SlideShare a Scribd company logo
1 of 89
Workload Isolation...
@asya999 #askAsya - ask me what you're doing wrong
(you might be doing it wrong)
7 Deadly Sins
(of bad MongoDB deployments)
5 Stages of Grief
(of bad MongoDB deployments)
10 Commandments
(of good MongoDB deployments)
Human Error
Power Outages
Fire
Server Room Issues
Unscheduled Updates & Patches
Workload Isolation?
HA
Global Write
Clusters
Scale out
Workload
Isolation
Why?
Scalability
Availability
Recoverability
Security
Scalability
Availability
Recoverability
Security
These Stories Are True
or they are based on stories that are true
Based* on real cases filed with MongoDB Support
All names changed
Some details* may have been omitted
* some cases may have been combined and/or embellished to make a point
* just boring ones, not the really embarrassing ones
January 9
The Case of Mistaken Delete
simple typo
replica set
had 45 days in the oplog
but oldest backup was 90+ days
but ... saved the DB files (all of them)
We accidentally remove()ed an entire collection.
Is there a way to undo?
recovery not for the faint of heart ... all data was recovered
Conclusion:
Replication ≠ Backups
Do regular backups.
Don't do production operations "ad hoc"
noSQL ≠ noDBA
We accidentally remove()ed an entire collection.
Is there a way to undo?
Scalability
Application
Driver
mongod
mongod
mongod
Replica set
Application
Driver
mongod
mongod
mongod
Shard 1
Application
Driver
mongos
mongod
mongod
mongod
mongod
mongod
mongod
Shard 1 Shard 2
Application
Driver
mongos
mongod
mongod
mongod
mongod
mongod
mongod
Shard 1 Shard 2
mongod
mongod
mongod
Shard 3
Application
Driver
mongos mongos•••
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
•••
Shard 1 Shard 2 Shard 3 Shard N
Application
Driver
mongos mongos•••
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
•••
Shard 1 Shard 2 Shard 3 Shard N
Application
Driver
mongos
Application
Driver
mongos mongos•••
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
•••
Shard 1 Shard 2 Shard 3 Shard N
Application
Driver
Application
Driver
mongos•••mongos
•••
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongos
•••
Shard 1 Shard 2 Shard 3 Shard N
DATA
DATA
DATA
DATA
mongos mongosmongos mongos
DATA
DATA
DATA
DATA
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongos
•••
Shard 1 Shard 2 Shard 3 Shard N
mongos mongosmongos mongos
DATA
DATA
DATA
DATA
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongos
•••
Shard 1 Shard 2 Shard 3 Shard N
mongos mongosmongos mongos
DATA
DATA
DATA
DATA
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongos
•••
Shard 1 Shard 2 Shard 3 Shard N
mongos mongosmongos mongos
DATA
DATA
DATA
DATA
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
mongod
app 0
•••
Shard 0 Shard 0 Shard 0 Shard 0
app 2 app 3app 1 app 4
www.etiennemansard.c om
Horizontal Scaling
SHARDING
SHARDING
SHARDING
SHARDING
SHARDING
SHARDING
SHARDING
SHARDING
SHARDING
SHARDING
SHARDING
SHARDING
SHARDING
SHARDING
2
SHARDING
2
3
SHARDING
2
4
3
SHARDING
5
2
4
3
SHARDING
6
5
2
4
3
6
SHARDING
SCATTER-GATHER
SHARDING
TARGETED
6
5
2
4
3
6
SHARDING
COMPARE THROUGHPUT
Query Scaling Rate Comparison
Number of shards
vs
Number of queries
Each query
target one shard
Per shard/system TOTAL
Each query
target all shard
Per shard/system TOTAL
1 10,000/10K 10K/10K
2 5,000/10K 10K/20K
5 2,000/10K 10K/50K
10 1,000/10K 10K/100K
If your application sends 10,000 queries
Query Scaling Rate Comparison
Number of shards
vs
Total query capacity
Each query
target one shard
Per shard/system TOTAL
Each query
target all shard
Per shard/system TOTAL
1 10K/10K 10K/10K
2 10K/20K 10K/10K
5 10K/50K 10K/10K
10 10K/100K 10K/10K
If each shard can process 10,000 queries
SHARDING
COMPARE LATENCY
66
SHARDING
COMPARE LATENCY
SHARDING
COMPARE LATENCY
Can I prove it?
Can I demonstrate it?
PROOF
Open Source
Reference Implementation
• Various Fanout Feed Models
• User Graph Implementation
• Content storage
Configurable models and options
Built-in benchmarking
https://github.com/mongodb-labs/socialite
Socialite: Architecture
GraphServiceProxy
ContentProxy
Optimized for Performance
User
timeline
cache
Schema
Indexing Horizontal Scaling
Operational Testing Built-in
User facing latency
Linear scaling of resources
Most important criteria?
• Generates realistic real-life-scale workload
• compared to Twitter, etc.
• Confirms architecture scales linearly
• without loss of responsiveness
Perfect Candidate to Benchmark
Comparison
Shard Everything Separate Clusters
Scalability
Availability
Reliability
Debug-ability
January 18
