Hadoop expertise on call, for clusters that still have to run
Apache SparkApache HiveCustomers keep Hadoop clusters running reliably through the migration period without maintaining scarce in-house Hadoop expertise, and recurring scheduler incidents are resolved at the configuration…
Overview
Hadoop operational expertise has been leaving the market for years, but the clusters are still in production and still carry regulated workloads. AceMQ provides named senior engineers, 24/7, for the clusters that have to keep running until the migration finishes.
Challenge
Common production incidents include a single queue consuming the cluster because its capacity and maximum-capacity settings were never reconciled, applications stuck in ACCEPTED because the ApplicationMaster resource percentage is exhausted, NodeManagers marked unhealthy from full local directories, and containers killed for exceeding physical memory when the real problem is JVM overhead outside the heap. These are diagnosable in minutes by someone who has seen them and can take days by someone who has not.
Environment
On-premises Hadoop clusters running YARN with Capacity or Fair Scheduler, mixed Spark, Hive, and MapReduce workloads.
Approach
AceMQ engineers work from ResourceManager and NodeManager logs, scheduler queue metrics, and container exit codes to separate a scheduling problem from a resource problem from a node problem. Stabilization comes first, followed by the queue configuration or memory sizing change that removes the recurrence.
Solution
- 115-minute emergency SLA with named senior engineers, 24/7, no tier-1 triage
- 2Queue capacity, maximum-capacity, and user-limit-factor review to resolve starvation and unbounded queue growth
- 3Diagnosis of applications stuck in ACCEPTED, including ApplicationMaster resource percentage exhaustion
- 4NodeManager health diagnosis covering local and log directory exhaustion and disk failure thresholds
- 5Container memory sizing across heap, overhead, and vmem ratio to stop kills that are really JVM overhead
- 6Preemption and node labeling configuration where mixed workloads must share a cluster with differing priorities
Outcome
Customers keep Hadoop clusters running reliably through the migration period without maintaining scarce in-house Hadoop expertise, and recurring scheduler incidents are resolved at the configuration level rather than by restarting the ResourceManager.
Technologies
Apache SparkApache HiveRelated Use Cases
Apache Hadoop NameNode Heap Remediation
Relieving NameNode heap pressure and long GC pauses caused by small-file sprawl across HDFS, before the cluster loses its metadata service.
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.
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 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 Broadcast Join Failure Support
Resolving jobs that fail after a dimension table grows past the broadcast threshold and the optimizer keeps trying to broadcast it anyway.
Starburst Federation Readiness Assessment
Evaluating whether a federated query layer will actually work against a given set of source systems before the platform is committed to.
Need Expert Apache Hadoop Support?
AceMQ's senior Apache Hadoop 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.