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Authors:
(1) Benzar Glen S. Grepon, Northern Bukidnon State College, Philippines ben.it2c@gmail.com (corresponding author);
(2) JC P. Margallo, Northern Bukidnon State College, Philippines;
(3) Jonathan B. Maserin, Northern Bukidnon State College, Philippines;
(4) Rio Al-Di A. Dompol, Northern Bukidnon State College, Philippines. Table of Links Abstract and Intro Literature Review Methodology Results Discussions Conclusions Recommendations Research Implications Practical Implications Declarations References and Author's Biography CONCLUSIONS The developed web application meets the objectives to provide the users. The web application is well-recommended and acceptable by the users. The researchers provided cheaper premium web hosting every year for its renewal. The researchers will incorporate ads for additional revenue for the web application and a registration form for the users to get an acknowledgment of their contribution to the post and to improve the web application. Agencies such as DPWH, RTA, and the like should be able to contribute accurate reports using the web application to ensure reliable and well-grounded information is delivered to the community. RECOMMENDATIONS It is recommended that the future development expand the coverage of the road updates, including the entire Philippine archipelago, for long-drive commuters and drivers to be more updated regarding road updates. Also, include using mobile applications for more user-friendly design and interactions. RESEARCH IMPLICATIONS The study allows viewing and reporting road updates with attachments as proof, informing commuters about what to expect when they travel. The system has been implemented in the municipality of Bukidnon and has been updated now and then for continuous quality improvement. PRACTICAL IMPLICATIONS The main benefit that the Road Updates Information System can do is to inform people who are constantly traveling to be updated on the current situation of a specific road by accessing the system and checking the map on areas to travel and see for  themselves the photos and description of a place where road construction, accidents and other causes that delay the traveling time of passengers and travelers. Aside from being informed of the road updates, citizens can also participate by uploading photos and descriptions of what is still going on the roads to the system and be viewed by people who try to access the web portal. DECLARATIONS Conflict of Interest No author has disclosed any conflicts of interest. Informed Consent The study did not involve humans as participants and only used scene text datasets that are already available online hence this is not applicable. Ethics Approval The conducted research uses only scene text datasets, which are already available online, and did not include humans as participants; hence this is not applicable. REFERENCES Altvater, A. (2023) What is agile methodology? tools, best practices & more, Stackify. Retrieved from https://stackify.com/agile-methodology Amado, T. and Dela Cruz,J.C. (2022).Localized Intelligent Traffic Monitoring System through Ontological Modelling. 2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Boracay Island, Philippines, pp. 1-5, doi: 10.1109/HNICEM57413.2022.10109419. Balahadia, F. F., Fabito, B. S., & Fernando, M. C. G. (2015). E-Participation: Incident mapping portal for local government units. In 2015 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1-6). IEEE. Dadvar, S., Lee, Y. J., Shin, H. S., & Khodaparasti, H. (2020). Integrated Highway Safety Information System (HSIS) datasets by means of a Roadway Safety Data Integrator (RSDI) tool. Data in Brief, 32, 106154. https://doi.org/10.1016/j.dib.2020.106154. ISO 25010. (n.d.). ISO 25000 PORTAL. https://iso25000.com/index.php/en/iso-25000- standards/iso-25010 Khan, M. E., Shadab, S. G. M., & Khan, F. (2020). Empirical study of software development life cycle and its various models. International Journal of Software Engineering, 8, 16-26. Khan, M., Shadab, S., & Khan, F. (2020). Empirical Study of Software Development LifeCycle and its Various Models. International Journal of Software Engineering (IJSE),8(2), 16-26. Khoo, H.L., Ong, G.P. (2013). Evaluating perceived quality of traffic information system using structural equation modeling. KSCE Journal of Civil Engineering, 17(4), 837-849. ScholarBank@NUS Repository. https://doi.org/10.1007/s12205-013-0248-6 Li, H. & Zhijian, L. (2010). The study and implementation of mobile GPS navigation system based on Google Maps. 2010 International Conference on Computer and Information Application, Tianjin, China, pp. 87-90, doi: 10.1109/ICCIA.2010.6141544. Luthfi, A. M., Karna, N., & Mayasari, R. (2019). Google maps API implementation on IOT platform for tracking an object using GPS. In 2019 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob) (pp. 126-131). IEEE. Macfarlane, J. (2023). Your navigation app is making traffic unmanageable. IEEE Spectrum. Retrieved April 17, 2023, from https://spectrum.ieee.org/your-navigation-app-ismaking-traffic-unmanageable McCall, M. (2021). Participatory Mapping and PGIS: Secerning Facts and Values, Representation and Representativity. International Journal of E-Planning Research (IJEPR), 10(3), 105-123. http://doi.org/10.4018/IJEPR.20210701.oa7 Markus Löchtefeld. (2019). DetourNavigator - Using Google Location History to Generate Unfamiliar Personal Routes. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA '19). Association for Computing Machinery, New York, NY, USA, Paper LBW1117, 1–6. https://doi.org/10.1145/3290607.3313047 Novikov, A., Borovskoy, A., Gorbun, Y., Terentyev A. and Pletney,M. (2021). Geographic Information Systems to Improve Road Safety. Systems of Signals Generating and Processing in the Field of on Board Communications, Moscow, Russia, 2021, pp. 1-6, doi: 10.1109/IEEECONF51389.2021.9416096. Slamet, C., Rahman, A., Sutedi, A., Darmalaksana, W., Ramdhani, M. A., & Maylawati, D. S. A. (2018). Social media-based identifier for natural disaster. In IOP conference series: materials science and engineering (Vol. 288, No. 1, p. 012039). IOP Publishing. Srivastava, A., Bhardwaj, S., & Saraswat, S. (2017). SCRUM model for agile methodology. In P. N. Astya, A.Swaroop, V. Sharma, M. Singh, & K. Gupta (Eds.), Proceedings of the International Conference on Computing, Communication, and Automation (ICCCA). Paper presented at the 2017 International Conference on Computing, Communication, and Automation, India (pp. 864-869). India: IEEE. Thalluri, L., Veeravalli, H., Rekapalli, D., Katragadda, S., Teja, T., & Sundar, C., (2022). Smart Vehicle Tracking System with Collision Avoidance. 7th International Conference on Communication and Electronics Systems (ICCES) (pp. 462-467). IEEE. Wörndl, W., Herzog, D. (2020). Mobile Applications for e-Tourism. In Xiang, Z., Fuchs, M., Gretzel, U., Höpken, W. (eds) Handbook of e-Tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-05324-6_17-1 Xu, R., Zhang, S., Xiong, P., Lin, A. and Ma, J. (2022). Towards Better Driver Safety: Empowering Personal Navigation Technologies with Road Safety Awareness. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, pp. 3004-3009, doi: 10.1109/ITSC55140.2022.9922545. Yang, S. Y., & Hsu, C. L. (2016). A location-based services and Google maps-based information master system for tour guiding. Computers & Electrical Engineering, 54, 87-105. Yamsaengsung, S., & Papasratorn, B. (2018). Towards Improving User Interaction with Navigation Apps: an Information Quality Perspective. KnE Social Sciences, 3(1), 119- 131.https://doi.org/10.18502/kss.v3i1.1401 Authors’ Biography Benzar Glen S. Grepon is the Director of the Information Management Office and the previous IT Program Head of Northern Bukidnon State College, specializing in research, Programming, Project Management, Management Information Systems, Network Management, and Administration JC P. Margallo is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College, specializing in Web development and programming. Jonathan B. Maserin is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College, specializing in Web development and programming. Rio Al-Di A. Dompol is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College and a computer programmer in the Information Management Office of NBSC, specializing in Web development, programming, Database Management and Mobile Design and Development. This paper is available on arxiv under CC 4.0 license. Authors: (1) Benzar Glen S. Grepon, Northern Bukidnon State College, Philippines ben.it2c@gmail.com (corresponding author); (2) JC P. Margallo, Northern Bukidnon State College, Philippines; (3) Jonathan B. Maserin, Northern Bukidnon State College, Philippines; (4) Rio