From e71e88b495e1b31bae7015542360720600b0c4cf Mon Sep 17 00:00:00 2001 From: Felipe Lopes Date: Sun, 8 Sep 2024 13:29:37 -0300 Subject: [PATCH] [pt-br] Localize 'sampling' page to Portuguese (Brazil) (#5138) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Janssen dos Reis Lima Co-authored-by: Emídio Neto <9735060+emdneto@users.noreply.github.com> Co-authored-by: Vitor Vasconcellos Co-authored-by: Ezzio Moreira Co-authored-by: Luiz Aoqui --- content/pt/docs/concepts/sampling/index.md | 215 ++++++++++++++++++ .../sampling/tail-sampling-process.png | Bin 0 -> 82548 bytes .../concepts/sampling/traces-venn-diagram.png | Bin 0 -> 113397 bytes 3 files changed, 215 insertions(+) create mode 100644 content/pt/docs/concepts/sampling/index.md create mode 100644 content/pt/docs/concepts/sampling/tail-sampling-process.png create mode 100644 content/pt/docs/concepts/sampling/traces-venn-diagram.png diff --git a/content/pt/docs/concepts/sampling/index.md b/content/pt/docs/concepts/sampling/index.md new file mode 100644 index 000000000000..6c08c415f0fe --- /dev/null +++ b/content/pt/docs/concepts/sampling/index.md @@ -0,0 +1,215 @@ +--- +title: Amostragem +description: + Aprenda sobre amostragem e explore as diferentes opções disponíveis no + OpenTelemetry. +weight: 80 +default_lang_commit: de60a8e2c5098e47eda3e12b006c5490bdb8a94e +--- + +Com [rastros](/docs/concepts/signals/traces), você pode observar as requisições +à medida que se movem de um serviço para outro em um sistema distribuído. +Rastreamento é muito útil tanto para análises de alto nível quanto para análises +aprofundadas de sistemas. + +Contudo, se a grande maioria das suas requisições é bem-sucedida e termina com +uma latência aceitável e sem erros, você não precisa de 100% dos seus rastros +para observar de forma significativa suas aplicações e sistemas. Você só precisa +da amostragem correta. + +![Ilustração mostrando que nem todos os dados precisam ser rastreados, e que uma amostra de dados é suficiente](traces-venn-diagram.png) + +## Terminologia + +É importante usar uma terminologia consistente ao discutir amostragem. Um rastro +ou trecho é considerado "amostrado" (_sampled_) ou "não amostrado" (_not +sampled_): + +- **Amostrado**: Um rastro ou trecho é processado e exportado. Por ter sido + escolhido pelo amostrador (_sampler_) como representativo do conjunto de + dados, ele é considerado "amostrado". +- **Não amostrado**: Um rastro ou trecho não é processado ou exportado. Por não + ter sido escolhido pelo amostrador, ele é considerado "não amostrado". + +Às vezes, as definições desses termos se confundem. Você pode encontrar alguém +dizendo que está "amostrando dados" ou que os dados não processados ou +exportados são considerados "amostrados". Essas afirmações estão incorretas. + +## Por que usar amostragem? + +A amostragem é uma das formas mais eficazes de reduzir os custos de +observabilidade sem perder visibilidade. Embora existam outras maneiras de +reduzir custos, como filtrar ou agregar dados, esses outros métodos não aderem +ao conceito de representatividade, que é crucial ao realizar análises +aprofundadas do comportamento de aplicações ou sistemas. + +Representatividade é o princípio de que um grupo menor pode representar com +precisão um grupo maior. Além disso, a representatividade pode ser verificada +matematicamente, o que significa que você pode ter alta confiança de que uma +amostra menor de dados representa com precisão o grupo maior. + +Além disso, quanto mais dados você gera, menos dados você realmente precisa para +ter uma amostra representativa. Para sistemas de alto volume, é muito comum que +uma taxa de amostragem de 1% ou menos represente com bastante precisão os outros +99% dos dados. + +## Quando usar amostragem + +Considere usar amostragem se você atender a qualquer um dos seguintes critérios: + +- Você gera 1.000 ou mais rastros por segundo. +- A maior parte dos seus rastros representa tráfego saudável com pouca variação + nos dados. +- Você tem alguns critérios comuns, como erros ou alta latência, que geralmente + significam que algo está errado. +- Você tem critérios específicos de domínio que você pode usar para determinar + dados relevantes além de erros e latência. +- Você consegue descrever algumas regras comuns que determinam se o dado deve + ser amostrado ou descartado. +- Você consegue