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Diffstat (limited to 'chapter/8/big-data.md')
| -rw-r--r-- | chapter/8/big-data.md | 4 |
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diff --git a/chapter/8/big-data.md b/chapter/8/big-data.md index c6baac0..cc11e28 100644 --- a/chapter/8/big-data.md +++ b/chapter/8/big-data.md @@ -14,13 +14,13 @@ This chapter is organized in - 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 - - PM of Pregel/GraphX + - PM of Pregel - 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 + - Spark SQL - how is it different from other above models? How does it leverage Spark execution engine and enhanced RDDs like data frames? what are its goals? whats a Dataframe API and how is it different from a RDD? - Execution Models - MapReduce (intermediate writes to disk) |