MongoDB performs best when documents are grouped together and data is written and indexed in a way that minimizes disk seeks. Specifically:
1) Group related documents together to reduce the number of random disk seeks needed to retrieve them.
2) Create indexes on fields that data is often inserted on to improve insertion performance and keep more data in memory.
3) Pre-allocate data on disk in the order it will be read to minimize disk seeks during queries. Reading sequentially stored data requires fewer seeks than randomly accessing documents.
performance analysis between sql ans nosqlRUFAI YUSUF
While traditional relational databases are still used in a large scope of applications, we have seen recently an explosion in the number of a new data bases technologies developed in particular for Big Data serving. Currently the main alternatives to RDMBS are NoSQL databases.
performance analysis between sql ans nosqlRUFAI YUSUF
While traditional relational databases are still used in a large scope of applications, we have seen recently an explosion in the number of a new data bases technologies developed in particular for Big Data serving. Currently the main alternatives to RDMBS are NoSQL databases.
To understand how to make your application fast, it's important to understand what makes the database fast. We will take a detailed look at how to think about performance, and how different choices in schema design affect your cluster performances depending on storage engines used and physical resources available.
A Front-Row Seat to Ticketmaster’s Use of MongoDBMongoDB
Ticketmaster is the world leader in selling tickets. After more than a decade of developing applications extensively on Oracle and MySQL, Ticketmaster made the move to MongoDB. The reasons for the move are generally in line with those of other companies – increased flexibility and performance, and decreased costs and time-to-market. In this session we’ll discuss how the conversion to MongoDB went at Ticketmaster and we’ll take a deeper dive into some of the successes and set-backs that we faced. We’ll give an overview of the MongoDB topology at Ticketmaster, discuss exactly what data we’re writing to MongoDB and comment on the MongoDB support model that we’re using. We’ll also touch on the transition from relational DBA to NoSQL DBA at Ticketmaster.
In this workshop we will deploy a pre-built Node website to Heroku, then hook it up to an mLabs MongoDB instance. We will then use both the Mongo Shell and a GUI based app to import and export data, save and modify documents, and run queries. Finally, we'll use our knowledge of Mongo queries to create a RESTful api for the Node app.
This is a workshop designed for experienced JavaScript developers. You must already be familiar with the following: JavaScript, Git, using a programming editor, running commands from the terminal, and launching a web server on your own machine.
QuestDB: ingesting a million time series per second on a single instance. Big...javier ramirez
In this session I will show you the technical decisions we made when building QuestDB, the open source, Postgres compatible, time-series database, and how we can achieve a million row writes per second without blocking or slowing down the reads.
SharePoint Search Topology and OptimizationMike Maadarani
This presentation covers the architecture of SharePoint Search Topology, how to extend search and how to optimize your search farm for better results. It describes how you can build your Search topology with PowerShell commands and it explains how you can use the Query Rules and Query Builder for a great search results.
These are the slides I presented at the Nosql Night in Boston on Nov 4, 2014. The slides were adapted from a presentation given by Steve Francia in 2011. Original slide deck can be found here:
http://spf13.com/presentation/mongodb-sort-conference-2011
Clickhouse Capacity Planning for OLAP Workloads, Mik Kocikowski of CloudFlareAltinity Ltd
Presented on December ClickHouse Meetup. Dec 3, 2019
Concrete findings and "best practices" from building a cluster sized for 150 analytic queries per second on 100TB of http logs. Topics covered: hardware, clients (http vs native), partitioning, indexing, SELECT vs INSERT performance, replication, sharding, quotas, and benchmarking.
Amazon DynamoDB is a fast and flexible NoSQL database service for applications that need consistent, single-digit millisecond latency at any scale. It is a fully managed cloud database and supports both document and key-value store models. Its flexible data model and reliable performance make it a great fit for mobile, web, gaming, ad tech, IoT, and many other applications.
