From OPeNDAP Documentation

How to build & deploy dmr++ files for Hyrax

What? Why?

It is a fast and flexible way to serve data stored in S3. The dmr++ encodes the location of the data content residing in a binary data file/object (e.g., an hdf5 file) so that it can be directly accessed, without the need for an intermediate library API, by using the file with the location information. The binary data objects may be on a local filesystem, or they may reside across the web in something like an S3 bucket.

How Does It Work?

The dmr++ ingest software reads a data file (see note) and builds a document that holds all of the file's metadata (the names and types of all of the variables along with any other information bound to those variables). This information is stored in a document we call the Dataset Metadata Response (DMR). The dmr++ adds some extra information to this (that's the '++' part) about where each variable can be found and how to decode those values. The dmr++ is simply an special annotated DMR document.

This effectively decouples the annotated DMR (dmr++) from the location of the granule file itself. Since dmr++ files are typically significantly smaller than the source data granules they represent, they can be stored and moved for less expense. They also enable reading all of the file's metadata in one operation instead of the iterative process that many APIs require.

If the dmr++ contains references to the source granules location on the web, the location of the the dmr++ file itself does not matter.

Software that understands the dmr++ content can directly access the data values held in the source granule file, and it can do so without having to retrieve the entire file and work on it locally, even when the file is stored in a Web Object Store like S3.

If the granule file contains multiple variables and only a subset of them are needed, the dmr++ enabled software can retrieve just the bytes associated with the desired variables parts.

note: The OPeNDAP software currently supports HDF5 and NetCDF4. Other formats can be supported, such as zarr.

Supported Data Formats

The dmr++ software currently works with hdf5 and netcdf-4 files. (The netcdf-4 format is a subset of hdf5 so hdf5 tools are utilized for both.) Other formats like zarr, hdf4, netcdf-3 are not currently supported by the dmr++ software, but support could be added if requested.


The hdf5 data format is quite complex and many of the options and edge cases are not currently supported by the dmr++ software.

These limitations and how to quickly evaluate an hdf5 or netcdf-4 file for use with the dmr++ software are explained below.

hdf5 filters

The hdf5 format has several filter/compression options used for storing data values. The dmr++ software currently supports data that utilize the H5Z_FILTER_DEFLATE, H5Z_FILTER_SHUFFLE, and H5Z_FILTER_FLETCHER32 filters. You can find more on hdf5 filters here.

hdf5 storage layouts

The hdf5 format also uses a number of "storage layouts" that describe various structural organizations of the data values associated with a variable in the granule file. The dmr++ software currently supports data that utilize the H5D_COMPACT, H5D_CHUNKED, and H5D_CONTIGUOUS storage layouts. These are all of the storage layouts defined by the hdf5 library, but others can be added. You can find more on hdf5 storage layouts here.

Is my hdf5 or netcdf-4 file suitable for dmr++?

To determine the hdf5 filters, storage layouts, and chunking scheme used in an hdf5 or netcdf-4 file you can use the command:

h5dump -H -p <filename>

To get a human readable assessment of the file that will show the storage layouts, chunking structure, and the filters needed for each variable (aka DATASET in the hdf5 vocabulary) h5dump info can be found here.

h5dump example output:

$ h5dump -H -p chunked_gzipped_fourD.h5
HDF5 "chunked_gzipped_fourD.h5" {
GROUP "/" {
  DATASET "d_16_gzipped_chunks" {
     DATASPACE  SIMPLE { ( 40, 40, 40, 40 ) / ( 40, 40, 40, 40 ) }
        CHUNKED ( 20, 20, 20, 20 )
        SIZE 2863311 (3.576:1 COMPRESSION)
     FILTERS {

Is my netcdf file netcdf-3 or netcdf-4?

It is an unfortunate state of affairs that the file suffix ".nc" is the commonly used naming convention for both netcdf-3 and netcdf-4 files. You can use the command:

ncdump -k <filename> 

to determine if a netcdf file is either classic netcdf-3 (classic) or netcdf-4 (netCDF-4).

  • The netcdf library must be installed on the system upon which the command is issued.

You can learn more in the NetCDF documentation here.

