Streaming Pipeline Parity Using Temporal Event Grouping
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Solution Overview
Problem
Maintaining streaming parity in large-scale, complex data pipelines is challenging due to continuous data streams without fixed boundaries, varying data event generation and consumption rates, non-1:1 relationships, and duplicate data events, which existing tools inadequately address.
Innovation Solution
Temporally group data events with a group identification tag, upload data points to a database and cache, and perform matching operations to identify and rectify mismatches, ensuring parity by tracking production and consumption across applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If streaming data events are continuously tracked across multiple hops, then data integrity and streaming parity are maintained, but system complexity and difficulty of tracking increase due to continuous streams, varying rates, non-1:1 relationships, and duplicate events
Solution Approach 1:
The patent segments the continuous streaming data into discrete temporal batches with unique identification tags. Each batch is treated as a separate unit for tracking purposes, allowing the system to manage complexity by processing finite segments rather than continuous infinite streams. This segmentation enables systematic tracking across multiple hops while maintaining streaming parity through batch-level reconciliation.
Solution Approach 2:
The patent introduces an intermediary batching mechanism that sits between data production and consumption points. This intermediary layer assigns temporal batch IDs to groups of events, creating a buffer that simplifies tracking by grouping individual events into manageable units. The intermediary handles the complexity of continuous streams, varying rates, and duplicate events, while presenting simplified batch-level information to upstream and downstream systems.
2Measurement precision
If temporal batching is implemented with group identification tags, then tracking precision and parity verification improve, but data processing overhead and storage requirements increase
Solution Approach 1:
The patent applies partial action by implementing temporal batching only at critical checkpoints in the data pipeline rather than continuously processing every single event. By batching events into temporal groups and verifying parity at batch boundaries, the system achieves sufficient precision for detecting data loss without the excessive overhead of tracking every individual event in real-time. This selective application of batching reduces processing overhead while maintaining adequate verification precision.
3Measurement precision
If duplicate data events are handled with temporal grouping, then tracking accuracy improves, but computational resources and time required for matching operations increase
Solution Approach 1:
The patent applies preliminary action by pre-assigning temporal batch ID tags to groups of events at the source before they traverse the pipeline. This preliminary grouping and tagging eliminates the need for complex real-time duplicate detection and matching operations downstream. By pre-organizing events into temporal batches with unique identifiers, the system achieves high tracking accuracy while minimizing computational resources and time required for verification, as the batching structure is already in place before matching operations begin.
Data Source
AI summary
In a pipeline, data events generated by a producer application are temporally grouped by using a group identification tag. For each data event, data points are generated and uploaded to a storage and cache at each point of production and consumption. The storage allows a matching of data events between the production point and the consumption point, thereby ensuring that streaming parity is maintained. In cases of mismatch, the cache allows for detecting missing data events, i.e., identifying data events that were generated by an upstream producer application, but not consumed by a downstream consumer. While being agnostic to the transformations applied by the various applications in the pipeline, the embodiments disclosed herein keep track of the output data events and input data events and precisely identify the missing data events.