Knowi provides an optional elastic store that can store and track query results. Unlike traditional warehouses that require complex ETL processes and pre-defined schema, the elastic store is is a flexible, scalable, schema-less warehouse.
This can provide a number of benefits, including:
- Cache/persistent layer for fast analysis and visualizations
- Leaves raw data at the source
- Typically used to store query results, results of multi-datasource joins, etc.
- Eliminates complex ETL processing
- Does not require a predefined schema
- Keeps multi-structured data intact
- Shields your raw datasources from your BI/reporting queries
- Build aggregations and data pipelines from datasets stored from this source
Note that this is optional. You can also use the direct query mode, that bypasses this data layer to interact with your raw datasource directly.
Data we receive is stored in a dataset based on a name (or a unique identifier). A dataset is a logical grouping of data that can seamlessly accommodate new fields when needed and can be combined from multiple datasources. A dataset is typically created from queries against a datasource, or, using our push API.
Overview
Data we receive is stored in a dataset based on a name (or a unique identifier). A dataset is a logical grouping of data that can seamlessly accommodate new fields when needed and can be combined from multiple datasources. A dataset is typically created from queries against a datasource, or, using our push API.
Access
To query datasets stored within the warehouse:
-
Create a new Knowi Warehouse Datasource (if one does not exist):
-
To query against the datasource, either:
a. select dataset name and then enter Cloud9QL directly or,
b. use data the Query Generator to dynamically generate the queries.
-
To set up a derived dataset from this dataset, set up the query and assign a schedule.
- If you wish to limit the data assets available to subsequent queries, please refer to our Access Control List documentation
Cloud9 Agent Configuration
In addition to the UI based approach, you can also query Knowi using your agent. This can be used to query data in the warehouse, apply custom processors and build derived datasets. For added security, your API key needs to be pre-approved by Cloud9 Charts (with an optional domain match) enable this functionality. Please Contact us to enable this.
Highlights:
- Your datasets stored within Cloud9 Charts can be queried and transformed.
- Typically, used to aggregate data into another dataset.
Datasource Configuration:
| Parameter | Comments |
|---|---|
| name | Unique Datasource Name. |
| datasource | Set value to cloud9charts |
| exportApiKey | Export API key from Cloud9 Charts |
Query Configuration:
| Query Config Params | Comments |
|---|---|
| entityName | Dataset Name Identifier |
| identifier | A unique identifier for the dataset. Either identifier or entityName must be specified. |
| dsName | Name of the datasource name configured in the datasource_XXX.json file to execute the query against. Required. |
| c9ExportEntityName | Entity Name of the dataset to pull data from. |
| queryStr | Required. Cloud9QL query to manipulate data in Cloud9 Charts. See Cloud9QL docs |
| frequencyType | One of minutes, hours, days,weeks,months. If this is not specified, this is treated as a one time query, executed upon Cloud9Agent startup (or when the query is first saved) |
| frequency | Indicates the frequency, if frequencyType is defined. For example, if this value is 10 and the frequencyType is minutes, the query will be executed every 10 minutes |
| startTime | Optional, can be used to specify when the query should be run for the first time. If set, the the frequency will be determined from that time onwards. For example, is a weekly run is scheduled to start at 07/01/2014 13:30, the first run will run on 07/01 at 13:30, with the next run at the same time on 07/08/2014. The time is based on the local time of the machine running the Agent. Supported Date Formats: MM/dd/yyyy HH:mm, MM/dd/yy HH:mm, MM/dd/yyyy, MM/dd/yy, HH:mm:ss,HH:mm,mm |
| overrideVals | This enables data storage strategies to be specified. If this is not defined, the results of the query is added to the existing dataset. To replace all data for this dataset within Cloud9 Charts, specify {"replaceAll":true}. To upsert data specify "replaceValuesForKey":["fieldA","fieldB"]. This will replace all existing records in Cloud9 Charts with the same fieldA and fieldB with the the current data and insert records where they are not present. |
Datasource Example:
[
{
"name":"c9cDatasource",
"datasource":"cloud9charts",
"exportApiKey":"hDIdFflk9ilgmdogpgpPMN1Rcsie"
}
]
Query Example:
[
{
"entityName":"Aggregated Data",
"dsName":"c9cDatasource",
"c9ExportEntityName":"Raw Data",
"queryStr":"select * where a is not null and a < 10800 ; select sum(a) as Total A, sum(b) as Total B, \"Some Type\" as Type order by b desc",
"overrideVals":{
"replaceValuesForKey":["Type"]
},
"frequencyType":"hourly",
"frequency":"2",
"startTime":"09"
}
]
In the example above, Raw Data dataset is queried for certain conditions, then aggregated and posted into an "Aggregated Data" dataset on a timed schedule.