Al-Di A. Dompol, Northern Bukidnon State College, Philippines. Authors: Authors: (1) Benzar Glen S. Grepon, Northern Bukidnon State College, Philippines ben.it2c@gmail.com (corresponding author); (2) JC P. Margallo, Northern Bukidnon State College, Philippines; (3) Jonathan B. Maserin, Northern Bukidnon State College, Philippines; (4) Rio Al-Di A. Dompol, Northern Bukidnon State College, Philippines. Table of Links Abstract and Intro Abstract and Intro Literature Review Literature Review Methodology Methodology Results Results Discussions Discussions Conclusions Conclusions Recommendations Recommendations Research Implications Research Implications Practical Implications Practical Implications Declarations Declarations References and Author's Biography References and Author's Biography CONCLUSIONS The developed web application meets the objectives to provide the users. The web application is well-recommended and acceptable by the users. The researchers provided cheaper premium web hosting every year for its renewal. The researchers will incorporate ads for additional revenue for the web application and a registration form for the users to get an acknowledgment of their contribution to the post and to improve the web application. Agencies such as DPWH, RTA, and the like should be able to contribute accurate reports using the web application to ensure reliable and well-grounded information is delivered to the community. RECOMMENDATIONS It is recommended that the future development expand the coverage of the road updates, including the entire Philippine archipelago, for long-drive commuters and drivers to be more updated regarding road updates. Also, include using mobile applications for more user-friendly design and interactions. RESEARCH IMPLICATIONS The study allows viewing and reporting road updates with attachments as proof, informing commuters about what to expect when they travel. The system has been implemented in the municipality of Bukidnon and has been updated now and then for continuous quality improvement. PRACTICAL IMPLICATIONS The main benefit that the Road Updates Information System can do is to inform people who are constantly traveling to be updated on the current situation of a specific road by accessing the system and checking the map on areas to travel and see for  themselves the photos and description of a place where road construction, accidents and other causes that delay the traveling time of passengers and travelers. Aside from being informed of the road updates, citizens can also participate by uploading photos and descriptions of what is still going on the roads to the system and be viewed by people who try to access the web portal. DECLARATIONS Conflict of Interest Conflict of Interest Conflict of Interest No author has disclosed any conflicts of interest. Informed Consent Informed Consent Informed Consent The study did not involve humans as participants and only used scene text datasets that are already available online hence this is not applicable. Ethics Approval Ethics Approval Ethics Approval The conducted research uses only scene text datasets, which are already available online, and did not include humans as participants; hence this is not applicable. REFERENCES Altvater, A. (2023) What is agile methodology? tools, best practices & more, Stackify. Retrieved from https://stackify.com/agile-methodology Amado, T. and Dela Cruz,J.C. (2022).Localized Intelligent Traffic Monitoring System through Ontological Modelling. 2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Boracay Island, Philippines, pp. 1-5, doi: 10.1109/HNICEM57413.2022.10109419. Balahadia, F. F., Fabito, B. S., & Fernando, M. C. G. (2015). E-Participation: Incident mapping portal for local government units. In 2015 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1-6). IEEE. Dadvar, S., Lee, Y. J., Shin, H. S., & Khodaparasti, H. (2020). Integrated Highway Safety Information System (HSIS) datasets by means of a Roadway Safety Data Integrator (RSDI) tool. Data in Brief, 32, 106154. https://doi.org/10.1016/j.dib.2020.106154. ISO 25010. (n.d.). ISO 25000 PORTAL. https://iso25000.com/index.php/en/iso-25000- standards/iso-25010 Khan, M. E., Shadab, S. G. M., & Khan, F. (2020). Empirical study of software development life cycle and its various models. International Journal of Software Engineering, 8, 16-26. Khan, M., Shadab, S., & Khan, F. (2020). Empirical Study of Software Development LifeCycle and its Various Models. International Journal of Software Engineering (IJSE),8(2), 16-26. Khoo, H.L., Ong, G.P. (2013). Evaluating perceived quality of traffic information system using structural equation modeling. KSCE Journal of Civil Engineering, 17(4), 837-849. ScholarBank@NUS Repository. https://doi.org/10.1007/s12205-013-0248-6 Li, H. & Zhijian, L. (2010). The study and implementation of mobile GPS navigation system based on Google Maps. 2010 International Conference on Computer and Information Application, Tianjin, China, pp. 87-90, doi: 10.1109/ICCIA.2010.6141544. Luthfi, A. M., Karna, N., & Mayasari, R. (2019). Google maps API implementation on IOT platform for tracking an object using GPS. In 2019 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob) (pp. 126-131). IEEE. Macfarlane, J. (2023). Your navigation app is making traffic unmanageable. IEEE Spectrum. Retrieved April 17, 2023, from https://spectrum.ieee.org/your-navigation-app-ismaking-traffic-unmanageable McCall, M. (2021). Participatory Mapping and PGIS: Secerning Facts and Values, Representation and Representativity. International Journal of E-Planning Research (IJEPR), 10(3), 105-123. http://doi.org/10.4018/IJEPR.20210701.oa7 Markus Löchtefeld. (2019). DetourNavigator - Using Google Location History to Generate Unfamiliar Personal Routes. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA '19). Association for Computing Machinery, New York, NY, USA, Paper LBW1117, 1–6. https://doi.org/10.1145/3290607.3313047 Novikov, A., Borovskoy, A., Gorbun, Y., Terentyev A. and Pletney,M. (2021). Geographic Information Systems to Improve Road Safety. Systems of Signals Generating and Processing in the Field of on Board Communications, Moscow, Russia, 2021, pp. 1-6, doi: 10.1109/IEEECONF51389.2021.9416096. Slamet, C., Rahman, A., Sutedi, A., Darmalaksana, W., Ramdhani, M. A., & Maylawati, D. S. A. (2018). Social media-based identifier for natural disaster. In IOP conference series: materials science and engineering (Vol. 288, No. 1, p. 012039). IOP Publishing. Srivastava, A., Bhardwaj, S., & Saraswat, S. (2017). SCRUM model for agile methodology. In P. N. Astya, A.Swaroop, V. Sharma, M. Singh, & K. Gupta (Eds.), Proceedings of the International Conference on Computing, Communication, and Automation (ICCCA). Paper presented at the 2017 International Conference on Computing, Communication, and Automation, India (pp. 864-869). India: IEEE. Thalluri, L., Veeravalli, H., Rekapalli, D., Katragadda, S., Teja, T., & Sundar, C., (2022). Smart Vehicle Tracking System with Collision Avoidance. 7th International Conference on Communication and Electronics Systems (ICCES) (pp. 462-467). IEEE. Wörndl, W., Herzog, D. (2020). Mobile Applications for e-Tourism. In Xiang, Z., Fuchs, M., Gretzel, U., Höpken, W. (eds) Handbook of e-Tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-05324-6_17-1 Xu, R., Zhang, S., Xiong, P., Lin, A. and Ma, J. (2022). Towards Better Driver Safety: Empowering Personal Navigation Technologies with Road Safety Awareness. