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Ken Owens
CTO Cisco Intercloud Services
07/15/15
How Cisco Migrated from
MapReduce Jobs to Spark
Jobs
1
Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public
Introduction
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Introduction
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Source: IDC 7
30M
New devices
connected
every week
78%
Workloads
processed
in Cloud DCs
by 2018
5TB+
of data per person
by 2020
180B
Mobile apps
downloaded
in 2015
277X
Data created
by IoE devices
v. end-user
The Uber Trend: Exponential Rise in Connectivity
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Exponential Trend
Linear Trend
Disruptive Stress
/Opportunity
Knee of Curve
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When Products Become Cloud-enabled, They Become
10X More Valuable
$23.19
$249.00
$18.01
$199.00
$5.99
$59.99
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SaaS
PaaS IaaS
A Broader Perspective than Hybrid Cloud Is Required…
Data Center Cloud Edge / IoT
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Hyperscale applications serving several
thousands of users very quickly
Traditional enterprise applications
IoE and increasing connectivity driving the need
for such workloads
Hadoop, Mobile back-ends, Gaming, Social
Small (~10%), yet rapidly growing
percentage of applications in the Cloud
ERP, CRM, Applications that leverage
traditional databases
Majority of applications being run
for/by Enterprises today
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and Hyperscale Application Deployment
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PaaS IaaS
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Applications
ERP, Financial, Client/Server,
CRM, email, …
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Source: Gartner, Lydia Leong
of CIOs currently
have a second
fast/agile mode
of operation
45%
Traditional
Mode
Requires
Reliability
(ITIL, CMMI, COBIT)
Nonlinear Mode
Accept Instability
(DevOps,
automation,
reusable)
Systems
of
Differentiation
Systems
of
Innovation
Systems
of
Record
Change
Governance
Bimodal IT Is the New Normal
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Intercloud
The
Intercloud
Web-scale Architecture
API-Driven Automation
Open, Secure, Compliant,
Hybrid IT
Internet
The
Internet
IP Based
Open Standards
World of Isolated Clouds
(2000s)
Individual custom-built clouds
without consistent APIs
Connected for application
acceleration with Open APIs
The Intercloud
Intercloud
Islands of Isolated
PC LAN Networks (1990s)
Multiple LANs using
a multitude of protocols
The Internet
Connected using industry-
standard IP protocol
We Must Connect the Clouds
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Interaction Analytics
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Omni-Channel Customer Journeys
Server
Logs
Social
& Chat
Mobile
Event
Streams
Call
Center
S/W
Download
Open Trouble
Ticket
Assign
Engineer
Update
Trouble Ticket
Close Trouble
Ticket
Resolve
Trouble Ticket
Read Support
Documents
View Design
Documents
View Tech
Documents
New
Registration
Bug Search FAQs
Contract
Details
Product
Details
Device
Coverage
Interaction Touch points
Channels
Journey
Case Resolution
Software Upgrade
The customers’ interaction with Cisco across multiple touch points to get the desired business
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• Software Upgrades
• Bug Inquiry
• Software Inquiry
• Trouble Ticket Lifecycle
• Device Troubleshooting
• New Registration
• Contract Renewal
• Customer Interest
Analytics
• Customer Experience
Analytics
• Resource Forecasting
• Security and
Compliance
Customer Journeys Behavioral Insights
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• Real-time Content
Optimization &
Recommendation
• Context Based
Predictive Alerts
• Implicit Personalization
Impact
Customer Interaction Analytics
From Journey to Outcome…
Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public
Server Logs
Customer Interaction Analytics
Big Data Platform
Synthesize customer journey maps into behavioral insights.
