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MONGODB’NİN İLİŞKİSEL VERİTABANLARI GİBİ KULLANILABİLMESİ

Yıl 2021, Cilt: 16 Sayı: 63, 151 - 172, 30.09.2021

Öz

Son 20 yılda teknoloji çok büyük bir hızla gelişmiştir. Gelişen teknoloji ile birlikte dijital ortamdaki verilerin boyutlarında da çok ciddi bir artış yaşanmıştır. Geleneksel olan ilişkisel veri tabanları dijital ortamdaki veri miktarlarını verimli bir şekilde saklayabilecek ve işlem yapabilecek gelişmeyi göstermede yeterlilik sağlayamamıştır. İlişkisel veri tabanları veri tabanı içerisindeki bütün verileri birbiri ile ilişkilendirebilirler ve gelişmiş sorgulama dili aracılığıyla kullanıcılarına detaylı sorgular, raporlar üretebilirler. Ancak ilişkisel veri tabanları performans olarak ve mali olarak ilişkisel olmayan veri tabanlarının oldukça gerisinde kalmıştır. İlişkisel veri tabanlarının ekonomik açıdan çokta verimli olmaması ve artan dijital veri miktarına bağlı olarak veri tabanı performansında da ciddi düşüşler göstermesi ilişkisel olmayan veri tabanlarının kullanımını ciddi şekilde arttırmıştır. İlişkisel olmayan veri tabanları özel olarak hazırlanmış yapılar olup performansı, kolay sürdürülebilirliği ve uygun maliyetli olması ile popülarite kazanmıştır. İlişkisel olmayan veri tabanları her ne kadar yapısı gereği ilişkisel olarak kullanılamasa da günümüzün gelişmiş programlama dilleri aracılığıyla ve gelişmiş bir veri tabanı mimarisi ile birlikte ilişkisel olarak kullanımı mümkündür. Bu çalışmada da günümüzün en gelişmiş ve en popüler ilişkisel olmayan veri tabanlarından birisi olan MongoDB’nin C# programlama dili aracılığıyla nasıl ilişkisel olarak kullanılabileceği ve bunun bizlere ne gibi kazançlar sağlayacağı üzerinde durulmuştur.

