Skip to content
Switch branches/tags

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time


A library for manipulating bioinformatics sequencing formats in Apache Spark.

Build Status Maven Central Javadocs License: MIT


This code grew out of, and was heavily inspired by, Hadoop-BAM and Spark-BAM. Spark-BAM has shown that reading BAMs for Spark can be both more correct and more performant than the Hadoop-BAM implementation. Furthermore, all known users of Hadoop-BAM are using Spark, not MapReduce, as their processing engine so it is natural to target the Spark API, which gives us higher-level primitives than raw MR.

Benchmarks: Disq is faster and more accurate than Hadoop-BAM, and at least as fast as Spark-BAM.

Support Matrix

This table summarizes the current level of support for each feature across the different file formats. See discussion below for details on each feature.

Filesystems - Hadoop (r/w)
Filesystems - NIO (r)
Compression NA NA NA
Multiple input files
Sharded output
Indexes - read heuristic NA NA
Indexes - read .bai/.crai NA NA
Indexes - write .bai/.crai NA NA
Indexes - read .sbi NA NA NA
Indexes - write .sbi NA NA NA
Indexes - write .tbi NA NA NA
Ordering guarantees
Queryname sorted guarantees NA NA
Stringency NA
Testing - large files
Testing - samtools and bcftools


The following discusses the features provided by the library.


The library should be able to read and write BAM, CRAM, SAM, and VCF formats, at a minimum. More formats will be added over time, as needed.

Format records are represented by htsjdk types: SAMRecord (for BAM/CRAM/SAM) and VariantContext (for VCF).

Spark RDDs are used for the collection type.

Writing files will create new files or overwrite existing files without warning.


Two filesystem abstractions are supported: the Hadoop filesystem (HDFS, local, and others such as S3), and Java NIO filesystems (local, S3, GCS, etc).

Only one filesystem abstraction is used for each operation (unlike current Hadoop-BAM, which mixes the two, e.g. using Hadoop for bulk loading, and the HDFS NIO plugin for metadata operations). The choice of which to use (Hadoop vs. NIO) is set by the user. Roughly speaking, Hadoop is best for HDFS clusters (including those running in the cloud), and NIO is appropriate for cloud stores.

NIO is only supported for reading, since writing has more complex commit semantics and is out of scope. For writing to a cloud store use a Hadoop filesystem such as S3a or the GCS connector.


For BAM and CRAM, compression is a part of the file format, so it is necessarily supported. Compressed SAM files are not supported.

For reading VCF, support includes BGZF-compressed (.vcf.bgz or .vcf.gz) and gzip-compressed files (.vcf.gz).

For writing (compressed) VCF, only BGZF-compressed files can be written (gzip is not splittable so it is a mistake to write this format).

Multiple input files

For reading BAM/CRAM/SAM and VCF, multiple files may be read in one operation. A path may either be a an individual file, or a directory. Directories are not processed recursively, so only the files in the directory are processed, and it is an error for the directory to contain subdirectories.

Directories must contain files with the same header. If files have different headers then the effect of reading the files is undefined.

File types may not be mixed: it is an error to process BAM and CRAM files, for example, in one operation.

Sharded output

For writing BAM/CRAM/SAM and VCF, by default whole single files are written, but the output files may optionally be sharded, for efficiency. A sharded BAM file has the following directory structure:

└── output.bam.sharded/
    ├── part-00000.bam
    ├── part-00001.bam
    ├── ...
    └── part-00009.bam

Note that output.bam.sharded is a directory and contains complete BAM files (with header and terminator), and a .bam extension. A similar structure is used for the other formats.

Sharded files are treated as a single file for the purposes of reading multiple inputs.


For reading BAM, if there is no index, then the file is split using a heuristic algorithm to find record boundaries. Otherwise, if a .sbi index file is found it is used to find splits. A regular .bai index file may optionally be used to find splits, although it does not protect against regions with very high coverage (oversampling) since it specifies genomic regions, not file regions.

For writing BAM, .bai indexes may optionally be created at the same time as writing the BAM file. Indexes are not created by default, and have to be explicitly enabled.

For reading CRAM, if there is a .crai index then it is used to find record boundaries. Otherwise, the whole CRAM file is efficiently scanned to read container headers so that record boundaries can be found.

SAM files and VCF files are split using the usual Hadoop file splitting implementation for finding text records.

For writing BGZF-compressed VCF, a tabix (.tbi) index may optionally be created at the same time as writing the VCF file. Indexes are not written by default, and have to be explicitly enabled.

Writing .crai indexes is not possible at present. These can be generated using existing tools, such as htsjdk/GATK/ADAM.


For reading BAM/CRAM/SAM and VCF, a range of intervals may be specified to restrict the records that are loaded. Intervals are specified using htsjdk's Interval class.

