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Profiler

Please see the set of transform project conventions for details on general project conventions, transform configuration, testing and IDE set up.

Summary

Profiler implement a word count. Typical implementation of the word count is done using map reduce. * It’s O(N2) complexity * shuffling with lots of data movement

Implementation here is using “streaming” aggregation, based on central cache:

  • At the heart of the implementation is a cache of partial word counts, implemented as a set of Ray actors and containing word counts processed so far.
  • Individual data processors are responsible for:
  • Reading data from data plane
  • tokenizing documents (we use pluggable tokenizer)
  • Coordinating with distributed cache to collect overall word counts

The complication of mapping this model to transform model is the fact that implementation requires an aggregators cache, that transform mode knows nothing about. The solution here is to use transform runtime to create cache and pass it as a parameter to transforms.

Transform runtime

Transform runtime is responsible for creation cache actors and sending their handles to the transforms themselves Additionally it writes created word counts to the data storage (as .csv files) and enhances statistics information with the information about cache size and utilization

Configuration and command line Options

The set of dictionary keys holding ProfilerTransform configuration for values are as follows:

  • doc_column - specifies name of the column containing documents

Running

Launched Command Line Options

When running the transform with the Python launcher (i.e. TransformLauncher), the following command line arguments are available in addition to the options provided by the launcher.

  --profiler_doc_column PROFILER_DOC_COLUMN
                        key for accessing data
 ```

These correspond to the configuration keys described above.

### Running the samples
To run the samples, use the following `make` targets

* `run-cli-sample` - runs src/ededup_transform_ray.py using command line args
* `run-local-sample` - runs src/ededup_local_ray.py
* `run-s3-sample` - runs src/ededup_s3_ray.py
    * Requires prior installation of minio, depending on your platform (e.g., from [here](https://min.io/docs/minio/macos/index.html)
      and [here](https://min.io/docs/minio/linux/index.html)
      and invocation of `make minio-start` to load data into local minio for S3 access.

These targets will activate the virtual environment and set up any configuration needed.
Use the `-n` option of `make` to see the detail of what is done to run the sample.

For example,
```shell
make run-cli-sample
...
Then 
ls output
To see results of the transform.

Transforming data using the transform image

To use the transform image to transform your data, please refer to the running images quickstart, substituting the name of this transform image and runtime as appropriate.