Splunk Search

Correlate data in JSON file without timestamp

Patrick_Peeters
Splunk Employee
Splunk Employee

I have a JSON input with different types, all representing a data point at a certain time. I have the start time of the event and am looking for a way to get all the data organised without having to revert to custom Python code - how do I do this?

The 'time' type in JSON depicts how many seconds since the start time a data point is happening across all these types.

Ideally I'm looking for something that looks like this based on the data below assuming the start time is 12:00:00, probably latlng even split but that's secondary for now:

_timedistancealtitudelatlng
12:00:003.851.5
-33.895627, 151.228228
12:00:015.251.6-33.895627, 151.228228
12:00:036.751.5-33.895627, 151.228228
12:00:048.951.5-33.895627, 151.228228

 

 

[
    {
        "type": "time",
        "data": [
            0,
            1,
            3,
            4
        ]
    }, 
    {
        "type": "distance",
        "data": [
            3.8,
            5.2,
            6.7,
            8.9
        ]
    },
    {
        "type": "altitude",
        "data": [
            51.5,
            51.6,
            51.5,
            51.5
        ]
    },
    {
        "type": "latlng",
        "data": [
            [
                -33.895627,
                151.228228
            ],
            [
                -33.895627,
                151.228228
            ],
            [
                -33.895627,
                151.228228
            ],
            [
                -33.895627,
                151.228228
            ],
        ]
    },
]

 

 

 

0 Karma
1 Solution

Patrick_Peeters
Splunk Employee
Splunk Employee

Managed to get it done via Python's pandas module, specifically pandas.DataFrame function. It works great for series and dataframe data like the one I had.

The result:

 

[
    {
        "time": 1580539600,
        "distance": 0,
        "altitude": 45.7,
        "heartrate": 77,
        "lat": -33.895302,
        "lon": 151.228177
    },
    {
        "time": 1580539601,
        "distance": 0,
        "altitude": 45.7,
        "heartrate": 77,
        "lat": -33.895302,
        "lon": 151.228177
    },
    {
        "time": 1580539602,
        "distance": 0,
        "altitude": 45.7,
        "heartrate": 77,
        "lat": -33.895325,
        "lon": 151.22817
    }
]

 

 

View solution in original post

Tags (3)
0 Karma

Patrick_Peeters
Splunk Employee
Splunk Employee

Managed to get it done via Python's pandas module, specifically pandas.DataFrame function. It works great for series and dataframe data like the one I had.

The result:

 

[
    {
        "time": 1580539600,
        "distance": 0,
        "altitude": 45.7,
        "heartrate": 77,
        "lat": -33.895302,
        "lon": 151.228177
    },
    {
        "time": 1580539601,
        "distance": 0,
        "altitude": 45.7,
        "heartrate": 77,
        "lat": -33.895302,
        "lon": 151.228177
    },
    {
        "time": 1580539602,
        "distance": 0,
        "altitude": 45.7,
        "heartrate": 77,
        "lat": -33.895325,
        "lon": 151.22817
    }
]

 

 

Tags (3)
0 Karma
Got questions? Get answers!

Join the Splunk Community Slack to learn, troubleshoot, and make connections with fellow Splunk practitioners in real time!

Meet up IRL or virtually!

Join Splunk User Groups to connect and learn in-person by region or remotely by topic or industry.

Get Updates on the Splunk Community!

Persistent Queue at TcpOut — One of Splunk's Most Practical Features

Splunk introduced persistent queueing at the tcpout layer as one of the most practical resilience features in ...

Catalog Is Now Generally Available on Splunk Cloud Platform

A Unified View of Your Data  Security logs, application events, business data, and historical telemetry often ...

Your Path to AgenticOps: AI Experiences for Every Splunk Practitioner

Your Path to AgenticOps: AI Experiences for Every Splunk Practitioner   Watch on demand us for a demo-driven ...