MongoDB to ScyllaDB: Technical Comparison and the Path to Success

What can you expect when migrating from MongoDB to ScyllaDB? This session provides a jumpstart based on what we’ve learned from working with your peers across hundreds of use cases. Discover how ScyllaDB’s architecture, capabilities, and performance compares to MongoDB’s. Then, hear about your MongoDB to ScyllaDB migration options and practical strategies for success, including our top do’s and don’ts.

1. MongoDB vs ScyllaDB: Path to Success Felipe Mendes, Solution Architect at ScyllaDB Pete Aven , Solution Architect at ScyllaDB

2. Meet The Team Felipe Cardeneti Mendes Published Author ScyllaDB Committer IT Specialist & Solution Architect Your photo goes here, smile :) Pete Aven Data Engineer Application Development ScyllaDB Solution Architect

3. Soccer Football Goal?

4. Survival Rule #1

5. Lower Node Count Efficient hardware usage results in direct savings Where Do You Wanna Be? Predictable, Low Latencies Consistent single-digit millisecond p99 latencies Autonomous Self-Tuning Self-optimizing, smaller footprint, easy to use Always-On Architecture Active-active deployments for mission-critical workloads

6. Architecture Differences

7. Data Model & Query Language MQL – Mongo Query Language Documents stored in BSON format Schemaless Favors Query Flexibility over Performance

8. Data Model & Query Language CQL or DynamoDB API Wide-Column Representation Schema-oriented Favors Performance over Query Flexibility

9. MongoDB – Replica Sets Reads Reads (optional) Reads (optional) REPLICA SET 3x replication Primary-Secondary Impact on Write Throughput Election on Failure Scaling Limitations Up to 50 members Arbiters Non-voting members Limited Storage/Replica* Affect Reads Scaling MongoDB Atlas *

10. MongoDB – Sharded Cluster A Shard is a Replica Set Adds Overhead & Complexity Further Availability Concerns Underutilized Capacity Sharding Performed Manually Hashed vs Ranged Still Require a Query Router Reads SHARDED CLUSTER 3 shards, 3x replication

11. ScyllaDB – Sharding & Replication Simplified Architecture Equal Nodes Sharded to the Core Maximizes Infrastructure Power Unlimited Scale Always-On No Limits Fully Autonomous App App

12. Independent Benchmark Results " ScyllaDB outperforms MongoDB in 132 of 133 measurements. " – Daniel Seybold , benchANT CTO Read the full report

13. "MongoDB is Web Scale" " The scalability results demonstrate that ScyllaDB achieves up to near linear scalability, while MongoDB shows less efficient horizontal scalability. " – Daniel Seybold , benchANT CTO Read the full report

14. Migration Options

15. It’s a Simple Matter of Programming(™)

16. Connectors Connecting ScyllaDB Shard-Aware Drivers

17. Collections vs Tables

18. Mental Exercise MemeGroup [ { id : simpsons, title : 'The Simpsons' , author : uid1, tags : [' homer ', ' liza '], photos : [pid1, pid2], views : 200 } { id : comics, title : 'Comics' , author : uid2, tags : [' drawing ', ' cartoon '], photos : [pid1, pid2, pid3], views : 300 } { id : matrix, title : 'Matrix' , author : uid1, tags : [' neo ', ' morpheus ', ' pill '], photos : [pid4, pid5], views : 50 } ] Memes [ { id : homer_grass, title : 'Homer Hiding' , location : p1, tags : [' hiding ', ' grass '], likes : 30 , views : 200 } { id : morpheus_pill, title : Choose Destiny ' , location : p2, tags : [' pills '], likes : 99 , views : 300 } { id : trollface, title : 'Troll face' , location : p3, tags : [' troll ', ' face ], likes : 9999999 , views : NaN } ] Queries: Display all Memes posted by a given Author, sorted by time Retrieve all Groups of Memes matching a particular Tag

