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CPU - 0.4 nanoseconds  \n2. Memory - 100 nanoseconds  \n3. SSD - 16 microseconds  \n4. Network - 150 miliseconds  \n---------------------------------------------------  \n* CPU -\u003e Twitter: 6000 tweets/s, cada um por volta de 200bytes  = 1.2 milhoes de bytes ou 1mb/s  \n* 2.5GHz CPU -\u003e 2.5 Bilhões operações por segundo. Cada operação tem um processamento X Bytes ~~ 8 bytes  \n* Então usariamos 0.01% da capacidade da cpu, não é um problema.  \n  \n* Knowing that tweets create approximately 104 billion bytes of data per day, (6000 tweets / second) x (86400 seconds / day) x (200 bytes / tweet) = 104 billion bytes / day. How long would it take the 2.5 GigaHertz CPU to analyze a full day of tweets? (say that for each operation, the CPU processes 8 bytes of data) Ans: 5.2 seconds  \n--------------------------------------------------\n* Mas há um detalhe...  Na maior parte do tempo a CPU não esta processando dados.  \n* A Memoria Leva 250X tempo que a CPU para encontrar o mesmo byte na memória.  \n--------------------------------------------------\n#### Processo de 1 hora de Tweets -\u003e Memoria:30ms, SSD:0.5s, HD:4s. Network ~~30s. \n--------------------------------------------------\n#### SPARK foi desenvolvido especificamente para otimizar o uso da memória, trabalhando com clusters de computadores conectados pela REDE, então é crucial otimizar a rede. Existe um TRADEOFF devido a uso da rede.    \n\n### Então, quando usar usar Big Data? Conheça esses numeros:    \n* CPU 200X mais rápida que memória.  \n* Memória é 15x mais rápida que SSD.  \n* SSD é usualmente 20x mais rápido que rede.   \n----------------------------------------------------  \n  \n\n\n  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fevertonsavio%2Fspark-big-data-analitycs","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fevertonsavio%2Fspark-big-data-analitycs","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fevertonsavio%2Fspark-big-data-analitycs/lists"}