Find out which Spark jobs earn their compute and which ones do not
Apache SparkDatabricksDelta Lake
KubernetesCustomers get a concrete, ordered backlog rather than a general efficiency report. Most estates contain enough over-provisioned and orphaned work to reduce compute spend substantially before any job…
Overview
Spark estates grow by accretion. Jobs get copied, resource requests get inflated to make a deadline once and never revised, and pipelines outlive the reports they fed. AceMQ profiles the whole estate and returns a ranked list of what to fix, what to consolidate, and what to delete.
Challenge
Nobody has a complete picture of which jobs run, what they cost, and who consumes their output. Executor counts and memory settings were chosen defensively rather than measured. Several pipelines compute nearly the same aggregate from the same source. A meaningful fraction of jobs process small enough volumes that Spark's overhead exceeds the work, and would run faster as a single-node process.
Environment
Apache Spark on Kubernetes, EMR, Dataproc, or Databricks, with jobs orchestrated by Airflow or a native scheduler.
Approach
AceMQ collects event logs and scheduler history across the estate to build per-job runtime, resource allocation, and utilization profiles. Allocated resources are compared against peak observed usage to find over-provisioning, and output lineage is traced to find pipelines whose consumers no longer exist. Each finding carries a specific recommended action rather than a general observation.
Solution
- 1Estate-wide job inventory with runtime, allocated resources, peak utilization, and cost per run
- 2Over-provisioning analysis comparing requested executor memory and cores against measured peak usage
- 3Duplicate and near-duplicate pipeline detection through source and output lineage
- 4Identification of small-volume jobs where Spark overhead exceeds the work and a single-node engine is faster and cheaper
- 5Orphaned pipeline detection where downstream consumers no longer read the output
- 6Prioritized remediation backlog ordered by cost saved against engineering effort required
Outcome
Customers get a concrete, ordered backlog rather than a general efficiency report. Most estates contain enough over-provisioned and orphaned work to reduce compute spend substantially before any job is rewritten.
Technologies
Apache SparkDatabricksDelta LakeKubernetesRelated Use Cases
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.
Apache Hadoop Cluster Exit Assessment
Establishing what is actually running on a Hadoop cluster, what it costs to keep, and what a defensible migration sequence and timeline look like.
Databricks Delta Small-File Remediation
Fixing Delta tables where streaming writes and over-partitioning have produced millions of tiny files, stalling reads and vacuum operations.
Databricks DBU Cost Governance
Right-sizing Databricks compute by moving scheduled work off all-purpose clusters and tightening autoscaling, instance selection, and idle timeouts.
Databricks Unity Catalog Migration
Migrating off the legacy Hive metastore to Unity Catalog with external location mapping, table upgrades, and a grant model that survives audit.
Redpanda Tiered Storage Assessment
Designing tiered storage for long retention so historical data lives in object storage without local disk dictating how long you can keep it.
Ready for an Apache Spark Health Check?
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.