập chỉ mục lại, các phương pháp hay nhất và giải pháp thay thế

了解 Elasticsearch 重新索引：何时重新索引、最佳实践和替代方案

Authors:
(1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu;
(2) Eric Bier, Palo Alto Research Center, USA bier@parc.com;
(3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com;
(4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov;
(5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Related Work Methodology Experiments Conclusion & Future work and References Abstract This paper describes a novel approach for generating accurate floor plans and 3D models of building interiors using scanned mesh data. Unlike previous methods, which begin with a high resolution point cloud from a laser range-finder, our approach begins with triangle mesh data, as from a Microsoft HoloLens. It generates two types of floor plans, a "pen-and-ink" style that preserves details and a drafting-style that reduces clutter. It processes the 3D model for use in applications by aligning it with coordinate axes, annotating important objects, dividing it into stories, and removing the ceiling. Its performance is evaluated on commercial and residential buildings, with experiments to assess quality and dimensional accuracy. Our approach demonstrates promising potential for automatic digitization and orientation of scanned mesh data, enabling floor plan and 3D model generation in various applications such as navigation, interior design, furniture placement, facilities management, building construction, and HVAC design. Keywords: Clustering based methods · Floor plans · Augmented Reality· 3D Models. 1 Introduction Floor plans are useful for many applications including navigating in building interiors; remodeling; efficient placement of furniture; placement of pipes; heating, ventilation, and air conditioning (HVAC) design; and preparing an emergency evacuation plan. Depending on the application, different kinds of floor plan are appropriate. For remodeling building interiors or designing HVAC systems, users may prefer a drafting-style floor plan that focuses on planar walls and removes furniture and other clutter. For furniture placement, navigation, or evacuation planning, users may prefer a more detailed floor plan that shows the positions of furniture, cabinets, counter tops, etc. In either case, producing a floor plan can be time consuming, requiring expert skills, such as measuring distances and angles or entering data into a CAD program. Furthermore, it may need to be done more than once because a building changes when walls and furniture are moved, added, or removed. So it is valuable to be able to generate floor plans automatically with little or no training. To generate floor plans, it helps to begin with accurate data that can be collected automatically. Laser range finders, smartphones, tablets, and augmented reality (AR) headsets are some of the devices that have made it easier to collect high-resolution building data in the form of RGBD images, point clouds, and triangle meshes. In this paper, we describe a method for generating drafting style and pen-and-ink-style floor plans by leveraging incomplete and imperfect triangle mesh data. This approach efficiently generates both types of floor plans accurately, supporting a wide range of applications. Main Contribution We describe a new method for generating accurate floor plans using poorly captured triangle mesh data from the Microsoft HoloLens 2. The main contributions are: – A modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, using blocks to capture wall height and thickness. – An orientation-based clustering method that finds walls at arbitrary angles. – The use of k-means clustering to rotate the mesh to the principal axes and to identify the floor and ceiling. – Generating two kinds of precise floor plans from incomplete mesh data. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; (2) Eric Bier, Palo Alto Research Center, USA bier@parc.com; (3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Authors: Authors: (1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; (2) Eric Bier, Palo Alto Research Center, USA bier@parc.com; (3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Abstract and Intro Related Work Related Work Methodology Methodology Experiments Experiments Conclusion & Future work and References Conclusion & Future work and References Abstract This paper describes a novel approach for generating accurate floor plans and 3D models of building interiors using scanned mesh data. Unlike previous methods, which begin with a high resolution point cloud from a laser range-finder, our approach begins with triangle mesh data, as from a Microsoft HoloLens. It generates two types of floor plans, a "pen-and-ink" style that preserves details and a drafting-style that reduces clutter. It processes the 3D model for use in applications by aligning it with coordinate axes, annotating important objects, dividing it into stories, and removing the ceiling. Its performance is evaluated on commercial and residential buildings, with experiments to assess quality and dimensional accuracy. Our approach demonstrates promising potential for automatic digitization and orientation of scanned mesh data, enabling floor plan and 3D model generation in various applications such as navigation, interior design, furniture placement, facilities management, building construction, and HVAC design. Keywords : Clustering based methods · Floor plans · Augmented Reality· 3D Models. Keywords 1 Introduction Floor plans are useful for many applications including navigating in building interiors; remodeling; efficient placement of furniture; placement of pipes; heating, ventilation, and air conditioning (HVAC) design; and preparing an emergency evacuation plan. Depending on the application, different kinds of floor plan are appropriate. For remodeling building interiors or designing HVAC systems, users may prefer a drafting-style floor plan that focuses on planar walls and removes furniture and other clutter. For furniture placement, navigation, or evacuation planning, users may prefer a more detailed floor plan that shows the positions of furniture, cabinets, counter tops, etc. In either case, producing a floor plan can be time consuming, requiring expert skills, such as measuring distances and angles or entering data into a CAD program. Furthermore, it may need to be done more than once because a building changes when walls and furniture are moved, added, or removed. So it is valuable to be able to generate floor plans automatically with little or no training. To generate floor plans, it helps to begin with accurate data that can be collected automatically. Laser range finders, smartphones, tablets, and augmented reality (AR) headsets are some of the devices that have made it easier to collect high-resolution building data in the form of RGBD images, point clouds, and triangle meshes. In this paper, we describe a method for generating drafting style and pen-and-ink-style floor plans by leveraging incomplete and imperfect triangle mesh data. This approach efficiently generates both types of floor plans accurately, supporting a wide range of applications. Main Contribution We describe a new method for generating accurate floor plans using poorly captured triangle mesh data from the Microsoft HoloLens 2. The main contributions are: Main Contribution – A modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, using blocks to capture wall height and thickness. – An orientation-based clustering method that finds walls at arbitrary angles. – The use of k-means clustering to rotate the mesh to the principal axes and to identify the floor and ceiling. – Generating two kinds of precise floor plans from incomplete mesh data. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Using Scanned Mesh Data for Auto-Digitized 3D Modeling: Abstract and Introduction

