This presentation discusses migrating data from other data stores to MongoDB Atlas. It begins by explaining why MongoDB and Atlas are good choices for data management. Several preparation steps are covered, including sizing the target Atlas cluster, increasing the source oplog, and testing connectivity. Live migration, mongomirror, and dump/restore options are presented for migrating between replicasets or sharded clusters. Post-migration steps like monitoring and backups are also discussed. Finally, migrating from other data stores like AWS DocumentDB, Azure CosmosDB, DynamoDB, and relational databases are briefly covered.
MongoDB is a document-oriented NoSQL database written in C++. It uses a document data model and stores data in BSON format, which is a binary form of JSON that is lightweight, traversable, and efficient. MongoDB is schema-less, supports replication and high availability, auto-sharding for scaling, and rich queries. It is suitable for big data, content management, mobile and social applications, and user data management.
This document provides an overview and introduction to MongoDB, an open-source, high-performance NoSQL database. It outlines MongoDB's features like document-oriented storage, replication, sharding, and CRUD operations. It also discusses MongoDB's data model, comparisons to relational databases, and common use cases. The document concludes that MongoDB is well-suited for applications like content management, inventory management, game development, social media storage, and sensor data databases due to its flexible schema, distributed deployment, and low latency.
Presented by Claudius Li, Solutions Architect at MongoDB, at MongoDB Evenings New England 2017.
MongoDB Atlas is the premier database as a service offering. Find out how MongoDB Atlas can help your team to deploy more easily, develop faster and easily manage deployment, maintenance, upgrades and expansions. We will also demonstrate some of the key features and tools that come with MongoDB Atlas.
The document discusses migrating from an RDBMS to MongoDB. It covers determining if a migration is worthwhile based on evaluating current pain points and target value. It also discusses the roles and responsibilities that will change during a migration, including data architects, developers, DBAs and more. Bulk migration techniques are reviewed including using mongoimport to import JSON data. System cutover is also mentioned as an important part of the migration process.
This document discusses how to achieve scale with MongoDB. It covers optimization tips like schema design, indexing, and monitoring. Vertical scaling involves upgrading hardware like RAM and SSDs. Horizontal scaling involves adding shards to distribute load. The document also discusses how MongoDB scales for large customers through examples of deployments handling high throughput and large datasets.
In this presentation, Raghavendra BM of Valuebound has discussed the basics of MongoDB - an open-source document database and leading NoSQL database.
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From these slides you'll learn how Galera integrates with MySQL 5.6 and Global Transaction IDs to enable cross-datacenter and cloud replication over high latency networks.
ABOUT GALERA CLUSTER
Galera Cluster for MySQL is a true multi-master MySQL replication plugin, and has been proven in mission-critical infrastructures of companies like Ping Identity, AVG Technologies, KPN and HP Cloud DNS. In this webcast you¹ll learn about the following Galera Cluster capabilities, including the latest innovations in the new 3.0 release:
Galera Cluster features and benefits
Support for MySQL 5.6
Integration with MySQL Global Transaction Identifiers
Mixing Galera synchronous replication and asynchronous MySQL replication
Deploying in WAN and Cloud environments
Handling high-latency networks
Management of Galera
The document is a slide presentation on MongoDB that introduces the topic and provides an overview. It defines MongoDB as a document-oriented, open source database that provides high performance, high availability, and easy scalability. It also discusses MongoDB's use for big data applications, how it is non-relational and stores data as JSON-like documents in collections without a defined schema. The presentation provides steps for installing MongoDB and describes some basic concepts like databases, collections, documents and commands.
MongoDB is an open-source, document-oriented database that provides high performance and horizontal scalability. It uses a document-model where data is organized in flexible, JSON-like documents rather than rigidly defined rows and tables. Documents can contain multiple types of nested objects and arrays. MongoDB is best suited for applications that need to store large amounts of unstructured or semi-structured data and benefit from horizontal scalability and high performance.
MongoDB Atlas makes it easy to set up, operate, and scale your MongoDB deployments in the cloud. From high availability to scalability, security to disaster recovery - we've got you covered.
Automated: With MongoDB Atlas, you no longer need to worry about operational tasks such as provisioning, configuration, patching, upgrades, backups, and failure recovery. MongoDB Atlas provides the functionality and reliability you need, at the click of a button.
