Snowpipe Streaming best practices¶

Cost optimization¶

As a best practice, we recommend calling the API with fewer Snowpipe Streaming clients that write more data per second. Use a Java or Scala application to aggregate data from multiple sources, such as IoT devices or sensors, and then use the Snowflake Ingest SDK to call the API to load data at higher flow rates. The API efficiently aggregates data across multiple target tables in an account.

A single Snowpipe Streaming client can open multiple channels to send data, but the client cost is only charged per active client. The number of channels does not affect the client cost. Therefore, we recommend using multiple channels per client for performance and cost optimization.

Using the same tables for both batch and streaming ingestion can also result in reduced Snowpipe Streaming compute costs due to pre-empted file migration operations. If Automatic Clustering is also enabled on the same table that Snowpipe Streaming is inserting into, compute costs for file migration might be reduced. The clustering operation will optimize and migrate data in the same transaction.

Performance recommendations¶

For optimal performance in high-throughput deployments, we recommend the following actions:

  • If you are loading multiple rows, using insertRows will be more efficient and cost effective than calling insertRow multiple times because less time is spent on locks.

    • Keep the size of each row batch passed to insertRows below 16 MB compressed.

    • The optimal size of row batches is between 10-16 MB.

  • Pass values for the TIME, DATE, and all TIMESTAMP columns as one of the supported types from the java.time package.

  • When creating a channel using OpenChannelRequest.builder, set the OnErrorOption to OnErrorOption.CONTINUE, and manually check the return value from insertRows for potential ingestion errors. This approach currently leads to a better performance than relying on exceptions thrown when OnErrorOption.ABORT is used.

  • When setting the default log level to DEBUG, make sure that the following loggers keep logging on INFO: their DEBUG output is very verbose, which can lead to a significant performance degradation.

    • net.snowflake.ingest.internal.apache.parquet

    • org.apache.parquet

  • Channels should be long lived when a client is actively inserting data and should be reused because offset token information is retained. Do not close channels after inserting data because data inside the channels is automatically flushed based on the time configured in MAX_CLIENT_LAG.

Latency recommendations¶

With Snowflake Ingest SDK versions 2.0.4 and later, you can use MAX_CLIENT_LAG to configure the data flush latency. By default, Snowpipe Streaming flushes data every 1 second for standard Snowflake tables (non-Apache Icebergâ„¢). The MAX_CLIENT_LAG configuration lets you override that and set it to your desired flush latency from 1 second to 10 minutes.

For the low-throughput scenarios where not much data is being generated, you might only be sending 1 row or 1 KB of data every second. This can result in longer query compilation time where lots of small partitions must be resolved to return your query results, especially when the query is triggered before the migration process compacts the partitions. Setting the MAX_CLIENT_LAG higher allows Snowpipe Streaming to buffer the inserted rows for the configured amount of time and create better sized partitions. This helps with query performance and migration significantly.

Therefore, set MAX_CLIENT_LAG as high as your target latency allows. For example, if you have a task that runs every 1 minute to merge or transform your streamed data, it would be optimal to set MAX_CLIENT_LAG to 50 or 55 seconds.

For streaming to Snowflake-managed Iceberg tables (supported by Snowflake Ingest SDK versions 3.0.0 and later), the default MAX_CLIENT_LAG is 30 seconds to ensure optimized Parquet files. You can set the property to a lower value, but we recommend not doing this unless there is a significantly high throughput.

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