HDX Data Freshness
HDX Data Freshness
The implementation of HDX freshness in Python reads all the datasets from the Humanitarian Data Exchange website (using the HDX Python library) and then iterates through them one by one performing a sequence of steps.
- It gets the dataset’s update frequency if it has one. If that update frequency is Never, then the dataset is always fresh.
- If not, it checks if the dataset and resource metadata have changed - this qualifies as an update from a freshness perspective. It compares the difference between the current time and update time with the update frequency and sets a status: fresh, due, overdue or delinquent.
- If the dataset is not fresh based on metadata, then the urls of the resources are examined. If they are internal urls (data.humdata.org - the HDX filestore, manage.hdx.rwlabs.org - CPS) then there is no further checking that can be done because when the files pointed to by these urls update, the HDX metadata is updated.
- If they are urls with an adhoc update frequency (proxy.hxlstandard.org, ourairports.com), then freshness cannot be determined. Currently, there is no mechanism in HDX to specify adhoc update frequencies, but there is a proposal to add this to the update frequency options. At the moment, the freshness value for adhoc datasets is based on whatever has been set for update frequency, but these datasets can be easily identified and excluded from results if needed.
- If the url is externally hosted and not adhoc, then we can open an HTTP GET request to the file and check the header returned for the Last-Modified field. If that field exists, then we read the date and time from it and check if that is more recent than the dataset or resource metadata modification date. If it is, we recalculate freshness.
- If the resource is not fresh by this measure, then we download the file and calculate an MD5 hash for it. In our database, we store previous hash values, so we can check if the hash has changed since the last time we took the hash.
- There are some resources where the hash changes constantly because they connect to an api which generates a file on the fly. To identify these, we hash again and check if the hash changes in the few seconds since the previous hash calculation.
Since there can be temporary connection and download issues with urls, the code has multiple retry functionality with increasing delays. Also as there are many requests to be made, rather than perform them one by one, they are executed concurrently using the asynchronous functionality that has been added to the most recent versions of Python.
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