In BoulderCodeHub/RWDataPlyr: Read and Manipulate Data from 'RiverWare'

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Problem

'Big' rdfs cannot be parsed by rdf_to_rwtbl2() nor by rdf_aggregate() or by rw_scen_aggregate(), which both rely on rdf_to_rwtbl2().
The underlying memory issue is with the underlying C++ code, even though R-studio shows that there is enough memory on the laptop.
Storing the data in the "long" format, with many string/character columns that are repeated for every row is memory intensive, but useful for the analysis and plotting we do.
Example: The 600 MB AZWU.rdf file for a 400 trace run takes up about 15 GB in memory

Solution

bigrdf_to_rwtbl() works similarly to rdf_to_rwtbl2() creating the same 'long' data frame, but does not store it in memory
It relies on parquet files as they do not have to store all data in memory, and batch parse the rdf files
The parquet files can be used in a standard dplyr pipeline, and then 'collected' into memory when they get to be a manageable size

Example

bigrdf_to_rwtbl() returns a connection to the parquet file, but not the data itself. This might take ~5 minutes to run. It will process 20 traces at a time, and will report out as it starts each chunk of 20 traces.

library(RWDataPlyr)
library(dplyr)
library(stringr)
rdf_path <- "//manoa.colorado.edu/bor/Shared/P26/Dec202024_NA_CCS_FA_Final_Runs/2016Dems,CRMMS_Trace12,ICSon,SuperEnsembleV3,CCS.9087.mdl,CCS.9047.rls/AZWU.rdf"

zz <- bigrdf_to_rwtbl(rdf_path, scenario = 'CCS', n_trace_per_chunk = 20)
zz

Then, you can work with the data in a typical dplyr pipeline, and move it into memory when it is smaller. Use collect() to move it into memory.

# get the annual depletion requested for each user
df <- zz |> 
  filter(str_ends(ObjectSlot, '\\.Depletion Requested')) |>
  group_by(Scenario, ObjectSlot, Year) |>
  summarise(Value = sum(Value)) |>
  collect()

Now df is a reasonable size (.1 MB) to keep in memory and use as you normally would.

Details

It is faster to work with data in memory than with the parquet connection, but only if you have enough memory. So, the goal should be to reduce the size and then move it into memory.
You could run collect(zz), but this will take ~14 GB of free memory
The n_trace_per_chunk variable controls how many traces are parsed on each call to the C++ code.
Increasing this will likely result in faster evaluation, but also takes more memory
Decreasing this will likely result in slower evaluation, but take less memory
If you get the out of memory error, try making this smaller; if you're working with a smaller rdf file, you can probably increase this. Setting it to a negative value will have it process all traces (but then you should probably use rdf_to_rwtbl2()).
The parquet data is stored in a temp directory, e.g., C:/Users/RAButler/AppData/Local/Temp/1/RtmpElIuIn/bigrdf_55b05e956495. This is a requirement of CRAN. It also means that if you run this and R is closed/reset, it will delete the parquet data.
So, if you want to save these data someplace, you can use bigrdf_move(df, 'path/to/move/to')
Then you can use arrow::open_dataset('path/to/move/to') to reconnect to these data in the future
rdf_to_rwtbl2() is faster than bigrdf_to_rwtbl(), so for smaller rdf files that's probably still the preferred method for getting the data into R