Thanks. Not sure why you say it is different, from a stream processing use case perspective both seems to accomplish the same thing while the implementation may take different approaches. If I want to aggregate and do stats in Storm, I would have to microbatch the tuples at some level. e.g. count of orders in last 1 minute, in Storm I have to write code to for sliding windows and state management, while Spark seems to provide operators to accomplish that. Tuple level operations such as enrichment, filters etc.. seems also doable in both.
On Mon, Jun 9, 2014 at 12:24 PM, Ted Dunning <[email protected]> wrote: > > They are different. > > Storm allows right now processing of tuples. Spark streaming requires > micro batching (which may be a really short time). Spark streaming allows > checkpointing of partial results in the stream supported by the framework. > Storm says you should roll your own or use trident. > > Applications that fit one like a glove are likely to bind a bit on the > other. > > > > > On Mon, Jun 9, 2014 at 12:16 PM, Rajiv Onat <[email protected]> wrote: > >> I'm trying to figure out whether these are competitive technologies for >> stream processing or complimentary? From the initial read, from a stream >> processing capabilities both provides a framework for scaling while Spark >> has window constructs, Apache Spark has a Spark Streaming and promises one >> platform for batch, interactive and stream processing. >> >> Any comments or thoughts? >> > >
