Google data centers

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Former Google data center in Eemshaven, Netherlands Google datacenter (2007) - panoramio - erwinboogert (2).jpg
Former Google data center in Eemshaven, Netherlands
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Google data centers are the large data center facilities Google uses to provide their services, which combine large drives, computer nodes organized in aisles of racks, internal and external networking, environmental controls (mainly cooling and humidification control), and operations software (especially as concerns load balancing and fault tolerance).

Contents

There is no official data on how many servers are in Google data centers, but Gartner estimated in a July 2016 report that Google at the time had 2.5 million servers. This number is changing as the company expands capacity and refreshes its hardware. [1]

Locations

The locations of Google's various data centers by continent are as follows: [2] [3]

ContinentLocationGeoProducts LocationCloud LocationTimelineDescription
North America Arcola (VA), USA 38°56′35.99″N77°31′27.61″W / 38.9433306°N 77.5243361°W / 38.9433306; -77.5243361 Loudoun County N. Virginia (us-east4)2017 - announced [4] [5]
North America Atlanta (GA), USA 33°44′59.04″N84°35′5.33″W / 33.7497333°N 84.5848139°W / 33.7497333; -84.5848139 Douglas County -2003 - launched350 employees
South America Cerrillos, Santiago, Chile 33°31′14″S70°43′18″W / 33.520515°S 70.721695°W / -33.520515; -70.721695 [6] -Santiago (southamerica-west1)2020 - announced [7]

2021 - launched [8]

Asia Changhua County, Taiwan 24°08′18.6″N120°25′32.6″E / 24.138500°N 120.425722°E / 24.138500; 120.425722 Changhua County Taiwan

(asia-east1)

2011 - announced

2013 - launched

60 employees
North America Clarksville (TN), USA 36°37′16″N87°15′47″W / 36.6211599°N 87.2630735°W / 36.6211599; -87.2630735 Montgomery County -2015 - announced
North America Columbus (OH), USA -Columbus (us-east5)2022 - launched [9]
North America Council Bluffs (IA), USA 41°13′17.7″N95°51′49.92″W / 41.221583°N 95.8638667°W / 41.221583; -95.8638667 Council Bluffs 2007 - announced

2009 - completed first phase completed

2012 and 2015 - expanded

130 employees
North America Council Bluffs (IA), USA 41°10′06″N95°47′46″W / 41.1682533°N 95.7961246°W / 41.1682533; -95.7961246 Iowa (us-central1)
Asia Delhi, India -Delhi (asia-south2)2020 - announced

2021 - launched [10]

Middle East Doha, Qatar -Doha (me-central1)2023 - launched [11]
Europe Dublin, Ireland 53°19′12.39″N6°26′31.43″W / 53.3201083°N 6.4420639°W / 53.3201083; -6.4420639 Dublin -2011 - announced

2012 - launched

150 employees [12]
Europe Eemshaven, Netherlands 53°25′31″N6°51′44″E / 53.4252171°N 6.8622574°E / 53.4252171; 6.8622574 Eemshaven Netherlands (europe-west4)2014 - announced

2016 - launched

2018, 2019 - expansion

200 employees
Europe Frankfurt, Germany 50°07′21″N8°58′27″E / 50.1226299°N 8.9741678°E / 50.1226299; 8.9741678 [13] -Frankfurt (europe-west3)2022 - expanded [14]
Europe Fredericia, Denmark 55°33′29.5″N9°39′20.8″E / 55.558194°N 9.655778°E / 55.558194; 9.655778 Fredericia -2018 - announced [15]

2020 - launched

€600M building costs
Europe Ghlin, Hainaut, Belgium 50°28′09.6″N3°51′55.7″E / 50.469333°N 3.865472°E / 50.469333; 3.865472 Saint-Ghislain Belgium (europe-west1)2007 - announced

2010 - launched

12 employees
Europe Hamina, Finland 60°32′11.68″N27°7′1.21″E / 60.5365778°N 27.1170028°E / 60.5365778; 27.1170028 Hamina Finland

(europe-north1)