Flexible: Only MongoDB Atlas combines the critical capabilities of relational databases with the innovations of NoSQL. Radically simplify development and operations by delivering a diverse range of capabilities in a single, managed database platform.
Secure: MongoDB Atlas provides multiple levels of security for your database. These include robust access control, network isolation using Amazon VPC, IP whitelists, encryption of data in-flight using TLS/SSL, and optional encryption of the underlying filesystem.
Scalable: MongoDB Atlas grows with you, all with the click of a button. You can scale up across a range of instance sizes, and scale-out with automatic sharding. And you can do it with zero application downtime.
Highly Available: MongoDB Atlas is designed to offer exceptional uptime. Recovery from instance failures is transparent and fully automated. A minimum of three copies of your data are replicated across availability zones and continuously backed up.
High Performance: MongoDB Atlas provides high throughput and low latency for the most demanding workloads. Consistent, predictable performance eliminates the need for separate caching tiers, and delivers a far better price-performance ratio compared to traditional database software.
The Right (and Wrong) Use Cases for MongoDBMongoDB
The document discusses the right and wrong use cases for MongoDB. It outlines some of the key benefits of MongoDB, including its performance, scalability, data model and query model. Specific use cases that are well-suited for MongoDB include building a single customer view, powering mobile applications, and performing real-time analytics. Cache-only workloads are identified as not being a good use case. The document provides examples of large companies successfully using MongoDB for these right use cases.
MongoDB is a non-relational database that stores data in JSON-like documents with dynamic schemas. It features flexibility with JSON documents that map to programming languages, power through indexing and queries, and horizontal scaling. The document explains that MongoDB uses JSON and BSON formats to store data, has no fixed schema so fields can evolve freely, and demonstrates working with the mongo shell and RoboMongo GUI.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
The document discusses data modeling for MongoDB. It begins by recognizing the differences between modeling for a document database versus a relational database. It then outlines a flexible methodology for MongoDB modeling including defining the workload, identifying relationships between entities, and applying schema design patterns. Finally, it recognizes the need to apply patterns like schema versioning, subset, computed, bucket, and external reference when modeling for MongoDB.
MongoDB WiredTiger Internals: Journey To TransactionsMydbops
MongoDB has adapted transaction feature (ACID Properties) in MongoDB 4.0. This talk focuses on the internals of how MongoDB adapted the ACID properties with Weird Tiger Engine. Weird tiger offers more future possibilities for MongoDB. This tech talk was presented at Mydbops Database Meetup on 27-04-2019 by Manosh Malai Senior Devops/NoSQL Consultant with Mydbops and Ranjith Database Administrator with Mydbops.
This was presented by the MongoDB team at the Singapore VIP event on 24th Jan 2019.
The presentation covers-
What is MongoDB
Why MongoDB
MongoDB As a Service, Serverless Platform and Mobile
MongoDB Atlas: Database as a Service (Available on AWS, Azure and Google Cloud)
Usecases
As an official MongoDB-as-a-Service offering from MongoDB Inc., the maker for MongoDB, Atlas is becoming a very popular service offering for those who wish to build their applications in the cloud, regardless on AWS, Azure or GCP. One less known cloud product offered on the Atlas platform is Stitch, A group of services designed to interact with Atlas in every conceivable way, including creating endpoints, triggers, user authentication flows, serverless functions, and a UI to handle all of this. Adding these together, you have a server-less solution running on top of MongoDB cloud.
The document discusses using MongoDB as a tick store for financial data. It provides an overview of MongoDB and its benefits for handling tick data, including its flexible data model, rich querying capabilities, native aggregation framework, ability to do pre-aggregation for continuous data snapshots, language drivers and Hadoop connector. It also presents a case study of AHL, a quantitative hedge fund, using MongoDB and Python as their market data platform to easily onboard large volumes of financial data in different formats and provide low-latency access for backtesting and research applications.
SQL vs NoSQL, an experiment with MongoDBMarco Segato
A simple experiment with MongoDB compared to Oracle classic RDBMS database: what are NoSQL databases, when to use them, why to choose MongoDB and how we can play with it.
