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Configure the Cloudant Database

To query the data from the database, you need to configure the Cloudant database. Therefore, we must set up an index, view, and search index.

Note: For more details, visit the IBM Cloud Cloudant documentation

The data format for the Shelly data saved in the database is essential, and this is an example of a data entry.

{
  "_id": "03e3f5f27e8742aea0035ff4bf0dd3ee",
  "_rev": "1-1c8c94efab24a8da925978234e7e4ce6",
  "result": {
    "emeters": [
      {
        "power": 178.08,
        "pf": 0.72,
        "current": 1.11,
        "voltage": 225.86,
        "is_valid": true,
        "total": 60608,
        "total_returned": 320.9
      },
      {
        "power": 4.71,
        "pf": 0.53,
        "current": 0.04,
        "voltage": 226.14,
        "is_valid": true,
        "total": 32062.3,
        "total_returned": 0
      },
      {
        "power": 195.98,
        "pf": 0.93,
        "current": 0.95,
        "voltage": 225.08,
        "is_valid": true,
        "total": 51733.8,
        "total_returned": 0
      }
    ],
    "date": "2024-01-08 21:41:50",
    "total_power": 378.77
  }
}

Examples for searches and indexes

1. Cloudant Search query index

The gif below shows an example usage of a search query. Search examples: `

  1. total_power:[1000 TO 2000
  2. date:2024*
  3. total_power:{2000 TO Infinity]
  4. (total_power:{2000 TO Infinity]) AND (date:"2024-01*")

Example for the payload in the Grafana query

{
   "search_option":"custom",
   "search_topic":"date",
   "search_string":"(phase1_power:[-5000 TO 0])"
}

You can find more detailed information related to queries with Lucene in the Lucene Apache Documentation

  • Example search query definition in the Cloudant UI:

  • Example of a function definition for a Search query index using JavaScript
function (doc) {
  index("_id", doc._id);
  if( doc.result.date){
    index("date", doc.result.date,{"store": true,"facet":true});
  }
  if(doc.result.total_power){
    index("total_power", doc.result.total_power,{"store": true,"facet":true});
  }
  var value = doc.result.emeters[0].total
  if(value){
    index("phase1_total", value,{"store": true,"facet":true});
  }
  value = doc.result.emeters[1].total
  if(value){
    index("phase2_total", value,{"store": true,"facet":true});
  }
  value = doc.result.emeters[2].total
  if(value){
    index("phase3_total", value,{"store": true,"facet":true});
  }
  value = doc.result.emeters[0].total_returned
  if(value){
    index("phase1_total_returned", value,{"store": true,"facet":true});
  } else {
    index("phase1_total_returned", 0 ,{"store": true,"facet":true});
  }
  value = doc.result.emeters[1].total_returned
  if(value){
    index("phase2_total_returned", value,{"store": true,"facet":true});
  } else {
    index("phase2_total_returned", 0 ,{"store": true,"facet":true});
  }
  value = doc.result.emeters[2].total_returned
  if(value){
    index("phase3_total_returned", value,{"store": true,"facet":true});
  } else {
    index("phase3_total_returned", 0 ,{"store": true,"facet":true});
  }
  value = doc.result.emeters[0].power
  if(value){
    index("phase1_power", value,{"store": true,"facet":true});
  }
  value = doc.result.emeters[1].power
  if(doc.result.emeters[1].power){
    index("phase2_power", doc.result.emeters[1].power,{"store": true,"facet":true});
  }
  value = doc.result.emeters[2].power
  if(value){
    index("phase3_power", value,{"store": true,"facet":true});
  }
}
  • Example return value format:
{
  "id": "828e77b8ebe8c3df6a916e1093775738",
  "order": [
    0.75,
    0
  ],
  "fields": {
    "phase3_power": 86.11,
    "total_power": 180.52,
    "phase2_total": 18946.3,
    "phase3_total_returned": 0,
    "phase3_total": 29507.8,
    "date": "2023-12-25 10:43:08",
    "phase1_total_returned": 271.5,
    "phase1_total": 36261.8,
    "phase1_power": 89.22,
    "phase2_power": 5.19,
    "phase2_total_returned": 0
  }
}

2. Cloudant query

The gif below shows an example usage of a Cloudant query.

  • Example Cloudant query
{
  "selector": {
    "_id": {
      "$gt": "0"
    }
  },
  "fields": [
    "_id",
    "_rev",
    "result.emeters.0.power",
    "result.emeters.1.power",
    "result.date",
    "result.total_power"
  ],
  "sort": [
    {
      "_id": "asc"
    }
  ]
}

3. Creation of the database, view, and query index in the IBM Cloud Cloudant service

Step 1: Ensure the shelly-cloudant-grafana-connection-server is up and running

Step 2: Create the database in the IBM Cloud Cloudant service

You can define the database name in the .env file.

  1. Open the shelly-cloudant-grafana server 
    open http://0.0.0.0:8081/docs
  2. This has opened a browser Swagger UI of the shelly-cloudant-grafana server. In this UI select the endpoint: db_create_cloudant_database
  3. Press Try it out
  4. Press Execute

The gif below shows the steps.

Step 3: Create the needed view and query index in the database

To access the data later, we create a view and query index in the database. Therefore, the documents are in the folder code/cloudant_config of the cloned GitHub project. These kinds of documents are called design documents.

  1. Select in the Swagger UI the endpoint db_create_doc_from_file
  2. Press Try it out
  3. Select the file with the document to upload: upload-1-view-all-data.json, upload-2-search_index_all_data_javascipt.json
  4. Press Execute
  5. Verify that the document was created correctly inside the Cloudant database. After the execution, you can see in the Response that the template file was used to create a document in the database.

Step 4: Verify the created design documents

To verify the created design documents.

  1. Select in the Swagger UI the endpoint db_get_all_design_docs
  2. Press Try it out
  3. Press Execute
  4. Verify