high_scalability high_scalability-2012 high_scalability-2012-1233 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: This is a guest post ( part 2 , part 3 ) by Greg Lindahl, CTO of blekko, the spam free search engine that had over 3.5 million unique visitors in March. Greg Lindahl was Founder and Distinguished Engineer at PathScale, at which he was the architect of the InfiniPath low-latency InfiniBand HCA, used to build tightly-coupled supercomputing clusters. Imagine that you're crazy enough to think about building a search engine. It's a huge task: the minimum index size needed to answer most queries is a few billion webpages. Crawling and indexing a few billion webpages requires a cluster with several petabytes of usable disk -- that's several thousand 1 terabyte disks -- and produces an index that's about 100 terabytes in size. Serving query results quickly involves having most of the index in RAM or on solid state (flash) disk. If you can buy a server with 100 gigabytes of RAM for about $3,000, that's 1,000 servers at a capital cost of $3 million, plus about $1 million per year of serve
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1 Crawling and indexing a few billion webpages requires a cluster with several petabytes of usable disk -- that's several thousand 1 terabyte disks -- and produces an index that's about 100 terabytes in size. [sent-6, score-0.52]
2 Putting the index into RAM on Amazon is very expensive, and only makes sense for a search engine with several % market share. [sent-13, score-0.247]
3 org/wiki/Paxos_algorithm Combinators In a conventional database system, updates to the database are done in transactions, in which the program locks one or more rows of the database, makes some changes, and then commits or aborts the entire transaction. [sent-28, score-0.244]
4 In blekko's datastore, we heavily rely on a construct called combinators to do processing at the database cell level. [sent-30, score-0.535]
5 A combinator is an atomic operation on a cell of a database that is associative and preferably commutative. [sent-31, score-0.869]
6 The fact that addition is associative and commutative means that we will (eventually) get the same answer in all 3 replicas of this cell. [sent-35, score-0.333]
7 The hierarchy of combinations means that the total number of transactions is dramatically reduced compared to a naive implementation, where every process talks directly to the 3 replicas of the cell, and every addition operation results in 3 immediate transactions. [sent-36, score-0.248]
8 The Logcount Combinator Search engines frequently need to count unique items in a set. [sent-37, score-0.28]
9 Examples include counting the number of inlinks to a website, the number of unique geographic areas linking to a website, and the number of unique Class-C IP networks linking to a website, and so on. [sent-38, score-0.477]
10 Keeping a perfect count would require keeping a lot of data, so we invented an approximate method , which can count a up to a billion things with accuracy of +- 50% in only 16 bytes. [sent-40, score-0.256]
11 The TopN Combinator Another common search engine operation is remembering the most important N items in a set. [sent-46, score-0.35]
12 The TopN combinator can represent the top N URLs in a finite-sized array that fits into a single cell of the database: The TopN combinator can be updated incrementally, as we crawl new webpages, and these updates are inexpensive. [sent-49, score-0.933]
13 It can be read back in a single disk operation, without needing indexing or sorting or reading of any data about URLs not in the top N. [sent-50, score-0.2]
14 So far we've used combinators as single cells in our database tables. [sent-55, score-0.348]
15 Meta-Combinators: The Hash Combinator If a cell in the database contains a hash of (key,value) pairs, the hash meta-combinator can be used to atomically update only some of the (key,value) pairs, leaving the rest unchanged. [sent-58, score-0.451]
16 This gives us considerable freedom to make the columns in the database the ones that make sense to the programmer, instead of having to promote extra things to be columns in order to be able to change them atomically. [sent-59, score-0.187]
17 Taking The Reduce Out Of Map/Reduce Since we represent our database tables with combinators, why not use the combinators to shuffle and reduce the output of our MapReduce jobs? [sent-60, score-0.581]
18 Then we can write MapJobs that iterate over a table in the database, and write their output back into the database using combinators. [sent-61, score-0.288]
19 A second feature of this method of wordcount is that the above function can also be used in a streaming context, to add the wordcounts of newly crawled documents to the existing counts in the table "/wordcount". [sent-64, score-0.206]
20 Our datastore: Looks like a tabular database Supports real-time and batch processing in the same cluster Supports high programmer productivity In the next installment of this series, we'll take a more detailed look at web crawling, and using combinators to implement a crawler. [sent-72, score-0.592]
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