The case of deleted database
Accidentally deleted DB directory
Restored recent backup... but mongod won't start
Backups were not good: taken incorrectly
Unable to start mongod process
Unable to start mongod process
Conclusion:
Most Important Part of Backups:
RESTORES
Test your backups
noSQL ≠ noDBA
Unable to start mongod process
Comparison
Shard Everything Separate Clusters
Scalability
Availability
Reliability
Debug-ability
January 13
The case of missing metadata
January 13:
"DBA" adds a new shard
"DBA" observes that data does not seem to be migrating to new shard
"DBA" sets out to "fix" the "problem"
By "re-sharding" the database/collection in question
Which doesn't work (because it's already sharded)
"Simple" solution: remove the config DB metadata for chunks!
Try resharding again!
Force it!
Sequence of Events
How did we help them fix it?
My colleague
The Operation
Result
Conclusion
noSQL ≠ noDBA
Everybody has a test
environment...
Some people are lucky enough
enough to have a totally separate
production environment.
May 22
The case of the single data center
At 4:30 PM, Friday alpha page comes in
Two senior support engineers work the ticket till 10 PM
The details:
33 node,
17 terabyte sharded cluster (11 shards, 3 node replica each),
single data center,
no journaling
no backups
Add to that:
power failure in the data center
no UPS
Result:
unreadable data on every node
noSQL ≠ noDBA
Sequence of Events
Bad things happen to good data centers
December 30
The case of disappearing data files
Dec 29 2013 10:35:00 AM: db.stats() is showing dataSize > fileSize
Dec 29 2013 04:44:00 PM: "there are data files viewed as missing by `mongod`"
Dec 30 2013 02:12:00 AM: Seeing incorrect fileSize on numerous servers
you can see a drop in fileSize on 12/28 in MMS with
no corresponding drop in the other size metrics.
Dec 30 2013 02:19:00 AM: "[do] these databases have anything in common,
especially with the xxxx DB from yesterday?
Dec 30 2013 02:22:00 AM: Nothing comes to mind ... that DBs have in common
Sequence of Events
Dec 30 2013 02:28:00 AM: We notice this all happening at the same time.
We think something might be deleting data files.
Dec 30 2013 02:31:00 AM: "Something is deleting data files outside mongod?"
Dec 29 2013 04:44:00 PM: "there are data files viewed as missing by `mongod`"
Dec 30 2013 02:12:00 AM: Seeing incorrect fileSize on numerous servers
you can see a drop in fileSize on 12/28 in MMS with
no corresponding drop in the other size metrics.
Dec 30 2013 02:19:00 AM: "[do] these databases have anything in common,
especially with the xxxx DB from yesterday?
Dec 30 2013 02:22:00 AM: Nothing comes to mind ... that DBs have in common
Sequence of Events
Dec 30 2013 02:28:00 AM: We notice this all happening at the same time.
We think something might be deleting data files.
Dec 30 2013 02:31:00 AM: "Something is deleting data files outside mongod?"
Dec 30 2013 02:57:00 AM: Yes. We deleted actual db files on both the primaries
and secondaries on the 28th.
Dec 29 2013 04:44:00 PM: "there are data files viewed as missing by `mongod`"
Dec 30 2013 02:12:00 AM: Seeing incorrect fileSize on numerous servers
you can see a drop in fileSize on 12/28 in MMS with
no corresponding drop in the other size metrics.
Dec 30 2013 02:19:00 AM: "[do] these databases have anything in common,
especially with the xxxx DB from yesterday?
Dec 30 2013 02:22:00 AM: Nothing comes to mind ... that DBs have in common
Sequence of Events
Dec 28 2013: Someone notices that they are low on disk space and as a solution
writes a shell script that finds every file on every disk on every
server that's bigger than 1GB in size and which hasn't been
accessed in >3 days.
And it then deletes it.
This script ran on every server deleting every database file bigger than 1GB
which hasn't been accessed in the previous few days...
Dec 30 2013 02:28:00 AM: We notice this all happening at the same time.
We think something might be deleting data files.
Dec 30 2013 02:31:00 AM: "Something is deleting data files outside mongod?"
Dec 30 2013 02:57:00 AM: Yes. We deleted actual db files on both the primaries
Ultimately, NO data was lost.
running `mongod` process keeps the "deleted" file from being removed
running `mongod` can recreate all data files via db.repair()
BUT... there is no disk space for db.repair()
Luckily, an extra server or two are "found" and allow rotating re-sync of new secondary in each
replica set.
Again: no data was lost. All data was fully and successfully recovered.
Guess the Outcome!
THANK YOU
Enjoy The Rest of the Day

