Concepts for a Replay Mechanism Sample Clauses

Concepts for a Replay Mechanism. ‌ The replay mechanism offers the possibility to replay older result-streams on demand. It is triggered in an interactive way enabling the application user to specify the parameters of the replay such as granularity and time frame, while not interrupting the real-time pro- cessing pipelines. The processing should be transparent to users as if they are still in the same pipeline. The ability of combining online and offline result delivery is critical to decision making in financial analysis and for a deeper understanding of current situations. The replay mechanism imposes some challenges due to the difference in the processing models of stream and batch data. The major functionality of Storm as well as of other stream processing frameworks (Spark Streaming1, Samza2) is to facilitate the ingestion of events in a real-time or near real-time manner. To do so, these frameworks are optimized for high-throughput processing, and tolerate relaxed accurateness guarantees3. The data 1xxxx://xxxxx.xxxxxx.xxx/streaming/ 2xxxx://xxxxx.xxxxxx.xxx/‌‌ 3Core Apache Storm guarantees that each message is processed at least once. Duplicates due to read Storm topology query online batch Summingbird program query Hadoop job ingest HDFS read write store3 store2 store1 Message Queue batch results key-value store client client library cannot be stored over long term because of limited memory capacity, and iterations over data is restricted. Such scenarios are well-handled in batch processing paradigm such as MapReduce [10] or Bulk Synchronous Parallel [11]. However, their disadvantages are the latency of delivering processing results (sometimes can be up to several seconds or minutes, or even hours), which makes online and interactive processing infeasible. write online results key-value store source1 source2 source3
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Related to Concepts for a Replay Mechanism

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