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Discount is a Spark-based tool for k-mer (genomic sequences of length k) counting and analysis. It is able to analyse large metagenomic-scale datasets while having a small memory footprint. It can be used as a standalone command line tool, but also as a general Spark library, including in interactive notebooks.

Discount aims to be an extremely scalable k-mer counter for Spark/HDFS. It has been tested on the Serratus dataset for a total of 5.59 trillion k-mers (5.59 x 10^12) with 1.57 trillion distinct k-mers.

This software includes Fastdoop by U.F. Petrillo et al [1]. We have also included compact universal hitting sets generated by PASHA [2].

For a detailed background and description, please see our paper on evenly distributed k-mer binning.


  1. Basics
  2. Advanced topics
  3. References


Compiling is optional. If you prefer not to compile Discount yourself, you can download a pre-built release from the Releases page.

Running Discount

Discount can run locally on your laptop, on a cluster, or in the cloud. It has been tested standalone with Spark 3.1.0, and also on AWS EMR and on Google Cloud Dataproc.

To run locally, download the Spark distribution (3.0 or later) (

Scripts to run Discount are provided for macOS and Linux. To run locally, copy to a new file called and edit the necessary variables in the file (at a minimum, set the path to your unpacked Spark distribution). This will be the script used to run Discount. It is also very helpful to point LOCAL_DIR to a fast drive, such as an SSD.

To run on AWS EMR, you may use In that case, change the example commands below to use that script instead, and insert your EMR cluster name as an additional first parameter when invoking. To run on Google Cloud Dataproc, please use instead.

K-mer counting

The following command produces a statistical summary of a dataset.

./ -k 55 /path/to/data.fastq stats

All example commands shown here accept multiple input files. The FASTQ and FASTA formats are supported, and must be uncompressed.

To submit an equivalent job to AWS EMR, after creating a cluster with id j-ABCDEF1234 and uploading the necessary files (the GCloud script works in the same way):

./ j-ABCDEF1234 -k 55 s3://my-data/path/to/data.fastq stats

As of version 2.3, minimizer sets for k >=19, m=10,11 are bundled with Discount and do not need to be specified explicitly. Advanced users may wish to override this (see the section on minimizers)

To generate a full counts table with k-mer sequences (in many cases larger than the input data), the count command may be used:

./ -k 55 /path/to/data.fastq count -o /path/to/output/dir --sequence

A new directory called /path/to/output/dir_counts (based on the location specified with -o) will be created for the output.

Usage of upper and lower bounds filtering, histogram generation, normalization of k-mer orientation, and other functions, may be seen in the online help:

./ --help

Chromosomes and very long sequences

If the input data contains sequences longer than 1,000,000 bp, you must use the --maxlen flag to specify the longest expected single sequence length. However, if the sequences in a FASTA file are very long (for example full chromosomes), it is essential to generate a FASTA index (.fai). Various tools can be used to do this, for example with SeqKit:

seqkit faidx myChromosomes.fasta

Discount will detect the presence of the myChromosomes.fasta.fai file and read the data efficiently. In this case, the parameter --maxlen is not necessary.

Repetitive or very large datasets

As of version 2.3, Discount contains two different counting methods, the "simple" method, which was the only method prior to this version, and the "pregrouped" method, which is essential for data that contains highly repetitive k-mers. Discount will try to pick the best method automatically, but we would advise users to do their own experiments. If Spark crashes with an exception about buffers being too large, the pregrouped method may also help. It can be forced with a command such as:

./ --method pregrouped -k 55 /path/to/data.fastq stats

Or, to force the simple method to be used:

./ --method simple -k 55 /path/to/data.fastq stats

While highly scalable, the pregrouped method may sometimes cause a slowdown overall (by requiring one additional shuffle), so it should not be used for datasets that do not need it. See the section on performance tuning.

Interactive notebooks

Discount is well suited for data analysis in interactive notebooks, and as of version 2.0 the API has been redesigned with this in mind. A demo notebook for Apache Zeppelin is included in the notebooks/ directory. It has been tested with Zeppelin 0.10 and Spark 3.1. To try this out, after downloading the Spark distribution, also download Zeppelin.
(The smaller "Netinst" distribution is sufficient, but an external Spark is necessary.) Then, load the notebook itself into Zeppelin through the browser to see example use cases and instructions.

Use as a library

You can add Discount as a dependency using the following syntax (SBT):

libraryDependencies += "com.jnpersson" %% "discount" % "2.3.0"

API docs for the current release are available here. Please note that Discount is still under heavy development and the API may change slightly even between minor versions.


  • Visiting http://localhost:4040 (if you run a standalone Spark cluster) in a browser will show progress details while Discount is running.

  • If you are running a local standalone Spark (everything in one process) then it is helpful to increase driver memory as much as possible (this can be configured in Pointing LOCAL_DIR to a fast drive for temporary data storage is also highly recommended.

  • The number of files generated in the output tables will correspond to the number of partitions Spark uses, which you can configure in the run scripts. However, we recommend configuring partitions for performance/memory usage (the default value of 200 is usually fine) and manually joining the files later if you need to.

