Key Performance Indicators for Managing MongoDB and Recommended Production Configurations

Jun 21, 2017Download as pptx, pdf1 like1,181 views

Speaker: Dwayne McNab, Database Architect, Vonage Level: 300 (Advanced) Track: Operations This session will focus on using Ops Manager for performance monitoring and profiling. We will share how we streamline configurations, automate complex tasks, and configure alerts from multiple sources at Vonage. We'll also discuss general infrastructure management, including automated upgrades, version control, backup and restore and alerting. Finally, we will discuss specific KPIs to measure and recommendations for production configurations. What You Will Learn: - Key performance indicators (KPIs) for managing MongoDB. - Performance monitoring considerations. - Ops manager best practices.

Vonage, Proprietary & Confidential
Using Ops Manager to Solve Operational
Nightmares
Dwayne McNab
Database Architect
@dwayne_mcnab

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Why Ops Manager is a useful tool

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• Easy to use UI

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 Streamline configurations
 Automate complex tasks
 Configure users and groups

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- Ease of managing your infrastructure
 Upgrades and downgrades
 Back up and restore

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Solve operational nightmares

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How should you monitor performance?

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- Track performance over time
 Find out what’s normal for your application
 Understand how your database performs under different workloads
 Know the peak hours of your application

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- Set different severity levels
 Warning if metric spikes 25% over normalcy
 Fatal when metric reaches 80%

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- System load
 Flush average should be less than a second
What KPI (Key Performance Indicator) should we consider
tracking?

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iIO Wait is not getting close to 100%

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Page Faults

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Page Faults

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Page Faults

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 Number of read/write queues
 Lock percentage
Keeping queues and locks low

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 Resident memory is memory that MongoDB owns in RAM
 Mapped memory is a virtual address for pages in memory
 Virtual memory is a copy of the virtual address of each pages of mapped memory.
It is used for journaling
Memory

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MMAPV1 Memory

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WiredTiger Memory

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- Replication
 Lag is the difference between the timestamp of the last op on a primary and
secondary
 Oplog length
ReplicaSet Health

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Replication Lag

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Replication Oplog Window

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Replication Headroom

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- Deployment on a budget
 Redundant Ops Manager servers
 Redundant backup servers and application database
Some recommended production topologies

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- High availability
 Multiple Ops Manager servers behind a load balancer
 Separate application database
 Separate blockstore database

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- Backup Database
 Blockstore
 Filesystem storage

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 More sleep
 Peace of mind
 More insight
 Confidence

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Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe. This talk covers: Common components of an IoT solution The challenges involved with managing time-series data in IoT applications Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance. How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.

MongoDB SoCal 2020: MongoDB Atlas Jump StartMongoDB

MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB

Our clients have unique use cases and data patterns that mandate the choice of a particular strategy. To implement these strategies, it is mandatory that we unlearn a lot of relational concepts while designing and rapidly developing efficient applications on NoSQL. In this session, we will talk about some of our client use cases, the strategies we have adopted, and the features of MongoDB that assisted in implementing these strategies.

MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB

Encryption is not a new concept to MongoDB. Encryption may occur in-transit (with TLS) and at-rest (with the encrypted storage engine). But MongoDB 4.2 introduces support for Client Side Encryption, ensuring the most sensitive data is encrypted before ever leaving the client application. Even full access to your MongoDB servers is not enough to decrypt this data. And better yet, Client Side Encryption can be enabled at the "flick of a switch". This session covers using Client Side Encryption in your applications. This includes the necessary setup, how to encrypt data without sacrificing queryability, and what trade-offs to expect.

MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB

MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB

MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB

When you need to model data, is your first instinct to start breaking it down into rows and columns? Mine used to be too. When you want to develop apps in a modern, agile way, NoSQL databases can be the best option. Come to this talk to learn how to take advantage of all that NoSQL databases have to offer and discover the benefits of changing your mindset from the legacy, tabular way of modeling data. We’ll compare and contrast the terms and concepts in SQL databases and MongoDB, explain the benefits of using MongoDB compared to SQL databases, and walk through data modeling basics so you feel confident as you begin using MongoDB.

MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB

MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB

The document discusses guidelines for ordering fields in compound indexes to optimize query performance. It recommends the E-S-R approach: placing equality fields first, followed by sort fields, and range fields last. This allows indexes to leverage equality matches, provide non-blocking sorts, and minimize scanning. Examples show how indexes ordered by these guidelines can support queries more efficiently by narrowing the search bounds.

MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB

MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB

The document describes a methodology for data modeling with MongoDB. It begins by recognizing the differences between document and tabular databases, then outlines a three step methodology: 1) describe the workload by listing queries, 2) identify and model relationships between entities, and 3) apply relevant patterns when modeling for MongoDB. The document uses examples around modeling a coffee shop franchise to illustrate modeling approaches and techniques.

MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB

MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business. This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented. In addition, we'll discuss future plans and opportunities and offer ample Q&A time with the engineers on the project.

MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB

Virtual assistants are becoming the new norm when it comes to daily life, with Amazon’s Alexa being the leader in the space. As a developer, not only do you need to make web and mobile compliant applications, but you need to be able to support virtual assistants like Alexa. However, the process isn’t quite the same between the platforms. How do you handle requests? Where do you store your data and work with it to create meaningful responses with little delay? How much of your code needs to change between platforms? In this session we’ll see how to design and develop applications known as Skills for Amazon Alexa powered devices using the Go programming language and MongoDB.

MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB

MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB

Il n’a jamais été aussi facile de commander en ligne et de se faire livrer en moins de 48h très souvent gratuitement. Cette simplicité d’usage cache un marché complexe de plus de 8000 milliards de $. La data est bien connu du monde de la Supply Chain (itinéraires, informations sur les marchandises, douanes,…), mais la valeur de ces données opérationnelles reste peu exploitée. En alliant expertise métier et Data Science, Upply redéfinit les fondamentaux de la Supply Chain en proposant à chacun des acteurs de surmonter la volatilité et l’inefficacité du marché.