Call Center
Mobility
Social
Event
Streams
Data
Sources
Data
Ingestion
CiscoDV
Kafka
Redis
ETL
Analytics
Model
Build Model
Activity
Refinement
Activity
Synthesis
Synthesized
Insights
Real-time Processing
Batch Analytics
Insight Services
CiscoDV
Interact
ImpalaHive
Pig ES
Zoomdata,Platfora
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Solution
Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public
AWS Platform
Component Cloud::
Hadoop
(Batch
Analytics)
Cloud::
Queries
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Queries)
Cloud::
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time
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Case for Cisco Intercloud Services for Analytics…
 Cisco Security and Compliance requirements
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confidential content cannot be uploaded to AWS. Cisco internal cloud
solution is a better fit.
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production system.
OpenStack
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Big Data Ecosystem
Cloudera’s Hadoop distribution version CDH 5.1.3., ELK Stack, Apache Kafka and Apache Storm.
Data virtualization & Cloud Integration
Access to data services and data stores via Cisco Data Virtualization
Runtime Services
Foundational PaaS capabilities including SLAs for uptime, performance, latency, data retention, issue escalation and
support priorities, issue resolution, problem management, deployment process, patch management.
API Services
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model it must support interoperability across platform service providers and promote the cloud concepts of extensibility and
flexibility.
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AWS to CIS Migration – Success Criteria
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 Successful automation of the end-end data process pipeline
 Build behavioral insight services
 Access to data and services via data discovery and visualization tools
 Meet the performance, scale and platform stability requirements
 Successful deployment of CiscoDV on CIS
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 Build and expose insight services for consumption by limited users

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AWS and CIS Data Node Sizing Comparison
Hadoop Cluster for Batch and Query Analytics
Node Service AWS Instance Type vCPU Mem Storage
Number of
Data Nodes
Comments
Data Nodes/
Node Master m3.2xlarge 8 30 2x80 GB 30
Each hadoop data node has 1500GB of EBS
available for HDFS storage
AWS Sizing
CCS Sizing
Node Service CCS Instance Type vCPU Mem Storage
Number of
Data Nodes
Comments
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Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public
Pilot Test Data
• Test performed on one day’s production data
• Total no. of records processed – 110,852,667
• Total data size – 32GB
• Total no. of M/R jobs in the data pipeline – 17
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Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public
CIS Performance of Batch Analytics – Limited Test
Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public
Test Details by M/R job
Job Name CCS 12
nodes:
cycle1
CCS 18
nodes:
cycle1
CCS 24
nodes:
cycle1
CCS 30
nodes:
cycle1
CCS 18
nodes:
cycle2
CCS 24
nodes:
cycle2
CCS 30
nodes:
cycle2
CCS 35
nodes:
cycle2
New_cleanse 249 176 143 117 82 67 55 51
Process_private_ip 27 14 11 10 7 5 6 6
join_web_and_ip_data 142 95 76 61 49 40 34 29
combine_ip_decorated_files 26 14 11 10 9 7 8 7
filterBotEntries 34 19 15 13 10 8 7 7
sessionize 71 64 69 62 60 63 15 13
firstActivitiesFilter 26 15 13 10 9 8 6 6
allOtherActivitiesFilter 29 18 13 13 11 9 7 6
matchFirstActivities 21 13 11 13 13 11 8 8
buildActivities 27 15 12 10 7 6 9 9
filterBUG 8 5 3 2 3 3 4 4
filterSEA 8 5 3 2 3 3 4 4
filterTCO 8 5 3 2 3 3 4 4
filterTDV 8 5 3 2 3 3 4 4
filterWDV 8 5 3 2 3 3 4 4
filterMOD 8 5 3 2 3 3 4 4
filterTOOL 8 5 3 2 3 3 4 4

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PoC: Analytics with Spark on CIS
Existing code
 Made in Ruby with Wukong to run on Hadoop
 A history of changes and modifications
 Script-based, steps communicate via intermediary files
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Sessionize
Cleanse
logs
cleanse
private web
decorate
sessionize
(cookie, time)
sessioned
match 1st
(IP, UA, time)
build actions merge
session PSV
add to hivebug tool
first, others, bots
1..7
onlyBots
first
others
private
Main
computation
happens here
cleansed
 Pre-process log records (‘cleanse’)
 Extract HTTP sessions (‘sessionize’)
 Extract user actions, such as ‘search’, ‘download
