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1
Intro
2
What is CDI?
3
Segment Timeline
4
Identity Resolution Use Cases
5
Problem
6
Identity Graph
7
Mappings
8
General Data Model
9
"Object" KV Kind
10
Mapping Object
11
Segment ID Index
12
Merge Object
13
Workload Profile Needs
14
Personas Data Flow
15
Aurora works, but...
16
Some Baseline Aurora Costs
17
Example Realistic Workload
18
Tuning Cookbook
19
Cluster Evolution
20
Infrastructure Overview
21
Terracode-foundationdb / cluster
22
AMI Launch Config / User Data
23
data.mount
24
configure-fdb
25
fdb-discovery
26
fdb-backup
27
Gameday & Chaos
28
Exactly Once Delivery
29
Dedupe Pros / Cons
Description:
Discover how Segment achieved a 5x cost reduction by migrating from AWS Aurora to FoundationDB in this informative conference talk. Explore the journey of Segment's identity resolution system, a critical component in translating customer interactions into Personas models. Learn about the architecture behind Segment's infrastructure, the decision-making process for selecting FoundationDB, and the significant benefits realized through this migration. Gain insights into the specific operational challenges faced during the transition and the strategies employed to overcome them. Delve into topics such as CDI, identity graphs, data models, workload profiles, cluster evolution, and infrastructure setup. Understand the importance of exactly once delivery and the pros and cons of deduplication in this context. This presentation offers valuable lessons for organizations considering similar database migrations to improve performance and reduce costs.

How We Saved 5x Migrating from Aurora to FoundationDB

Linux Foundation
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