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Morph, an Ethereum layer 2 leading a Consumer Blockchain revolution, has announced the launch of its latest advancement, its Holesky Testnet. This new development comes on the heels of their successful Morph Sepolia Testnet and introduces a highly anticipated platform that showcases significant improvements in blockchain infrastructure and application. Already known for its commitment to consumer-centric solutions, Morph stands as a unique player in the space by focusing on real-world utility over the often speculative nature of many blockchain applications. Unlike traditional platforms that prioritize DeFi and trading, Morph aims to provide services that people can use daily, making blockchain technology an integral part of everyday life. The launch of the Holesky Testnet with its enhanced capabilities is a crucial step towards realizing this vision, bridging the gap between sophisticated technology and practical, everyday applications. Key Features and Innovations of the Morph Holesky Testnet: Enhanced Performance and Infrastructure The Holesky Testnet operates using Ethereum Holesky as Layer 1, elevating standards for performance and aligning closely with the infrastructure of the anticipated mainnet. This transition promises users a seamless and advanced preview of what to expect with the full launch. Advanced Features EIP-4844 Optimistic zkEVM Integration: This integration significantly reduces transaction costs, making blockchain operations more efficient.
Revamped Bridge Mechanism: The updated bridge mechanism now allows for withdrawals to be finalized in just one transaction, enhancing the user experience.
Robust Decentralized Sequencer Network: With fully decentralized modules and an increased number of sequencers, the network's backbone is stronger than ever, ensuring unparalleled reliability and security. Economic System Innovations The testnet introduces a novel economic model centered around a decentralized sequencer network, positioning Morph to set new market trends and standards. Impact on the Morph Community For testnet users, prior contributions to the Sepolia testnet are well recorded. Users are now encouraged to transition to the Holesky testnet to benefit from lower fees and superior functionality. For developers, Morph recommends migrating contracts from the Sepolia to the Holesky testnet. Detailed guidance on this process can be found here. The explorer is accessible for tracking deployments and interactions at Holesky Testnet Explorer. Engaging with Morph’s New Testnet: Developers new to Morph's platform can quickly get up to speed through the comprehensive documentation available. For those already familiar with the Sepolia testnet, the core steps to engage with Holesky remain consistent. Start by deep diving into Morph's technology on the About Morph Page, then understand the inner workings of Morph’s infrastructure, and start the development journey with a quickstart guide. For more, review comprehensive Developer Documentation to build innovative blockchain applications. Morph invites the global tech community to join in this revolutionary phase. This new testnet is a significant leap towards realizing a consumer-centric blockchain ecosystem that promises to redefine interaction with technology. About Morph Morph is a fully permissionless EVM L2 that uses a combination of optimistic and zero knowledge rollup technology to enable limitless possibilities in finance, gaming, social media, and entertainment. Morph is the first Layer 2 on Ethereum to launch with a decentralized sequencer, aligning it with several core principles of web3 — decentralization, censorship resistance, and security. The blockchain was built with mainstream audiences like gamers and social media users in mind, making it a user-friendly option for developers who require a chain to build these types of apps on. Website | Twitter |Discord | Telegram |Medium｜Linkedin This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. Morph, an Ethereum layer 2 leading a Consumer Blockchain revolution, has announced the launch of its latest advancement, its Holesky Testnet. This new development comes on the heels of their successful Morph Sepolia Testnet and introduces a highly anticipated platform that showcases significant improvements in blockchain infrastructure and application. Already known for its commitment to consumer-centric solutions, Morph stands as a unique player in the space by focusing on real-world utility over the often speculative nature of many blockchain applications. Unlike traditional platforms that prioritize DeFi and trading, Morph aims to provide services that people can use daily, making blockchain technology an integral part of everyday life. The launch of the Holesky Testnet with its enhanced capabilities is a crucial step towards realizing this vision, bridging the gap between sophisticated technology and practical, everyday applications. Key Features and Innovations of the Morph Holesky Testnet: Enhanced Performance and Infrastructure The Holesky Testnet operates using Ethereum Holesky as Layer 1, elevating standards for performance and aligning closely with the infrastructure of the anticipated mainnet. This transition promises users a seamless and advanced preview of what to expect with the full launch. Advanced Features EIP-4844 Optimistic zkEVM Integration: This integration significantly reduces transaction costs, making blockchain operations more efficient. Revamped Bridge Mechanism: The updated bridge mechanism now allows for withdrawals to be finalized in just one transaction, enhancing the user experience. Robust Decentralized Sequencer Network: With fully decentralized modules and an increased number of sequencers, the network's backbone is stronger than ever, ensuring unparalleled reliability and security. EIP-4844 Optimistic zkEVM Integration: This integration significantly reduces transaction costs, making blockchain operations more efficient. Revamped Bridge Mechanism: The updated bridge mechanism now allows for withdrawals to be finalized in just one transaction, enhancing the user experience. Robust Decentralized Sequencer Network: With fully decentralized modules and an increased number of sequencers, the network's backbone is stronger than ever, ensuring unparalleled reliability and security. Economic System Innovations The testnet introduces a novel economic model centered around a decentralized sequencer network, positioning Morph to set new market trends and standards. Impact on the Morph Community For testnet users, prior contributions to the Sepolia testnet are well recorded. Users are now encouraged to transition to the Holesky testnet to benefit from lower fees and superior functionality. For developers, Morph recommends migrating contracts from the Sepolia to the Holesky testnet. Detailed guidance on this process can be found here . The explorer is accessible for tracking deployments and interactions at Holesky Testnet Explorer . here here Holesky Testnet Explorer Holesky Testnet Explorer Engaging with Morph’s New Testnet: Developers new to Morph's platform can quickly get up to speed through the comprehensive documentation available. For those already familiar with the Sepolia testnet , the core steps to engage with Holesky remain consistent. Start by deep diving into Morph's technology on the About Morph Page , then understand the inner workings of Morph’s infrastructure, and start the development journey with a quickstart guide . For more, review comprehensive Developer Documentation to build innovative blockchain applications. Sepolia testnet About Morph Page About Morph Page understand the inner workings understand the inner workings quickstart guide quickstart guide comprehensive Developer Documentation comprehensive Developer Documentation Morph invites the global tech community to join in this revolutionary phase. This new testnet is a significant leap towards realizing a consumer-centric blockchain ecosystem that promises to redefine interaction with technology. About Morph Morph is a fully permissionless EVM L2 that uses a combination of optimistic and zero knowledge rollup technology to enable limitless possibilities in finance, gaming, social media, and entertainment. Morph is the first Layer 2 on Ethereum to launch with a decentralized sequencer, aligning it with several core principles of web3 — decentralization, censorship resistance, and security. The blockchain was built with mainstream audiences like gamers and social media users in mind, making it a user-friendly option for developers who require a chain to build these types of apps on. Website | Twitter |Discord | Telegram |Medium｜Linkedin Website | Twitter | Discord | Telegram | Medium｜ Linkedin Website Website Twitter Twitter Discord Discord Telegram Telegram Medium ｜ Medium Linkedin This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Join Morph’s Revolution With The Launch Of Holesky Testnet