More Related Content

What's hot

Building Data Driven Products With Ruby - RubyConf 2012
Building Data Driven Products With Ruby - RubyConf 2012Building Data Driven Products With Ruby - RubyConf 2012
Building Data Driven Products With Ruby - RubyConf 2012Ryan Weald
 
MongoDB @ Frankfurt NoSql User Group
MongoDB @  Frankfurt NoSql User GroupMongoDB @  Frankfurt NoSql User Group
MongoDB @ Frankfurt NoSql User GroupChris Harris
 
Cassandra summit keynote 2014
Cassandra summit keynote 2014Cassandra summit keynote 2014
Cassandra summit keynote 2014jbellis
 
MongoDB World 2018: Overnight to 60 Seconds: An IOT ETL Performance Case Study
MongoDB World 2018: Overnight to 60 Seconds: An IOT ETL Performance Case StudyMongoDB World 2018: Overnight to 60 Seconds: An IOT ETL Performance Case Study
MongoDB World 2018: Overnight to 60 Seconds: An IOT ETL Performance Case StudyMongoDB
 
Cassandra Summit 2013 Keynote
Cassandra Summit 2013 KeynoteCassandra Summit 2013 Keynote
Cassandra Summit 2013 Keynotejbellis
 
Tokyo cassandra conference 2014
Tokyo cassandra conference 2014Tokyo cassandra conference 2014
Tokyo cassandra conference 2014jbellis
 
Optimizing Slow Queries with Indexes and Creativity
Optimizing Slow Queries with Indexes and CreativityOptimizing Slow Queries with Indexes and Creativity
Optimizing Slow Queries with Indexes and CreativityMongoDB
 
Web Server Scheduling
Web Server SchedulingWeb Server Scheduling
Web Server SchedulingDavid Evans
 
