High Performance MongoDB Applications on IBM Power8

Keshav RanganathanAnalytics Solutions Offering Manager – Power Systems, IBM

Buzz MoschettiSenior Solutions Architect, MongoDB

May 12, 2016

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MongoDB

The leading post-relational database

Document Data Model

Open-Source

Fully Featured

High Performance

Scalable

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home: 1234567890, mobile: 1234568138 }}

MongoDB Company Overview

~550 employees 2200+ customers

Over $311 million in fundingOffices in NY & Palo Alto and

across EMEA, and APAC

The best way to run MongoDB in your data

center.

Automated.

Supported.

Secured.

What’s included?

Enterprise-Grade Support

Commercial License

Ops Manager or Cloud Manager Premium

Encrypted & In-Memory Storage Engines

Compass (GUI for exploration)

BI Connector

Advanced Security

Platform Certification

On-Demand Training

MongoDB Enterprise Edition

Leading Organizations Use MongoDB

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Gartner Says MongoDB is a Leader

{ _id: “123”, title: "MongoDB: The Definitive Guide", authors: [ { _id: "kchodorow", name: "Kristina Chodorow“ }, { _id: "mdirold", name: “Mike Dirolf“ } ], published_date: ISODate(”2010-09-24”), pages: 216, language: "English", thumbnail: BinData(0,"AREhMQ=="), publisher: { name: "O’Reilly Media", founded: 1980, locations: ["CA”, ”NY” ] }}

What is a Document?

Nexus Architecture

Scalability& Performance

Always On,Global Deployments

FlexibilityExpressive Query Language

& Secondary Indexes

Strong Consistency

Enterprise Management& Integrations

MongoDB 3.0 Set The Stage…

7x-10x Performance, 50%-80% Less Storage

How: WiredTiger Storage Engine•Same data model, query language, & ops•100% backwards compatible API•Non-disruptive upgrade•Storage savings driven by native compression•Write performance gains driven by

• Document-level concurrency control• More efficient use of HW threads

•Much better ability to scale vertically

MongoDB 3.0MongoDB 2.6

Performance

MongoDB 3.4 + POWER: Efficient, Performant MongoDB

• Much better ability to scale vertically

+ • Document Validation Rules• Encryption at rest• BI Connector (SQL bridge)• Compass• New Relic & AppDynamics integration• Backup snapshots on filesystem• $lookup (“left outer JOIN”)

4X

Threads per core*

4X Mem. Bandwidth*

4XMore cache* @ Lower Latency

SMT=Simultaneous Multi-Threading OLTP = On-Line Transaction Processing

These design decisions result in best performance for data centric workloads like:

Database, NoSQL, Big Data Analytics, OLTP

POWER8: Designed for data to deliver breakthrough performance

POWER8SMT8

x86Hyperthread

Parallel Processing

POWER8pipe

Data flow

x86 pipe POWER8

x86 POWER8 + OpenPOWER

x86

Power Scale-out servers are affordable, easy-to-deploy and energy efficient with superior cloud economics and security.

Scale-out Systems• 1 or 2 sockets

• Up to 24 cores

• Up to 192 threads

• Up to 2 TB memory

• Running AIX, IBM i, Linux

Enterprise Systems*• Up to 16 sockets

• Up to 192 cores

• Up to 1536 threads

• Up to 16 TB memory

• Running AIX, IBM i, Linux

Power Enterprise servers are designed for data and offer businesses the ultimate in resiliency, availability, security, and performance.

IBM Power Systems with POWER8, designed to take on the most complex data challenges

Supported by Canonical

Community / 3rd Party Support

Converged

The IBM Power Systems Linux Portfolio

•Design and TCA cost optimized for cognitive workloads on scale out deployments (cloud and cluster)

•Solutions for cognitive, HPC and Big Data IBM Support

running

The LC Line

The L Line

PurePower

Enterprise& IFLs

IaaS

Scale-Out, Linux-Only

ConvergedInfrastructure

Scale-Up

•Enterprise level RAS for single system deployments

•Solutions for Big Data & Analytics

•Converged infrastructure offering

•Rapid time to value and simplicity of management

•Enterprise level robustness and IFL capability

•Solution editions for in memory databases

•(HANA, DB2 BLU)

•Hosted cloud and hybrid cloud solutions

•Rapid deployments and POCs

System CostServer + RHEL OS + MongoDB Subscription

$34,161($20,872 + $1,299 + $11,990)

$38,976($25,687 + $1,299 + $11,990)

MongoDB result - YCSBTotal operations per second (OPS)

297.5 k 169.5 k

OPS per dollar 8.7 ops/$ 4.35 ops/$

100% Better

IBM Power S822LC

(16-core, 256GB)

$20,872

HP DL380 Gen9(24-core, 256GB)

$25,687

MongoDBon

Linux on POWER8vs.

Linux on Intel Haswell

Significantly reduce operating costs running MongoDB on IBM Power SystemsLower TCA + higher performance = more efficient use of IT dollars

• Based on IBM internal testing of single system and OS image running Yahoo Cloud Services Benchmark (YCSB) 0.6.0, workload at 50/50 read/write factor. Conducted under laboratory condition, individual result can vary based on workload size, use of storage subsystems & other conditions.

