S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Ben-Ari - Codemotion Rome...
-
Upload
codemotion -
Category
Technology
-
view
22 -
download
0
Transcript of S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Ben-Ari - Codemotion Rome...
S3, Cassandra or Outer Space? Dumping Time Series Data using Spark
Demi Ben-Ari - VP R&D @
ROME 24-25 MARCH 2017
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” – IAFInterested in almost every kind of technology – A True Geek
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
Scala & Spark (Architecture)
Scala REPL Scala Compiler
Spark Runtime
Scala Runtime
JVM
File System (eg. HDFS,
Cassandra, S3..)Cluster Manager (eg. Yarn, Mesos)
What kind of DSL is Apache Spark
● Centered around Collections● Immutable data sets equipped with functional transformations
● These are exactly the Scala collection operations
mapflatMap filter...
reducefold aggregate...
unionintersection ...
Spark is A Multi-Language Platform
● Why to use Scala instead of Python?○ Native to Spark, Can use
everything without translation
○ Types help
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
Data Flow Diagram
External Data
Source
Analytics Layers
Data Pipeline
Parsed Raw
Entity Resolution Process
Building insightson top of the entities
Data Output Layer
Anomaly Detection
Trends
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
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
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
The Problem - Receiving DATA
Beginning state, no data, and the timeline begins
T = 0
Level 3 Entity
Level 2 Entity
Level 1 Entity
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
The Problem - Receiving DATA
T = 10
Level 3 Entity
Level 2 Entity
Level 1 Entity
Computation sliding window sizeLevel 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)
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
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
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/
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◦ ……
MongoDB for Serving
Worker 1
Worker 2
….
….
…
…
Worker N
MongoDBReplica Set
Spark Cluster
Master
Write
Read
Spark Slave - Server Specs
● Instance Type: r3.xlarge● CPU’s: 4● RAM: 30.5GB● Storage: ephemeral● Amount: 10+
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)
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
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
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
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
Workflow with Cassandra
Worker 1
Worker 2
….
….
…
…
Worker N
Cassandra Cluster
Spark Cluster
Write
Read
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
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
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
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
Parsed Raw
Static / Aggregated
Data
Spark Analytics Layers
UI Serving
Downscaled Data
Heavy Fusion
Process
How it looks after all?
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
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)
● LinkedIn● Twitter: @demibenari● Blog:
http://progexc.blogspot.com/● [email protected]
● “Big Things” Community
�Meetup, YouTube, Facebook, Twitter
● GDG Cloud