Replication and Replica Sets
Replication and Replica SetsReplication and Replica Sets
Replication and Replica SetsMongoDB
 

What's hot (10)

Building Data Driven Products With Ruby - RubyConf 2012
Building Data Driven Products With Ruby - RubyConf 2012Building Data Driven Products With Ruby - RubyConf 2012
Building Data Driven Products With Ruby - RubyConf 2012
 
MongoDB @ Frankfurt NoSql User Group
MongoDB @  Frankfurt NoSql User GroupMongoDB @  Frankfurt NoSql User Group
MongoDB @ Frankfurt NoSql User Group
 
Cassandra summit keynote 2014
Cassandra summit keynote 2014Cassandra summit keynote 2014
Cassandra summit keynote 2014
 
MongoDB World 2018: Overnight to 60 Seconds: An IOT ETL Performance Case Study
MongoDB World 2018: Overnight to 60 Seconds: An IOT ETL Performance Case StudyMongoDB World 2018: Overnight to 60 Seconds: An IOT ETL Performance Case Study
MongoDB World 2018: Overnight to 60 Seconds: An IOT ETL Performance Case Study
 
Cassandra Summit 2013 Keynote
Cassandra Summit 2013 KeynoteCassandra Summit 2013 Keynote
Cassandra Summit 2013 Keynote
 
Tokyo cassandra conference 2014
Tokyo cassandra conference 2014Tokyo cassandra conference 2014
Tokyo cassandra conference 2014
 
Optimizing Slow Queries with Indexes and Creativity
Optimizing Slow Queries with Indexes and CreativityOptimizing Slow Queries with Indexes and Creativity
Optimizing Slow Queries with Indexes and Creativity
 
Web Server Scheduling
Web Server SchedulingWeb Server Scheduling
Web Server Scheduling
 
Replication and Replica Sets
Replication and Replica SetsReplication and Replica Sets
Replication and Replica Sets
 
MySQL Rises with JSON Support
MySQL Rises with JSON SupportMySQL Rises with JSON Support
MySQL Rises with JSON Support
 

Similar to MongoDB.local Austin 2018: Workload Isolation: Are You Doing it Wrong?

Big Data Analytics: Finding diamonds in the rough with Azure
Big Data Analytics: Finding diamonds in the rough with AzureBig Data Analytics: Finding diamonds in the rough with Azure
Big Data Analytics: Finding diamonds in the rough with AzureChristos Charmatzis
 
MongoDB Days UK: Tales from the Field
MongoDB Days UK: Tales from the FieldMongoDB Days UK: Tales from the Field
MongoDB Days UK: Tales from the FieldMongoDB
 
Databases benoitg 2009-03-10
Databases benoitg 2009-03-10Databases benoitg 2009-03-10
Databases benoitg 2009-03-10benoitg
 
DataDay 2023 Presentation - Notes
DataDay 2023 Presentation - NotesDataDay 2023 Presentation - Notes
DataDay 2023 Presentation - NotesMax De Marzi
 
A gentle introduction to the world of BigData and Hadoop
A gentle introduction to the world of BigData and HadoopA gentle introduction to the world of BigData and Hadoop
A gentle introduction to the world of BigData and HadoopStefano Paluello
 
Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...
Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...
Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...NoSQLmatters
 
MongoDB: Optimising for Performance, Scale & Analytics
MongoDB: Optimising for Performance, Scale & AnalyticsMongoDB: Optimising for Performance, Scale & Analytics
MongoDB: Optimising for Performance, Scale & AnalyticsServer Density
 
Optimizing MongoDB: Lessons Learned at Localytics
Optimizing MongoDB: Lessons Learned at LocalyticsOptimizing MongoDB: Lessons Learned at Localytics
Optimizing MongoDB: Lessons Learned at Localyticsandrew311
 
Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...
Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...
Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...MongoDB
 
