Dear R Developers, I would like to suggest the creation of a new S4 object class for On-Disk data.frames which do not fit in RAM memory, which could be called disk.data.frame() It could be based in rsqlite for example (By translating R syntax to SQL syntax for example), and the syntax and way of working of the disk.data.frame() class could be exactly the same than with data.frame objects. When the session is of, is such disk.data.frames are not saved, and implicit DROP TABLE could be done in all the schemas created in rsqlite. Nowadays, with the SSD disk drives such new data.frame() class could have sense, specially when dealing with Big Data. It is true that this new class might be slower than regular data.frame, data.table or tibble classes, but we would be able to handle much more data, even if it is at cost of speed. Also with data sampling, and use of a regular odbc connection we could do all the work, but for people who do not know how to use RDBMS or specific purpose R packages for this job, this could work. Another option would be to base this new S4 class on feather files, but maybe making it with rsqlite is simply easier. A GitHub project could be created for such purpose, so that all the community can contribute (included me :D ). Thank you, Juan [[alternative HTML version deleted]]
It is not needed. There is a large community of developer using SparkR. spark.apache.org/docs/latest/sparkr.html It does exactly what you want. On 3 September 2017 at 20:38, Juan Telleria <jtelleriar at gmail.com> wrote:> Dear R Developers, > > I would like to suggest the creation of a new S4 object class for On-Disk > data.frames which do not fit in RAM memory, which could be called > disk.data.frame() > > It could be based in rsqlite for example (By translating R syntax to SQL > syntax for example), and the syntax and way of working of the > disk.data.frame() class could be exactly the same than with data.frame > objects. > > When the session is of, is such disk.data.frames are not saved, and > implicit DROP TABLE could be done in all the schemas created in rsqlite. > > Nowadays, with the SSD disk drives such new data.frame() class could have > sense, specially when dealing with Big Data. > > It is true that this new class might be slower than regular data.frame, > data.table or tibble classes, but we would be able to handle much more > data, even if it is at cost of speed. > > Also with data sampling, and use of a regular odbc connection we could do > all the work, but for people who do not know how to use RDBMS or specific > purpose R packages for this job, this could work. > > Another option would be to base this new S4 class on feather files, but > maybe making it with rsqlite is simply easier. > > A GitHub project could be created for such purpose, so that all the > community can contribute (included me :D ). > > Thank you, > Juan > > [[alternative HTML version deleted]] > > ______________________________________________ > R-devel at r-project.org mailing list > stat.ethz.ch/mailman/listinfo/r-devel
On 4 September 2017 at 11:35, Suzen, Mehmet wrote: | It is not needed. There is a large community of developer using SparkR. | spark.apache.org/docs/latest/sparkr.html | It does exactly what you want. I hope you are not going to mail a sparkr commercial to this list every day. As the count is now at two, this may be an excellent good time to stop it. Dirk -- dirk.eddelbuettel.com | @eddelbuettel | edd at debian.org