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, pp. 3004-3009, doi: 10.1109/ITSC55140.2022.9922545. Yang, S. Y., & Hsu, C. L. (2016). A location-based services and Google maps-based information master system for tour guiding. Computers & Electrical Engineering, 54, 87-105. Yamsaengsung, S., & Papasratorn, B. (2018). Towards Improving User Interaction with Navigation Apps: an Information Quality Perspective. KnE Social Sciences, 3(1), 119- 131.https://doi.org/10.18502/kss.v3i1.1401 Authors’ Biography Benzar Glen S. Grepon is the Director of the Information Management Office and the previous IT Program Head of Northern Bukidnon State College, specializing in research, Programming, Project Management, Management Information Systems, Network Management, and Administration Benzar Glen S. Grepon JC P. Margallo is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College, specializing in Web development and programming. JC P. Margallo Jonathan B. Maserin is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College, specializing in Web development and programming. Jonathan B. Maserin Rio Al-Di A. Dompol is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College and a computer programmer in the Information Management Office of NBSC, specializing in Web development, programming, Database Management and Mobile Design and Development. Rio Al-Di A. Dompol This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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Database pricing models are hard. As a developer looking for a managed database, one of the most annoying (and yet crucial) aspects of the search process involves evaluating not only the upfront price of the solution for your database size but also how the pricing works and how well it will scale. On top of the nuances when evaluating database pricing (e.g., “How much does the bill increase as my data grows?”, “Am I being charged per writes or reads?”, and “Do I have to pay more per backups?”), developers tend to overlook one aspect: the way a database's pricing model is structured will influence how you manage your data and assess your Postgres database size. Different pricing models require different approaches to running PostgreSQL. For example, if you’re locked into a large disk, you might not prioritize reducing your database size. If you’re charged per data read, you might think twice about running certain queries, and if you’re charged per data ingress, you may be cautious about the frequency and volume of newly ingested data. Each pricing element subtly influences the strategies and behaviors you’ll end up adopting, pushing you toward a particular way of managing and interacting with your database to ensure both cost-efficiency and optimal performance. , with great customer feedback. In this blog post, we’ll explore the operational advantages we’ve observed from our customers since transitioning to this model, together with tactics on how to optimally manage your PostgreSQL storage in a usage-based model. At Timescale, we transitioned to a usage-based storage model a few months ago Usage-based storage models are becoming increasingly popular in the database world—who wants to pay for disk space they’re not using? Still, a usage-based model doesn’t come without consequences, and you need to consider some best practices to navigate it effectively. Quick Recap on PostgreSQL Storage Models To lay out a common ground for our discussion on managing your database size in a usage-based model, let’s start by quickly covering how pricing works in our PostgreSQL platform ( ) and how it compares to other managed PostgreSQL products, like Amazon RDS for PostgreSQL and Amazon Aurora. Timescale Starting yesterday (💥), we offer two types of database services in the Timescale platform: , where developers can create PostgreSQL databases with dynamic compute to save money without sacrificing performance. Dynamic PostgreSQL services Time Series services, where developers can create PostgreSQL databases boosted with extra performance and scalability via hypertables, columnar compression, , and tiered storage. continuous aggregates Let’s focus on how we price storage on our platform. Both these services come with a usage-based model for storage, which means the following: Developers are charged for the volume they use, without small print—no minimum database size, no minimum scaling steps. If, by the end of the month, you’ve used 128 GB, that’s what you’ll be billed for. You can start at 1 GB and grow to TBs. There are no charges based on data written, data transfers, or queries run. No need to allocate storage when creating a database or scaling. No need to manually downsize disks. To bring this home, let’s lay out the differences between this model, Amazon RDS PostgreSQL, and Amazon Aurora: In summary, here are the three main takeaways from our comparison: Both Timescale and Aurora charge for your actual database size. In contrast, RDS PostgreSQL charges for provisioned storage. You can’t downsize volumes in RDS. Timescale does not charge for data written, data transfers, or query operations. Both RDS and Aurora charge per data transfer, extra backup storage, and you might incur some extra I/O charges, depending on which type of storage you’re picking. Timescale has no minimum scaling steps, whereas Aurora scales in 10 GB increments after starting with 10 GB. As you can see, Timescale’s model is closer to , with the difference that Timescale lacks scaling steps and extra charges for things like data transfer. In contrast, RDS is a good representation of the allocation-based model, even if traditionally found in database vendors operating on this model. Aurora I/O-Optimized RDS lacks the “storage tiers” How Usage-Based Pricing Improves PostgreSQL Storage Management As we previously introduced, different pricing models imply different database experiences. When we transitioned from an allocation-based to a usage-based model, our customers told us they saw immediate improvements in three operational areas: They didn’t need to provision storage upfront when starting a database, which led to fewer overprovisioning mistakes and, therefore, lower storage bills. They didn’t have to think about storage while scaling up. They could downscale—e.g., if they deleted data, they stopped paying for it. Usage-based models eliminate the problem of storage overprovisioning In traditional allocation-based models, developers often find themselves predicting their storage needs, which, very often, leads to storage overprovisioning. We observed this across our fleet when Timescale operated on a usage-based model: the majority of our customers were not using their full disk capacity, which means they were essentially paying for storage space they weren't yet using. Usage-based models eliminate this guessing game (and the consequences of wrong guesswork). You don’t need to think about storage while scaling up “Timescale's usage-based storage means I don't have to think about disk size anymore. Our storage cost is down 30 %, and I didn't have to do anything!" Adam McCrea, (Timescale customer) Judoscale In usage-based models, the storage scales seamlessly as your database grows. A main source of stress in traditional allocation-based models is the danger of running out of disk space, which can lead to numerous problems ranging from application downtime and lost transactions to data corruption in the worst-case scenarios. With usage-based models, developers no longer have to vigilantly monitor their storage to avoid hitting a storage wall. At the same time, they don’t have to worry about heavy autoscaling steps or tier jumps. You can downscale (stop paying for data you deleted) Last but not least, usage-based models allow you to “downscale.” If you delete data (or manage to reduce your database size considerably), you start paying less per storage, which sounds only fair. As we’ll see in the next section, Timescale has a few features to help customers reduce their PostgreSQL database size. Adopting a usage-based model allowed our customers to immediately realize savings when reducing disk usage, which incentivized them to keep their database lean. How to Effectively Navigate a Usage-Based Model: Tips to Reduce Your Postgres Database Size The benefits we just mentioned improve the developer experience of working with storage significantly, which is why usage-based models are becoming very popular. But usage-based pricing comes with an obvious (yet profound) consequence: it forces developers to adopt good data practices to reduce their PostgreSQL database size as much as possible. When you know that your storage costs are directly linked to the disk size you’re actually using, there’s a newfound incentive to be more judicious with data storage. If you’re operating in a usage-based model for storage, it becomes your responsibility to ensure you’re storing data efficiently. In a way, this can be considered a “drawback” of usage-based models, and it requires some work—but this is actually a blessing in disguise. In traditional allocation-based models, data hygiene can be somewhat overlooked because the costs are fixed: if you’re locked into a 500 GB disk in RDS, and your database is 200 GB, you don’t seem to have a big incentive to make storage usage efficient. But here’s the thing: good data practices are not just about saving money. To scale a PostgreSQL database, it is essential to keep it optimized. By adopting good PostgreSQL data management practices, you will not only see better performance but your life as a database administrator will get much easier. With this in mind, let’s run through some practices that will help you navigate a usage-based model for storage as effectively as possible, reducing your PostgreSQL database size in a systematic way. In the particular case of Timescale, we have some particularly helpful features, which we’ll cover as well. Reduce bloat in your PostgreSQL database A first must-do in a usage-based model is to pay attention to the specifics of how PostgreSQL storage works, i.e., you must reduce