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When in-game cheating becomes the norm, does the gaming industry have a major problem on its hands? A global study of the gaming industry would say yes. It was found that 77% of online gamers are likely to stop playing a multiplayer game online if they think other players are cheating, and 48% of online gamers are likely to buy less in-game content. Add in the accrued value of digital assets as a key feature of blockchain gaming, and developers have an obligation to tackle this problem head-on. An attractiveness to bending the rules is not something limited to the world of online gaming; it is rife in all parts of society. However, as social psychologist Corey Butler points out ‘anonymity (playing with strangers or online) and opportunity can tip the scales toward greater cheating. When strong incentives like money or grades are on the line, otherwise honest people might be tempted to cheat. If somebody with a competitive nature is presented with the opportunity to easily outperform the other at a task, then it's safe to say that many will bite the bullet and risk the reward for the easy win—factor in the monetary value of rewards in Web3 gaming, and this desire to win increases significantly. However, when it comes to multiplayer online games, the competition is designed around entertainment, fun, and the community. These core values of gaming make cheating a stain on the industry as a whole. Cheating is the new norm. Cheating in some of the largest games has become an economy of its own, with many platforms and YouTube channels dedicated to introducing cheating, hacks, and navigating the ways to cheat. Don't believe us, simply type fortnite cheat terms into a google search. Combating this trend is critical for maintaining the reputation of these brands. Simon Vieira, CEO of MixMob, a Solana-based gaming platform, believes the temptation to cheat is rife, but it is nothing new, “Web3 games offer enhanced incentives due to the tangible value of game coins and digital items. However, this also creates a greater temptation to cheat. That said, traditional gaming has previously addressed similar issues. For instance, real money gaming employs methods such as biotic randomness to obscure certain game aspects like matchmaking. This approach reduces predictability, thus diminishing the incentives to cheat while still maintaining a skill-based gaming environment. We apply these strategies at MixMob to ensure fairness and keep the game engaging for all players,” says Vieira. When hacking games, cheaters now have an endless stack of tools to choose from when it comes to infiltrating game mechanics with glitching and farming to using aimbots in the software and speed hacks. This problem is exacerbated in Web3 gaming as it lacks an authoritative host. “Fair play is crucial in multi-player games as it directly impacts player engagement and retention. A fair environment ensures that players feel valued and that the game remains competitive and enjoyable for everyone,” notes Vieira. Furthermore, with AI, it is easier than ever to predict patterns of player behavior and create new ways to exploit and manipulate gaming data. So, how can this problem be addressed without compromising the gaming experience? Speaking to Tashi Protocol CEO Sandeep Bhatia, it is clear that consensus will play a future role in blockchain gaming. “Dynamite as the network transport for multiplayer games can prevent certain forms of cheating like 'lag switching' or local state hacking. The benefit of having consensus on top of a peer-to-peer network layer ensures that players cannot cheat, and the architecture reduces costs.” Frustration with the publishers Publishers are tackling the issue of one player, or several players at a time, by banning them and using anti-cheat software. Just last month, Activision, a gaming giant and creator of Call of Duty, banned 27,000 accounts from the game. However, this isn't deterring new cheats coming into the sector. Tackling it from the outset in the process of the game studio is the only way to ensure that both the publishers and the gamers benefit in the long run. To restore confidence, creators of blockchain games must demonstrate trust from the outset. “Shifting from a traditional gaming infrastructure to adopt a decentralized, serverless framework will optimize the entire Web3 gaming experience and solve the issue of cheating from the ground up. Not only can we reduce the costs by eliminating the need for centralized servers, but Tashi's Dynamite ensures fair distribution and protection against common security threats,” says Bhatia. Once a player sees that a game is compromised, it ruins the fun element and may even deter them from entering the environment because of security fears. In addition, the influx of bots and other hacks has led to unfair advantages within some of the most loved games. What can blockchain game publishers do? Gaming studios must take the necessary steps to demonstrate their commitment to fair play. This is essential in the blockchain space as it continues to build trust with the traditional gaming communities. At MixMob, they leverage blockchain transparency to allow the community to participate in monitoring and reporting cheaters actively. “This community refereeing not only helps maintain fairness but also fosters a sense of responsibility and integrity among players, and it has worked great for us,” says Vieira. “Web3 game publishers must take decisive steps to limit cheating. The actions taken should vary based on the severity of the cheating incident. Potential measures include banning, suspending, or permanently removing players involved in unfair practices. Additionally, publishers can adopt proven strategies from traditional gaming, such as introducing randomness to some parts of the game to reduce predictability, which helps deter cheating,” he continues. Game developers who are keen to implement anti-cheat measures are often outplayed by hackers who have mastered the art of cheating and the avoidance of detection. Sure, developers can ban cheaters who are caught in the act or embed cheat detection software. However, this doesn't prevent cheating from occurring from the outset. The vulnerabilities have already been exploited by the time the developer has their hands on the activities. If, however, a game creator starts the development process using the Tashi Consensus Protocol, it has eliminated the act of cheating from the initial design stage. “Gamers rely on trust that game developers would have implemented the best practices to ensure cheaters are punished. That is why Tashi works to educate all its gaming partners on the novel ways blockchain solves cheating issues. We make our tech easy to use and guide all our partners who might come from the Web2 world and are new to using blockchain tech,” added Bhatia. While anti-cheat software like Riot Game's Vanguard or Activision Blizzard's Ricochet stands as the frontline defense, the second layer can utilize the transparency provided by blockchain technology to fight against bad actors. Implementing innovative approaches to game development, like the Tashi Dynamite Consensus Engine, pushes gaming to a new era of fair play and reclaims the values that true gamers want to see in the game development stage. In addition to fostering a culture of fair play, a paradigm shift is necessary in the approach to combating cheating in online gaming. By 2027, the number of users in the Online Games market is expected to amount to 1.2bn users. The projected revenue in the Online Games market worldwide is expected to reach US$27.97bn in 2024. Reclaiming the gaming sector for true gamers who value the entire gaming experience is crucial as blockchain games position themselves alongside some of the gaming