O Bigtable é um sistema de armazenamento de dados proprietário compactado, de alto desempenho construído no sistema do Google File e algumas outras tecnologias Google. Em 6 de maio de 2015, uma versão pública do Bigtable foi disponibilizada.[1][2]

O desenvolvimento do Bigtable começou em 2004[3] e agora é usado por várias aplicações do Google, como o indexamento web,[4] MapReduce, que é usado frequentemente para gerar modificar dados armazenados no Bigtable,[5] Google Maps,[6] Google Book Search, Google Earth, Blogger.com, Google Code, YouTube,[7] e Gmail.[8] As razões do Google para desenvolver seu próprio banco de dados incluem escalabilidade e melhor controle das características de desempenho.[9]

Bibliografia editar


  1. «Announcing Google Cloud Bigtable: The same database that powers Google Search, Gmail and Analytics is now available on Google Cloud Platform». Google Blog. 6 de maio de 2015. Consultado em 21 de setembro de 2016 
  2. «Get started with Google Cloud Datastore - a fast, powerful, NoSQL database» 
  3. Kumar, Aswini, Whitchcock, Andrew, ed., Google's Bigtable, First an overview. Bigtable has been in development since early 2004 and has been in active use for about eight months (about February 2005). .
  4. Chang et al. 2006.
  5. Chang et al. 2006, p. 3: ‘Bigtable can be used with MapReduce, a framework for running large-scale parallel computations developed at Google. We have written a set of wrappers that allow a Bigtable to be used both as an input source and as an output target for MapReduce jobs’
  6. Whitchcock, Andrew, Google's Bigtable, There are currently around 100 cells for services such as Print, Search History, Maps, and Orkut .
  7. Cordes, Kyle (12 de julho de 2007), YouTube Scalability (talk), Their new solution for thumbnails is to use Google’s Bigtable, which provides high performance for a large number of rows, fault tolerance, caching, etc. This is a nice (and rare?) example of actual synergy in an acquisition. .
  8. «How Entities and Indexes are Stored», Google App Engine, Google Code .
  9. Chang et al. 2006, Conclusion: ‘We have described Bigtable, a distributed system for storing structured data at Google... Our users like the performance and high availability provided by the Bigtable implementation, and that they can scale the capacity of their clusters by simply adding more machines to the system as their resource demands change over time... Finally, we have found that there are significant advantages to building our own storage solution at Google. We have gotten a substantial amount of flexibility from designing our own data model for Bigtable.’