Automatic Data Virtualization - Supporting XML based abstractions on HDF5 Datasets

1. Automatic Data Virtualization - Supporting XML based abstractions on HDF5 Datasets Swarup Kumar Sahoo Gagan Agrawal

2. Roadmap • Motivation • Introduction • System Overview • XQuery, Low and High Level schema and HDF5 storage • Compiler Analysis and Algorithm • Experiment • Summary and Future Work

3. Motivation • Emergence of grid-based data repositories • Can enable sharing of data • Emergence of applications that process large datasets • Complicated by complex and specialized storage formats • Need for easily portable applications • Compatibility with web/grid services

4. Data Virtualization An abstract view of data dataset Data Virtualization Data Service • By Global Grid Forum’s DAIS working group: • A Data Virtualization describes an abstract view of data. • A Data Service implements the mechanism to access and process data • through the Data Virtualization

5. Introduction : Automatic Data Virtualization • Goal : Enable Automatic creation of efficient data services • Support a high-level or abstract view of data • Data is stored in low-level format • Application development: • assume a high-level or virtual view • Application Execution: • On actual low-level layout

6. Overview of Our Automatic Data Virtualization Work • Previous work on XML Based virtualization • Techniques for XQuery Compilation (Li and Agrawal, ICS 2003, DBPL 2003) • Supporting XML Based high-level abstraction on flat-file datasets (LCPC 2003, XIME-P 2004) • Relational Table/SQL Based Implementation • Supporting SQL Select and Where (HPDC 2004) • Supporting SQL-3 Aggregations (LCPC 2004)

7. XQuery ??? XML XML-based Virtualization HDF5 NetCDF TEXT … RDBMS

8. Challenges and Contributions • Challenges • Compiler generates efficient data processing code • Uses the information about the low-level layout and mapping between virtual and low-level layout • Challenge in compilation • High level to low level • to ensure high locality in processing of large datasets • Contributions of this paper • An improved data- centric transformation algorithm • An implementation specific to HDF5 as the low-level format

9. System Overview System Overview High level XML Schema XQuery Source Code Low level XML Schema Mapping Schema Compiler Generated Code HDF5 Library Processor and Disk

10. XQuery and HDF5 • High-level declarative languages ease application development • XQuery is a high-level language for processing XML datasets • Derived from database, declarative, and functional languages! • HDF5: • Hierarchical Data Format • Widely used in scientific communities • A case study with a format which has optimized access libraries

11. Use of XML Schemas • High-level schema • XML is used to provide a virtual view of the dataset • Low-level schema • reflects actual physical layout in HDF5 • Mapping schema: • describes mapping between each element of high-level schema and low-level schema

12. Oil Reservoir Simulation • Support cost-effective Oil Production • Simulations on a 3-D grid • 17 variables and cell locations in 3-D grid at each time step • Computation of bypassed regions • Expression to determine if a cell is bypassed for a time-step • Within a spatial region and range of time steps • Grid cells that are bypassed for every time-step in the range Oil Reservoir management

13. High-Level Schema < xs:element name="data" maxOccurs="unbounded" > < xs:complexType > < xs:sequence > < xs:element name="x" type="xs:integer"/ > < xs:element name="y" type="xs:integer"/ > < xs:element name="z" type="xs:integer"/ > < xs:element name="time" type="xs:integer"/ > < xs:element name="velocity" type="xs:float"/ > < xs:element name="mom" type="xs:float"/ > < /xs:sequence > < /xs:complexType > < /xs:element >

14. High-Level XQuery Code Of Oil Reservoir management unordered( for $i in ($x1 to $x2) for $j in ($y1 to $y2) for $k in ($z1 to $z2) let $p := document("OilRes.xml")/data where ($p/x=$i) and ($p/y = $j) and ($p/z = $k) and ($p/time >= $tmin) and ($p/time <= $tmax) return <info> <coord> {$i, $j, $k} </x-coord> <summary> { analyze($p) } </summary> </info> )

15. Low-Level Schema <file name="info"> <sequence> <group name="data"> <attribute name="time"> <datatype> integer </datatype> <dataspace> <rank> 1 </rank> <dimension> [1] </dimension> </dataspace> </attribute> <dataset name="velocity"> <datatype> float </datatype> <dataspace> <rank> 1 </rank> <dimension> [x] </dimension> </dataspace> </dataset> .............. </group> </sequence> </file>

16. Mapping Schema //high/data/velocity //low/info/data/velocity //high/data/time //low/info/data/time //high/data/mom //low/info/data/mom [index(//low/info/data/velocity, 1)] //high/data/x //low/coord/x [index(//low/info/data/velocity, 1)]

17. Compiler Analysis • Problem with direct translation : • Each let expression involves complete scan over dataset • So final code will need several passes over the data • Solution : • Apply Data Centric Transformations to read a portion HDF5 dataset only once

18. Requires 3 Scans Naïve Strategy Output Dataset

19. Data Centric Strategy Output Datasets Requires just one scan

20. Data Centric Transformation • Overall Idea in Data-Centric Transformation • Iterate over each data element in actual storage • Find out iterations of the original loop in which they are accessed. • Execute computation corresponding to those iterations. • Previous Work • Pingali et al.: blocking • Ferreira and Agrawal: data-parallel Java on disk-resident datasets • Li and Agrawal: XQuery, invert getData functions • Our contribution: • Use Low-Level and Mapping Schema • Extend the idea when multiple datasets need to be accessed

21. Data Centric Transformation • Mapping Function T : Iteration space → High-Level data • Mapping Function C : High-Level data → Low-Level data • Mapping Function C · T = M : Iteration space → Low-Level data • Our Goal is to compute M-1.

22. Data Centric Transformation • Choose one dataset as base dataset S1 from n datasets to be accessed • Apply M1-1 to compute set of iterations. • The expression Mi·M1-1 gives the portion of dataset Si that needs to be accessed along with S1 • Choice of base dataset might impact the data locality.

23. Choice of Base Dataset • Min-IO-Volume Strategy • Minimize repeated access to any dataset • Min-Seek-Time Strategy • Minimize any discontinuity in access

24. Template for Generated Code Generated_Query { Create an abstract iteration space using Source code. Allocate and initialize an array of output element corresponding to iteration space. For k = 1, …, NO_OF_CHUNKS { Read kth chunk of dataset S1 using HDF5 functions and structural tree. Foreach of the other datasets S2, … , Sn access the required chunk of the dataset. Foreach data element in the chunks of data { compute the iteration instance. apply the reduction computation and update the output. } } }

25. Experiment 200*200*200 grid with 10 time steps (1.28 GB) 50*50*50 Storage Chunk Size

26. Experiment 50*50*50 grid with 200 time steps (400 MB) 25*25*25 Storage Chunk Size

27. Key Observations • Overall minimum execution time • Min-IO-Volume strategy when read chuck size matches storage chunk size • Execution time • Very sensitive to Read Chunk-Size in Min-IO-Volume Strategy • Not sensitive to Read Chunk-Size in Min-Seek-Time Strategy due to buffering of Storage chunks

28. Summary • Compiler techniques • Support High-level abstractions on complex low-level data formats • Enables use of the same source code across a variety of data formats • Perform data centric transformations automatically • Experimental result shows minor change in strategy can affect performance significantly • Future Work • Cost models to guide strategy and chunk size selection • Compare performance with manual implementations • parallelizing data processing • extend applicability of the algorithm to more general class of queries