By Jim Crist
*This post is reprinted with permission from Jim Crist’s blog. The original post can be found here.
In this post, I introduce Skein, a new tool and library for deploying applications on Apache YARN. I provide background on why this work was necessary, and demonstrate deploying a simple Python application on a YARN cluster.
Apache YARN is the resource management and job scheduling framework native to Hadoop clusters. It is responsible for scheduling applications on the cluster (deciding where and when an application gets the resources it requested) and provisioning these resources in a secure and robust way.
Many data-processing frameworks (e.g. Spark, Flink, Storm, etc.) support YARN as a deployment option. As a contributor to Dask, I sought to improve our deployment support for YARN. This proved difficult for several reasons:
- YARN is a JVM-only framework, and requires a non-trivial amount of Java to get things working (for example, Spark’s YARN support is ~6000 lines of Scala). Dask is written in Python.
- Applications that deploy on YARN need to distribute their resources with them. For Java applications this is straightforward – just bundle everything into a JAR and you’re done. With Python things aren’t so easy.
- YARN security (if enabled) uses Kerberos, which can be tricky to support properly. Kerberos is so difficult to get right that one of the core Hadoop developers wrote a book comparing it to the horrors found in H.P. Lovecraft novels. This, coupled with the myriad configuration options YARN supports, can make testing applications difficult.
I’m fairly happy with the set of tools we’ve developed to solve these problems. The remainder of this post discusses them in detail.
Skein: Easy Deployment on YARN
Skein is a declarative tool for deploying applications on YARN. Users write application specifications either in YAML or using the native Python API, and Skein handles deploying and managing them on YARN.
- Skein applications are written declaratively using a specification reminiscent of docker-compose. While YARN is extremely flexible, Skein is opinionated about how an application should be structured. Sane defaults and reduced options help simplify the user API and greatly reduce the amount of code needed to deploy on YARN.
- Every Skein application contains a key-value store running on the application master. This provides a way for containers to share runtime configuration parameters (e.g., dynamically chosen addresses and ports), as well as coordinate state between containers.
- Skein applications are dynamic. Containers can be started and stopped at runtime, allowing for services to scale to your needs.
- Skein was designed “API first,” meaning both the Python API and CLI are first-class-citizens, and should (hopefully) feel natural and intuitive (if you find any rough edges, please file an issue).
- Skein contains two (unfortunately necessary-ish) Java processes written as gRPC services. This provides a clear separation between the application language and Java, and means that other language bindings besides Python are possible, allowing other languages to take advantage of this work.
- Skein is tested on multiple Hadoop configurations, including both simple and kerberossecurity, to help ensure support across all clusters.
Example: Echo Server
To illustrate the intended workflow, we’ll implement a simple echo server and client, and use Skein to deploy on YARN.
The full code for this example can be found here.
The Echo Server
Since the server could be run on any machine, we may not be sure what ports are available on that machine, or the host address of the machine as seen from the edge node. To handle this we start the server on a dynamic port, and then determine the hostname and port at runtime.
# Setup the server with a dynamically chosen port loop = asyncio.get_event_loop() coro = asyncio.start_server(handle_echo, '0.0.0.0', 0, loop=loop) server = loop.run_until_complete(coro) # Determine the dynamically chosen address host = socket.gethostbyname(socket.gethostname()) port = server.sockets.getsockname() address = '%s:%d' % (host, port)
To communicate this dynamic address back to the client, we store the address in the key-value store. To allow scaling up to multiple server instances (a bit contrived for this example) we append the server’s container_id to a fixed prefix (‘address.’) to ensure a unique key.
# Form a unique key to store the address using the current container id key = 'address.%s' % skein.properties.container_id
We then store the server address in the key-value store. Note that we set the current container_id as the owner of the key. This makes use of Skein’s key-value store ownership model. When the server’s container exits (whether successfully or due to failure), the key will be deleted. This helps ensure that when servers shut down, the address is no longer available to the echo client.
# Connect to the application master app = skein.ApplicationClient.from_current() # The key-value store only accepts bytes as values value = address.encode() # Store the server address in the key-value store, assigning the current # container as the owner of the key. This ensures that the key is deleted if # the container exits. app.kv.put(key, value, owner=skein.properties.container_id)
The remainder of the echo server implementation is generic asyncio operations – providing a handler, starting up the server, and running the event loop until shutdown.
The Echo Client
When using either the CLI or the Python API, most operations require an application id. This is a unique identifier for your application in YARN, and is used both by Skein and by external tools (for example, the yarn CLI command). In our example echo-client here we provide the application id via the command-line, and then use it to connect to the application.
# Get the application id from the command-line args app_id = sys.argv # Connect to the application app = skein.Client().connect(app_id)
Before we can send a message to the echo server, we first need to get its address. This again is done through the key-value store. However, instead of getting the address of a single echo server, we’ll loop through all registered addresses and message each of them. To do this we use the get_prefix method to get all keys that start with address..
async def echo_all(app, message): """Send and recieve a message from all running echo servers""" # Loop through all registered server addresses for address in app.kv.get_prefix('address.').values(): # Parse the host and port from the stored address host, port = address.decode().split(':') port = int(port) # Send the message to the echo server await tcp_echo_client(message, loop, host, port)
The remainder of the client implementation is generic – provide a async function to message each server, start the event loop, and run until all futures have completed.
Packaging the Python Environment
Skein doesn’t mandate a specific way of distributing application files/executables. File resources may already exist on every node, or may need to be distributed with the application. For Python applications, one way of handling this is to use the conda package manager to create a Python environment, and conda-pack to package that environment for distribution. This is what we’ll do here.
