Building a Scalable and Modern Infrastructure at CARFAX

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The CARFAX vehicle history database contains over twelve billion documents in a twelve shard cluster that replicates to multiple data centers. This will be a step by step walk through of how we deploy our servers, manage high volume reads and writes, and our configuration for high availability. By automating everything from the operating system install up we are able deploy complete replica clusters quickly and efficiently. Using distributed processing and message queuing we load millions of new documents each day with a projected growth over a billion records per year. Through the use of tagging, server configuration, and read settings we deliver content with high consistency and availability.

Technology

A Scalable and Modern
Infrastructure at CARFAX

About Me
• Jai Hirsch – Senior Systems Architect, Data
Technologies at CARFAX
• Long-time Java and Database Developer
• Data and Distributed Processing Enthusiast
• Github: https://github.com/JaiHirsch
• Twitter: @JaiHirsch
• Blog: http://jaihirsch.github.io/straw-in-a-haystack/

“CARFAX helps millions of people buy and sell used cars with
more confidence”

NoSQL Before it Was Cool
Proprietary Key Value Store on
OpenVMS Developed by
CARFAX in 1984

Why MongoDB?
Legacy structures mapped to
documents
High availability using replica sets
Platform Independence
Support

MongoDB at CARFAX
Our Production Environment
The Legacy Database and High
Volume Loads
High Availability Reads

Server Deployment
AUTOMATE
AUTOMATE
AUTOMATE
AUTOMATE

Server Configuration
12 Shards with two spare
servers racked for failover
• OS: Linux
• MongoDB 2.4.9
• 128 GIGs of RAM
• 1.8 TB of Drive Space
• 10K RPM SAS Drives

Loading Millions to Billions of Records per Day
AUTOMATE
AUTOMATE
AUTOMATE
AUTOMATE

First Attempt To Load Was Completely
CPU Bound

Not Acceptable!
45 Days to
Backload the
Legacy Database

Acceptable!
Billion+ inserts per
Day!
9 Days to Backload

The MongoDB Implementation
13.6 billion+ documents
 1.5 billion+ new documents per
year
 Document size: ~ 800 Bytes

VHR Uses 200+ Documents
With Embedded Keys

Millions of Reports per Day
AUTOMATE
AUTOMATE
AUTOMATE

Each Data
center is
Tagged
Each Replica
Set is Tagged

Lets Wrap It Up
Don’t buy a used car without a
CARFAX report
Grok your data and working set
Architect for your load volume
Scale your reads to meet demand
30

Keys To Success
AUTOMATE EVERYTHING
Test Many Configurations
Grid Computing is Awesome
Shard Early, Shard Often

Friends Don’t
Let Friends Use
Default Ulimits!

Thank You!
 The migration was a
success due to the
incredible teams at
CARFAX and MongoDB
 We are always looking
for great people to join
us.
www.carfax.com/careers

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debabhi2

The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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Joaquim Jorge

Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?

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Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.

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Building a Scalable and Modern Infrastructure at CARFAX

1. A Scalable and Modern Infrastructure at CARFAX

2. About Me • Jai Hirsch – Senior Systems Architect, Data Technologies at CARFAX • Long-time Java and Database Developer • Data and Distributed Processing Enthusiast • Github: https://github.com/JaiHirsch • Twitter: @JaiHirsch • Blog: http://jaihirsch.github.io/straw-in-a-haystack/

3. “CARFAX helps millions of people buy and sell used cars with more confidence”

4. CARFAX Vehicle History Report

5. Documents on the Report

6. NoSQL Before it Was Cool Proprietary Key Value Store on OpenVMS Developed by CARFAX in 1984

7. Never mind that sh*t! Here comes Mongo!

8. Why MongoDB? Legacy structures mapped to documents High availability using replica sets Platform Independence Support

9. MongoDB at CARFAX Our Production Environment The Legacy Database and High Volume Loads High Availability Reads

10. Our Production Environment

11. Server Deployment AUTOMATE AUTOMATE AUTOMATE AUTOMATE

12. Server Configuration 12 Shards with two spare servers racked for failover • OS: Linux • MongoDB 2.4.9 • 128 GIGs of RAM • 1.8 TB of Drive Space • 10K RPM SAS Drives

13.

14. The Future

15.

16. Extract, Transform, Load

17. Loading Millions to Billions of Records per Day AUTOMATE AUTOMATE AUTOMATE AUTOMATE

18. First Attempt To Load Was Completely CPU Bound

19. Not Acceptable! 45 Days to Backload the Legacy Database

20. Distributed Processing

21. Acceptable! Billion+ inserts per Day! 9 Days to Backload

22. The MongoDB Implementation 13.6 billion+ documents  1.5 billion+ new documents per year  Document size: ~ 800 Bytes

23. VHR Uses 200+ Documents With Embedded Keys

24. High Availability Reads

25. Millions of Reports per Day AUTOMATE AUTOMATE AUTOMATE

26. Read Scalability With Tagging

27. Each Data center is Tagged Each Replica Set is Tagged

28. 5X More Reports per Second

29. But we can do More!

30. Lets Wrap It Up Don’t buy a used car without a CARFAX report Grok your data and working set Architect for your load volume Scale your reads to meet demand 30

31. Keys To Success AUTOMATE EVERYTHING Test Many Configurations Grid Computing is Awesome Shard Early, Shard Often

32. And Remember

33. Friends Don’t Let Friends Use Default Ulimits!

34. Thank You!  The migration was a success due to the incredible teams at CARFAX and MongoDB  We are always looking for great people to join us. www.carfax.com/careers

Building a Scalable and Modern Infrastructure at CARFAX

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