Multi-dimensional Index on Hadoop Distributed File System

1
Multi-dimensional Index on Hadoop Distributed
File System

• Haojun Liao, Jizhong Han and Jinyun Fang

- Vikas Gonti
2
Contents

• Introduction
• Design Overview
• Details of Implementation
• Experimental Evaluation
• Conclusion and Future work

3
Introduction

• Why we need to improve the query performance?
• Large Voluminous Data
• Cost
• Scalability
• Spatial access methods.

4
HDFS

• HDFS is an open source implementation of DFS

5
Index Structure in HDFS

• HDFS offers an effective way of manipulating and
  maintenance index as in the single processor
  environment.
• No significant modifications of the original
  index structure are required to enable its
  appropriate function.
• Query processing using index can be integrated
  with MapReduce framework towards query
  efficiency.
• Answering queries by using access methods can be
  more efficient than by using sequential scanning.

6
Design Overview

• R-Tree like Index Structure
• a disk-based hierarchical structure based on
  B-tree, is one of the most common used
  multi-dimensional index.
• R-Tree node is implemented as a disk page.
• Index nodes are of the same size in a index.

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Structure of Index Frame
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Query Processing

• Only a small portion of index nodes are involved
  in handling high selectivity query types, i.e.,
  point query, range query, and nearest neighbor
  query.

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Details of Implementation

• Buffer Management
• Node Size
• Index Structure
• Query Optimization issues
• Data Transfer Model

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Details of Implementation

• Buffer Management
• Different strategies are applied to internal
  nodes and leaf nodes of index.
• Internal nodes accounts for small fraction of
  total space.
• All the internal nodes are kept in buffer once
  loaded.
• Access frequently.
• In Contrast,
• Only a certain number of buffer pages are
  allocated for leaf nodes.
• Relatively large space.
• Visited on demand.
• More buffer for leaf nodes will decrease the disk
  access and reduce the response time.

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Buffer Management (cont..)

• Buffer strategy
• Internal nodes are pinned in buffer once loaded
  for future node access.
• Leaf nodes are allocated limited number of buffer
  pages averagely distributed in each data nodes
  that are managed by LRU policy
• Data transferring procedure
• Once the data request emerges, data node check
  its buffer first.
• If the required data packet is hold in the
  buffer, it is sent to client.
• Otherwise, disk access is invoked.

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Node Size

• Many factors are involved in the determination of
  index node size, such as transfer overhead, I/O
  costs and CPU time.
• Small Index nodes incurs more data transmission
  times.
• Large index node will be used in order to reduce
  the data transfer costs.
• Large node requires more data to be processed in
  query
• The size of the index node should be aligned to
  the size of data packet, the minimum unit for
  data transferring in HDFC.
• Avoid the transmitting of the unnecessary data.

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Index Structure.

• Meta-data locates in the front of file,
  accounting for 1kb disk space.
• Internal nodes, as well as metadata, need to fit
  in the first data chunk.
• The rest space of the first data chunk of the
  index file is left blank for the extension of
  internal nodes.

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Index Structure (cont..)

• Leaf nodes are grouped in several data chunks,
  according to the location proximity.
• We align the next leaf node to the start position
  of the next data chunk.
• In internal node, the entry information includes
  the sub-tree identifications and the MBR
• In leaf node, entry information includes the MBR
  information and pointer to corresponding data
  object.

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Query Optimization issues

• Ordered entries can speed up the in-memory search
  procedure by reducing computing needs.
• Once the entries of each index nodes is sorted
  according to some spatial criterion, the order
  can be well preserved in a static environment.
• The point and range query processing within each
  node costs O(n/2w) time, where n is the number
  of entries in each node and w is the cardinality
  of the object set.

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Data Transfer Model

• Data node pushes data to client in the form of
  data packet of 64k by default to obtain the best
  performance for sequential reads.
• We implement a new data transfer protocol to
  facilitate the random reads for block-based index
  structure.
• The main difference with PUSH model is that Data
  node is blocked after transmitting one data
  packet.
• Data node is blocked in favor of the random
  access where the client might not need the
  sequential data packets.

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Data Transfer Model (Cont..)
Original Push Model
Our Transfer Model
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Experimental Evaluations

• Datasets
• The following real world datasets are used for
  the experimentation.
• CAR contains 2,249,727 road segments extracted
  from Tiger/Line datasets
• HYD contains 40,995,718 line segments
  representing rivers of China and
• TLK contains up to 157,425,887 points extracted
  from the elevation data of China.

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Data transfer overhead

• Transferred data is compared with the required
  data by varying size and count of read operations
  on HYD data set.

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Performance

• This result is based on TLK dataset. The data
  packet during transfer ranges from 8k to 64.
• This new protocol offers the best performance for
  all access patterns when the data packet is set
  to 64k.

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Effects of index node size

• Response time of range query and point query on
  CAR dataset.
• The worst performance of range query is when the
  index node size is set 16k, and the performance
  degenerates rapidly with the increase of query
  window.
• The response time for point query is longest when
  index node size is set 8k because the index has
  more level and more node visits are involved with
  smaller node size.

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Effects of Buffer

• Vary the buffer size to evaluate the effects of
  buffer on query performance, whereas other
  parameters are kept constant.
• We perform 100 range queries of 1 of total space
  on TLK dataset.
• The response time decreases as the available
  buffer increases. The further improvement of
  response time can be expected with the increase
  of buffer size.

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Conclusion Future work

• Conclusion
• We propose a method for organizing hierarchical
  structures applied to both B-tree and R-tree on
  HDFS.
• We investigate several systematic parameters like
  node size, index distribution, buffer, and query
  processing techniques
• Data transfer protocol specified for block-wise
  random reads integrate with HDFS
• Future work
• Investigate the problem of combination of
  MapReduce and index structure.
• Explore efficient multi-dimensional data
  distribution strategy according to index
  structure to further enhance the I/O performance

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• Questions ?

-- Thank You