What does AceMQ Apache Spark support include?

Our Spark support covers 24/7 emergency incident response with a 15-minute SLA, root cause analysis on every P1, performance tuning for partitioning and shuffle behavior, pipeline architecture review, cluster sizing and cost optimization, and migration or upgrade support. Every ticket reaches a named senior engineer with no tier-1 triage layer.

Can you diagnose executor OOM errors?

Yes, this is one of the most common Spark issues we see. We start with Spark UI stage detail to check for partition skew and broadcast join sizing, since those cause the large majority of executor OOM incidents that don't trace back to a genuine data volume increase.

Do you support Spark on Databricks, EMR, and Dataproc equally?

Yes. The core Spark engine issues — skew, shuffle spill, memory tuning — are consistent across managed platforms; what differs is the platform-specific autoscaling and cluster policy layer, which we also cover for each of the major managed offerings.

Can you help with a Hadoop-to-Spark migration?

Yes. We support both legacy Hadoop clusters and modern Spark deployments, which means we plan migrations from people who understand what's actually being left behind — not just consultants who only know the destination platform.

How fast do you respond to a Spark production outage?

P1 emergencies — jobs failing cluster-wide, a pipeline that stopped producing output — get a 15-minute first response with a senior engineer joining a live bridge. P2 critical issues are 1 hour, P3 high-priority 4 hours, and P4 standard questions next business day. These are contractual targets.

Do you support both batch and Structured Streaming Spark jobs?

Yes. We support Spark 3.x for both batch ETL and Structured Streaming workloads, and because we also support Flink, we bring the same event-time and state-management thinking to Spark streaming jobs that we apply to dedicated streaming engines.

How is AceMQ Apache Spark support priced?

Support is priced as an annual contract based on your cluster footprint, required SLA tier, and coverage hours — not per incident or per ticket. Contact us for a quote; we turn them around within 24 hours.

Apache Spark Support

24/7 Apache Spark Support with a 15-Minute Emergency SLA

AceMQ supports Apache Spark in production across Databricks, EMR, Dataproc, and Kubernetes — executor OOM from partition skew, shuffle spill tanking runtime, and driver crashes from an unbounded collect(). Every ticket reaches a named senior engineer who reads Spark UI stage detail live.

Senior Apache Spark engineers on call right now — 24/7/365

15 min emergency SLA24 /7 global coverage130 + enterprise customers26 + countries served

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Trusted for mission-critical Apache Spark by teams in finance, healthcare, defense, and telecom

Escalation Path

Your first hour of a Apache Spark outage

Most vendors publish an SLA number. This is what actually happens, minute by minute, when you page a senior AceMQ engineer.

T+0

You page us

Phone, email, or Slack — any channel reaches the on-call senior engineer directly. No web form, no tier-1 queue.

T+15

Named engineer live

A senior engineer who already knows your environment joins a live bridge. Zero cold-start, no re-explaining your topology.

T+30

Root cause isolated

Direct broker access, log and metric review, and a working hypothesis with a rollback plan before we touch anything.

Post

Written RCA

Documented root cause, the fix applied, and the prevention steps — delivered after every P1, not just when asked.

Response Times

SLA tiers, contractually guaranteed

Every tier reaches a senior Apache Spark engineer. There is no tier-1 triage layer to get through.

P1 — Emergency

15 min

Production down, messages not flowing, cluster or broker failure

P2 — Critical

1 hour

Severe degradation, rising error rates, approaching capacity limits

P3 — High

4 hours

Performance issues, configuration problems, non-critical failures

P4 — Standard

Next day

Questions, guidance, best practices, non-urgent improvements

Incident Triage

Apache Spark problems we fix every week

These are real symptoms from real Apache Spark production environments — and the first thing our engineers check when one comes in.

Symptom you're seeingWhat we check firstTypical resolution

Executors OOM mid-job with no code change

What we check firstSpark UI stage detail for partition size skew, and whether a broadcast join is broadcasting a table larger than spark.sql.autoBroadcastJoinThreshold.

Typical resolutionUnder 2 hours

Job runtime doubles, heavy shuffle spill to disk

What we check firstShuffle read/write metrics in stage detail against executor memory fraction. Insufficient shuffle buffer relative to partition count forces spill that looks like a network problem but isn't.

Typical resolution1–3 hours

Output stage writes thousands of tiny files

What we check firstOutput partition count against actual data volume — usually a missing coalesce or repartition before write, or overly granular partitionBy columns.

Typical resolution1–2 hours

Driver crashes with 'ran out of memory'

What we check firstJob code for collect() or toPandas() calls on a dataset that grew past what fits in driver memory since the job was last tuned.

Typical resolutionUnder 2 hours

Dynamic allocation won't scale executors under load

What we check firstspark.dynamicAllocation.maxExecutors against cluster resource manager queue caps — a scheduler queue limit often silently caps scale-out below the configured Spark max.

Typical resolution1–3 hours

Stages retry repeatedly on a specific node pool

What we check firstCluster manager logs for spot or preemptible instance reclamation events. Fetch failures cascade when shuffle data lived only on nodes that just got reclaimed.

Typical resolution2–4 hours

One task runs for hours while its stage siblings finish in minutes

What we check firstPartition key cardinality for skew. Salting the join key, or enabling adaptive query execution's skew join handling, is the standard fix.

Typical resolution1–3 hours

A job behaves differently in local dev than on the cluster

What we check firstSpark config diff between spark-defaults.conf and SparkSession builder overrides, plus Scala/Python/JAR version mismatches between environments.