bloat on a regular basis. This will help you not only with your database size but also with your I/O requirements. We’ll point you toward some basics in this section, , and we’ve also written . but this HN thread has some great advice a blog post on table bloat in PostgreSQL A proactive approach to optimizing PostgreSQL storage begins with keeping a close eye on dead tuples. Dead tuples, if left unchecked, can accumulate and lead to unnecessary storage overheads. The extension can be a great ally to understanding how tables manage these tuples. Monitor dead tuples. pgstattuple() To effectively combat table bloat, you might want to configure autovacuum to run more frequently. Rather than globally adjusting autovacuum settings in postgresql.conf, it's more precise to fine-tune these settings on a per-table basis. This caters to the fact that different tables can have varying tendencies for accumulating dead tuples. It's also crucial to monitor long-running transactions that might hinder autovacuum operations. Vacuum frequently. While autovacuum and vacuum address dead tuples, reclaiming unused pages demands a different approach. Although VACUUM FULL can be utilized for this purpose, it has the inherent limitation of locking the entire table. can help you with this. Reclaim unused pages. Pg_repack Do some PostgreSQL fine-tuning Systematically reducing your PostgreSQL database size is closely related to being able to scale your PostgreSQL database effectively, i.e., keeping things fast and agile while you’re adding more and more data. Keeping an eye on (and perhaps adjusting) some key PostgreSQL parameters can help. . Here are some aspects you need to consider: This article reviews the most important performance parameters : controls the memory used for PostgreSQL’s page cache, and it’s typically set to 25 % of the system’s total RAM. shared_buffers : it sets the memory allocated per operation within a query. In Timescale, the recommended setting is . work_mem (25 % of RAM) / max_connections : it sets the max number of concurrent connections allowed to the database. Connection poolers can help manage many short-lived connections. max_connections : it determines the max number of worker processes PostgreSQL can initiate. If using Timescale, the formula to set this parameter is: . max_worker_processes max_worker_processes = 3 + timescaledb.max_background_workers + max_parallel_workers : it specifies the max number of workers supporting parallel queries. The default is to set this equal to the number of available CPUs. max_parallel_workers : it determines the max number of concurrent autovacuum processes. In Timescale, it is set to 10 by default. autovacuum_max_workers Practice indexing hygiene Regularly optimizing indexes will help keep your PostgreSQL efficient, especially as the database scales. While indexes can help you improve query performance when used wisely, excessive indexes can create issues in large PostgreSQL deployments. The most obvious consequence of excessive indexing is increased storage consumption, as every index necessitates separate storage, which grows proportionally with the size of the tables. This overhead can become more significant when tables are partitioned, like in Timescale’s hypertables. Unnecessary indexes can also be counterproductive in improving your write operations, as each new data entry or modification implies concurrent index updates, leading to more I/O operations and potential table bloat. Monitoring your indexes will help you identify which ones are no longer being used, keeping things lean. One way to do this is by using : pg_stat_user_indexes -- Retrieve indexes that might not be used or are infrequently used
SELECT
    schemaname,
    relname AS table_name,
    indexrelname AS index_name,
    idx_scan AS times_used
FROM
    pg_stat_user_indexes
WHERE
    idx_scan = 0
    OR idx_scan < some_low_threshold -- replace 'some_low_threshold' with a value that makes sense for your context
ORDER BY
    idx_scan ASC, schemaname, relname; If an index has a value of 0 in the idx_scan column, it means it hasn't been used since the last time the statistics were reset, meaning that it could be a candidate for optimization. By setting a low threshold for , you can also identify infrequently used indexes. idx_scan Shrink your large tables via Timescale compression One of Timescale's standout features is its native support for columnar compression, which can drastically reduce the disk space used by large tables without compromising query performance. Compression improves the performance of many queries. Timescale's compression works by converting regular row-based data into a more compact columnar format. This process is particularly effective for time-series data, where many columns may contain repetitive values; we can achieve impressive compression rates (+90 %) by applying different compression algorithms depending on each data type. This means that your large tables can be shrunk up to 10x. In Timescale, compression is enabled on a table-by-table basis via a time-based compression policy. For example, this code enables compression in a particular hypertable and automatically compresses data older than two weeks: -- Enable compression on the hypertable
ALTER TABLE your_hypertable SET (
  timescaledb.compress,
  timescaledb.compress_segmentby = 'some_column'
);
-- Set a compression policy to compress data older than two weeks
SELECT add_compression_policy('your_hypertable', INTERVAL '2 weeks'); To learn more about compression, . check out our documentation Set up a data management strategy: Move older data to cheaper storage, delete the data you no longer need The previous tactics are very helpful in reducing your database size, but there is another avenue you can leverage in Timescale to scale your storage effectively: tiered storage. By creating a simple tiering policy, you can move older, less-accessed data to a bottomless object storage layer: -- This policy moves all data older than one month to object storage 
SELECT add_tiering_policy('example', INTERVAL '1 month); This object store has the following characteristics: It comes with a lower price point than our high-performance storage, allowing you to store many TBs of data for much less. It is infinite, meaning you can store as much data as you’d like. This tiered storage architecture is ingrained in Timescale’s backend. The object storage is not a “bucket” detached from your database—instead, it’s an integral part of it. Queries will transparently access both storage layers without any action needed from you—you can just keep using standard SQL over the same schema. Once your tiering policy is set, there’s nothing else to consider! We recommend moving data to the low-cost storage layer once it’s not being frequently accessed by your application since there’s a performance cost (the object store is not as fast as our regular storage). But you can keep running ad-hoc queries over this data comfortably (e.g., queries that are not critical for the user experience of your customers and for which top performance is not essential). Editor’s note: We’ll share exciting news on tiered storage soon. 🎉 Stay tuned! On top of offering this low-cost storage layer, Timescale makes it easy to set up data retention policies to delete data you no longer need: -- Set a data retention policy to drop data older than 10 months
SELECT add_retention_policy('your_hypertable', INTERVAL '10 months'); You can combine data retention policies like the one above with continuous aggregates to effectively downsample your dataset—e.g., reducing granularity from one second to one minute, deleting the one-second values but keeping the one-minute aggregates. . Read this blog post to see an example of how to do this and proactively manage long-term data Usage-Based Models and the Data Management Paradigm While usage-based models may seem nothing but a pricing change on the surface, they bring about a paradigm shift in how developers think about their database size and how they perceive and handle data. Usage-based models promote a culture of continuous improvement, where the focus shifts from mere storage management to database health and efficiency. This requires some discipline at first, but once your mindset shifts and you learn some techniques, you’ll be in the best place to scale your PostgreSQL database efficiently and effectively. Timescale has valuable tools to help you systematically reduce your PostgreSQL database size, like compression, tiered storage, and data retention policies. This allows you to effectively scale your PostgreSQL databases in a usage-based model. . To experience it yourself, sign up for Timescale—you can use it for free for the first 30 days - Written by . Carlota Sota Also published here.

