Getting Started with the DAOS Hadoop Filesystem

Here, we describe the steps required to build and deploy the DAOS Hadoop filesystem, and the configurations to access DAOS in Spark. We assume DAOS servers and agents have already been deployed in the environment; otherwise, they can be deployed by following the DAOS installation guide.

Build DAOS Hadoop Filesystem

The DAOS Java and Hadoop filesystem implementation have been merged into the DAOS repository. Below are the steps to build the Java jar files for the DAOS Java and DAOS Hadoop filesystem. These jar files are required when running Spark. You can ignore this section if you already have the pre-built jars.

$ git clone
$ cd daos
$ git checkout <desired branch or commit>
## assume DAOS is built and installed to <daos_install> directory
$ cd src/client/java
$ mvn clean package -DskipITs -Ddaos.install.path=<daos_install>

After build, the package daos-java--assemble.tgz will be available under distribution/target.

Deploy DAOS Hadoop Filesystem

After unzipping daos-java-<version>-assemble.tgz, you will get the following files.

  • daos-java-<version>.jar and hadoop-daos-<version>.jar These files need to be deployed on every compute node that runs Spark. Place them in a directory, e.g., $SPARK_HOME/jars, that are accessible to all the nodes or copy them to every node.

  • daos-site-example.xml The file contains DAOS configuration and needs to be properly configured with the DAOS pool UUID, container UUID, and a few other settings. Rename it to daos-site.xml and place it in Spark's conf ($SPARK_HOME/conf) directory.

Configure Spark to use DAOS

  • To access DAOS Hadoop filesystem in Spark, add the jar files to the classpath of the Spark executor and driver. This can be configured in Spark's configuration file spark-defaults.conf.
spark.executor.extraClassPath   /path/to/daos-java-<version>.jar:/path/to/hadoop-daos-<version>.jar
spark.driver.extraClassPath     /path/to/daos-java-<version>.jar:/path/to/hadoop-daos-<version>.jar
  • Next, export all DAOS related env variables and the following env variable in This enables signal chaining in JVM to better interoperate with DAOS native code that installs its own signal handlers. It ensures that signal calls are intercepted so that they do not actually replace the JVM's signal handlers if the handlers conflict with those already installed by the JVM. Instead, these calls save the new signal handlers, or "chain" them behind the JVM-installed handlers. Later, when any of these signals are raised and found not to be targeted at the JVM, the DAOS's handlers are invoked.
$ export LD_PRELOAD=<YOUR JDK HOME>/jre/lib/amd64/
  • Configure daos-site.xml. If the DAOS pool and container have not been created, we can use the following command to create them and get the pool UUID, container UUID, and service replicas.
$ dmg pool create --scm-size=<scm size> --nvme-size=<nvme size>
$ daos cont create --pool <pool UUID> --svc <service replicas> --type POSIX

After that, configure daos-site.xml with the pool and container created.

    <value>your pool UUID</value>
    <description>UUID of DAOS pool</description>
    <value>your container UUID</value>
    <description>UUID of DAOS container created with "--type posix"</description>
    <value>your pool service replicas</value>
    <description>service list separated by ":" if more than one service</description>

The default pool and container are configured by fs.daos.pool.uuid and fs.daos.container.uuid. The default DAOS filesystem can be accessed by URI daos://default:1 in Spark. In HDFS, the URI is composed by a master host name (or IP address) and a port for example hdfs://:8020. In DAOS, we don't use host name and port to connect, instead we use pool UUID and container UUID to specify the DFS filesystem. We do not put the UUIDs in URI as UUID is not a valid port number. Instead, the hostname default maps to the default pool configured by fs.daos.pool.uuid and the port 1 maps to the default container configured by fs.daos.container.uuid.

It is also possible to configure multiple pools and containers in the daos-site.xml and use different URI to access them. For example, to access another container in the default pool using URI daos://default:2, we can configure the container UUID in c2.fs.daos.container.uuid. To access another pool and container using daos://pool1:3, we can configure the pool UUID in pool1.fs.daos.pool.uuid and container UUID in c3.fs.daos.container.uuid. See examples,

"daos://default:1" reads values of "fs.daos.pool.uuid" and "fs.daos.container.uuid"
daos://default:2" reads values of "fs.daos.pool.uuid" and "c2.fs.daos.container.uuid"
"daos://pool1:3" reads values of "pool1.fs.daos.pool.uuid" and "c3.fs.daos.container.uuid"

Different URIs represent different DAOS filesystem and they can also be configured with different settings like the read buffer size, etc. For example, to configure the filesystem represented by daos://default:2, we use property name prefixed with c2, i.e., *c2.fs.daos.*. To configure the filesystem represented by daos://pool1:3, we use property name prefixed with pool1c3, i.e., *pool1c3.fs.daos.*. If no specific configurations are set, they fall back to the configuration set for the default pool and container started with *fs.daos*..

One tricky example is to access same DAOS filesystem with two Hadoop FileSystem instances. One instance is configured with preload enabled in the daos-site.xml. The other instance is preload disabled. With above design, you can use two different URIs, daos://default:1 and daos://default:2. In the daos-site.xml, you can set fs.daos.container.uuid and c2.fs.daos.container.uuid to same the container UUID. Then set fs.daos.preload.size to a value greater than 0 and c2.fs.daos.preload.size to 0.

Access DAOS in Spark

All Spark APIs that work with the Hadoop filesystem will work with DAOS. We use the daos:// URI to access files stored in DAOS. For example, to read people.json file from the root directory of DAOS filesystem, we can use the following pySpark code:

df ="daos://default:1/people.json")