Self-hosted GitLab EE

• Install/Requirements
  • Performance tuning configurations of the various associated/required applications.
• Self-host the product documentation
  • Redirect /help links to Docs
    • Redirect /help links
• Synch Repos Across Domains
  • Migrate projects and groups by using file exports ---

Configuration

Q:

Provide sane performance tuning of GitLab for self-hosted single host serving some 500 python developers.

A:

Optimizing a self-hosted GitLab instance for 500 Python developers requires careful tuning of GitLab’s services, including Gitaly, PostgreSQL, Puma (Rails), Redis, and Workhorse. Below is a sane performance tuning guide to improve responsiveness, repository operations, and overall stability for a single-host deployment.

1. System and Hardware Considerations

Recommended Hardware for 500 Developers

• CPU: 16+ cores (Preferably AMD EPYC / Intel Xeon)
• RAM: 64GB+ (more if hosting CI/CD on the same server)
• Storage:
  • SSD/NVMe for repositories (/var/opt/gitlab/git-data/repositories)
  • Dedicated disk for PostgreSQL (/var/opt/gitlab/postgresql)
• Network: 10GbE recommended for high concurrency
• OS: RHEL 9 / Ubuntu 22.04 LTS

2. GitLab Configuration (/etc/gitlab/gitlab.rb)

Run sudo gitlab-ctl reconfigure after making changes.

2.1. Puma - Optimize Web API Requests

Puma replaces Unicorn as the default web server. Tune it for better parallel request handling.

puma['worker_processes'] = 6  # Adjust based on CPU cores (8–12 cores = 6 workers)
puma['min_threads'] = 4
puma['max_threads'] = 16

2.2. Gitaly - Optimize Git Performance

Gitaly is responsible for repository operations (clone, push, fetch).

gitaly['concurrency'] = [
  { 'rpc' => '/gitaly.SmartHTTPService/PostUploadPack', 'max_per_repo' => 10 },
  { 'rpc' => '/gitaly.SSHService/UploadPack', 'max_per_repo' => 10 }
]
gitaly['graceful_restart_timeout'] = '30s'
gitaly['cgroups_memory_enabled'] = true
gitaly['cgroups_memory_limit'] = '4GB'

• Git Smart HTTP

This setting ensures smart HTTP and SSH Git operations do not overload the system.

2.3. PostgreSQL - Optimize Database Performance

For large repositories and heavy API usage, increase PostgreSQL memory and connections.

postgresql['max_connections'] = 400
postgresql['shared_buffers'] = "16GB"
postgresql['work_mem'] = "256MB"
postgresql['maintenance_work_mem'] = "512MB"
postgresql['effective_cache_size'] = "48GB"

Ensure PostgreSQL runs on NVMe SSDs.

2.4. Redis - Optimize Caching

GitLab uses Redis for caching, reducing database load.

redis['maxmemory'] = "16GB"
redis['maxmemory_policy'] = "allkeys-lru"

2.5. Workhorse - Optimize Git HTTP Handling

Workhorse acts as a reverse proxy for Git HTTP requests.

gitlab_workhorse['listen_network'] = "tcp"
gitlab_workhorse['listen_addr'] = "0.0.0.0:8181"

This allows faster internal request processing.

2.6. Sidekiq - Optimize Background Jobs

Sidekiq processes background jobs (issues, CI/CD triggers).

sidekiq['concurrency'] = 20

This ensures background tasks don’t get backlogged.

3. Git Performance Tuning

3.1. Optimize Git Garbage Collection

GitLab runs periodic Git garbage collection (GC), which should be optimized for large repositories.

Edit:

sudo -u git -H git config --global gc.auto 0
sudo -u git -H git config --global pack.threads 4
sudo -u git -H git config --global pack.windowMemory 256m

Then, schedule Git garbage collection off-peak hours:

crontab -e

Add:

0 2 * * * sudo gitlab-rake gitlab:cleanup:repos

4. Linux Kernel & System Tweaks

4.1. Increase File Descriptors

Large teams mean high concurrent Git operations.

