In trying to get Ancho's wheels turning again, I've spent some time in the last week trying to figure out how to translate my concepts into running software. As you may recall, one of Ancho's design goals is to allow fairly large models to run faster by running them in parallel on a cluster.
These are all just my perceptions, so if you're reading this and I got something completely wrong, please let me know.
Mesos is a project of the Berkeley AMPLab, part of their so-called BDAS (pronounced "bad-ass") stack. Its purpose is to allow multiple compute scheduling frameworks to share the same computing cluster without having to statically partition nodes to specific frameworks. The other alternative here was YARN, which is the resource manager used in most Hadoop distributions. I read some academic papers on Mesos, as well as for Google's Omega scheduler.
The Omega paper helped me understand the architectural differences between Mesos and YARN. Mesos listens for announcements that computing slots (units CPU & RAM) are available, offers them to running frameworks based on policies you can set, and the frameworks can accept or reject the available resources. This has some drawbacks, as identified in the Omega paper, but it generally works better in multi-framework environments than YARN's philosophy of centralizing control over what runs where. It's easier in practice for each framework to figure out what it wants or doesn't want.
So, I've chosen Mesos as my preferred underlying resource manager.
Hadoop Ecosystem vs. BDAS
The choices here were basically the Hadoop ecosystem (MapReduce, HDFS, Hive, Pig, Mahout, etc) or something else. In all honesty, I have always had trouble understanding how the various parts of Hadoop fit together. There are so many moving parts that I would want to use a curated distribution (Cloudera, Hortonworks or MapR) and Hortonworks is the only distro that's fully open.
The Hadoop distributions are all YARN-based. Mesos makes more sense to me conceptually, and if Ancho will need to be a framework running on top of the resource manager, I need to program against something I understand. Also, Mesos explicitly supports tasks that run in Docker-based executors. I like Docker, I feel like I understand what it's doing, and I want to use it for sandboxing user-written model code, so this has a lot of appeal.
BDAS isn't quite as neatly packaged, but it also seems like it's easier to put together just the components that I need without needing a Master's degree in compiler theory.
A long time ago I decided that of all the NoSQL databases, I prefer Cassandra. As above, I feel like I understand what it's doing, and its data model fits well with what Ancho models will generate and then need to analyze. Cassandra is essentially a very large multi-level hashtable. It makes more sense to me than trying to use something with file semantics like HDFS, even if database semantics are added on top of it with a layer like Hive. Also, it seems like it should have better performance for my use cases.
Apache Spark seems like the way to go on this, for several reasons. It's fast, it's directly integrated with Mesos, it can read and write directly to Cassandra, and its Python API is a first-class Spark citizen. I feel like I understand the "Resilient Distributed Dataset" (RDD) abstraction that forms the core of Spark.
On top of Spark, their machine learning library MLlib seems to offer a lot of the functions I will need for computing summary statistics after an Ancho model has run. (It also says that it interoperates with NumPy, although I haven't seen any details about this.) It would be nice to have access to Pandas for this, but Pandas is not designed to run across distributed data as far as I know, and SparklingPandas (Pandas on top of Spark) doesn't seem to be very actively developed.
Of the things I can imagine needing a distributed computing framework for, Spark can do it all.
So, my architecture diagram ends up looking something like this:
The usage scenario would run something like this:
- Users author their models in Python against an Ancho API. Development, testing and small scale runs of the model can be done locally without any cluster infrastructure.
- To run the model in a cluster, the user would submit the model to an Ancho Cluster (running atop Mesos as a framework) by one of two means:
- Submitting the needed parameters by POSTing a JSON file (or something similar) to a REST-like interface. There would probably be a command line tool for this, but the API would allow jobs to be submitted by an automated process.
- Logging into a web site that allows users to submit jobs, monitor their execution, and get the results.
- Parameters submitted to the Ancho Cluster for a model run would include:
- Location of your model code -- Ancho can accept a tarball, or check it out of a source control repository, or pull it from S3, or whatever.
- Any parameters you need to submit to your Run: starting values, number of sequences to run, target values, etc.
- Once the Run is submitted, the Ancho Cluster framework would start one or more docker executors across the Mesos cluster to run your model. Those executors would download the model, install any Python packages required (as specified in pip-requirements.txt) and begin running Sequences. After each Sequence completes, it reports its results back to the Ancho framework (and stored in Cassandra), which eventually declares the Run complete and decommissions most of the executors.
- A few remaining executors will be told to generate the summary statistics about the Run using Spark/MLlib. I originally considered having the framework do the summary work, but this would make it impossible for users to define their own summary functions. I think that Cassandra's security model will allow me to restrict user code to accessing just the user's own data (only certain keyspaces/tables), but I may need to revisit this.
- The final results will be exposed as a data structure to users of the RESTy interface. Web users could be presented something more interactive, such as an IPython session with the result data preloaded, or something prettier and easier to digest, such as a PDF generated via ReportLab.
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