Metrics at Scale @ UBER (Mantas Klasavicius Technology Stream)

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Lviv IT Arena is a conference specially designed for programmers, designers, developers, top managers, inverstors, entrepreneurs and startuppers. Annually it takes place at the beginning of October in Lviv at Arena Lviv stadium. In 2016 the conference gathered more than 1800 participants and over 100 speakers from companies like Microsoft, Philips, Twitter, UBER and IBM. More details about the conference at itarena.lviv.ua.

Technologie

Metrics at scale @UBER
Mantas Klasavičius

About Me
Senior software engineer @ Uber

About Me
Senior software engineer @ Uber
<metric_path> <value> <timestamp>

UBER
6 continents
72 countries
425 cities
>5 million a day
>1000 engineers
7 years

UBER in Vilnius
3y ago
>20 engineers4 Teams:
- Observability
- Databases
- Foundations
- DevExp

Hypergrowth defines us...
Growth of Services

Metrics
Metrics @UBER is a first class citizen
T0 Service
Handling ~500M telemetry timeseries
Writing ~3M values/sec and running ~1K queries/sec
50M minutes worth of data per sec
Growing >25% month over month

Metrics Collection
Considered choices
Netflix AtlasBlueflood
Update graphite

Metrics Collection
Cassandra is a figure of epic tradition and of tragedy.
High write throughput
Cassandra data model supports time series
data-store - DTCS
Cassandra's native TTL support

Metrics Collection
Cassandra - our use case
Separate clusters for different types of data
Clusters spans multiple datacenters
Dynamically control to which cluster data is written
Forcibly deleting old data
https://github.com/m3db/m3db/

Metrics Collection
Metrics as free resource
*.application_1431728998581_0361.*
*. Connections.10_30_3_24.0x64d11081baa1837.*
*. ply_1b09f59b-a3cf-4b9a-99b4-93e8eb16722c.*
*. check-<uid_or_uuid>.*

Metrics Collection
Cost accounting and metrics about metrics

Observability: Past, Present, and Future
Metrics Visualization
aggregate = fillNulls target | sum;
fetch name:requests.errors caller:cn
| aggregate
| asPercent (fetch name:requests caller:cn | aggregate)
| anomalies
| sort max
| tail 10
M3QL - Query Like It’s Bash
tail(
sort(
anomalies(
asPercent(
sum(fillNulls(stats.counts.cn.*.requests.errors)),
sum(fillNulls(stats.counts.cn.*.requests))
), max
)
), 10
)

Metrics Visualization
Graphite Way vs. M3QL

Observability: Past, Present, and Future
Alerting based on metrics
Query Based Alerting
graphite.absolute_threshold(
‘scale(sumSeries(transformNull(stats.*.counts.api.velocity_filter.uber.views.*.*.blocked, 0)), 0.1)’,
alias=’velocity filter blocked requests’,
warning_over=0.1,
critical_over=10.0,
)

Observability: Past, Present, and Future
Alerting based on metrics
Classic Thresholding
Classic high / low thresholds have some intrinsic problems.
• Labor-intensive: each threshold is hand-tuned and manually
updated.
• Too sensitive: hard to set thresholds for metrics with large
fluctuations, even if there’s an obvious pattern.
• Not sensitive enough: thresholds take a long time to catch
slow degradations.
• Poor UX: configuring really good alerts requires specialized
knowledge of the query language.
• No guidance: system doesn’t offer automated root cause
exploration.

Observability: Past, Present, and Future
Alerting based on metrics
• Zero config: thresholds are set and maintained automatically.
• Dynamic adjustment: thresholds cope with noise, underlying growth, seasonality
and rollouts.
• Rapid detection: embarrassingly parallel algorithm is efficient enough for minute-
by-minute analysis at scale.
• Integrated UX: work within our existing telemetry and alert configuration systems.
• Helpful: automated root cause analysis.
In short, the only input is a list of business-critical metrics.
Intelligent Monitoring

Observability: Past, Present, and Future
Alerting based on metrics
The max lower threshold
exceeds the min upper
threshold
Dynamic Thresholds

Observability: Past, Present, and Future
Alerting based on metrics
Outage Detection
< 1% outages missed.
6.5 out of 10 alerts are true issues.