Edit /etc/security/limits.conf:

gitlab    soft    nofile    1000000
gitlab    hard    nofile    1000000

4.2. Optimize TCP/IP Stack

Edit /etc/sysctl.conf:

fs.file-max = 1000000
net.core.somaxconn = 1024
net.ipv4.tcp_tw_reuse = 1
net.ipv4.tcp_fin_timeout = 15
net.ipv4.tcp_keepalive_time = 300
net.core.rmem_max = 8388608
net.core.wmem_max = 8388608

Apply with:

sudo sysctl -p

5. Monitoring & Troubleshooting

5.1. Enable GitLab’s Embedded Prometheus Monitoring

In /etc/gitlab/gitlab.rb:

prometheus_monitoring['enable'] = true

Check GitLab’s built-in metrics:

sudo gitlab-ctl status
sudo gitlab-ctl tail

5.2. Check Gitaly Performance

sudo gitlab-rake gitlab:gitaly:status

5.3. Debug PostgreSQL Slow Queries

sudo gitlab-psql -c "SELECT * FROM pg_stat_activity WHERE state != 'idle';"

6. CI/CD Considerations

For 500 Python developers, CI/CD can overload GitLab.

• Move CI/CD Runners to Separate Machines

  • Use GitLab Runner on dedicated nodes:

    sudo gitlab-runner register
    

  • Runners should have high CPU & RAM.

• Enable Cache for Python Dependencies

  cache:
  key: pip-cache
  paths:
    - .pip-cache/
  

7. Backup Strategy

Configure backups in /etc/gitlab/gitlab.rb:

gitlab_rails['backup_keep_time'] = 604800  # Keep backups for 7 days
gitlab_rails['backup_path'] = "/var/opt/gitlab/backups"

Schedule daily backups:

crontab -e

0 3 * * * sudo gitlab-backup create

Final Steps

1. Apply Changes:

   sudo gitlab-ctl reconfigure
   sudo gitlab-ctl restart
   

2. Monitor Performance:

   sudo gitlab-ctl status
   

3. Stress Test:

   url=https://gitlab.example.com/
   ab -n 1000 -c 100 $url
   

Summary of Key Optimizations

This configuration ensures GitLab scales efficiently for 500 developers while optimizing repository access, background jobs, and CI/CD workflows.

Q:

Do those Puma/Gitaly/PostgreSQL/Redis/Git configurations differ from defaults of GitLab v17.x?

A:

Yes, the configurations differ from the default settings in GitLab v17.x in several key ways. Below, I'll compare the defaults vs. recommended settings for Puma, Gitaly, PostgreSQL, Redis, and Git, highlighting why changes are necessary for scaling to 500 Python developers on a single-host GitLab instance.

1. Puma (Web Application Server)

GitLab v17.x Default:

puma['worker_processes'] = 2
puma['min_threads'] = 4
puma['max_threads'] = 4

• Limited to 2 workers → inefficient for high-concurrency environments.
• Thread count (4) is low, which may cause request queuing under load.

Recommended for 500 Devs:

puma['worker_processes'] = 6  # Scale workers based on available CPU cores
puma['min_threads'] = 4
puma['max_threads'] = 16

• Why? More workers allow better parallel request handling for GitLab UI/API.
• Why? Higher threads allow handling multiple concurrent requests per worker.

2. Gitaly (Git RPC Service)

GitLab v17.x Default:

gitaly['concurrency'] = [
  { 'rpc' => '/gitaly.SmartHTTPService/PostUploadPack', 'max_per_repo' => 5 },
  { 'rpc' => '/gitaly.SSHService/UploadPack', 'max_per_repo' => 5 }
]
gitaly['graceful_restart_timeout'] = '0s'

• Limits Git operations to 5 concurrent uploads per repository.
• No memory cgroups, so Gitaly can overconsume RAM.

Recommended for 500 Devs:

gitaly['concurrency'] = [
  { 'rpc' => '/gitaly.SmartHTTPService/PostUploadPack', 'max_per_repo' => 10 },
  { 'rpc' => '/gitaly.SSHService/UploadPack', 'max_per_repo' => 10 }
]
gitaly['graceful_restart_timeout'] = '30s'
gitaly['cgroups_memory_enabled'] = true
gitaly['cgroups_memory_limit'] = '4GB'

• Why? Increase concurrent Git upload handling for large teams.
• Why? Memory cgroups prevent Gitaly from consuming too much RAM.

3. PostgreSQL (Database)

GitLab v17.x Default:

postgresql['max_connections'] = 200
postgresql['shared_buffers'] = "256MB"
postgresql['work_mem'] = "4MB"
postgresql['maintenance_work_mem'] = "64MB"
postgresql['effective_cache_size'] = "4GB"

• Only 200 connections → insufficient for 500 developers using GitLab UI/API.
• Low cache and memory allocation → Slower queries under heavy load.

