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Diffstat (limited to 'chapter/8')
| -rw-r--r-- | chapter/8/big-data.md | 16 |
1 files changed, 12 insertions, 4 deletions
diff --git a/chapter/8/big-data.md b/chapter/8/big-data.md index 1696878..b0bf6f9 100644 --- a/chapter/8/big-data.md +++ b/chapter/8/big-data.md @@ -6,13 +6,21 @@ by: "Jingjing and Abhilash" ## Introduction `JJ: Placeholder for introduction` The booming Internet has generated big data... -This chapter is organized in by +This chapter is organized in - Programming Models - Data parallelism (most popular, standard map/reduce/functional pipelining) - - Limitations, iteration difficult due to the execution model of MapReduce/Hadoop + - PM of MapReduce: basic, limitation, pipelining > FlumeJava + - PM of Dryad: can support DAG computation, limitations: low-level, `Q: Should this go to execution model?` + - PM of Spark, RDD/lineage: can support iterative algorithm, interactive analytics - Large-scale Parallelism on Graphs - - Querying: DryadLINQ, Pig, Hive, possible Spark SQL + - PM of Pregel/GraphX + - Querying: more declarative `Q: put here or in the execution model?` + - DryadLINQ, SQL-like, use Dryad as execution engine; + - Pig, on top of Hadoop, independent of execution platform, in theory can compiled into DryadLINQ too; what is the performance gain/lost? Easier to debug? + - Hive, SQL-like, on top of Hadoop, what is the performance gain/lost. + - Dremel, query natively w/o translating into MP jobs + - Spark SQL, on top of Spark - Execution Models - MapReduce (intermediate writes to disk) @@ -23,7 +31,7 @@ This chapter is organized in by - Limitations ? - Performance - Things people are building on top of MapReduce/Spark - - FlumeJava? ...Etc + - // FlumeJava? ...Etc - Ecosystem, everything interoperates with GFS or HDFS, or makes use of stuff like protocol buffers so systems like Pregel and MapReduce and even MillWheel... ## Programming Model |