patch’, ‘open manual’, ‘open a bug’
Ruby: Scripts with temp files
 Each box on the figure is a script in a separate file
 They pipe Gb of data as input and output
 Random matching of nodes to data for sessionizing
 Lots of redundant shuffling
Ruby Flow
global sort in time
global group by IP
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Sessionize
Cleanse
logs
cleanse
private web
decorate
sessionize
(cookie, time)
sessioned
match 1st
(IP, UA, time)
build actions merge
session PSV
add to hivebug tool
first, others, bots
1..7
onlyBots
first
others
private
Main
computation
happens here
cleansed
 Same flow, but each box is a Java or Scala function
No intermediate temp files
 Steps are chained by Spark, often without any need for
intermediate data
 If still needed, the data is stored in memory and local
disk as much as possible
Local computation
 Cleansing is computed on nodes local to data blocks
(same as Ruby)
 Sessions are built per IP
 On separate nodes each handling a single IP range
 One copied to the node on partition the data remains
local
Spark Flow
global partition by IP
local sort in time
Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public
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 Logs of a single day: 52 Gb
 Total of 110 mil records
 Where 53 mil records are kept after pre-filtering
 Producing over 1 mil user actions
 Cluster of 30 nodes
 Ruby
 Runtime 140 min
 Spark
 Runtime 7 min (20 times faster )
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Presentation data center transformation cisco’s virtualization and cloud jo...

The document discusses Cisco's journey towards virtualization and cloud computing. It describes Cisco's global data center strategy, which includes building a new world-class data center in Allen, Texas and developing a cloud strategy and services called CITEIS. CITEIS provides infrastructure as a service using Cisco UCS, Nexus switches, and other Cisco technologies to enable automated, self-service provisioning and elastic infrastructure capacity.

Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public
 Extracting sessions means sort in time and group by IP
 Ruby:
 sorting in time and per-IP grouping is performed across the whole cluster (very bad, lots of IO)
 Spark is good at dealing with partitions:
 per-IP groups are placed on different machines (partitions)
 global sort in time is replaced by many local per-IP sorts done on machines responsible for
extracting sessions for specific groups of IP addressed
 Other improvements
 Avoid redundant temp files, redundant (de)-serialization of objects (comes with Java/Scala), stages
keep data in memory when possible (comes with Spark)
 Cache results of user agent resolution that are heavy on regular expressions
Why?
CiscoDV on CIS
Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public
Data Virtualization for Intercloud Analytics
Customer Benefits
 Discover data beyond the enterprise: Virtual integration that combines traditional
enterprise data, Big Data stores on CIS and AWS, cloud data from SaaS providers and,
Cisco Customers and Partners
 Seamless interoperability offers easy access to data across distributed data sources
in the intercloud analytics platform
 Universal data governance maximizes enforcement of data security rules
 Analytics Data Hubs: Deployment flexibility to build hybrid/virtual sandboxes that
enable nimble data discovery and rapid data analytics to support multiple LOBs
 Deliver data to any number of analytics tools.
Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public
Use Case 1: Get Case Interactions
Use Case Description # of cases opened by company X that
are currently open. (other variations
would include cases by company,
trends etc.)
CiscoDV Value CiscoDV enforces data security rules to
restrict access on the intercloud
platform to customer sensitive data.
Data Sources SalesForce
Intercloud Solution CIS CiscoDV service can access the
“sanitized” version of CSOne data
through JDBC from RIDES(SWTG
CiscoDV) API.
Connection Type DV on hybrid cloud  Enterprise data
store

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DevOps tools became very popular with the adoption of public cloud, but Operational teams now realize that their benefits can be extended to enterprise data centers. In reality, cloud native tools can help bridge public clouds and private data centers by enabling a common framework to manage applications and their underlying infrastructure components. In this session you’ll learn about the latest Cisco ACI integrations with Hashicorp Terraform and Consul to deliver a powerful solution for end-to-end on-prem and cloud infrastructure deployments.

