The job that worked for two years until the lookup table got bigger
Apache SparkDelta LakeApache HiveThe failing job returns to service the same day, and the pattern stops recurring because statistics are maintained and stale hints are removed across the codebase rather than only in the job that brok…
Overview
Broadcast joins are the fastest option when one side is small, and a reliable source of production outages when that side quietly stops being small. AceMQ provides named senior engineers to diagnose and resolve these failures during the incident, not after it.
Challenge
A dimension table crosses the broadcast threshold and the driver either runs out of memory materializing it or the broadcast times out waiting for executors to receive it. Stale statistics make it worse — the optimizer plans a broadcast based on a row count collected months ago. An explicit broadcast hint left in the code makes it worse still, because the optimizer has no discretion to back out. The failure appears suddenly in a job nobody has touched.
Environment
Apache Spark on YARN, Kubernetes, EMR, or Databricks, running joins against dimension tables that grow over time.
Approach
AceMQ engineers confirm from the physical plan whether a broadcast is being attempted and why, then compare the actual build-side size against the threshold and the driver's available memory. Immediate resolution is usually a threshold correction, hint removal, or driver memory adjustment; the durable fix is statistics maintenance and a review of every remaining explicit broadcast hint in the codebase.
Solution
- 115-minute emergency SLA with named senior engineers, 24/7, no tier-1 triage
- 2Physical plan review to confirm broadcast selection and measure the real build-side size against the configured threshold
- 3autoBroadcastJoinThreshold and broadcastTimeout correction sized against actual driver memory
- 4Removal or scoping of explicit broadcast hints that override the optimizer regardless of table growth
- 5Statistics refresh and ANALYZE TABLE scheduling so the optimizer plans against current row counts
- 6Adaptive Query Execution configuration so the plan can fall back to a sort-merge join at runtime
Outcome
The failing job returns to service the same day, and the pattern stops recurring because statistics are maintained and stale hints are removed across the codebase rather than only in the job that broke.
Technologies
Apache SparkDelta LakeApache HiveRelated Use Cases
Apache Spark Executor OOM and Partition Skew Remediation
Fixing nightly jobs where a handful of skewed keys concentrate data onto a few executors and drive repeated out-of-memory failures.
Apache Spark Shuffle and Spill Tuning
Reducing shuffle write volume and disk spill on nightly batch jobs so the processing window fits inside the reporting deadline.
Need Expert Apache Spark Support?
AceMQ's senior Apache Spark engineers have handled this exact type of engagement before. Whether you need architectural guidance, hands-on remediation, or an ongoing managed partnership, we're ready to help.