Stephen McHenry - Chanecellor of Site Reliability Engineering, Google

Woulda, Coulda, Shoulda The World of Tera, Peta & Exa Stephen McHenry Chancellor of Site Reliability Engineering April 22, 2009 Google Confidential and Proprietary

Overview •  Mission Statement •  Some History •  Planning for •  Failure •  Expansion •  Applications •  Infrastructure •  Hardware •  The Future Google Confidential and Proprietary

Google’s Mission To organize the world’s information and make it universally accessible and useful Google Confidential and Proprietary

Overview •  Mission Statement •  Some History •  Planning for •  Failure •  Expansion •  Applications •  Infrastructure •  Hardware •  The Future Google Confidential and Proprietary

Lego Disk Case One of our earliest storage systems Google Confidential and Proprietary

Peak of google.stanford.edu (circa 1997) Google Confidential and Proprietary

The Infamous “Corkboard” Google Confidential and Proprietary

Many Corkboards (1999) Google Confidential and Proprietary

A Data Center in 1999… Google Confidential and Proprietary

Another Data Center, Spring 2000 Note the Cooling Google Confidential and Proprietary

google.com (new data center 2001) Google Confidential and Proprietary

google.com (3 days later) Google Confidential and Proprietary

Current Data Center Google Confidential and Proprietary

Overview •  Mission Statement •  Some History •  The Challenge •  Planning for •  Failure •  Expansion •  Applications •  Infrastructure •  Hardware •  The Future Google Confidential and Proprietary

Just For Reference Terabyte – 1012 Bytes -1,000,000,000,000 Bytes Petabyte – 1015 Bytes – 1000 Terabytes 1,000,000,000,000,000 Bytes Exabyte – 1018 Bytes – 1 Million Terabytes 1,000,000,000,000,000,000 Bytes Zettabyte – 1021 Bytes – 1 Billion Terabytes 1,000,000,000,000,000,000,000 Bytes Yottabyte – 1024 Bytes – 1 Trillion Terabytes 1,000,000,000,000,000,000,000,000 Bytes Google Confidential and Proprietary

How much information is out there? How large is the Web? •  Tens of billions of documents? Hundreds? •  ~10KB/doc => 100s of Terabytes Then there’s everything else •  Email, personal files, closed databases, broadcast media, print, etc. Estimated 5 Exabytes/year (growing at 30%)* 800MB/year/person – ~90% in magnetic media Web is just a tiny starting point Source: How much information 2003 Google Confidential and Proprietary

Google takes its mission seriously Started with the Web (html) Added various document formats •  Images •  Commercial data: ads and shopping (Froogle) •  Enterprise (corporate data) •  News •  Email (Gmail) •  Scholarly publications •  Local information •  Maps •  Yellow pages •  Satellite images •  Instant messaging and VoIP •  Communities (Orkut) •  Printed media •  … Google Confidential and Proprietary

Ever-Increasing Computation Needs more Every Google service sees data continuing growth in computational needs •  More queries   More users, happier users more queries •  More data   Bigger web, mailbox, blog, etc. better results •  Better results   Find the right information, and find it faster Google Confidential and Proprietary

When Your Data Center Reaches 170o F o Google Confidential and Proprietary

The Joys of Real Hardware Typical first year for a new cluster: ~0.5 overheating (power down most machines in <5 mins, ~1-2 days to recover) ~1 PDU failure (~500-1000 machines suddenly disappear, ~6 hours to come back) ~1 rack-move (plenty of warning, ~500-1000 machines powered down, ~6 hours) ~1 network rewiring (rolling ~5% of machines down over 2-day span) ~20 rack failures (40-80 machines instantly disappear, 1-6 hours to get back) ~5 racks go wonky (40-80 machines see 50% packetloss) ~8 network maintenances (4 might cause ~30-minute random connectivity losses) ~12 router reloads (takes out DNS and external vips for a couple minutes) ~3 router failures (have to immediately pull traffic for an hour) ~dozens of minor 30-second blips for dns ~1000 individual machine failures ~thousands of hard drive failures slow disks, bad memory, misconfigured machines, flaky machines, etc. Google Confidential and Proprietary

Components of Web Search Crawling process Get link from Crawler (Spider): Expired pages list from index Fetch page   Collects the documents List of links to Parses page •  Tradeoff between size and speed to explore extract links Add URL •  High networking bandwidth requirements Add to queue •  Be gentle to serving hosts while doing it Indexer:   Generates the index - similar to the back of a book (but big!)   Requires several days on thousands of computers   More than 20 billion web documents •  Web, Images, News, Usenet messages, …   Pre-compute query-independent ranking (PageRank, etc) Query serving:   Processes user queries   Finding all relevant documents •  Search over tens of Terabytes, 1000s of times/second   Scoring - Mix of query dependent and independent factors Google Confidential and Proprietary

Google Query Serving Infrastructure Misc. servers query Spell checker Google Web Server Ad Server Doc servers Index servers I0 I1 I2 IN D0 D1 DM … … Replicas Replicas I0 I1 I2 IN D0 D1 DM … … I0 I1 I2 IN D0 D1 DM Doc shards Index shards Elapsed time: 0.25s, machines involved: 1000+ Google Confidential and Proprietary