diferenciar seus serviços, para que serviços de alto e baixo + volume sejam amostrados de forma diferente. +- Você tem a capacidade de rotear dados não amostrados (para cenários de "por + via das dúvidas") para sistemas de armazenamento de baixo custo. + +Por fim, considere seu orçamento em geral. Se você tem um orçamento limitado +para observabilidade, mas pode dedicar tempo para realizar uma amostragem +eficaz, então a amostragem geralmente vale a pena. + +## Quando não usar amostragem + +A amostragem pode não servir para você. Você talvez queira evitar a amostragem +se atender a qualquer um dos seguintes critérios: + +- Você gera poucos dados (dezenas de pequenos rastros por segundo ou menos). +- Você só usa dados de observabilidade de forma agregada, portanto, pode + pré-agregar os dados. +- Você está limitado por outros fatores, como por exemplo, regulamentações que + proíbem descartar dados (e não pode rotear dados não amostrados para + armazenamento de baixo custo). + +Por fim, considere os seguintes três custos associados à amostragem: + +1. O custo direto de computação para amostrar dados de forma eficaz, como um + _proxy_ de amostragem pela cauda. +2. O custo indireto de engenharia para manter metodologias de amostragem + eficazes à medida que mais aplicações, sistemas e dados são envolvidos. +3. O custo indireto de oportunidade de perder informações críticas com técnicas + de amostragem ineficazes. + +A amostragem, embora eficaz na redução dos custos de observabilidade, pode +introduzir outros custos inesperados se não for realizada corretamente. Poderia +ser mais barato alocar mais recursos para observabilidade, seja através de um +fornecedor ou com recursos computacionais em hospedagem própria, dependendo do +seu _backend_ de observabilidade, da natureza dos seus dados e das suas +tentativas de realizar amostragem de forma eficaz. + +## Amostragem pela Cabeça + +A amostragem pela cabeça (_head sampling_) é uma técnica de amostragem usada +para tomar uma decisão de amostragem o mais cedo possível. A decisão de amostrar +ou descartar um trecho ou um rastro não é feita inspecionando o rastro como um +todo. + +Por exemplo, a forma mais comum de amostragem pela cabeça é a +[Amostragem de Probabilidade Consistente](/docs/specs/otel/trace/tracestate-probability-sampling/#consistent-probability-sampling). +Isso também é conhecido como Amostragem Determinística. Neste caso, uma decisão +de amostragem é tomada com base no ID do rastro e na porcentagem desejada de +rastros a serem amostrados. Isso garante que rastros inteiros sejam amostrados - +sem trechos faltantes - a uma taxa consistente, como 5% de todos os rastros. + +As vantagens da amostragem pela cabeça são: + +- Fácil de entender +- Fácil de configurar +- Eficiente +- Pode ser feita em qualquer ponto do _pipeline_ de coleta de rastros + +A principal desvantagem da amostragem pela cabeça é que não é possível tomar uma +decisão de amostragem com base nos dados do rastro inteiro. Por exemplo, você +não pode garantir que todos os rastros com um erro sejam amostrados apenas com a +amostragem pela cabeça. Para essa situação e muitas outras, você precisa da +amostragem pela cauda. + +## Amostragem pela Cauda + +A amostragem pela cauda (_tail sampling_) é onde a decisão de amostrar um rastro +acontece considerando todos ou a maioria dos trechos dentro do rastro. A +Amostragem de Cauda oferece a opção de amostrar seus rastros com base em +critérios específicos derivados de diferentes partes de um rastro, o que não é +uma opção com a Amostragem pela Cabeça. + +![Ilustração mostrando como os trechos se originam de um trecho raiz. Após os trechos serem concluídos, o processador de amostragem pela cauda toma uma decisão de amostragem.](tail-sampling-process.png) + +Alguns exemplos de como você pode usar a Amostragem pela Cauda incluem: + +- Sempre amostrar rastros que contenham um erro +- Amostrar rastros com base na latência geral +- Amostrar rastros com base na presença ou valor de atributos específicos em um + ou mais trechos de um rastro; por exemplo, amostrar mais rastros originados de + um serviço recém-implantado +- Aplicar diferentes taxas de amostragem a rastros com base em certos critérios, + como quando os rastros vêm apenas de serviços de baixo volume versus rastros + de serviços de alto volume. + +Como você pode ver, a