Recommended for 500 Devs:

postgresql['max_connections'] = 400
postgresql['shared_buffers'] = "16GB"
postgresql['work_mem'] = "256MB"
postgresql['maintenance_work_mem'] = "512MB"
postgresql['effective_cache_size'] = "48GB"

• Why? Increase max connections to 400 to handle API calls and web UI requests.
• Why? Higher shared_buffers allows caching more query results in RAM.

4. Redis (Caching Service)

GitLab v17.x Default:

redis['maxmemory'] = "0"  # Unlimited
redis['maxmemory_policy'] = "noeviction"

• Unlimited Redis memory usage can cause Out-of-Memory (OOM) issues.
• No eviction policy, meaning Redis will refuse new writes when full.

Recommended for 500 Devs:

redis['maxmemory'] = "16GB"
redis['maxmemory_policy'] = "allkeys-lru"

• Why? Set a 16GB cache limit to prevent Redis from consuming all system RAM.
• Why? Use Least Recently Used (LRU) eviction to remove old cache entries.

5. Git Configuration (Garbage Collection & Packing)

GitLab v17.x Default:

git config --global gc.auto 1
git config --global pack.threads 1

• Git automatically runs garbage collection (gc.auto 1), which is inefficient for large repos.
• Packing operations (pack.threads 1) use a single thread, slowing down large repository maintenance.

Recommended for 500 Devs:

sudo -u git -H git config --global gc.auto 0
sudo -u git -H git config --global pack.threads 4
sudo -u git -H git config --global pack.windowMemory 256m

• Why? Disable auto-GC and run it via cron at off-peak hours.
• Why? Use 4 threads for packing, improving performance for large repos.

6. Workhorse (Git HTTP Proxy)

GitLab v17.x Default:

gitlab_workhorse['listen_network'] = "unix"
gitlab_workhorse['listen_addr'] = "/var/opt/gitlab/gitlab-workhorse/socket"

• Uses Unix sockets, which are fine for low-traffic environments but may not scale well under high load.

Recommended for 500 Devs:

gitlab_workhorse['listen_network'] = "tcp"
gitlab_workhorse['listen_addr'] = "0.0.0.0:8181"

• Why? Switch to TCP sockets for better network concurrency under load.

7. Sidekiq (Background Job Processing)

GitLab v17.x Default:

sidekiq['concurrency'] = 5

• Only 5 workers, causing backlogs in notifications, CI/CD triggers, etc.

Recommended for 500 Devs:

sidekiq['concurrency'] = 20

• Why? Increases background job throughput for faster task processing.

8. TCP/IP Kernel Tweaks

GitLab v17.x Default:

• Defaults to generic kernel networking settings, which can be suboptimal for heavy concurrent traffic.

Recommended for 500 Devs (/etc/sysctl.conf)

fs.file-max = 1000000
net.core.somaxconn = 1024
net.ipv4.tcp_tw_reuse = 1
net.ipv4.tcp_fin_timeout = 15
net.ipv4.tcp_keepalive_time = 300
net.core.rmem_max = 8388608
net.core.wmem_max = 8388608

• Why? Reduce connection wait times and increase socket buffers for high-concurrency Git operations.

Summary of Key Differences

Final Steps

1. Apply changes:

   sudo gitlab-ctl reconfigure
   sudo gitlab-ctl restart
   

2. Monitor GitLab:

   sudo gitlab-ctl status
   

3. Check PostgreSQL slow queries:

   sudo gitlab-psql -c "SELECT * FROM pg_stat_activity WHERE state != 'idle';"
   

These optimizations should ensure smooth operation for 500 developers, reducing latency in Git operations and improving UI/API response times.

Q:

Actual memory of host needn't be the sum of those "memory" settings?

A1:

You're absolutely correct to think critically about the memory settings for each service. The memory configurations for PostgreSQL, Redis, Gitaly, etc., are all separate and typically don't add up directly to the total available physical memory. However, they do need to be managed carefully, as each service will use its portion of the memory based on the settings.

Here's an explanation of how memory settings for these services interact:

1. PostgreSQL Memory Settings

The key settings like shared_buffers, work_mem, and effective_cache_size configure how PostgreSQL uses memory, but they don’t directly sum up to the total memory PostgreSQL consumes.

• shared_buffers: This is the amount of memory PostgreSQL uses for caching data in memory. It’s typically a portion of the total system RAM, but not all of it. For high-performance systems, this should generally be set to around 25-40% of total system memory, but it can be lower for very large systems where disk I/O speed is optimized.