Strategies for Database Size Reduction in PostgreSQL: A Usage-Based Approach

PostgreSQL-এ ডাটাবেসের আকার হ্রাসের কৌশল: একটি ব্যবহার-ভিত্তিক পদ্ধতি

Strategien zur Reduzierung der Datenbankgröße in PostgreSQL: Ein nutzungsbasierter Ansatz

Estrategias para reducir el tamaño de la base de datos en PostgreSQL: un enfoque basado en el uso

Stratégies de réduction de la taille des bases de données dans PostgreSQL : une approche basée sur l'utilisation

PostgreSQL में डेटाबेस आकार में कमी के लिए रणनीतियाँ: एक उपयोग-आधारित दृष्टिकोण

PostgreSQL でのデータベース サイズ削減の戦略: 使用量ベースのアプローチ

PostgreSQL의 데이터베이스 크기 감소 전략: 사용량 기반 접근 방식

Estratégias para redução do tamanho do banco de dados no PostgreSQL: uma abordagem baseada no uso

Стратегии уменьшения размера базы данных в PostgreSQL: подход, основанный на использовании

PostgreSQL'de Veritabanı Boyutunu Küçültme Stratejileri: Kullanıma Dayalı Bir Yaklaşım

Các chiến lược giảm kích thước cơ sở dữ liệu trong PostgreSQL: Cách tiếp cận dựa trên việc sử dụng

PostgreSQL 中数据库大小减小的策略：基于使用的方法

A Journey to the Center of the Earth by Jules Verne, is part of the HackerNoon Books Series. You can jump to any chapter in this book . FIRST LESSONS IN CLIMBING here CHAPTER V - FIRST LESSONS IN CLIMBING At Altona, a suburb of Hamburg, is the Chief Station of the Kiel railway, which was to take us to the shores of the Belt. In twenty minutes from the moment of our departure we were in Holstein, and our carriage entered the station. Our heavy luggage was taken out, weighed, labeled, and placed in a huge van. We then took our tickets, and exactly at seven o'clock were seated opposite each other in a firstclass railway carriage. My uncle said nothing. He was too busy examining his papers, among which of course was the famous parchment, and some letters of introduction from the Danish consul which were to pave the way to an introduction to the Governor of Iceland. My only amusement was looking out of the window. But as we passed through a flat though fertile country, this occupation was slightly monotonous. In three hours we reached Kiel, and our baggage was at once transferred to the steamer. We had now a day before us, a delay of about ten hours. Which fact put my uncle in a towering passion. We had nothing to do but to walk about the pretty town and bay. At length, however, we went on board, and at half past ten were steaming down the Great Belt. It was a dark night, with a strong breeze and a rough sea, nothing being visible but the occasional fires on shore, with here and there a lighthouse. At seven in the morning we left Korsor, a little town on the western side of Seeland. Here we took another railway, which in three hours brought us to the capital, Copenhagen, where, scarcely taking time for refreshment, my uncle hurried out to present one of his letters of introduction. It was to the director of the Museum of Antiquities, who, having been informed that we were tourists bound for Iceland, did all he could to assist us. One wretched hope sustained me now. Perhaps no vessel was bound for such distant parts. Alas! a little Danish schooner, the , was to sail on the second of June for Reykjavik. The captain, M. Bjarne, was on board, and was rather surprised at the energy and cordiality with which his future passenger shook him by the hand. To him a voyage to Iceland was merely a matter of course. My uncle, on the other hand, considered the event of sublime importance. The honest sailor took advantage of the Professor's enthusiasm to double the fare. Valkyrie "On Tuesday morning at seven o'clock be on board," said M. Bjarne, handing us our receipts. "Excellent! Capital! Glorious!" remarked my uncle as we sat down to a late breakfast; "refresh yourself, my boy, and we will take a run through the town." Our meal concluded, we went to the Kongens-Nye-Torw; to the king's magnificent palace; to the beautiful bridge over the canal near the Museum; to the immense cenotaph of Thorwaldsen with its hideous naval groups; to the castle of Rosenberg; and to all the other lions of the place-none of which my uncle even saw, so absorbed was he in his anticipated triumphs. But one thing struck his fancy, and that was a certain singular steeple situated on the Island of Amak, which is the southeast quarter of the city of Copenhagen. My uncle at once ordered me to turn my steps that way, and accordingly we went on board the steam ferry boat which does duty on the canal, and very soon reached the noted dockyard quay. In the first instance we crossed some narrow streets, where we met numerous groups of galley slaves, with particolored trousers, grey and yellow, working under the orders and the sticks of severe taskmasters, and finally reached the Vor-Frelser's-Kirk. This church exhibited nothing remarkable in itself; in fact, the worthy Professor had only been attracted to it by one circumstance, which was, that its rather elevated steeple started from a circular platform, after which there was an exterior staircase, which wound round to the very summit. "Let us ascend," said my uncle. "But I never could climb church towers," I cried, "I am subject to dizziness in my head." "The very reason why you should go up. I want to cure you of a bad habit." "But, my good sir—" "I tell you to come. What is the use of wasting so much valuable time?" It was impossible to dispute the dictatorial commands of my uncle. I yielded with a groan. On payment of a fee, a verger gave us the key. He, for one, was not partial to the ascent. My uncle at once showed me the way, running up the steps like a schoolboy. I followed as well as I could, though no sooner was I outside the tower, than my head began to swim. There was nothing of the eagle about me. The earth was enough for me, and no ambitious desire to soar ever entered my mind. Still things did not go badly until I had ascended 150 steps, and was near the platform, when I began to feel the rush of cold air. I could scarcely stand, when clutching the railings, I looked upwards. The railing was frail enough, but nothing to those which skirted the terrible winding staircase, that appeared, from where I stood, to ascend to the skies. "Now then, Henry." "I can't do it!" I cried, in accents of despair. "Are you, after all, a coward, sir?" said my uncle in a pitiless tone. "Go up, I say!" To this there was no reply possible. And yet the keen air acted violently on my nervous system; sky, earth, all seemed to swim round, while the steeple rocked like a ship. My legs gave way like those of a drunken man. I crawled upon my hands and knees; I hauled myself up slowly, crawling like a snake. Presently I closed my eyes, and allowed myself to be dragged upwards. "Look around you," said my uncle in a stern voice, "heaven knows what profound abysses you may have to look down. This is excellent practice." Slowly, and shivering all the while with cold, I opened my eyes. What then did I see? My first glance was upwards at the cold fleecy clouds, which as by some optical delusion appeared to stand still, while the steeple, the weathercock, and our two selves were carried swiftly along. Far away on one side could be seen the grassy plain, while on the other lay the sea bathed in translucent light. The Sund, or Sound as we call it, could be discovered beyond the point of Elsinore, crowded with white sails, which, at that distance looked like the wings of seagulls; while to the east could be made out the far-off coast of Sweden. The whole appeared a magic panorama. But faint and bewildered as I was, there was no remedy for it. Rise and stand up I must. Despite my protestations my first lesson lasted quite an hour. When, nearly two hours later, I reached the bosom of mother earth, I was like a rheumatic old man bent double with pain. "Enough for one day," said my uncle, rubbing his hands, "we will begin again tomorrow." There was no remedy. My lessons lasted five days, and at the end of that period, I ascended blithely enough, and found myself able to look down into the depths below without even winking, and with some degree of pleasure. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jules Verne (2006). A Journey to the Center of the Earth. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/files/18857/18857-h/18857-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