Building dmr++ files with get_dmrpp

The application that builds the dmr++ files is a command line tool called get_dmrpp. It in turn utilizes other executables such as build_dmrpp, reduce_mdf, merge_dmrpp (which rely in turn on the hdf5_handler and the hdf5 library), along with a number of UNIX shell commands.

All of these components are install with each recent version of the Hyrax Data Server

You can see the get_dmrpp usage statement with the command: get_dmrpp -h

Using get_dmrpp

The way that get_dmrpp is invoked controls the way that the data are ultimately represented in the resulting dmr++ file(s).

The get_dmrpp application utilizes software from the Hyrax data server to produce the base DMR document which is used to construct the dmr++ file.

The Hyrax server has a long list of configuration options, several of which can substantially alter the the structural and semantic representation of the dataset as seen in the dmr++ files generated using these options.

Command line options

The command line switches provide a way to control the output of the tool. In addition to common options like verbose output or testing modes, the tool provides options to build extra (aka 'sidecar') data files that hold information needed for CF compliance if the original HDF5 data files lack that information (see the missing data section ). In addition, it is often desirable to build dmr++ files before the source data files are uploaded to a cloud store like S3. In this case, the URL to the data may not be known when the dmr++ is built. We support this by using placeholder/template strings in the dmr++ and which can then be replaced with the URL at runtime, when the dmr++ file is evaluated. See the '-u' and '-p' options below.


The fully qualified path to the top level data directory. Data files read by get_dmrpp must be in the directory tree rooted at this location and their names expressed as a path relative to this location. The value may not be set to / or /etc The default value is /tmp if a value is not provided. All the data files to be processed must be in this directory or one of its subdirectories. If get_dmrpp is being executed from same directory as the data then -b `pwd` or -b . works as well.
This option is used to specify the location of the binary data object. It’s value must be an http, https, or file (file://) URL. This URL will be injected into the dmr++ when it is constructed. If option -u is not used; then the template string OPeNDAP_DMRpp_DATA_ACCESS_URL will be used and the dmr++ will substitute a value at runtime.
The path to an alternate bes configuration file to use.
The path to an optional addendum configuration file which will be appended to the default BES configuration. Much like the site.conf file works for the full server deployment it will be loaded last and the settings there-in will have an override effect on the default configuration.


The name of the file to create.

Verbose Output Modes

Show help/usage page.
verbose mode, prints the intermediate DMR.
Very verbose mode, prints the DMR, the command and the configuration file used to build the DMR, and does not remove temporary files.
Just print the DMR that will be used to build the DMR++


Run ALL hyrax tests on the resulting dmr++ file and compare the responses the ones generated by the source hdf5 file.
Run hyrax inventory tests on the resulting dmr++ file and compare the responses the ones generated by the source hdf5 file.
Run hyrax value probe tests on the resulting dmr++ file and compare the responses the ones generated by the source hdf5 file.

Missing Data Creation

Build a 'sidecar' file that holds missing information needed for CF compliance (e.g., Latitude, Longitude and Time coordinate data).
Provide the URL for the Missing data sidecar file. If this is not given (but -M is), then a template value is used in the dmr++ file and a real URL is substituted at runtime.
The path to the file that contains missing variable information for sets of input data files that share common missing variables. The file will be created if it doesn't exist and the result may be used in subsequent invocations of get_dmrpp (using -r) to identify the missing variable file.

AWS Integration

The get_dmrpp application supports both S3 hosted granules as inputs, and uploading generated dmr++ files to an S3 bucket.