Performance Tipping Points - Hitting Hardware Bottlenecks
Performance Tipping Points - Hitting Hardware BottlenecksPerformance Tipping Points - Hitting Hardware Bottlenecks
Performance Tipping Points - Hitting Hardware BottlenecksMongoDB
 
Data oriented design and c++
Data oriented design and c++Data oriented design and c++
Data oriented design and c++Mike Acton
 
Running 400-node Cassandra + Spark Clusters in Azure (Anubhav Kale, Microsoft...
Running 400-node Cassandra + Spark Clusters in Azure (Anubhav Kale, Microsoft...Running 400-node Cassandra + Spark Clusters in Azure (Anubhav Kale, Microsoft...
Running 400-node Cassandra + Spark Clusters in Azure (Anubhav Kale, Microsoft...DataStax
 
Use Your MySQL Knowledge to Become a MongoDB Guru
Use Your MySQL Knowledge to Become a MongoDB GuruUse Your MySQL Knowledge to Become a MongoDB Guru
Use Your MySQL Knowledge to Become a MongoDB GuruTim Callaghan
 
Lessons Learned Migrating 2+ Billion Documents at Craigslist
Lessons Learned Migrating 2+ Billion Documents at CraigslistLessons Learned Migrating 2+ Billion Documents at Craigslist
Lessons Learned Migrating 2+ Billion Documents at CraigslistJeremy Zawodny
 
Tales from the Field
Tales from the FieldTales from the Field
Tales from the FieldMongoDB
 
How to overcome mysterious problems caused by large and multi-tenancy Hadoop ...
How to overcome mysterious problems caused by large and multi-tenancy Hadoop ...How to overcome mysterious problems caused by large and multi-tenancy Hadoop ...
How to overcome mysterious problems caused by large and multi-tenancy Hadoop ...DataWorks Summit/Hadoop Summit
 

Similar to MongoDB.local Austin 2018: Workload Isolation: Are You Doing it Wrong? (20)

Big Data Analytics: Finding diamonds in the rough with Azure
Big Data Analytics: Finding diamonds in the rough with AzureBig Data Analytics: Finding diamonds in the rough with Azure
Big Data Analytics: Finding diamonds in the rough with Azure
 
MongoDB Days UK: Tales from the Field
MongoDB Days UK: Tales from the FieldMongoDB Days UK: Tales from the Field
MongoDB Days UK: Tales from the Field
 
Databases benoitg 2009-03-10
Databases benoitg 2009-03-10Databases benoitg 2009-03-10
Databases benoitg 2009-03-10
 
DataDay 2023 Presentation - Notes
DataDay 2023 Presentation - NotesDataDay 2023 Presentation - Notes
DataDay 2023 Presentation - Notes
 
A gentle introduction to the world of BigData and Hadoop
A gentle introduction to the world of BigData and HadoopA gentle introduction to the world of BigData and Hadoop
A gentle introduction to the world of BigData and Hadoop
 
druid.io
druid.iodruid.io
druid.io
 
System Design.pdf
System Design.pdfSystem Design.pdf
System Design.pdf
 
Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...
Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...
Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...
 
MongoDB: Optimising for Performance, Scale & Analytics
MongoDB: Optimising for Performance, Scale & AnalyticsMongoDB: Optimising for Performance, Scale & Analytics
MongoDB: Optimising for Performance, Scale & Analytics
 
Hadoop bank
Hadoop bankHadoop bank
Hadoop bank
 
Optimizing MongoDB: Lessons Learned at Localytics
Optimizing MongoDB: Lessons Learned at LocalyticsOptimizing MongoDB: Lessons Learned at Localytics
Optimizing MongoDB: Lessons Learned at Localytics
 
Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...
Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...
Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...
 
