Also it supports cycles in the graph. It's like comparing a bicycle to an airplane. On Nov 16, 2014 2:03 PM, "Vladi Feigin" <[email protected]> wrote:
> Storm is much more sophisticated then just filter-pipe pattern. > It provides > 1. Reliability: guarantees that every spout tuple will be fully > processed. Actually it provides : at-most-once delivery(no ackers) , > at-least-once > delivery(ackers) and exactly-once (Trident) semantic for the message > delivering/processing > 2. Various types of tuples grouping > 3. Scaling-out distributed system > Vladi > > > On Sun, Nov 16, 2014 at 8:29 PM, Patrick Wiener <[email protected]> > wrote: > >> So basically Storm’s core concept can be compared to >> pipes-and-filters-pattern BUT provides a more „user-friendly“ framework >> than e.g. a unix based pipes-and-filters processing. >> >> btw: I haven’t come across with TRIDENT yet. Just starting to dive deeper >> into Storm as a potential technology for a real-time analytics architecture >> (e.g. KAFKA+STORM+NODE+D3). >> >> Am 16.11.2014 um 17:06 schrieb Nathan Leung <[email protected]>: >> >> Storm supports fan outs, joins, various data groupings, and easier >> scalability than the canonical Unix based pipes and filters processing. >> On Nov 16, 2014 10:53 AM, "Andres Gomez Ferrer" <[email protected]> >> wrote: >> >>> Firstly your thoughts are correct :), but do you know Trindet’s api? >>> >>> Trindet provides functions and filters equivalent to bolts >>> >>> ____________________________________________________________ >>> Andrés Gómez >>> *Developer* >>> redborder.net / [email protected] >>> mobile: +34 606224922 >>> http://lnkd.in/sHnbJe >>> >>> <LogoEneo1-300x119.png> <LogoRedBorder.png> >>> ____________________________________________________________ >>> >>> >>> En 16 de noviembre de 2014 en 15:25:10, Patrick Wiener ( >>> [email protected]) escrito: >>> >>> >>> Hey everybody, >>> >>> I am working on a universities project towards a comparison towards >>> pipes-and-filters-pattern and Storm. >>> Since I am new to Storm and its topology and operating mode I hope you >>> can help me evaluating my train of thoughts. >>> >>> *Bolts* can be considered as Filters, whereas the *Spout* equals >>> the „Data Source“ (internal view) *pushing* tuples (data) to the >>> downstream bolt. >>> Finally the last bolt within the topology pushes tuples into a Data >>> Sink, e.g. Redis. >>> For the external view the Spout is also *pulling* (not shown in >>> picture) from external sources such as Kafka. >>> >>> Overall, Spouts implement pull and push mechanism and Bolts only push >>> mechanism >>> >>> I know this might seem trivial to you guys but i really hope for some >>> constructive help. >>> >>> >>> <34AC046A-F649-4A4E-9DA6-5FB7FE16868F> >>> >>> >> >