• work_mem: This is the amount of memory used for sorting and hashing operations per query. It can be a concern if many queries use a lot of memory, but PostgreSQL will only allocate this amount for each operation. In practice, it doesn’t sum up to a strict total — PostgreSQL will use it per query execution context.

• effective_cache_size: This is more of a hint to PostgreSQL about how much memory is available for caching, used by the query planner. It should generally be set to 50-75% of the total system memory, as it's a guideline for query optimization.

2. Redis Memory Settings

The maxmemory setting tells Redis how much memory it can use for caching. It doesn't "add up" with other services, and Redis will try to stay within this limit, evicting keys as necessary based on the eviction policy (allkeys-lru in the example).

3. Gitaly Memory Settings

The memory settings for Gitaly are primarily related to how much memory Gitaly can use for its own internal operations, including handling repository access and storing temporary files. These settings will use memory resources, but not to the extent of directly competing with the entire host's memory.

4. Puma Memory Settings

Puma's memory use depends on the number of worker processes and threads. Each worker uses a set amount of memory, so the memory per worker can add up (along with threads), but again, the total memory usage will not be strictly the sum of all worker memory unless all are fully utilized. Generally, you want to make sure each worker has enough memory, but you don't want to have too many workers such that you run out of memory on the system.

How to Monitor and Tune:

• Total Memory Usage: You should make sure that the total memory usage for all services doesn’t exceed the available memory of the host. The system’s swap space (if any) should also be monitored to avoid performance issues.
• Process Isolation: Services like PostgreSQL, Redis, and Gitaly may run on separate processes and may have specific memory limits. Each service is designed to work efficiently within its own limits.
• Host Memory: If your host has 64GB RAM, you wouldn't necessarily allocate all of it to these services. You might leave some memory for the OS and other applications.

Example:

If you set: - PostgreSQL shared_buffers = 16GB - Redis maxmemory = 16GB - Gitaly memory settings = 4GB

Then you could aim for something like: - 16GB + 16GB + 4GB = 36GB for these services, leaving the remaining memory (roughly 28GB) for the OS, Puma, caching, and other overhead.

The sum of the "memory" settings for services is a guideline, not an exact total, and you should monitor the system’s overall memory usage to ensure that your host is not overloaded. Tools like htop, vmstat, or GitLab's built-in metrics can help you track this.

Conclusion:

While it's good practice to tune memory for these services, the actual system memory usage won't be the sum of all the configured memory limits. As long as you leave sufficient memory for the operating system and other processes, these services will run efficiently within their own memory limits without exceeding the host's capacity. Regular monitoring is key to understanding how these services interact with your server's memory.

A2:

You're absolutely right! The actual physical memory (RAM) requirement of the host is not necessarily the sum of all the individual "memory" settings from Puma, Gitaly, PostgreSQL, Redis, and other services. These values represent allocation limits, caches, or buffers, which do not all get fully utilized at the same time.

How to Estimate Real Memory Usage

Rather than summing up all memory values, we consider: 1. Memory allocation vs. actual usage: Services typically do not use their full allocation unless under extreme load. 2. Overlapping memory usage: Some caches (PostgreSQL, Redis) may hold redundant data, reducing total needs. 3. OS-level memory management: Linux aggressively caches disk reads and reclaims memory dynamically.

Breakdown of Major Services and Realistic RAM Usage

Total Estimated RAM Requirement

Instead of summing up the maximum allocations (≈58 GB), we estimate: - Minimum required RAM: 32GB - Optimal RAM: 64GB - For heavy CI/CD workloads: 128GB+

Key Considerations

1. PostgreSQL's shared_buffers doesn't use all memory immediately. It only keeps frequently accessed data in RAM.
2. Redis does not always use full maxmemory. If the cache is not full, it uses less.
3. Gitaly uses memory in bursts during Git operations. Idle time consumption is low.
4. Sidekiq jobs vary in memory needs depending on how many are running.
5. Linux dynamically allocates unused RAM to disk caching. This improves performance without reserving actual memory.

What Happens If RAM is Exceeded?

If total memory demand exceeds available RAM, GitLab will start swapping, which significantly degrades performance. To prevent this: - Enable swap (if necessary, for buffer):

  sudo fallocate -l 8G /swapfile
  sudo chmod 600 /swapfile
  sudo mkswap /swapfile
  sudo swapon /swapfile
  echo "/swapfile none swap sw 0 0" | sudo tee -a /etc/fstab

• Monitor memory usage with htop or glances.
• Use cgroups (systemd or cgroup limits) to prevent single services from consuming all RAM.