Kaynakça

  • Baruffa, G., Femminella, M., Pergolesi, M., & Reali, G. (2019). Comparison of MongoDB and Cassandra databases for spectrum monitoring As-a-Service. IEEE Transactions on Network and Service Management, 17(1), 346-360.
  • Boicea, A., Radulescu, F., & Agapin, L. I. (2012). MongoDB vs Oracle--database comparison. In 2012 third international conference on emerging intelligent data and web technologies (pp. 330-335). IEEE.
  • Chopade, M. R. M., ve Dhavase, N. S. (2017). Mongodb, couchbase: Performance comparison for image dataset. In 2017 2nd International Conference for Convergence in Technology (I2CT) (pp. 255-258). IEEE.
  • Győrödi, C., Győrödi, R., Pecherle, G., & Olah, A. (2015). A comparative study: MongoDB vs. MySQL. In 2015 13th International Conference on Engineering of Modern Electric Systems (EMES) (pp. 1-6). IEEE.
  • Hou, B., Shi, Y., Qian, K., ve Tao, L. (2017). Towards analyzing mongodb noSQL security and designing injection defense solution. In 2017 IEEE 3rd international conference on big data security on cloud (bigdatasecurity), ieee international conference on high performance and smart computing (hpsc), and ieee international conference on intelligent data and security (ids) (pp. 90-95). IEEE.
  • Jung, M. G., Youn, S. A., Bae, J., ve Choi, Y. L. (2015). A study on data input and output performance comparison of mongodb and postgreSQL in the big data environment. In 2015 8th International Conference on Database Theory and Application (DTA) (pp. 14-17). IEEE.
  • Lou, Y., ve Ye, F. (2018). Research on Data Query Optimization Based on SparkSQL and MongoDB. In 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) (pp. 144-147). IEEE.
  • Mearaj, I., Maheshwari, P., ve Kaur, M. J. (2018). Data Conversion from Traditional Relational Database to MongoDB using XAMPP and NoSQL. In 2018 Fifth HCT Information Technology Trends (ITT) (pp. 94-98). IEEE.
  • Öztürk, S., ve Atmaca, H. E. (2017). İlişkisel ve ilişkisel olmayan (NoSQL) veri tabanı sistemleri mimari performansının yönetim bilişim sistemleri kapsamında incelenmesi. Bilişim Teknolojileri Dergisi, 10(2), 199-209.
  • Patil, M. M., Hanni, A., Tejeshwar, C. H., ve Patil, P. (2017). A qualitative analysis of the performance of MongoDB vs MySQL database based on insertion and retriewal operations using a web/android application to explore load balancing—Sharding in MongoDB and its advantages. In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 325-330). IEEE.
  • Prabagaren, G. (2014). Systematic approach for validating Java-MongoDB schema. In International Conference on Information Communication and Embedded Systems (ICICES2014) (pp. 1-4). IEEE.
  • Putri, F. K., ve Kwon, J. (2017). A distributed system for finding high profit areas over big taxi trip data with MognoDB and spark. In 2017 IEEE International Congress on Big Data (BigData Congress) (pp. 533-536). IEEE.
  • Rezapour, M., Moradi, M., ve Ghadiri, N. (2015). Performance evaluation of SQL and MongoDB databases for big e-commerce data. In 2015 International Symposium on Computer Science and Software Engineering (CSSE) (pp. 1-7). IEEE.
  • Yuan, M., Liu, K., Zhang, L., ve Zou, C. (2018). Research on Big Data Storage Model of Oilfield Assay Data Based on MongoDB. In 2018 IEEE 4th International Conference on Computer and Communications (ICCC) (pp. 1863-1866). IEEE.
  • Matallah, H., Belalem, G., & Bouamrane, K. (2021). Comparative Study Between the MySQL Relational Database and the MongoDB NoSQL Database. International Journal of Software Science and Computational Intelligence (IJSSCI), 13(3), 38-63.
  • Malik, A., Burney, A., & Ahmed, F. (2020). A Comparative Study of Unstructured Data with SQL and NO-SQL Database Management Systems. Journal of Computer and Communications, 8 (4), 59-71.
  • URL 1 - https://koraypeker.com/2019/03/16/modern-veri-tabanlari (Erişim Tarihi: 08.04.2021)
  • URL 2 - https://www.red-gate.com/ (Erişim Tarihi: 15.04.2021)
  • URL 3 - https://www.turkz.org/ (Erişim Tarihi: 21.04.2021)
  • URL 4 - http://chadwickspencer.com/myblog/index.php/2018/04/ (Erişim Tarihi: 27.03.21)

USING MONGODB LIKE RELATIONAL DATABASES

Yıl 2021, Cilt: 16 Sayı: 63, 151 - 172, 30.09.2021

Öz

Technology has developed rapidly in the last 20 years. Along with the developing technology, there has been a significant increase in the size of the data in the digital environment. Traditional relational databases have not been able to efficiently store and process the amount of data in the digital environment. Relational databases can associate all the data in the database with each other and can generate detailed queries and reports for their users through the advanced query language. However, relational databases lag far behind non-relational databases in performance and financially. The fact that relational databases are not economically efficient and that the database performance decreases due to the increasing amount of digital data has seriously increased the use of non-relational databases. Non-relational databases are specially prepared structures and have gained popularity with their performance, easy maintainability, and cost-effectiveness. Although non-relational databases cannot be used relationally due to their structure, it is possible to use relationally with today's advanced programming languages and advanced database architecture. In this study, it is emphasized how MongoDB, one of the most advanced and most popular non-relational databases of today, can be used relationally through the C # programming language and what benefits this will provide for us.

