Difference between revisions of "KeystonePerformance"
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=== Identify CPU, Disk, Memory, Database bottlenecks === | === Identify CPU, Disk, Memory, Database bottlenecks === | ||
| + | ==== Methodology ==== | ||
| + | ====== Test #1, Create users in parallel and look for CPU, disk or memory bottleneck. ====== | ||
| + | # Install RDO [http://openstack.redhat.com/] on a bare metal | ||
| + | # Create one instance of ''m1.medium'' flavor and other of type ''m1.large'', so that we can have different CPU and memory config | ||
| + | # Install KeyStone on both of the above created instances | ||
| + | # Using python multiprocessing module create users in parallel using keystoneclient.v2_0 module on each one of them. | ||
| + | ## key.users.create(<user>, "test", "test@test.com") | ||
| + | # Collect the CPU, Disk, Memory and Database related stats while user creation is in progress. | ||
=== Effect of caching - memcached === | === Effect of caching - memcached === | ||
Revision as of 13:02, 13 December 2013
Contents
- 1 Keystone Performance
Keystone Performance
This is to track the performance work related to Keystone.
Work Items
Identify CPU, Disk, Memory, Database bottlenecks
Methodology
Test #1, Create users in parallel and look for CPU, disk or memory bottleneck.
- Install RDO [1] on a bare metal
- Create one instance of m1.medium flavor and other of type m1.large, so that we can have different CPU and memory config
- Install KeyStone on both of the above created instances
- Using python multiprocessing module create users in parallel using keystoneclient.v2_0 module on each one of them.
- key.users.create(<user>, "test", "test@test.com")
- Collect the CPU, Disk, Memory and Database related stats while user creation is in progress.
