Choosing hardware for big data analysis is difficult because of the many options and variables involved. The problem is more complicated when you need a full cluster for big data analytics. This session will cover the basic guidelines and architectural choices involved in choosing analytics hardware for Spark and Hadoop. I will cover processor core and memory ratios, disk subsystems, and network architecture. This is a practical advice oriented session, and will focus on performance and cost tradeoffs for many different options.
Exploring the Future Potential of AI-Enabled Smartphone Processors
High Performance Hardware for Data Analysis
1. HIGH PERFORMANCE
HARDWARE FOR DATA
ANALYSIS
Michael Pittaro
Michael_Pittaro@dell.com
O P E N
D A T A
S C I E N C E
C O N F E R E N C E_
BOSTON 2015
@opendatasci
3. 3
About This Talk
• We can’t cover everything about hardware in a 30 minute session.
• We can go deep enough to help you
– Understand tradeoffs and balanced architectures
– Ask the right questions about choices
– Learn from what others are doing
• My Approach Today
1. Why look at high performance hardware ?
2. Look at a production cluster design
3. Look at the choices and tradeoffs behind the scene
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Why consider High Performance Hardware ?
• Choice of hardware can have large impacts
– On performance
– On budget
• Understanding the hardware helps with the software
– Scalable and parallel systems deal with both
• Data is heavy
– Local clusters are persistent
– Large data transfer may not be a viable option.
• Cloud hosting may not be an option
– You can’t or won’t delegate critical infrastructure to third parties.
– You need every bit of performance you can get.
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Reference Architectures Fill The Gap
• Tested Server Configurations
• Tested Network Configurations
• Recommended Software Configuration
– Application and Workload Software
– OS Infrastructure
– Operational Infrastructure
• Opinionated Point of View
– Based on real world experience
• Recommended starting point
– Customization is possible
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The secret to a good architecture is balance
Price
Performance
Fault Zones
Application Workload
Software
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Server Choices
• 4 Socket Servers (e.g. Dell R920)
– Optimized for enterprise applications - Large RDBMS servers, SAP, SAP HANA,
Microsoft Exchange
– Very large memory available (6 TB)
– Often use direct or network attached storage
• ‘Blade’ Servers (e.g. Dell M620, M1000e Chassis)
– Pluggable Processor and Storage modules
– Backplane and Chassis has a lot of shared interconnect logic
– Flexibility for enterprise applications - Virtualization is popular
• 2 Socket Servers (e.g. Dell R620, R630, R720, R730)
– Many options available
– 1U and 2U chassis footprints
– Developed for Web Hosting and Large Scale-Out Clusters
– Dell Internal Storage – 12 x 3.5” drives, 24 x 2.5” drives (in chassis)
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• Assume 1-1.5 Hadoop tasks per core
– allows headroom for other processes
• Hyperthreading
– Enable for Hadoop, Spark
– for others: it depends
• Hadoop: aim for 1 core / disk spindle
• Impala: can handle more spindles and cores easily
• Spark: I/O depends on back end storage
• Faster processor is better
– Most Hadoop jobs are I/O bound, not processor bound
– Hadoop compression uses processor cycles
– Less cores with a faster clock is often a good tradeoff
– The Map / Reduce balance depends on actual workload
– It’s hard to optimize more without knowing the actual workload
Selecting Processors
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Intel Xeon Dual Socket Processor Architecture
Haswell CPU
Up to 18 cores
TDP: Up to 145 W (SVR); 160 W (WS)
Socket Socket-R3
Scalability 2S capability
Memory
4xDDR4 channels
1333, 1600, 1866 (2 DPC), 2133 (1 DPC)
RDIMM, LRDIMM
QPI
2xQPI 1.1 channels
6.4, 8.0, 9.6 GT/s
PCIe
PCIe 3.0 (2.5, 5, 8 GT/s)
PCIe Extensions: Dual Cast, Atomics
40xPCIe*3.0
Intel® Xeon®
processor
E5-2600 v3
Intel® Xeon®
processor
E5-2600 v3
QPI
2 Channels
DDR4
LAN
Up to
4x10GbE
PCIe* 3.0, 40 lanes
Intel® C610
series
chipset
WBG
DDR4
DDR4
DDR4
DDR4
DDR4
DDR4
DDR4
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Intel Processor Generations
Product Xeon E5-2600 E5-2600 V2 E5-2600 V3
Microarchitecture SandyBridge IvyBridge Haswell
Cores / Threads 8 / 16 12/24 18/36
Last Level Cache Up to 20MB Up to 30 MB Up to 45 MB
Max Memory Speed 1600 MT/S
DDR3
1866 MT/s
DDR3
2133 MT/s
DDR4
QPI (GT/s) 2 channels
6.4, 7.2, 8.0
2 channels
6.4, 7.2, 8.0
2 channels
6.4, 8.0, 9.6
Max DIMMS 12 12 12
Max Clock Speed 3.1GHz / 3.8GHz 3.7 GHz / 3.8GHz 3.7 Ghz / 3.8Ghz
Process Tech 32nm 22nm 22nm
Year 2012 2013 2014
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Selecting Memory
• DDR3 versus DDR4, RDIMM versus LRDIMM
– DDR3 is cheaper now, DDR4 is faster (15%)
• DIMM Sizes
– 8GB, 16GB, 32GB, 64GB, 128GB
• Sweet Spot Varies
– DDR4 around 32GB right now
• Balance the memory banks
– 4 memory channels per processor
– 4 x 16GB better than 2 x 32GB
• Server Class Memory
– It’s all ECC checked
– Dell Server BIOS options to optimize checking method
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Selecting Disks
• 3.5” Drives
– 3TB, 4TB, 6TB per drive
– Pricing sweet spot is 3TB
– Use enterprise grade drives, not consumer !!
