DataDog log downloader

Download logs from DataDog via the API. This allows you to download a large number of logs matching a particular query rather than being bound by the 5000 limit imposed on the export button in the UI. Logs are output as a JSON file.

Usage

DD_API_KEY=... DD_APP_KEY=... npx github:wegift/datadog-downloader --query '"Redeem failed"'

Authentication

You will need an API key and an app key to access the DataDog api. These should be provided in environment variables as seen above.

API keys are global for a DataDog account and can be found in organization settings. App keys are personal to your profile and can be generated in personal settings.

Options

--query     The filter query (aka search term). Take care when quoting on the command line, single quote the entire query for best results.

--index     Which index to read from, default 'main'

--from      Start date/time defaults to 1y ago
--to        End date/time, omit for results up to the current time

--pageSize  How many results to download at a time, default 1000 limit of 5000

--output    Path of json file to write results to, default 'results.json'

--format
    json: Save the final output as a single JSON object (default)
    ndjson: Stream the output to New Line Delimited JSON file (Less memory intensive on larger datasets)

--cursor    Next Page cursor position, useful to restart from a certain point if it crashed (Only works with streamable data formats)

--append    Enable appending data stream to output file instead of overwriting (Only works with streamable data formats)

Note: Date/times are parsed by JS Date constructor, e.g. 2022-01-01

Local Dev

Run npm install.

Copy .env.example to .env and add a valid DataDog API key and app key.

Run

node index.mjs --query '"Redeem token failure" -@redeem_failure_reason:"Invalid token"'

Caveats

Logs are not streamed, they are all stored in memory and stringified / written as as single action. I have tested with 25k logs and there were no issues, the resultant JSON file was only 100mb so it seems likely that you could download 100k+ without running into memory or performance limits but ymmv.

Contributing

Pull requests welcome!