Performance Tipping Points - Hitting Hardware Bottlenecks
Performance Tipping Points - Hitting Hardware BottlenecksPerformance Tipping Points - Hitting Hardware Bottlenecks
Performance Tipping Points - Hitting Hardware Bottlenecks
 
Data oriented design and c++
Data oriented design and c++Data oriented design and c++
Data oriented design and c++
 
Running 400-node Cassandra + Spark Clusters in Azure (Anubhav Kale, Microsoft...
Running 400-node Cassandra + Spark Clusters in Azure (Anubhav Kale, Microsoft...Running 400-node Cassandra + Spark Clusters in Azure (Anubhav Kale, Microsoft...
Running 400-node Cassandra + Spark Clusters in Azure (Anubhav Kale, Microsoft...
 
Use Your MySQL Knowledge to Become a MongoDB Guru
Use Your MySQL Knowledge to Become a MongoDB GuruUse Your MySQL Knowledge to Become a MongoDB Guru
Use Your MySQL Knowledge to Become a MongoDB Guru
 
Lessons Learned Migrating 2+ Billion Documents at Craigslist
Lessons Learned Migrating 2+ Billion Documents at CraigslistLessons Learned Migrating 2+ Billion Documents at Craigslist
Lessons Learned Migrating 2+ Billion Documents at Craigslist
 
Tales from the Field
Tales from the FieldTales from the Field
Tales from the Field
 
Data corruption
Data corruptionData corruption
Data corruption
 
How to overcome mysterious problems caused by large and multi-tenancy Hadoop ...
How to overcome mysterious problems caused by large and multi-tenancy Hadoop ...How to overcome mysterious problems caused by large and multi-tenancy Hadoop ...
How to overcome mysterious problems caused by large and multi-tenancy Hadoop ...
 

More from MongoDB

MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
 
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
 
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB
 
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB
 
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB
 
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
 
MongoDB SoCal 2020: MongoDB Atlas Jump Start
 MongoDB SoCal 2020: MongoDB Atlas Jump Start MongoDB SoCal 2020: MongoDB Atlas Jump Start
MongoDB SoCal 2020: MongoDB Atlas Jump StartMongoDB
 
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB
 
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB
 
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
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 .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
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 Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
 
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB
 
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB
 
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB
 
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB
 
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB
 
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB
 
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
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 .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB
 

More from MongoDB (20)

MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
 
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
 
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
 
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
 
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
 
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
 
MongoDB SoCal 2020: MongoDB Atlas Jump Start
 MongoDB SoCal 2020: MongoDB Atlas Jump Start MongoDB SoCal 2020: MongoDB Atlas Jump Start
MongoDB SoCal 2020: MongoDB Atlas Jump Start
 
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
 
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
 
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
 
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
 
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
 
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
 
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
 
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
 
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
 
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
 
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
 
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
 
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
 

Recently uploaded

Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
Maximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxMaximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxOnBoard
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhisoniya singh
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationRidwan Fadjar
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 3652toLead Limited
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsMemoori
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationSafe Software
 
Unlocking the Potential of the Cloud for IBM Power Systems
Unlocking the Potential of the Cloud for IBM Power SystemsUnlocking the Potential of the Cloud for IBM Power Systems
Unlocking the Potential of the Cloud for IBM Power SystemsPrecisely
 
Pigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping ElbowsPigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping ElbowsPigging Solutions
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticscarlostorres15106
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...shyamraj55
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Patryk Bandurski
 

Recently uploaded (20)

08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
Maximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxMaximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptx
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 Presentation
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
The transition to renewables in India.pdf
The transition to renewables in India.pdfThe transition to renewables in India.pdf
The transition to renewables in India.pdf
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
 
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
 
Unlocking the Potential of the Cloud for IBM Power Systems
Unlocking the Potential of the Cloud for IBM Power SystemsUnlocking the Potential of the Cloud for IBM Power Systems
Unlocking the Potential of the Cloud for IBM Power Systems
 
Pigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping ElbowsPigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping Elbows
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food Manufacturing
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
 

MongoDB.local Austin 2018: Workload Isolation: Are You Doing it Wrong?