2009 - announced

2011 - first phase completed

2022 - expansion

6 buildings, 400 employees [16]
North America Henderson (NV), USA 36°03′20″N115°00′37″W / 36.0556248°N 115.0102258°W / 36.0556248; -115.0102258 Henderson Las Vegas (us-west4)2019 - announced [17]

2020 - launched

64-acres; $1.2B building costs [18] [19]
Asia Hong Kong, Hong Kong -Hong Kong (asia-east2)2017 - announced [20]

2018 - launched [21]

Asia Inzai, Japan 35°49′04″N140°07′57″E / 35.8177804°N 140.1323916°E / 35.8177804; 140.1323916 Inzai -2023 - launched
Asia Jakarta, Indonesia -Jakarta (asia-southeast2)2020 - launched [22]
Asia Koto-Ku, Tokyo, Japan -Tokyo

(asia-northeast1)

2016 - launched [23]
North America Leesburg (VA), USA 39°3′6.38″N77°32′20.38″W / 39.0517722°N 77.5389944°W / 39.0517722; -77.5389944 Loudoun County N. Virginia (us-east4)2017 - announced [4] [5]
North America Lenoir (NC), USA 35°53′54.78″N81°32′50.58″W / 35.8985500°N 81.5473833°W / 35.8985500; -81.5473833 Lenoir -2007 - announced

2009 - launched

over 110 employees
AsiaLok Yang Way, Pioneer, Singapore 1°19′26″N103°41′36″E / 1.3239859°N 103.693253°E / 1.3239859; 103.693253 [24] Singapore Singapore (asia-southeast1)2022 - launched
Europe London, UK -London

(europe-west2)

2017 - launched [25]
North America Los Angeles (CA), USA -Los Angeles (us-west2)
Europe Madrid, Spain 40°31′10″N3°20′27″W / 40.5195333°N 3.3409366°W / 40.5195333; -3.3409366 -Madrid (europe-southwest1)2022 - launched [26]
Pacific Melbourne, Australia -Melbourne

(australia-southeast2)

2021 - launched [27]
Europe Middenmeer, Noord-Holland, The Netherlands 52°47′24″N5°01′45″E / 52.7901053°N 5.0292193°E / 52.7901053; 5.0292193 [28] Middenmeer Netherlands (europe-west4)2019 - announced [29]
North America Midlothian (TX), USA 32°26′35″N97°03′44″W / 32.443170°N 97.062324°W / 32.443170; -97.062324 Midlothian Dallas (us-south1)2019 - announced

2022 - launched [30]

375-acres; $600M building costs [31]
Europe Milan, Italy -Milan (europe-west8)2022 - launched [32]
North America Moncks Corner (SC), USA 33°03′50.8″N80°02′36.1″W / 33.064111°N 80.043361°W / 33.064111; -80.043361 Berkeley County South Carolina (us-east1)2007 - launched

2013 - expanded

150 employees
North America Montreal, Quebec, Canada [33] -Montréal (northamerica-northeast1)2018 - launched [34] 62.4-hectares; $600M building costs [35]
Asia Mumbai, India -Mumbai (asia-south1)2017 - launched [36]
North America New Albany (OH), USA 40°03′41″N82°45′31″W / 40.0613352°N 82.7586278°W / 40.0613352; -82.7586278 New Albany -2019 - announced400-acres; $600M building costs [37] [38]
Asia Osaka, Japan -Osaka

(asia-northeast2)

2019 - launched [39]
South America Osasco, São Paulo, Brazil -São Paulo (southamerica-east1)2017 - launched [40]
North America Papillion (NE), USA 41°08′00″N96°08′39″W / 41.1332915°N 96.144178°W / 41.1332915; -96.144178 Papillion -2019 - announced275-acres; $600M building costs [41] [42]
Europe Paris, France -Paris (europe-west9)2022 - launched [43]
North America Pryor Creek (OK), USA 36°14′28.1″N95°19′48.22″W / 36.241139°N 95.3300611°W / 36.241139; -95.3300611 Mayes County -2007 - announced

2012 - expanded

over 400 employees, [44] land at MidAmerica Industrial Park
South America Quilicura, Santiago, Chile 33°21′30.5″S70°41′50.4″W / 33.358472°S 70.697333°W / -33.358472; -70.697333 Quilicura -2012 - announced

