Install the Spark jar that matches both your Minecraft version and server loader, restart the server, grant yourself the spark or spark.profiler permission, then run /spark profiler start while the lag is happening. After a few minutes of normal play, run /spark profiler stop; Spark uploads the profile and gives you a shareable report with an interactive flamegraph showing where tick time was spent.
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What Spark is and what it can tell you
Spark is a server profiler for Minecraft. It samples what the server threads are doing so you can connect a lag symptom—such as low TPS, long ticks, or a recurring spike—to the code or activity consuming time. The result is a web-based report rather than a single “lag score.”
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Spark’s official download page is spark.lucko.me/download. Command syntax and flags can change between releases, so check the version-specific reference at spark.lucko.me/docs/Command-Usage when a command is rejected or behaves differently from the examples here.
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Check your server before installing
- Identify the loader: Paper and Spigot use the plugin system; Fabric, Forge and NeoForge use the mod system.
- Identify the Minecraft version: download a Spark build made for that version, not merely the newest file on the download page.
- Confirm access: you need console access or an account that can run server commands. Operator status works, or an administrator can grant the Spark permission nodes.
- Inspect startup output: some third-party guidance claims that certain recent Paper builds include Spark already, but that is not established for every build. Search the startup console for Spark before adding another jar; duplicate installations can create confusing errors.
- Keep a rollback path: stop the server before replacing a jar, and retain the previous file until the new installation has started successfully.
Install Spark on each server loader
1. Download the matching jar
Open the official Spark download page and select the file for your Minecraft version and loader. A plugin jar is not interchangeable with a Fabric, Forge or NeoForge mod jar. If you are checking the project’s Spigot resource page, it is available at spigotmc.org/resources/spark.57242; the official download page remains the place to match a release to your loader and game version.
2. Put the file in the correct directory
| Server software | Installation directory | What to do |
|---|---|---|
| Paper | plugins/ |
Copy the Spark plugin jar into the server’s plugins folder. |
| Spigot | plugins/ |
Copy the Spark plugin jar into the server’s plugins folder. |
| Fabric | mods/ |
Copy the Spark mod jar into the mods folder. |
| Forge | mods/ |
Copy the Spark mod jar into the mods folder. |
| NeoForge | mods/ |
Copy the Spark mod jar into the mods folder. |
Do not put a mod jar in plugins/ or a plugin jar in mods/. The loader will either ignore the file or report a dependency or format error during startup.
3. Restart completely
Stop the server cleanly, place the jar, and start it again. A full restart is the supported way to load Spark. Do not rely on a hot-reload command: reloaders can leave plugin state or class loaders in an inconsistent state and make profiling results harder to trust.
4. Verify the installation
Once the server is online, run:
/spark versionA version response confirms that the command is registered. If the command is unknown, check the loader directory, the jar’s Minecraft-version compatibility, and the startup log for a dependency or Java error before trying to profile.
Give yourself permission to use Spark
The official permission nodes are spark and spark.profiler. An operator can also run the commands. On a permission-managed server, grant the appropriate node to the administrator group through your permission manager, then reconnect or refresh permissions if that manager requires it.
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| Access method | When it is useful | Important detail |
|---|---|---|
| Operator status | Fast troubleshooting on a private or trusted server | Op access is broader than Spark alone, so avoid it for users who should only profile. |
spark |
Granting the Spark command family to an admin group | Use the node supported by your permission manager. |
spark.profiler |
Restricting access specifically to profiler commands | Other Spark diagnostic commands may require additional permission nodes. |
If a command returns a permission error even though Spark is installed, test from the server console. Console commands bypass in-game permission issues and help distinguish an access problem from a syntax problem.
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Run your first profile
Use this sequence for a normal performance investigation:
- Get players into the situation that causes the lag. Reproduce the issue with the normal number of players, loaded regions and active machines whenever possible.
