There is a trend in the industry today back to using transactions based on next generation databases that provide strong consistency and global scale. And this consistency is the highest level of consistency - meaning more than just read my own writes. But also read before write, compare and swap, etc.
6. ● DNS, IoT, Time Series, Monitoring, Web Metrics,
Followers, etc.
● Modify Business to Fit - Allow for Inconsistency
○ Inventory - Oversell and cancel order if
necessary
○ Bank - Overdraft and charge
Where Event Sourcing Works
7. We live in a stateful and finite world!
- James Ward
But ….
8. Where Event Sourcing Breaks
When Order of Events Matters
Can not write from 2 hosts and
guarantee ordering
Reconcile Diverging History of
Events
Possible to Lose History
10. Differing Views of Strong Consistency
Write / Read Local Quorum
No Read before Write
No Compare and Swap
No Snapshot Isolation
11. Write Skew
Two concurrent
transactions each
determine what they are
writing based on reading a
data set which overlaps
what the other is writing
begriffs.com
12. Can we Ignore Consistency?
Pushes consistency problem into
the application where they are
far harder to solve
Eventual Consistency really is a
necessary Evil
13. Consistency in the
Application Tier
Events Sourcing w/ Command
Serialization
Serialization Points - Kafka
Consumers
What happens when they fail?
14. Cloud Native / Microservices
Partitioning of the Application Domain and Related
Data
Increased Coordination between Data
Number of Microservices Increases Number of
Interactions
Exponential Growth in Complexity
19. Value of Transactions
Decrease Application Tier Complexity
Reduce Cognitive Overhead
Increased Developer Productivity
Increased Focus on Business Value
20. ● Deal with People's Money
● Financial Transactions
● Inventory
● Stock Exchange
● Auctions
● Points
When do you need ACID TXN?
21. Do we really have to sacrifice AP to get C?
But there are two important caveats: first, you only need
forfeit something during an actual partition, and even
then there are many mitigations. Second, the actual
theorem is about 100% availability, while the interesting
discussion here is about the tradeoffs involved for realistic
high availability.
Eric Brewer Spanner Paper
22. Problems with CAP, and Yahoo’s little
known NoSQL system - Daniel Abadi
Tradeoff between latency (L) and
consistency under normal conditions
23. Google Spanner
● Private global network along with GPS receivers and atomic
clocks.
● Atomic Clocks for Serializability Guarantees
● AdWords & Google Play for 5+ years
● Google Cloud Platform Control Plane
● No Scheduled Downtime
● Can do maintenance by moving data between nodes.
24. CockroachDB
Hybrid Logical Clock similar to a vector
clock for ordering of transactions
Challenges with clock skew
Waits up to 250 MS on reads
25. What is Fauna?
Globally distributed, transactional database
Born out of Twitter and the Calvin Pape
Functional / Type-Safe Query Language
Indexes, Constraints and Views
26. Fauna Transaction Resolution
Resolves transactions before committing to DB
Execute phase and Commit phase
All Transaction w/ in a epoch our ordered and
applied as a batch
Uses a RAFT replicated log that derives a total
order of all transactions.
27. Distributed ACID Transaction Engine
Consensus protocols from Raft and Calvin Paper
Can span thousands of records and indexes
Designed for high throughput and WAN replication
Ensures multi-node and multi- datacenter
correctness
28. Security, Identity, Isolation
Native Security and Access Control
Row-level security, identity, and
isolation
Complete Data Isolation across datasets
29. Quality of Service Managed Resource Scheduler
Operating System Like Design
Single Cluster for Differing and
Competing Workloads
30. Temporality
FaunaDB keeps all
instances of a data, not
overwriting them but
creating new ones when a
write is performed. This is
useful especially when
auditing data, verifying its
evolution over time.
32. Instances
Each record stored as an object.
All objects are instances of a specific class.
Every instance has an identifier called a ref.
An instance’s ref encodes its class along with a
unique id
34. Similar to Traditional RDBMS
Indexes Terms and Values
Transform, cover, and order
instance data
Enforce unique constraints.
Referenced explicitly in query
expressions;
Optimizer does not make index
applications
Indexes
35. Example
“Smart
Contract”
● User Nodes w/ Simulated Account Balance
● Server Node acts as Intermediary and
Notary on Contract
● User Nodes Propose Borrowing from Peers
● Peer Accepts or Rejects Proposal
● Contract is Created between Peers Lending
Fixed Amount
