This data denormalization is inherent to your microservices architecture and increases performance.
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Examples of data that can’t be deleted in the span of an API call include data that is exported to offline snapshots, or data that exists in multiple backend systems and caches. In this case, your erasure pipeline has to get a bit more complicated.
The downside of this approach is that it assumes every data deletion task can be completed within the span of an API call, usually seconds or milliseconds, when it may take longer. Once the API calls have succeeded for each piece of data, the data has been deleted and your erasure pipeline is finished. Your erasure pipeline can call that API to perform data deletion tasks. In order to reach every piece of data, your pipeline will need to support each of these three processing methods.ĭata mutable via a real-time API is the simplest. Offline warehoused data will be mutable via (3) a parallel-distributed processing framework like MapReduce. Online data will be mutable via (1) a real-time API or (2) an asynchronous mutator. The data you find will usually be accessible to you in one of three ways. So your first job will be to use your knowledge of your organization, the expertise of your peers, and organization-wide communication channels to compile a list of all relevant data.
Data about a given event, user, or record could be in online or offline datasets, and may be owned by disparate parts of your organization.
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We’ll also touch on common problems and how to ensure ongoing maintenance of an erasure pipeline.įirst, you’ll need to find the data that needs to be deleted. In this post, we’ll discuss how to set up an erasure pipeline, including data discoverability, access, and processing. At Twitter, we call this process “erasure” and coordinate data deletion between systems using an erasure pipeline. One solution is to think of data deletion not as an event, but as a process. This data is often distributed throughout your microservices architecture, requiring coordination between systems and teams to delete it. There will always be data, however, that spans multiple datasets and records. A common solution is to set an organization-wide standard of per-dataset or per-record retentions. This poses challenges to ensuring that data is deleted. Microservices architectures tend to distribute responsibility for data throughout an organization.
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