We regularly test Pydoop on Ubuntu only, but it should also work on other Linux distros and (possibly with some tweaking) on macOS. Other platforms are not supported. Additional requirements:

  • Python 2 or 3, including header files (e.g., apt-get install python-dev, yum install python-devel);

  • setuptools >= 3.3;

  • Hadoop >=2. We run regular CI tests with recent versions of Apache Hadoop 2.x and 3.x, but we expect Pydoop to also work with other Hadoop distributions. In particular, we have tested it on Amazon EMR (see Using Pydoop on Amazon EMR).

These are both build time and run time requirements. At build time you will also need a C++ compiler (e.g., apt-get install build-essential, yum install gcc gcc-c++) and a JDK (a JRE is not sufficient).


  • Avro Python implementation to enable Avro I/O (run time only). Note that the pip packages for Python 2 and 3 are named differently (respectively avro and avro-python3).

Environment Setup

To compile the HDFS extension module, Pydoop needs the path to the JDK installation. You can specify this via JAVA_HOME. For instance:

export JAVA_HOME="/usr/lib/jvm/java-8-openjdk-amd64"

Note that Pydoop is interested in the JDK home (where include/jni.h can be found), not the JRE home. Depending on your Java distribution and version, these can be different directories (usually the former being the latter’s parent). If JAVA_HOME is not found in the environment, Pydoop will try to locate the JDK via Java system properties.

Pydoop also includes some Java components, and it needs Hadoop libraries to be in the CLASSPATH in order to build them. This is done by calling hadoop classpath, so make sure that the hadoop executable is in the PATH. For instance, if Hadoop was installed by unpacking the tarball into /opt/hadoop:

export PATH="/opt/hadoop/bin:/opt/hadoop/sbin:${PATH}"

The Hadoop class path is also needed at run time by the HDFS extension. Again, since Pydoop picks it up from hadoop classpath, ensure that hadoop is in the PATH, as shown above. pydoop submit must also be able to call the hadoop executable.

Additionally, Pydoop needs to read part of the Hadoop configuration to adapt to specific scenarios. If HADOOP_CONF_DIR is in the environment, Pydoop will try to read the configuration from the corresponding location. As a fallback, Pydoop will also try ${HADOOP_HOME}/etc/hadoop (in the above example, HADOOP_HOME would be /opt/hadoop). If HADOOP_HOME is not defined, Pydoop will try to guess it from the hadoop executable (again, this will have to be in the PATH).

Building and Installing

Install prerequisites:

pip install --upgrade pip
pip install --upgrade -r requirements.txt

Install Pydoop via pip:

pip install pydoop

To install a pre-release (e.g., alpha, beta) add --pre:

pip install --pre pydoop

You can also install the latest development version from GitHub:

git clone
cd pydoop
python build
python install --skip-build

If possible, you should install Pydoop on all cluster nodes. Alternatively, it can be distributed, together with your MapReduce applications, via the Hadoop distributed cache (see Installation-free Usage).


  1. not found: try the following:

    export LD_LIBRARY_PATH="${JAVA_HOME}/jre/lib/amd64/server:${LD_LIBRARY_PATH}"
  2. non-standard include/lib directories: the setup script looks for includes and libraries in standard places – read for details. If some of the requirements are stored in different locations, you need to add them to the search path. Example:

    python build_ext -L/my/lib/path -I/my/include/path -R/my/lib/path
    python build
    python install --skip-build

    Alternatively, you can write a small setup.cfg file for distutils:


    and then run python install.

    Finally, you can achieve the same result by manipulating the environment. This is particularly useful in the case of automatic download and install with pip:

    export CPATH="/my/include/path:${CPATH}"
    export LD_LIBRARY_PATH="/my/lib/path:${LD_LIBRARY_PATH}"
    pip install pydoop

Testing your Installation

After Pydoop has been successfully installed, you might want to run unit tests and/or examples to verify that everything works fine. Here is a short list of things that can go wrong and how to fix them. For full details on running tests and examples, see .travis.yml.

  1. Incomplete configuration: make sure that Pydoop is able to find the hadoop executable and configuration directory (check the above section on environment setup).

  2. Cluster not ready: wait until all Hadoop daemons are up and HDFS exits from safe mode (hadoop dfsadmin -safemode wait).

  3. HDFS tests may fail if your NameNode’s hostname and port are non-standard. In this case, set the HDFS_HOST and HDFS_PORT environment variables accordingly.

  4. Some HDFS tests may fail if not run by the cluster superuser, in particular capacity, chown and used. To get superuser privileges, you can either start the cluster with your own user account or set the dfs.permissions.superusergroup Hadoop property to one of your unix groups (type groups at the command prompt to get the list of groups for your current user), then restart the HDFS daemons.

Using Pydoop on Amazon EMR

You can configure your EMR cluster to automatically install Pydoop on all nodes via Bootstrap Actions. The main difficulty is that Pydoop relies on Hadoop being installed and configured, even at compile time, so the bootstrap script needs to wait until EMR has finished setting it up:

while [ ! -f \${RM_PID} ] && [ ! -f \${NM_PID} ]; do
  sleep 2
export JAVA_HOME=/etc/alternatives/java_sdk
sudo -E pip install pydoop
echo "${PYDOOP_INSTALL_SCRIPT}" | tee -a /tmp/
chmod u+x /tmp/
/tmp/ >/tmp/pydoop_install.out 2>/tmp/pydoop_install.err &

The bootstrap script creates the actual installation script and calls it; the latter, in turn, waits for either the resource manager or the node manager to be up (i.e., for YARN to be up whether we are on the master or on a slave) before installing Pydoop. If you want to use Python 3, install version 3.6 with yum:

sudo yum -y install python36-devel python36-pip
sudo alternatives --set python /usr/bin/python3.6

The above instructions have been tested on emr-5.12.0.

Trying Pydoop without installing it

You can try Pydoop on a Docker container. The Dockerfile is in the distribution root directory:

docker build -t pydoop .
docker run --name pydoop -d pydoop

This spins up a single-node, pseudo-distributed Hadoop cluster with HDFS, YARN and a Job History server. Before attempting to use the container, wait a few seconds until all daemons are up and running.

You may want to expose some ports to the host, such as the ones used by the web interfaces. For instance:

docker run --name pydoop -p 8088:8088 -p 9870:9870 -p 19888:19888 -d pydoop

Refer to the Hadoop docs for a complete list of ports used by the various services.