For reading BAM/CRAM/SAM, when intervals are specified it is also possible to load unplaced unmapped reads if desired.

Ordering Guarantees

This library does not do any sorting, so it is up to the user to understand what is being read or written. Furthermore, no checks are carried out to ensure that the records being read or written are consistent with the header. E.g. it is possible to write a BAM file whose header says it is queryname sorted, when in fact its records are unsorted.

For reading a single BAM/SAM file, the records in the RDD are ordered by the BAM sort order header (unknown, unsorted, queryname, or coordinate). For reading multiple BAM/SAM files, the records in the RDD are ordered within each file, and files are ordered lexicographically. If the BAM/SAM files were written using a single invocation of the write method in this library for an RDD that was sorted, then the RDD that is read back will retain the sort order, and the sort order honors the sort order in the header of any of the files (the headers will all be the same). If the BAM/SAM files are not globally sorted, then they should be treated as unknown or unsorted.

CRAM and VCF files are always sorted by position. If reading multiple files that are not globally sorted, then they should be treated as unsorted, and should not be written out unless they are sorted first.

Queryname Sorted Guarantees

For reading queryname sorted BAM or SAM, paired reads must never be split across partitions. This allows applications to be sure that a single task will always be able to process read pairs together.

CRAM files must be coordinate sorted (not queryname sorted), so this provision is not applicable.


For reading BAM/CRAM/SAM, the stringency settings from htsjdk are supported.


All read and write paths are tested on real files from the field (multi-GB in size).

Samtools and Bcftools are used to verify that files written with this library can be read successfully.


Add the dependency from Maven Central

// First get a Spark context
JavaSparkContext jsc = ...

// Read a BAM file into a Spark RDD
JavaRDD<SAMRecord> reads = HtsjdkReadsRddStorage.makeDefault(jsc)

Implementation notes for developers

The library requires Java 8 or later. The surface area of the API has deliberately been kept small by exposing only a few public classes in the top-level disq package, and putting all the private implementation classes in impl packages. Users should not access anything in impl. While it is not possible to enforce this, anything in impl is not subject to release versioning rules (i.e. it could be removed in any release).

The naming of classes in the public API reflects the fact that they work with htsjdk and Spark RDDs: e.g. HtsjdkReadsRddStorage. In the future it will be possible to have alternative models that are not htsjdk (or are a different version), or that use Spark datasets or dataframes.

As a general rule, any code that does not have a Spark or Hadoop dependency, or does not have a "distributed" flavor belongs in htsjdk. This rule may be broken during a transition period while the code is being moved to htsjdk.

Interoperability tests

Some tests use Samtools and Bcftools to check that files created with this library are readable with them (and htsjdk).

To run the tests first install Samtools and Bcftools. (Version 1.4 of samtools, and 1.3 of bcftools were used. The latter was needed to avoid this bug.)

Then, when running tests, specify where the binaries are on your system as follows:

mvn test \
    -Ddisq.samtools.bin=/path/to/bin/samtools \

Real world file testing

The files used for unit testing are small and used to test particular features of the library. It is also valuable to run tests against more realistic files, which tend to be a lot larger.

RealWorldFilesIT is an integration test that will recursively find files that it can parse in a given directory, and count the number of records in each of them. The counts are compared against the equivalent htsjdk code, as well as Samtools and Bcftools commands (if configured as above).

The following tests all the GATK 'large' files (BAM, CRAM, VCF) that have been copied to the local filesystem beforehand:

mvn verify \$GATK_HOME/src/test/resources/large \$GATK_HOME/src/test/resources/large/human_g1k_v37.20.21.fasta \
    -Ddisq.samtools.bin=$SAMTOOLS_HOME/bin/samtools \

Performing a release

To perform a release you need commit rights to the repository, a key in the KEYS file, and an account on with permission to write to the disq-bio Maven group ID. You also need update your Maven ~/.m2/settings.xml file to include your Sonatype credentials as described here.

To perform a snapshot deployment run

mvn clean deploy -Prelease

If you get the error gpg: signing failed: Inappropriate ioctl for device then run this first:

export GPG_TTY=$(tty)

If your GitHub account is using two factor authentication then you may need to use a token when prompted for a password. See this article.

To perform a release deployment run

mvn release:clean release:prepare
mvn release:perform

You will be prompted for the version number (note that the default may not be correct), and the release tag, which should be of the form disq-x.y.z.

Inspect and release the deployed artifacts manually by logging into Find the repository you just created, under "Staging Repositories", then "Close" and "Release" it.

You should see the artifacts appear in the central repository in around 10 minutes.


A library for manipulating bioinformatics sequencing formats in Apache Spark




Code of conduct


No packages published