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 and Hadoop. We assume that the DAOS servers and agents have already been deployed in the environment. Otherwise, they can be deployed by following the DAOS Installation Guide.
!!!note DAOS support for Spark and Hadoop is not available in DAOS 1.0. It is targeted for the DAOS 1.2 release.
Build DAOS Hadoop Filesystem¶
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 and Hadoop. You can ignore this section if you already have the pre-built jars.
$ git clone https://github.com/daos-stack/daos.git $ 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-<version>-assemble.tgz will be available
Deploy DAOS Hadoop Filesystem¶
daos-java-<version>-assemble.tgz, you will get the
They need to be deployed on every compute node that runs Spark or Hadoop.
Place them in a directory, e.g.,
$SPARK_HOME/jars for Spark and
$HADOOP_HOME/share/hadoop/common/lib for Hadoop, which is accessible to all
the nodes or copy them to every node.
You have two choices, with or without UNS path, to
construct DAOS URI. If you choose the second choice, you have to copy the file
to your application config directory, e.g.,
$SPARK_HOME/conf for Spark and
$HADOOP_HOME/etc/hadoop for Hadoop. Then do some proper configuration and
rename it to
daos-site.xml. For the second choice,
daos-site.xml is optional.
See next section for details.
Configure DAOS Hadoop FileSystem¶
DAOS Environment Variable¶
Export all DAOS related env variables and the following env variable in
your application, e.g.,
spark-env.sh for Spark and
hadoop-env.sh for Hadoop.
The following env 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/libjsig.so
DAOS FileSystem binds to schema "daos". DAOS URIs are in the format of "daos://[authority]//[path]". Both authority and path are optional. There are two types of DAOS URIs, with and without DAOS UNS path depending on where you want the DAOS Filesystem to get initialized and configured.
With DAOS UNS Path¶
The simple form of URI is "daos:///\<your uns path>[/sub path]".
"\<your path>" is your OS file path created with the
daos command or Java DAOS UNS
DaosUns.create(). The "[sub path]" is optional. You can create the UNS
path with below command.
$ daos cont create --pool <pool UUID> --svc <svc list> -path <your path> --type=POSIX
$ java -Dpath="your path" -Dpool_id="your pool uuid" -cp ./daos-java-1.1.0-shaded.jar io.daos.dfs.DaosUns create
After creation, you can use below command to see what DAOS properties set to the path.
$ getfattr -d -m - <your path>
Without DAOS UNS Path¶
The simple form of URI is "daos:///[sub path]". Please check description of
for how to configure filesystem. In this way, preferred configurations are in
daos-site.xml which should be put in right place, e.g., Java classpath, and
loadable by Hadoop DAOS FileSystem.
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.
<configuration> ... <property> <name>fs.daos.pool.uuid</name> <value>your pool UUID</value> <description>UUID of DAOS pool</description> </property> <property> <name>fs.daos.container.uuid</name> <value>your container UUID</value> <description>UUID of DAOS container created with "--type posix"</description> </property> <property> <name>fs.daos.pool.svc</name> <value>your pool service replicas</value> <description>service list separated by ":" if more than one service</description> </property> ... </configuration>
You may want to connect to two DAOS servers or two DFS instances mounted to different containers in one DAOS server from same JVM. Then, you need to add authority to your URI to make it unique since Hadoop caches filesystem instance keyed by "schema + authority" in global (JVM). It applies to the both types of URIs described above.
Tune More Configurations¶
If your DAOS URI is the mapped UUIDs, you can follow descriptions of each
config item in daos-site-example.xml
to set your own values in loadable
If your DAOS URI is the UNS path, your configurations, except those set by DAOS
UNS creation, in
daos-site.xml can still be effective. To make configuration
source consistent, an alternative to the configuration file
daos-site.xml is to
set all configurations to the UNS path. You put the configs to the same UNS
path with below command.
# install attr package if get "command not found" error $ setfattr -n user.daos.hadoop -v "fs.daos.server.group=daos_server:fs.daos.pool.svc=0" <your path>
$ java -Dpath="your path" -Dattr=user.daos.hadoop -Dvalue="fs.daos.server.group=daos_server:fs.daos.pool.svc=0" -cp ./daos-java-1.1.0-shaded.jar io.daos.dfs.DaosUns setappinfo
For the "value" property, you need to follow pattern, key1=value1:key2=value2.. .. And key should be from daos-site-example.xml. If value contains characters of '=' or ':', you need to escape the value with below command.
$ java -Dop=escape-app-value -Dinput="daos_server:1=2" -cp ./daos-java-1.1.0-shaded.jar io.daos.dfs.DaosUns util
You'll get escaped value, "daos_server\u003a1\u003d2", for "daos_server:1=2".
If you configure the same property in both
daos-site.xml and UNS path, the
daos-sitem.xml takes priority. If user set Hadoop configuration before
initializing Hadoop DAOS FileSystem, the user's configuration takes priority.
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
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
Access DAOS in Spark¶
All Spark APIs that work with the Hadoop filesystem will work with DAOS. We use
daos:// URI to access files stored in DAOS. For example, to read the
people.json file from the root directory of DAOS filesystem, we can use the
following pySpark code:
df = spark.read.json("daos://default:1/people.json")
Configure Hadoop to Use DAOS¶
$HADOOP_HOME/etc/hadoop/core-site.xml to change fs.defaultFS to
daos://default:1 or "daos://uns/\<your path>". Then append below configuration
to this file and
<property> <name>fs.AbstractFileSystem.daos.impl</name> <value>io.daos.fs.hadoop.DaosAbsFsImpl</value> </property>
DAOS has no data locality since it is remote storage. You need to add below
configuration to the scheduler configuration file, like
<property> <name>yarn.scheduler.capacity.node-locality-delay</name> <value>-1</value> </property>
capacity-scheduler.xml to other nodes.
Access DAOS in Hadoop¶
If everything goes well, you should see “/user” directory being listed after issuing below command.
$ hadoop fs -ls /
You can also play around with other Hadoop commands, like -copyFromLocal and
-copyToLocal. You can also start Yarn and run some mapreduce jobs on Yarn. Just
make sure you have DAOS URI,
daos://default:1/, set correctly in your job.
If you use Omni-path PSM2 provider in DAOS, you'll get connection issue in Yarn container due to PSM2 resource not being released properly in time.