1.
0-60: Tesla’s
Streaming
Data Platform
Jesse Yates
Staff Engineer
Kafka Summit SF
September 30th, 2019

2.
• Staff Engineer @ Tesla, Big Data
• “Big Data” & Cloud specialist
• Apache HBase Committer
• Apache Phoenix PMC
• Occasional blogger
• Triathlete
Who am I?

3.
Agenda
• Challenges
• Design Overview
• Build a data flow
• Operations

4.
Data Challenges
Usual suspects
• Volume
• Velocity
• Variety
IoT Challenges
• Bursty
• Low-latency
• Payload explosion

5.
100s of Powerpacks
10s pods/pack
1000s signals/sec/pod

6.
Payload Explosion

7.
A little bit of history
Trillions/day

8.
Designing an
ETL System

9.
Design Requirements
• ”Just works”
• Flexible batching
• One-stream, one-app
• Scale with multiple degrees of freedom

10.
Kafka Channels
• Backpressure, buffering, non-blocking, fault tolerant
• Mostly configurable, extendable as needed
• Highly Composable
Limit functionality to increase operability

11.
Channel Building Blocks
Database FileSystem
Broadcast
Filter Batch
Source
KafkaToFileSystem
KafkaToDB
KafkaToCanonical
KafkaToKafka

12.
Composable Components
Mirror
Raw to
Canonical
Storage
Payload (K:V) (K:V)

13.
Let’s build a
data flow!
Not really,
because,
you know,
live demos

14.
A Simple Use Case
• Gzipped, custom event format
• Collected in an edge Kafka cluster
• Land in central DataLake for analysts
• …
• Profit!

15.
Mirror from Edge Kafka
• Its just another Channel application
• No surprise bugs or operations
• Regex mapping
• Sampling
edge.cool_data
cool_data
edge.legacy_data

16.
Our Flow
10%
Mirror

17.
Raw to Canonical
• Many-to-many
• Decoders: gzip, b64
• Built-in parsers: JSON, CSV
• Custom Parser
• Flexible for unplanned uses
• High parallelizable
Kafka
Source
Commit
Decode
Parse
Produce

18.
Parser API
public parse(byte[]) :: Iterator<Map<String, Object>>
• Exception during call -> Skip record
• Exception during iteration -> Halt the stream
• Sometimes means early materialization

19.
Using Our Custom Parser

20.
Our Flow
10%
Mirror
Custom
Parser to
Avro

21.
KafkaToFileSystem
• Parquet format
• System-time partitioned
• Many-to-one
• Native batching
/root
/cool.db
/sys_date=2019-09-30
/channel.cool_data
/somedata.parquet.1

22.
Our Flow
10%
Mirror
Custom
Parser to
Avro
FS HDFS

23.
Monitoring &
Operations

24.
Kafka Monitoring
• Kafka is very simple to monitor + observe
• One dashboard can tell you everything at a glance
But… people don’t think in offsets and counts
% SLOs and time-based lag monitoring

25.
Operations
• Many open source tools
• Kafka Monitor https://github.com/linkedin/kafka-monitor
• Burrow https://github.com/linkedin/Burrow
• Cruise Control https://github.com/linkedin/cruise-control
• Our own tools https://github.com/teslamotors/kafka-helmsman
• Freshness tracker
• Topic Enforcer
• Rolling Restart

26.
Kubernetes
• Dynamic scalability
• Incidents or usual growth
• Handle daily peaks
• Load smearing across
streams
• Not free – infra is non-trivial

27.
What about when things go sideways
• A rack fails
• Your database chokes
• The network is having a bad day
And your users need their data RIGHT NOW!

28.
Channels Backfill
• “Freshest” data can be ingest
immediately
• Looks just like a regular channel
• Just select a range in the past &
deploy

29.
Channels Backfill

30.
Summary
• Lots of kinds of data + IoT challenges
• Simplicity for operations at scale
• Backpressure, non-blocking, high-throughput
• Flexibly configuration based

31.
A note on global warming

32.
Accelerate the world’s transition to
sustainable energy
We are hiring!
Jesse Yates@jesse_yates jyates@tesla.com

33.
Backup Slides

34.
Future Directions
• S3 storage as first-class citizen
• Managing hundreds of flows with multiple steps
• Internal library
• Self-service flows exposed to users
• Open source?