Essay on Google,Advanced search
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The sorter also generates a list of wordIDs and offsets it into the reverse index. The DuffSimulink function generates a new dictionary for the searcher along with the LeX icon generated by the indexer. The searcher is run by a web server and answers queries using dictionaries built by DopCopION, inverted indexes, and PageRanks. Scalability refers to the effective and efficient operation of distributed systems in different scales from the intranet of small enterprises to the Internet. If the number of resources and users surge, the system can still maintain its effectiveness.
There are three challenges to achieve scalability. For example, if a search engine server cannot handle all the access requirements, it is necessary to increase the number of servers in order to avoid performance bottlenecks. In this respect, Google considers scalability in three dimensions: 1 being able to process more data x 2 being able to process more queries y 3 seeking better results z. However, in order to be scalable, other functions, including indexing, ranking, and searching, require highly distributed solutions. The management of these data sets has a great demand on the performance of the distributed system. In this case, the scalability of the hierarchical algorithm is obviously better than that of the linear algorithm, but the performance loss cannot be completely avoided.
Therefore, the better the performance is, the better the network search operation can be completed within 0. Only in this way can Google make more profits from the sale of advertisements. If there are too many Internet addresses, the Internet address will be exhausted. For this, Google does not have a good solution at present, because if we use bit Internet address, there is no doubt that many software components need to be modified. The availability of distributed system mainly depends on the extent to which new resource sharing services can be added and used by multiple clients.
To meet these needs, Google has developed a physical architecture. There are many information resources with high value to users in distributed system, so it is very important to protect the security of these resources. In addition to this demand, GFS faces the challenge of managing distribution and the risk of increased hardware failure. Ensuring safety of data as well as being able to scale up to thousands of computers while managing multiple terabytes of data can thus be considered the key challenges faced by GFS. So Google made an important decision not to use any of the existing distributed file systems. Instead it decided to develop a new file system. The biggest difference with other file systems is that it optimizes the use of large files i.
Gigabyte to multi-terabyte , resulting in the majority of files is considered immutable, and can be read many times with only one write. A GFS cluster consists of a single master and multiple chunk servers and is accessed by multiple clients. These machines are common Linux process machines that can run user level server processes. The stored files are divided into fixed size blocks, each with a globally unique bit chunk handle. Chunk servers store on local disks as Linux files，It can read and write at the same time. Chunk data which is assigned by chunk handle and data range. To improve GFS performance, every chunk needs to be replicated to at least three servers.
Chunk master maintains the metadata of the whole GFS. In a certain period, the chunk master will ask every chunk server to upload state through HeartBeat messages. Data bearing communication, which does not need to be linked to the Linux Vnode layer, directly connects to the chunk server. Neither the client nor the chunk server caches file data. This approach without storing data not only avoids the inability to cache because the working set is too large, but also makes the client and the whole system consistent. The buffer of Linux stores all the frequently accessed data in memory, so chunk servers do not need to cache file data, which greatly improves the performance and speed of GFS. Communication protocols The setting and selection of communication protocols is very important for the overall design of a system.
Google adopts a simple, minimal and efficient remote call protocol. Communication of the remote call protocol requires a serialization component to transform the procedure call data. So, Google developed a protocol buffer, which is a simplified, high-performance serialization component. Google also uses a separate protocol to publish and subscribe. Protocol buffers focuses on data description and subsequent data serialization. It wants to provide a simple, efficient, extensible way to specify and serialize data independent of language and platform. The serialized data can be stored, transferred, or any scenario that needs to serialize the data format.
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Case Study: Google Search Engine,2. Microsoft Academic
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