The document discusses time series data processing using Apache Spark. It begins with an introduction of the speaker and an overview of Spark. It then describes the structure and flow of maritime location data. The challenges of processing this streaming time series data are discussed, including delays in data arrival that can cause holes in computations. The document outlines the evolution of their solution from using MongoDB to using Apache Spark with Cassandra and S3 for improved performance and scalability. It concludes with lessons about understanding data characteristics and choosing the right persistence layers.

1. S3, Cassandra or Outer Space? Dumping
Time Series Data using Spark
Demi Ben-Ari - VP R&D @
ROME 24-25 MARCH 2017

2. About Me
Demi Ben-Ari, Co-Founder & VP R&D @ Panorays
● BS’c Computer Science – Academic College Tel-Aviv Yaffo
● Co-Founder
○ “Big Things” Big Data Community
○ Google Developer Group Cloud
In the Past:
● Sr. Data Engineer - Windward
● Team Leader & Sr. Java Software Engineer
Missile defense and Alert System - “Ofek” – IAF
Interested in almost every kind of technology – A True Geek

3. Agenda
● Spark brief overview and Catch Up
● Data flow and Environment
● What’s our time series data like?
● Where we started from - where we got to
○ Problems and our decisions
○ Evolution of the solution
● Conclusions

4. Spark Brief Overview & Catchup

5. Scala & Spark (Architecture)
Scala REPL Scala Compiler
Spark Runtime
Scala Runtime
JVM
File System
(eg. HDFS,
Cassandra, S3..)
Cluster Manager
(eg. Yarn, Mesos)

6. What kind of DSL is Apache Spark
● Centered around Collections
● Immutable data sets equipped with functional transformations
● These are exactly the Scala collection operations
map
flatMap
filter
...
reduce
fold
aggregate
...
union
intersection
...

7. Spark is A Multi-Language Platform
● Why to use Scala instead of
Python?
○ Native to Spark, Can use
everything without
translation
○ Types help

8. So Bottom Line…
What’s Spark???

9. United Tools Platform

10. United Tools Platform - Single Framework
Batch
InteractiveStreaming
Single Framework

11. Data flow and Environment
(Our Use Case)

12. Structure of the Data
● Maritime Analytics Platform
● Geo Locations + Metadata
● Arriving over time
● Different types of messages being reported by satellites
● Encoded
● Might arrive later than actually transmitted

13. Data Flow Diagram
External
Data
Source
Analytics
Layers
Data Pipeline
Parsed
Raw
Entity Resolution
Process
Building insights
on top of the entities
Data Output
Layer
Anomaly
Detection
Trends

14. Environment Description
Cluster
Dev Testing
Live
Staging
ProductionEnv
OB1K
RESTful Java Services

15. Basic Terms
● Missing Parts in Time Series Data
◦ Data arriving from the satellites
● Might be causing delays because of bad transmission
◦ Data vendors delaying the data stream
◦ Calculation in Layers may cause Holes in the Data
● Calculating the Data layers by time slices

16. Basic Terms
● Idempotence
is the property of certain operations in mathematics and computer
science, that can be applied multiple times without changing the
result beyond the initial application.
● Function: Same input => Same output

17. Basic Terms
● Partitions == Parallelism
◦ Physical / Logical partitioning
● Resilient Distributed Datasets (RDDs) == Collections
◦ fault-tolerant collection of elements that can be operated on in
parallel.
◦ Applying immutable transformations and actions over RDDs

18. What RDD’s really are?

19. So…..

20. The Problem - Receiving DATA
Beginning state, no data, and the timeline
begins
T = 0
Level 3 Entity
Level 2 Entity
Level 1 Entity

21. The Problem - Receiving DATA
T = 10
Level 3 Entity
Level 2 Entity
Level 1 Entity
Computation sliding window size
Level 1 entities data arrives
and gets stored

22. The Problem - Receiving DATA
T = 10
Level 3 Entity
Level 2 Entity
Level 1 Entity
Computation sliding window size
Level 3 entities are created on
top of Level 2’s Data
(Decreased amount of data)
Level 2 entities are created
on top of Level 1’s Data
(Decreased amount of
data)

23. The Problem - Receiving DATA
T = 20
Level 3 Entity
Level 2 Entity
Level 1 Entity
Computation sliding window size
Because of the sliding window’s
back size, level 2 and 3 entities
would not be created properly and
there would be “Holes” in the Data
Level 1 entity's
data arriving late

24. Solution to the Problem
● Creating Dependent Micro services forming a data pipeline
◦ Mainly Apache Spark applications
◦ Services are only dependent on the Data - not the previous
service’s run
● Forming a structure and scheduling of “Back Sliding Window”
◦ Know your data and its relevance through time
◦ Don’t try to foresee the future – it might Bias the results

25. Starting point & Infrastructure

26. How we started?
● Spark Standalone – via ec2 scripts
◦ Around 5 nodes (r3.xlarge instances)
◦ Didn’t want to keep a persistent HDFS – Costs a lot
◦ 100 GB (per day) => ~150 TB for 4 years
◦ Cost for server per year (r3.xlarge):
- On demand: ~2900$
- Reserved: ~1750$
● Know your costs: http://www.ec2instances.info/

