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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>fst package for dealing with big data</title>
<meta charset="utf-8" />
<meta name="author" content="" />
<link href="libs/remark-css-0.0.1/default.css" rel="stylesheet" />
<link href="libs/remark-css-0.0.1/default-fonts.css" rel="stylesheet" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# fst package for dealing with big data
## ༼ʘ̚ل͜ʘ̚༽
---
# Links
- Slides: https://quinnasena.github.io/fst/fst_slides.html#1
- fst ducumentation: https://cran.r-project.org/web/packages/fst/fst.pdf
- tidyfst ducmentation: https://cran.r-project.org/web/packages/tidyfst/tidyfst.pdf
- Detailed info: https://www.r-bloggers.com/lightning-fast-serialization-of-data-frames-using-the-fst-package/
---
# The problem
.pull-left[
- 10 treatments applied to origin dataframe
- Second level of another 10 treatments = 100 treatments
- Third level of 10 treatments = 1000 (total 1111 dataframes)
- Each dataframe is 4750 rows (total = 5277250 rows)
- Multiplied by 100 replicates...
- All of the treatments need to be analysed
]
.pull-right[
<!-- -->
]
---
# The problem
- Origin dataframe (with all treatments) = 18 Gb
- Analysis of model = 45 Gb output
- Further analysis needs to read in 45 Gb data and do further computations
- in R subsetting dataframes creates a copy of the original dataframe, both of which are stored in RAM.
- Impossible on my personal laptop and an inefficient use of resources from NeSI
---
# Hello fst
```r
### Create something big(ish) 30000 rows, 200 variables plus an alphabetical id column
a_lot_of_data <- as.data.frame(
matrix(
rnorm(3000000),
nrow = 30000,
ncol = 200
)
) %>%
mutate(id = rep(LETTERS[1:20], each=1500))
### How big?
print(object.size(a_lot_of_data), units = "Mb")
```
```
## 46 Mb
```
```r
### Save as RDS and as FST
saveRDS(a_lot_of_data, "a_lot_of_data.Rds")
fst::write.fst(a_lot_of_data, "a_lot_of_data.fst")
```
---
# Write speed
.pull-left[
## Rds
```r
write_speed_rds <- microbenchmark(
saveRDS(a_lot_of_data, "a_lot_of_data.rds"),
times = 1
)
# speed in GB/s
as.numeric(object.size(a_lot_of_data)) / write_speed_rds$time
```
```
## [1] 0.01027072
```
```r
# speed in seconds
write_speed_rds$time/1e9
```
```
## [1] 4.699365
```
]
.pull-right[
## fst
```r
write_speed_fst <- microbenchmark(
write_fst(a_lot_of_data, "a_lot_of_data.fst"),
times = 1
)
# speed in GB/s
as.numeric(object.size(a_lot_of_data)) / write_speed_fst$time
```
```
## [1] 0.3079743
```
```r
# speed in seconds
write_speed_fst$time/1e9
```
```
## [1] 0.1567204
```
]
---
# Size matters, the smaller the better...
```r
### Read in both formats
a_lot_of_data_rds <- readRDS("a_lot_of_data.rds")
a_lot_of_data_fst <- fst("a_lot_of_data.fst")
```
--
```r
### How Big are they now?
## Rds, same as original
print(object.size(a_lot_of_data_rds), units = "Mb")
```
```
## 46 Mb
```
```r
## fst, a lot smaller than original
print(object.size(a_lot_of_data_fst), units = "Kb")
```
```
## 16.3 Kb
```
---
# Subsetting Rds
```r
### With Rds format we read in the entire dataframe and subset
subset_rds <- a_lot_of_data_rds[a_lot_of_data_rds$id == "E", c("V98", "id")]
print(object.size(subset_rds), units = "Kb")
```
```
## 30.2 Kb
```
```r
head(subset_rds)
```
```
## V98 id
## 6001 0.3032403 E
## 6002 -0.3079970 E
## 6003 -1.1808409 E
## 6004 -0.3918794 E
## 6005 -0.5470373 E
## 6006 1.3056930 E
```
```r
## Size total = 30.2 Kb + 46 Mb
```
---
# Subsetting fst
```r
### fst subsets *without* reading entire dataframe into memory
subset_fst <- a_lot_of_data_fst[a_lot_of_data_fst$id == "E", c("V98", "id")]
head(subset_fst)
```
```
## V98 id
## 1 0.3032403 E
## 2 -0.3079970 E
## 3 -1.1808409 E
## 4 -0.3918794 E
## 5 -0.5470373 E
## 6 1.3056930 E
```
```r
print(object.size(subset_fst), units = "Kb")
```
```
## 24.3 Kb
```
```r
## Size total = 16.3 Kb + 24.3 Kb
```
---
# Read fst
```r
### fst can read-in part of a dataset but I think it only takes row numbers as an argument
read_subset_fst <- read_fst("a_lot_of_data.fst", c("id","V98"), 1000, 2000)
head(read_subset_fst)
```
```
## id V98
## 1 A 1.8664596
## 2 A 0.7979371
## 3 A 0.7523801
## 4 A 0.9208977
## 5 A 1.8916638
## 6 A -1.2030774
```
```r
print(object.size(read_subset_fst), units = "Kb")
```
```
## 16.6 Kb
```
```r
## Size total = 16.6 Kb
```
---
# tidyfst
- fst does not handle like a regular dataframe (although can be converted after subsetting)
```r
### Subsetting can also be done with tidyfst package that replicates some tidyverse functions
subset_tidyfst <- filter_dt(a_lot_of_data_fst, id == "C") %>%
select_dt(V98, id) #the tidyfst way of filtering fst data tables
head(subset_tidyfst)
```
```
## V98 id
## <num> <char>
## 1: -1.9373604 C
## 2: 1.5531283 C
## 3: -0.2260059 C
## 4: -0.2987208 C
## 5: 1.0594286 C
## 6: -1.4902994 C
```
---
# Pros and cons
.pull-left[
## Rds
- Capable of saving a list of outputs as one object (fst seems to only save a single object)
- Saved output read back in same format, i.e., class (df, matrix...)
\----------
<br>
- Slower to read/write
]
.pull-right[
## fst
- Can save huge amounts of memory!
- read/write is much much faster than any other package
<br>
\----------
<br>
- Reads back as fst format which handles like a data.table rather than data.frame
- I still save Rds as well as fst.
]
---
# (ง°ل͜°)ง
<img src="multithreading.png" width="673" style="display: block; margin: auto;" />
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