Learning Objectives:
Understand the differences between relational and non-relational databases
Learn about common use cases for DynamoDB across gaming, ad tech, IoT, and more
See how DynamoDB helps customers handle spikes in traffic and save development time for new feature launches
Who Should Attend:
Developers, IT Decision Makers, and Executives interested in learning more about Amazon Web Services’ serverless NoSQL service to scale mobile, web, IoT, ad tech, and gaming apps
MongoDB World 2019: Finding the Right MongoDB Atlas Cluster Size: Does This I...MongoDB
How do you determine whether your MongoDB Atlas cluster is over provisioned, whether the new feature in your next application release will crush your cluster, or when to increase cluster size based upon planned usage growth? MongoDB Atlas provides over a hundred metrics enabling visibility into the inner workings of MongoDB performance, but how do apply all this information to make capacity planning decisions? This presentation will enable you to effectively analyze your MongoDB performance to optimize your MongoDB Atlas spend and ensure smooth application operation into the future.
Partner Webinar: The Scaling Checklist for MongoDB - 100GB and beyondMongoDB
MongoHQ knows there is something special about 100 GB of data. Our customers that hit 100 GB are running core pieces of their business on a scalable MongoDB platform. In this presentation, we will walk through a cloud focused scaling checklist that will help you quickly and securely blow past the 100 GB milestone. Using customer examples and best practice MongoDB use cases, we'll help prepare you to get to the data size your business needs.
To understand how to make your application fast, it's important to understand what makes the database fast. We will take a detailed look at how to think about performance, and how different choices in schema design affect your cluster performances depending on storage engines used and physical resources available.
A Front-Row Seat to Ticketmaster’s Use of MongoDBMongoDB
Ticketmaster is the world leader in selling tickets. After more than a decade of developing applications extensively on Oracle and MySQL, Ticketmaster made the move to MongoDB. The reasons for the move are generally in line with those of other companies – increased flexibility and performance, and decreased costs and time-to-market. In this session we’ll discuss how the conversion to MongoDB went at Ticketmaster and we’ll take a deeper dive into some of the successes and set-backs that we faced. We’ll give an overview of the MongoDB topology at Ticketmaster, discuss exactly what data we’re writing to MongoDB and comment on the MongoDB support model that we’re using. We’ll also touch on the transition from relational DBA to NoSQL DBA at Ticketmaster.
In this workshop we will deploy a pre-built Node website to Heroku, then hook it up to an mLabs MongoDB instance. We will then use both the Mongo Shell and a GUI based app to import and export data, save and modify documents, and run queries. Finally, we'll use our knowledge of Mongo queries to create a RESTful api for the Node app.
This is a workshop designed for experienced JavaScript developers. You must already be familiar with the following: JavaScript, Git, using a programming editor, running commands from the terminal, and launching a web server on your own machine.
QuestDB: ingesting a million time series per second on a single instance. Big...javier ramirez
In this session I will show you the technical decisions we made when building QuestDB, the open source, Postgres compatible, time-series database, and how we can achieve a million row writes per second without blocking or slowing down the reads.
SharePoint Search Topology and OptimizationMike Maadarani
This presentation covers the architecture of SharePoint Search Topology, how to extend search and how to optimize your search farm for better results. It describes how you can build your Search topology with PowerShell commands and it explains how you can use the Query Rules and Query Builder for a great search results.
These are the slides I presented at the Nosql Night in Boston on Nov 4, 2014. The slides were adapted from a presentation given by Steve Francia in 2011. Original slide deck can be found here:
http://spf13.com/presentation/mongodb-sort-conference-2011
Clickhouse Capacity Planning for OLAP Workloads, Mik Kocikowski of CloudFlareAltinity Ltd
Presented on December ClickHouse Meetup. Dec 3, 2019
Concrete findings and "best practices" from building a cluster sized for 150 analytic queries per second on 100TB of http logs. Topics covered: hardware, clients (http vs native), partitioning, indexing, SELECT vs INSERT performance, replication, sharding, quotas, and benchmarking.
Amazon DynamoDB is a fast and flexible NoSQL database service for applications that need consistent, single-digit millisecond latency at any scale. It is a fully managed cloud database and supports both document and key-value store models. Its flexible data model and reliable performance make it a great fit for mobile, web, gaming, ad tech, IoT, and many other applications.
