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Scala: Lingua Franca of Fast Data
Jamie AllenSr. Director of Global Solutions Architects
• Why Scala?• Who is doing this?• What is Fast Data?• Architecting for Fast Data
Agenda
• Cloud portability versus native control• Application correctness versus speed of development• Modularity versus global namespace• Concise syntax versus boilerplate• Multi-threaded simplicity via abstractions versus low-level control
Tradeoffs
• REPL• Type safety• Modularity• Concise syntax• Multi-threaded simplicity• Data-centric semantics• Managed runtime for cloud portability• Ecosystem
Scala is the local optimum
Scala is the local optimum
The JVM is a primary reason for Scala’s success
• No REPL or Notebook• Not a data-centric language, particularly collections semantics
Why not Java?
• Data-centric language, has all of the wonderful collections semantics we want• No type safety• No modularity
Why not Python?
• Weak type safety• Collections are too elemental• Native execution is a non-starter, so Go is the only option• Garbage collection is not generational
Why not Go or C++?
• Scala just so happened to fit well in this space• Performance• Correctness• Conciseness
• Scala will evolve• Other languages will come in time
Scala is NOT the end of the road
Who is doing this?
One Caveat: Apache Beam and TensorFlow
Why Scala?At the time we started, I really wanted a PL that supports a language-integrated interface (where people write functions inline, etc)… However, I also wanted to be on the JVM in order to easily interact with the Hadoop filesystem and data formats for that. Scala was the only somewhat popular JVM language that offered this kind of functional syntax and was also statically typed (letting us have some control over performance), so we chose that. Today there might be an argument to make the first version of the API in Java with Java 8, but we also benefitted from other aspects of Scala in Spark, like type inference, pattern matching, actor libriaries, etc.Matei Zaharia, creator of Spark
What is Fast Data?
A bit of history: Hadoop
YARN
HDFS
MRjob#1
MRjob#2
Flume Sqoop
DBs
SlaveNode
DiskDiskDiskDiskDisk
NodeMgr
DataNode
Master
ResourceManager
NameNode
Worker
Hadoop strengths• Lowest capital expenditure for big data• Excellent for ingesting and integrating diverse datasets• Flexible
• Classic analytics (aggregations and data warehousing)• Machine learning
Hadoop weaknesses• Complex administration• YARN requires dedicated cluster• MapReduce foibles
• Poor performance• Imperative programming model• No stream processing support
Fast Data with Spark
Spark• 100x faster as a replacement for Hadoop MapReduce• Uses much fewer machines and resources• Incredible support from the community and enterprise
Spark use cases• Primarily anomaly detection
• Risk management• Fraud detection• Odds recalculation
• Spam filters• Update search engine results quickly
• Spark had it with RDDs• They removed it with the DataFrames API• Brought it back with DataSets, but not as comprehensively as RDDs
Type safety
Why not Flink?• Flink has much better stream handling for low latency systems that Spark currently
lacks• Event timing• Watermarks• Triggers
• Exactly-once semantics• Pipeline portability via Apache Beam integration
Why not Flink?
Architecting for Fast Data
This isn’t enough
Old and busted
Traditional application architectures and platforms are obsolete.Gartner
How do we avoid messing this up?
• At the API• In our source• For our data
We want isolation
Wikipedia, Creative Commons, created by DFoerster
We want realistic data management• Use CQRS and Event Sourcing, not CRUD• Transactions, especially distributed, will not work• Consistency is an anti-pattern at scale• Distributed locks and shared data will limit you• Data fabrics break all of these conventions
Think in terms of compensation, not prevention.Kevin Webber, Lightbend
We want to ACID v2• Associativity, not Atomicity• Commutativity, not Consistency• Idempotent, not Isolation• Distributed, not Durable
Wikipedia, Creative Commons, created by Weston.pace
New hotness
Mesos,YARNonBareMetal,Cloud
HDFS,S3,CFSv2SQL/NoSQL
Core
Streaming SQL
MLlib GraphX
Fast Data Architecture
HTTP/RESTInternet
ReacHveServices
LogsandOtherFiles
Actors
Cluster …Persist
AkkaStreams
WebServices
Learning Spark• Go to http://bigdatauniversity.com, built by IBM
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