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From DataFrames to Tungsten:
A Peek into Spark’s Future
Reynold Xin @rxin
Spark Summit, San Francisco
June 16th, 2015
DataFrame
noun
Making Spark accessible to everyone (data
scientists, engineers, statisticians, …)
Tungsten
noun
Making Spark faster & prepare for the next
five years.
How do DataFrames and
Tungsten relate to each other?
Google Trends for “dataframe”
Single-node tabular data structure, with API for
relational algebra (filter, join, …)
math and stats
input/output (CSV, JSON, …)
ad infinitum
Data frame: lingua franca for “small data”
	
  
head(flights)	
  
#>	
  Source:	
  local	
  data	
  frame	
  [6	
  x	
  16]	
  
#>	
  	
  
#>	
  	
  	
  	
  year	
  month	
  day	
  dep_time	
  dep_delay	
  arr_time	
  arr_delay	
  carrier	
  tailnum	
  
#>	
  1	
  	
  2013	
  	
  	
  	
  	
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  517	
  	
  	
  	
  	
  	
  	
  	
  	
  2	
  	
  	
  	
  	
  	
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  11	
  	
  	
  	
  	
  	
  UA	
  	
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#>	
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  4	
  	
  	
  	
  	
  	
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  20	
  	
  	
  	
  	
  	
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#>	
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#>	
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  ...	
  	
  	
  	
  	
  ...	
  
	
  
Spark DataFrame
>	
  head(filter(df,	
  df$waiting	
  <	
  50))	
  	
  #	
  an	
  example	
  in	
  R	
  
##	
  	
  eruptions	
  waiting	
  
##1	
  	
  	
  	
  	
  1.750	
  	
  	
  	
  	
  	
  47	
  
##2	
  	
  	
  	
  	
  1.750	
  	
  	
  	
  	
  	
  47	
  
##3	
  	
  	
  	
  	
  1.867	
  	
  	
  	
  	
  	
  48	
  
	
  
Distributed data frame for Java, Python, R, Scala
Similar APIs as single-node tools (Pandas, dplyr), i.e. easy to learn
data size
KB MB GB TB PB
Existing
Single-node
Data Frames
Spark
DataFrame
It is not Spark vs Python/R,
but Spark and Python/R.
Spark and Python/R
Spark
DF
scalability
multi-core
multi-machines
Python/R
DF
Viz
Machine
Learning
Stats
wealth
of
libraries
Spark RDD Execution
Java/Scala
API
JVM
Execution
Python
API
Python
Execution
opaque closures
(user-defined functions)
Spark DataFrame Execution
DataFrame
Logical Plan
Physical
Execution
Catalyst
optimizer
Intermediate representation for computation
Spark DataFrame Execution
Python
DF
Logical Plan
Physical
Execution
Catalyst
optimizer
Java/Scala
DF
R
DF
Intermediate representation for computation
Simple wrappers to create logical plan
Benefit of Logical Plan: Simpler Frontend
Python : ~2000 line of code (built over a weekend)
R : ~1000 line of code
i.e. much easier to add new language bindings (Julia, Clojure, …)
Performance
0 2 4 6 8 10
Java/Scala
Python
Runtime for an example aggregation workload
RDD
Benefit of Logical Plan:
Performance Parity Across Languages
0 2 4 6 8 10
Java/Scala
Python
Java/Scala
Python
R
SQL
Runtime for an example aggregation workload (secs)
DataFrame
RDD
What about Tungsten?
Hardware Trends
Storage
Network
CPU
Hardware Trends
2010
Storage
50+MB/s
(HDD)
Network 1Gbps
CPU ~3GHz
Hardware Trends
2010 2015
Storage
50+MB/s
(HDD)
500+MB/s
(SSD)
Network 1Gbps 10Gbps
CPU ~3GHz ~3GHz
Hardware Trends
2010 2015
Storage
50+MB/s
(HDD)
500+MB/s
(SSD)
10X
Network 1Gbps 10Gbps 10X
CPU ~3GHz ~3GHz L
Tungsten: Preparing Spark for Next 5 Years
Substantially speed up execution by optimizing CPU efficiency, via:
(1)  Runtime code generation
(2)  Exploiting cache locality
(3)  Off-heap memory management
From DataFrame to Tungsten
Python
DF
Logical Plan
Java/Scala
DF
R
DF
Tungsten
Execution
5PM
Deep Dive into Project Tungsten
Developer Track by Josh Rosen
Initial Performance Results
0
200
400
600
800
1000
1200
1x 2x 4x 8x
Runtime(seconds)
Data set size (relative)
Tungsten-off
Tungsten-on
Python Java/Scala RSQL …
DataFrame
Logical Plan
LLVMJVM GPU NVRAM
Unified API, One Engine, Automatically Optimized
Tungsten
backend
language
frontend
…
Tungsten Execution
PythonSQL R Streaming
DataFrame
Advanced
Analytics
Spark Office Hours Today
Databricks booth A1
Topic Area
1:00-1:45 Core, YARN, Ops
1:45-2:30 Core/SQL/Data Science
3:00-3:40 Streaming
3:40-4:15 Core, Python, R
4:30-5:15 Machine Learning
5:15-6:00 Matei Zaharia

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