FIRST LESSONS IN CLIMBING

The inspiration to delve into this topic came from a YouTube video titled “The Metaverse: Seven Metaverse Companies That Are Making It a Reality.” This seven-minute-long video explains how seven famous tech companies are shaping the metaverse industry. Chances are that 2023 might just be another year for the industry to grow bigger. metaverse According to Precedence Research, a consulting firm that provides industry insights and market analysis, the is estimated to reach $1.3 trillion by 2030 based on four market drivers which include, increasing investment in the metaverse, rising demands in the media and entertainment industry, increasing uses of metaverse platforms, and rising popularity of interaction models and digital identities. metaverse market size https://youtu.be/SQSWn1CMrv8?embedable=true In this article, we explore some of the most popular metaverse companies in the world and their niches bearing in mind that the number of companies and brands entering the metaverse space is fast growing. Decentraland Decentraland is a popular metaverse platform running on the Ethereum blockchain technology. It was founded by Ari Meilich and Esteban Ordano in 2015, and its niche is virtual land ownership and development. Decentraland users are incentivized to buy, sell, and trade virtual land using its native ERC-20 token, MANA. Above all, there is the freedom to create their own unique experiences within the virtual world, and it grants users ownership and control over their virtual assets. Sandbox Sandbox is a popular gaming metaverse platform where users can create, share, and monetize their virtual assets. It is a user-generated virtual world that provides a variety of tools and assets for developing games and building social spaces. Sandbox has a thriving community for creators and players to collaborate on gaming projects and share ideas. The Sandbox was founded in 2012 by Sebastien Borget, Arthur Madrid, and Nicolas Pouard. It started as a mobile game studio known as Pixowl before gravitating toward blockchain technology. Roblox Roblox has millions of users as a leading gaming platform in the virtual realm. It is known for its vibrant environment and a wide range of game options and virtual worlds where users can create virtual immersive experiences and play games. It also has virtual hangouts where millions of users can connect with friends and join virtual events. Users can also learn coding through the Roblox Studio, and it is tailored to captivate and serve metaverse users of all ages. Roblox was founded in 2004 by David Baszucki and Erik Cassel as Dynablocks, a physics-based educational tool. It later had a change of name and transitioned to a user-generated content platform and virtual gaming community. Voxels Metaverse Voxels Metaverse, formerly known as Cryptovoxels, was founded by Ben Nolan in 2018. It is an Ethereum-based metaverse platform that is a bit similar to Decentraland. For example, Voxels Metaverse and Decentraland run on the Ethereum blockchain and allow users to earn from playing games. In the Voxels Metaverse environment, there are endless possibilities for creativity and exploration. Users can build, explore, and trade their voxel-based virtual creations. Vowels Metaverse is built around the concepts of decentralization and blockchain ownership, which allows users to have full control over their virtual land and assets. Spatial Spatial is an immersive collaboration platform that was designed to host remote meetings and events in its virtual environment. Its market niche is metaverse-based virtual conferencing which allows users to link up, interact, and collaborate, regardless of physical distance. Spatial combines VR and AR technologies to deliver remote collaboration and its unique settings  have continued to attract various industries that are seeking to incorporate the metaverse technology. One of the immersive qualities of Spatial is that it uses AI to generate data from images of its users and physical environments to create accurate 3D models. Spatial was founded in 2016 by Anand Agarawala and Jinha Lee who were inspired to create immersive augmented and virtual reality experiences. ALTAVA Group ALTAVA Group is a luxury Metaverse platform that offers a unique opportunity for leading brands to connect their in-person and digital experiences through the power of blockchain technology. ALTAVA's most notable NFT is the BAGC Collection, a golf-themed derivative of the Bored Ape Yacht Club (BAYC) licensed from Elite Apes, The Sandbox, and Animoca Brands. ALTAVA has blue-chip equity and token investors such as Hyundai, LG, Spartan, and Animoca Brands. It also has a Second Skin NFT Collection that links directly to WOY, the company's own Metaverse, available on the App Store and Play Store. High Fidelity High Fidelity was in 2013 founded by Philip Rosedale with the aim of creating a next-generation virtual reality platform. High Fidelity is a blockchain-based metaverse platform that focuses on social interaction, events, and commerce in virtual reality. It provides the tools for users to create and customize their virtual avatars, attend virtual meetings, and engage in virtual trade. Its aim is to promote social connections and meaningful experiences within the virtual realm. Upland Upland is a metaverse company that combines virtual property trading with real-world locations. It was founded in 2018 by Dirk Lueth and Idan Zuckerman. Upland allows its users to buy, sell, and trade virtual assets that mirror real-world addresses, enabling them to own and manage virtual assets tied to actual geographic locations. It offers a unique combination of virtual and real-world experiences for users to explore and engage with. The MetaVRse The MetaVRse is a well-known development studio in augmented reality and virtual reality experiences. It was founded by Alan Smithson in 2016 as a platform for immersive content and applications that leverage AR, MR, and VR technologies for the needs of various industries, including entertainment, marketing and training, retail, real estate, and health care. NeosVR NeosVR is a user-generated virtual reality platform founded by Karel Hulec in 2014. It provides a platform for creation, collaboration, and exploration. NeosVR users can build their own virtual environments, interact with others, and develop interactive experiences using a wide range of tools and assets. Its focus is primarily on its community of creators who push the boundaries of virtual reality experience. Conclusion The metaverse is a rapidly evolving field with a vast potential to shape the future of virtual experiences. The rapid growth of the metaverse industry is spurred by the growing number of  companies and brands entering this space. As a result of this, it has experienced a boost in innovation, and creativity, and it has created endless possibilities to explore the virtual worlds. Each metaverse platform has its unique features, communities, and opportunities for users to explore. In gaming, socializing, creating, or conducting business, the metaverse is capable of creating a new frontier for human interaction and experience, and with the continued advancements and improvements, it is destined to transform the way we connect, collaborate, and experience the virtual world.

