Running Production MongoDB Lightning Talk

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The document discusses modeling data with MongoDB and techniques for optimizing database performance. It uses the example of the Stark family tree to illustrate how to model related data in arrays and embedded documents. It also covers indexing, monitoring operations with currentOp(), and terminating long-running queries or operations with db.killOp() to improve database efficiency.

Technology

Let’s talk about production
MongoDB
Database Happiness
Developer Fun

It starts with the data model
_id name home p_id
0 Ned Stark Winterfell 0
1 Arya Stark Winterfell 1
2 Bran Stark Winterfell 2
p_id proficiencies
0
keeping a cool
head
1
pulling off the short
hair look
2 riding piggyback

$Arrays in MongoDB { _id: ObjectId(“000”), name: “Ned Stark”, relatives: [ ObjectId(“001”), ObjectId(“002”) ] } { _id: ObjectId(“000”), name: “Ned Stark”, relatives: [ { _id: ObjectId(“001”), name: “Arya Stark”, … }, { _id: ObjectId(“002”), name: “Bran Stark”, … } ] } vs.$

$What are “total normalization” trade-offs? { _id: ObjectId(“000”), name: “Ned Stark”, relatives: [ ObjectId(“001”), ObjectId(“002”) ] }$

Here is Ned Stark.
His relatives are:
ObjectId(“001”) ObjectId(“002”)

$And “partial denormalization”? { _id: ObjectId(“000”), name: “Ned Stark”, relatives: [ { _id: ObjectId(“001”), name: “Arya Stark”, img: “../arya.png” } { _id: ObjectId(“002”), name: “Bran Stark”, img: “../bran.png” } ] }$

Here is Ned Stark.
His relatives are:
Arya Stark Bran Stark

Rules on Arrays
High reads, low updates/writes
Array should be bound in size
Don’t need to frequently access
embedded items individually

#1 cause of slow databases:
(lack of) indexes

cursor.explain() tells us if we’re
indexed
{
"cursor" : "<Cursor Type and Index>",
"n" : <num>,
"nscannedObjects" : <num>,
"nscanned" : <num>,
"nscannedObjectsAllPlans" : <num>,
"nscannedAllPlans" : <num>,
"scanAndOrder" : <boolean>,
"indexOnly" : <boolean>,
"millis" : <num>,
"indexBounds" : { <index bounds> },
"allPlans" : [
{ "cursor" : "<Cursor Type and Index>",
"n" : <num>,
"nscannedObjects" : <num>,
"nscanned" : <num>,
"indexBounds" : { <index bounds> }
},
...
]
}

Remember our indexing
essentials
scanAndOrder
n vs nScanned
NO BasicCursor!!!!!!!!!!!!! NO!
Tier 3
Tier 2
Tier 1

Running Production MongoDB Lightning Talk

How to save your database
• mongod logs
• db.currentOp() & db.killOp()

There’s lots of good info in the
logs
• 2014-10-09T02:26:58.994-0700 [conn367784] query gotDB.people query: {
$query: { isAlive: true }, $orderby: { age: 1 } } planSummary: IXSCAN { isAlive:
1 }, cursorid:1736436984952 ntoreturn:10 ntoskip:0 nscanned:7677
nscannedObjects:7677 scanAndOrder:1 keyUpdates:0 numYields:0
locks(micros) r:115557 nreturned:10 reslen:34036 115ms
• 2014-10-09T21:26:58.994-0700 [conn127156] command gotDB.$cmd
command: mapReduce { $msg: "query not recording (too large)" }
planSummary: COLLSCAN keyUpdates:0 numYields:3843 locks(micros)
W:2840 r:85085260 w:26899 reslen:187 76657ms

$Use currentOp() to triage slow operations db.currentOp().inprog.forEach( function(op) { if(op.secs_running > 5) printjson(op); } )$

$Terminate slowness { "locks": { "^myDB": "R" }, "ns": "myDB.bar", "op": "query", "opid": 1344808, "query": {}, "secs_running": 53, "waitingForLock": false } db.killOp(1344808)$

Thanks for listening!
chris@mongolab.com
@chrisckchang
@mongolab

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Managing airport and airspace data is no small task, especially when you’re expected to deliver it in AIXM format without spending a fortune on specialized tools. But what if there was a smarter, more affordable way? Join us for a behind-the-scenes look at how Sweco partnered with Swedavia, the Swedish airport operator, to solve this challenge using FME and Esri. Learn how they built automated workflows to manage periodic updates, merge airspace data, and support data extracts – all while meeting strict government reporting requirements to the Civil Aviation Administration of Sweden. Even better? Swedavia built custom services and applications that use the FME Flow REST API to trigger jobs and retrieve results – streamlining tasks like securing the quality of new surveyor data, creating permdelta and baseline representations in the AIS schema, and generating AIXM extracts from their AIS data. To conclude, FME expert Dean Hintz will walk through a GeoBorders reading workflow and highlight recent enhancements to FME’s AIXM (Aeronautical Information Exchange Model) processing and interpretation capabilities. Discover how airports like Swedavia are harnessing the power of FME to simplify aviation data management, and how you can too.