Advanced topics


Discount counts k-mers by constructing super k-mers (supermers) and shuffling these into bins. Each bin corresponds to a minimizer, which is the minimal m-mer for some m < k in each k-mer, for some ordering of a minimizer set. The choice of minimizer set and ordering does not affect k-mer counting results, but can have a big effect on performance. By default, Discount will use internal minimizers, which are packaged into the jar from the resources/PASHA directory. These are universal hitting sets, available for k >= 19, m=9,10,11. By default, they will be ordered by sampled frequency in the dataset being analysed, prioritising uncommon minimizers over common ones.

We provide some additional minimizer sets at You can also generate your own set using PASHA (described below).

To manually select a minimizer set, it is possible to point Discount to a file containing a set, or to a directory containing minimizer sets. For example:

./ -m 10 --minimizers resources/PASHA/minimizers_55_10.txt -k 55 /path/to/data.fastq stats

In this case, minimizers have length 10 (m=10) and the supplied minimizer set will work for any k >= 55.

If you instead supply a directory, the best minimizer set in that directory will be chosen automatically, by looking for files with the name minimizers_{k}_{m}.txt:

./ -m 10 --minimizers resources/PASHA -k 55 /path/to/data.fastq stats

It is also possible (but less efficient ) to operate without a minimizer set, in which case all m-mers will become minimizers. This can be done using the --allMinimizers flag. Currently, this flag is must be used when k < 19 as we do not supply minimizer sets in that range:

./ -k 17 --allMinimizers /path/to/data.fastq stats

Generating a universal hitting set

For optimal performance, compact universal hitting sets (of m-mers) should be used as minimizer sets. They may be generated using the PASHA tool. Precomputed sets for many values of k and m may also be downloaded from the PASHA website. (Note that the PASHA authors use the symbols (k, L) instead of (m, k), which we use here. Their k corresponds to minimizer length, which we denote by m.)

A universal set generated for some pair of parameters (k, m) will also work for a larger k. However, the larger the gap, the greater the number of bins generated by Discount and the shorter the superkmers would be. This can negatively impact performance.

Computed sets must be combined with their corresponding decycling set (available at the link above), for example as follows:

cat PASHA11_30.txt decyc11.txt > minimizers_30_11.txt

This produces a set that is ready for use with Discount.

Evaluation of minimizer orderings

Discount can be used to evaluate the bin distributions generated by various minimizer orderings. See docs/ for details.

Performance tuning for large datasets

The philosophy of Discount is to achieve performance through a large number of small and evenly sized bins, which are grouped into a large number of modestly sized Spark partitions. This reduces memory pressure as well as CPU time for many of the algorithms that need to run. For most datasets, the default settings should be fine. However, for huge datasets or constrained environments, the pointers below may be helpful.

  1. Increase m. For very large datasets, with e.g. more than 1011 total k-mers, m=11 (or more) may be helpful. This would generate a larger number of bins (which would be smaller) by using a larger universal hitting set. However, if m is too large relative to the dataset, then a slowdown may be expected. As of version 2.3, we have tested up to m=13.
  2. Increase the number of partitions. This can be done in the run scripts. However, if the number is too large, shuffling will be slower, sometimes dramatically so.
  3. Increase the number of input splits by reducing the maximum split size. This affects the number of tasks in the hashing stage. This can also be done in the run scripts. The same caveat as above applies.

For very large datasets, it is helpful to understand where the difficulties come from. For a repetitive dataset, using --method pregrouped will have large benefits. On the other hand, for a highly complex dataset with many distinct k-mers, increasing m can help by spreading the k-mers into more bins. For some datasets, it may be necessary to use both of these techniques.

In general, it is helpful to monitor CPU usage to make sure that the job is not I/O bound (if it is well configured, CPU utilisation should be close to 100% on average). To help with I/O pressure, fast SSDs and/or different partition sizes may help.

If memory pressure is too high (high GC time), then assigning more memory or increasing m may help.

Compiling Discount

To compile the software, the SBT build tool ( is needed. Discount is by default compiled for Scala 2.12/Spark 3.1. An experimental Scala 2.13 branch is also available.

The command sbt assembly will compile the software and produce the necessary jar file in target/scala-2.12/Discount-assembly-x.x.x.jar. This will be a "fat" jar that also contains some necessary dependencies.

API documentation may be generated using the command sbt doc.


If you find Discount useful in your research, please cite our paper:

Johan Nyström-Persson, Gabriel Keeble-Gagnère, Niamat Zawad, Compact and evenly distributed k-mer binning for genomic sequences, Bioinformatics, 2021;, btab156,


Contributions are very welcome, for example in the form of bug reports, pull requests, or general suggestions.


  1. Petrillo, U. F., Roscigno, G., Cattaneo, G., & Giancarlo, R. (2017). FASTdoop: A versatile and efficient library for the input of FASTA and FASTQ files for MapReduce Hadoop bioinformatics applications. Bioinformatics, 33(10), 1575–1577.
  2. Ekim, al.(2020). A Randomized Parallel Algorithm for Efficiently Finding Near-Optimal Universal Hitting Sets.
    In R. Schwartz, editor, Research in Computational Molecular Biology, pages 37–53, Cham.Springer International Publishing