Note

This documentation is for a prior release of Kinetica. For the latest documentation, click here.

/get/records/json

URL: https://<aws.fqdn>/<aws.cluster.name>/gpudb-0/get/records/json

Overview

Kinetica provides a direct JSON egress service to allow records to be retrieved as raw JSON from the database.

Several options can be used to identify & filter the data being requested. There is also limited support for aggregation.

JSON objects will be returned as an array:

Single or Multiple Records
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[
    {
        "id": 1,
        "category": "Furniture",
        "name": "Clamp-on Lamps",
        "description": "Strong, steadfast fasteners; localized lighting for laboring",
        "stock": 10
    },
    {
        "id": 14,
        "category": "Technology",
        "name": "Dot Matrix Printers",
        "description": "Impactful industrial implements for impressing ink iteratively",
        "stock": 27
    }
]

For examples, see Examples or JSON Egress Snippets.

Usage

The base JSON egress endpoint URL is:

https://<aws.fqdn>/<aws.cluster.name>/gpudb-0/get/records/json

Authentication credentials should be passed with the request.

Endpoint-specific options can be passed as request parameters, as well.

Parameters

Name	Default	Description
`table_name`		Name of the table from which to request records, in [schema_name.]table_name format.
`column_names`	`*`	Names of the columns to extract from `table_name`.
`offset`	`0`	Positive integer indicating the number of initial results to skip (this can be useful for paging through the results).
`limit`	`-9999`	Positive integer indicating the maximum number of results to be returned, or -9999 to indicate that the maximum number of results allowed by the server should be returned. The number of records returned will never exceed the server's own limit, defined by the `max_get_records_size` parameter in the server configuration. Use response parameter `has_more_records` to see if more records exist in the result to be fetched, and request parameters `offset` & `limit` to request subsequent pages of results.
`expression`		Filter expression to apply to the table data. In the case of requests with aggregation in them, this filter will be applied before the aggregation occurs.
`order_by`		Comma-separated list of the columns to sort by as well as the sort order; e.g., `timestamp asc, x desc`. Note This only applies to scalar requests. Requests with aggregation will be sorted on the first aggregation value, in ascending order.
`having`		Filter expression to apply to the aggregated table data. Note This only applies to requests with aggregation in them.

Examples

Below are templates of requesting table data as JSON, via the /get/records/json REST endpoint:

cURL
Python

cURL
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KINETICA_URL=https://abcdefg.cloud.kinetica.com/hijklmn/gpudb-0
USERNAME=auser
PASSWORD=apassword
TABLE_NAME=product

# Quote the URL when passing multiple options, or any & will stop the URL
#   parsing and run the URL parsed up to that point as a background job
curl -sS ${KINETICA_URL}/get/records/json?table_name=${TABLE_NAME} \
--header "Content-Type: application/json" \
--user "${USERNAME}:${PASSWORD}"
Python
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kinetica_url = "https://abcdefg.cloud.kinetica.com/hijklmn/gpudb-0"
username = "auser"
password = "apassword"
table_name = "product"
kinetica_url += "/get/records/json?table_name=" + table_name

response = requests.get(kinetica_url, auth = (username, password))

Endpoint-specific options can be passed as URL-encoded data parameters instead of being appended to the URL:

cURL
Python

cURL
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KINETICA_URL=https://abcdefg.cloud.kinetica.com/hijklmn/gpudb-0
USERNAME=auser
PASSWORD=apassword
TABLE_NAME=product

# Using data-urlencode can make params easier to read than appending to URL
#   Use the G option to pass the data-urlencode options as GET query parameters
curl -sSG ${KINETICA_URL}/get/records/json \
--header "Content-Type: application/json" \
--user "${USERNAME}:${PASSWORD}" \
--data-urlencode "table_name=${TABLE_NAME}" \
--data-urlencode "column_names=name,category,description,stock" \
--data-urlencode "expression=stock > 100000" \
--data-urlencode "order_by=name"
Python
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kinetica_url = "https://abcdefg.cloud.kinetica.com/hijklmn/gpudb-0"
username = "auser"
password = "apassword"
table_name = "product"

response = requests.post(
    kinetica_url + "/get/records/json",
    params = {
        "table_name": table_name,
        "column_names": ["name", "category", "description", "stock"],
        "expression": "stock > 100000",
        "order_by": "name"
    },
    auth = (username, password)
)

Requests containing aggregation functions are also supported:

cURL
Python

cURL
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KINETICA_URL=https://abcdefg.cloud.kinetica.com/hijklmn/gpudb-0
USERNAME=auser
PASSWORD=apassword
TABLE_NAME=product

# Using data-urlencode can make params easier to read than appending to URL
#   Use the G option to pass the data-urlencode options as GET query parameters
curl -sSG ${KINETICA_URL}/get/records/json \
--header "Content-Type: application/json" \
--user "${USERNAME}:${PASSWORD}" \
--data-urlencode "table_name=${TABLE_NAME}" \
--data-urlencode "column_names=category,COUNT(1) AS total_products" \
--data-urlencode "expression=stock >= 1000" \
--data-urlencode "having=COUNT(1) > 1"
Python
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kinetica_url = "https://abcdefg.cloud.kinetica.com/hijklmn/gpudb-0"
username = "auser"
password = "apassword"
table_name = "product"

response = requests.post(
    kinetica_url + "/get/records/json",
    params = {
        "table_name": table_name,
        "column_names": ["category", "COUNT(1) AS total_products"],
        "expression": "stock >= 1000",
        "having": "COUNT(1) > 1"
    },
    auth = (username, password)
)

Responses

The endpoint calls will respond with one of two message formats, one for successes and one for failures:

Success
Failure

Success
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{
    "status": "OK",
    "data":
    {
        "records":
        [
            {
                "<record_1_column_1>": "<record_1_value_1>",
                ...
                "<record_1_column_N>": "<record_1_value_N>"
            },
            ...
            {
                "<record_N_column_1>": "<record_N_value_1>",
                ...
                "<record_N_column_N>": "<record_N_value_N>"
            }
        ],
        "total_number_of_records": <count of records retrieved>,
        "has_more_records": <whether more records exist in the result set>
    }
}
Failure
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{
    "status": "ERROR",
    "message": "<retrieval failure error message>"
}

An example of each of these is as follows:

Success
Failure

Success
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{
    "status": "OK",
    "data": {
        "records": [
            {
                "category": "Technology",
                "total_products": 2
            },
            {
                "category": "Office Supplies",
                "total_products": 3
            }
        ],
        "total_number_of_records": 2,
        "has_more_records": false
    }
}
Failure
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{
    "status": "ERROR",
    "message": "order_by not supported with aggregate(s) (E/Ec:162)"
}