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DynamoDB Data Modeling for Performance Workshop

The modeling decisions you make early in a DynamoDB project — how you structure partition keys, design access patterns, and use indexes — can come back to bite you at scale. Join DynamoDB consultant Uriel Bitton and ScyllaDB engineers for a hands-on look at how to optimize your data modeling. We’ll start by focusing on data modeling within DynamoDB itself, then discuss additional options that are available for those who have moved DynamoDB workloads to ScyllaDB (via its DynamoDB-compatible API, Alternator). We'll cover: - Access-pattern-driven design and why it changes how you structure your tables - Partition key strategies that avoid hot spots and uneven throughput - How to model your way around DynamoDB's per-partition throughput limits or avoid it with ScyllaDB Alternator - Index design and where it hurts performance - Working around DynamoDB limits with ScyllaDB Alternator - Tips for hybrid vector search data modeling with ScyllaDB Alternator

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Uriel Bitton, AWS Cloud Consultant, Dynasight Guilherme Nogueira, Technical Director, ScyllaDB DynamoDB Data Modeling for Performance
Guilherme Nogueira 2 + Technical Director @ ScyllaDB + Previously Solutions Architect + Publishing + Streaming + Automotive + Helping users be successful at scale
Uriel Bitton 3 + AWS Cloud Consultant + DynamoDB data modelling consultant + Founder of Dynasight + Helping startups understand DynamoDB and build scalable & cost efficient databases on AWS. Dynasight
Agenda 4 + What is ScyllaDB + ScyllaDB Alternator for DynamoDB workloads + Design patterns + Modeling around limitations + Index performance + Hybrid Vector Search with Alternator + Q&A + Wrap
What is ScyllaDB? 5
6 Shard-per-Core Unique Close-to-Metal Architecture Built in C++ Everything Asynchronous Shared Nothing Shard per Core Specialized Cache Network Processor NUMA Storage Shards (per core) uniqu e ScyllaDB technology
Guaranteed SLA Perfect horizontal & Vertical Scale 1000 Nodes Cluster 8000 Cluster K8S Deployment +200TB per Node >256 Cores per Node 1B Operations per Second Userspace I/O Scheduler Disk Query Commitlog Compaction Queue Queue Queue 0.5msec Shards (per core) uniqu e ScyllaDB technology
+400 Gamechangers Leverage ScyllaDB Seamless experiences across content + devices Corporate fleet management Real-time analytics 2,000,000 SKU -commerce management Video recommendation management Low-latency global sports betting Real time fraud detection for 6M daily transactions Uber scale, mission critical chat & messaging app Network security threat detection Power ~50M X1 DVRs with billions of reqs/day Precision healthcare via Edison AI Inventory hub for retail operations Property listings and updates Cryptocurrency exchange app Geography-based recommendations Global operations- Avon, Body Shop + more Predictable performance for on sale surges GPS-based exercise tracking Serving dynamic live streams at scale Powering India's top social media platform Personalized advertising to players Distribution of game assets in Unreal Engine Real-time ML-driven recommendations Connecting millions of people around the globe
9 ScyllaDB Alternator For DynamoDB workloads
Efficiency Shard-per-core Workload Prioritization Built-in cache Capacity-based Pricing Throttling-free Scaling Cloud agnostic 10
ScyllaDB's DynamoDB-compatible API + Native DynamoDB API + Same efficiency + Works as-is + No changes to table or queries 11 + Run anywhere + Any cloud, VM, k8s, bare metal + Tables, Indexes, Streams + Types, drivers
Question: What are your main challenges with DynamoDB? ● High throughput use-cases ● Latency ● Cost ● Lock-in ● Others (tell us in chat!) Poll time
Why design for performance? 13
+ Built to scale + Performs the same @ 10GB, 10TB datasets + Restrictions enforced for performance + Strict primary keys + Physical separation of the data + Hard limits (item and page sizes) + Requires change of mindset + Especially from Relational world 14 DynamoDB design
15 Access-pattern-driven design
+ Relational model + Start with the data + Joins, Foreign Keys… + Performance issues = Indexes + Lower learning curve + Harder to scale + Favors flexibility 16 Start with the queries + NoSQL model + Start with the access patterns + Read + Write + Map keys to allow precise fetches + Point queries + “Pre-join” the data + Designed for scalability + Trades-off performance vs flexibility
How is data accessed? + Read paths + Map Primary Key, Sort Key + Map necessary Indexes + Write paths + How does data flow into your system? + Real-time + Batch + Inserts, updates, appends, deletes + Plan for write efficiency + Point writes + Batches for multiple items 17 Access patterns
Partition key strategies 18
19 Tables must be designed around queries. + Design partition keys for highly granular access + How to map cardinality early on? + Why is cardinality important? + Uniqueness + Users interact with different parts of the database + Avoid tripping limits DynamoDB Table Structure
20 Model around DynamoDB limitations
They are there for a reason. But often surprises users. + 400KB max Item size + Pagination + 1MB page size + What happens when Filter is applied? + Indexes consume Units and not obvious cost + How does it work on ScyllaDB Alternator? 21 DynamoDB Limits
Index design and performance 22
But present a challenge for design. + Cardinality in Indexes + What happens if 99.9% of Items have State = ACTIVE? + Write overhead + Cost + But also performance + What happens if State = ACTIVE exceeds ops threshold? + Best practices + Beware of projection (KEYS_ONLY, INCLUDE, ALL) + 20 GSIs per table – never do that! + Leverage Sparse Indexes + Skips items without the indexed attribute name 23 Indexes are powerful
Hybrid Vector Search with Alternator Integrated Vector Search for DynamoDB workloads 24
+ Part of ScyllaDB Cloud + Index ScyllaDB Data using Alternator API + High Throughput per core + Low Consistent Latency ScyllaDB Vector Search 25
26 Vector Search Vector Database Saved in Searched in Embeddings Embeddings Document Image Audio Transformed into User’s Search Query Transformed into 1 2 3 Perform Similarity Search Accurate Search Result To Provide
Q&A 27
28 Wrap up
Designing for DynamoDB performance requires careful planning + Key cardinality + Choice of Indexes + Types of operations ScyllaDB is a valid DynamoDB replacement: + DynamoDB-compatible API + Run applications anywhere + Better performance + Lower cost 29 Wrap-up
Thank you for joining us today. @scylladb scylladb/ slack.scylladb.com @scylladb company/scylladb/ scylladb/ Uriel Bitton uriel@dynasight.app linkedin.com/in/urielbitton Guilherme Nogueira (Gui) guilherme.nogueira@scylladb.com