Typical resolutionSame day

Resolution times reflect typical Apache Spark engagements under an active AceMQ support contract. Every P1 closes with a written root-cause analysis.

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Support In Practice

Apache Spark problems we've already solved

Representative engagements showing how these incidents get diagnosed and closed under an AceMQ support contract.

All use cases

Assessment

Databricks DBU Cost Governance

Global Payments Processor

Right-sizing Databricks compute by moving scheduled work off all-purpose clusters and tightening autoscaling, instance selection, and idle timeouts.

DatabricksApache SparkDelta Lake

Read case study

Remediation

Databricks Delta Small-File Remediation

Digital Retail Platform

Fixing Delta tables where streaming writes and over-partitioning have produced millions of tiny files, stalling reads and vacuum operations.

DatabricksDelta LakeApache Spark

Read case study

Support

Databricks Job Cluster Failure Support

Insurance Services Group

Named-engineer support for production Databricks job failures — driver OOM, spot reclamation, library conflicts, and workflow retry storms.

DatabricksApache SparkDelta Lake

Read case study

Remediation

Apache Spark Executor OOM and Partition Skew Remediation

Telecommunications Operator

Fixing nightly jobs where a handful of skewed keys concentrate data onto a few executors and drive repeated out-of-memory failures.

Apache SparkApache HadoopDelta Lake

Read case study

Support

Apache Spark Broadcast Join Failure Support

Industrial Automation Manufacturer

Resolving jobs that fail after a dimension table grows past the broadcast threshold and the optimizer keeps trying to broadcast it anyway.

Apache SparkDelta LakeApache Hive

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Assessment

Apache Spark Workload and Cost Assessment

Media Streaming Provider

Profiling a Spark estate to find over-provisioned jobs, redundant pipelines, and workloads better served by something other than Spark.

Apache SparkDatabricksDelta Lake

Read case study

Assessment

Apache Hadoop Cluster Exit Assessment

Regional Utility Operator

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 HadoopApache HiveApache Spark

Read case study

Remediation

Apache Hadoop NameNode Heap Remediation

Global Logistics Operator

Relieving NameNode heap pressure and long GC pauses caused by small-file sprawl across HDFS, before the cluster loses its metadata service.

Apache HadoopApache HiveApache Spark

Read case study

What's Included

Everything in your Apache Spark support contract

No add-on pricing for incidents. No per-ticket charges. One contract covers the whole surface.

Emergency Incident Response

Jobs failing cluster-wide, an executor OOM loop, or a pipeline that stopped producing output. A senior engineer joins a live bridge within 15 minutes.

Root Cause Analysis

Every P1 closes with a written RCA: what failed, why, the fix applied, and the configuration change that prevents recurrence.

Performance Tuning

Partitioning strategy, shuffle configuration, and memory sizing tuned against your actual data volumes and skew profile — not generic Spark config defaults.

Pipeline Architecture Review

Job design review to catch skewed joins, unbounded collect() calls, and small-file output patterns before they become a 2am page.

Cluster Sizing & Cost Optimization

Executor sizing and dynamic allocation configuration matched against actual job resource needs, so you're not paying for headroom you never use.

Migration & Upgrade Support

Spark version upgrades with compatibility testing, and migration off legacy Hadoop-based Spark deployments onto modern cloud-native platforms.

Anywhere You Run It

We support Apache Spark wherever it's deployed

Cloud, Kubernetes, bare metal, hybrid, and air-gapped — including environments where you can't give us outbound network access.

DatabricksAmazon EMRAWS GlueGoogle DataprocAzure HDInsight & SynapseKubernetes (Spark-on-K8s)YARNStandalone clustersOn-premise & bare metalHybrid cloudSpark 3.x (Structured Streaming & batch)

Why AceMQ

What you get that you don't get elsewhere

Named Engineers, Zero Cold Start

The same senior engineers stay on your account. They know your job topology and where your skew lives — so a P1 starts with the Spark UI, not you re-explaining your pipeline.

No Tier-1 Triage Layer

You reach a senior Spark engineer directly by phone, email, or Slack. No help desk, no escalation approval process standing between you and a fix.

Batch and Streaming, Both Deeply

We support Spark alongside Flink, which means we think in the same terms for batch and streaming compute rather than treating them as unrelated disciplines.

Hadoop-to-Modern Migration Expertise

Many Spark clusters still run on legacy Hadoop infrastructure. We support both, so a migration off Hadoop onto cloud-native Spark is planned by people who've done the move, not just the destination.

Genuine Follow-the-Sun Coverage

Engineers across 26+ countries and every time zone. Your 3am stage failure is someone's mid-afternoon — no overnight skeleton crew.

Full-Stack, Not Just the JVM

Spark problems are frequently cluster manager, cloud storage, or network problems wearing a stack trace. We diagnose across the whole path, not just Spark's own logs.

FAQ

Apache Spark support questions

One Skewed Join Shouldn't Cost You an Afternoon

Whether you need emergency response tonight or a support contract that catches partition skew before it becomes a 6-hour job, AceMQ staffs every engagement with a named senior Apache Spark engineer. Support quotes returned within 24 hours.

Get in Touch

Talk to a Support Expert

Send us a message and we'll follow up within one business day — or book a free 30-min consultation directly.

305-204-2607

info@acemq.com

66 W. Flagler St. 9th Floor
Miami, FL 33130

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