MongoDB.local Atlanta: Modern Data Backup and Recovery from On-Premises to th...MongoDB
This document summarizes MongoDB's modern data backup and recovery strategies, both for on-premises and cloud environments. It outlines MongoDB's current on-premises backup architecture and its limitations. The document then introduces MongoDB's new backup strategy of taking WiredTiger checkpoints directly to long-term storage to reduce overhead. It also discusses MongoDB Atlas, which provides automatic multi-region backups and point-in-time restore capabilities. The document demos these backup features and previews future improvements like direct agent backups to optimize the process.
Discover MongoDB Atlas and MongoDB Stitch - DEM02-S - Mexico City AWS SummitAmazon Web Services
Learn about the modernization of application development using the MongoDB platform on AWS. In this session, discover key capabilities of MongoDB Atlas for on-demand cluster deployment, high availability, horizontal scalability, and geographically distributed operations. Additionally, learn how to quickly build a website or mobile application that is backed by MongoDB and that uses the MongoDB Stitch serverless platform.
Ch-Ch-Ch-Ch-Changes: Taking Your MongoDB Stitch Application to the Next Level...MongoDB
MongoDB Stitch is a serverless platform designed to help you easily and securely build an application on top of MongoDB Atlas. It lets developers focus on building applications rather than on managing data manipulation code, service integration, or backend infrastructure. MongoDB Stitch also makes it simple to respond to backend changes immediately, allowing you to simplify client side code and build complex flows more easily. This talk will cover ways that MongoDB Stitch helps you respond to changes in your database and take your applications to the next level.
MongoDB Days Silicon Valley: Winning the Dreamforce Hackathon with MongoDBMongoDB
Presented by Greg Deeds, CEO, Technology Exploration Group
Experience level: Introductory
A two person team using MongoDB and Salesforce.com created a geospatial machine learning tool from various datasets, parsing, indexing, and mapreduce in 24 hours. The amazing hack that beat 350 teams from around the world designer Greg Deeds will speak on getting to the winners circle with MongoDB power. It was MongoDB that proved to be the teams secret weapon to level the playing field for the win!
MongoDB Schema Design: Practical Applications and ImplicationsMongoDB
Presented by Austin Zellner, Solutions Architect, MongoDB
Schema design is as much art as it is science, but it is central to understanding how to get the most out of MongoDB. Attendees will walk away with an understanding of how to approach schema design, what influences it, and the science behind the art. After this session, attendees will be ready to design new schemas, as well as re-evaluate existing schemas with a new mental model.
MongoDB has been conceived for the cloud age. Making sure that MongoDB is compatible and performant around cloud providers is mandatory to achieve complete integration with platforms and systems. Azure is one of biggest IaaS platforms available and very popular amongst developers that work on Microsoft Stack.
Presented at DevIntersection / AngleBrackets 2014. I showed how to set up, develop and run NoSQL solutions for the cloud on Windows and Linux using Windows Azure. Also show you how to build multi-tier applications in the cloud that access NoSQL data. This session included an introduction to our Platform-as-a-Service offerings for MongoDB and CouchDB, as well as prepackaged Linux VMs that run Cassandra, Riak, Redis and other NoSQL data stores with a few clicks. We’ll also introduce you to the Developer Centers for Windows Azure, the Azure SDKs, our selection of plugins for popular open source developer tools, DevOps services, and other tools and materials we’ve developed to make life easier for application developers.
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
<b>Elevate MongoDB with ODBC/JDBC </b>[4:05 pm - 4:25 pm]<br />Adoption for MongoDB is growing across the enterprise and disrupting existing business intelligence, analytics and data integration infrastructure. Join us to disrupt that disruption using ODBC and JDBC access to MongoDB for instant out-of-box integration with existing infrastructure to elevate and expand your organization’s MongoDB footprint. We'll talk about common challenges and gotchas that shops face when exposing unstructured and semi-structured data using these established data connectivity standards. Existing infrastructure requirements should not dictate developers’ freedom of choice in a database
This document discusses building robust data pipelines that stream events between applications and services in real time using MongoDB and Confluent. It outlines how event-driven architectures with Apache Kafka and MongoDB can help customers address challenges like reacting to new data sources in real time, modernizing applications, and gaining insights from data. Specific use cases are discussed like application modernization, microservices, analytics, and IoT. Customer examples are provided from healthcare, financial services, and other industries. The benefits of MongoDB's document data model and transactions are highlighted. Finally, the document demonstrates MongoDB Atlas and Confluent Platform capabilities.
