ACM DEBS Grand Challenge: Continuous Analytics on Geospatial Data Streams with WSO2 Complex Event Processor

DEBS Grand Challenge: Continuous
Analytics on Geospatial Data
Streams with WSO2 Complex Event
Processor
Sachini Jayasekara, Srinath Perera,
Miyuru Dayarathna,
Sriskandarajah Suhothayan
WSO2 Inc.

Problem
o Dataset Taxi rides collected
from New York in year 2013[1]
o Each line has timestamp, start
end locations, fare details etc.
o 13K cars, 173 million events
o 2 Queries
o Queries based on 0.5km and
0.25km cells over New York.
[1]. Chris Whong (http://chriswhong.com/open-data/foil_nyc_taxi/)

CEP Operators
1. Filters or transformations (process a single event)
from Ball[v>10]
select .. insert into ..
2. Windows + aggregation (track window of events:
time, length)
from Ball#window.time(30s) select avg(v) ..
3. Joins (join two event streams to one)
from Ball#window.time(30s) as b join Players as p on p.v <
b.v
4. Patterns (state machine implementation)
from Ball[v>10], Ball[v<10]*,Ball[v>10] select ..
5. Event tables (map a database as an event stream)
Define table HitV (v double) using .. db info ..

Complex Event Processing
see http://goo.gl/BaPFYA for more info.

Query 1: Frequent Routes
o Output 10 most frequent routes in last 30 minutes
o Need to output when value has changed ( current
time derived from event’s timestamp attribute)

Query 2: Profitable Areas
o Find the cells that are most profitable for taxi
drivers at the given moment.
o Profitability = median (fare + tip) for last 15 minutes
divided by the number of taxi drivers who have
dropped-off and have not taken a new trip in the
last 30 minutes per cell.

Optimizations
o WSO2 CEP
o Object Pooling
o Only keep required Attributes (e.g., in window)
o Algorithmic
o String Lookup
o Reusing windows
o Avoid Join
o FrequentK
o Counting Pattern
o Median (Bucket)
o Fully use the computer

Avoid Joins
o Q2 process median and taxi counting in parallel
o But join is expensive due to ordering
o Instead, calculate median, enrich the event with
results, use enriched event to calculate empty taxi,
then divide median by empty taxi without a join.

Taxi Counting Pattern Optimizations
o Query creates a state machine to track taxi’s state,
and update counts accordingly
o Slow with CEP pattern as it searches all states to
check for expiration
o Fixed by keeping states sorted by starting time (2X
improvement)

Fully use the Computer
o So far, we remove unnecessary operations!!
o Now we have to use all 4 cores of the VM
o How?
o Data Partition
o Pipeline
o Pipeline with single buffer

Data Partition : Issues
o Need to reorder and send timing updates
o But savings due to partition is small (e.g. frequentK is O(log
(n)) and execution in a partition take O(log(n/p))
o All savings lost when reordering

Execution Pipeline
o Break different stages to a pipeline
o Now we can use 6 threads ( 1 and 6 does IO so OK)
o 125K/sec now, but 50ms latency
o Bottleneck is moving events between queues

Circular Buffer based Pipeline
o One circular buffer with sequence barriers using
LMAX disruptor
o Avoid cost of moving events, reduce GC, and works
well with the cache
o 2X more throughput and 0ms latency

Results
o Pretty good on real HW (8 core) and AWS ( 4 core),
but not as good on VirtualBox ( 4 core)
o Can run on 512M heap size with only 10% slowdown

Results: Speedup vs. Concurrency
o Compared against single node version
o Real HW scaled well, AWS less and VM scale up was
very small

Results: Latency vs. Throughput
o each point is (env, thread count, size of buffer)

Conclusion
o All changes except final
circular buffer in WSO2
CEP 4.0 ( released 2015
Q3)
o WSO2 CEP is free and
available under Apache
Open source Licence
o Fast and flexible, and
already used in many
critical use cases.

ACM DEBS Grand Challenge: Continuous Analytics on Geospatial Data Streams with WSO2 Complex Event Processor

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ACM DEBS Grand Challenge: Continuous Analytics on Geospatial Data Streams with WSO2 Complex Event Processor