Understanding Multi-Table Insert (MTI) in Snowflake and Our Encounter with Deadlocks

What is MTI (Multi-Table Insert) in Snowflake?

Multi-Table Insert (MTI) is a feature in Snowflake that allows inserting data into multiple tables from a single query. This is particularly useful when dealing with partitioned tables where incoming data needs to be distributed based on specific conditions, such as date-based partitions.

While MTI is designed for efficiency, we recently encountered a critical issue where it caused significant performance bottlenecks, leading to a deadlock situation.

The Issue: MTI Query Stuck in a Long-Running State

We noticed that our MTI query was consuming all available resources in our Medium-sized warehouse, causing other queries to be pushed into a queuing state. Despite running for an extended period, the MTI query failed twice internally, was automatically re-triggered by Snowflake, and eventually became stuck.

What made this even more concerning was that the query was not inserting any data but continued to consume resources indefinitely, creating a "hallucination effect" where it appeared active but was making no actual progress.

Why Did This Happen?

The root cause was related to the way MTI handles large-scale data inserts. We were performing a full refresh of three years' worth of data, significantly increasing both the data volume and concurrent inserts. This led to:

• Spilling of data to local and remote disk as the warehouse ran out of memory.
• Resource contention, where the logic required for execution couldn't proceed due to insufficient available resources.
• A deadlock scenario, where data was waiting for the process to execute, and the process was waiting for resources that were occupied by the data itself.

Despite Snowflake acknowledging this issue and opening an internal investigation, we needed an immediate workaround.

What Did We Try?

1. Increasing Warehouse Size (M → XL): Surprisingly, this did not resolve the issue. The problem persisted even with more compute power.
2. Reducing Warehouse Concurrency (8 → 4): Lowering concurrency did not yield any significant improvements in our case.
3. Batching the Data Load: A more controlled approach, but time-consuming.
4. Using Snowpark-Optimized Warehouse: This was the breakthrough—our process executed successfully with Snowpark-optimized warehouses.

How Does Snowpark-Optimized Warehouse Handle Data Differently?

Snowpark-optimized warehouses are designed specifically for high-performance analytical workloads, offering several advantages over standard virtual warehouses:

• Better Resource Management: Unlike standard WH, which can face disk spills and memory constraints, Snowpark-optimized WH efficiently manages memory allocation, reducing the chances of deadlocks.
• Optimized Execution Engine: It utilizes an enhanced execution model that prioritizes complex transformations and concurrent processing, making it well-suited for ML, data science, and large-scale ETL operations.
• Adaptive Query Execution: It dynamically adjusts the resource allocation to prevent bottlenecks caused by excessive data volume and concurrent operations.

Key Takeaways

• MTI can be powerful but requires careful execution, especially for large-scale data loads.
• Increasing warehouse size is not always the solution—understanding workload behavior is crucial.
• Snowpark-optimized warehouses can significantly improve performance for high-memory and complex operations.
• Investigations by Snowflake Devs are ongoing, but proactive adjustments to our ETL pipeline helped us navigate the issue.

Have you encountered similar challenges with Snowflake? Let’s discuss in the comments! ??

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