S3 Hosted granules are supported by default,
When the get_dmrpp application sees that the name of the input file is an S3 URL it will check to see if the AWS CLI is configured and if so get_dmrpp will attempt retrieve the granule and make a dmr++ utilizing whatever other options have been chosen.
Example: get_dmrpp -b `pwd` s3://bucket_name/granule_object_id
The -U command line parameter for get_dmrpp instructs the get_dmrpp application to upload the generated dmr++ file to S3, but only when the following conditions are met:
  • The name of the input file is an S3 URL
  • The AWS CLI has been configured with credentials that provide r+w permissions for the bucket referenced in the input file S3 URL.
  • The -U option has been specified.
If all three of the above are true then get_dmrpp will copy the retrieve the granule, create a dmr++ file from the granule, and copy the resulting dmr++ file (as defined by the -o option) to the source S3 bucket using the well known NGAP sidecar file naming convention: s3://bucket_name/granule_object_id.dmrpp
Example: get_dmrpp -U -o foo -b `pwd` s3://bucket_name/granule_object_id

hdf5_handler Configuration

Because get_dmrpp uses the hdf5_handler software to build the dmr++ the software must inject the hdf5_handler's configuration.

The default configuration is large, but any valued may be altered at runtime.

Here are some of the commonly manipulated configuration parameters with their default values:


Note to DAACs with existing Hyrax deployments.

If your group is already serving data with Hyrax and the data representations that are generated by your Hyrax server are satisfactory, then a careful inspection of the localized configuration, typically held in /etc/bes/site.conf, will help you determine what configuration state you may need to inject into get_dmrpp.

The H5.EnableCF option

Of particular importance is the H5.EnableCF option, which instructs the get_dmrpp tool to produce Climate Forecast convention (CF) compatible output based on metadata found in the granule file being processed.

Changing the value of H5.EnableCF from false to true will have (at least) two significant effects.

It will:

  • Cause get_dmrpp to attempt to make the dmr++ metadata CF compliant.
  • Remove Group hierarchies (if any) in the underlying data granule by flattening the Group hierarchy into the variable names.

By default get_dmrpp the H5.EnableCF option is set to false:

H5.EnableCF = false

There is a much more comprehensive discussion of this key feature, and others, in the HDF5 Handler section of the Appendix in the Hyrax Data Server Installation and Configuration Guide

Missing data, the CF conventions and hdf5

Many of the hdf5 files produced by NASA and others do not contain the domain coordinate data (such as latitude, longitude, time, etc.) as a collection of explicit values. Instead information contained in the dataset metadata can used to reproduce these values.

In order for a dataset to be Climate Forecast (CF) compatible it must contain these domain coordinate data values.

The Hyrax hdf5_handler software, utilized by the get_dmrpp application, can create this data from the dataset metadata. The get_dmrpp application places these generated data in a “sidecar” file for deployment with the source hdf5/netcdf-4 file.

Hyrax - Serving data using dmr++ files

There are three fundamental deployment scenarios for using dmr++ files to serve data with the Hyrax data server.

This can be simple categorized as follows: The dmr++ file(s) are XML files that contain a root dap4:Dataset element with a dmrpp:href attribute whose value is one of:

  1. An http(s):// URL referencing to the underlying granule files via http.
  2. A file:// URL that references the granule file on the local filesystem in a location that is inside the BES' data root tree.
  3. The template string OPeNDAP_DMRpp_DATA_ACCESS_URL

Each will discussed in turn below.

Note: By default Hyrax will automatically associate files whose name ends with ".dmrpp" with the dmr++ handler.

Using dmr++ with http(s) URLs

If the dmr++ files that you wish to serve contain dmrpp:href attributes whose values are http(s) URLs then there are 2+1 steps to serve the data:

  1. Place the dmr++ files on the local disk inside of the directory tree identified by the BES.Catalog.catalog.RootDirectory in the BES configuration
  2. Ensure that the Hyrax AllowedHosts list is configured to allow Hyrax to access those target URLs. This can be accomplished by adding new regex entires to the AllowedHosts list in /etc/bes/site.conf, creating that file as need be.
  3. If the data URLs require authentication to access then you'll need to configure Hyrax for that too.

Using dmr++ with file URLs

Using dmr++ files with locally held files can be useful for verifying that dmr++ functionality is working without relying on network access that may have data rate limits, authenticated access configuration, or security access constraints. Additionally, in many cases the dmr++ access to the locally held data may be significantly faster than through the native netcdf-4/hdf5 data handlers.

In order to use dmr++ files that contain file:// URLs:

  1. Place the dmr++ files on the local disk inside of the directory tree identified by the BES.Catalog.catalog.RootDirectory in the BES configuration
  2. Ensure the the dmr++ files contain only file:// URLs that refer to data granule files inside of the directory tree identified by the BES.Catalog.catalog.RootDirectory in the BES configuration.

