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Google data centers
Google uses large data center facilities 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).
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.
The locations of Google's various data centers by continent are as follows:
The original hardware (circa 1998) that was used by Google when it was located at Stanford University included:
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".
At the time, on average, a single search query read ~100 MB of data, and consumed CPU cycles. During peak time, Google served ~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 five 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 four 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.
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Google data centers
Google uses large data center facilities 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).
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.
The locations of Google's various data centers by continent are as follows:
The original hardware (circa 1998) that was used by Google when it was located at Stanford University included:
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".
At the time, on average, a single search query read ~100 MB of data, and consumed CPU cycles. During peak time, Google served ~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 five 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 four 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.
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