RTree Spatial Indexing with MongoDB - MongoDC
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Transcript of RTree Spatial Indexing with MongoDB - MongoDC
WHY WE CHOSE MONGODB TO PUT BIG-DATA ‘ON THE MAP’
JUNE 2012
@nknize+Nicholas Knize
“The 3D UDOP allows near real time visibility of all SOUTHCOM Directorates information in one location…this capability allows for unprecedented situational awareness and information sharing”
-Gen. Doug Frasier
TST PRODUCTS
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• Expose enterprise data in a geo-temporal user defined environment
• Provide a flexible and scalable spatial indexing framework for heterogeneous data
• Visualize spatially referenced data on 3D globe & 2D maps• Manage real-time data feeds and mobile messaging • View data over geo-rectified imagery with 3D terrain• Support mission planning and simulation• Provide real-time collaboration and sharing
ISPATIAL OVERVIEW
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• Horizontally scalable – Large volume / elastic
• Vertically scalable – Heterogeneous data types (“Data Stack”)
• Smartly Distributed – Reduce the distance bits must travel
• Fault Tolerant – Replication Strategy and Consistency model
• High Availability – Node recovery
• Fast – Reads or writes (can’t always have both)BIG DATA STORAGE CHARACTERISTICS
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Desired Data Store Characteristic for ‘Big Data’
• Cassandra– Nice Bring Your Own Index (BYOI) design– … but Java, Java, Java… Memory management can be an issue– Adding new nodes can be a pain (Token Changes, nodetool)– Key-Value store…good for simple data models
• Hbase– Nice BigTable model– Theory grounded heavily in C.A.P, inflexible trade-offs– Complicated setup and maintenance
• CouchDB– Provides some GeoSpatial functionality (Currently being rewritten)– HEAVILY dependent on Map-Reduce model (complicated design)– Erlang based – poor multi-threaded heap management
NOSQL OPTIONS
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Subset of Evaluated NoSQL Options
Why MongoDB for Thermopylae?• Documents based on JSON – A GEOJSON match made in heaven!
• C++ - No Garbage Collection Overhead! Efficient memory management design reduces disk swapping and paging
• Disk storage is memory mapped, enabling fast swapping when necessary
• Built in auto-failover with replica sets and fast recovery with journaling
• Tunable Consistency – Consistency defined at application layer
• Schema Flexible – friendly properties of SQL enable easy port
• Provided initial spatial indexing support – Point based limited!WHY TST LIKES MONGODB
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MONGODB SPATIAL INDEXER
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... The Spatial Indexer wasn’t quite right
• MongoDB (like nearly all relational DBs) uses a b-Tree – Data structure for storing sorted data in log time– Great for indexing numerical and text documents (1D attribute data)– Cannot store multi-dimension (>2D) data – NOT COMPLEX GEOMETRY
FRIENDLY
DIMENSIONALITY REDUCTION
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How does MongoDB solve the dimensionality problem?
• Space Filling (Z) Curve – A continuous line that
intersects every point in a two-dimensional plane
• Use Geohash to represent lat/lon values– Interleave the bits of a
lat/long pair– Base32 encode the result
GEOHASH BTREE ISSUES
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• Neighbors aren’t so close!– Neighboring points on the
Geoid may end up on opposite ends of the plane
– Impacts search efficiency
• What about Geometry?– Doesn’t support > 2D– Mongo uses Multi-
Location documents which really just indexes multiple points that link back to a single document
Issues with the Geohash b-Tree approach
Case 3:
Case 4:
Multi-Location Document (aka. Polygon) Search Polygon
Case 1:
Case 2:
Success!
Success!
Fail!
Fail!
Mongo Multi-location Document Clipping Issues($within search doesn’t always work w/ multi-location)
MULTI-LOCATION CLIPPING
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• Constrain the system to single point searches– Multi-dimension support will be exponentially complex (won’t scale)
• Interpolate points along the edge of the shape– Multi-dimension support will be exponentially complex (won’t scale)
• Customize the spatial indexer– Selected approach
SOLUTIONS TO GEOHASH PROBLEM
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Potential Solutions
CUSTOM TUNED SPATIAL INDEXER
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Thermopylae Custom Tuned MongoDB for Geo
TST Leverage’s Guttman’s 1984 Research in R/R* Trees• R-Trees organize any-dimensional data by representing
the data as a minimum bounding box. • Each node bounds it’s children. A node can have many
objects in it (max: m min: ceil(m/2) )• Splits and merges optimized by minimizing overlaps• The leaves point to the actual objects (stored on disk
probably)• Height balanced – search is always O(log n)
Spatial Indexing at Scale with R-Trees
RTREE THEORY
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Spatial data represented as minimum bounding rectangles (2-dimension), cubes (3-dimension), hexadecant (4-dimension)
Index represented as: <I, DiskLoc> where:
I = (I0, I1, … In) : n = number of dimensionsEach I is a set in the form of [min,max] describing MBR range along a
dimension
R*-Tree Spatial Index Example• Sample insertion result for 4th order
tree• Objectives:
1. Minimize area2. Minimize overlaps3. Minimize margins4. Maximize inner node utilization
a b cd e f g h i j k l
m n o p
R*-TREE INDEX OBJECTIVES
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Insert
• Similar to insertion into B+-tree but may insert into any leaf; leaf splits in case capacity exceeded.– Which leaf to insert into?– How to split a node?