– SATA or SAS. SAS slightly faster.
– 3.0 GB/sec is fine, 6.0 Gb/sec is a waste with spinning drives
• 2.5” Drives
– 800GB and 1.2 TB
– More expensive than 3.5” drives
– more spindles and performance
• SATA Solid State Drives
– 6.0 Gb/sec
– 2.5” and 1.8” options
– Expensive for now
– Not as deterministic as spindles
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• Hadoop scales processing and storage together
– The cluster grows by adding more data nodes
– The ratio of processor to storage is the main adjustment
• Generally, aim for a 1 spindle / 1 core ratio
– I/O is large blocks (64Mb to 256Mb)
– Primarily sequential read/write, very little random I/O
– 8 tasks will be reading or writing 8 individual spindles
• Drive Sizes and Types
– NL SAS or Enterprise SATA 6 Gb/sec
– Drive size is mainly a price decision
• Depth per node
– Up to 48 TB/node is common
– 112 Tb / node is possible
– Consider how much data is ‘active’
– Very deep storage impacts recovery performance
Spindle / Core / Storage Depth Optimization
1
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Network and Switches
• Simple Tree Structure
– Top of Rack (TOR) for each rack / group of nodes
– Racks feed up to a Cluster or Aggregation Switch
– All switching is at Layer 2 (Ethernet)
› No fancy routing or layer 3 (IP) packet inspection
– Most switches are 48 ports in this class
• Switch Characteristics
– Line rate switching at 10Gbps
– Deep buffers to handle bursts
– Virtual Link Trunking (VLT)– two switches act as one, with failover
– Uplinks are 40GbE
• High Availability and Performance
– Use two 10GbE links to alternate switches
– Bond at the Linux level into a single device
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Model Data Node
Configuration
Comments RA
R730Xd Dual socket, 12 cores,
24 x 2.5” spindles
Most popular platform for
Hadoop
C8000 Dual socket, 16 cores,
16 x 3.5” spindles
Popular for deep/dense
Hadoop applications
C6100 /
C6105
Dual socket, 8/12 cores,
12 x 3.5” spindles
Two node version. C6100 is
hardware EOL
C2100 Dual Socket, 12 cores,
12 x 3.5” spindles
Popular, hardware EOL but
often repurposed for
Hadoop
R620 Dual Socket, 8 cores,
10 x 2.5” spindles
1U form factor
C6220 Dual-socket, 8 cores,
6 x 2.5” spindles
Core/spindle ratio is not
ideal for Hadoop.
In the Wild – Dell Customer Hadoop Configurations
2
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• GPU’s
– Possible, not seen too often with Hadoop
• Ingest / Streaming
– Usually a custom configuration for high speed capture/loading (e.g. Kafka, Storm)
• Dell PowerEdge VRTX
– Designed as a ‘mini-blade’ for branch offices
– Could make a killer data science workstation
What I haven’t talked about!
25. 25
High Performance Hardware for Data Analysis
• Choosing hardware for big data analysis is difficult because of the many options and variables involved. The problem is more
complicated when you need a full cluster for big data analytics.
• This session will cover the basic guidelines and architectural choices involved in choosing analytics hardware for Spark and
Hadoop. I will cover processor core and memory ratios, disk subsystems, and network architecture. This is a practical advice
oriented session, and will focus on performance and cost tradeoffs for many different options.