Note: For Hyrax, a correctly formatted file URL must start with the protocol file:// followed by the full qualified path to the data granule, for example:


so the the completed URL will have three slashes after the first colon:


Using dmr++ with the template string. (NASA)

Another way to serve dmr++ files with Hyrax is to build the dmr++ files without valid URLs but with a template string that is replaced at runtime. If no target URL is supplied to get_drmpp at the time that the dmr++ is generated the template string: OPeNDAP_DMRpp_DATA_ACCESS_URL will added to the file in place of the URL. The at runtime it can be replaced withe the correct value.

Currently the only implementation of this is Hyrax's NGAP service which, when deployed in the NASA NGAP cloud, will accept "restified path" URLs that are defined as having a URL path component with two mandatory and one optional parameters:

MANDATORY: "/collections/UMM-C:{concept-id}"
OPTIONAL:  "/UMM-C:{ShortName} '.' UMM-C:{Version}"
MANDATORY: "/granules/UMM-G:{GranuleUR}"


When encountering this type of URL Hyrax will decompose it and use the content to formulate a query to the NASA CMR in order to retrieve the data access URL for the granule and for the dmr++ file. It then retrieves the dmr++ file and injects the data URL so that data access can proceed as described above.

More on the Restified Path can be found here,

Recipe: Building and testing dmr++ files

There are two recipes shown here, one using Hyrax docker containers and a second using the container that is part of the EOSDIS Cumulous task. Prerequisites:

  • Docker daemon running on a system that also supports a shell (the examples use bash in this section)

Recipe: Building dmr++ files using a Hyrax docker container.

  1. Acquire representative granule files for the collection you wish to import. Put them on the system that is running the Docker daemon. For this recipe we will assume that these files have been placed in the directory:
  2. Get the most up to date Hyrax docker image:
    docker pull opendap/hyrax:snapshot
  3. Start the docker container, mounting your data directory on to the docker image at /usr/share/hyrax:
    docker run -d -h hyrax -p 8080:8080 --volume /tmp/dmrpp:/usr/share/hyrax --name=hyrax opendap/hyrax:snapshot
  4. Get a first view of your data using get_dmrpp with it's default configuration.
    1. If you want you can build a dmr++ for an example "input_file" using a docker exec command:
      docker exec -it hyrax get_dmrpp -b /usr/share/hyrax -o /usr/share/hyrax/input_file.dmrpp -u "file:///usr/share/hyrax/input_file" "input_file"
    2. Or if you want more scripting flexibility you can login to the docker conainer to do the same:
      1. Login to the docker container:
        docker exec -it hyrax /bin/bash
      2. Change working dir to data dir:
        cd /usr/share/hyrax
      3. This sets the data directory to the current one (-b $(pwd)) and sets the data URL (-u) to the fully qualified path to the input file.
        get_dmrpp -b $(pwd) -o foo.dmrpp -u "file://"$(pwd)"/your_test_file" "your_test_file"
    Now that you have made a dmr++ file, use the running Hyrax server to view and test it by pointing your browser at:
  5. You can also batch process all of your test granules, if you want to go that route. This script assumes your ingestable data files end with '.h5'.
    The resulting dmr++ files should contain the correct file:// URLs and be correctly located so that they may be tested with the Hyrax service running in the docker instance.
# This script will write each output file as a sidecar file into 
# the same directory as its associated input granule data file.