Learning Objectives:
Understand the differences between relational and non-relational databases
Learn about common use cases for DynamoDB across gaming, ad tech, IoT, and more
See how DynamoDB helps customers handle spikes in traffic and save development time for new feature launches
Who Should Attend:
Developers, IT Decision Makers, and Executives interested in learning more about Amazon Web Services’ serverless NoSQL service to scale mobile, web, IoT, ad tech, and gaming apps
MongoDB World 2019: Finding the Right MongoDB Atlas Cluster Size: Does This I...MongoDB
How do you determine whether your MongoDB Atlas cluster is over provisioned, whether the new feature in your next application release will crush your cluster, or when to increase cluster size based upon planned usage growth? MongoDB Atlas provides over a hundred metrics enabling visibility into the inner workings of MongoDB performance, but how do apply all this information to make capacity planning decisions? This presentation will enable you to effectively analyze your MongoDB performance to optimize your MongoDB Atlas spend and ensure smooth application operation into the future.
Partner Webinar: The Scaling Checklist for MongoDB - 100GB and beyondMongoDB
MongoHQ knows there is something special about 100 GB of data. Our customers that hit 100 GB are running core pieces of their business on a scalable MongoDB platform. In this presentation, we will walk through a cloud focused scaling checklist that will help you quickly and securely blow past the 100 GB milestone. Using customer examples and best practice MongoDB use cases, we'll help prepare you to get to the data size your business needs.
AWS Summit 2013 | India - Understanding the Total Cost of (Non) Ownership, Ki...Amazon Web Services
Explore the financial considerations of owning and operating a traditional data center or managed hosting provider versus utilizing cloud infrastructure. This session will consider many cost factors which can be overlooked when comparing models, such as training, support contracts and software licensing. The presentation will additionally also cover as to how the TCO in an on-premise data center can become significantly higher when considering factors like scalability, flexibility & security when compared to a cloud platform. Learn how to further reduce your current costs on AWS and improve your spend predictability.
Building a Large Scale SEO/SEM Application with Apache SolrRahul Jain
Slides from my talk on "Building a Large Scale SEO/SEM Application with Apache Solr" in Lucene/Solr Revolution 2014 where I talk how we handle Indexing/Search of 40 billion records (documents)/month in Apache Solr with 4.6 TB compressed index data.
Abstract: We are working on building a SEO/SEM application where an end user search for a "keyword" or a "domain" and gets all the insights about these including Search engine ranking, CPC/CPM, search volume, No. of Ads, competitors details etc. in a couple of seconds. To have this intelligence, we get huge web data from various sources and after intensive processing it is 40 billion records/month in MySQL database with 4.6 TB compressed index data in Apache Solr.
Due to large volume, we faced several challenges while improving indexing performance, search latency and scaling the overall system. In this session, I will talk about our several design approaches to import data faster from MySQL, tricks & techniques to improve the indexing performance, Distributed Search, DocValues(life saver), Redis and the overall system architecture.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
5. Group Documents Together (1)
Stats system. Data = 3x RAM.
Single document per day
1.6s to read an entire year
Single document per month
0.3s to read an entire year
6. Group Documents Together (2)
Fewer random seeks
= Faster
Grouped documents
= Less overhead
= More in working set
See: http://bit.ly/foursquare-metrics-mongodb
7. Unusual Indices (1)
Index on metric type then date:
Inserts started at 10k/sec
Dropped to 2.5k/sec after 20m inserts
Index on date then metric type:
Inserts stayed at 10k/sec
No hit on query performance
8. Unusual Indices (2)
Only inserting to one side of index
Rebalancing hits less of the index
⇒ Less to flush to disk
⇒ More will be in memory
9. Pre-Allocate for Locality (1)
Pre-allocate data in read order
Data written in key then date order
6.6ms to query data for a year
Data written in date then key order
62ms to query data for a year
10. Pre-Allocate for Locality (2)
Data exists on disk in the order it is written
(ignoring resized documents)
Reading 12 random documents from disk
= 12 seeks
Reading 12 documents written at same time
= 1 seek + 11 sequential reads
11. MANDATORY
NOTICE:
Always benchmark
your use case
![[More]
MongoDB Performance
Tips
Colin Howe
@colinhowe
http://www.colinhowe.co.uk](https://image.slidesharecdn.com/moremongodbperformancetips-120423141130-phpapp02/85/More-MongoDB-Performance-Tips-1-320.jpg)
