Optimizing Search in Un-Sharded Large-Scale Distributed...
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Optimizing Search in Un-Sharded Large-Scale Distributed Systems This work is supported in part by the National Science Foundation under awards NSF-1461260 (REU). OVERVIEW Storage systems are increasingly distributed Discovery and access are crucial for management and analysis of data Nodes in unsharded environments more autonomous and network traffic decreased No general model for searching in unsharded environments. MOTIVATION •Search Discover files based on names and contents Emphasis on speed and scalability Support for near-real-time discovery •Environment Each document remains intact on each node Information stored in system not necessarily balanced among nodes OBJECTIVES • Lucene Handles indexing, query processing, searching and scoring of documents Near real time indexing to search capabilities New challenges relating to efficiently discovering, accessing, managing, and analyzing distributed data Search framework does not rely on sharding Applicable to a range of distributed storage models Compares hierarchical index structure • Test Bed 90000 Wiki documents per m3.large node common, rare, non-existent queries Evaluated against Solr and Grep ARCHITECTURE • Server-Client Model Client interface and a server exists on each node Server gets query and begins searching while taking care of query distribution and result collection • Query Distribution Spanning tree constructed using membership list of nodes, allowing for dynamic changes in cluster membership Spanning tree allows queries to be distributed efficiently and reduces network traffic Optimized by sending queries to nodes with larger indexes first, which are more likely to have a longer searchtime RESULTS • Results Lower overhead Faster and scaled better than Solr and Grep Easy to integrate fast, scalable text search for unsharded environments Tree-based query distribution model Faster search than alternatives when scaled CONTRIBUTION Evaluation with ElasticSearch Fault Tolerance Smarter distribution structure Integration into Globus and FusionFS EVALUATION Suraj Chafle 1 , Jonathan J. Wu 2 , Ioan Raicu 1 , Kyle Chard 3 1 Department of Computer Science, Illinois Institute of Technology, Chicago, IL 2 Department of Computer Science, Washington University in St. Louis, St. Louis, MO 3 Department of Computer Science,` University of Chicago, Chicago, IL 0.01 0.1 1 10 100 1000 3 7 15 31 Time of Search (s) Number of Nodes Grep: Common DistSearch: Common Solr: Common Grep: Nonexistant DistSearch: Nonexistant Solr: Nonexistant Grep: Rare DistSearch: Rare Solr: Rare FUTURE WORK ACKNOWLEDGEMENTS
Transcript of Optimizing Search in Un-Sharded Large-Scale Distributed...
Optimizing Search in Un-Sharded Large-Scale Distributed Systems
This work is supported in part by the NationalScience Foundation under awards NSF-1461260(REU).
OVERVIEW
Storage systems are increasingly distributed
Discovery and access are crucial for management and analysis of data
Nodes in unsharded environments more autonomous and network traffic decreased
No general model for searching in unshardedenvironments.
MOTIVATION
•Search
Discover files based on names and contents
Emphasis on speed and scalability
Support for near-real-time discovery
•Environment
Each document remains intact on each node
Information stored in system not necessarily balanced among nodes
OBJECTIVES
• Lucene
Handles indexing, query processing, searching andscoring of documents
Near real time indexing to search capabilities
New challenges relating to efficiently discovering, accessing, managing, and analyzing distributed data
Search framework does not rely on sharding
Applicable to a range of distributed storage models
Compares hierarchical index structure
• Test Bed
90000 Wiki documents per m3.large node
common, rare, non-existent queries
Evaluated against Solr and Grep
ARCHITECTURE
• Server-Client Model
Client interface and a server exists on each node
Server gets query and begins searching whiletaking care of query distribution and resultcollection
• Query Distribution
Spanning tree constructed using membership listof nodes, allowing for dynamic changes in clustermembership
Spanning tree allows queries to be distributedefficiently and reduces network traffic
Optimized by sending queries to nodes with largerindexes first, which are more likely to have alonger searchtime
RESULTS
• Results
Lower overhead
Faster and scaled better than Solr and Grep
Easy to integrate fast, scalable text search forunsharded environments
Tree-based query distribution model
Faster search than alternatives when scaled
CONTRIBUTION
Evaluation with ElasticSearch
Fault Tolerance
Smarter distribution structure
Integration into Globus and FusionFS
EVALUATION
Suraj Chafle1, Jonathan J. Wu2, Ioan Raicu1, Kyle Chard3
1Department of Computer Science, Illinois Institute of Technology, Chicago, IL2Department of Computer Science, Washington University in St. Louis, St. Louis, MO
3Department of Computer Science,` University of Chicago, Chicago, IL
0.01
0.1
1
10
100
1000
3 7 15 31
Tim
e o
f S
ea
rch
(s
)
Number of Nodes
Grep: Common
DistSearch: Common
Solr: Common
Grep: Nonexistant
DistSearch: Nonexistant
Solr: Nonexistant
Grep: Rare
DistSearch: Rare
Solr: Rare
FUTURE WORK
ACKNOWLEDGEMENTS