amostragem pela cauda permite um grau muito maior de +sofisticação na forma como você amostra os dados. Para sistemas maiores que +devem amostrar telemetria, é quase sempre necessário usar a Amostragem pela +Cauda para equilibrar o volume de dados com a utilidade desses dados. + +Existem três principais desvantagens na amostragem pela cauda atualmente: + +1. A amostragem pela cauda pode ser difícil de implementar. Dependendo dos tipos + de técnicas de amostragem a sua disposição, nem sempre é uma decisão do tipo + "configurar e esquecer". À medida que seus sistemas mudam, suas estratégias + de amostragem também mudarão. Para um sistema distribuído grande e + sofisticado, as regras que implementam estratégias de amostragem também podem + ser grandes e sofisticadas. +2. A amostragem pela cauda pode ser difícil de operar. O(s) componente(s) que + implementam a amostragem pela cauda devem ser sistemas com estado que podem + aceitar e armazenar uma grande quantidade de dados. Dependendo dos padrões de + tráfego, isso pode exigir dezenas ou até centenas de nós de computação que + utilizam recursos de maneira diferente. Além disso, um amostrador pela cauda + pode precisar "recorrer" a técnicas de amostragem menos intensivas + computacionalmente se não conseguir acompanhar o volume de dados que está + recebendo. Por conta desses fatores, é crucial monitorar os componentes de + amostragem pela cauda para garantir que eles tenham os recursos necessários + para tomar as decisões corretas de amostragem. +3. Atualmente, os amostradores pela cauda frequentemente acabam sendo + tecnologias específicas de fornecedores. Se você está usando um fornecedor + pago para Observabilidade, as opções mais eficazes de amostragem pela cauda + disponíveis para você podem ser limitadas ao que o fornecedor oferece. + +Por fim, para alguns sistemas, a amostragem pela cauda pode ser usada em +conjunto com a Amostragem pela Cabeça. Por exemplo, um conjunto de serviços que +produz um volume extremamente alto de dados de rastreamento pode primeiro usar a +amostragem pela cabeça para amostrar apenas uma pequena porcentagem dos rastros +e, posteriormente, no _pipeline_ de telemetria usar a amostragem pela cauda para +tomar decisões de amostragem mais sofisticadas antes de exportar para um +_backend_. Isso é frequentemente feito com o intuito de proteger o _pipeline_ de +telemetria contra sobrecarga. + +## Suporte + +### Collector + +O OpenTelemetry Collector inclui os seguintes processadores de amostragem: + +- [Processador de Amostragem Probabilística](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/probabilisticsamplerprocessor) +- [Processador de Amostragem pela Cauda](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/tailsamplingprocessor) + +### SDKs de Linguagens de programação + +Para as implementações específicas de cada linguagem da API e SDK do +OpenTelemetry, você vai encontrar suporte para amostragem nas respectivas +páginas de documentação: + +{{% sampling-support-list " " %}} + +### Fornecedores + +Muitos [fornecedores](/ecosystem/vendors) oferecem soluções abrangentes de +amostragem que incorporam amostragem pela cabeça, amostragem pela cauda e outros +recursos que podem atender a necessidades sofisticadas de amostragem. Essas +soluções também podem ser otimizadas especificamente para o _backend_ do +fornecedor. Se você está enviando telemetria para um fornecedor, considere usar +as soluções de amostragem deles. diff --git a/content/pt/docs/concepts/sampling/tail-sampling-process.png b/content/pt/docs/concepts/sampling/tail-sampling-process.png new file mode 100644 index 0000000000000000000000000000000000000000..03dd798de939cf18fd932437ad57f741ab18aba4 GIT binary patch literal 82548 zcmeEuby!qg7cY$ph)5~@S|Hs>g9xZ}cPI=!Lk``js7NTnkb)@PAT=~9AgRZg9b-2d-$pHH7h&z!Sk?G?YZ_S);5XX>hoWasG4;o#tq-Mf2769Lx!Py6H1<;{rykg_#<|M|$waItlAvvq{9oQ(V6p(E5y^2(KyiGKb0K_|%5_V-K>*PqJ* z7RYn*golrtm*=!@Znjqcmu@Fdesuf3t{>A$oD3$WZtDqhFt}q220>f_(xgO0g(bdE z^RFkr3H_s|{_mcA{QN?HcKzeg-(BS#9UNV>oE}(!r1*Yz`Qz3ftxpISQ**He0X#k7 z+)49)-TT>Ig6D*-f3W$748Pw6geY}Rg6Ee)NS)J?)B{vi2It-#S#3|;MbsS37~!?D zhY}U}+A7L=k>%yx;IpJxZa&v8$+N##2j4NQo9OhL@(>94*kQQQWIvD)FTOp3DTX29 zV!;M!7jN=CKYL9UkHw{{m|n)?oom9J2VEw%FI($vwW^LD)UQH24=rY!(v8%~@YIkvC4Gli4onk7&G8gt8e2eZu)uRwp9k}AL=u%WTg$xcZ!G*v6Y2|ck*Y!d{ 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