Exploring Popular Metaverse Platforms And Their Niches

Bacteria: Chapter II - BACTERIA IN WATER

The OWASP Top 10 is a standard awareness document for developers and web application security. It represents a broad consensus about the most critical security risks to web applications. In this article, I want to cover the second part of the TOP 10 vulnerabilities and how to protect against them using .NET. 6. Security Misconfiguration Security misconfiguration can happen at any level of an application stack, including the network services, platform, web server, application server, database, frameworks, custom code, and pre-installed virtual machines, containers, or storage. Such flaws frequently give attackers unauthorized access to some system data or functionality. What to do? (When using TLS) Redirect a request made over Http to https: app.UseHttpsRedirection(); //Startup.cs Send the anti-forgery token with every POST/PUT request: To automatically validate all requests other than GET, HEAD, OPTIONS and TRACE you need to add a global action filter with the attribute inside your Startup.cs AutoValidateAntiforgeryToken services.AddMvc(options =>
{
    options.Filters.Add( AutoValidateAntiforgeryTokenAttribute());
}); new If you need to also validate the token on GET, HEAD, OPTIONS or TRACE - requests you can add the attribute to the controller method (for MVC controllers) or parent class (for Razor pages): ValidateAntiforgeryToken [ ]
[ ] HttpGet ValidateAntiforgeryToken IActionResult ( ) public DoSomethingDangerous [ ]
[ ] : HttpGet ValidateAntiforgeryToken public class SafeModel PageModel In case you can't use a global action filter, add the attribute to your controller classes or razor page models: AutoValidateAntiforgeryToken [ ] AutoValidateAntiforgeryToken public class UserController [ ] : AutoValidateAntiforgeryToken public class SafeModel PageModel 7. Cross-Site Scripting (XSS) XSS is the second most prevalent issue in the OWASP Top 10, and is found in around two-thirds of all applications. The impact of XSS is moderate for reflected and DOM XSS, and severe for stored XSS, with remote code execution on the victim’s browser, such as stealing credentials, sessions, or delivering malware to the victim. What to do? DO NOT: Trust any data the user sends you, prefer white lists (always safe) over black lists DO NOT: Use the [AllowHTML] attribute or helper class @Html.Raw unless you really know that the content you are writing to the browser is safe and has been escaped properly. 8. Insecure Deserialization The impact of deserialization flaws cannot be overstated. These flaws can lead to remote code execution attacks, one of the most serious attacks possible. What to do? Validate User Input Malicious users are able to use objects like cookies to insert malicious information to change user roles. In some cases, hackers are able to elevate their privileges to administrator rights by using a pre-existing or cached password hash from a previous session. Prevent Deserialization of Domain Objects Run the Deserialization Code with Limited Access Permissions If a deserialized hostile object tries to initiate a system processes or access a resource within the server or the host's OS, it will be denied access and a permission flag will be raised so that a system administrator is made aware of any anomalous activity on the server. Don't allow the datastream to define the type of object that the stream will be deserialized to. You can prevent this by for example using the DataContractSerializer or XmlSerializer if at all possible. Where JSON.Net is being used make sure the TypeNameHandling is only set to None. TypeNameHandling = TypeNameHandling.None If JavaScriptSerializer is to be used then do not use it with a JavaScriptTypeResolver. If you must deserialise data streams that define their own type, then restrict the types that are allowed to be deserialized. One should be aware that this is still risky as many native .Net types potentially dangerous in themselves. 9. Using Components with Known Vulnerabilities Prevalence of this issue is very widespread. Component-heavy development patterns can lead to development teams not even understanding which components they use in their application or API, much less keeping them up to date. While some known vulnerabilities lead to only minor impacts, some of the largest breaches to date have relied on exploiting known vulnerabilities in components. What to do? Keep the .Net framework updated with the latest patches Keep your packages up to date, many will contain their own vulnerabilities. NuGet Run the against your application as part of your build process and act on any high level vulnerabilities. OWASP Dependency Checker 10. Insufficient Logging & Monitoring Attackers rely on the lack of monitoring and timely response to achieve their goals without being detected. Most successful attacks start with vulnerability probing. Allowing such probes to continue can raise the likelihood of successful exploit to nearly 100%. What to do? Ensure all login, access control failures and server-side input validation failures can be logged with sufficient user context to identify suspicious or malicious accounts. Establish effective monitoring and alerting so suspicious activities are detected and responded to in a timely fashion. Log all errors from the Startup.cs, so that anytime an error is thrown it will be logged. { (env.IsDevelopment())
    {
        _isDevelopment = ;
        app.UseDeveloperExceptionPage();
    } app.UseExceptionHandler(errorApp =>
    {
        errorApp.Run( context =>
        { errorFeature = context.Features.Get<IExceptionHandlerFeature>(); exception = errorFeature.Error;

            Log.Error(String.Format( , exception.StackTrace.ToString()));
        });
    });

    app.UseAuthentication();
    app.UseMvc();
} ( ) public void Configure IApplicationBuilder app, IHostingEnvironment env if true //Log all errors in the application async var var "Stacktrace of error: {0}" Monitor key performance indicators. In .NET a great option to add monitoring capabilities is . Application Insights Thanks for reading! - Part one https://hackernoon.com/owasp-top-10-net-protection-a-guide-part-1-w92r3wis P - Read more about OWAS https://owasp.org/www-project-top-ten/

OWASP Top 10 .NET Protection Guide (Part 2)

When you tend to use one library for a certain period of time, you get used to it. But, you need to evolve and learn something new every day. If you are still stuck up with Matplotlib(Which is amazing), Seaborn(This is amazing too), Pandas(Basic, yet easy Visualization) and Bokeh, You need to move on and try something new. Many amazing visualization libraries are available in python, which turns to be very versatile. Here, I’m going to discuss about these amazing libraries: Plotly Cufflinks Folium Altair + Vega D3.js (My best Pick, i code in JS too) If you are aware and use the libraries mentioned above then you are on the right track of evolution. They can help in generating some amazing visualizations and syntax ain’t difficult too. Generally, I prefer more of plotly+cufflinks and Alright, lets get back to the basics: D3.js. Plotly Plotly is an open-source, interactive and browser-based graphing library for ython. Plotly is a library that allows you to create interactive plots that you can use in dashboards or websites (you can save them as html files or static images). Plotly is built on top of plotly.js which in turn is built on D3.js and it is a high-level charting library. plotly comes with over 30 chart types, including scientific charts, statistical charts, 3D graphs, financial charts and more. The best things about plotly is that you can use it in Jupyter Notebooks, as well as stand alone HTML pages. You can also use it online on their site but i prefer more to use it offline and you can also save the visualization as an image. It’s pretty simple to use it and get it to work. — Method to use it in Jupyter Notebook (Offline) First, install the plotly library. pip install plotly Then open jupyter notebook and type this: from plotly import __version__
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True) The syntax is quite simpler or simplest, i say? In P_andas_, you use and here, you use This changes the whole definition of the visualization. dataframe.plot() dataframe.iplot(). “ i ” With just one line, i generated this scatter plot. You can customize it as you want. Remember to specify mode markers or you’ll just get some cluster of lines. Scatter plot generated using plotly Please note that as the data increases, plotly begins to choke. So, I would only use plotly when I have less than 500K data points. Try it all in your Jupyter Notebook. Cufflinks Cufflinks binds Plotly directly to pandas dataframes. The combination is just amazing, the power of plotly combined with flexibility of Pandas. It is more effective than plotly. The syntax is even simpler than plotly. With plotly’s Python library, you can describe figures with DataFrame’s series and index, but with cufflinks you can plot it directly. Here is an example: df = cf.datagen.lines()

py.iplot([{
    'x': df.index,
    'y': df[col],
    'name': col
}  for col in df.columns]) With Plotly df.iplot(kind='scatter') With Cufflinks Cufflinks makes it much easier to plot stuffs. You can also generated amazing 3D charts with cufflinks too. I have generated this 3D with just couple of lines of code. 3D chart with Cufflinks You can always try it out in your Jupyter Notebook. — Quick Hack: Set in the config: c.NotebookApp.iopub_data_rate_limit = 1.0e10 Import it the following way: import plotly.graph_objs as go
import plotly.plotly as py
import cufflinks as cf
from plotly.offline import iplot, init_notebook_mode
cf.go_offline()
# Set global theme
cf.set_config_file(world_readable=True, theme=’pearl’, offline=True)
init_notebook_mode() And it works inline. Next, I’m going to talk about yet another amazing Viz library. Folium Folium is built on the data wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library. You can manipulate your data in python, then visualize it in a Leaflet map via folium. Folium is turning out be an amazing library for plotting spatial data. You can also generate heat maps and choropleth maps using folium. Let’s learn something about folium: Maps are defined as a object, addition of other folium objects on top of the to improve the map rendered folium.Map folium.Map can be done You can use different map tiles for the map rendered by Folium, such as MapBox, OpenStreetMap , and several other tiles, for that you can visit or . this github repo folder this documentation page You can also select different map projections. Many projections are available out there. Let’s generate a Choropleth map with Geojson of US unemployment. Here is the snippet: map = folium.Map([43, -100], zoom_start=4)

choropleth = folium.Choropleth(
    geo_data=us_states,
    data=state_data,
    columns=['State', 'Unemployment'],
    key_on='feature.id',
    fill_color='YlGn',
    name='Unenployment',
    show=False,
).add_to(m)

