Mongodb debugging-performance-problems

2 likes1,689 views

The document discusses strategies for improving MongoDB performance, emphasizing the importance of application speed over database speed. Key solutions include denormalization, indexing, sharding, and scaling the database. Various performance metrics and query execution plans are provided to illustrate and support the suggested solutions.

MongoDB
Debugging Performance Problems
@ConradIrwin

50M crashes/day
4TB data / 100GB index
15 nodes / 3* availability zones

I don't care if MongoDB is slow
I care if my app is slow

How to make app fast again it's slow because
of the way I'm

$db.errors. find({project_id: x}). sort({ _id: -1}). limit(30)$

$db.errors. find({project_id: x}). sort({ _id: -1}). limit(30). explain()$

${ "cursor" : "BtreeCursor _id_ reverse", "isMultiKey" : false, "n" : 0, "nscannedObjects" : 227756, "nscanned" : 227756, "nscannedObjectsAllPlans" : 227756, "nscannedAllPlans" : 227756, "scanAndOrder" : false, "indexOnly" : false, "nYields" : 1779, "nChunkSkips" : 0, "millis" : 461, "indexBounds" : { "_id" : [ [ { "$maxElement" : 1 }, { "$minElement" : 1 } ] ] }, "server" : "Jaroussky.local:27017", "filterSet" : false }$

${ "cursor" : "BtreeCursor _id_ reverse", "nscanned" : 227756, "indexBounds" : {"_id" : [ [{"$maxElement" : 1}, {"$minElement" : 1}] ] } }$

$db.errors.ensureIndex( {project_id: 1, _id: -1} )$

${"cursor" : "BtreeCursor project_id_1__id_1", "nscanned" : 6, "indexBounds" : { "project_id" : [[ ObjectId(x),ObjectId(x) ]], "_id" : [[ {"$minElement" : 1}, {"$maxElement" : 1} ]] }}$

insert query update delete getmore command flushes mapped
5 97 57 *0 95 127|0 0 320g
5 98 61 *0 113 146|0 0 320g
8 94 61 *0 95 137|0 0 320g
vsize res faults locked db idx miss % qr|qw ar|aw
641g 8.65g 7 bugsnag:47.7% 0 0|0 0|0
641g 8.66g 2 bugsnag:21.0% 0 0|0 0|0
641g 8.64g 3 bugsnag:23.4% 0 0|0 0|0
netIn netOut conn set repl time
47k 73k 145 bugsnag1 PRI 06:34:05
63k 99k 146 bugsnag1 PRI 06:34:06
98k 124k 146 bugsnag1 PRI 06:34:07

command locked db time
127|0 bugsnag:47.7% 06:34:05
146|0 bugsnag:21.0% 06:34:06
137|0 bugsnag:23.4% 06:34:07

$sh.shardCollection("errors" { project_id: 1, _id: -1 })$

command locked db time
83|0 bugsnag:3.1% 06:46:12
69|0 bugsnag:4.1% 06:46:13
73|0 bugsnag:2.4% 06:46:14

Only 1 query...
Indexed properly...
Lock % ok...

Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz
xvdi 0.00 2.00 15.00 743.00 436.00 7905.50 22.01
xvdi 0.00 2.00 0.00 663.00 0.00 6706.00 20.23
xvdi 0.00 1.00 0.00 726.00 0.00 5593.50 15.41
avgqu-sz await r_await w_await svctm %util
11.02 14.54 5.33 14.73 0.60 45.60
8.04 12.12 0.00 12.12 0.51 33.60
13.01 17.92 0.00 17.92 0.50 36.40

rkB/s wkB/s await %util
7660 138 14.73 83.60
2248 2043 12.12 65.20
7905 436 17.92 45.60

db.stats()['indexSize']
= 3852071824
= 3932614656
free -b (m3.medium)

db.stats()['indexSize']
= 3852071824
= 15775363072
free -b (r3.large)

Main query seems fine...
"Quick" queries sometimes slow...

ns total read write 20
14-06-22T19:11:35
bugsnag.events 95ms 0ms 95ms
bugsnag.errors 80ms 6ms 74ms
bugsnag.system.namespaces 26ms 26ms 0ms
bugsnag.projects 12ms 5ms 7ms
bugsnag.users 15ms 2ms 13ms
bugsnag.error_aggregates 4ms 0ms 4ms
bugsnag.deploys 4ms 3ms 1ms
bugsnag.event_tallies 3ms 0ms 3ms

ns tot r w
bugsnag.events 95 0 95
bugsnag.errors 80 6 74
bugsnag.projects 12 5 7
bugsnag.users 15 7 8

NewRelic / Skylight
explain()
mongostat
iostat
mongotop
...

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The document outlines a comprehensive methodology for data modeling in MongoDB, contrasting document-based with tabular modeling approaches. It details various modeling patterns, such as the use of fields, arrays, and sub-documents, through specific use cases like a coffee shop franchise and social networks. The document also emphasizes the importance of schema design patterns and practical considerations for schema evolution and workload management.

MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB

The document presents David Munguia's insights on leveraging MongoDB for real-time alerting in healthcare, detailing Humana's journey and lessons learned since their kickoff in November 2017. Key lessons include understanding access patterns, utilizing resources effectively, and embracing a mindset shift away from SQL to MongoDB's JSON structure. The presentation culminates in the success of their deployment, which significantly improved ETL processing times and reporting efficiency.

MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB

The document provides best practices for working with IoT and time-series data using MongoDB, discussing key concepts such as time-series data definitions, architecture design, and schema creation. It emphasizes efficient data management strategies, like data retention policies and archiving methods, while presenting case studies and practical examples of MongoDB's capabilities. Additionally, it offers insights on querying and visualizing time-series data and promotes resources for further engagement with MongoDB projects.

MongoDB SoCal 2020: MongoDB Atlas Jump StartMongoDB

The document outlines a presentation on MongoDB Atlas, a global cloud database that offers deployment, scaling, and operational automation. It highlights the service's key features, including security, multi-region capabilities, and analytics, with a focus on guiding users through the setup process. Attendees are encouraged to explore MongoDB Atlas and participate in related tutorials and initiatives.

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

The document introduces key individuals involved in modernization practices at Infosys, focusing on the use of MongoDB to meet new data demands. It discusses real-world use cases, particularly for a major American technology conglomerate, detailing the design of an observability layer powered by MongoDB. The architecture addresses challenges related to data volume, velocity, and variety, enabling high availability and distributed query capabilities.

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

The document provides a detailed overview of MongoDB's encryption methods, including client-side encryption and data handling practices for sensitive information such as Social Security Numbers (SSNs). It discusses various encryption techniques, schemas, potential attack scenarios, and compliance with regulations like GDPR. The implementation details cover the use of auto-encryption options and key management, emphasizing the importance of protecting user data throughout its lifecycle.

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

The document outlines the setup and use of MongoDB with Kubernetes through the MongoDB Operator and Open Service Broker, highlighting the necessary configurations and resources for deployment. It includes commands for cluster creation, service configuration, and secret management, emphasizing the ease of connecting MongoDB with Kubernetes services. Additionally, considerations for maintenance and architectural options are discussed to aid in decision-making for deployment strategies.

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

The document outlines a presentation by Munish Kapoor on utilizing MongoDB Charts for data analysis, emphasizing that insights from data are crucial for all roles in organizations. It discusses various analytical questions related to movies, such as trends in length and quality, and how to filter and share insights effectively. Key takeaways highlight the importance of making sense of increasing data volumes and leveraging MongoDB Charts for analysis and collaboration.

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

The document discusses the transition from SQL databases to MongoDB, highlighting the mental shift required and providing a comparison of terms and concepts between the two. It outlines the advantages of MongoDB, such as the flexible schema and document-based storage, which allows for easier data manipulation and scaling. Additionally, it includes examples illustrating how to model data and perform operations in both types of databases.

MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB

The document presents an overview of MongoDB Atlas, a global cloud database service emphasizing its features such as automated maintenance, scalability, enterprise-grade security, and data locality. It highlights the importance of understanding forward-looking statements and risks associated with future developments. Additionally, the document offers insights on how to quickly set up and deploy a MongoDB cluster for users.

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

The document discusses the capabilities and improvements of the MongoDB 4.2 aggregation framework, highlighting its use in powering queries, updates, and views. It provides various examples of data pipelines, showcasing stages such as $match, $group, and $sort, and emphasizes the performance optimizations and additional features available in this version. Additionally, it covers how to leverage aggregation pipelines for updates and the unification of different languages in data handling.

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

The document discusses MongoDB Atlas Data Lake, which aims to efficiently manage vast amounts of data in the cloud, addressing the challenges enterprises face with data storage and querying. It outlines the architecture, configuration options, and security measures for customer data management while emphasizing the importance of emulating MongoDB's functionalities. Future developments include support for more MongoDB operators, optimizations, new file formats, and integrations with major cloud providers.

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

The document provides a comprehensive overview of developing Alexa skills using MongoDB and Go, including connections to MongoDB, the use of AWS Lambda for serverless execution, and configurations for Alexa skills. It outlines the structure of intents and utterances necessary for skill functionality, as well as the importance of using unique and pronounceable invocation names. Additionally, the document offers resources such as a GitHub example and information on obtaining MongoDB Atlas credits.

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

The document outlines the features and benefits of the Realm on-device object database, developed for seamless mobile app development, emphasizing offline capabilities and smooth synchronization with MongoDB. It highlights the importance of user experience, real-time data updates, and conflict resolution in app development. Additionally, it provides a small assignment example of a todo app to illustrate the practical application of these concepts.

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

Le document aborde comment le machine learning transforme la supply chain en répondant à des défis tels que l'opacité des informations et les déséquilibres de l'offre et de la demande. Il décrit les opérations d'Upply, notamment son utilisation de données massives pour optimiser le matching entre chargeurs et transporteurs, ainsi que son infrastructure technique basée sur des modèles de prévisions avancés. Le document mentionne également l'adoption de MongoDB pour améliorer la gestion des données et les performances de la plateforme.