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9-1-1 Addressing: End-to-End Automation Using FMESafe Software

This session will cover a common use case for local and state/provincial governments who create and/or maintain their 9-1-1 addressing data, particularly address points and road centerlines. In this session, you'll learn how FME has helped Shelby County 9-1-1 (TN) automate the 9-1-1 addressing process; including automatically assigning attributes from disparate sources, on-the-fly QAQC of said data, and reporting. The FME logic that this presentation will cover includes: Table joins using attributes and geometry, Looping in custom transformers, Working with lists and Change detection.

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Presented at All Things Open RTP Meetup Presented by William Hill - Developer Advocate, NVIDIA Title: Agentic AI for Developers and Data Scientists Build an AI Agent in 10 Lines of Code and the Concepts Behind the Code Abstract: In this talk we will demonstrate building a working data science AI agent in 10 lines of basic Python code in a Colab notebook. Our AI Agent will perform LLM prompt-driven visual analysis using open-source libraries. In this session we will show how to develop an AI Agent using GPUs through NVIDIA’s developer program and Google Colab notebooks. After coding our AI Agent, we will break down the 10 lines of code. We will show the key components and open source library integrations that enable the agent's functionality, focusing on practical implementation details and then the theoretical concepts. The presentation concludes with a survey of current LLM technologies and the latest trends in developing AI applications for enthusiasts and enterprises

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מרכז עיבודים תעשייתי בעל 3/4/5 צירים, עד 22 החלפות כלים עם כל אפשרויות העיבוד הדרושות. בעל שטח עבודה גדול ומחשב נוח וקל להפעלה בשפה העברית/רוסית/אנגלית/ספרדית/ערבית ועוד.. מסוגל לבצע פעולות עיבוד שונות המתאימות לענפים שונים: קידוח אנכי, אופקי, ניסור, וכרסום אנכי.

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Design patterns like Event Sourcing and Event Streaming have long become standards for building real-time analytics systems. However, when the system load becomes nonlinear with fast and often unpredictable spikes, it's crucial to respond quickly in order not to lose real-time operating itself. In this talk, I’ll share my experience implementing and using a tool like Temporal.io. We'll explore the evolution of our system for maintaining real-time report generation and discuss how we use Temporal both for short-lived pipelines and long-running background tasks.

Security Tips for Enterprise Azure SolutionsMichele Leroux Bustamante

Delivering solutions to Azure may involve a variety of architecture patterns involving your applications, APIs data and associated Azure resources that comprise the solution. This session will use reference architectures to illustrate the security considerations to protect your Azure resources and data, how to achieve Zero Trust, and why it matters. Topics covered will include specific security recommendations for types Azure resources and related network security practices. The goal is to give you a breadth of understanding as to typical security requirements to meet compliance and security controls in an enterprise solution.

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Hi, I’m Aly Owens, I have a special pleasure to stand here as over a decade ago I graduated from CityU as an international student with an MBA program. I enjoyed the diversity of the school, ability to work and study, the network that came with being here, and of course the price tag for students here has always been more affordable than most around. Since then I have worked for major corporations like T-Mobile and Microsoft and many more, and I have founded a startup. I've also been teaching product management to ensure my students save time and money to get to the same level as me faster avoiding popular mistakes. Today as I’ve transitioned to teaching and focusing on the startup, I hear everybody being concerned about Ai stealing their jobs… We’ll talk about it shortly. But before that, I want to take you back to 1997. One of my favorite movies is “Fifth Element”. It wowed me with futuristic predictions when I was a kid and I’m impressed by the number of these predictions that have already come true. Self-driving cars, video calls and smart TV, personalized ads and identity scanning. Sci-fi movies and books gave us many ideas and some are being implemented as we speak. But we often get ahead of ourselves: Flying cars,Colonized planets, Human-like AI: not yet, Time travel, Mind-machine neural interfaces for everyone: Only in experimental stages (e.g. Neuralink). Cyberpunk dystopias: Some vibes (neon signs + inequality + surveillance), but not total dystopia (thankfully). On the bright side, we predict that the working hours should drop as Ai becomes our helper and there shouldn’t be a need to work 8 hours/day. Nobody knows for sure but we can require that from legislation. Instead of waiting to see what the government and billionaires come up with, I say we should design our own future. So, we as humans, when we don’t know something - fear takes over. The same thing happened during the industrial revolution. In the Industrial Era, machines didn’t steal jobs—they transformed them but people were scared about their jobs. The AI era is making similar changes except it feels like robots will take the center stage instead of a human. First off, even when it comes to the hottest space in the military - drones, Ai does a fraction of work. AI algorithms enable real-time decision-making, obstacle avoidance, and mission optimization making drones far more autonomous and capable than traditional remote-controlled aircraft. Key technologies include computer vision for object detection, GPS-enhanced navigation, and neural networks for learning and adaptation. But guess what? There are only 2 companies right now that utilize Ai in drones to make autonomous decisions - Skydio and DJI.