Microsoft Azure DocumentDB is a NoSQL document database service that is part of Microsoft Azure. It allows for the storage and querying of JSON documents and offers rich query capabilities over schema-free data using SQL and JavaScript. DocumentDB provides scalability, availability, and predictable performance for cloud applications.
MongoDB in the Middle of a Hybrid Cloud and Polyglot Persistence ArchitectureMongoDB
The Sage Data Cloud enables next-generation cloud and mobile services via a Hybrid Cloud and Polyglot Persistence Architecture. Come learn how MongoDB and other cloud data stores make this a reality, and get an insight into our learnings and operations.
This document discusses MongoDB and the needs of Rivera Group, an IT services company. It notes that Rivera Group has been using MongoDB since 2012 to store large, multi-dimensional datasets with heavy read/write and audit requirements. The document outlines some of the challenges Rivera Group faces around indexing, aggregation, and flexibility in querying datasets.
Eagle6 is a product that use system artifacts to create a replica model that represents a near real-time view of system architecture. Eagle6 was built to collect system data (log files, application source code, etc.) and to link system behaviors in such a way that the user is able to quickly identify risks associated with unknown or unwanted behavioral events that may result in unknown impacts to seemingly unrelated down-stream systems. This session is designed to present the capabilities of the Eagle6 modeling product and how we are using MongoDB to support near-real-time analysis of large disparate datasets.
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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
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Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
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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
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We are honored to launch and host this event for our UiPath Polish Community, with the help of our partners - Proservartner!
We certainly hope we have managed to spike your interest in the subjects to be presented and the incredible networking opportunities at hand, too!
Check out our proposed agenda below 👇👇
08:30 ☕ Welcome coffee (30')
09:00 Opening note/ Intro to UiPath Community (10')
Cristina Vidu, Global Manager, Marketing Community @UiPath
Dawid Kot, Digital Transformation Lead @Proservartner
09:10 Cloud migration - Proservartner & DOVISTA case study (30')
Marcin Drozdowski, Automation CoE Manager @DOVISTA
Pawel Kamiński, RPA developer @DOVISTA
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
09:40 From bottlenecks to breakthroughs: Citizen Development in action (25')
Pawel Poplawski, Director, Improvement and Automation @McCormick & Company
Michał Cieślak, Senior Manager, Automation Programs @McCormick & Company
10:05 Next-level bots: API integration in UiPath Studio (30')
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
10:35 ☕ Coffee Break (15')
10:50 Document Understanding with my RPA Companion (45')
Ewa Gruszka, Enterprise Sales Specialist, AI & ML @UiPath
11:35 Power up your Robots: GenAI and GPT in REFramework (45')
Krzysztof Karaszewski, Global RPA Product Manager
12:20 🍕 Lunch Break (1hr)
13:20 From Concept to Quality: UiPath Test Suite for AI-powered Knowledge Bots (30')
Kamil Miśko, UiPath MVP, Senior RPA Developer @Zurich Insurance
13:50 Communications Mining - focus on AI capabilities (30')
Thomasz Wierzbicki, Business Analyst @Office Samurai
14:20 Polish MVP panel: Insights on MVP award achievements and career profiling
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The Rise of Supernetwork Data Intensive ComputingLarry Smarr
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The International Conference for High Performance Computing, Networking, Storage, and Analysis
St. Louis, Missouri
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Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
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This webinar will cover a step-by-step guide to Cosmos, an open source package from Astronomer that helps you easily run your dbt Core projects as Airflow DAGs and Task Groups, all with just a few lines of code. We’ll walk through:
- Standard ways of running dbt (and when to utilize other methods)
- How Cosmos can be used to run and visualize your dbt projects in Airflow
- Common challenges and how to address them, including performance, dependency conflicts, and more
- How running dbt projects in Airflow helps with cost optimization
Webinar given on 9 July 2024
Kief Morris rethinks the infrastructure code delivery lifecycle, advocating for a shift towards composable infrastructure systems. We should shift to designing around deployable components rather than code modules, use more useful levels of abstraction, and drive design and deployment from applications rather than bottom-up, monolithic architecture and delivery.
RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptxSynapseIndia
Your comprehensive guide to RPA in healthcare for 2024. Explore the benefits, use cases, and emerging trends of robotic process automation. Understand the challenges and prepare for the future of healthcare automation
Quantum Communications Q&A with Gemini LLM. These are based on Shannon's Noisy channel Theorem and offers how the classical theory applies to the quantum world.
4. #MDBLocal
Why MongoDB? A: Next Gen Multi-Model data platform
Mobile
Apps
MongoDB is the most powerful data management platform in the market today
01
10JSON
Flexible Multi-Structured Schema is designed to adapt to changes
GeoSpatial
GeoJSON
2D &
2DSphere
Relational
Left-Outer Join
Views
Schema Validation
Key/Value
Horizontal Scale
In-Memory
Binaries
Files & Metadata
Encrypted
Search
Text Search
Multiple Languages
Faceted Search
Graph
Graph &
Hierarchical
Recursive
Lookups
Document
Rich JSON
Data Structures
Flexible Schema
8. #MDBLocal
Prep Items: Atlas Cluster Sizing
What is the current cluster hardware like?
RAM
Disk (size & speed)
CPUs
What is the workload like?
Reads / Sec?
Writes / Sec?
Docs / Sec?
Peak Connections?
APM: DataDog, NewRelic, ?
cmd line: mongostat, mongotop,
iostat, top, free, vmstat,
etc.
MongoDB Shell:
db.serverStatus().connections
9. #MDBLocal
Prep Items: Atlas Cluster Sizing
On-Prem or Cloud Reserved Instances
Most-likely Overprovisioned
Let ATLAS AUTO-SCALE
figure it out!
Match the current hardware
Run performance tests hours / days
Upscale: CPU or RAM > 75% (1 hr)
Dowscale: CPU and RAM < 50% (72 hrs)
10. #MDBLocal
Prep Items: Expert Atlas Cluster Sizing
#Shards by Storage = Total Storage ÷ Max Storage Per Shard
#Shards by RAM = Total RAM ÷ Max RAM Per Shard
#Shards by Cores = Total Cores ÷ Max Cores Per Shard
#Shards by IOPS = Total IOPS ÷ Max IOPS Per Shard
#Shards by Network Bandwidth = Peak Gbps ÷ Gbps Capacity Per Shard
#Shards by Disk Bandwidth = Peak Mbps ÷ Mbps Capacity Per Shard
Complete MongoDB Atlas Sizing Talk from MDBW19:
https://www.slideshare.net/mongodb/mongodb-world-2019-finding-the-right-mongodb-atlas-cluster-size-does-this-instance-make-my-app-look-fast
Work with your local MongoDB Solution Architect
11. #MDBLocal
Prep Items: Version, Driver & Retries
Ensure your current driver is 3.6+ compatible
As of Feb 2020 Atlas is 3.6+
You can still migrate from 2.6+!!
3.6 Retryable Writes
4.2 Retryable Reads
Fault Resiliency
12. #MDBLocal
Prep Items: Connectivity
● IP Whitelist | VPC Peer | Private Endpoint
● Create Users & Permissions
● Use SRV connection strings (3.6+)
vs.
13. #MDBLocal
Prep Items: Test Basic Ops mgeneratejs '{
"_id": "$objectid",
"dateTime": "$date",
"createdAt": "$date",
"Action" :"$string",
"severityLevel": "$integer",
"source": "$string",
"display": "$string",
"deviceServerIp": "$ip",
"details": {
"ipAddress": "$ip",
"macAddress": "$string",
"userId": "SYSTEM",
"method": "method"
}}' --jsonArray -n 1000000 | mongoimport -
-jsonArray --port 27017 --upsert -d atlas -c
iot
Test, Test, Test
● Simulate Production Traffic
● Your own test suite
● POCDriver
> https://github.com/johnlpage/POCDriver
● mgeneratejs
> https://github.com/rueckstiess/mgeneratejs
14. #MDBLocal
Prep Items: Increase OpLog on Source Cluster
Initial Sync
Scans every document
Replicates to target cluster
Source OpLog
Must be large enough to contain entire
initial sync oplog window in order to
replicate data changes that occurred
during initial sync
Initial Sync
Source OpLog
15. #MDBLocal
Prep Items: Upscale Target Cluster
Recommend upscale by 1+ tier higher
Consider higher IOPS too
Increase disk size lower cost alternative
over provisioned IOPS.