2015 - launched

up to 20 employees expected. A million dollar investment plan to increase capacity at Quilicura was announced in 2018. [45]
North America Reno (NV), USA 39°30′04″N119°25′46″W / 39.5011558°N 119.4295537°W / 39.5011558; -119.4295537 Storey County -2017 - 1,210 acres of land bought in the Tahoe Reno Industrial Center [46]

2018 - announced

2018 November - project approved by the state of Nevada [47] [48]

North America Salt Lake City (UT), USA -Salt Lake City (us-west3)2020 - launched [49]
Asia Seoul, South Korea -Seoul

(asia-northeast3)

2020 - launched [50]
Pacific Sydney, Australia -Sydney

(australia-southeast1)

2017 - launched [51]
Middle East Tel Aviv, Israel [52] -Tel Aviv (me-west1)2022 - launched [53]
North America The Dalles (OR), USA 45°37′57.04″N121°12′8.16″W / 45.6325111°N 121.2022667°W / 45.6325111; -121.2022667 The Dalles Oregon (us-west1)2006 - launched80 full-time employees
North America Toronto, Canada -Toronto (northamerica-northeast2)2021 - launched [54]
Europe Turin, Italy 45°08′48″N7°44′32″E / 45.146729°N 7.742147°E / 45.146729; 7.742147 -Turin (europe-west12)2023 - launched [55]
South America Vinhedo, São Paulo, Brazil São Paulo (southamerica-east1)
Europe Warsaw, Poland -Warsaw (europe-central2)2019 - announced

2021 - launched [56]

Asia Wenya, Jurong West, Singapore 1°21′04.8″N103°42′35.2″E / 1.351333°N 103.709778°E / 1.351333; 103.709778 Singapore Singapore (asia-southeast1)2011 - announced

2013 - launched

2015 - expanded

North America Widows Creek (Bridgeport) (AL), USA 34°54′48.4″N85°44′53.1″W / 34.913444°N 85.748083°W / 34.913444; -85.748083 [57] Jackson County -2018 - broke ground
Europe Zürich, Switzerland 47°26′45″N8°12′39″E / 47.4459257°N 8.2109085°E / 47.4459257; 8.2109085 [58] -Zurich (europe-west6)2018 - announced

2019 - launched [59]

Europe Austria 2022 - announced [60]
Europe Berlin, Germany [61] Berlin (europe-west10)2021 - announced [62] 2023 August - launched [63]
Middle East Dammam, Saudi Arabia 2021 - announced [64]
Europe Athens, Greece 2022 - announced [60]
North America Kansas City, Missouri 2019 - announced [65]
Middle East Kuwait 2023 - announced [66]
Asia Malaysia 2022 - announced [67]
Pacific Auckland, New Zealand 2022 - announced [67]
Europe Oslo, Norway 2022 - announced [60]
North America Querétaro, Mexico 2022 - announced [68]
Africa Johannesburg, South Africa Johannesburg (africa-south1)2022 - announced [60] 2024 - launched
Europe Sweden 2022 - announced [60]
Asia Tainan City, Taiwan -Taiwan

(asia-east1)

2019 September - announced [69] [70] [71]
Asia Thailand 2022 - announced [67]
Asia Yunlin County, Taiwan -Taiwan (asia-east1)2020 September - announced [72]
North America Mesa (AZ), USA 2023 - construction started [73]
Europe Waltham Cross, Hertfordshire, UK 51°41′44″N0°02′55″W / 51.695666°N 0.048728°W / 51.695666; -0.048728 2024 January - announced [74]
South America Canelones, Uruguay 34°48′56″S55°59′44″W / 34.815481°S 55.995683°W / -34.815481; -55.995683 2024 - construction started [75]

2026 - inauguration expected [76]

Hardware

Original hardware

Google's first production server rack, circa 1998 Googles First Server.jpg
Google's first production server rack, circa 1998

The original hardware (circa 1998) that was used by Google when it was located at Stanford University included: [77]

Google Cluster

The state of Google infrastructure in 2003 was described in a report by Luiz André Barroso, Jeff Dean, and Urs Hölzle as a "reliable computing infrastructure from clusters of unreliable commodity PCs". [78]

On average, a single search query requires reads ~100 MB of data, and consumes CPU cycles. During peak time, Google serves ~1000 queries per second. To handle this peak load, they built a compute cluster with ~15,000 commodity-class PCs instead of expensive supercomputer hardware to save money. To make up for the lower hardware reliability, they wrote fault tolerant software.