# The underlying GeoJson and StepColormap objects are reachable
print(type(choropleth.geojson))
print(type(choropleth.color_scale))

folium.LayerControl(collapsed=False).add_to(m)

map.save(os.path.join('results', 'GeoChoro.html'))

map This is just a basic one, you can add markers, pop ups and a lot more to it. Here is how it would look like. Map with leaflet and folium Altair + Vega Altair is a declarative statistical visualization library and it is based on Vega and Vega-Lite. Altair enables you to build a wide range of statistical visualizations quickly with a powerful and concise visualization grammar. You need to install it the following way, if you are using Jupyter Notebook. It also includes some example vega datasets. pip install -U altair vega_datasets notebook vega Altair’s main dependency is Vega, in order to make the plots to be visible on the screen, you need to install it and also, you need to run this command for every new session. alt.renderers.enable(‘notebook’) Data in Altair is built around the Pandas Dataframe. One of the defining characteristics of statistical visualization is that it begins with tidy Dataframes. You can also save the plot as an image or open it in vega editor for more options. It’s definitely not the best one out there, but definitely worth a try for the sake of creators hard work. Here is an example, I’m using cars dataset for this; import altair as alt
from vega_datasets import data

source = data.cars()

brush = alt.selection(type='interval')

points = alt.Chart().mark_point().encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    color=alt.condition(brush, 'Origin:N', alt.value('lightgray'))
).add_selection(
    brush
)

bars = alt.Chart().mark_bar().encode(
    y='Origin:N',
    color='Origin:N',
    x='count(Origin):Q'
).transform_filter(
    brush
)

alt.vconcat(points, bars, data=source) Scatter plot and Histogram with Altair and Vega You can try it out in your own Notebook and let me know if you get stuck anywhere! D3.js (Data Driven Documents) is a JavaScript library for manipulation of documents based on the data. You can bring data to life using HTML, SVG, and CSS. D3 does not require you to tie tie yourself to any proprietary framework because modern browsers have it all that D3 needs and it is also used for combining powerful visualization components and for a data-driven approach to DOM manipulation. D3.js D3.js is the best data visualization library out in the market. I prefer to use it almost every time. You can use it with python and as well as with R. Originally, it works with JavaScript and that becomes quite tough because JS has wide range of functions and requires much learning and experience but if you are a JS pro then you don’t need to make a second thought. Although, and has made it a bit simpler, just a bit! But you get the best stuff out there with this library. Python R D3py has 3 three main dependencies: Numpy Pandas NetworkX I would suggest you to use it with JavaScript or R, not with python because the version is out of date and was last updated in 2016. Though, it was just a thin python wrapper for D3.js. R has an interface for D3 visualizations. With , you can bind data from R to D3 visualizations. D3 visualizations created with work just like R plots within RStudio, R Markdown documents, and Shiny applications. You can install the package from CRAN as follows: r2d3 r2d3 r2d3 install.packages(“r2d3”) You can make some amazing visualizations with this one, Let me show you a couple of it here. Sequences Sunburst — ’s Block (Source) Kerry Rodden Activity Status of an Year — Kunal Dhariwal (Me, lol) From basics to High end, you can build anything with D3.js, Don’t forget to try it out. If you encounter any error or need any help, you can always make a comment or ping me on LinkedIn. : LinkedIn https://bit.ly/2u4YPoF : Github https://bit.ly/2SQV7ss P.S- Special Thanks to the creators and contributors of those amazing libraries. I hope that this has helped you to enhance your knowledge base :) Follow me for more! Thanks for your read and valuable time!

Taking Data Visualization to Another Level

Borrowing-to-lend for loan-to-buy customers Toyota Financial Services has to be one of the most fascinating, fraudulent entities operating in the world right now. Initially fuelled by cheap Japanese interest rates when it was first begun in the early 2000s, this “ within a factory” is one of the world’s most opaque, byzantine and powerful global financing organisations that you have never heard of. bank Today, TFS is the focus of Toyota’s operations, issuing billions in zero percent and cut-rate financing offers a year and guzzling a similarly sizeable chunk of money from the fixed income markets. Take, for instance, the company’s 50 billion euro note issuance last September. This was a collaboration between no less than 7 issuers spanning four continents all organised under the giant TFS banner. Not content with borrowing in just euros and yen, in July this earlier, Toyota issued its first dollar-denominated bonds: $2 billion of 3-, 5-, and 10-year commercial paper. That quick capital raise was shortly followed up with a further $3 billion issuance of USD-denominated retail notes aimed at American private investors — debt that your mother and father might put their savings into. The retail notes are “a way to expand and diversify TFS’ funding sources and provide a competitive investment alternative to investors,” Mike Groff, TFS’s new President, at the time. said in a press release That’s not all, though — there are the for environmentally-conscious lenders (a cool $5.5 billion), , another , and there was even a . Oh, not to mention lending the company solicits, too. Plus the standard fare of Japanese long bonds and other debt tranches the company issued this year via its myriad network of franchisors. By all accounts, it all adds up to a sizeable chunk of borrowing, representing more than 3 times the company’s steadily relaxing net income. green bonds the 2021 $750m 3.183% yield debt 300 million euros of 3-year debt $300 million 1-year tranche which redeems this month the $500 million of overnight The recent borrowing splurge is yet another unconventional policy play from Akio Toyoda, the current chief executive of Toyota since 2009. Toyoda is the third-generation heir to the family empire. It was his own grandfather Sakichi Toyoda, no less, the company’s founder, who once famously quipped, “it is more difficult to spend than to earn money.” That was back when the company used to stockpile cash and amortise loans before the year was up. How times have changed. , TFS launched a new office earlier this year, alongside a handful of other countries which saw the manufacturer’s finance empire expanding. However, investors might be forgiven for wondering where all this borrowing is going. After all, Koji Kobayashi, Toyota’s chief financial officer and an executive vice president, told Toyota’s November earnings conference attendees that he intended to cut costs by another full 50% over the coming year. In Ireland manufacturing “It would be good if our annual cost savings could reach 600 billion yen ($5.3 billion),” Koji-san , expressing concerns at what he forecast as “difficult” conditions for the company going ahead. That was despite having posted a record net profit on industry-wide record profit margins. (Toyota’s margins, at 8.9% trounce Honda’s, at around 6%, and Ford’s, which are as low as 3%.) said at the time , where Toyota Motor has a rather less well-respected reputation, may offer some insight into where all this debt is headed. The Japanese auto-maker laid off 50,000 factory workers in Altona a few years ago, many of them entire families. Needless to say, the effect on sales was felt across the 400-odd dealership franchises throughout the country, denting the company’s usually PR-self-conscious image badly. (Aussies tend not to forgive and forget too quickly if you fuck them.) The challenge has been — how to get sales back on track? A peek at Australia “At the moment, the (dealer) reputation may not be too great but I think we can turn that around and we think this is the way to do it. So we are pretty positive about this,” TFS president and CEO John Chandler told about the company’s latest sales-side innovation: an algorithm-based credit-matching engine. The algo, which is essentially nothing more complex or fancy than what a bank uses to credit score you for a loan, is, according to Mr. Chandler, the company’s best potential bet yet in getting sales back on track: GoAutoNews Premium “We are seeing some dealers talking of putting the finance decision earlier in the sales process,” he said. “When they can integrate that into the early discussion, particularly quoting the rates, that will become part of the whole process and we won’t necessarily have to hand the customer over to the finance guy at the end of the sales part. Affordability is a key part of the decision.” Of course, giving customers wads of cash to buy your products so that they end up paying you more on the back end is all very well, but when you are borrowing chunks of cash to do so that is rather a different proposition, surely? At that point, not only are you exposing the investor in the bond to the credit risk of the corporation and its technology systems, but you are actually exposing them to the credit risk of the borrower. More importantly still, to what extent does the manufacturer have an incentive to make sure that such loans are profitably issued? That will be the subject of our next post on this topic.

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