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Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public
Use Case 2: Get Customer Journey
Use Case Description Customer interactions on the web
pertaining to bug search and case
submission process. Foundational data
can be used to explore trends and feed
into content recommendation models
CiscoDV Value Direct access to Data on CIS Intercloud Analytics
Platform
Data Sources SAS Analytics
Intercloud Solution By direct network access to the Impala
Server, the CIS CiscoDV server
connects to the Impala Service in
Hadoop also on CIS as a Data Source.
SQL Queries configured in CiscoDV
execute Impala queries
Connection Type DV on hybrid cloud  VPC Big Data
platform
Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public
Use Case 3: Get Bug Interactions
Use Case
Description
Another foundational data service that provides
a breakdown of customer exposure or interest
in bugs. The service can be refined further to
look at trends specific to a company or a
product for further analytics.
CiscoDV Value Real-time data federation that accesses
extremely large data in CIS Intercloud Analytics
platform and join that with Bug Data accessed
via departmental CiscoDV instance (RIDES)
Data Sources SASA Analytics and QDDTS via RIDES
Intercloud
Solution
By building on the access to the Impala Server,
the DV server can join the Bug Data from the
Enterprise Data Stores with the HDFS data to
provide a federated view.
Connection
Type
DV on hybrid cloud  VPC Big Data platform
and Enterprise data store
Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public
CiscoDV on Intercloud Analytics Platform (CIS)
Scenario 1
CIS Cisco DV to Cisco
Enterprise Data Store
Scenario 2
CIS CiscoDV to Impala and
Hive on CIS Intercloud
Analytics Platform
Scenario 3
CIS Cisco DV to Hive on AWS
Big Data Cluster
Scenario1
Scenario 3
StampedeCon 2015 Keynote

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Cisco data center training for ibmCisco data center training for ibm
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This document provides an overview and summary of Cisco's Data Center networking and storage solutions, with a focus on the new Cisco MDS 9710 Director. Some key points: - Cisco offers a multi-protocol portfolio including Fibre Channel, FCoE, and IP networking solutions to address growing data and connectivity demands in modern data centers. - The Cisco MDS 9710 is the newest storage director that provides the highest scalability, availability, and investment protection in the industry for large scale data centers. - It supports up to 384 line-rate 16Gbps Fibre Channel ports or 48-port 10GbE FCoE modules in a single chassis. This provides 3 times the performance of competing

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Presentation capturing the cloud opportunity
Presentation   capturing the cloud opportunityPresentation   capturing the cloud opportunity
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Download & Share Technology Presentations http://goo.gl/k80oY0 Student Guide & Best http://goo.gl/6OkI77

Cisco Connect Toronto 2018 sd-wan - delivering intent-based networking to t...
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This document discusses Cisco SD-WAN and its ability to deliver intent-based networking to branches and the WAN. It begins by noting the business challenges of traditional network architectures in supporting modern needs around mobility, cloud applications, and security. It then introduces Cisco SD-WAN as a software-defined solution that provides automated, predictive, and business-intent driven networking through centralized control, application-aware policies, hybrid WAN transport, and integrated security and analytics capabilities. Key components of the Cisco SD-WAN architecture are also summarized, including the data, control, management, and orchestration planes.

