Why We Chose MongoDB for Big Spatial Data

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
ACCOMPLISHING THE IMPOSSIBLE

• 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

Desired Data Store Characteristic for ‘Big Data’

• 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

Subset of Evaluated NoSQL Options
• 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

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

... 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

MONGODB SPATIAL INDEXER

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

DIMENSIONALITY REDUCTION

Issues with the Geohash b-Tree approach

• 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

GEOHASH BTREE ISSUES

Mongo Multi-location Document Clipping Issues
($within search doesn’t always work w/ multi-location)
Case 1: Success! Case 3: Fail!

Case 2: Success! Case 4: Fail!

Multi-Location Document (aka. Polygon) Search Polygon
MULTI-LOCATION CLIPPING

Potential Solutions

• 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

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)

CUSTOM TUNED SPATIAL INDEXER

Spatial Indexing at Scale with R-Trees

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 dimensions
Each I is a set in the form of [min,max] describing MBR range along a dimension

RTREE THEORY

mn o p
R*-Tree Spatial Index Example
• Sample insertion result for 4th order
tree
• Objectives: a b cd e f g h i jk l

1. Minimize area
2. Minimize overlaps
3. Minimize margins
4. Maximize inner node utilization

R*-TREE INDEX OBJECTIVES

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

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.
n
m

o p

• Insert into m.

m

• Insert into n.

n

• Insert into o.

o

• Insert into p.

p

mn o p

Query
• Start at root a b cd e f g h i jk l
• Find all overlapping MBRs
• Search subtrees recursively

n
m
a

o p
a x
a

mn o p

Query
a b cd e f g h i jk l
• Search m.

e n
a m
a
a
b
a g a
o p
c
d x
x

R*-Tree Leverages B-Tree Base Data Structures (buckets)

R*-TREE MONGODB IMPLEMENTATION

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

SD-r*Tree Data Structure Illustration

a a a
c c

d0 r1 b r1 b
Data Node Spatial
Coverage

c
b d0 d1 c b d0 r2 d
e

e d1 d2 d

• di = Data Node (Chunk)
• ri = Coverage Node
Leveraged work from Litwin, Mouza, Rigaux 2007

SD-r*Tree DATA STRUCTURE

SD-r*Tree Structure Distribution

a
c GeoShard 2 GeoShard 3

r1 b
d1 d2

mongos
c
b d0 r2 d
r1 r2 GeoShard 1
e

d0
e d1 d2 d

SD-r*TREE STRUCTURE DISTRIBUTION

GeoSharding Alternative – 3D / 4D Hilbert Scanning Order

GEO-SHARDING ALTERNATIVE

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

X-Tree Performance Results
(Berchtold, Keim, Kriegel – 1996)

X-TREE PERFORMANCE

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

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

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

THANK YOU
Questions?

Nicholas Knize
nknize@t-sciences.com

THANK YOU

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

ENTERPRISE
PARTNER

WHO ARE THESE GUYS?

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

Key Customers - Commercial

Cleveland USGIF Las Vegas Baltimore
Cavaliers Motor Speedway Grand Prix

iSpatial framework serves thousands of mobile devices
COMMERCIAL CUSTOMERS

Why We Chose MongoDB for Big Spatial Data

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Semelhante a Why We Chose MongoDB for Big Spatial Data

Semelhante a Why We Chose MongoDB for Big Spatial Data (20)

Último

Último (20)

Why We Chose MongoDB for Big Spatial Data

Notas do Editor