- Start sampling:
/spark profiler start - Let the profile collect data. Hosting guides commonly suggest about three to five minutes with players online. The official command reference does not mandate a duration, so use a period long enough to include the problem without collecting unrelated activity.
- Stop and publish the profile:
/spark profiler stop - Open the link Spark prints. A completed profile uploads to Spark’s viewer automatically and returns a shareable report URL.
The correct stop command is /spark profiler stop. One third-party example uses /spark profiler --stop, but that is not the form shown in the official command list and should not be your default.
Profiler commands and options
| Command | Purpose |
|---|---|
/spark profiler start |
Begin a profile using the current defaults. |
/spark profiler stop |
End the active profile and upload the result for viewing. |
/spark profiler info |
Show information about the current profiler session. |
/spark profiler open |
Open or view the current profile without stopping it. |
/spark profiler cancel |
Discard the active profile without uploading it. |
These options can be appended to the start command. Use the exact spelling and syntax accepted by your installed version; the complete reference is at spark.lucko.me/docs/Command-Usage.
--timeout <seconds>stops the profile automatically after the specified number of seconds. For example,/spark profiler start --timeout 300collects for five minutes.--thread *profiles all threads, while--thread <name>limits collection to a named thread when you are investigating one worker.--allocprofiles memory allocations instead of only focusing on normal execution sampling. Use it when the symptom points to allocation or garbage-collection activity.--interval <ms>changes the sampling interval in milliseconds. The command reference reports a default value of 4; confirm the exact default and accepted range for your Spark build.--only-ticks-over <ms>records only ticks exceeding the selected duration. A commonly used spike-hunting example is/spark profiler start --only-ticks-over 60.--ignore-sleepingomits sleeping threads from the sample, which can make active work easier to see in a busy report.--comment <text>attaches a note such as the area, player action or timestamp you are testing. Put multi-word text in quotes if your command interface requires it.--save-to-filesaves the profile locally instead of relying only on the automatic upload flow. Check the command output for the exact path and file handling on your version.
Choose the right capture for the problem
Continuous, general lag
Start a normal profile and leave it running for roughly three to five minutes while the server operates under representative load. Have players move, fight, build and use the systems that are normally active. A short profile taken during an empty-server lull will mostly describe idle behavior.
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Short, repeatable spikes
Use a threshold so the report concentrates on slow ticks:
/spark profiler start --only-ticks-over 60The value is a filter, not a universal definition of lag. A 60 ms threshold is an example for isolating ticks that exceed that duration; choose a threshold that matches the symptom you are trying to capture.
One worker or subsystem
If you already know the relevant thread name, add --thread <name>. Use --thread * when you want the complete multi-thread picture. Restricting the scope can make a targeted investigation easier, but it can also hide work occurring on another thread.
Memory or garbage-collection symptoms
Use --alloc for allocation-focused profiling. Pair that profile with Spark’s memory commands—especially /spark gc, /spark gcmonitor, /spark heapsummary and /spark heapdump—when the issue is memory churn rather than long game ticks.
Read the Spark report
1. Open the generated page
After /spark profiler stop, Spark presents a link to the uploaded report. Open it in a browser and preserve the URL when asking for help; the page is designed to be shared.
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2. Start with the flamegraph
The interactive flamegraph visualizes where sampled time was spent. Wider sections represent a larger share of the collected samples. Select a wide stack and follow it downward to see the call path that led to the work, then use the report’s navigation to inspect the surrounding methods and threads.
3. Separate server work from an isolated outlier
Look for patterns that match the reproduction notes. If the profile was collected while a machine was running, a stack associated with that machine is more relevant than an unrelated one-time task. If the profile used --only-ticks-over, remember that the page is intentionally showing slow ticks rather than an average of every tick.
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- A report dominated by one repeating stack suggests a specific task or subsystem worth disabling or testing in isolation.
- Many different stacks spread across the report may indicate broad workload pressure rather than one clear culprit.
- An allocation profile answers a different question from a normal execution profile; do not treat allocation totals as direct tick-time measurements.