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StampedeCon 2015 Keynote

  • 1. Ken Owens CTO Cisco Intercloud Services 07/15/15 How Cisco Migrated from MapReduce Jobs to Spark Jobs 1
  • 2. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Introduction
  • 3. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Introduction
  • 4. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Introduction
  • 5. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Introduction
  • 6. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Introduction
  • 7. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Source: IDC 7 30M New devices connected every week 78% Workloads processed in Cloud DCs by 2018 5TB+ of data per person by 2020 180B Mobile apps downloaded in 2015 277X Data created by IoE devices v. end-user The Uber Trend: Exponential Rise in Connectivity
  • 8. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Exponential Trend Linear Trend Disruptive Stress /Opportunity Knee of Curve Exponential Growth Drives Opportunities Peter Diamandis: BOLD
  • 9. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public When Products Become Cloud-enabled, They Become 10X More Valuable $23.19 $249.00 $18.01 $199.00 $5.99 $59.99
  • 10. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public SaaS PaaS IaaS A Broader Perspective than Hybrid Cloud Is Required… Data Center Cloud Edge / IoT
  • 11. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Hyperscale applications serving several thousands of users very quickly Traditional enterprise applications IoE and increasing connectivity driving the need for such workloads Hadoop, Mobile back-ends, Gaming, Social Small (~10%), yet rapidly growing percentage of applications in the Cloud ERP, CRM, Applications that leverage traditional databases Majority of applications being run for/by Enterprises today CIOs Need to Embrace Both Traditional and Hyperscale Application Deployment
  • 12. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public SaaS PaaS IaaS Application Portability and Interoperability Is the Key Traditional Applications ERP, Financial, Client/Server, CRM, email, … Cloud Native Applications IoT, BigData,Analytics, Gaming, ... Data Center Cloud Edge / IoT
  • 13. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Source: Gartner, Lydia Leong of CIOs currently have a second fast/agile mode of operation 45% Traditional Mode Requires Reliability (ITIL, CMMI, COBIT) Nonlinear Mode Accept Instability (DevOps, automation, reusable) Systems of Differentiation Systems of Innovation Systems of Record Change Governance Bimodal IT Is the New Normal Source: Gartner, Lydia Leong
  • 14. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Intercloud The Intercloud Web-scale Architecture API-Driven Automation Open, Secure, Compliant, Hybrid IT Internet The Internet IP Based Open Standards World of Isolated Clouds (2000s) Individual custom-built clouds without consistent APIs Connected for application acceleration with Open APIs The Intercloud Intercloud Islands of Isolated PC LAN Networks (1990s) Multiple LANs using a multitude of protocols The Internet Connected using industry- standard IP protocol We Must Connect the Clouds
  • 16. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Omni-Channel Customer Journeys Server Logs Social & Chat Mobile Event Streams Call Center S/W Download Open Trouble Ticket Assign Engineer Update Trouble Ticket Close Trouble Ticket Resolve Trouble Ticket Read Support Documents View Design Documents View Tech Documents New Registration Bug Search FAQs Contract Details Product Details Device Coverage Interaction Touch points Channels Journey Case Resolution Software Upgrade The customers’ interaction with Cisco across multiple touch points to get the desired business outcome.
  • 17. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public • Software Upgrades • Bug Inquiry • Software Inquiry • Trouble Ticket Lifecycle • Device Troubleshooting • New Registration • Contract Renewal • Customer Interest Analytics • Customer Experience Analytics • Resource Forecasting • Security and Compliance Customer Journeys Behavioral Insights • Boost Self Service • Real-time Content Optimization & Recommendation • Context Based Predictive Alerts • Implicit Personalization Impact Customer Interaction Analytics From Journey to Outcome…