giants. When in-game cheating becomes the norm, does the gaming industry have a major problem on its hands? A global study of the gaming industry would say yes. It was found that 77% of online gamers are likely to stop playing a multiplayer game online if they think other players are cheating, and 48% of online gamers are likely to buy less in-game content. Add in the accrued value of digital assets as a key feature of blockchain gaming, and developers have an obligation to tackle this problem head-on. study study An attractiveness to bending the rules is not something limited to the world of online gaming; it is rife in all parts of society. However, as social psychologist Corey Butler points out ‘anonymity (playing with strangers or online) and opportunity can tip the scales toward greater cheating. When strong incentives like money or grades are on the line, otherwise honest people might be tempted to cheat. If somebody with a competitive nature is presented with the opportunity to easily outperform the other at a task, then it's safe to say that many will bite the bullet and risk the reward for the easy win—factor in the monetary value of rewards in Web3 gaming, and this desire to win increases significantly. However, when it comes to multiplayer online games, the competition is designed around entertainment, fun, and the community. These core values of gaming make cheating a stain on the industry as a whole. Cheating is the new norm. Cheating in some of the largest games has become an economy of its own, with many platforms and YouTube channels dedicated to introducing cheating, hacks, and navigating the ways to cheat. Don't believe us, simply type fortnite cheat terms into a google search. Combating this trend is critical for maintaining the reputation of these brands. Simon Vieira, CEO of MixMob, a Solana-based gaming platform, believes the temptation to cheat is rife, but it is nothing new, “Web3 games offer enhanced incentives due to the tangible value of game coins and digital items. However, this also creates a greater temptation to cheat. That said, traditional gaming has previously addressed similar issues. For instance, real money gaming employs methods such as biotic randomness to obscure certain game aspects like matchmaking. This approach reduces predictability, thus diminishing the incentives to cheat while still maintaining a skill-based gaming environment. We apply these strategies at MixMob to ensure fairness and keep the game engaging for all players,” says Vieira. When hacking games, cheaters now have an endless stack of tools to choose from when it comes to infiltrating game mechanics with glitching and farming to using aimbots in the software and speed hacks. This problem is exacerbated in Web3 gaming as it lacks an authoritative host. “Fair play is crucial in multi-player games as it directly impacts player engagement and retention. A fair environment ensures that players feel valued and that the game remains competitive and enjoyable for everyone,” notes Vieira. Furthermore, with AI, it is easier than ever to predict patterns of player behavior and create new ways to exploit and manipulate gaming data. So, how can this problem be addressed without compromising the gaming experience? Speaking to Tashi Protocol CEO Sandeep Bhatia, it is clear that consensus will play a future role in blockchain gaming. “Dynamite as the network transport for multiplayer games can prevent certain forms of cheating like 'lag switching' or local state hacking. The benefit of having consensus on top of a peer-to-peer network layer ensures that players cannot cheat, and the architecture reduces costs.” Frustration with the publishers Publishers are tackling the issue of one player, or several players at a time, by banning them and using anti-cheat software. Just last month, Activision , a gaming giant and creator of Call of Duty, banned 27,000 accounts from the game. However, this isn't deterring new cheats coming into the sector. Activision Activision Tackling it from the outset in the process of the game studio is the only way to ensure that both the publishers and the gamers benefit in the long run. To restore confidence, creators of blockchain games must demonstrate trust from the outset. “Shifting from a traditional gaming infrastructure to adopt a decentralized, serverless framework will optimize the entire Web3 gaming experience and solve the issue of cheating from the ground up. Not only can we reduce the costs by eliminating the need for centralized servers, but Tashi's Dynamite ensures fair distribution and protection against common security threats,” says Bhatia. Once a player sees that a game is compromised, it ruins the fun element and may even deter them from entering the environment because of security fears. In addition, the influx of bots and other hacks has led to unfair advantages within some of the most loved games. What can blockchain game publishers do? Gaming studios must take the necessary steps to demonstrate their commitment to fair play. This is essential in the blockchain space as it continues to build trust with the traditional gaming communities. At MixMob, they leverage blockchain transparency to allow the community to participate in monitoring and reporting cheaters actively. “This community refereeing not only helps maintain fairness but also fosters a sense of responsibility and integrity among players, and it has worked great for us,” says Vieira. “Web3 game publishers must take decisive steps to limit cheating. The actions taken should vary based on the severity of the cheating incident. Potential measures include banning, suspending, or permanently removing players involved in unfair practices. Additionally, publishers can adopt proven strategies from traditional gaming, such as introducing randomness to some parts of the game to reduce predictability, which helps deter cheating,” he continues. Game developers who are keen to implement anti-cheat measures are often outplayed by hackers who have mastered the art of cheating and the avoidance of detection. Sure, developers can ban cheaters who are caught in the act or embed cheat detection software. However, this doesn't prevent cheating from occurring from the outset. The vulnerabilities have already been exploited by the time the developer has their hands on the activities. If, however, a game creator starts the development process using the Tashi Consensus Protocol, it has eliminated the act of cheating from the initial design stage. “Gamers rely on trust that game developers would have implemented the best practices to ensure cheaters are punished. That is why Tashi works to educate all its gaming partners on the novel ways blockchain solves cheating issues. We make our tech easy to use and guide all our partners who might come from the Web2 world and are new to using blockchain tech,” added Bhatia. While anti-cheat software like Riot Game's Vanguard or Activision Blizzard's Ricochet stands as the frontline defense, the second layer can utilize the transparency provided by blockchain technology to fight against bad actors. Implementing innovative approaches to game development, like the Tashi Dynamite Consensus Engine, pushes gaming to a new era of fair play and reclaims the values that true gamers want to see in the game development stage. In addition to fostering a culture of fair play, a paradigm shift is necessary in the approach to combating cheating in online gaming. By 2027, the number of users in the Online Games market is expected to amount to 1.2bn users. The projected revenue in the Online Games market worldwide is expected to reach US$27.97bn in 2024. Reclaiming the gaming sector for true gamers who value the entire gaming experience is crucial as blockchain games position themselves alongside some of the gaming giants.

