Question
SopraSteriaNext
FR
Last activity: 28 Aug 2025 11:05 EDT
pega Archival Pipelinemechanism , Unclear which case IDs were archived a given day
We are using Pega Customer Service 24.1.1, hosted on Pega Cloud 3.
When archival is configured in the pipeline mechanism, it definitely archives cases, how can we quantify the list of case IDs that are archived as per the job scheduler run?
Other alternatives tried, we can use pySearchFieldWorkResponsive , with filter archived cases , it list down 1000 cases for a given class , it doesnt list the archived date , it can show resolved date , pyIsArchiced , but thats not useful.
The info is saved in a ZIP file. The name of the ZIP file contains the archived date, but I cannot browse its contents.
/archive/archivedclasses/xxxx-Cxxx-Work-xxxxx/2019/11/30/ArchivalFile_PEGA_WEB_5CC8F466F9_M89JR_2025JULY3_9409_AT7.zip.
HCA Healthcare
US
SopraSteriaNext
FR
@Sairohith Thanks for your response
I was advised by another Pega expert. It's a pity that the archiving mechanism doesn't allow easy traceability.
1.use the Repository API to fetch each file: yes its possibile to reccursively iterate to find zip files , not an issue
1.1unzip : This is the problem, am looking for native OOTB solutions to unzip or read the manifest, we are on pega cloud any customisations potentially could impact pega upgrades , Other concern is space occupied by unzipped files , clening them up after the csv is parsed
1.2 parse the CSV : can do
Thanks
Vishruthi
@VishruthiReddy
Here’s the clean way to get a per-day list: each archive ZIP written by the in-memory pipeline contains a CSV “manifest” with one row per archived item (columns include pzInsKey, pxObjClass, parentInsKey, pzPVStream), and the folder path already encodes the archive date, so you can treat that CSV as the source of truth for “which cases were archived on a given day.” Point a Repository (PCFS) or a File Data Set at /archive/archivedclasses/<Your-Work-Class>/<YYYY>/<MM>/<DD>/, iterate all ZIPs for that date, use the Repository API to fetch each file, unzip, parse the CSV, and store/show the list (either write to a custom table or drive a simple report). If you can’t browse PCFS directly, use the Dev Studio Repository browser or ask Pega Cloud to grant read access for that path. For run-level context and counts, use the in-memory pipeline job (pyPegaArchiverUsingPipeline) plus the Archival results/Log-ArchivalSummary views to correlate which ZIPs were produced in that window.Portal search (pySearchFieldWorkResponsive) is fine for quick lookups but won’t show the archive date or guarantee membership of a specific run—use the manifest instead. If you need to restore or inspect more, follow the “Retrieving archived Case data” guidance; but for daily auditing, the manifest is the reliable list.