  • 18. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Server Logs Customer Interaction Analytics Big Data Platform Synthesize customer journey maps into behavioral insights. Call Center Mobility Social Event Streams Data Sources Data Ingestion CiscoDV Kafka Redis ETL Analytics Model Build Model Activity Refinement Activity Synthesis Synthesized Insights Real-time Processing Batch Analytics Insight Services CiscoDV Interact ImpalaHive Pig ES Zoomdata,Platfora
  • 19. AWS and CIS Intercloud Solution
  • 20. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public AWS Platform Component Cloud:: Hadoop (Batch Analytics) Cloud:: Queries (Interactive Queries) Cloud:: Streams (Near Real- time Analytics) Virtual Machines 30 6 5 AWS Instance Sizing m3.2xlarge c3.xlarge m3.xlarge Virtual Cores 8/VM 4/VM 4/VM RAM 30GB/VM 7.5GB/VM 15GB/VM Disk 1.5 TB/VM 1.5 TB/VM 1.5 TB/VM
  • 21. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Case for Cisco Intercloud Services for Analytics…  Cisco Security and Compliance requirements • Workloads that deal with personally identifiable data and Cisco confidential content cannot be uploaded to AWS. Cisco internal cloud solution is a better fit.  Customer journey beyond the enterprise • Applications are hosted on AWS • Partner systems hosted on AWS and other cloud providers Presence in AWS and other cloud services required to support these scenarios for end-end customer journey insights.  Data virtualization integrated in the CIS Analytics Stack • Connect data from multiple clouds and multiple big data platforms  Integrated visualization toolset
  • 22. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public CIS Analytics Platform
  • 23. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public CIS Analytics Platform Requirements Infra Provisioning Deploy a virtual private cloud (VPC) on CIS with compute, storage and memory requirements comparable to the current production system. OpenStack Icehouse OpenStack with Neutron, Nova, and Swift installed. Big Data Ecosystem Cloudera’s Hadoop distribution version CDH 5.1.3., ELK Stack, Apache Kafka and Apache Storm. Data virtualization & Cloud Integration Access to data services and data stores via Cisco Data Virtualization Runtime Services Foundational PaaS capabilities including SLAs for uptime, performance, latency, data retention, issue escalation and support priorities, issue resolution, problem management, deployment process, patch management. API Services Provide both fine-grained and coarse-grained access to the all service layers of the CIS Analytics Platform. In the hybrid cloud model it must support interoperability across platform service providers and promote the cloud concepts of extensibility and flexibility.
  • 24. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public AWS to CIS Migration – Success Criteria  Successful synthesis of customer interaction data  Successful automation of the end-end data process pipeline  Build behavioral insight services  Access to data and services via data discovery and visualization tools  Meet the performance, scale and platform stability requirements  Successful deployment of CiscoDV on CIS  Connect HDFS and Hive DS with CiscoDV via Hive and Impala  Build and expose insight services for consumption by limited users
  • 25. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public AWS and CIS Data Node Sizing Comparison Hadoop Cluster for Batch and Query Analytics Node Service AWS Instance Type vCPU Mem Storage Number of Data Nodes Comments Data Nodes/ Node Master m3.2xlarge 8 30 2x80 GB 30 Each hadoop data node has 1500GB of EBS available for HDFS storage AWS Sizing CCS Sizing Node Service CCS Instance Type vCPU Mem Storage Number of Data Nodes Comments Data Nodes/ Node Master GP-2XLarge 8 32 50 35 Each hadoop data node has 1500GB of EBS available for HDFS storage Less than AWS sizing (Storage)
  • 26. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Pilot Test Data • Test performed on one day’s production data • Total no. of records processed – 110,852,667 • Total data size – 32GB • Total no. of M/R jobs in the data pipeline – 17 • Two test cycles • Cycle 1: Heterogeneous CCS nodes (vCPUs, storage, memory) • Cycle 2: Homogeneous CCS nodes
  • 27. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public CIS Performance of Batch Analytics – Limited Test