Conclusion

• Do NOT add up all memory settings—they represent limits, not actual usage.
• 64GB RAM is a good target for 500 developers with Git, API, and CI/CD usage.
• If hosting runners for CI/CD, aim for 128GB+.

Q:

If the CI/CD pipelines use Kubernetes executor and GitLab runners are of a remote (k8s) service, do the advised configurations change at all?

A:

Yes, if CI/CD pipelines use a Kubernetes executor and GitLab runners are on a remote Kubernetes cluster, GitLab's resource requirements on the main server decrease, but some optimizations remain necessary.

Key Differences with Kubernetes Runners

1. GitLab no longer runs CI/CD jobs locally → No need for high Sidekiq concurrency.
2. GitLab's PostgreSQL, Redis, Puma, and Gitaly still require tuning, but memory pressure is lower.
3. Workhorse and API performance remain crucial, as pipelines generate API requests and Git operations.

How Do Configuration Recommendations Change?

How These Changes Impact Performance

✅ Lower PostgreSQL & Redis load:

• CI/CD job logs and artifacts now persist in Kubernetes storage instead of PostgreSQL/Redis on GitLab.
• Fewer writes to PostgreSQL → lower memory and disk I/O.

✅ Lower Sidekiq load:

• CI/CD job execution moves to Kubernetes, so fewer jobs run inside GitLab.
• Background jobs still needed for:
  • Pipeline scheduling
  • CI/CD event processing
  • Artifact expiration
  • Merge request automation
• Reduce Sidekiq concurrency from 20 to 8.

✅ Gitaly & Workhorse remain critical:

• Pipelines still clone repositories from GitLab → Gitaly still needs high concurrency.
• Runners access repositories via HTTP(S) → Workhorse should stay in TCP mode.

✅ Git garbage collection remains necessary:

• Even though runners are external, Git repositories still accumulate loose objects.
• Run Git GC regularly to avoid repo bloat.

Revised Configuration (/etc/gitlab/gitlab.rb)

If runners move to Kubernetes, apply these changes:

1. PostgreSQL - Reduce Memory & Connections

postgresql['max_connections'] = 300
postgresql['shared_buffers'] = "12GB"
postgresql['work_mem'] = "128MB"
postgresql['effective_cache_size'] = "36GB"

• Why? Fewer CI/CD database queries.

2. Redis - Reduce Memory Allocation

redis['maxmemory'] = "8GB"
redis['maxmemory_policy'] = "allkeys-lru"

• Why? CI/CD cache now handled in Kubernetes.

3. Sidekiq - Reduce Background Worker Concurrency

sidekiq['concurrency'] = 8

• Why? Fewer local CI/CD jobs to process.

4. Keep Puma, Gitaly, and Workhorse Settings

puma['worker_processes'] = 6
puma['max_threads'] = 16

gitaly['concurrency'] = [
  { 'rpc' => '/gitaly.SmartHTTPService/PostUploadPack', 'max_per_repo' => 10 },
  { 'rpc' => '/gitaly.SSHService/UploadPack', 'max_per_repo' => 10 }
]
gitaly['cgroups_memory_enabled'] = true
gitaly['cgroups_memory_limit'] = '4GB'

gitlab_workhorse['listen_network'] = "tcp"
gitlab_workhorse['listen_addr'] = "0.0.0.0:8181"

• Puma stays highly concurrent for UI/API requests.
• Gitaly still needs high concurrency for cloning/fetching.
• Workhorse remains optimized for Git HTTP traffic.

5. Keep Git Garbage Collection Active

sudo -u git -H git config --global gc.auto 0
sudo -u git -H git config --global pack.threads 4
sudo -u git -H git config --global pack.windowMemory 256m

• Why? Even with external runners, repositories still accumulate junk objects.

How Much RAM is Now Needed?

• With Kubernetes runners, GitLab can run well on 32GB, but 48GB is safer for heavy repo traffic.
• If hosting large repositories (GBs in size), keep 64GB+.

Final Thoughts

✅ With external runners, GitLab requires ~30% less memory
✅ PostgreSQL and Redis workloads drop significantly
✅ Workhorse and Gitaly remain critical for repo access
✅ Git garbage collection is still necessary

If your Kubernetes cluster is also handling GitLab registry/artifacts,
we can fine-tune object storage, persistent volumes, and caching strategies!