From Manual to Auto Searching- FME in the Driver's SeatSafe Software

Finding a specific car online can be a time-consuming task, especially when checking multiple dealer websites. A few years ago, I faced this exact problem while searching for a particular vehicle in New Zealand. The local classified platform, Trade Me (similar to eBay), wasn’t yielding any results, so I expanded my search to second-hand dealer sites—only to realise that periodically checking each one was going to be tedious. That’s when I noticed something interesting: many of these websites used the same platform to manage their inventories. Recognising this, I reverse-engineered the platform’s structure and built an FME workspace that automated the search process for me. By integrating API calls and setting up periodic checks, I received real-time email alerts when matching cars were listed. In this presentation, I’ll walk through how I used FME to save hours of manual searching by creating a custom car-finding automation system. While FME can’t buy a car for you—yet—it can certainly help you find the one you’re after!

Daily Lesson Log MATATAG ICT TEchnology 8LOIDAALMAZAN3

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From Manual to Auto Searching- FME in the Driver's SeatSafe Software

Running Production MongoDB Lightning Talk

1. featuring special guest Sean Bean

2. Let’s talk about production MongoDB Database Happiness Developer Fun

3. But it doesn’t need to be this way.

4. Let’s use House Stark as an example

5. It starts with the data model _id name home p_id 0 Ned Stark Winterfell 0 1 Arya Stark Winterfell 1 2 Bran Stark Winterfell 2 p_id proficiencies 0 keeping a cool head 1 pulling off the short hair look 2 riding piggyback

6. Arrays in MongoDB { _id: ObjectId(“000”), name: “Ned Stark”, relatives: [ ObjectId(“001”), ObjectId(“002”) ] } { _id: ObjectId(“000”), name: “Ned Stark”, relatives: [ { _id: ObjectId(“001”), name: “Arya Stark”, … }, { _id: ObjectId(“002”), name: “Bran Stark”, … } ] } vs.

7. What are “total normalization” trade-offs? { _id: ObjectId(“000”), name: “Ned Stark”, relatives: [ ObjectId(“001”), ObjectId(“002”) ] }

8. Here is Ned Stark. His relatives are: ObjectId(“001”) ObjectId(“002”)

9. And “partial denormalization”? { _id: ObjectId(“000”), name: “Ned Stark”, relatives: [ { _id: ObjectId(“001”), name: “Arya Stark”, img: “../arya.png” } { _id: ObjectId(“002”), name: “Bran Stark”, img: “../bran.png” } ] }

10. Here is Ned Stark. His relatives are: Arya Stark Bran Stark

11. Rules on Arrays High reads, low updates/writes Array should be bound in size Don’t need to frequently access embedded items individually

12. #1 cause of slow databases: (lack of) indexes

13. cursor.explain() tells us if we’re indexed { "cursor" : "<Cursor Type and Index>", "n" : <num>, "nscannedObjects" : <num>, "nscanned" : <num>, "nscannedObjectsAllPlans" : <num>, "nscannedAllPlans" : <num>, "scanAndOrder" : <boolean>, "indexOnly" : <boolean>, "millis" : <num>, "indexBounds" : { <index bounds> }, "allPlans" : [ { "cursor" : "<Cursor Type and Index>", "n" : <num>, "nscannedObjects" : <num>, "nscanned" : <num>, "indexBounds" : { <index bounds> } }, ... ] }

14. Remember our indexing essentials scanAndOrder n vs nScanned NO BasicCursor!!!!!!!!!!!!! NO! Tier 3 Tier 2 Tier 1

16. How to save your database • mongod logs • db.currentOp() & db.killOp()

17. There’s lots of good info in the logs • 2014-10-09T02:26:58.994-0700 [conn367784] query gotDB.people query: { $query: { isAlive: true }, $orderby: { age: 1 } } planSummary: IXSCAN { isAlive: 1 }, cursorid:1736436984952 ntoreturn:10 ntoskip:0 nscanned:7677 nscannedObjects:7677 scanAndOrder:1 keyUpdates:0 numYields:0 locks(micros) r:115557 nreturned:10 reslen:34036 115ms • 2014-10-09T21:26:58.994-0700 [conn127156] command gotDB.$cmd command: mapReduce { $msg: "query not recording (too large)" } planSummary: COLLSCAN keyUpdates:0 numYields:3843 locks(micros) W:2840 r:85085260 w:26899 reslen:187 76657ms

18. Use currentOp() to triage slow operations db.currentOp().inprog.forEach( function(op) { if(op.secs_running > 5) printjson(op); } )

19. Terminate slowness { "locks": { "^myDB": "R" }, "ns": "myDB.bar", "op": "query", "opid": 1344808, "query": {}, "secs_running": 53, "waitingForLock": false } db.killOp(1344808)

20. Thanks for listening! [email protected] @chrisckchang @mongolab

Editor's Notes

#12: query too large
#18: note 2.6 logs check to see connections are authenticating, where connections are coming from, what operations are slow, etc. what’s going on to my database right now

Running Production MongoDB Lightning Talk

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Editor's Notes