Turn off Auto-Scale
Force Failover before migration
17. #MDBLocal
Comparing Options
Live Migrate mongomirror dump/restore or import
RS or Sharded
Built-in cutover
RS only
Sharded: Professional Services
All deployments
Great for most customers Can avoid network hop Downtime proportional to data size
Built-in Atlas UI
Must temporarily allow
network access (hop)
Works with Network peering
User-controlled cut-over
Sharded -> RS
18. #MDBLocal
Behind the scenes
1. initial sync - copying documents
and building indexes that already
exist on the source deployment.
2. oplog sync - tailing and applying
entries from the oplog (delta).
○ “CDC” - Continues replicating
as live data is changing
○ resumable from here
19. #MDBLocal
Migration Dry Run
Prod ⇒ Staging/QA Atlas Cluster
Dry-run:
Connectivity & Security
Time to perform initial sync
Restart App(s) with
new Connection
Run initial sync at least 2 times
1) Build Staging site with Initial Sync but w/o Cutover
a) Measure time
2) Repeat w/Cutover
a) Let LM / MM reach 0s replication lag
b) Restarting Apps pointing to new Cluster
c) Test, Test, Test
30. 30
This presentation contains “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933,
as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. Such forward-looking statements are
subject to a number of risks, uncertainties, assumptions and other factors that could cause actual results and the timing of
certain events to differ materially from future results expressed or implied by the forward-looking statements. Factors that
could cause or contribute to such differences include, but are not limited to, those identified our filings with the Securities
and Exchange Commission. You should not rely upon forward-looking statements as predictions of future events.
Furthermore, such forward-looking statements speak only as of the date of this presentation.
In particular, the development, release, and timing of any features or functionality described for MongoDB products
remains at MongoDB’s sole discretion. This information is merely intended to outline our general product direction and it
should not be relied on in making a purchasing decision nor is this a commitment, promise or legal obligation to deliver
any material, code, or functionality. Except as required by law, we undertake no obligation to update any forward-looking
statements to reflect events or circumstances after the date of such statements.
Safe Harbor Statement
32. #MDBLocal
Let’s choose a few
MongoDB “compatible” Key-value stores Relational DBMS
AWS DocumentDB
Azure CosmosDB
AWS DynamoDB
33. #MDBLocal
AWS DocumentDB
● Compatible with MongoDB 3.6
● Use the same MongoDB Drivers/SDKs, Tools and
Applications with Amazon DocumentDB
● Automatic Patching, Failover and Recovery
● Integrated with AWS services (CloudWatch, etc.)
● Functional Differences:
https://docs.aws.amazon.com/documentdb/latest/developerguide/functio
nal-differences.html
34. #MDBLocal
AWS DocumentDB Feature Gap vs. MongoDB
Fails > 60%* of MongoDB correctness tests
• Extensive testing, debugging & refactoring
required to migrate to DocumentDB
Lags mainline features by 5 years
• No retryable reads + writes
• No transactions
• No support for storage or index compression
• Missing many aggregation stages that allow
expressive data handling
• No lossless decimal type
• No search and geospatial queries
• Indexes are not copied over via the utilities
(mongodump and mongorestore)
• No materialized views
MongoDB’s most
important value is
developer productivity
These limitations can
significantly reduce
that value
*60% for 3.6, 64% for 4.2* https://www.mongodb.com/atlas-vs-amazon-documentdb/compatibility
35. #MDBLocal
AWS DocumentDB Feature Gap vs. MongoDB
Not based on the MongoDB server
emulates the MongoDB API
does not provide complete functionality
Yet, Developers are directed to use official
MongoDB Drivers, Documentation and University
to learn how to connect and develop?
What is this experience like? ...
