Map reduce advantages over parallel databases
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MapReduce has several advantages over parallel databases for processing large datasets: 1) MapReduce can handle heterogeneous systems with different storage systems more easily than parallel databases which require data copying and analysis. 2) Complex functions are more straightforward to express in MapReduce's simple map and reduce model compared to SQL in parallel databases which can require complicated user defined functions. 3) MapReduce provides better fault tolerance than parallel databases by using techniques like batching, sorting, grouping and smart task scheduling during data transfers between mapping and reducing tasks.
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Map reduce advantages over parallel databases
- 1. M a p R e d u c e a d va n t a g e s o ve r p a r a l l e l d a t a b a s e s MAPREDUCE 1
- 2. Outline • Introduction • Heterogeneous Systems • Complex Functions • Fault Tolerance • Performance • Conclusion 2
- 3. MapReduce: A Flexible Data Processing Tool • Map Reduce is a programming model MapReduce is composed of : • Map function key/value pairs • Reduce function key/values • Processes many terabytes of data • System easy to use 3
- 4. Paper • By Andrew Pavloetal • Comparison paper • MapReduce is a major step backwards 4
- 5. Heterogeneous Systems • MapReduce provides a simple model for analyzing data in such heterogeneous systems • Storage systems like relational database or file systems • In parallel database : • input must be copied • then analyze 5
- 6. Complex Functions • Map & Reduce functions are simple and straight forward SQL equivalent. • Pavloetal pointed that some times it very complicated to be expressed in SQL • User Defined Functions(UDFs) • Buggy some times • MapReduce is a better framework 6
- 7. Fault Tolerance (1/2) • Two models to transfer data between mappers and reducers: • Pull model (move) • Push model (write) • Pull model create many files and disks (Pavloetal ) • MapReduce used : • batching, sorting and grouping • Smart scheduling for reads 7
- 8. Fault Tolerance (2/2) • MapReduce do not use push model due to fault- tolerance • Fault tolerance will be more important to process these data efficiently. 8
- 9. Performance (1/2) • Merging results (cost) : • Merging isn’t necessary when the next consumer of MapReduce is: • another MapReduce • not another MapReduce 9
- 10. Performance (2/2) • Data loading: • Hadoop can analyze data 5 to 50 times faster than the time needed to load data to parallel database 10
- 11. Conclusion • MapReduce is a highly effective and efficient tool for large-scale fault-tolerant data analysis • MapReduce is very useful in a heterogeneous system • MapReduce provides a good framework 11