The structure of the cluster consists of 5 parts. Central Google Web servers (GWS) face the public Internet. Upon receiving a user request, the Google Web server communicates with a spell checker, an advertisement server, many index servers, many document servers. Each of the 4 parts responds to a part of the request, and the GWS assembles their responses and serves the final response to the user.

The raw documents were ~100 TB, and the index files were ~10 TB. The index files are sharded, and each shard is served by a "pool" of index servers. Similarly, the raw documents are also sharded. Each query to the index file results in a list of document IDs, which are then sent to the document servers to retrieve the title and the keyword-in-context snippets.

There were several CPU generations in use, ranging from single-processor 533MHz Intel-Celeron-based servers to dual 1.4GHz Intel Pentium III. Each server contains one or more hard drives, 80 GB each. Index servers have less disk space than document servers. Each rack has two Ethernet switches, one per side. The servers on each side interconnect via a 100-Mbps. Each switch has a ~250 MB/sec uplink to a central switch that connects to all racks.

The design objectives include:

Due to the massive parallelism, scaling up hardware scales up the thoroughput linearly, i.e. doubling the compute cluster doubles the number of queries servable per second.

The cluster is made of server racks at 2 configurations: 40 x 1u per side with 2 sides, or 20 x 2u per side with 2 sides. The power consumption is 10 kW per rack, at a density of 400 W/ft^2, consuming 10 MWh per month, costing $1,500 per month.

Production hardware

As of 2014, Google has used a heavily customized version of Debian Linux. They migrated from a Red Hat-based system incrementally in 2013. [79]

The customization goal is to purchase CPU generations that offer the best performance per dollar, not absolute performance. How this is measured is unclear, but it is likely to incorporate running costs of the entire server, and CPU power consumption could be a significant factor. [80] Servers as of 2009–2010 consisted of custom-made open-top systems containing two processors (each with several cores [81] ), a considerable amount of RAM spread over 8 DIMM slots housing double-height DIMMs, and at least two SATA hard disk drives connected through a non-standard ATX-sized power supply unit. [82] The servers were open top so more servers could fit into a rack. According to CNET and a book by John Hennessy, each server had a novel 12-volt battery to reduce costs and improve power efficiency. [81] [83]

According to Google, their global data center operation electrical power ranges between 500 and 681 megawatts. [84] [85] The combined processing power of these servers might have reached from 20 to 100 petaflops in 2008. [86]

Network topology

Details of the Google worldwide private networks are not publicly available, but Google publications [87] [88] make references to the "Atlas Top 10" report that ranks Google as the third largest ISP behind Level 3.

In order to run such a large network, with direct connections to as many ISPs as possible at the lowest possible cost, Google has a very open peering policy. [89]

From this site, we can see that the Google network can be accessed from 67 public exchange points and 69 different locations across the world. As of May 2012, Google had 882 Gbit/s of public connectivity (not counting private peering agreements that Google has with the largest ISPs). This public network is used to distribute content to Google users as well as to crawl the internet to build its search indexes. The private side of the network is a secret, but a recent disclosure from Google [90] indicate that they use custom built high-radixswitch-routers (with a capacity of 128 × 10 Gigabit Ethernet port) for the wide area network. Running no less than two routers per datacenter (for redundancy) we can conclude that the Google network scales in the terabit per second range (with two fully loaded routers the bi-sectional bandwidth amount to 1,280 Gbit/s).

These custom switch-routers are connected to DWDM devices to interconnect data centers and point of presences (PoP) via dark fiber.