Ads System As challenging as search •  But with some transactional semantics Problem: find useful ads based on what the user is interested in at that moment •  A form of mind reading Two systems •  Ads for search results pages (search for tires or restaurants) •  Ads for web browsing/email (or ‘content ads’)   Extract a contextual meaning from web pages   Do the same thing for data from a gazillion advertisers   Match those up and score them   Do it faster than the original content provider can respond to the web page! Google Confidential and Proprietary

Example: Sunday NY Times Google Confidential and Proprietary

Language Translation (by Machine) Information is more useful if more people can understand it Translation is a long-standing, challenging Artificial Intelligence problem Key insight: •  Transform it into a statistical modeling problem •  Train it with tons of data! Doubling training corpus size  Chinese-English Arabic-English ~0.5% higher score Google Confidential and Proprietary

Data + CPUs = Playground Substantial fraction of internet available for processing Easy-to-use teraflops/petabytes Cool problems, great fun… Google Confidential and Proprietary

Learning From Data Searching for Britney Spears… Google Confidential and Proprietary

Query Frequency Over Time Queries containing “eclipse” Queries containing “world series” Queries containing “full moon” Queries containing “summer olympics” Queries containing “watermelon” Queries containing “opteron” Google Confidential and Proprietary

WhiteHouse.gov/openforquestions Google Confidential and Proprietary

A Simple Challenge For Our Computing Platform 1.  Create the world’s largest computing infrastructure 2.  Make sure we can afford it Need to drive efficiency of the computing infrastructure to unprecedented levels   indices containing more documents   updated more often   faster queries   faster product development cycles   … Google Confidential and Proprietary

Systems Infrastructure Google File System (GFS) Map Reduce Big Table Google Confidential and Proprietary

GFS: Google File System Planning – For unprecedented quantities of data storage & failure(s) Google has unique FS requirements •  Huge read/write bandwidth •  Reliability over thousands of nodes •  Mostly operating on large data blocks •  Need efficient distributed operations GFS Usage @ Google •  Many clusters •  Filesystem clusters of up to 5000+ machines •  Pools of 10000+ clients •  5+ PB Filesystems •  40 GB/s read/write load in single cluster •  (in the presence of frequent HW failures) Google Confidential and Proprietary

GFS Setup Replicas Misc. servers GFS Master Client Masters GFS Master Client Client C1 C1 C0 C0 C5 … C2 C3 C2 C5 C5 Machine 2 Machine N Machine 1 •  Master manages metadata •  Data transfers happen directly between clients/ machines Google Confidential and Proprietary

MapReduce – Large Scale Processing Okay, GFS lets us store lots of data… now what? We need to process that data in new and interesting ways! •  Fast: locality optimization, optimized sorter, lots of tuning work done... •  Robust: handles machine failure, bad records, … •  Easy to use: little boilerplate, supports many formats, … •  Scalable: can easily add more machines to handle more data or reduce the run-time •  Widely applicable: can solve a broad range of problems •  Monitoring: status page, counters, … The Plan – Develop a robust compute infrastructure that allows rapid development of complex analyses, and is tolerant to failure(s) Google Confidential and Proprietary

MapReduce – Large Scale Processing MapReduce: •  a framework to simplify large-scale computations on large clusters •  Good for batch operations •  User writes two simple functions: map and reduce •  Underlying library/framework takes care of messy details •  Greatly simplifies large, distributed data processing Lots of uses inside Google Ads Sawmill (Logs Analysis) Froogle Search My History Google Earth Search quality Google Local Spelling Google News Web search indexing Google Print …many other internal projects ... Machine Translation Google Confidential and Proprietary

Large Scale Processing – (semi) Structured Data Why not just use commercial DB? •  Scale is too large for most commercial databases •  Even if it weren’t, cost would be very high   Building internally means system can be applied across many projects for low incremental cost •  Low-level storage optimizations help performance significantly   Much harder to do when running on top of a database layer Okay, traditional relational databases are woefully inadequate at this scale… now what? The Plan – Build a large scale, distributed solution for semi- structured data, that is resistant to failure(s) Google Confidential and Proprietary

Large Scale Processing – (semi) Structured Data BigTable: •  A large-scale storage system for semi-structured data •  Database-like model, but data stored on thousands of machines.. •  Fault-tolerant, persistent •  Scalable Thousands of servers   Terabytes of in-memory data   Petabytes of disk-based data   Millions of reads/writes per second, efficient scans   billions of URLs, many versions/page (~20K/version)   Hundreds of millions of users, thousands of queries/sec   100TB+ of satellite image data   •  Self-managing   Servers can be added/removed dynamically   Servers adjust to load imbalance •  Design/initial implementation started beginning of 2004 Google Confidential and Proprietary