- If the report does not include the lag event, collect another profile with a longer window, a better reproduction, or a threshold suited to the spike.
Spark shows where sampled activity occurred; it does not automatically prove that a plugin, mod or configuration setting is defective. Use the stack, reproduction conditions and controlled changes together before removing a component.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use Spark’s other diagnostics
Profiling is one part of the command family. These checks can establish whether the server is currently unhealthy and can help you decide what to capture next.
| Command | What it helps check |
|---|---|
/spark health |
General server-health information. |
/spark ping |
Ping-related response information. |
/spark tps |
Ticks-per-second status. |
/spark tickmonitor |
Tick-duration monitoring for spotting slow ticks. |
/spark gc |
Current garbage-collection information. |
/spark gcmonitor |
Ongoing garbage-collection monitoring. |
/spark heapsummary |
A summary of heap usage. |
/spark heapdump |
A heap dump for deeper memory investigation; treat the resulting file as sensitive. |
/spark activity |
Activity information that can add context to a profile. |
Run the commands your installed version exposes; availability and permission requirements can vary by loader and release.
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Automatic profiling and proxy or client commands
Background profiling
Spark’s configuration includes spark.backgroundProfiler. When enabled, it can start profiling automatically at server startup. This is useful when the problem appears during unattended restarts or before an administrator can log in, but it also creates profiles you may not need. Review the generated data and disable the setting when you have finished the investigation.
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The command prefix changes outside a standard server context:
/sparkbis used for BungeeCord./sparkvis used for Velocity./sparkcis used for a Forge or Fabric client.
On a normal Paper or Spigot server, use /spark; on a Fabric, Forge or NeoForge server, use the Spark command exposed by that installation.
Troubleshoot installation and profiling failures
| Symptom | Likely cause | Fix |
|---|---|---|
/spark version is unknown |
The jar is in the wrong folder, incompatible with the loader, or failed during startup. | Check whether the file belongs in plugins/ or mods/, verify the Minecraft version, restart, and read the startup console. |
| The server reports a mod or plugin dependency error | The downloaded build does not match the loader or game version. | Remove the incompatible jar and download the matching Spark build; do not mix plugin and mod files. |
| The command is denied | Your account lacks spark or spark.profiler, or you are not an operator. |
Run the command from console, obtain operator status, or ask an administrator to grant the required node. |
stop does not end the profile |
The syntax was copied from a different guide or release. | Use /spark profiler stop, then check the command reference for your installed version. |
| The report shows no useful lag | The problem did not occur during collection, or the profile was taken with no representative players or activity. | Reproduce the issue while profiling, use a longer window, add a descriptive comment, or apply --only-ticks-over for a repeatable spike. |
| No report link appears after stopping | The profile may still be processing, upload may have failed, or local saving was requested. | Check the command output and server log. If you used --save-to-file, locate the local file path reported by Spark. |
| Two Spark installations appear in logs | A bundled copy and a manually installed jar may both be present. | Inspect the startup messages, keep the intended compatible copy, remove the duplicate, and restart. |
A repeatable workflow for diagnosing Minecraft server lag
- Record the Minecraft version, loader, player count and the exact action that causes the slowdown.
- Check
/spark health,/spark tpsand/spark tickmonitorto establish whether the symptom is active. - Start a normal profile during representative play; use
--only-ticks-overwhen the issue is a brief, repeatable spike. - Stop the profile with
/spark profiler stopand save the generated link with notes about what happened and when. - Inspect the flamegraph for stacks that coincide with the reproduction, rather than choosing the widest stack without context.
- If memory behavior is suspected, collect an allocation profile and use the GC and heap commands for supporting evidence.
- Change one server component or setting at a time, reproduce the issue again, and compare the new profile with the original conditions.
This approach turns Spark from a one-off screenshot into a controlled diagnostic record: a known installation, a documented reproduction, a profile that includes the event, and a report you can share with the people maintaining the server.
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