# Create a new environment with all dependencies $ conda create -y -n demo -c conda-forge python skein conda-pack ... # Activate the environment $ conda activate demo # Package the environment into environment.tar.gz $ conda-pack -o environment.tar.gz Collecting packages... Packing environment at '/home/jcrist/miniconda/envs/demo' to 'environment.tar.gz' [########################################] | 100% Completed | 16.6s # See the size of the output environment $ du -h environment.tar.gz 102M environment.tar.gz
During YARN Resource Localization this environment can then be unpacked and linked as a directory in every container.
For more information on file distribution in Skein, see the distributing files docs.
The Application Specification
With a completed server and client implementation, we now need to write the application specification. We’ll only make use of a few of the specification fields here; the full schema can be found in the specification docs.
The echo server specification can be found here, and is also duplicated below:
name: echoserver services: server: resources: vcores: 1 memory: 256 files: # A packaged conda environment to be distributed with the # application. During YARN resource localization this will be # automatically unpacked into the directory ``environment``. environment: environment.tar.gz # The server implementation. server.py: server.py commands: # Activate the conda environment - source environment/bin/activate # Start the server - python server.py
We define a single service server, and specify that each instance needs one virtual-core (usually equal to one CPU, cluster specific) and 256 MB of memory. For file resources, we specify the packaged Conda environment, as well as the server script. These will be mapped to ./environment/ and ./server.py in the container environment respectively. Finally we provide a list of commands to run to start the service. For some services this may be more complicated, but here it’s just activating the packaged Conda environment and running the server script.
Running the Application
# Start the application, and store the application id as APPID $ APPID=`skein application submit spec.yaml`
This validates the specification, uploads any necessary file resources to HDFS, and then submits the application to YARN. To check on the status of the application we can use the skein application status command:
# Check the application status $ skein application status $APPID APPLICATION_ID NAME STATE STATUS application_1534186866311_0009 echoserver RUNNING UNDEFINED
This shows 2 running containers: one for the application master, and one for our echo server. You can also navigate to the YARN Web-UI to check on the status of the application, based on the given application ID:
Trying out our echo client:
$ python client.py $APPID Connecting to server at 172.18.0.4:41846 Sent: 'Hello World!' Received: 'Hello World!'
And it works! We see communication with a single echo server; the dynamic address was found at 172.18.0.4:41846, and the message was sent and returned successfully.
Next, let’s try scaling up the number of echo servers using the skein container scale command:
# Scale to 4 server instances $ skein container scale $APPID --service server --number 4 # List all ``server`` containers for this application $ skein container ls $APPID --service server SERVICE ID STATE RUNTIME server server_0 RUNNING 2m server server_1 RUNNING 4s server server_2 RUNNING 3s server server_3 RUNNING 2s
Running the echo client again:
$ python client.py $APPID python client.py $APPID Connecting to server at 172.18.0.4:41846 Sent: 'Hello World!' Received: 'Hello World!' Connecting to server at 172.18.0.4:42547 Sent: 'Hello World!' Received: 'Hello World!' Connecting to server at 172.18.0.4:37295 Sent: 'Hello World!' Received: 'Hello World!' Connecting to server at 172.18.0.4:45087 Sent: 'Hello World!' Received: 'Hello World!'
This time we see communication with 4 different echo servers, one for each server instance.
Finally, we can shutdown our application using the skein application shutdown command:
# Shutdown the application $ skein application shutdown $APPID # Show the application was shutdown $ skein application status $APPID APPLICATION_ID NAME STATE STATUS application_1534186866311_0009 echoserver FINISHED SUCCEEDED
Note that if the python server.py command exited itself (perhaps via a shutdown endpoint on the server), then the manual shutdown command wouldn’t be necessary. This can be nice for things like batch processing jobs that have a distinct end, as they can then be submitted and run to completion without further human intervention.
To review, in the above example we
- Wrote a demo echo server and client.
- Added YARN deployment support using Skein
- Packaged the application dependencies using conda-pack
- Started, scaled, and stopped the echo server on YARN
All without writing a line of Java! Additionally, the Python code that was needed to support YARN deployment was relatively short. While this example was simplistic, we’ve found that real-world applications (such as the dask-yarn library) remain just as clear and concise (although this is more of a testament to Python than to Skein).
As mentioned at the top, due to the myriad configuration options, testing that an application works on all YARN clusters can be difficult. The YARN documentation is pretty adamant about this.
If you don’t test your YARN application in a secure Hadoop cluster, it won’t work.
To test Skein, an external tool hadoop-test-cluster was developed. This is a pip-installable tool for creating and working with tiny dockerized test clusters. Images with both simple and kerberos security configurations are available, and the tool is written to allow extending with further options.
Assuming you have docker already installed, using a kerberized test cluster is as easy as
# Start the cluster, mounting the local directory $ htcluster startup --image kerberos --mount .:workdir # Login $ htcluster login # Or run a command externally $ htcluster exec -- py.test mylibrary # Shutdown the cluster $ htcluster shutdown
Making the tests easy to run locally has eased development, and helps ensure Skein is robust across different Hadoop deployments.
Review and Future Work
We presented three new tools:
- Skein for easy deployment of applications on YARN.
- conda-pack for packaging the dependencies of these applications for distribution.
- hadoop-test-cluster for no-fuss testing of Hadoop applications locally.
Taken together, these tools help provide a workflow for bringing Python applications to a traditionally Java based ecosystem.
These tools are currently being used to deploy Dask on YARN in the dask-yarn libary. Similar work is being investigated for deploying Ray on YARN, as well as adding a non-Spark kernelspec to Jupyter Enterprise Gateway.
If this workflow looks useful to you, please feel free to reach out on github. Issues, pull-requests, and discussions are welcome!
This work was made possible by my employer, Anaconda Inc., as well as contributions and feedback from the larger Python community.