Q:

How to load test the protected Git service endpoints, e.g., git ls-remote $host/$repo.git, which require authentication, using Apache Bench?

A:

To load test protected Git service endpoints such as git ls-remote $host/$repo.git, which require authentication, using Apache Bench (ab), follow these steps:

1. Gather Required Credentials

For Git HTTP authentication, you typically use: - Username & Password (Basic Auth) - Personal Access Token (PAT) (GitLab, GitHub) - Session Cookies (If using GitLab with login)

2. Format the Authenticated Request

Git servers usually require Basic Authentication, which can be provided using: - Username & Password: https://username:password@$host/$repo.git - Access Token (Preferred): https://oauth_token@$host/$repo.git

Example: Constructing an Authenticated URL

GIT_HOST="git.example.com"
REPO_PATH="group/repo.git"
USERNAME="testuser"
PASSWORD="supersecret"
ACCESS_TOKEN="glpat-xxxxxxxxxxxxxx"

# Basic Auth (Less Secure)
AUTH_URL="https://$USERNAME:$PASSWORD@$GIT_HOST/$REPO_PATH"

# Personal Access Token (More Secure)
AUTH_URL="https://$ACCESS_TOKEN@$GIT_HOST/$REPO_PATH"

3. Use Apache Bench (ab) for Load Testing

Basic Test

To test the response time and throughput:

ab -n 100 -c 10 "$AUTH_URL/info/refs?service=git-upload-pack"

• -n 100 → Total 100 requests
• -c 10 → 10 concurrent connections
• /info/refs?service=git-upload-pack → The endpoint Git uses for ls-remote

More Intensive Load Test

ab -n 1000 -c 50 "$AUTH_URL/info/refs?service=git-upload-pack"

• Simulates 1000 total requests with 50 concurrent users

4. If Password Authentication Fails (Use -H Header)

Some Git servers block username:password in URLs. Use an Authorization header instead:

ab -n 100 -c 10 -H "Authorization: Basic $(echo -n "$USERNAME:$PASSWORD" | base64)" "https://$GIT_HOST/$REPO_PATH/info/refs?service=git-upload-pack"

For Personal Access Token (PAT):

ab -n 100 -c 10 -H "Authorization: Basic $(echo -n "oauth:$ACCESS_TOKEN" | base64)" "https://$GIT_HOST/$REPO_PATH/info/refs?service=git-upload-pack"

5. Using Session Cookies (For GitLab)

If GitLab requires login, use session cookies: 1. Log in via curl:

   curl -c cookies.txt -X POST -d "username=myuser&password=mypass" "https://git.example.com/users/sign_in"

1. Use Apache Bench with cookies:

   ab -n 100 -c 10 -C "cookie_value_from_cookies.txt" "https://git.example.com/$REPO_PATH/info/refs?service=git-upload-pack"
   

6. Analyzing Apache Bench Output

After running ab, you’ll get: - Requests per second (Requests/sec): Higher is better. - Time per request (Time per request): Lower is better. - Failed requests: Ensure 0 failed requests.

7. Monitor GitLab Server Load During Test

While running ab, monitor server resources:

htop   # CPU/RAM usage
iotop  # Disk I/O usage
dstat  # Network usage

8. Avoid Being Blocked

🔹 Use -r to prevent aborting on socket failures:

ab -n 1000 -c 50 -r "$AUTH_URL/info/refs?service=git-upload-pack"

🔹 Rate-limit requests (-p) to avoid overloading the Git server:

ab -n 1000 -c 10 -p 500 "https://$GIT_HOST/$REPO_PATH/info/refs?service=git-upload-pack"

Summary

✔ Use Apache Bench (ab) to test git ls-remote using /info/refs?service=git-upload-pack
✔ Use -H "Authorization: Basic <base64> for authentication
✔ Monitor server load (htop, iotop, dstat)
✔ Use -r to prevent premature failures

Would you like to extend testing to Git pushes/pulls (git-receive-pack)? 🚀

Q:

The real issue is git clone $url responding HTTP 500, but we don't want to slam the server with lots of such requests, thinking that would ruin everyone's day.

A:

... you don't want to overload the server with excessive test requests. Here’s a structured approach to debug this without causing unnecessary load.

1. Understanding the git clone HTTP Request

When running:

git clone https://$GIT_HOST/$REPO_PATH.git

Git makes multiple HTTP requests to the server:

1. Fetch repository metadata (ls-remote)
   
   • GET /$REPO_PATH.git/info/refs?service=git-upload-pack
2. Negotiate packfile transfer
   
   • POST /$REPO_PATH.git/git-upload-pack
3. Download objects in batches
   
   • Multiple POST requests until all objects are fetched.