BigTable Usage Useful for structured/semi-structured data   URLs - Contents, crawl metadata, links, anchors, pagerank, …   Per-user data - User preference settings, recent queries/search results, …   Geographic data - Physical entities, roads, satellite imagery, annotations, … Production use or active development for ~70 projects: Google Print   My Search History   Orkut   Crawling/indexing pipeline   Google Maps/Google Earth   Blogger   …   Currently ~500 BigTable cells Largest bigtable cell manages ~3000TB of data spread over several thousand machines (larger cells planned) Google Confidential and Proprietary

A Simple Challenge For Our Computing Platform 1.  Create the world’s largest computing infrastructure 2.  Make sure we can afford it Need to drive efficiency of the computing infrastructure to unprecedented levels   indices containing more documents   updated more often   faster queries   faster product development cycles   … Google Confidential and Proprietary

Innovative Solutions Needed In Several Areas Server design and architecture Power efficiency System software Large scale networking Performance tuning and optimization System management and repairs automation Google Confidential and Proprietary

Pictorial History •  Brainstorming Circa 2003 •  Container-based data centers •  Battery per server instead of traditional UPS 99.9% efficient backup power! o  •  Application of best practices leads to PUE below 1.2 Google Confidential and Proprietary

Pictorial History Prototype arriving at Google, Jan 2005 Google Confidential and Proprietary

Pictorial History The first crane was too small -- Take 2 Google Confidential and Proprietary

Pictorial History Google prototypes first airborne data center Google Confidential and Proprietary

Pictorial History And into the parking garage we go Google Confidential and Proprietary

Data Center Vitals •  Capacity: 10 MW IT load •  Area: 75000 sq ft total under roof •  Overall power density: 133W/sq ft •  Prototype container delivered January 2005 •  Data center built 2004-2005 •  Construction completed September, 2005 •  Went live November 21, 2005 Google Confidential and Proprietary

Additional Vitals •  45 containers, approx. 40000 servers •  Single and 2-story on facing sides of hangar •  Bridge crane for container handling Google Confidential and Proprietary

Planning for the Future •  Manage Total Cost of Ownership •  Reduce Water Usage •  Reduce Power Consumption •  Manage E-Waste Google Confidential and Proprietary

Total Cost of Ownership - TCO Earnings and sustainability are (often) aligned •  Careful application of best practices leads to much lower energy use which leads to lower TCO for facilities – Examples: Manage air flow - avoid hot/cold mixing o  Raise the inlet temperature o  Use free cooling (Belgium has no o  chillers!) Optimize power distribution o  •  Don't need exotic technologies •  But: need to break down traditional silos Between capex and opex o  Between facilities and IT o  Manage everyone by impact on TCO o  Google Confidential and Proprietary

Water resources management is the next \"elephant in the room\" we are all going to have to address. Google Confidential and Proprietary

A Great Wave Rising: The coming U.S. crisis in water policy Lake Powell 53% full (from ESPN!) Shasta Lake Google Confidential and Proprietary

Lake Mead water could dry up by 2021* Lake Mead historical levels Lake Mead - 45% full * Scripps Institution of Oceanography, UCSD, Feb 2008. Lake Oroville - new docks Google Confidential and Proprietary

Georgia’s Lake Lanier March 4, 2007 February 11, 2008 Google Confidential and Proprietary

Lake Hartwell, GA – November 2008 Google Confidential and Proprietary

Water – The Next “Big Elephant” Why? •  Water resources are becoming (a lot) scarcer and more variable How do data centers fit in? •  For every 10 MW consumed, the average data center uses ~150,000 gallons of water per day for cooling. •  Upstream of the data center, the same 10 MW of delivered power consumes 480,000 gallons of water per day to generate that power. References: U.S. Dept. of Energy – Energy Demands On Water Resources – Dec., 2006 National Renewable Energy Laboratory - Consumptive Water Use for U.S. Power Production - Dec., 2003 USGS - Water Use At Home - Jan., 2009 Google Confidential and Proprietary

Water Consumption (gpd) by DC Type Factoid: The typical 'water-less' DC uses about a third more water than the evaporatively cooled Google DC Using less power is the most significant factor for reducing water consumption Google Confidential and Proprietary

Water Recycling: Our data center in St. Ghislain, Belgium Google's data center in Belgium uses 100% reclaimed water from an industrial canal Google Confidential and Proprietary

Power - Cutting waste / Smarter computing Fact: The typical PC wastes half the electricity it uses Fact: Over 60% of all corporate PCs are left on overnight ________________________________________________ •  End-user devices are the largest portion of IT footprint •  Power efficiency is critical as billions of devices are deployed •  The technology exists today to save energy and money Buy power efficient laptops / PCs / servers Google saves $30 per server every year Enable power management Power management suites: ROI < 1 year Transition to lightweight devices Reduce power from 150W to less than 5W Potential: 50% emissions reduction Google Confidential and Proprietary

E-waste is a Growing Problem •  Hazardous •  High volume because of obsolescence •  Ubiquitous (computers, appliances, consumer electronics, cell phones) Solutions •  4 R's: Reduce, reuse, repair, recycle •  Dispose of remainder responsibly Google Confidential and Proprietary

Thank you! Google Confidential and Proprietary

Stephen McHenry - Chanecellor of Site Reliability Engineering, Google

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