19. Mental Exercise Queries: Display all Memes posted by a given Author, sorted by time Retrieve all Groups of Memes matching a particular Tag CREATE TABLE MemeByAuthor ( author uuid, m eme_id uuid, post_time timeuuid, t itle text, t ags frozen<set<text>>, p ath text, PRIMARY KEY (author, post_time) ) CREATE TABLE MemeGroup ( group_id uuid, a uthor_id uuid, title text, t ags set<text>, memes uuid, PRIMARY KEY (group_id, memes) ) CREATE INDEX ON MemeGroup (tags); Adapted from StackOverflow

20. ScyllaDB JSON Support INSERT INTO mytable JSON '{ "\"_id\"": 1, "title": "Reservoir Dogs", "director": ...}' SELECT and INSERT statements

21. Leave JSON “As-Is” ID #1 document

22. Hybrid Approach ID #1 D ocument Document ID #1

23. Before Migrating Understand the differences between a document vs wide-column Denormalize and design a query-driven approach Optimize your model for fast retrieval & ingestion Take the opportunity to re-evaluate your access patterns! Get to know the available data-types, indexes, collections and UDTs in ScyllaDB Implement highly concurrent asynchronous code in your application BLAST ScyllaDB – It WILL handle it!

24. Migration Walkthrough ETL Application Writes & Reads Reads Forklift Historical Data Source Connector MongoDB Change Streams ScyllaDB Sink Connector Writes & Reads

25. Success Stories

26. Trillions of messages 60% fewer nodes 5ms p99 "We knew we were not going to use MongoDB sharding because it is complicated to use and not known for stability" 2015 – Million 2016 – Billion 2019 – Trillion Stanislav Vishnevskiy How Discord Stores Billions and Trillions of Messages

27. Unleashing the Power of Data with Scylla "We were looking for something really specific. A highly scalable, and high performance NoSQL database. The answer was simple, ScyllaDB is a better fit for our use case." João Pedro Voltani

28. MongoDB Versus ScyllaDB, in Production at Numberly " We are happy users of both, because we select the use cases based on their strengths " We use ScyllaDB for real-time and latency sensitive pipelines, mixed batch and real-time workloads, and web backends in GraphQL ( imposing a schema ). We see a trend into ScyllaDB today. Alexys Jacob Numberly adoption MongoDB OSS (AGPL) MongoDB 1.4 stable Cassandra OSS (Apache) MongoDB is very strong in web backends and real time queries over unpredictable behavioral data. We need to query and operate on top of this data. This is where MongoDB flexibility is good for us. Cassandra 1.0 Cassandra 2.0 Cassandra 3.0 ScyllaDB OSS (AGPL) ScyllaDB 1.0 ScyllaDB 2.0 ScyllaDB 3.0 2016 2017 2018 2019 2015 2013 2011 2010 2008 2009 MongoDB 2.0 MongoDB 3.0 WiredTiger (3.2) MongoDB 4.0 (SSPL) MongoDB 4.2 Numberly adoption MongoDB 3.6

29. Takeaways ‹#› ScyllaDB is the database for Performance ScyllaDB and MongoDB are different databases Knowing the differences is key for a successful transition Identify the optimal data model Follow a query-driven & performance-oriented design approach Both have a large driver and connectors ecosystem The path for migration is straightforward, and we're here to help ! Customers met their Performance challenges with ScyllaDB

30. Stay in Touch Felipe Cardeneti Mendes [email protected] @cardeneti82118 @fee-mendes Find me on LinkedIn! Pete Aven [email protected] @peteaven @wpaven Find me on LinkedIn!

31. ScyllaDB Summit 2024 Styles 2024 Summit color palette #1B58EF # 05CEE8 # 00EFB6 # F244CD # 8158FF #EEEEEE # FFA522 #4D4D4D The default body font is Roboto Condensed. You can adjust the size as needed. You can also use Roboto (the uncondensed version). For code you should use Roboto Mono and you can set it on this dark background