  • 28. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Test Details by M/R job Job Name CCS 12 nodes: cycle1 CCS 18 nodes: cycle1 CCS 24 nodes: cycle1 CCS 30 nodes: cycle1 CCS 18 nodes: cycle2 CCS 24 nodes: cycle2 CCS 30 nodes: cycle2 CCS 35 nodes: cycle2 New_cleanse 249 176 143 117 82 67 55 51 Process_private_ip 27 14 11 10 7 5 6 6 join_web_and_ip_data 142 95 76 61 49 40 34 29 combine_ip_decorated_files 26 14 11 10 9 7 8 7 filterBotEntries 34 19 15 13 10 8 7 7 sessionize 71 64 69 62 60 63 15 13 firstActivitiesFilter 26 15 13 10 9 8 6 6 allOtherActivitiesFilter 29 18 13 13 11 9 7 6 matchFirstActivities 21 13 11 13 13 11 8 8 buildActivities 27 15 12 10 7 6 9 9 filterBUG 8 5 3 2 3 3 4 4 filterSEA 8 5 3 2 3 3 4 4 filterTCO 8 5 3 2 3 3 4 4 filterTDV 8 5 3 2 3 3 4 4 filterWDV 8 5 3 2 3 3 4 4 filterMOD 8 5 3 2 3 3 4 4 filterTOOL 8 5 3 2 3 3 4 4
  • 29. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public PoC: Analytics with Spark on CIS Existing code  Made in Ruby with Wukong to run on Hadoop  A history of changes and modifications  Script-based, steps communicate via intermediary files Goal  Revise, rethink and reimplement with Spark on CIS  Open for advanced cloud analytics  Improve maintainability by moving away from aging Ruby on Hadoop
  • 30. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Sessionize Cleanse logs cleanse private web decorate sessionize (cookie, time) sessioned match 1st (IP, UA, time) build actions merge session PSV add to hivebug tool first, others, bots 1..7 onlyBots first others private Main computation happens here cleansed  Pre-process log records (‘cleanse’)  Extract HTTP sessions (‘sessionize’)  Extract user actions, such as ‘search’, ‘download patch’, ‘open manual’, ‘open a bug’ Ruby: Scripts with temp files  Each box on the figure is a script in a separate file  They pipe Gb of data as input and output  Random matching of nodes to data for sessionizing  Lots of redundant shuffling Ruby Flow global sort in time global group by IP
  • 31. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Sessionize Cleanse logs cleanse private web decorate sessionize (cookie, time) sessioned match 1st (IP, UA, time) build actions merge session PSV add to hivebug tool first, others, bots 1..7 onlyBots first others private Main computation happens here cleansed  Same flow, but each box is a Java or Scala function No intermediate temp files  Steps are chained by Spark, often without any need for intermediate data  If still needed, the data is stored in memory and local disk as much as possible Local computation  Cleansing is computed on nodes local to data blocks (same as Ruby)  Sessions are built per IP  On separate nodes each handling a single IP range  One copied to the node on partition the data remains local Spark Flow global partition by IP local sort in time
  • 32. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public  Volumes  Logs of a single day: 52 Gb  Total of 110 mil records  Where 53 mil records are kept after pre-filtering  Producing over 1 mil user actions  Cluster of 30 nodes  Ruby  Runtime 140 min  Spark  Runtime 7 min (20 times faster ) Runtime comparison
  • 33. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public  Extracting sessions means sort in time and group by IP  Ruby:  sorting in time and per-IP grouping is performed across the whole cluster (very bad, lots of IO)  Spark is good at dealing with partitions:  per-IP groups are placed on different machines (partitions)  global sort in time is replaced by many local per-IP sorts done on machines responsible for extracting sessions for specific groups of IP addressed  Other improvements  Avoid redundant temp files, redundant (de)-serialization of objects (comes with Java/Scala), stages keep data in memory when possible (comes with Spark)  Cache results of user agent resolution that are heavy on regular expressions Why?
  • 35. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Data Virtualization for Intercloud Analytics Customer Benefits  Discover data beyond the enterprise: Virtual integration that combines traditional enterprise data, Big Data stores on CIS and AWS, cloud data from SaaS providers and, Cisco Customers and Partners  Seamless interoperability offers easy access to data across distributed data sources in the intercloud analytics platform  Universal data governance maximizes enforcement of data security rules  Analytics Data Hubs: Deployment flexibility to build hybrid/virtual sandboxes that enable nimble data discovery and rapid data analytics to support multiple LOBs  Deliver data to any number of analytics tools.