An HTTP 500 (Internal Server Error) suggests: - The Git HTTP backend (Gitaly, Workhorse, or Nginx) is failing. - The server is overloaded or misconfigured. - A specific repository operation (e.g., object packing) is triggering an error.

2. How to Test Without Slamming the Server

To troubleshoot without flooding GitLab, use controlled and targeted tests:

Option 1: Isolate git ls-remote First

Since git clone first calls ls-remote, check if it fails first:

git ls-remote https://$GIT_HOST/$REPO_PATH.git

If this also returns HTTP 500, the issue is likely Gitaly or GitLab Workhorse.

Option 2: Clone a Tiny Repo (Low Impact Test)

Instead of testing with a large repo, clone a small one:

git clone --depth 1 https://$GIT_HOST/$SMALL_TEST_REPO.git

• If this works, the problem is large repository cloning.
• If this also fails, the problem is likely system-wide.

Option 3: Clone with Debug Logging

Run git clone with detailed debug logs:

GIT_TRACE=1 GIT_CURL_VERBOSE=1 git clone https://$GIT_HOST/$REPO_PATH.git

• Check where the request fails.

• Look for errors like:

  fatal: HTTP error 500
  

  or

  POST git-upload-pack (500 Internal Server Error)
  

3. Check Server Logs for Root Cause

On the GitLab server, analyze logs in real-time while running git clone:

Gitaly Logs (Git backend)

sudo tail -f /var/log/gitlab/gitaly/current

• Look for RPC error or failed to process upload-pack.

Workhorse Logs (Git HTTP proxy)

sudo tail -f /var/log/gitlab/gitlab-workhorse/current

• Check if Workhorse is failing when proxying Git HTTP requests.

Nginx Logs (GitLab Web Requests)

sudo tail -f /var/log/gitlab/nginx/gitlab_access.log
sudo tail -f /var/log/gitlab/nginx/gitlab_error.log

• Look for 500 Internal Server Error.

PostgreSQL Logs (Database issues)

sudo tail -f /var/log/gitlab/postgresql/current

• If CI/CD jobs or hooks are failing, it might be a DB overload issue.

4. Possible Causes of HTTP 500 in git clone

Based on logs, here are common reasons and fixes:

5. Safe Load Testing Without Overloading the Server

To simulate git clone without impacting production, use Apache Bench (ab) with rate-limiting:

Option 1: Test Without Downloading Objects

Instead of cloning everything, only request metadata:

ab -n 10 -c 2 "https://$GIT_HOST/$REPO_PATH.git/info/refs?service=git-upload-pack"

• Simulates 10 lightweight clone requests.
• Does not download full repository data.

Option 2: Rate-Limited git clone

Clone with bandwidth limits:

git clone --depth=1 --single-branch --progress https://$GIT_HOST/$REPO_PATH.git &
sleep 2 && kill $!

• Runs a partial fetch for 2 seconds, then stops.
• Prevents overloading the server.

Option 3: Clone Specific Large Objects

If a repo has large files, fetch only a single large object:

git fetch --depth 1 origin <large_commit_sha>

• Checks if large object retrieval is the issue.

6. Final Steps: Fixing HTTP 500 Issues

✅ If Gitaly is the issue:

• Increase memory limits in gitaly.toml:

  [memory]
  max_rss = "6GB"
  

• Restart Gitaly:

  sudo gitlab-ctl restart gitaly
  

✅ If Nginx is the issue:

• Increase timeout limits in /etc/gitlab/gitlab.rb:

  nginx['proxy_read_timeout'] = 300
  nginx['proxy_connect_timeout'] = 300
  

• Restart Nginx:

  sudo gitlab-ctl restart nginx
  

✅ If Workhorse is the issue:

• Increase timeout:

  gitlab_workhorse['proxy_timeout'] = 300
  

• Restart Workhorse:

  sudo gitlab-ctl restart gitlab-workhorse
  

✅ If Large Repo Clones Are the Issue:

• Run Git Garbage Collection:

  sudo -u git -H git gc --aggressive /var/opt/gitlab/git-data/repositories/@hashed/<repo_path>
  

• Encourage shallow clones:

  git clone --depth 1 https://$GIT_HOST/$REPO_PATH.git
  

7. Summary

Q:

Indeed, git ls-remote by itself generates the same pattern and rate of HTTP 500 responses !