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Authors:
(1) Anna-Katharina Wickert, Technische Universität Darmstadt, Darmstadt, Germany (wickert@cs.tu-darmstadt.de);
(2) Lars Baumgärtner, Technische Universität Darmstadt, Darmstadt, Germany (baumgaertner@cs.tu-darmstadt.de);
(3) Florian Breitfelder, Technische Universität Darmstadt, Darmstadt, Germany (florian.breitfelder@tu-darmstadt.de);
(4) Mira Mezini, Technische Universität Darmstadt, Darmstadt, Germany (mezini@cs.tu-darmstadt.de). Table of Links Abstract and 1 Introduction 2 Background 3 Design and Implementation of Licma and 3.1 Design 3.2 Implementation 4 Methodology and 4.1 Searching and Downloading Python Apps 4.2 Comparison with Previous Studies 5 Evaluation and 5.1 GitHub Python Projects 5.2 MicroPython 6 Comparison with previous studies 7 Threats to Validity 8 Related Work 9 Conclusion, Acknowledgments, and References 2 BACKGROUND Crypto libraries enable developers to use crypto primitives, like symmetric key encryption or password-based encryption (PBE), in their application. However, studies have shown that developers struggle to use those APIs correctly and securely. For example, Krüger et al. [8] show that 95 % of the Android applications using crypto have at least one crypto misuse. In the remainder of the paper, we define a crypto misuse, in short misuse, as a usage of a crypto library which is a formally correct use of the API, e.g., not causing a crash, but is unsafe. In this paper, we focus on six commonly discussed crypto misuses [4, 13] which we will introduce next. For AES encryption, a block mode is used to combine several blocks for encryption and decryption. However, some block modes are considered insecure by experts. Rule 1 (§1) and rule 2 (§2) cover two insecure usages of block modes. §1 prohibits the usage of ECB which is deterministic and not secure. §2 focus on a nonrandom initialization vector (IV) for the block mode CBC. Due to the predictability of the IV, the usage of CBC is deterministic and not secure. Further, a key protects confidential information. Thus, rule 3 (§3) states that the key should be confidential and not a static one hard-coded in the source. For PBE, a salt and the number of iterations harden the password against dictionary attacks. Rule 4 (§4) highlights that a non-random salt reduces the security of the crypto primitive to using no salt, which is considered insecure. Rule 5 (§5) concentrates on the fact that several iterations are required to harden the crypto primitive against attacks. Thus, Egele et al. [4] suggest the minimum recommended value of 1,000 iterations as defined in the PBE standard PKCS#5. A seed can ensure that the output of a random number generator is non-deterministic. Rule 6 (§6) covers that static seeds lead to deterministic outputs. Common Java [4] and C/C++ [13] libraries expect a seed, while the analyzed Python APIs avoid this by design. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Anna-Katharina Wickert, Technische Universität Darmstadt, Darmstadt, Germany (wickert@cs.tu-darmstadt.de); (2) Lars Baumgärtner, Technische Universität Darmstadt, Darmstadt, Germany (baumgaertner@cs.tu-darmstadt.de); (3) Florian Breitfelder, Technische Universität Darmstadt, Darmstadt, Germany (florian.breitfelder@tu-darmstadt.de); (4) Mira Mezini, Technische Universität Darmstadt, Darmstadt, Germany (mezini@cs.tu-darmstadt.de). Authors: Authors: (1) Anna-Katharina Wickert, Technische Universität Darmstadt, Darmstadt, Germany (wickert@cs.tu-darmstadt.de); (2) Lars Baumgärtner, Technische Universität Darmstadt, Darmstadt, Germany (baumgaertner@cs.tu-darmstadt.de); (3) Florian Breitfelder, Technische Universität Darmstadt, Darmstadt, Germany (florian.breitfelder@tu-darmstadt.de); (4) Mira Mezini, Technische Universität Darmstadt, Darmstadt, Germany (mezini@cs.tu-darmstadt.de). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2 Background 3 Design and Implementation of Licma and 3.1 Design 3 Design and Implementation of Licma and 3.1 Design 3.2 Implementation 3.2 Implementation 4 Methodology and 4.1 Searching and Downloading Python Apps 4 Methodology and 4.1 Searching and Downloading Python Apps 4.2 Comparison with Previous Studies 4.2 Comparison with Previous Studies 5 Evaluation and 5.1 GitHub Python Projects 5 Evaluation and 5.1 GitHub Python Projects 5.2 MicroPython 5.2 MicroPython 6 Comparison with previous studies 6 Comparison with previous studies 7 Threats to Validity 7 Threats to Validity 8 Related Work 8 Related Work 9 Conclusion, Acknowledgments, and References 9 Conclusion, Acknowledgments, and References 2 BACKGROUND Crypto libraries enable developers to use crypto primitives, like symmetric key encryption or password-based encryption (PBE), in their application. However, studies have shown that developers struggle to use those APIs correctly and securely. For example, Krüger et al. [8] show that 95 % of the Android applications using crypto have at least one crypto misuse. In the remainder of the paper, we define a crypto misuse, in short misuse, as a usage of a crypto library which is a formally correct use of the API, e.g., not causing a crash, but is unsafe. In this paper, we focus on six commonly discussed crypto misuses [4, 13] which we will introduce next. For AES encryption, a block mode is used to combine several blocks for encryption and decryption. However, some block modes are considered insecure by experts. Rule 1 (§1) and rule 2 (§2) cover two insecure usages of block modes. §1 prohibits the usage of ECB which is deterministic and not secure. §2 focus on a nonrandom initialization vector (IV) for the block mode CBC. Due to the predictability of the IV, the usage of CBC is deterministic and not secure. Further, a key protects confidential information. Thus, rule 3 (§3) states that the key should be confidential and not a static one hard-coded in the source. For PBE, a salt and the number of iterations harden the password against dictionary attacks. Rule 4 (§4) highlights that a non-random salt reduces the security of the crypto primitive to using no salt, which is considered insecure. Rule 5 (§5) concentrates on the fact that several iterations are required to harden the crypto primitive against attacks. Thus, Egele et al. [4] suggest the minimum recommended value of 1,000 iterations as defined in the PBE standard PKCS#5. A seed can ensure that the output of a random number generator is non-deterministic. Rule 6 (§6) covers that static seeds lead to deterministic outputs. Common Java [4] and C/C++ [13] libraries expect a seed, while the analyzed Python APIs avoid this by design. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Addressing the Risks of Misusing Crypto Libraries