  • 36. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Use Case 1: Get Case Interactions Use Case Description # of cases opened by company X that are currently open. (other variations would include cases by company, trends etc.) CiscoDV Value CiscoDV enforces data security rules to restrict access on the intercloud platform to customer sensitive data. Data Sources SalesForce Intercloud Solution CIS CiscoDV service can access the “sanitized” version of CSOne data through JDBC from RIDES(SWTG CiscoDV) API. Connection Type DV on hybrid cloud  Enterprise data store
  • 37. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Use Case 2: Get Customer Journey Use Case Description Customer interactions on the web pertaining to bug search and case submission process. Foundational data can be used to explore trends and feed into content recommendation models CiscoDV Value Direct access to Data on CIS Intercloud Analytics Platform Data Sources SAS Analytics Intercloud Solution By direct network access to the Impala Server, the CIS CiscoDV server connects to the Impala Service in Hadoop also on CIS as a Data Source. SQL Queries configured in CiscoDV execute Impala queries Connection Type DV on hybrid cloud  VPC Big Data platform
  • 38. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Use Case 3: Get Bug Interactions Use Case Description Another foundational data service that provides a breakdown of customer exposure or interest in bugs. The service can be refined further to look at trends specific to a company or a product for further analytics. CiscoDV Value Real-time data federation that accesses extremely large data in CIS Intercloud Analytics platform and join that with Bug Data accessed via departmental CiscoDV instance (RIDES) Data Sources SASA Analytics and QDDTS via RIDES Intercloud Solution By building on the access to the Impala Server, the DV server can join the Bug Data from the Enterprise Data Stores with the HDFS data to provide a federated view. Connection Type DV on hybrid cloud  VPC Big Data platform and Enterprise data store
  • 39. Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public CiscoDV on Intercloud Analytics Platform (CIS) Scenario 1 CIS Cisco DV to Cisco Enterprise Data Store Scenario 2 CIS CiscoDV to Impala and Hive on CIS Intercloud Analytics Platform Scenario 3 CIS Cisco DV to Hive on AWS Big Data Cluster Scenario1 Scenario 3

Editor's Notes

  1. FABIO – a few items from Pankaj and Liz Monday: Per the John Chambers slides I sent you Monday night, please be sure to fully address digitization in the opener, so Pankaj can connect to John’s opening remarks. Set the stage here for what the digital transformation is and why it dries IoE and cloud. Explain where we came from, where we are today – exponential growth and a magnitude of changes still to come. Please see new VNI, to see if there are any newer/better stats re the Data Center. Pankaj feels the top 3 data points are ok in this slide, but perhaps we could find better ones for the bottom 2 data points? Maybe uplevel them a bit? ------------------------------------------------------- The world is changing. The digital transformation is turning traditional business models on their heads. We are seeing unprecedented growth in the explosion of devices and mobile apps and in data utilization. IoE – IoE devices create 277 times the data that the end user is creating. But only a fraction of it ever reaches the data center. A Boeing 787 for example, generates 40 TB of data per every hour of flight time. But only 0.5 TB is ultimately transmitted to the data center. Mobility: In 2014, global mobile data traffic grew 1.7x or 69%… In 2014 alone, 77B+ mobile apps downloaded… by 2015 180B apps (233% increase) Internet… IDC predicts by 2017, there will be 3.6 billion global Internet users… More than 1/2 the world population Big Data… By 2020 there will be more than 5,000 GB of data for every person on Earth These massive changes are putting tremendous stress on the data center. The traditional data center model has to evolve in order to meet demand today and into the future.
  2. We know how to fix this We’re going to do for cloud what we did for data. You couldn’t move data between the networks – they weren’t connected. Cisco unified those worlds The world of cloud today is a world of isolated clouds. There’s no workload or data portability. “Amazon is hotel California – you can never leave, and that data is staying there” Our vision is to connect all these clouds together into the Intercloud - whether private, public , or hybrid through technology and innovation Intercloud is going to connect these clouds together in the same way we connected data together. No one cloud model or single cloud approach, such as the massively scalable clouds from Amazon, Google or Microsoft will win alone in this space