36. #MDBLocal
Possible Migration Options
Method Considerations
Offline mongodump / mongorestore
Does not dump admin database
Recreate user(s) (DocumentDB does not provide RBAC*)
Online
build-your-own
Does not support Kinesis Streams, Data Pipeline, etc.
Change Streams (limited) could be used (likely very fragile)
*https://docs.aws.amazon.com/documentdb/latest/developerguide/fu
nctional-differences.html#functional-differences.mongodump-
mongorestore
37. #MDBLocal
[ec2-user@ip-172-31-1-79 dump]$ mongodump --host sigsdocdb.caexbcw7y6up.us-west-
2.docdb.amazonaws.com:27017 --username snarvaez --ssl --sslCAFile /home/ec2-user/rds-
combined-ca-bundle.pem
2020-02-24T05:01:23.523+0000writing SigsTest.coll to
2020-02-24T05:01:23.525+0000done dumping SigsTest.coll (1 document)
[ec2-user@ip-172-31-1-79 bin]$ ./mongomirror --host rs0/sigsdocdb.caexbcw7y6up.us-west-
2.docdb.amazonaws.com:27017 --username snarvaez --ssl --sslCAFile /home/ec2-user/rds-
combined-ca-bundle.pem --destination Cluster0-shard-0/cluster0-shard-00-00-
tlsla.mongodb.net:27017,cluster0-shard-00-01-tlsla.mongodb.net:27017,cluster0-shard-00-02-
tlsla.mongodb.net:27017 --destinationUsername snarvaez
mongomirror version: 0.9.1
git version: 0bc45282784aa74bc25c336412efca7f84749aa4
Go version: go1.12.13
os: linux
arch: amd64
compiler: gc
2020-02-24T05:02:56.564+0000Error initializing mongomirror: could not initialize source
connection: could not connect to server: server selection error: server selection timeout
current topology: Type: Single
Servers:
Addr: sigsdocdb.caexbcw7y6up.us-west-2.docdb.amazonaws.com:27017, Type: Unknown, State:
Connected, Average RTT: 0, Last error: connection(sigsdocdb.caexbcw7y6up.us-west-
2.docdb.amazonaws.com:27017[-121]) connection is closed
38. #MDBLocal
Azure CosmosDB
Advertised Strengths
1. Globally Distributed
2. Linearly Scalable
3. Schema-Agnostic Indexing
4. Multi-Model
5. Multi-API and Multi-Language Support
6. Multi-Consistency Support
7. Indexes Data Automatically
8. High Availability
9. Guaranteed Low Latency
10. Multi-Master Support
39. #MDBLocal
Azure CosmosDB Feature Gap vs. MongoDB
Also not based on the MongoDB server - It emulates the MongoDB API
Large feature gaps vs. mainline
● No multi document ACID Transactions, Materialized Views, Retryable Writes, Lossless
Decimals, Text Search, Schema Validation, etc.
● 3.2 and 3.6 modes. 3.2 clusters cannot be upgraded to 3.6 at this time (Feb 2020)
● Numerous Incompatibilities
Many operations work differently and are not documented - left to developers to figure out
Scalability needs Handling + Rapid Cost Escalations
● RUs determine scalability - developers need error handling when max RUs exceeded
Azure Only - Lock-in
40. #MDBLocal
Possible migration options
Method Considerations
Offline mongodump / mongorestore
Not an option - backups cannot be restored to another target
Offline Via Azure Data Factory* or
Azure DocumentDB Data Migration Tool*
ETL Export to JSON / mongoimport
Online
build-your-own
Via Change Feed
Similar to using Change Streams + Azure Functions to write to Atlas
* https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-cosmos-db-mongodb-api
* https://www.microsoft.com/en-us/download/details.aspx?id=46436
* https://docs.microsoft.com/en-us/azure/cosmos-db/change-feed
41. #MDBLocal
AWS DynamoDB
DynamoDB is a wide-column key/value store. Each
entry is called Item and consists of Attributes.
Widely used in AWS Ecosystem ⇒ AWS Only
Migration may required due to
● Increased / Unpredictable Cost
● Functionality insufficient for Business or Dev
Productivity - App has outgrown the data store
● etc. https://aws.amazon.com/blogs/database/choosing-the-right-
dynamodb-partition-key/