Invisible Online: A Family Guide to Private and Secure Online Living

Boosting Development Efficiency With the Specification-First Principle

Authors: (1) Jonathan H. Rystrøm. Table of Links Abstract and Introduction Previous Literature Methods and Data Results Discussions Conclusions and References A. Validation of Assumptions B. Other Models C. Pre-processing steps C Pre-processing steps Dealing with a dataset with millions of rows and complex types like ”categories” and ”dates” requires special engineering considerations. This section outlines the pre-processing steps required to get the data from Ni et al. (2019) in an analysis-ready shape. All pre-processing of the data was done using python (Van Rossum, 2007). This is particularly because of the rich ecosystem of scientific packages. For this project we use numpy (Harris et al., 2020), pandas (McKinney, 2011), and numba (Lam et al., 2015) for efficient large-scale data processing. We also use scikit-learn (Pedregosa et al., 2011) to efficiently parse categories (see repository for implementation). Most computations were performed on the Oxford Internet Institute’s HPC cluster. This allowed us to benefit from multi-core processing (Gorelick & Ozsvald, 2020) and increased RAM. The first step is creating a dataset of category relevance for the books (see repository for details). Here, we simply take the original gzipped file and extract a list of categories and item ID (asin). This drastically reduces the file size, so we can do the computations in memory. The next step is preparing the rating data. We start by filtering the dataset to only have users with more than 20 ratings. This reduces the dataset considerably as we saw in Fig. 2. We then left-join the data with the category similarity data described above. Each row now consists of a user id, category id, timestamp, and preference score (i.e. the rating multiplied by category relevance; see eq. 1) for each rating that the user has made for any given category. Note, that each individual rating can be represented in multiple rows if a book has multiple categories (which most have). Finally, we summarise the data to get the sum of preference scores and amount of ratings per user, category, and quarter. This gives us a further reduced dataset that is more manageable to work with. Revealed preferences are defined as the weighted sum of ratings and category relevance (eq. 1), so this decision is mainly one of granularity. This paper is under CC 4.0 license. available on arxiv

