1. CS 239 – Big Data Systems Fall 2018
Harry Xu
UCLA

2. My Research Background
Programming languages and compilers
Static and dynamic program analysis
Compiler
Runtime system
Big Data systems
Dataflow systems
Graph systems
Distributed systems
Single-machine disk-based systems
Some industrial experience
Microsoft – created and solely developed an optimizing compiler for Cosmos/Scope that improved the overall performance of production jobs by up to 3X
IBM – created and developed a series of profiling tools for large-scale systems
Big Data system support for
scalable program analysis
Language/runtime support for
scalable systems

3. BigDatalog
Application
Circle
Infrastructure
Circle

4. This Course: Big Data Systems
What it is about
Low-level infrastructures
Programming models
Runtimes
Scalability and efficiency
What it is NOT about
High-level applications
Workloads
Data collection and usage
An example
We are going to discuss some papers on machine learning systems
We are NOT going to discuss learning models and algorithms
because I don’t know much about them

5. Industrial Relevance Many papers came directly from industry
GFS, MapReduce, Bigtable, Spanner, TensorFlow (Google)
HDFS (Yahoo)
Azure, Trill, Dryad, Naiad (Microsoft)
Spark, Tachyon (Databricks)
Applications v.s. systems
Many people can develop applications
Few people can develop systems
Applications are specific to domains while skills required to build infrastructures are generic

6. Goals to Achieve
Understand what systems are available for data analytics
Understand fundamental challenges in system design
Understand how to design a customized system for a certain workload
Gain experience with system development by proposing and implementing a new idea

7. What This Course is Related To
Distributed systems
Database systems
Computer Architecture
Networking
Storage (memory, disk, file system, etc.)
Graph algorithms
Statistics
Machine learning

8. Aspects of Big Data Processing
Where to put data?
How to process data at scale?
How to process different types of data?
Structured data
Unstructured data
Streaming data
Graph data
Data for model training
How to take advantage of technological advances
How to make processing efficient?

9. Topics Covered (I) Distributed storage systems Dataflow engines
HDFS, GFS, Bigtable, Spanner, and Azure storage
Dataflow engines
MapReduce, Dryad, AsterixDB, Spark
Batch processing
Hive, Spark SQL, and SCOPE
Resource Management
Mesos, YARN, LATE, Borg, Sparrow

10. Topics Covered (II) Stream processing Graph processing
Storm, Flink, Kafka, Naiad, Trill, SVE, Drizzle
Graph processing
Pregel, Ligra, GraphChi, Xstream, GridGraph
Machine learning
TensorFlow, Parameter Servers, Project Adam

11. Why Do We Need Those Systems
Enablers
Better performance
Scalability
Efficiency
Energy
Easy/flexible programmability

12. Course Structure Paper critiques Presentation
Due before each presentation day
Presentation
20-25 mins
Participation in active discussion
Project
2-3 students form a group, working on an innovative idea in system development

13. Things about Presentations/Critiques
Reuse slides as much as possible
A good rule of thumb is to follow this order
What problems does the paper solve?
Why are they (serious) problems?
Why aren’t they already solved?
What are the main challenges?
How did the authors overcome them?
What evidence did the authors show that the problems is solved?
Questions, concerns, opportunities for improvement