O paralelismo é controlado pelo número de
nós no cluster do Bigtable. Cada nó gerencia um ou mais intervalos de chaves,
mas eles podem se mover entre os nós como parte do
balanceamento de carga. Para mais informações, confira Entender o desempenho na documentação do Bigtable.
Você é cobrado pelo número de nós nos clusters da sua instância. Confira Preços do Bigtable.
Performance
A tabela a seguir mostra as métricas de desempenho das operações de gravação de E/S
do Bigtable. As cargas de trabalho foram executadas em um worker e2-standard2 usando
o SDK do Apache Beam 2.48.0 para Java. Eles não usaram o Runner v2.
Essas métricas são baseadas em pipelines de lote simples. Elas servem para comparar o desempenho
entre conectores de E/S e não representam necessariamente pipelines reais.
O desempenho do pipeline do Dataflow é complexo e depende do tipo de VM, dos dados
processados, do desempenho de origens e coletores externos e do código do usuário. As métricas se baseiam
na execução do SDK do Java e não representam as características de desempenho de outros
SDKs da linguagem. Para mais informações, confira Desempenho do E/S do Beam.
Práticas recomendadas
Em geral, evite usar transações. Não há garantia de que as transações sejam
idempotentes, e o Dataflow pode invocá-las várias vezes devido
a novas tentativas, gerando valores inesperados.
Um único worker do Dataflow pode processar dados para muitos intervalos
de chaves, levando a gravações ineficientes no Bigtable. O uso de
GroupByKey para agrupar dados por chave do Bigtable pode melhorar significativamente
o desempenho de gravação.
Se você grava grandes conjuntos de dados no Bigtable, chame o
withFlowControl. Essa configuração limita automaticamente a
taxa de tráfego ao Bigtable para garantir que os servidores
do Bigtable tenham recursos suficientes disponíveis para veicular os dados.
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 2025-09-04 UTC."],[[["\u003cp\u003eThe Apache Beam Bigtable I/O connector facilitates writing data from Dataflow to Bigtable, and pre-built Google Dataflow templates can also be used depending on the use case.\u003c/p\u003e\n"],["\u003cp\u003eBigtable cluster nodes dictate parallelism, with each node managing key ranges that can shift during load balancing, and node count directly affects Bigtable costs.\u003c/p\u003e\n"],["\u003cp\u003ePerformance metrics for Bigtable I/O write operations were measured at 65 MBps or 60,000 elements per second using a specific setup, though real-world pipeline performance can vary greatly.\u003c/p\u003e\n"],["\u003cp\u003eAvoid using transactions when writing to Bigtable with Dataflow due to potential issues with idempotency and retries, and use \u003ccode\u003eGroupByKey\u003c/code\u003e for improved write efficiency.\u003c/p\u003e\n"],["\u003cp\u003eUtilizing \u003ccode\u003ewithFlowControl\u003c/code\u003e is advised when writing substantial datasets to Bigtable to automatically manage traffic and prevent Bigtable server overload.\u003c/p\u003e\n"]]],[],null,["# Write from Dataflow to Bigtable\n\nTo write data from Dataflow to Bigtable, use the\nApache Beam [Bigtable I/O connector](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/io/gcp/bigtable/package-summary.html).\n| **Note:** Depending on your scenario, consider using one of the [Google-provided Dataflow templates](/dataflow/docs/guides/templates/provided-templates). Several of these write to Bigtable.\n\nParallelism\n-----------\n\nParallelism is controlled by the number of\n[nodes](/bigtable/docs/instances-clusters-nodes#nodes) in the\nBigtable cluster. Each node manages one or more key ranges,\nalthough key ranges can move between nodes as part of\n[load balancing](/bigtable/docs/overview#load-balancing). For more information,\nsee [Understand performance](/bigtable/docs/performance) in the\nBigtable documentation.\n\nYou are charged for the number of nodes in your instance's clusters. See\n[Bigtable pricing](/bigtable/pricing).\n\nPerformance\n-----------\n\nThe following table shows performance metrics for Bigtable I/O\nwrite operations. The workloads were run on one `e2-standard2` worker, using\nthe Apache Beam SDK 2.48.0 for Java. They did not use Runner v2.\n\n\nThese metrics are based on simple batch pipelines. They are intended to compare performance\nbetween I/O connectors, and are not necessarily representative of real-world pipelines.\nDataflow pipeline performance is complex, and is a function of VM type, the data\nbeing processed, the performance of external sources and sinks, and user code. Metrics are based\non running the Java SDK, and aren't representative of the performance characteristics of other\nlanguage SDKs. For more information, see [Beam IO\nPerformance](https://beam.apache.org/performance/).\n\n\u003cbr /\u003e\n\nBest practices\n--------------\n\n- In general, avoid using transactions. Transactions aren't guaranteed to be\n idempotent, and Dataflow might invoke them multiple times due\n to retries, causing unexpected values.\n\n- A single Dataflow worker might process data for many key\n ranges, leading to inefficient writes to Bigtable. Using\n `GroupByKey` to group data by Bigtable key can significantly\n improve write performance.\n\n- If you write large datasets to Bigtable, consider calling\n [`withFlowControl`](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/io/gcp/bigtable/BigtableIO.Write.html#withFlowControl-boolean-). This setting automatically rate-limits\n traffic to Bigtable, to ensure the Bigtable\n servers have enough resources available to serve data.\n\nWhat's next\n-----------\n\n- Read the [Bigtable I/O connector](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/io/gcp/bigtable/package-summary.html) documentation.\n- See the list of [Google-provided templates](/dataflow/docs/guides/templates/provided-templates)."]]