Preparing Complex Datasets for Amazon's Recommender System Study

Automated solutions have been around for over a decade in the cybersecurity space. However, their use has been confined to very surface-level areas—it’s time this changed. With the recent advancements in generative AI, the technology has proven to have a higher ceiling for application, including time-consuming yet vital processes like cyber discovery. AI In fact, organizations that automate cyber defense mechanisms will experience a in security breaches by 2026. So, it’s evident that automation will become the norm for many cybersecurity processes. This is especially important given the more targeted and aggressive attacks, such as advanced persistent threats (APTs), that have been launched in the past year. 66% reduction APTs make cybersecurity much murkier. If a company experiences an attack of this nature, they’re as good as gone; the sophistication of these threats can penetrate the most robust systems when mildly overlooked. One of many examples from last year is the highly secure USBs, gaining access to government data via APTs by subtly infecting and taking over every device they connected them to. malicious actors that targeted Here’s where more advanced automation comes in to keep APTs at bay. Let’s see how adopting these new practices can highly benefit your security team and business. Faster and more controlled threat intelligence turnarounds The APT protection market is set to grow by a staggering annually until 2032 when it will be worth $51.5 billion. The increasing severity of these attacks is what’s driving CISOs to spend more of their budget protecting their company’s attack surfaces — starting with proper threat intelligence and asset discovery methods. 18% Automation can speed up asset discovery, and time is of the essence when it comes to data security. A typical discovery process for a company of around 10,000 employees would take a security team a couple of days to finish. By automating this process, the timeline plummets to just one day. Instead of outsourcing this process to cybersecurity companies, in-house security teams can cut the middleman and use threat intelligence tools to automate the discovery process for them. This is possible with SaaS programs that use open source and proprietary codes to create a workflow, allowing companies to run tailored discovery as often as they see fit. The time difference between manual and automated can be a costly APT attack or a safe system. More precise and accurate discovery to bridge the skills gap You wouldn’t begin securing your systems without knowing exactly what needs to be done. It would be a waste of both time and money. This is why discovery is so crucial to threat intelligence and successful cybersecurity practices — it’s the foundation for protecting attack surfaces. And for discovery to be worthwhile, it must be done right. However, the cybersecurity talent shortage is making it harder for companies to properly protect their systems, especially when are being affected by the lack of professionals globally. Automation is helping bridge this gap and giving companies peace of mind for their foundational cybersecurity strategies. 71% of organizations By using threat intelligence SaaS tools that automate discovery, security teams can forego a discovery expert while knowing this crucial piece of the puzzle is still being handled with precision and accuracy by an AI workflow. APTs don’t let up, and minimal human error could open the gates for malicious actors to break in and set up shop. Increased human power in other specialized activities CISOs that human error is their organization’s biggest cyber vulnerability. Discovery can be a very time-consuming task, and human processes can become faulty at any point in the job. Now that threat intelligence automation is a more accessible and developed practice, security leaders shouldn’t have to place their human power in these intensive and often flawed activities. highly agree After adopting SaaS tools for discovery, security teams will rest assured their discovery needs will be handled by software that takes less time and significantly reduces errors in threat detection. What will this translate to? More time applying threat intelligence data into more robust security protocols like vulnerability prioritization and remediation, meeting data security compliance requirements, and other activities that require human input. Keeping APTs away from your company encompasses several steps in your cybersecurity practices. Automation is making it easier for security teams to spend their time on valuable activities while others run on AI workflows with less margin for errors. In this new age of AI and automation, these tools are becoming more affordable and user-friendly to address skills shortages and the increase in complex cyber attacks. Ultimately, automating certain tasks is a win-win for security teams, budgeting, and cybersecurity measures — bringing in convenience, precision, and affordability. , co-founder, Trickest Nenad Zaric

Ensuring Cyber Resilience: The Imperative of Automating the APT Discovery Process

