Openflow Snowflake 部署成本和扩展注意事项¶

Openflow compute pool costs¶

The total costs for running Openflow are based on the number and types of instances used by Snowpark Container Service compute pools in your Snowflake account.

Openflow uses compute pools for two different purposes:

• Openflow Management Services

  Openflow Management Services run as part of an Openflow deployment. They use a compute pool to manage the Openflow deployment. This compute pool begins running as soon as you create a deployment. It continues to run as long as the deployment is active.

  caution:

  The compute pool associated with the Openflow Management Services continues to run and incurs costs, even if there are no runtimes running.

• Openflow runtimes

  Openflow uses compute pools to run the Openflow runtimes. The number of compute pools required and the number of nodes within each compute pool are scaled based on the number of runtimes that are currently running.

  When all runtimes associated with a runtime are stopped, the compute pool associated with the runtimes is scaled down to 0 nodes. No costs are incurred for a runtime compute pool when it is not in use.

Credits are billed per-second with a 5 minute minimum. For information on the rate per Snowpark Container Services Compute Instance Family per hour, refer to Table 1(d) in the Snowflake Service Consumption Table.

The following views in the Account Usage schema provide additional details on Openflow compute costs:

• METERING_DAILY_HISTORY

• METERING_HISTORY

Compute pool costs related to Openflow appear under SERVICE_TYPE as OPENFLOW_COMPUTE_SNOWFLAKE.

For more information on compute costs in Snowflake, see 了解计算成本.

Snowpark Container Services infrastructure costs¶

In addition to compute pool costs, there are costs associated with additional Snowpark Container Services infrastructure, including storage and data transfer.

For additional information, see Snowpark Container Services 成本.

Data ingestion costs¶

Costs are incurred when loading data into Snowflake using services such as Snowpipe or Snowpipe Streaming. These costs are based on the volume of data ingested.

Additionally, some connectors may require a warehouse and will incur warehouse costs. For example, database CDC connectors require a warehouse for both the initial snapshots and ongoing incremental Change Data Capture (CDC).

Telemetry data ingestion costs¶

When using an event table to store telemetry data for Openflow, Snowflake charges for sending logs and metrics to Openflow deployments. There are also charges for sending runtime telemetry data to your event table within Snowflake.

The rate for credits per GB of telemetry data is specified in Table 5 in the Snowflake Service Consumption Table This item is referred to as Telemetry Data Ingest.

将运行时映射到 Snowflake 计算池¶

The runtime type you choose determines the runtime pods that are scheduled on the associated compute pool. Using a larger runtime type will result in a larger compute pool being used, which will incur higher costs.

The runtime sizes and their scaling behavior are described in the following table:

Openflow scales the underlying Snowflake Compute Pools when additional compute pool nodes need to be scheduled, based on CPU consumption, and up to the maximum node setting set during runtime creation.

Compute pools are configured with a minimum size of 0 nodes and a maximum of 50 nodes. The required size is dynamically adjusted depending on the CPU and memory requirements of the runtimes.

If there are no resource demands, for example, if the runtime is not running, a compute pool scales down to 0 nodes after 600 seconds (10 minutes).

计算 Openflow - Snowflake Deployment 使用量的示例¶

You created an Openflow Snowflake Deployment and have not created any runtimes.

• Openflow_Control_Pool_0 计算池正在运行一个 CPU_X64_S 实例

• Total Openflow consumption = 1 CPU_X64_S instance-hour

You created one small runtime with Min Nodes = 1 and Max Nodes = 2. Runtime stays at 1 node for 1 hour.

• Openflow_Control_Pool_0 计算池正在运行 1 个 CPU_X64_S 实例

• INTERNAL_OPENFLOW_0_SMALL 计算池正在运行 1 个 CPU_X64_S 实例

• Openflow 总使用量 = 2 CPU_X64_S 实例小时

You created two small runtimes with min/max of two nodes each, and one large runtime with min/max of 10 nodes. These Runtimes are active for one hour.

• Openflow_Control_Pool_0 计算池正在运行 1 个 CPU_X64_S 实例

  • Two small runtimes at two nodes = INTERNAL_OPENFLOW_0_SMALL Compute Pool is running with 2 CPU_X64_S instances = 2 CPU_X64_S instance-hours

  • One large runtime at 10 nodes = INTERNAL_OPENFLOW_0_LARGE Compute Pool is running with 4 CPU_X64_L instances = 4 CPU_X64_L instance-hours

• Total Openflow consumption = 3 CPU_X64_S instance-hours + 4 CPU_X64_L instance-hour

You created one medium runtime with one node. After 20 minutes, it scales to two nodes. After 20 minutes, it scales back down to one node and runs for another 20 minutes.

• Openflow_Control_Pool_0 计算池正在运行 1 个 CPU_X64_S 实例

• One medium runtime scaling up to two medium runtimes = INTERNAL_OPENFLOW_0_MEDIUM Compute Pool is running with 1 CPU_X64_SL instance = 1 CPU_X64_SL instance-hour

• Openflow 总使用量 = 1 CPU_X64_S 实例小时 + 1 CPU_X64_SL 实例小时

You created one medium runtime with two nodes, then suspended it after 30 minutes.

• Openflow_Control_Pool_0 计算池正在运行 1 个 CPU_X64_S 实例

• One medium runtime at one node = INTERNAL_OPENFLOW_0_MEDIUM Compute Pool is running with 1 CPU_X64_SL instance

• 30 分钟 = 1/2 小时

• Openflow 总使用量 = 1 CPU_X64_S 实例小时 +1/2 CPU_X64_SL 实例小时