ELF: Efficient Lightweight Fast Stream Processing at Scale

Stream processing has become a key means for gaining rapid insights from webserver-captured data. Challenges include how to scale to numerous, concurrently running streaming jobs, to coordinate across those jobs to share insights, to make online changes to job functions to adapt to new requirements or data characteristics, and for each job, to efficiently operate over different time windows.

The ELF stream processing system addresses these new challenges. Implemented over a set of agents enriching the web tier of datacenter systems, ELF obtains scalability by using a decentralized "many masters" architecture where for each job, live data is extracted directly from webservers, and placed into memory-efficient compressed buffer trees (CBTs) for local parsing and temporary storage, followed by subsequent aggregation using shared reducer trees (SRTs) mapped to sets of worker processes. Job masters at the roots of SRTs can dynamically customize worker actions, obtain aggregated results for end user delivery and/or coordinate with other jobs.

An ELF prototype implemented and evaluated for a larger scale configuration demonstrates scalability, high per-node throughput, sub-second job latency, and sub-second ability to adjust the actions of jobs being run.