2023 was quite a ride for the trucking industry. We faced rising fuel prices, economic roller coasters, freight recessions, driver shortages, and a battle with inflation, which just wouldn't quit. It's been tough out there. As the founder of a startup with dispatch services in the U.S., I've got a front-row seat to the challenges facing our industry. My company aggregates over 100,000 loads daily, so I've seen firsthand how these trends are impacting carriers. My conclusion: To survive, a trucking company needs to know what’s coming, be flexible, and use modern tools. Here’s how to adapt to the trends that will shape the U.S. trucking industry. Use Data to Avoid Losses Everyone in the industry is feeling the squeeze. The trucking industry is facing its toughest challenge since 2008. Truckstop reported that in 2023, 77% of owner-operators had to book shorter routes due to rising costs, while 65% had booked fewer loads altogether. The American Transportation Research Institute ranks the economy as the biggest industry challenge, followed closely by truck parking availability and fuel prices. At the beginning of 2023, our inner data of aggregated loads predicted potential industry issues. I realized that margins would fall, and many operators would go bankrupt. That happened to Convoy, Yellow Corp, and other big players who were shutting down and laying off staff en masse. To be among the survivors, we took a risk to change our business model and focused on using AI tools. Being able to predict what’s coming helped my startup generate profit for three months last year. Now, we are once again close to breaking even. Carriers who don't use analytics will continue to run loads at a loss. With data analytics, truckers can identify the most efficient routes, predict load volumes, delivery times, market trends, seasonal fluctuations, and many other factors.  Previously, this information was only available to large companies. Now even small carriers can gain insights with tools like Truckerpath, DAT, or Cargofy (disclaimer: I'm biased, it's my startup). Switch to a Subscription Model It’s the era of the carriers who offer Logistics-as-a-Service. Looking to cut costs, e-commerce and other companies are seeking LaaS tools to manage their transportation services through cloud-based platforms. Instead of handling the logistics themselves, they prefer to outsource it to a third party. And this is where you can come into play. I saw firsthand how well it works. Launching a subscription-based model in 2023 allowed us to generate a more stable revenue stream. However, the benefits of LaaS extend beyond financial stability. By adopting a subscription model, carriers improve their efficiency by implementing transportation management systems (TMS), accounting software, and data analytics. They can better analyze financial data, gain insights about shippers’ debts, and manage contracts –– all in one place. Also, carriers can offer value-added services to shippers, such as real-time tracking and visibility. Start Using AI As Much as Possible AI tools increase truck drivers' revenue by an average of 17% per year, our internal analytics shows. By analyzing big data on supply-demand dynamics, seasonality, and freight availability, AI algorithms can guide truckers toward high-paying loads that minimize empty miles. Predictive matching helps carriers dismiss loads that lead to deadhead miles and wasted time. AI is also transforming the role of truck dispatchers. Instead of making numerous phone calls to find viable loads, they use AI and get pre-matched opportunities with calculated rates. With AI’s help, an individual dispatcher can now efficiently handle 2-3 times as many trucks. I don’t believe AI will fully replace the human interaction required for negotiations. But, it dramatically reduces manual work and empowers dispatchers to secure better loads in a fraction of the time. Trim Your Team (Sorry) The trucking industry has seen its fair share of ups and downs when it comes to employment. When times are good, companies hire thousands of workers, but when the market takes a turn, layoffs can be just as common. The  American Trucking Associations reported that, in 2022, a whopping 8.4 million people worked in trucking-related jobs across the economy. 3.54 million of those people were truck drivers. In 2023, many large companies such as Flexport, Uber Freight, Flock Freight, and Lipsey Logistics had to trim their teams. As the founder of a trucking startup, I also had to make the tough choice to downsize my team by 30%. This decision was not taken lightly, but it was essential to ensure the company's survival. Otherwise, my company would go bust, and I’d have to dismiss 100% of my staff. 2023 was quite a ride for the trucking industry. We faced rising fuel prices, economic roller coasters, freight recessions, driver shortages, and a battle with inflation, which just wouldn't quit. It's been tough out there. As the founder of a startup with dispatch services in the U.S., I've got a front-row seat to the challenges facing our industry. My company aggregates over 100,000 loads daily, so I've seen firsthand how these trends are impacting carriers. My conclusion: To survive, a trucking company needs to know what’s coming, be flexible, and use modern tools. Here’s how to adapt to the trends that will shape the U.S. trucking industry. Use Data to Avoid Losses Use Data to Avoid Losses Everyone in the industry is feeling the squeeze. The trucking industry is facing its toughest challenge since 2008. Truckstop reported that in 2023, 77% of owner-operators had to book shorter routes due to rising costs, while 65% had booked fewer loads altogether. The American Transportation Research Institute ranks the economy as the biggest industry challenge, followed closely by truck parking availability and fuel prices. 77% 77% 77% biggest industry challenge , biggest industry challenge biggest industry challenge At the beginning of 2023, our inner data of aggregated loads predicted potential industry issues. I realized that margins would fall, and many operators would go bankrupt. That happened to Convoy, Yellow Corp, and other big players who were shutting down and laying off staff en masse. To be among the survivors, we took a risk to change our business model and focused on using AI tools. Being able to predict what’s coming helped my startup generate profit for three months last year. Now, we are once again close to breaking even. Carriers who don't use analytics will continue to run loads at a loss. With data analytics, truckers can identify the most efficient routes, predict load volumes, delivery times, market trends, seasonal fluctuations, and many other factors.  Previously, this information was only available to large companies. Now even small carriers can gain insights with tools like Truckerpath, DAT, or Cargofy (disclaimer: I'm biased, it's my startup). Carriers who don't use analytics will continue to run loads at a loss. With data analytics, truckers can identify the most efficient routes, predict load volumes, delivery times, market trends, seasonal fluctuations, and many other factors.  Previously, this information was only available to large companies. Now even small carriers can gain insights with tools like Truckerpath, DAT, or Cargofy (disclaimer: I'm biased, it's my startup). Switch to a Subscription Model Switch to a Subscription Model It’s the era of the carriers who offer Logistics-as-a-Service. Looking to cut costs, e-commerce and other companies are seeking LaaS tools to manage their transportation services through cloud-based platforms. Instead of handling the logistics themselves, they prefer to outsource it to a third party. And this is where you can come into play. I saw firsthand how well it works. Launching a subscription-based model in 2023 allowed us to generate a more stable revenue stream. However, the benefits of LaaS extend beyond financial stability. By adopting a subscription model, carriers improve their efficiency by implementing transportation management systems (TMS), accounting software, and data analytics. They can better analyze financial data, gain insights about shippers’ debts, and manage contracts –– all in one place. Also, carriers can offer value-added services to shippers, such as real-time tracking and visibility. Start Using AI As Much as Possible Start Using AI As Much as Possible AI tools increase truck drivers' revenue by an average of 17% per year, our internal analytics shows. By analyzing big data on supply-demand dynamics, seasonality, and freight availability, AI algorithms can guide truckers toward high-paying loads that minimize empty miles. Predictive matching helps carriers dismiss loads that lead to deadhead miles and wasted time. AI is also transforming the role of truck dispatchers. Instead of making numerous phone calls to find viable loads, they use AI and get pre-matched opportunities with calculated rates. With AI’s help, an individual dispatcher can now efficiently handle 2-3 times as many trucks. I don’t believe AI will fully replace the human interaction required for negotiations. But, it dramatically reduces manual work and empowers dispatchers to secure better loads in a fraction of the time. Trim Your Team (Sorry) Trim Your Team (Sorry) The trucking industry has seen its fair share of ups and downs when it comes to employment. When times are good, companies hire thousands of workers, but when the market takes a turn, layoffs can be just as common. The American Trucking Associations reported that, in 2022, a whopping 8.4 million people worked in trucking-related jobs across the economy. 3.54 million of those people were truck drivers. American Trucking Associations American Trucking Associations American Trucking Associations In 2023, many large companies such as Flexport, Uber Freight, Flock Freight, and Lipsey Logistics had to trim their teams. As the founder of a trucking startup, I also had to make the tough choice to downsize my team by 30%. This decision was not taken lightly, but it was essential to ensure the company's survival. Otherwise, my company would go bust, and I’d have to dismiss 100% of my staff.

2024 Will Be Tough for the U.S. Trucking Industry: Here’s How Carriers Can Adapt

Generative Artificial Intelligence for Software Engineering: Background

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