Planned milestones for future releases

[MarcusKlik]

The currently planned features planned for fst:

version 0.8.4:

Intermediate release to fix the Clang 6.0 build errors (#118)

version 0.8.6:

• Multi-threaded serialization of character columns
• Basic data.table interface:

ft <- fst("1.fst")

ft[1:1000, .(ColA, ColB)]  # on-disk row subsetting + column selection

ft[ColA > 50, .(ColA)]  # on-disk subsetting using simple expression + column selection

ft[ColA == median(ColB), .(ColB)]  # subseting using custom expression + column selection

ft[ColA == ColB, .(ColSum = ColA + ColB)]  # subsetting + compute on column selection

Note that there is no grouping functionality in this basic interface (yet), but there will be:
- on-disk (random) logical row sub-setting (requiring only memory for selected rows) (argument i)
- on-disk row sub-setting using an expression (requiring only memory for columns used the expression) (argument i)
- on-disk column selection (argument j)
- computations on column selection (compute on j)

• Basic dplyr interface:

  • filter
  • select
  • slice
  • collect
  • sample_n and sample_frac (only needs memory for data in the returned sample)

• Hashing of column data

version 0.8.8:

• For the data.table interface:

  • operator := for column binding to an existing fst file
  • rbindlist to row bind multiple fst files into a single file

• For the dplyr interface:

  • add_row
  • add_column
  • mutate

version 0.8.8 and later

Later features (in random order):

• Add on-disk grouping functionality. That requires on-disk sorting, which can be done using a merge sort algorithm.

• lapply like functionality creating a fst file using a list of inputs (csv's, custom methods, etc.)

• interoperability:
  a) import data from Apache Parguet files
  b) Python interface
  c) C++ interface library
  d) Julia interface, ...

• advanced operations:
  a) Parallel grouping for specific methods (like +,-,*,/,sum,mean, etc. these methods need a C++ implementation for parallel operations)
  b) binary search on table key columns (extremely fast sub-setting of a key range)
  c) Merge operations on multiple fst files (right join to start with, like in data.table)
  d) multiple fst-files represent a single data set
  e) set of fst-files can be sorted in parallel into a new set of fst files. This avoids the slow end-phase of sorting algorithms like merge sort.
  f) user-defined map-reduce operations that can be used on the fst file(s) in parallel. Simple example: a custom median method using 1) sum and count each chunk 2) take results from 1) to calculate median.
  g) fill a data set range with specific rows from a fst file, overwriting data in-memory (Fill a data.table range with specific rows from read.fst #29).

• performance and security enhancements:
  a) encryption
  b) SIMD upgrades to the bit-shifters and pre-serialization filters used in fst
  c) a plug-in system (C++) for custom compressors to allow users to come up with faster or better compressors
  d) better character columns compression
  e) high compression mode for slow IO (network) speeds (Very slow writing to network drive when using compression (Windows 7) #23).

This list is subject to a lot of change depending on features and issues requested/reported by users of the fst package :-)

[krlmlr]

dplyr also implements some operations internally ("hybrid evaluation"), maybe there's potential for code reuse?

[shrektan]

Is it possible to add the ability to write empty data frame to your milestones? It’s a very useful feature, at least for myself, thanks.

[MarcusKlik]

Hi @krlmlr, that's a very nice feature! And yes, I think that it will greatly speed up processing to be able to use the fst internal representation for common methods (a logical vector takes only 2 bits per element in the internal format for example).

The interface will have to be build around an offline-object representing a fst file. All operations should be performed on that abstract object so that they can be parallelized automatically. For summarise that could be done by processing groups in parallel. But you can't call custom R methods from any other thread than the master thread. That means that data could be loaded from disk in the background, but processing would still be single threaded. Perhaps we could use the hybrid evaluation to replace known methods by multi-threaded ones, that would be great.

Thanks for the idea!

[MarcusKlik]

Hi @shrektan, thanks for your question! Writing empty tables is on the list for release v0.8.6. A discussion on that can also be found in #99.

Indeed, it would be nice to be able to use the empty table to store metadata. Also, when row binding is supported, you could have a situation where an empty table would be the first result of an apply call for example.

[shrektan]

That’s great. Also, I’m so exited to see the idea of the data.table interface feature. If that’s possible, I can only say a big WOW. Looking forward to that.

[xiaodaigh]

github.com/xiaodaigh/disk.frame i am experimenting with a data.table-like on-disk data manipulation package bacled by fst. early signs promising

…

On 9 Jan. 2018 11:59 pm, "Xianying Tan" ***@***.***> wrote: That’s great. Also, I’m so exited to see the idea of the data.table interface feature. If that’s possible, I can only say a big WOW. Looking forward to that. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#117 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AESfJSWjIrXPeYLEwqqoLIEyUUOQ5zGCks5tI2K3gaJpZM4RLfcy> .

[MarcusKlik]

Hi @xiaodaigh, your package is very impressive! Would it be possible to run the disk.frame interface against a parallel cluster (with each node processing some chunks)?

One of the bottlenecks if you have a lot of data is that you can use many threads to read data from file (for example with fst) but running (custom)R methods always has to be done on the (single) main thread. Combining a cluster with a fst back-end would give you the option to tune where you need the the most CPU power 👍

[xiaodaigh]

Firstly it's a great compliment from the creator of fst! I wouldn't call my package impressive if the bar is fst. Anyway it's still early stages but thanks!

@MarcusKlik > Would it be possible to run the disk.frame interface against a parallel cluster (with each node processing some chunks)?

I think it's possible and probably will be done at some point. One thing I am not certain about is that I am not sure how working with clusters in R will be like. I know Julia was designed to work well with clusters so I was thinking about building something in Julia and make it call R (hopefully Julia's native DataFrame will catch up in performance and it can get a native fst reader).

Crazy idea: we can a start a company making a fst-backed distributed data manipulation tool that maximizes single machine performance as well.

[MarcusKlik]

That's already a nice sales pitch you have there :-)