From a datacenter view, the network starts at the rack level, where 19-inch racks are custom-made and contain 40 to 80 servers (20 to 40 1U servers on either side, while new servers are 2U rackmount systems. [91] Each rack has an Ethernet switch). Servers are connected via a 1 Gbit/s Ethernet link to the top of rack switch (TOR). TOR switches are then connected to a gigabit cluster switch using multiple gigabit or ten gigabit uplinks. [92] The cluster switches themselves are interconnected and form the datacenter interconnect fabric (most likely using a dragonfly design rather than a classic butterfly or flattened butterfly layout [93] ).

From an operation standpoint, when a client computer attempts to connect to Google, several DNS servers resolve www.google.com into multiple IP addresses via Round Robin policy. Furthermore, this acts as the first level of load balancing and directs the client to different Google clusters. A Google cluster has thousands of servers, and once the client has connected to the server additional load balancing is done to send the queries to the least loaded web server. This makes Google one of the largest and most complex content delivery networks. [94]

Google has numerous data centers scattered around the world. At least 12 significant Google data center installations are located in the United States. The largest known centers are located in The Dalles, Oregon; Atlanta, Georgia; Reston, Virginia; Lenoir, North Carolina; and Moncks Corner, South Carolina. [95] In Europe, the largest known centers are in Eemshaven and Groningen in the Netherlands and Mons, Belgium. [95] Google's Oceania Data Center is located in Sydney, Australia. [96]

Data center network topology

To support fault tolerance, increase the scale of data centers and accommodate low-radix switches, Google has adopted various modified Clos topologies in the past. [97]

Project 02

Google data center in The Dalles, Oregon Google data center.jpg
Google data center in The Dalles, Oregon

One of the largest Google data centers is located in the town of The Dalles, Oregon, on the Columbia River, approximately 80 miles (129 km) from Portland. Codenamed "Project 02", the complex was built in 2006 and is approximately the size of two American football fields, with cooling towers four stories high. [98] [99] The site was chosen to take advantage of inexpensive hydroelectric power, and to tap into the region's large surplus of fiber optic cable, a remnant of the dot-com boom. A blueprint of the site appeared in 2008. [100]

Summa papermill

In February 2009, Stora Enso announced that they had sold the Summa paper mill in Hamina, Finland to Google for 40 million Euros. [101] [102] Google invested 200 million euros on the site to build a data center and announced additional 150 million euro investment in 2012. [103] [104] Google chose this location due to the availability and proximity of renewable energy sources. [105]

Floating data centers

In 2013, the press revealed the existence of Google's floating data centers along the coasts of the states of California (Treasure Island's Building 3) and Maine. The development project was maintained under tight secrecy. The data centers are 250 feet long, 72 feet wide, 16 feet deep. The patent for an in-ocean data center cooling technology was bought by Google in 2009 [106] [107] (along with a wave-powered ship-based data center patent in 2008 [108] [109] ). Shortly thereafter, Google declared that the two massive and secretly-built infrastructures were merely "interactive learning centers, [...] a space where people can learn about new technology." [110]

Google halted work on the barges in late 2013 and began selling off the barges in 2014. [111] [112]

Software

Most of the software stack that Google uses on their servers was developed in-house. [113] According to a well-known former Google employee in 2006, C++, Java, Python and (more recently) Go are favored over other programming languages. [114] For example, the back end of Gmail is written in Java and the back end of Google Search is written in C++. [115] Google has acknowledged that Python has played an important role from the beginning, and that it continues to do so as the system grows and evolves. [116]

The software that runs the Google infrastructure includes: [117]

Google has developed several abstractions which it uses for storing most of its data: [125]

Software development practices

Most operations are read-only. When an update is required, queries are redirected to other servers, so as to simplify consistency issues. Queries are divided into sub-queries, where those sub-queries may be sent to different ducts in parallel, thus reducing the latency time. [91]

To lessen the effects of unavoidable hardware failure, software is designed to be fault tolerant. Thus, when a system goes down, data is still available on other servers, which increases reliability.

