This document discusses Pinot, Uber's real-time analytics platform. It provides an overview of Pinot's architecture and data ingestion process, describes a case study on modeling trip data in Pinot, and benchmarks Pinot's performance on ingesting large volumes of data and answering queries in real-time.

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Pinot: Near Real-Time Analytics @ Uber

2. U B E R | Data
Xiang Fu
Sr Software Engineer II @ Uber
Streaming Analytics Team
Quick Introduction

3. U B E R | Data
Uber Scale
Messages Bytes
Apache Kafka Trillion per day ~PB per day
Streaming Analytics
Platform
Billions processed
per day
100s of TB
processed per day
Pinot 100s of Billions 10s of TB

4. U B E R | Data
Agenda
● Pinot @ Uber
● Architecture
● Case Study
● Pinot Perf

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Pinot @ Uber

6. U B E R | Data
Experimentation platform (Internal Dashboard)
A / B Tests
See progress of
tests in real-time

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UberEats (Realtime User Facing Product)
UberEats Restaurant
Manager
“What is my revenue for
past 90 days?”

8. U B E R | Data
Many More…
• UberPool Analytics
• Mobile Analytics
...

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Architecture

10. U B E R | Data
Pinot Workflow
Athena-X
Hive/Spark SQL/oozie
● Projection, Filtering
● Window Aggregation
● Join

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Pinot Realtime: Self Service
● Projection, Filtering
● Window Aggregation
● Join

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Case Study

13. U B E R | Data
Pinot Data Model
Column Name Column Type Filtering Compression Indexing
RiderId SingleValue/
Dimension
Yes Dictionary Sorted
DriverId SingleValue/
Dimension
Yes Dictionary Inverted
TripId SingleValue/
Dimension
No No Dictionary No
PickUpPoints MultiValue/
Dimension
No No Dictionary No
TripFare SingleValue/
Metric
No No Dictionary No
Step 1
List Column Spec
Step 2
Analyze Query Pattern
Step 3
Decide Compression &
Indexing Strategy

14. U B E R | Data
Pinot Data Ingestion
Realtime Ingestion:
Consumer Type Scalability Consistency
High Level Consumer Hard to scale beyond one node
Sacrificing consistency during
failures
Low Level Consumer Scalable beyond one node
Strong consistency guarantees even
during failure
Segment Persistence: 500k msg or 6 hours
Offline Ingestion:
Using Oozie to schedule daily incremental backfill from Hive to Pinot

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Pinot Perf

16. U B E R | Data
Pinot Realtime Ingestion
Hardware 4 base SKU boxes(24 cores, 128G RAM)
Consumer Type HLC LLC
Peak Traffic(msg/sec/box) 20k 200k
Peak Traffic(bytes/sec/box) 4M 40M
Storage Kafka Pinot
Total Data Volume(GB) 500 60

17. U B E R | Data
Pinot/Druid Data Size
Raw Data:
500M Rows, 30 columns
Raw Json: 391.9G
Three Storage Tiers
in Pinot/Druid
- Segments in Deep Storage
(NFS or HDFS)
- Local Disk Cache
- Memory

18. U B E R | Data
Pinot/Druid Query Performance
Max Duration:
select max(duration) from trips
Count All Grouped by City:
select count(*) from trips
group by city_id top 10000
Count All in One Month:
select count(*) from trips
where Month = '201601'
Count All in SF:
select count(*) from trips
where city_id=1 group by Month
Unique Drivers in SF:
select distinctCountHLL(driver_uuid)
from trips where city_id=1
Unique Drivers By Date:
select distinctCountHLL(driver_uuid)
from trips group by Date

19. U B E R | Data
Pinot/Druid Concurrent Query
Query: select count(*) from trips group by city_id

20. U B E R | Data
Guaranteed SLA for Site Facing Products
Aggregation on Rider
trips:
select count(*) from trips
where riderId = x and
date > 20170225

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Thank you