synthetic

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Overview

The synthetic package provides tooling to greatly symplify the creation of synthetic datasets for testing purposes. It’s features include:

• Creation of dataset templates that can be used to generate arbitrary large datasets
• Creation of column templates that can be used to define column data with custom range and distribution
• Automatic creation of dataset templates from existing datasets
• Many pre-defined templates to help you generate synthetic datasets with little effort
• Extented benchmark framework to help test the performance of serialization options such as fst, arrow, fread / fwrite, sqlite, etc.

By using a standardized method of serialization benchmarking, benchmark results become more reliable and more easy to compare over various solutions, as can be seen further down in this introduction.

Synthetic datasets

Most R users will probably be familiar with the iris dataset as it’s widely used in package examples and tutorials:

library(dplyr)

iris %>%
  as_tibble()
#> # A tibble: 150 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#>  1          5.1         3.5          1.4         0.2 setosa 
#>  2          4.9         3            1.4         0.2 setosa 
#>  3          4.7         3.2          1.3         0.2 setosa 
#>  4          4.6         3.1          1.5         0.2 setosa 
#>  5          5           3.6          1.4         0.2 setosa 
#>  6          5.4         3.9          1.7         0.4 setosa 
#>  7          4.6         3.4          1.4         0.3 setosa 
#>  8          5           3.4          1.5         0.2 setosa 
#>  9          4.4         2.9          1.4         0.2 setosa 
#> 10          4.9         3.1          1.5         0.1 setosa 
#> # ... with 140 more rows

But what if you need a dataset of a million rows? The synthetic package makes that straightforward. Simply define a dataset template using synthetic_table():

library(synthetic)

# define a synthetic table
synt_table <- synthetic_table(iris)

and generate a custom number of rows:

synt_table %>%
  generate(1e6) # a million rows
#> # A tibble: 1,000,000 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species   
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>     
#>  1          5.7         3.8          1.7         0.3 setosa    
#>  2          5.8         2.7          5.1         1.9 virginica 
#>  3          6.1         2.8          4           1.3 versicolor
#>  4          5.1         3.5          1.4         0.2 setosa    
#>  5          7.2         3.6          6.1         2.5 virginica 
#>  6          6           2.2          5           1.5 virginica 
#>  7          6.8         3            5.5         2.1 virginica 
#>  8          6.2         3.4          5.4         2.3 virginica 
#>  9          7.1         3            5.9         2.1 virginica 
#> 10          6.4         2.8          5.6         2.2 virginica 
#> # ... with 999,990 more rows

You can also select specific columns:

synt_table %>%
  generate(1e6, "Species")  # single column
#> # A tibble: 1,000,000 x 1
#>    Species   
#>    <fct>     
#>  1 versicolor
#>  2 virginica 
#>  3 setosa    
#>  4 versicolor
#>  5 versicolor
#>  6 virginica 
#>  7 versicolor
#>  8 versicolor
#>  9 setosa    
#> 10 virginica 
#> # ... with 999,990 more rows

Building templates from existing datasets

Benchmarking serialization

Benchmarks performed With synthetic have the following features:

• Each measurement of serialization speed uses a unique dataset (avoid disk caching)
• A read is not executed immediately after a write of the same dataset (avoid disk caching)
• All (column-) data is generated on the fly using predefined generators (no need to download large test sets)
• A wide range of data profiles can be used for the creation of synthetic data (understand dependencies on data format and profile)
• Object- en file sizes are recorded and speeds automatically calculated (reproducible results)
• A progress bar shows percentage done and time remaining (know when to go and get a cup of coffee)
• Only the actual serialization speed is benchmarked (measure only what must be measured)
• Multithreaded solutions are correctly measured (unlike some benchmark techniques)

But most importantly, with the use of synthetic, complex benchmarks are reduced to a few simple statements, increasing your productivity and reproducibility!

Walkthrough: setting up a benchmark

A lot of claims are made on the performance of serializers and databases, but the truth is that all solutions have their own strenghts and weaknesses.

some more text here

Define the template of a test dataset:

library(synthetic)
library(fst)
library(arrow)

# generator for 'fst benchmark' dataset
generator <- table_generator(
  "fst benchmark",
  function(nr_of_rows) {
    data.frame(
      Logical = sample_logical(nr_of_rows, true_false_na_ratio = c(85, 10, 5)),
      Integer = sample_integer(nr_of_rows, max_value = 100L),
      Real    = sample_integer(nr_of_rows, 1, 10000, max_distict_values = 20) / 100,
      Factor  = as.factor(sample(labels(UScitiesD), nr_of_rows, replace = TRUE))
    )}
)

Do some benchmarking on the fst format:

library(dplyr)

synthetic_bench() %>%
  bench_generators(generator) %>%
  bench_streamers(streamer_fst()) %>%
  bench_rows(1e7) %>%
  collect()

Congratulations, that’s your first structured benchmark :-)

Now, let´s add a second streamer and allow for two different sizes of datasets:

synthetic_bench() %>%
  bench_generators(generator) %>%
  bench_streamers(streamer_fst(), streamer_parguet()) %>%  # two streamers
  bench_rows(1e7, 5e7) %>%
  collect()

As you can see, although benchmarking two solutions at different sizes is more complex than the single solution benchmark, with synthetic it´s just a matter of expanding some of the arguments.

Let´s add two more streamers and add compression settings to the mix:

synthetic_bench() %>%
  bench_generators(generator) %>%
  bench_streamers(streamer_rds(), streamer_fst(), streamer_parguet(), streamer_feather()) %>%
  bench_rows(1e7, 5e7) %>%
  bench_compression(50, 80) %>%
  collect()