Observability: Past, Present, and Future
Alerting based on metrics
F3
stats.foo
anomalies(stats.foo)

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Mada co-founded Branch in 2014, masterminding the community-driven marketing strategy behind the company’s early, explosive success. Today, Branch is the mobile growth platform of choice for over 50,000 apps and 2 billion monthly users around the world, and one of Silicon Valley’s fastest-growing Unicorns. Mada currently leads marketing for Branch, and she remains deeply involved in the company’s global culture initiatives, which have led to several industry awards. Born and raised in Romania, Mada came to the US to study Electrical and Computer Engineering at Cornell University and then earned her Masters of Engineering and MBA from Stanford. Mada enjoys playing the latest viral mobile games, binge-watching the hottest sci-fi shows, and photographing Branch events. Mada regularly speaks about mobile growth at top global tech events, including Web Summit and SaaStr, and she is the host for the How I Grew This podcast series and an instructor for a Growth Strategies course at Stanford Continuing Studies. Awards: 2016 Linkedin’s Next Wave – 150 top professionals in 15 industries all under 35 2019 Marketers That Matter – Winner in the Building Brand B2B category Top Women Entrepreneurs In Cloud Innovation 2019 Winner Speech Overview: The mobile growth trends you need to know about in 2021 – 2020 have changed our world – from COVID to big platform changes when it comes to mobile technology you have to stay ahead of the curve. This talk explores the mobile marketing landscape and the key trends you need to know.

Mada Seghete, Branch. Mobile Growth Trends

IT Arena

Julia Petryk is the Head of PR at MacPaw, a globally acclaimed developer from Ukraine. Julia has 10+ years’ experience in digital marketing and online communications. She currently oversees the MacPaw’s global PR strategy, leads a team of PR practitioners, and maintains the company’s online presence in Western media. Julia is a mentor for tech startups and an author of her own online course “Product PR”.

Julia Petryk, MacPaw. Product PR: a how-to guide

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Yaroslav has been working in IT industry since 2008, delivering more than 50 projects in different domains across the globe. In the last few years, he’s been cooperating with high profile clients to provide guidance and support on their organizations’ journey to digital transformation. He worked with the biggest retailers in North America and tech giants such as Cisco, Dell, Suse, and Canonical. His most significant recent project is Data Science Platform (ML) developed in collaboration with Dell, Canonical, SUSE, and Intel (officially announced on February 2020). Today, he is a head of the IT Advisory Group at the Intellias Technology Office. Speech Overview: We will discuss the reasons why Kubernetes won’t change a lot outside the infrastructure providers’ world and why it’s not the best option for you. Why is it more important to understand the concept behind this platform rather than use Kubernetes itself? How Kubernetes managed to become the platform for computation, while OpenStack didn’t? What other alternatives might work better for you? When to use and not to use Kubernetes?

Yaroslav Ravlinko, Intellias. You don’t need Kubernetes. You need to understa...

IT Arena

Yaroslav is in software development focusing on Cloud since before the Cloud. It is his work and hobby at the same time. Concepts, architecture, solutioning and hands-on implementation along with leadership, management, and processes were his responsibilities working and consulting in Canada, USA, Ukraine, Austria. Anton worked as Engineering Manager, leading 5 products at the same time. Took part in due-diligence, importing companies

Yaroslav Novytskyy, Anton Vasylenko, N-iX. Migrating to the cloud: options an...

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Kostiantyn Bokhan, a technical lead at N-IX, focuses on data science projects. He leads data science projects in several areas: Computer vision, NLP, and signal processing as well as consults clients regarding digital transformations with AI. When free, he conducts research in the deep machine learning area. Kostiantyn has been an associate professor and faculty member of several universities since 2002. His research focuses on machine learning, deep learning, signal, and image processing. He received a PhD degree in network and telecommunications systems with research in digital signal processing in 2013. He has served on the scientific committees and review boards of several conferences. Speech Overview: Applying machine learning to make business applications and services intelligent is more than just training models and serving them. It requires implementing end-to-end and continuously repeatable cycles of training, testing, deploying, monitoring, and operating the models. Continuous delivery for machine learning (CD4ML) is a technique that enables reliable end-to-end cycles of development, deploying, and monitoring machine learning models. There are a lot of tools and frameworks that can be used to implement CD4ML. One of them is Kubeflow. Our experience of using Kubeflow for implementing CD4ML for the manufacturing area based on Azure Kubernetes service will be described in this speech.

Kostiantyn Bokhan, N-iX. CD4ML based on Azure and Kubeflow

IT Arena

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