jm + millwheel   2

The world beyond batch: Streaming 101 - O'Reilly Media
To summarize, in this post I’ve:

Clarified terminology, specifically narrowing the definition of “streaming” to apply to execution engines only, while using more descriptive terms like unbounded data and approximate/speculative results for distinct concepts often categorized under the “streaming” umbrella.

Assessed the relative capabilities of well-designed batch and streaming systems, positing that streaming is in fact a strict superset of batch, and that notions like the Lambda Architecture, which are predicated on streaming being inferior to batch, are destined for retirement as streaming systems mature.

Proposed two high-level concepts necessary for streaming systems to both catch up to and ultimately surpass batch, those being correctness and tools for reasoning about time, respectively.

Established the important differences between event time and processing time, characterized the difficulties those differences impose when analyzing data in the context of when they occurred, and proposed a shift in approach away from notions of completeness and toward simply adapting to changes in data over time.

Looked at the major data processing approaches in common use today for bounded and unbounded data, via both batch and streaming engines, roughly categorizing the unbounded approaches into: time-agnostic, approximation, windowing by processing time, and windowing by event time.
streaming  batch  big-data  lambda-architecture  dataflow  event-processing  cep  millwheel  data  data-processing 
august 2015 by jm
_MillWheel: Fault-Tolerant Stream Processing at Internet Scale_ [paper, pdf]
from VLDB 2013:

MillWheel is a framework for building low-latency data-processing applications that is widely used at Google. Users specify a directed computation graph and application code for individual nodes, and the system manages persistent state and the continuous flow of records, all within the envelope of the framework’s fault-tolerance guarantees.

This paper describes MillWheel’s programming model as well as its implementation. The case study of a continuous anomaly detector in use at Google serves to motivate how many of MillWheel’s features are used. MillWheel’s programming model provides a notion of logical time, making it simple to write time-based aggregations. MillWheel was designed from the outset with fault tolerance and scalability in mind. In practice, we find that MillWheel’s unique combination of scalability, fault tolerance, and a versatile programming model lends itself to a wide variety of problems at Google.
millwheel  google  data-processing  cep  low-latency  fault-tolerance  scalability  papers  event-processing  stream-processing 
august 2013 by jm

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