Blog
The latest news from Google AI
Site Reliability Engineers: “solving the most interesting problems”
Wednesday, July 25, 2012
Posted by Chris Reid, Sydney Staffing team
I recently sat down with Ben Appleton, a Senior Staff Software Engineer, to talk about his recent move from Software Engineer (SWE) on the Maps team to Site Reliability Engineering (SRE). In the interview, Ben explains why he transitioned from a pure development role to a role in production, and how his work has changed:
Chris
: Tell us about your path to Google.
Ben
: Before I joined Google I didn’t consider myself a “software engineer”. I went to the University of Queensland and graduated with a Bachelor’s Degree in Electrical Engineering and Mathematics, before going on to complete a Ph.D. My field of research was image segmentation, extending graph cuts to continuous space for analyzing X-rays and MRIs. At a conference in France I met a friend of my Ph.D. advisor’s, and he raved about Google, commenting that they were one of the only companies that really understood technology. I’d already decided academia wasn’t for me, so I interviewed for a general Software Engineering role at Google. I enjoyed the interviews, met some really smart people, and learned about some interesting stuff they were working on. I joined the Maps team in Sydney in 2005 and spent the next 6 years working on the
Maps API
.
Chris
: Tell us about some of the coolest work you did for Google Maps, and how you applied your research background.
Ben
: My background in algorithms and computational geometry was really useful. We were basically making browsers do stuff they’re not designed to do, such as rendering millions of vectors or warping images, inventing techniques as we went. On the server-side we focused on content distribution, pushing tiles or vectors from Google servers down through caches to the user’s browser, optimizing for load and latency at every stage. On the client-side, we had to make the most of limited processors with new geometric algorithms and clever prefetching to hide network latency. It was really interesting work.
Chris
: I understand you received company-wide recognition when you were managing the Maps API team. Tell us more about what that entailed.
Ben
: In September 2008, when managing the Maps API, my team received an award that was recognized Google-wide, which is a big honor. My main contributions were latency optimizations, stability, enterprise support, and Street View integration. The award was in recognition of strong sustained growth of the Maps API, in relation to the number of sites using it, and total views per day. Currently the Google Maps API is serving more than 600,000 websites.
Chris
: So what prompted the move to Site Reliability Engineering (SRE)?
Ben
: In my experience, a lot of software engineers don’t understand what SREs do. I’d worked closely with SREs, particularly those in Sydney supporting Maps, and had formed a high opinion of them. They’re a very strong team - they’re smart and they get things done. After 6 years working on the Maps API I felt it was time for a change. In Sydney there are SWE teams covering most of the product areas, including Chrome and Apps, Social and Blogger, Infrastructure Networking and the Go programming language, as well as Maps and GeoCommerce. I talked to all of them, but chose SRE because in my opinion, they’re solving the most interesting problems.
Chris
: How would you describe SRE?
Ben
: It really depends on the individual. At one end are the Systems Administrator types, sustaining ridiculously large systems. But at the other end are the Software Engineers like me. As SREs get more experienced this distinction tends to be blurred. The best SREs think programmatically even if they don’t do the programming. For me, I don’t see a difference in my day-to-day role. When I was working on the Maps API I was the primary on-call one week in three, whereas in SRE the typical on-call roster is one week in six. When you’re primary on-call it just means you’re the go-to person for the team, responsible for when something breaks or pushing new code into production. I was spending 50% of my time doing coding and development work, and as an SRE this has increased to 80%.
Chris
: Wow! So as an SRE in Production, you’re spending less time on-call and more time writing code than you were as a SWE on the Maps team?
Ben
: Yes! I’m not managing a team now, but I’m definitely spending more time coding than I was before. I guess the average SRE spends 50% of their time doing development work, but as I said, it depends on the person and it ranges from 20-80%.
Chris
: What does your team do?
Ben
: In Sydney there are SRE teams supporting Maps, Blogger, App Engine, as well as various parts of the infrastructure and storage systems. I’m working on Blobstore, an infrastructure storage service based on Bigtable which simplifies building and deploying applications that store users' binary data (BLOBs, or "Binary Large OBjects"). Example BLOBs include images, videos, or email attachments - any data objects that are immutable and long-lived. The fact that we're storing user data means that Blobstore must be highly available for reads and writes, be extremely reliable (so that we never lose data), and be efficient in terms of storage usage (so that we can provide large amounts of storage to users at low cost).
Chris
: Tell us more about some of the problems you’re solving, and how they differ with those you faced as a SWE in a development role.
Ben
: With the massive expansion in online data storage, we’re solving problems at a scale never before seen. Due to the global nature of our infrastructure, we think in terms of load balancing at many levels: across regions, across data centers within a region, and across machines within a data center. The problems we’re facing in SRE are much closer to the metal. We’re constantly optimizing resource allocation and efficiency and scalability of Google’s massive computer systems, as opposed to developing new features for a product like Maps. So the nature of the work is very similar to SWE, but the problems are bigger and there is a strong focus on scale.
Chris
: Are you planning on staying in SRE for a while?
Ben
: Yeah. I signed up for a six month rotation program called “Mission Control,” the goal of which is to teach engineers to understand the challenges of building and operating a high reliability service at Google scale. In other words, it’s an SRE training program. In my first three months of Mission Control I’ve been on-call twice, and always during office hours so there were SREs to help me when I got into trouble...which I did. I’ve got no intention of going back to SWE at the end of the six months and plan to stay in SRE for at least a few years. Right now the problems seem more interesting. For example, last year’s storage solutions are facing additional strain from the growth of Gmail, Google+ and Google Drive. So you’re constantly reinventing.
Chris
: What advice do you have for Software Engineers contemplating a role in SRE?
Ben
: SRE gives you the opportunity to work on infrastructure at a really big scale in a way you don’t get to in SWE. Whereas SWE is more about developing new features, SRE is dealing with bigger problems and more complex engineering due to the sheer scale. SRE is a great way to learn how systems really work in order to become a great engineer.
If you’re interesting in applying for a Site Reliability Engineering role, please note that we advertise the roles in several different ways to reflect the diversity of the team. The two main roles are “Software Engineer, Google.com” and “Systems Engineer, Google.com”. We use the term “Google.com” to signify that the roles are in Production as opposed to R&D. You can find all the openings listed on the
Google jobs site
. We’re currently hiring across many regions, including Sydney in Australia, and of course Mountain View in California.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2020
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