Kaynakça

  • Baruffa, G., Femminella, M., Pergolesi, M., & Reali, G. (2019). Comparison of MongoDB and Cassandra databases for spectrum monitoring As-a-Service. IEEE Transactions on Network and Service Management, 17(1), 346-360.
  • Boicea, A., Radulescu, F., & Agapin, L. I. (2012). MongoDB vs Oracle--database comparison. In 2012 third international conference on emerging intelligent data and web technologies (pp. 330-335). IEEE.
  • Chopade, M. R. M., ve Dhavase, N. S. (2017). Mongodb, couchbase: Performance comparison for image dataset. In 2017 2nd International Conference for Convergence in Technology (I2CT) (pp. 255-258). IEEE.
  • Győrödi, C., Győrödi, R., Pecherle, G., & Olah, A. (2015). A comparative study: MongoDB vs. MySQL. In 2015 13th International Conference on Engineering of Modern Electric Systems (EMES) (pp. 1-6). IEEE.
  • Hou, B., Shi, Y., Qian, K., ve Tao, L. (2017). Towards analyzing mongodb noSQL security and designing injection defense solution. In 2017 IEEE 3rd international conference on big data security on cloud (bigdatasecurity), ieee international conference on high performance and smart computing (hpsc), and ieee international conference on intelligent data and security (ids) (pp. 90-95). IEEE.
  • Jung, M. G., Youn, S. A., Bae, J., ve Choi, Y. L. (2015). A study on data input and output performance comparison of mongodb and postgreSQL in the big data environment. In 2015 8th International Conference on Database Theory and Application (DTA) (pp. 14-17). IEEE.
  • Lou, Y., ve Ye, F. (2018). Research on Data Query Optimization Based on SparkSQL and MongoDB. In 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) (pp. 144-147). IEEE.
  • Mearaj, I., Maheshwari, P., ve Kaur, M. J. (2018). Data Conversion from Traditional Relational Database to MongoDB using XAMPP and NoSQL. In 2018 Fifth HCT Information Technology Trends (ITT) (pp. 94-98). IEEE.
  • Öztürk, S., ve Atmaca, H. E. (2017). İlişkisel ve ilişkisel olmayan (NoSQL) veri tabanı sistemleri mimari performansının yönetim bilişim sistemleri kapsamında incelenmesi. Bilişim Teknolojileri Dergisi, 10(2), 199-209.
  • Patil, M. M., Hanni, A., Tejeshwar, C. H., ve Patil, P. (2017). A qualitative analysis of the performance of MongoDB vs MySQL database based on insertion and retriewal operations using a web/android application to explore load balancing—Sharding in MongoDB and its advantages. In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 325-330). IEEE.
  • Prabagaren, G. (2014). Systematic approach for validating Java-MongoDB schema. In International Conference on Information Communication and Embedded Systems (ICICES2014) (pp. 1-4). IEEE.
  • Putri, F. K., ve Kwon, J. (2017). A distributed system for finding high profit areas over big taxi trip data with MognoDB and spark. In 2017 IEEE International Congress on Big Data (BigData Congress) (pp. 533-536). IEEE.
  • Rezapour, M., Moradi, M., ve Ghadiri, N. (2015). Performance evaluation of SQL and MongoDB databases for big e-commerce data. In 2015 International Symposium on Computer Science and Software Engineering (CSSE) (pp. 1-7). IEEE.
  • Yuan, M., Liu, K., Zhang, L., ve Zou, C. (2018). Research on Big Data Storage Model of Oilfield Assay Data Based on MongoDB. In 2018 IEEE 4th International Conference on Computer and Communications (ICCC) (pp. 1863-1866). IEEE.
  • Matallah, H., Belalem, G., & Bouamrane, K. (2021). Comparative Study Between the MySQL Relational Database and the MongoDB NoSQL Database. International Journal of Software Science and Computational Intelligence (IJSSCI), 13(3), 38-63.
  • Malik, A., Burney, A., & Ahmed, F. (2020). A Comparative Study of Unstructured Data with SQL and NO-SQL Database Management Systems. Journal of Computer and Communications, 8 (4), 59-71.
  • URL 1 - https://koraypeker.com/2019/03/16/modern-veri-tabanlari (Erişim Tarihi: 08.04.2021)
  • URL 2 - https://www.red-gate.com/ (Erişim Tarihi: 15.04.2021)
  • URL 3 - https://www.turkz.org/ (Erişim Tarihi: 21.04.2021)
  • URL 4 - http://chadwickspencer.com/myblog/index.php/2018/04/ (Erişim Tarihi: 27.03.21)
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Anıl Yıldız 0000-0003-4607-6660

Parvaneh Shams 0000-0003-1467-3284

Zafer Güney 0000-0003-1974-4264

Yayımlanma Tarihi 30 Eylül 2021
Gönderilme Tarihi 14 Haziran 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 16 Sayı: 63

Kaynak Göster

APA Yıldız, A., Shams, P., & Güney, Z. (2021). MONGODB’NİN İLİŞKİSEL VERİTABANLARI GİBİ KULLANILABİLMESİ. Anadolu Bil Meslek Yüksekokulu Dergisi, 16(63), 151-172.


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