On this instance, we are able to see that the quantity of bytes processed elevated considerably between jobs. That is probably the rationale for slowness. We are able to moreover confirm this with the truth that total_slot_ms elevated, which means that it had extra slots out there and nonetheless took longer.

Mitigation Choices

1. Clustering: Relying in your question, you might be able to use clustering to assist enhance the efficiency. Clustering will assist queries that use filtering and aggregation, as clustering types related columns collectively. It will cut back the quantity of information scanned, however will solely present giant efficiency enhancements for tables higher than a gigabyte. 

2. Decrease enter knowledge: So as to mitigate this, attempt to discover out if there may be any strategy to optimize the question textual content to learn solely the required knowledge. Some choices to do that embody filtering early, comparable to including WHERE statements to start with of the question to filter out pointless information or modify the SELECT assertion to solely embody the wanted columns, slightly than a SELECT *.

3. Denormalize your knowledge: in case your knowledge includes parent-child or different hierarchical relationships, attempt to use nested and repeated fields in your schema. This permits BigQuery to parallelize execution and full quicker.

3. Elevated Shuffle Reminiscence

Background
Whereas jobs use slots for the compute assets, in addition they use shuffle reminiscence to maintain monitor of the job’s state to transition knowledge between execution stages because the question progresses. This shared state finally permits for parallel processing and optimizations of your question. Your shuffle reminiscence is correlated to the quantity of slots out there in a reservation.

As a result of shuffle is an in-memory operation, there may be solely a finite quantity of reminiscence out there for every stage of the question. If there may be an excessive amount of knowledge being processed at any cut-off date, comparable to a big be part of, or if there’s a excessive knowledge skew between joins, it’s doable {that a} stage can develop into too intensive and exceed its shuffle reminiscence quota. At this level, shuffle bytes will spill to disk, which causes queries to decelerate.

Monitoring
To diagnose this, you must have a look at two totally different metrics: each the shuffle reminiscence consumed by the job in addition to the slots used. Your shuffle reminiscence quota is tied to your slot capability, so a steady quantity of slots alongside a rise within the quantity of shuffle spilled to disk would point out that this could possibly be the foundation trigger.

Evaluate the combination shuffle_output_bytes_spilled_to_disk from the TIMELINE view. A rise in bytes spilled to disk means that the roles are caught, slightly than working quick sufficient to finish on time.



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