R*-TREE INSERT EXAMPLE
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Insert—Leaf Selection
• Follow a path from root to leaf.• At each node move into subtree whose MBR area
increases least with addition of new rectangle.
mn
o p
Modified from Dr. Sahni (UoF Advanced Data Structures)
Insert—Leaf Selection
• Insert into m.
m
Modified from Dr. Sahni (UoF Advanced Data Structures)
Insert—Leaf Selection
• Insert into n.
n
Modified from Dr. Sahni (UoF Advanced Data Structures)
Insert—Leaf Selection
• Insert into o.
o
Modified from Dr. Sahni (UoF Advanced Data Structures)
Insert—Leaf Selection
• Insert into p.
p
Modified from Dr. Sahni (UoF Advanced Data Structures)
mn
o p
aa
a
x
a b cd e f g h i j k l
m n o p
Query• Start at root• Find all overlapping MBRs• Search subtrees recursively
Modified from Dr. Sahni (UoF Advanced Data Structures)
Query
• Search m.
mn
o p
a
a
x x
a b cd e f g h i j k l
m n o p
a
aa
b
cd
e
g
Modified from Dr. Sahni (UoF Advanced Data Structures)
R*-Tree Leverages B-Tree Base Data Structures (buckets)
R*-TREE MONGODB IMPLEMENTATION
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Geo-Sharding – (in work)Scalable Distributed R* Tree (SD-r*Tree)
“Balanced” binary tree, with nodes distributed on a set of servers:
• Each internal node has exactly two children
• Each leaf node stores a subset of the indexed dataset
• At each node, the height of the subtrees differ by at most one
• mongos “routing” node maintains binary tree
GEO-SHARDING
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d0 d1
r1d0Data Node Spatial
Coverage
a a
b
c
cb d0
r1
a
b
c
c
b
d2d1
ed
d
r2
e
SD-r*Tree Data Structure Illustration
• di = Data Node (Chunk)• ri = Coverage Node
Leveraged work from Litwin, Mouza, Rigaux 2007
SD-r*Tree DATA STRUCTURE
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SD-r*Tree Structure Distribution
d0
r1
a
b
c
c
b
d2d1
ed
d
r2
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r2
d1 d2
d0
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GeoShard 2 GeoShard 3
GeoShard 1
mongos
SD-r*TREE STRUCTURE DISTRIBUTION
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GeoSharding Alternative – 3D / 4D Hilbert Scanning Order
GEO-SHARDING ALTERNATIVE
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Next Steps: Beyond 4-Dimensions - X-Tree(Berchtold, Keim, Kriegel – 1996)
Normal Internal Nodes Supernodes Data Nodes
• Avoid MBR overlaps
• Avoid node splits (main cause for high overlap)
• Introduce new node structure: Supernodes – Large Directory nodes of variable size
BEYOND 4-DIMENSIONS
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X-TREE PERFORMANCE
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X-Tree Performance Results(Berchtold, Keim, Kriegel – 1996)
T-Sciences Custom Tuned Spatial Indexer
• Optimized Spatial Search – Finds intersecting MBR and recurses into those nodes
• Optimized Spatial Inserts – Uses the Hilbert Value of MBR centroid to guide search – 28% reduction in number of nodes touched
• Optimize Deletes – Leverages R* split/merge approach for rebalancing tree when nodes become over/under-full
• Low maintenance – Leverages MongoDB’s automatic data compaction and partitioning
CONCLUSION
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Example Use Case – OSINT (Foursquare Data)
• Sample Foursquare data set mashed with Government Intel Data (poly reports)
• 100 million Geo Document test (3D points and polys)
• 4 server replica set
• ~350ms query response
• ~300% improvement over PostGIS
EXAMPLE
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Community Support
• Thermopylae contributes fixes to the codebase– http://github.com/mongodb
• TST will work with 10gen to fold into the baseline
• Active developer collaboration– IRC: #mongodb freenode.net
FIND US
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Backup
Thermopylae Sciences & Technology – Who are we?
• Advanced technology w/ 160+ employees• Core customers in national security, venues and
events, military and police, and city planning• Partnered with Google and imagery providers• Long term relationship focused – TS/SCI Staff TST + 10gen + Google = Game-changing approach
WHO ARE THESE GUYS?
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ENTERPRISEPARTNER
Key Customers - Government• US Dept of State Bureau of Diplomatic Security
– Build and support 30 TB Google Earth Globe with multi-terabytes of individual globes sent to embassies throughout the world. Integrated Google Earth and iSpatial framework.
• US Army Intelligence Security Command– Provide expertise in managing technology integration – prime
contractor providing operations, intelligence, and IT support worldwide. Partners include IBM, Lockheed Martin, Google, MIT, Carnegie Mellon. Integrated Google Earth and iSpatial framework.
• US Southern Command– Coordinate Intelligence management systems spatial data collection,
indexing, and distribution. Integrated Google Earth, iSpatial, and iHarvest.
– Index large volume imagery and expose it for different services (Air Force, Navy, Army, Marines, Coast Guard)
GOVERNMENT CUSTOMERS
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COMMERCIAL CUSTOMERS
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Key Customers - Commercial
ClevelandCavaliers
USGIF Las VegasMotor Speedway
BaltimoreGrand Prix
iSpatial framework serves thousands of mobile devices