Performance Analysis · Apache Spark

Analyze Spark logs
without burning
all your tokens.

Spark UI logs can reach 1 GB. No LLM processes that directly. SprkLogs makes the analysis work — you upload the log, get a technical diagnosis with bottlenecks and recommendations. No absurd token cost, no blown context window.

Analyze my log How it works →
1 GB+ logs supported
Bottlenecks prioritized by impact
Evidence from the job itself

What SprkLogs delivers

You bring
The Spark job event log — can be 400 MB, can be 1 GB.
Spark History Server
SprkLogs delivers
Technical diagnosis with bottlenecks by stage, identified root cause and recommendations ready to implement.

Works for logs no LLM could process directly. File size is no longer a limitation.

The problem Spark UI logs can reach 1 GB. Sending that directly to an LLM doesn’t work — context blows up or the cost is absurd.
The temptation Upload the log to ChatGPT and expect an analysis. In practice: truncation, hallucination or context error.
What SprkLogs does Processes the log internally before any AI call. You get the diagnosis, not the token bill.

How it works

Five steps.
One analysis.

From the raw file to the technical diagnosis with evidence from the job itself.

01
Event log ingestion
You upload the Spark event log ZIP and, optionally, the PySpark job .py files. No extra configuration. No server to set up. INPUT
02
Smart reduction
The system transforms hundreds of MB into a compact technical report of a few KB. Only the signals that matter: KPIs by stage, slow tasks, anomalous patterns. The noise is discarded. CORE FEATURE
03
Async analysis
The reduced report — not the original log — is sent to the LLM. The analysis runs in the background and you follow it in real time. No timeout. No blown context. BACKEND
04
Diagnosis with evidence
The panel shows an executive summary, stage table and recommendations prioritized by impact. Each finding is traceable to the real log data — not a generic assumption. OUTPUT
05
Export for action
Output in Markdown and JSON. Ready for the PR, Confluence or the sprint optimization plan. EXPORT

The challenge

1 GB+ logs
don’t fit
in an LLM.

Spark UI generates huge log files — proportional to the job’s complexity and duration. A realistic production pipeline easily produces hundreds of MB or over 1 GB of execution data.

Sending this directly to an AI is not an option: the model truncates, context blows up, or the token bill arrives before the diagnosis.

The solution

SprkLogs processes
the log before
the AI.

The system processes the log internally and delivers to the AI only what is needed for a precise technical diagnosis — the performance signals by stage, problem indicators and relevant patterns.

Result: you analyze jobs of any size, without token cost proportional to the file and without context limits.

Log directly to LLM Unfeasible or too expensive
With SprkLogs Complete diagnosis
Supported log size 1 GB+

Why not just throw it at ChatGPT

Because
the log
doesn’t fit.

The logic seems simple: upload the log, ask for analysis. But a production Spark log has a size proportional to the job’s complexity — and any attempt to send it directly to an LLM hits the context limit or an unfeasible cost.

Before SprkLogs, the real alternative was manually navigating Spark UI, stage by stage, and building context by hand. Hours of work for what SprkLogs delivers in minutes.

Without SprkLogs

Log sent directly to LLM. Context blows up, model truncates the data or rejects the file.

With SprkLogs

The system processes the log before the AI. LLM receives what it needs to diagnose — not the entire file.

Without SprkLogs

Generic analysis with no basis in the job. The AI suggests things that have no relation to your execution context.

With SprkLogs

Each recommendation is traceable to real data: stage, duration, task distribution, detected pattern.

Without SprkLogs

Token cost proportional to file size — unfeasible for production logs.

With SprkLogs

Fixed and predictable cost, regardless of whether the log is 100 MB or 1 GB.

Differentials

What sets us
apart from
the obvious.

We are not a ChatGPT wrapper for logs. We are a reduction and analysis system specialized in Spark.

01

Works with real logs

Production logs reach 1 GB. SprkLogs was built for this — no file size limitation or absurd token cost.

02

Diagnosis with evidence

Each recommendation is traceable to a real execution data point. Stage ID, duration, task distribution — not generic assumptions.

03

AI providers

OpenAI or Gemini. Choose the provider that fits your privacy policy and data governance.

04

Hybrid desktop architecture

Local log processing before any external call. A solid foundation for sensitive data privacy strategy.

05

Interface for engineers

Built for those who live in Spark UI. Correct technical terminology, direct flow, no abstraction layer for non-experts.

06

Output ready to use

Exportable Markdown and JSON. Paste it in the PR, Confluence or use as the basis for the sprint optimization plan.

For whom

Those who live inside Spark.

Data Engineer

Slow jobs
in production.

Diagnose the root cause without hours of manual Spark UI reading. SprkLogs delivers the prioritized bottleneck with stage ID, metric and recommendation specific to your job.

Data Analyst

PySpark without
knowing tuning.

You write the job, it runs slowly, you don’t know where to start. SprkLogs explains in clear technical language what is wrong and how to fix it.

Data Architect

Decisions with
real evidence.

Validate architecture changes with real execution data. Justify repartitioning, shuffle tuning and infrastructure changes with metrics extracted from the job itself.

Ready to start

Bring your log.
Leave with the diagnosis.

Upload your Spark job event log and receive a complete technical diagnosis — no blown context, no wasted tokens.

Analyze my Spark log Download .exe installer How it works →

Hybrid desktop architecture · Log reduction before AI · Evidence-based diagnosis