# The target directory to search for data files 
echo "target_dir: ${target_dir}";

# Search the target_dir for names matching the regex \*.h5 
for infile in `find "${target_dir}" -name \*.h5`
    echo " Processing: ${infile}"

    infile_base=`basename "${infile}"`
    echo "infile_base: ${infile_base}"

    bes_dir=`dirname "${infile}"`
    echo "    bes_dir: ${bes_dir}"

    echo "     Output: ${outfile}"

    get_dmrpp -b "${bes_dir}" -o "${outfile}" -u "file://${infile}" "${infile_base}"

Remember that you can use the Hyrax server that is running in the docker container to view and test the dmr++ files you just created by pointing your browser at:


Testing and qualifying dmr++ files

In the previous section/step we created some initial dmr++ files using the default configuration. It is crucial to make sure that they provide the representation of the data that you and your users are expecting, and that they will work correctly with the Hyrax server. (See the following sections for details). If the generated dmr++ files do not match expectations then the default configuration of the get_dmrpp may need to be amended using the -s parameter. If the data are currently being served by your DAAC's on-prem team this is where understanding exactly what the localizations made to the configurations of the on-prem Hyrax instances deployed for the collection is important. These localization will probably need to be injected into get_drmpp in order to produce the correct data representation in the dmr++ files.

Flattening Groups

By default get_dmrpp will preserve and show group hierarchies. If this is not desired, say for CF-1.0 compatibility, then you can change this by creating a small amendment to get_dmrpp's default configuration. First create the amending configuration file:

echo "H5.EnableCF=true" > site.conf

Then, change the invocation of get_dmrpp in the above example by adding the -s switch:

get_dmrpp -s site.conf -b `pwd` -o "${dmrpp_file}" -u "file://"`pwd`"/${file}" "${file}"

And re-run the dmr++ production as shown above.

DAP representations

We have test and assurance procedures for DAP4 and DAP2 protocols below. Both are important. For legacy datasets the DAP2 request API is widely used by an existing client base and should continue to be supported. Since DAP4 subsumes DAP2 (but with somewhat different API semantics) It should be checked for legacy datasets as well. For more modern datasets that content DAP4 types such as Int64 that are not part of the DAP2 specification or implementations we will need to relying eliding the instances of unmapped types, or return an error when this is encountered.

# Test Constants:
# Granule URL

Inspect the dmr++ files

  1. Do the dmr++ files have the expected dmrpp:href URL(s)?
    head -2 ${GRANULE_FILE}.dmrpp

Check DAP4 DMR Response

Inspect ${gf_url}.dmrpp.dmr

  1. Get the document, save as foo.dmr:
    curl -L -o foo.dmr "${gf_url}.dmr"
  2. Is each variable's data type correct and as expected?
  3. Are the associated dimensions correct?

DAP4 Check binary data response

For a particular granule GRANULE_FILE and a particular variable VARIABLE_NAME (Where VARIABLE_NAME is a full qualified DAP4 name.):

curl -L -o dap4_subset_file "${gf_url}.dap?dap4.ce=VARIABLE_NAME"
curl -L -o dap4_subset_dmrpp "${gf_url}.dmrpp.dap?dap4.ce=VARIABLE_NAME"
cmp dap4_subset_file dap4_subset_dmrpp

DAP4 UI test

  • View and exercise the DAP4 Data Request Form {gf_url}.dmr.html

DAP2 Check DDS Response

  1. Inspect ${gf_url}.dds
    1. Is each variable's data type correct and as expected?
    2. Are the associated dimensions correct?
  2. Compare DMR++ DDS with granule file DDS.
    For a particular granule GRANULE_FILE and a particular variable VARIABLE_NAME (Where VARIABLE_NAME is a DAP2 name.):
    curl -L -o dap2_dds_file "${gf_url}.dds"
    curl -L -o dap2_dds_dmrpp "${gf_url}.dds"
    cmp dap2_dds_file dap2_dds_dmrpp

DAP2 Check binary data response

For a particular granule GRANULE_FILE and a particular variable VARIABLE_NAME (Where VARIABLE_NAME is a DAP2 name.):

curl -L -o dap2_subset_file "${gf_url}.dods?VARIABLE_NAME"
curl -L -o dap2_subset_dmrpp "${gf_url}.dmrpp.dods?VARIABLE_NAME"
cmp dap2_subset_file dap2_subset_dmrpp

Note: One might consider doing this with two or more variables.

DAP2 UI Test

  • View and exercise the DAP2 Data Request Form located here: {gf_url}.html
  • Try it in Panoply!
    • Open Panoply.
    • From the File menu select Open Remote Dataset...
    • Paste the {gf_url}.html into the resulting dialog box.