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Platform for Real-Time Production Operations

Prepared for LSPE Meet-up
November 21, 2013
DataTorrent in Hadoop Ecosystem

• Most powerful Hadoop platform for real-time stream computations
• Massive Real-Time Production Monitoring, Analytics, and Alerting
– Systems monitoring: Resource Utilization, Logs Analysis
– Predictive Maintenance, DOS Attack, Launch Validation etc.
DataTorrent Technology Stack
Malhar – Open Source Operators and Apps Library
(Apache v2 License)
SLA

Alerts

Tools

Web Services

State
Snapshot

Security

Scalability

Fault
Tolerance

Partitioning

Dynamic
Modifications

StrAM (Stream Application Master)
DataTorrent’s Platform Differentiators
.
Extreme Scalability

•
•

•

Automatically scale to
changing loads
Sub-second latency with
linear scalability
Complex monitoring
applications with massive
computations

Mission Critical

•
•

•

Built-in Stateful Faulttolerance. 24/7 uptime
guaranteed
Predictive Analysis, and
trouble shooting

Update your application
while it's running!

Hadoop-Native

•
•

•

Runs on your existing
Apache Hadoop cluster.
Develop faster with our
open-source framework.
Integrate seamlessly with
your existing monitoring
stack.
Stream Processing
Stream 3

Stream 1

Data
Load

Stream 4

Stream 2

Window 3

•
•
•
•
•

Window 2

Window 1

A Stream is a sequence of data events with schema
An Operator takes input streams and compute output streams
An Application is a Directed Acyclic Graph (DAG)
In-memory asynchronous distributed computations
A Streaming Window is an atomic batch of sequential data events
DataTorrent Hadoop GRID
1

4

3
2

DT
Console

dtCLI

6
5
Resource
Manager

NM
MapReduce

NM

DT
Gateway

NM

NM
MapReduce

StrAM

MapReduce

3

1

MapReduce

MapReduce

2

5

4

6

MapReduce
Live Demonstration
Open Sourced Production Operations Application
Real-Time Dashboards and Actions
•
•
•
•
•
•

DOS Attack
Predictive maintenance of servers
Pre and post Launch analysis
404 Response
Root cause analysis for LAMP architecture
Segmentation
–
–
–
–

•

Geo Location
Gender, Age
Resource usage (urls)
Etc.

URL Analysis
– Response times
– Patterns

• Seamless integration into monitoring
stacks
How to get Started?
• DataTorrent
• Try Sandbox (https://datatorrent.com)
• Free for small to medium enterprises: Contact us for details
• Malhar Open Source (Apache 2.0) project
• https://github.com/DataTorrent/Malhar
• malhar-users@googlegroups.com
• Applications available Jan 2014
• LogStream: Site Operations
• Map-Reduce Monitor
DataTorrent Inc.
3200 Partrick Henry, 2nd Fl
Santa Clara, CA 95054

info@datatorrent.com
www.datatorrent.com

Twitter.com/DataTorrent
Facebook.com/DataTorrent
Platform Capabilities
Scale able High
Performance
• Throughput in Billions Events/Sec
• Latency in Milliseconds

Powerful Tools
• GUI For Cluster Performance Monitoring
• GUI and Debuggers for Event Data
• Test Framework, Certification, Versioning
• CLI, Macros

Easy To Use
Fault-Tolerance
• No State loss, No Message loss node outage recovery
• State Management
• Efficient State Checkpointing

• Library of Operator Templates
• Focus On Business Logic
• Connectors to Current Tools
• HDFS, Hbase, MySql, ActiveMQ
• APIs for Tool Integrations

Adaptability

Native YARN
Application

• Runtime Scaling and Resource Optimization
• Dynamic Application Modification

•Integrates with Hadoop 2.0 Distributions
•Apache, Cloudera, Hortonworks, MapR, Pivotal
•Co-Exists with Existing Batch Infrastructure
•Multi-Tenancy with Existing Hadoop Applications
Appendix

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Data torrent meetup-productioneng

  • 1. Platform for Real-Time Production Operations Prepared for LSPE Meet-up November 21, 2013
  • 2. DataTorrent in Hadoop Ecosystem • Most powerful Hadoop platform for real-time stream computations • Massive Real-Time Production Monitoring, Analytics, and Alerting – Systems monitoring: Resource Utilization, Logs Analysis – Predictive Maintenance, DOS Attack, Launch Validation etc.
  • 3. DataTorrent Technology Stack Malhar – Open Source Operators and Apps Library (Apache v2 License) SLA Alerts Tools Web Services State Snapshot Security Scalability Fault Tolerance Partitioning Dynamic Modifications StrAM (Stream Application Master)
  • 4. DataTorrent’s Platform Differentiators . Extreme Scalability • • • Automatically scale to changing loads Sub-second latency with linear scalability Complex monitoring applications with massive computations Mission Critical • • • Built-in Stateful Faulttolerance. 24/7 uptime guaranteed Predictive Analysis, and trouble shooting Update your application while it's running! Hadoop-Native • • • Runs on your existing Apache Hadoop cluster. Develop faster with our open-source framework. Integrate seamlessly with your existing monitoring stack.
  • 5. Stream Processing Stream 3 Stream 1 Data Load Stream 4 Stream 2 Window 3 • • • • • Window 2 Window 1 A Stream is a sequence of data events with schema An Operator takes input streams and compute output streams An Application is a Directed Acyclic Graph (DAG) In-memory asynchronous distributed computations A Streaming Window is an atomic batch of sequential data events
  • 8. Open Sourced Production Operations Application Real-Time Dashboards and Actions • • • • • • DOS Attack Predictive maintenance of servers Pre and post Launch analysis 404 Response Root cause analysis for LAMP architecture Segmentation – – – – • Geo Location Gender, Age Resource usage (urls) Etc. URL Analysis – Response times – Patterns • Seamless integration into monitoring stacks
  • 9. How to get Started? • DataTorrent • Try Sandbox (https://datatorrent.com) • Free for small to medium enterprises: Contact us for details • Malhar Open Source (Apache 2.0) project • https://github.com/DataTorrent/Malhar • malhar-users@googlegroups.com • Applications available Jan 2014 • LogStream: Site Operations • Map-Reduce Monitor DataTorrent Inc. 3200 Partrick Henry, 2nd Fl Santa Clara, CA 95054 info@datatorrent.com www.datatorrent.com Twitter.com/DataTorrent Facebook.com/DataTorrent
  • 10. Platform Capabilities Scale able High Performance • Throughput in Billions Events/Sec • Latency in Milliseconds Powerful Tools • GUI For Cluster Performance Monitoring • GUI and Debuggers for Event Data • Test Framework, Certification, Versioning • CLI, Macros Easy To Use Fault-Tolerance • No State loss, No Message loss node outage recovery • State Management • Efficient State Checkpointing • Library of Operator Templates • Focus On Business Logic • Connectors to Current Tools • HDFS, Hbase, MySql, ActiveMQ • APIs for Tool Integrations Adaptability Native YARN Application • Runtime Scaling and Resource Optimization • Dynamic Application Modification •Integrates with Hadoop 2.0 Distributions •Apache, Cloudera, Hortonworks, MapR, Pivotal •Co-Exists with Existing Batch Infrastructure •Multi-Tenancy with Existing Hadoop Applications