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| author | msabhi <abhi.is2006@gmail.com> | 2016-11-17 16:43:04 -0500 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2016-11-17 16:43:04 -0500 |
| commit | ce6727b47cbd0d4f0ac3407b498982a09c4b3e50 (patch) | |
| tree | 5c2b662d3403feb24382c1ab057a2a727f561332 /chapter/8 | |
| parent | d63ceb8aa8c280d789e3163426ea86bff16997e0 (diff) | |
Update big-data.md
Diffstat (limited to 'chapter/8')
| -rw-r--r-- | chapter/8/big-data.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/chapter/8/big-data.md b/chapter/8/big-data.md index 6a3edd3..eef8a8f 100644 --- a/chapter/8/big-data.md +++ b/chapter/8/big-data.md @@ -15,7 +15,7 @@ In BSP model + At every superstep, a processor receives input at the beginning, performs computation and outputs at the end. + Barrier synchronization synchs all the processors at the end of every superstep. + -A notable feature of the model is the complete control on data through communication between every processor at every superstep. <br /> ++A notable feature of the model is the complete control on data through communication between every processor at every superstep. <br /> Though similar to map reduce model, BSP preserves data in memory across supersteps and helps in reasoning iterative graph algorithms.<br /> `Pregel` is an implementation of classic BSP model by Google (PageRank) to analyze large graphs exclusively. It was followed by open source implementations - Apache’s Giraph and Hama; which were BSP models built on top of Hadoop. Pregel is highly scalable, fault-tolerant and can successfully represent larger complex graphs. Google claims the API becomes easy once a developer adopts “think like a vertex” mode. |