将数据推送到 cosmos DB 时 Azure 流分析作业降级
Azure Stream Analytics Job degrading while pushing data to cosmos DB
我有从 Azure IoT 中心推送的数据 -> 流分析 -> CosmosDB
我有 1 个模拟设备，我的 cosmos DB 集合有 1000 个 RU/s 工作正常。
现在我已经将它设为 10 个模拟设备，并且我的 Cosmos DB 集合扩展到 15000 RU/s 我的流分析仍然在下降。
是否需要增加收集的并行连接数？
我们能否使其更优化作为 Cosmos DB 的 Azure 定价，取决于吞吐量和 RU
Can we make it more optimal as Azure pricing of Cosmos DB, depend on
throughput and RUs?
我只是想在这里与您分享一些关于提高 Cosmos db 写入性能的想法。
1.Consistency等级
基于document：
Depending on what levels of read consistency your scenario needs
against read and write latency, you can choose a consistency level on
your database account.
您可以尝试将 Consistency Level 设置为 Eventually。详情请参考here.
2.Indexing:
基于文档：
by default, Azure Cosmos DB enables synchronous indexing on each CRUD
operation to your collection. This is another useful option to control
the write/read performance in Azure Cosmos DB.
请尝试设置惰性索引。另外，删除无用的索引。
3.Partition:
基于document：
Azure Cosmos DB unlimited are the recommended approach for
partitioning your data, as Azure Cosmos DB automatically scales
partitions based on your workload. When writing to unlimited
containers, Stream Analytics uses as many parallel writers as previous
query step or input partitioning scheme.
请对您的集合进行分区并在输出中传递分区键以提高写入性能。
我有从 Azure IoT 中心推送的数据 -> 流分析 -> CosmosDB
我有 1 个模拟设备，我的 cosmos DB 集合有 1000 个 RU/s 工作正常。 现在我已经将它设为 10 个模拟设备，并且我的 Cosmos DB 集合扩展到 15000 RU/s 我的流分析仍然在下降。
是否需要增加收集的并行连接数？
我们能否使其更优化作为 Cosmos DB 的 Azure 定价，取决于吞吐量和 RU
Can we make it more optimal as Azure pricing of Cosmos DB, depend on throughput and RUs?
我只是想在这里与您分享一些关于提高 Cosmos db 写入性能的想法。
1.Consistency等级
基于document：
Depending on what levels of read consistency your scenario needs against read and write latency, you can choose a consistency level on your database account.
您可以尝试将 Consistency Level 设置为 Eventually。详情请参考here.
2.Indexing:
基于文档：
by default, Azure Cosmos DB enables synchronous indexing on each CRUD operation to your collection. This is another useful option to control the write/read performance in Azure Cosmos DB.
请尝试设置惰性索引。另外，删除无用的索引。
3.Partition:
基于document：
Azure Cosmos DB unlimited are the recommended approach for partitioning your data, as Azure Cosmos DB automatically scales partitions based on your workload. When writing to unlimited containers, Stream Analytics uses as many parallel writers as previous query step or input partitioning scheme.
请对您的集合进行分区并在输出中传递分区键以提高写入性能。