That is, from above:

Since git clone first calls ls-remote, check if it fails first:

>git ls-remote https://$GIT_HOST/$REPO_PATH.git
>```
>If this also returns **HTTP 500**, 
>the issue is likely **Gitaly or GitLab Workhorse**.


# A:

Great observation! If `git ls-remote $URL` alone is generating **HTTP 500 responses** at the same rate and pattern as `git clone`, this strongly suggests the issue lies in **GitLab’s Git HTTP service**, specifically:

### **Possible Causes**
1. **Gitaly is failing under load** (Git backend service)
2. **GitLab Workhorse is misconfigured or timing out**
3. **Nginx reverse proxy limits are being exceeded**
4. **A specific repository operation (`git-upload-pack`) is failing**
5. **File descriptor limits or memory constraints on GitLab components**

---

## **1. Verify Gitaly is the Issue**
Since `git ls-remote` interacts **directly with Gitaly**, check if Gitaly is failing.

### **Check Gitaly Logs in Real-Time**

bash sudo tail -f /var/log/gitlab/gitaly/current

- Look for **errors or RPC failures**:

rpc error: code = Internal desc = failed to call git-upload-pack

### **Manually Test Gitaly**
Try an RPC request to Gitaly:

bash sudo gitlab-rake gitlab:gitaly:status

If Gitaly is failing, restart it:

bash sudo gitlab-ctl restart gitaly

**If Gitaly is repeatedly failing**, **increase its memory limits**:

toml [gitaly] concurrency = 20 memory_limit = "6GB"

Then reload:

bash sudo gitlab-ctl reconfigure

---

## **2. Check GitLab Workhorse**
Since Workhorse proxies Git HTTP traffic, **it may be timing out**.

### **Check Workhorse Logs**

bash sudo tail -f /var/log/gitlab/gitlab-workhorse/current

- Look for:

500 Internal Server Error upstream prematurely closed connection

### **Increase Workhorse Timeout**
Edit `/etc/gitlab/gitlab.rb`:

ruby gitlab_workhorse['proxy_timeout'] = 300

Then restart:

bash sudo gitlab-ctl restart gitlab-workhorse

---

## **3. Increase Nginx Limits**
If GitLab is behind **Nginx**, **Nginx may be rejecting requests** due to:
- **Connection limits**
- **Timeout settings**

### **Check Nginx Logs**

bash sudo tail -f /var/log/gitlab/nginx/gitlab_error.log

- Look for errors like:

502 Bad Gateway 500 Internal Server Error client sent too many requests

### **Increase Timeout & Connections**
Edit `/etc/gitlab/gitlab.rb`:

ruby nginx['proxy_read_timeout'] = 300 nginx['proxy_connect_timeout'] = 300 nginx['worker_connections'] = 16384 nginx['worker_rlimit_nofile'] = 100000

Then restart:

bash sudo gitlab-ctl restart nginx

---

## **4. Verify Git is Failing on `git-upload-pack`**
### **Manually Test Git HTTP API**
Instead of `git ls-remote`, run:

bash curl -v "https://$GIT_HOST/$REPO_PATH.git/info/refs?service=git-upload-pack"

- If this **also returns HTTP 500**, it confirms **GitLab’s Git service** is failing.
- If it **succeeds**, the issue is related to **Git concurrency limits**.

### **Increase Gitaly Concurrency**

toml [gitaly] concurrency = 20

Then restart:

bash sudo gitlab-ctl restart gitaly

---

## **5. Check File Descriptors & Memory Limits**
If **too many requests cause failures**, increase system limits.

### **Check Open Files Limit**

bash ulimit -n

If **less than 100000**, increase it in `/etc/security/limits.conf`:

gitlab soft nofile 100000 gitlab hard nofile 100000

### **Check System Memory**

bash free -h

If **Gitaly is OOM-killing**, increase swap:

bash sudo fallocate -l 8G /swapfile sudo chmod 600 /swapfile sudo mkswap /swapfile sudo swapon /swapfile

---

## **6. Safe Testing Without Overloading GitLab**
Instead of running many `git clone` commands:
- Test **only metadata fetching**:

bash ab -n 10 -c 2 "https://$GIT_HOST/$REPO_PATH.git/info/refs?service=git-upload-pack"

- Use **shallow cloning**:

bash git clone --depth 1 https://$GIT_HOST/$REPO_PATH.git ```

Summary of Fixes

Let me know which logs show 500 errors, and we’ll narrow it down! 🚀