Search infrastructure

Google data center in The Dalles, Oregon Google Data Center, The Dalles.jpg
Google data center in The Dalles, Oregon

Index

Like most search engines, Google indexes documents by building a data structure known as inverted index. Such an index obtains a list of documents by a query word. The index is very large due to the number of documents stored in the servers. [94]

The index is partitioned by document IDs into many pieces called shards. Each shard is replicated onto multiple servers. Initially, the index was being served from hard disk drives, as is done in traditional information retrieval (IR) systems. Google dealt with the increasing query volume by increasing number of replicas of each shard and thus increasing number of servers. Soon they found that they had enough servers to keep a copy of the whole index in main memory (although with low replication or no replication at all), and in early 2001 Google switched to an in-memory index system. This switch "radically changed many design parameters" of their search system, and allowed for a significant increase in throughput and a large decrease in latency of queries. [130]

In June 2010, Google rolled out a next-generation indexing and serving system called "Caffeine" which can continuously crawl and update the search index. Previously, Google updated its search index in batches using a series of MapReduce jobs. The index was separated into several layers, some of which were updated faster than the others, and the main layer wouldn't be updated for as long as two weeks. With Caffeine, the entire index is updated incrementally on a continuous basis. Later Google revealed a distributed data processing system called "Percolator" [131] which is said to be the basis of Caffeine indexing system. [123] [132]

Server types

Google's server infrastructure is divided into several types, each assigned to a different purpose: [91] [94] [133] [134] [135]

There are also "canary requests", whereby a request is first sent to one or two leaf servers to see if the response time is reasonable. If not, then the request fails. This provides security. [136]

Security

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In October 2013, The Washington Post reported that the U.S. National Security Agency intercepted communications between Google's data centers, as part of a program named MUSCULAR. [137] [138] This wiretapping was made possible because, at the time, Google did not encrypt data passed inside its own network. [139] This was rectified when Google began encrypting data sent between data centers in 2013. [140]

Environmental impact

Google data center in Mayes County, Oklahoma at MidAmerica Industrial Park Google Mayes County P0004991a.jpg
Google data center in Mayes County, Oklahoma at MidAmerica Industrial Park

Google's most efficient data center runs at 35 °C (95 °F) using only fresh air cooling, requiring no electrically powered air conditioning. [141]

In December 2016, Google announced that—starting in 2017—it would purchase enough renewable energy to match 100% of the energy usage of its data centers and offices. The commitment will make Google "the world's largest corporate buyer of renewable power, with commitments reaching 2.6 gigawatts (2,600 megawatts) of wind and solar energy". [142] [143] [144]

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SAP HANA is an in-memory, column-oriented, relational database management system developed and marketed by SAP SE. Its primary function as the software running a database server is to store and retrieve data as requested by the applications. In addition, it performs advanced analytics and includes extract, transform, load (ETL) capabilities as well as an application server.

Google Cloud Platform (GCP) is a suite of cloud computing services offered by Google that provides a series of modular cloud services including computing, data storage, data analytics, and machine learning, alongside a set of management tools. It runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search, Gmail, and Google Docs, according to Verma et al. Registration requires a credit card or bank account details.

In computing, energy proportionality is a measure of the relationship between power consumed in a computer system, and the rate at which useful work is done. If the overall power consumption is proportional to the computer's utilization, then the machine is said to be energy proportional. Equivalently stated, for an idealized energy proportional computer, the overall energy per operation is constant for all possible workloads and operating conditions.

The history of Microsoft SQL Server begins with the first Microsoft SQL Server database product – SQL Server v1.0, a 16-bit relational database for the OS/2 operating system, released in 1989.

<span class="mw-page-title-main">Swisscows</span> Web search engine operated by Hulbee AG

Swisscows is a web search engine launched in 2014, a project of Hulbee AG, a company based in Egnach, Switzerland. It uses semantic data recognition that gives faster answers to queries and claims to not store users' data. Swisscows also deems itself family-friendly, with explicit results entirely omitted. The engine's servers are based in underground data centers under the Swiss Alps, and geographically outside of EU and US.

RavenDB is an open-source document-oriented database written in C#, developed by Hibernating Rhinos Ltd. It is cross-platform, supported on Windows, Linux, and Mac OS. RavenDB stores data as JSON documents and can be deployed in distributed clusters with master-master replication.

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