Amazon DynamoDB - A serverless database for everyone

Transcript

1. simalexan A serverless database for everyone Amazon DynamoDB
2. simalexan Aleksandar Simovic Senior Software Engineer @ ScienceExchange AWS Serverless Hero coauthor of “Serverless Applications with Node.js” book AWS SAM & Lambda Builders Contributor Co-organizer of JS Belgrade, Serverless Belgrade, Wardley Maps Belgrade meetups
3. simalexan Which database should I choose? NoSQL Only RDB!
4. simalexan If relational databases solve
 all of our needs,
 
 why do we need NoSQL?
5. simalexan (1967-71)
 
 $1 Million per 1 MB $200 per 92k IPS Cost of Resources (2019) 
 
 $0.02 per 1 GB
 $500 per 300 bill. IPS
6. simalexan <<< Storage cost CPU time cost
7. simalexan SQL NoSQL Relational Hierarchical (Denormalized) Vertical Scale Horizontal Scale Queries Instantiated Views Maximize Storage Maximize compute (CPU)
8. simalexan Amazon DynamoDB Amazon’s NoSQL
9. simalexan Serverless DB
 (No patches / updates) Amazon DynamoDB Consistent and Fast
 (4M transc. / sec) Document or Key-Value Scales per Any Load Access control (Fine grained access 
 table, items, attributes, values) Event Driven Model
 (Connected to AWS Lambda)
10. simalexan • 2007, published paper • One of the authors is Werner Vogels 
 (CTO of Amazon)
 • Used by Amazon from day 1
 Amazon e-commerce Shopping Cart • 2012, GA Built by AWS / Used By AWS
11. simalexan • Hardware provisioning • Cross-availability zone replication
 (replicas whenever needed)
 • Monitoring and handling of failures • Patches / updates / fixes Managed for you
12. simalexan • First 25 GB stored per month is free • $0.25 per GB-month • Write $1.25 / million req units • Read $0.25 / million req units Pay-per-use database!
13. simalexan • Automatically partitioned • Partitions completely independent • No limits on workloads Scaling
14. simalexan Autoscaling
15. simalexan • Single digit (1-9) millisecond Put / Get • Custom SSD based platform
 - performance independent of table size
 - no need for working set to fit in memory Performance
16. simalexan • “Replication? We don’t need stinking replication” • “Automated” • “multi-region” • "multiple replica”
 tables 
 (one per region that you choose)
 • DynamoDB treats as a single unit Global Tables
17. simalexan Structure
18. simalexan • Scalar data types:
 - string, 
 - number, 
 - binary • MultiValue data types
 (string set, number set, binary set) Data Types Available
19. simalexan • Data Index by a Primary Key • Types of primary keys:
 - partitionKey (hash)
 - partitionKey + range Indexing Scientist field year Marie Curie Chemistry 1911 Marie Curie Physics 1903 John Bardeen Physics 1956 Leonid Hurwicz Economics 2007 hash + range example
20. simalexan DynamoDB Table
21. simalexan DynamoDB Table
22. simalexan Uniquely identifies a single item Partition Key Unordered hash index Allows table partitioning for scale 
 (chop up and throw in a storage node - automatic routing for service request)
23. simalexan Two attributes to uniquely identify an item Partition Sort Key Items arranged by the sort key No limits on the number of items per partition key
24. simalexan Local index to a single partition key Local Secondary Index Another sort key attribute 
 (alternate range key)
25. simalexan Country Scientist field year topic France Marie Curie Chemistry 1903 Radioactivity France Esther Duflo Economics 2019 Alleviating Global Poverty Get items 
 country = “France”
 scientist begin_with “Marie” NO Local Secondary Index
26. simalexan Get items 
 country = “France”
 year > 2000 Local Secondary Index Country Scientist field year topic France Marie Curie Chemistry 1903 Radioactivity France Esther Duflo Economics 2019 Alleviating Global Poverty LSI
 Partition Key: scientist
 range: year

27. simalexan Global Secondary Index (GSI) Index across all partitions
 
 Alternate partition (+sort) key
 Use composite sort keys for compound indexes
28. simalexan Artist Song Year of Release Album AC/DC Shoot to Thrill 1980 Back in Black Florian Pellieser Quintet Coup de foudre
 a Thessalonique 2018 Coup de foudre
 a Thessalonique NO Global Secondary Index Get items 
 artist = “AC/DC”
 order by song ASC
29. simalexan Artist Song Year of Release Album AC/DC Shoot to Thrill 1980 Back in Black Florian Pellieser Quintet Coup de foudre
 a Thessalonique 2018 Coup de foudre
 a Thessalonique Global Secondary Index Get items 
 album = “Back In Black”
 order by song DESC GSI 
 Partition Key: album
 range: song
 * this is why the GSI costs extra *
30. simalexan Updating the 
 Global Secondary Index (GSI) Update req Async updates Table Client GSIs Response
31. simalexan 1. Join two columns 2. Use “begins with” Composite Key is_verified date TRUE 11-05-2019 TRUE 11-05-2019 FALSE is_verified_date TRUE_11-05-2019 TRUE_11-05-2019 FALSE Useful for multivalue filter (and sort sometimes)
32. simalexan Limitations Item max size 400kb
 
 Max 5 LSI
 
 Initial limit of 20 max GSI
 Max 32 levels deep
 
 Max 40,000 reads and 40,000 write req. units / table
 
 256 tables per Account per AWS Region
33. simalexan Modeling / Queries?
34. simalexan SQL vs NoSQL SELECT * FROM aircrafts
 INNER JOIN bookings
 WHERE… SELECT * FROM aircrafts
35. simalexan Use Case Context Modeling steps Rick Houlihan Review -> Repeat -> Review Data modeling Avoid relational database patterns, use one table Access patterns • Read Write Workloads • Query dimensions and aggregations • Nature of the app (OLTP, OLAP, DSS) • ER Model • Data Lifecycle (TTL, Backup) • Data Sources • Query aggregations • Document all workflows • 1 application service = 1 table • Identify primary keys • Define indexes for secondary access
 patterns
36. simalexan Example Modeling by Rick Houlihan
37. simalexan Example Modeling by Rick Houlihan
38. simalexan Example Modeling by Rick Houlihan
39. simalexan Example Modeling by Rick Houlihan Relational Approach
40. simalexan Example Modeling by Rick Houlihan NoSQL Approach
41. simalexan Example Modeling by Rick Houlihan NoSQL Approach (Orders and Drivers)
42. simalexan Example Modeling by Rick Houlihan NoSQL Approach (Vendors and Deliveries)
43. simalexan Serverless Event Model
44. simalexan • As data enters a database,
 it can also leave.
 • AWS DynamoDB Streams
 • API Gateway -> DynamoDB
 DynamoDB -> Lambda Event Driven Model
45. simalexan Some Use cases Duolingo Amazon Shopping Cart CapitalOne Nordstrom Snapchat Nike GE Aviation Lyft Netflix Samsung
46. simalexan When (not) to use DynamoDB Real-time analytics and queries Complex queries and joins Bad use cases Good use cases Very high read/write Key-value simple queries Consistently low-latency Autosharding / multiple node scaling No tuning No size throughout put limits OLTP OLAP
47. simalexan serverless.pub/book “Serverless Applications with Node.js" claudia40 40% off Hvala!