Contents

Contents
- EII TimeSeriesProfiler

EII TimeSeriesProfiler

This module calculates the SPS (Samples Per Second) of any EII time-series modules based on the stream published by that respective module.

Prerequisites

TimeSeriesProfiler expects a set of config, interfaces and public or private keys to be present in ETCD as a prerequisite. To achieve this, ensure an entry for TimeSeriesProfiler with its relative path from IEdgeInsights([WORK_DIR]/IEdgeInsights/) directory is set in the time-series.yml file present in build/usecases([WORK_DIR]/IEdgeInsights/build/usecases) directory. Following is an example:
```
AppContexts:
- ConfigMgrAgent
- Visualizer/multimodal-data-visualization-streaming/eii/
- Visualizer/multimodal-data-visualization/eii
- DataStore
- Kapacitor
- Telegraf
- tools/TimeSeriesProfiler
```
With the previous pre-requisite done, please run the following command:
```
python3 builder.py -f ./usecases/time-series.yml
```

EII TimeSeriesProfiler Mode

By default, the EII TimeSeriesProfiler supports “sps” mode.

SPS mode

This mode is enabled by setting the “mode” key in config([WORK_DIR]/IEdgeInsights/tools/TimeSeriesProfiler/config.json) to “sps”. This mode calculates the samples per second of any EII module by subscribing to that module’s respective stream.
```
"mode": "sps"
```

Note

For running TimeSeriesProfiler in SPS mode, it is recommended to keep PROFILING_MODE set to false in .env([WORK_DIR]/IEdgeInsights/build/.env) for better performance.

EII TimeSeriesProfiler Configuration

total_number_of_samples

If mode is set to’sps’, the average SPS is calculated for the number of samples set by this variable. Setting it to (-1) will run the profiler forever unless terminated by stopping the container TimeSeriesProfiler manually. total_number_of_samples should never be set as (-1) for ‘sps’ mode.
export_to_csv

Setting this switch to true exports csv files for the results obtained in TimeSeriesProfiler.

Running TimeSeriesProfiler

The configuration required to run rfc_classifier.py UDF is as follows:

In Kapacitor config.json(``[WORK_DIR]/IEdgeInsights/Kapacitor/config.json``) , update “task” key as follows:

"task": [{
   {
    "tick_script": "rfc_task.tick",
    "task_name": "random_forest_sample"
    }
}]

In kapacitor.conf(``[WORK_DIR]/IEdgeInsights/Kapacitor/config/kapacitor.conf``) update udf section:

[udf.functions.rfc]
   prog = "python3.7"
   args = ["-u", "/EII/udfs/rfc_classifier.py"]
   timeout = "60s"
   [udf.functions.rfc.env]
      PYTHONPATH = "/EII/go/src/github.com/influxdata/kapacitor/udf/agent/py/"

Keep the config.json([WORK_DIR]/IEdgeInsights/tools/TimeSeriesProfiler/config.json) file as follows:

{
 "config": {
     "total_number_of_samples": 10,
     "export_to_csv": "False"
 },
 "interfaces": {
     "Subscribers": [
         {
             "Name": "default",
             "Type": "zmq_tcp",
             "EndPoint": "ia_datastore:65032",
             "PublisherAppName": "DataStore",
             "Topics": [
                 "rfc_results"
             ]
         }
     ]
 }
}

In .env([WORK_DIR]/IEdgeInsights/build/.env): Set the profiling mode to true.

Set environment variables accordingly in config.json([WORK_DIR]/IEdgeInsights/tools/TimeSeriesProfiler/config.json).

#. Set the required output stream or streams and the appropriate stream config in the config.json([WORK_DIR]/IEdgeInsights/tools/TimeSeriesProfiler/config.json) file. #.

To run this tool in IPC mode, the user must make the following changes to the subscribers interface section of [config.json] (./config.json)**:
{
  "type": "zmq_ipc",
  "EndPoint": "/EII/sockets"
}

To provision, build, and run the tool along with the EII time-series recipe or stack, see README.md.
Run the following command to see the logs:
```
docker logs -f ia_timeseries_profiler
```