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| -rw-r--r-- | chapter/8/big-data.md | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/chapter/8/big-data.md b/chapter/8/big-data.md index d17d2b1..89e3d6a 100644 --- a/chapter/8/big-data.md +++ b/chapter/8/big-data.md @@ -13,9 +13,9 @@ In BSP model + Computation consists of several steps called as supersets. + The processors involved have their own local memory and every processor is connected to other via a point-to-point communication. + 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 /> ++ Barrier synchronization synchs all the processors at the end of 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. |