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authorSytse Sijbrandij <sytse@gitlab.com>2014-08-21 15:15:31 +0000
committerSytse Sijbrandij <sytse@gitlab.com>2014-08-21 15:15:31 +0000
commita83451a5c052b594ae602923d0beb1195ea6bac8 (patch)
tree0c4caa7499c2e56fee5ae6587d599c87303f7a3e
parent2a532498185a8ffeb02b96c04296b1e843959b09 (diff)
parentecd946be4ce3efffa88c60f2e36a72394561b187 (diff)
downloadgitlab-ce-a83451a5c052b594ae602923d0beb1195ea6bac8.tar.gz
Merge branch 'doc/memory_recommendations' into 'master'
Swap is required on servers with 1GB RAM See merge request !1032
-rw-r--r--doc/install/requirements.md14
1 files changed, 7 insertions, 7 deletions
diff --git a/doc/install/requirements.md b/doc/install/requirements.md
index 938e48ddd74..53f6ccc8c34 100644
--- a/doc/install/requirements.md
+++ b/doc/install/requirements.md
@@ -55,13 +55,13 @@ You will either need to configure 512MB or 1.5GB of swap space.
With 512MB of swap space you must configure only one unicorn worker.
With one unicorn worker only git over ssh access will work because the git over http access requires two running workers (one worker to receive the user request and one worker for the authorization check).
If you use SSD storage and configure 1.5GB of swap space you can use two Unicorn workers, this will allow http access but it will still be slow.
-- 1GB supports up to 100 users (we highly recommend adding al least 1GB of swap space, this is a must if you have individual repositories under 250MB)
-- **2GB** is the **recommended** memory size and supports up to 500 users
-- 4GB supports up to 2,000 users
-- 8GB supports up to 5,000 users
-- 16GB supports up to 10,000 users
-- 32GB supports up to 20,000 users
-- 64GB supports up to 40,000 users
+- 1GB RAM + 1GB swap supports up to 100 users
+- **2GB RAM** is the **recommended** memory size and supports up to 500 users
+- 4GB RAM supports up to 2,000 users
+- 8GB RAM supports up to 5,000 users
+- 16GB RAM supports up to 10,000 users
+- 32GB RAM supports up to 20,000 users
+- 64GB RAM supports up to 40,000 users
Notice: The 25 workers of Sidekiq will show up as separate processes in your process overview (such as top or htop) but they share the same RAM allocation since Sidekiq is a multithreaded application.