27. Know Your Costs

28. Decision
● Working with S3 as the persistence layer
◦ Pay extra for
- Put (0.005 per 1000 requests)
- Get (0.004 per 10,000 requests)
◦ 150TB => ~210$ for 4 years of Data
● Same format as HDFS (CSV files)
◦ s3n://some-bucket/entity1/201412010000/part-00000
◦ s3n://some-bucket/entity1/201412010000/part-00001
◦ ……

29. What about the serving?

30. MongoDB for Serving
Worker 1
Worker 2
….
….
…
…
Worker N
MongoDB
Replica Set
Spark
Cluster
Master
Write
Read

31. Spark Slave - Server Specs
● Instance Type: r3.xlarge
● CPU’s: 4
● RAM: 30.5GB
● Storage: ephemeral
● Amount: 10+

32. MongoDB - Server Specs
● MongoDB version: 2.6.1
● Instance Type: m3.xlarge (AWS)
● CPU’s: 4
● RAM: 15GB
● Storage: EBS
● DB Size: ~500GB
● Collection Indexes: 5 (4 compound)

33. The Problem
● Batch jobs
◦ Should run for 5-10 minutes in total
◦ Actual - runs for ~40 minutes
● Why?
◦ ~20 minutes to write with the Java mongo driver – Async
(Unacknowledged)
◦ ~20 minutes to sync the journal
◦ Total: ~ 40 Minutes of the DB being unavailable
◦ No batch process response and no UI serving

34. Alternative Solutions
● Sharded MongoDB (With replica sets)
◦ Pros:
- Increases Throughput by the amount of shards
- Increases the availability of the DB
◦ Cons:
- Very hard to manage DevOps wise (for a small team of
developers)
- High cost of servers – because each shared need 3 replicas

35. Workflow with MongoDB
Worker 1
Worker 2
….
….
…
…
Worker N
Spark
Cluster
Master
Write
Read
Master

36. Our DevOps – After that solution
We had no
DevOps guy at
that time at all
☹

37. Alternative Solutions
● Apache Cassandra
◦ Pros:
- Very large developer community
- Linearly scalable Database
- No single master architecture
- Proven working with distributed engines like Apache Spark
◦ Cons:
- We had no experience at all with the Database
- No Geo Spatial Index – Needed to implement by ourselves

38. The Solution
● Migration to Apache Cassandra
● Create easily a Cassandra cluster using DataStax Community
AMI on AWS
◦ First easy step – Using the spark-cassandra-connector
(Easy bootstrap move to Spark ⬄ Cassandra)
◦ Creating a monitoring dashboard to Cassandra

39. Workflow with Cassandra
Worker 1
Worker 2
….
….
…
…
Worker N
Cassandra
Cluster
Spark
Cluster
Write
Read

40. Result
● Performance improvement
◦ Batch write parts of the job run in 3 minutes instead of ~ 40
minutes in MongoDB
● Took 2 weeks to go from “Zero to Hero”, and to ramp up a
running solution that work without glitches

41. So (Again)?

42. Transferring the Heaviest Process
● Micro service that runs every 10 minutes
● Writes to Cassandra 30GB per iteration
◦ (Replication factor 3 => 90GB)
● At first took us 18 minutes to do all of the writes
◦ Not Acceptable in a 10 minute process

43. Cluster On OpsCenter - Before

44. Transferring the Heaviest Process
● Solutions
◦ We chose the i2.xlarge
◦ Optimization of the Cluster
◦ Changing the JDK to Java-8
- Changing the GC algorithm to G1
◦ Tuning the Operation system
- Ulimit, removing the swap
◦ Write time went down to ~5 minutes (For 30GB RF=3)
Sounds good right? I don’t think so

45. Cloud Watch After Tuning
Cloud Watch After Tuning

46. The Solution
● Taking the same Data Model that we held in Cassandra (All of the
Raw data per 10 minutes) and put it on S3
◦ Write time went down from ~5 minutes to 1.5 minutes
● Added another process, not dependent on the main one,
happens every 15 minutes
◦ Reads from S3, downscales the amount and Writes them to
Cassandra for serving

47. Parsed
Raw
Static /
Aggregated
Data
Spark Analytics Layers
UI Serving
Downscaled
Data
Heavy
Fusion
Process
How it looks after all?

48. Conclusion
● Always give an estimate to your data
◦ Frequency
◦ Volume
◦ Arrangement of the previous phase
● There is no “Best” persistence layer
◦ There is the right one for the job
◦ Don’t overload an existing solution

49. Conclusion
● Spark is a great framework for distributed collections
◦ Fully functional API
◦ Can perform imperative actions
● “With great power,
comes lots of partitioning”
◦ Control your work and
data distribution via partitions
● https://www.pinterest.com/pin/155514993354583499/ (Thanks)

50. Questions?

51. ● LinkedIn
● Twitter: @demibenari
● Blog:
http://progexc.blogspot.com/
● demi.benari@gmail.com
● “Big Things” Community
Meetup, YouTube, Facebook,
Twitter
● GDG Cloud