• IBM Power System S822LC; 16 cores (2 x 8c chips) / 128 threads, POWER8; 3.3 GHz, 256 GB memory, MongoDB 3.3.5 RHEL 7.2. Competitive stack: HP Proliant DL380 Gen9; 24 cores (2 x 12c chips) / 48 threads; Intel E5-2690 v3; 2.6 GHz; 256 GB memory, MongoDB 3.3,5 RHEL 7.2 . Both server priced with 2 x 1TB SATA 7.2K rpm HDD, 1 Gb 2-port, 2 x 16gbps FCA. Configurations represent the highest processor frequency for that specific processor running the MongoDB server on 1 socket & the YCSB application workload on the 2nd socket. Both systems used fiber attached file system on IBM Flash 900.

Pricing is based on web pricing for S822LC http://www-03.ibm.com/systems/power/hardware/s822lc-commercial/buy.html and HP DL380 Gen9 https://h22174.www2.hp.com/SimplifiedConfig/Index MongoDB https://www.mongodb.com/compare/mongodb-oracle Page: 6

2.6Xbetter per

core

So Why are We Seeing an Advantage on P8??

Power 8 Threads per coreSMT 8

4.2 GHz

X86 2 Threads per coreSMT 2

2.5 GHz

Client 2: On-premises infrastructure – Consumer creditWorkload: Internal customer proprietary application workload

Internal business process management softwareObjective: Show over 2 x higher system level performance and delivers price performance advantage.Comparison: Intel 2 socket x86 IvyBridgeIBM solution: S824L servers – 4 core VM

Integrate with existing customer storage environmentPOC result: POC result: 3.7x per-core over x86, Initial 50 servers sold.

Experiences on Key Workload – in Customer POCs

Client 1: Database as a Service – Major TelComWorkload: Yahoo Cloud Services Benchmark (YCSB) – Java-

based workload with non-industry specific data with selectable DB size & read/write mix

Objective: Show better per-VM performance and higher VM density to deliver price performance advantage.

Comparison: Intel 12-c / 2 socket x86 Haswell (HP)IBM solution: S822LC 20-c server – 4 core VMs

Integrate with existing FCoE 40GB Cloud Storage Offer additional storage growth on IBM ESS

POC result: 5-20% better performance per VM with 4 x VM density

Both highly virtualized open source cloud environments & on-premises system of record

deployments are showing major gains in performance and price efficacy on Linux

on Power

Rapid Build Program

“The combination of Linux on IBM POWER8 with MongoDB Enterprise Server makes an ideal enterprise Database-as-a-Service platform. Leveraging POWER8’s performance benefits and the RapidBuild program, enterprises can build MongoDB-as-a-Service and private cloud environments that reduce installation time, enhance security, reduce TCO, and ensure high availability.”

Alan Chhabra, Vice President, WW Partners at MongoDB

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MongoDB-As-A-Service

The Main Goal:•Efficient provisioning of MongoDB

But Also:•Better control / transparency of database use (data too!)•Economy of scale in monitoring and management

• Backup / restore• Version upgrades

•Opportunity to employ several cost allocation models•Opportunity for self-service

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Macroarchitecture

Developer / DBA

PortalOpsManager

OtherProvisioningTechnology

(puppet, chef, …)

Application

ManagedPool Of

Servers &Storage

ServiceBookkeeping

Service Support

App Servers &

DBsCompassBI Connector

ApplicationApplication

Application

DevOps Infrastructure Engineering & Operations

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Dedicated Replica Set Per Application

4 cores32G RAM

4 cores32G RAM

4 cores32G RAM

App 1

8 cores64G RAM

8 cores64G RAM

8 cores64G RAM

App 2

4 cores32G RAM

4 cores32G RAM

4 cores32G RAM

App n

…

Pros:Greatest de-coupling of infrastructure risk and cross application riskAbility to have very different, specific machine configs per appEasy to model allocations / chargeback

Cons:XNo on-demand capability and generally lengthy rack and wire-up timeXPower and data center space hogXFear of underprovisioning bare metal leads to chronic overprovisioning

GRADE: B-

= locally attached storage

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Dedicated Replica Set + Centralized Storage

4 cores32G RAM

4 cores32G RAM

4 cores32G RAM

App 1

4 cores32G RAM

4 cores32G RAM

4 cores32G RAM

App 2

4 cores32G RAM

4 cores32G RAM

4 cores32G RAM

App n

…

Pros:Potentially easier to manageLarger volume sizes possible

Cons:XNo on-demand capability and generally lengthy rack and wire-up timeXPower and data center space hogXRisk of storage fabric failure

GRADE: C+

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“Striping”: Efficient MaaS

Pros:Far fewer machinesOn-demand creation of clusterEasy to vary size of “stripe” Can use locally attached storage or arrays

Cons:XMachine failure OR maintenance triggers many voting events

=

Ap

p 1

Ap

p 2

Ap

p 3

Ap

p n

GRADE: A

Q&A

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Thank you