# qs2

qs2: a framework for efficient serialization

`qs2` is the successor to the `qs` package. The goal is to have reliable and fast performance for saving and loading objects in R.

The `qs2` format directly uses R serialization (via the `R_Serialize`/`R_Unserialize` C API) while improving underlying compression and disk IO patterns. If you are familiar with the `qs` package, the benefits and usage are the same.

    qs_save(data, "myfile.qs2")
    data <- qs_read("myfile.qs2")

Use the file extension `qs2` to distinguish it from the original `qs` package. It is not compatible with the original `qs` format.

## Installation

    install.packages("qs2")

On Mac or Linux, you can enable multi-threading by compiling from source. It is enabled by default on Windows.

    remotes::install_cran("qs2", type = "source", configure.args = " --with-TBB --with-simd=AVX2")

Multi-threading in `qs2` uses the `Intel Thread Building Blocks` framework via the `RcppParallel` package.

## Converting qs2 to RDS

Because the `qs2` format directly uses R serialization, you can convert it to RDS and vice versa.

    file_qs2 <- tempfile(fileext = ".qs2")
    file_rds <- tempfile(fileext = ".RDS")
    x <- runif(1e6)

    # save `x` with qs_save
    qs_save(x, file_qs2)

    # convert the file to RDS
    qs_to_rds(input_file = file_qs2, output_file = file_rds)

    # read `x` back in with `readRDS`
    xrds <- readRDS(file_rds)
    stopifnot(identical(x, xrds))

## Validating file integrity

The `qs2` format saves an internal checksum. This can be used to test for file corruption before deserialization via the `validate_checksum` parameter, but has a minor performance penalty.

    qs_save(data, "myfile.qs2")
    data <- qs_read("myfile.qs2", validate_checksum = TRUE)

# The qdata format

The package also introduces the `qdata` format which has its own serialization layout and works with only data types (vectors, lists, data frames, matrices).

It will replace internal types (functions, promises, external pointers, environments, objects) with NULL. The `qdata` format differs from the `qs2` format in that it is NOT a general.

The eventual goal of `qdata` is to also have interoperability with other languages, particularly `Python`.

    qd_save(data, "myfile.qs2")
    data <- qd_read("myfile.qs2")

## Benchmarks

A summary across 4 datasets is presented below.

#### Single-threaded

Algorithm | Compression | Save Time (s) | Read Time (s)
--- | --- | --- | ---
qs2 | 7.96 | 13.4 | 50.4
qdata | 8.45 | 10.5 | 34.8
base::serialize | 1.1 | 8.87 | 51.4
saveRDS | 8.68 | 107 | 63.7
fst | 2.59 | 5.09 | 46.3
parquet | 8.29 | 20.3 | 38.4
qs (legacy) | 7.97 | 9.13 | 48.1

#### Multi-threaded (8 threads)

Algorithm | Compression | Save Time (s) | Read Time (s)
--- | --- | --- | ---
qs2 | 7.96 | 3.79 | 48.1
qdata | 8.45 | 1.98 | 33.1
fst | 2.59 | 5.05 | 46.6
parquet | 8.29 | 20.2 | 37.0
qs (legacy) | 7.97 | 3.21 | 52.0

- `qs2`, `qdata` and `qs` with `compress_level = 3`
- `parquet` via the `arrow` package using zstd `compression_level = 3`
- `base::serialize` with `ascii = FALSE` and `xdr = FALSE`

Datasets used

- `1000 genomes non-coding VCF` 1000 genomes non-coding variants (2743 MB)
- `B-cell data` B-cell mouse data, Greiff 2017 (1057 MB)
- `IP location` IPV4 range data with location information (198 MB)
- `Netflix movie ratings` Netflix ML prediction dataset (571 MB)

These datasets are openly licensed and represent a combination of numeric and text data across multiple domains. See `inst/analysis/datasets.R` on Github.