#!/usr/bin/env python
# This is an example showing a current SONAS configuration with 3
# interface node and a management node. When run with deterministic
# IPs there are gratuitous IP reassignments.
from ctdb_takeover import Cluster, Node, process_args
process_args()
addresses = ['A', 'B', 'C', 'D', 'E', 'F', 'G']
c = Cluster()
for i in range(3):
c.add_node(Node(addresses))
c.add_node(Node([]))
c.recover()
c.random_iterations()
#!/usr/bin/env python
from ctdb_takeover import Cluster, Node, process_args
process_args()
addresses10 = ['10.4.20.{0:d}'.format(n) for n in range(154, 168)]
addresses172a = ['172.20.106.{0:d}'.format(n) for n in range(110, 124)]
addresses172b = ['172.20.107.{0:d}'.format(n) for n in range(110, 117)]
c = Cluster()
#for i in range(7):
# c.add_node(Node([addresses10, addresses172]))
for i in range(4):
c.add_node(Node([addresses172a, addresses172b]))
for i in range(3):
c.add_node(Node(addresses10))
c.recover()
c.random_iterations()
#!/usr/bin/env python
# 2 IP groups, both on the same 5 nodes, with each group on different
# interfaces/VLANs. One group has many more addresses to test how
# well an "imbalanced" configuration will balance...
from ctdb_takeover import Cluster, Node, process_args
process_args()
addresses20 = ['192.168.20.{0:d}'.format(n) for n in range(1, 13)]
addresses128 = ['192.168.128.{0:d}'.format(n) for n in range(1, 5)]
c = Cluster()
for i in range(5):
c.add_node(Node([addresses20, addresses128]))
#for i in range(3):
# c.add_node(Node([addresses20]))
c.recover()
c.random_iterations()
#!/usr/bin/env python
# 2 IP groups, across 2 nodes, with each group on different
# interfaces. 4 addresses per group. A nice little canonical 2 node
# configuration.
from ctdb_takeover import Cluster, Node, process_args
process_args()
addresses1 = ['192.168.1.%d' % n for n in range(1, 5)]
addresses2 = ['192.168.2.%d' % n for n in range(1, 5)]
# Try detecting imbalance with square root of number of nodes? Or
# just with a parameter indicating how unbalanced you're willing to
# accept...
c = Cluster()
for i in range(2):
c.add_node(Node([addresses1, addresses2]))
c.recover()
c.random_iterations()
#!/usr/bin/env python
from ctdb_takeover import Cluster, Node, process_args
process_args()
addresses10 = ['10.4.20.%d' % n for n in range(154, 168)]
addresses172a = ['172.20.106.%d' % n for n in range(110, 124)]
addresses172b = ['172.20.107.%d' % n for n in range(110, 117)]
c = Cluster()
#for i in range(7):
# c.add_node(Node([addresses10, addresses172]))
for i in range(4):
c.add_node(Node([addresses172a, addresses172b]))
for i in range(3):
c.add_node(Node(addresses10))
c.recover()
c.random_iterations()
#!/usr/bin/env python
# This is a contrived example that makes the balancing algorithm fail
# for nondeterministic IPs (run with "-dv --nd" to see the failure).
from ctdb_takeover import Cluster, Node, process_args
process_args()
addresses1 = ['A', 'B', 'C', 'D']
addresses2 = ['B', 'E', 'F']
c = Cluster()
for i in range(2):
c.add_node(Node(addresses1))
c.add_node(Node(addresses2))
c.recover()
c.unhealthy(1)
c.recover()
c.healthy(1)
c.recover()
#!/usr/bin/env python
# 2 groups of addresses, combined into 1 pool so the checking
# algorithm doesn't know about the groups, across 2 nodes.
from ctdb_takeover import Cluster, Node, process_args
process_args()
addresses20 = ['192.168.20.%d' % n for n in range(1, 13)]
addresses21 = ['192.168.21.%d' % n for n in range(1, 5)]
c = Cluster()
for i in range(2):
c.add_node(Node(addresses20 + addresses21))
c.recover()
c.random_iterations()
#!/usr/bin/env python
# 4 IP groups, across 10 nodes, with each group on different
# interfaces/VLANs. 80 addresses in total but not evenly balanced, to
# help check some of the more extreme behaviour.
from ctdb_takeover import Cluster, Node, process_args
process_args()
addresses1 = ['192.168.1.%d' % n for n in range(1, 41)]
addresses2 = ['192.168.2.%d' % n for n in range(1, 21)]
addresses3 = ['192.168.3.%d' % n for n in range(1, 11)]
addresses4 = ['192.168.4.%d' % n for n in range(1, 11)]
# Try detecting imbalance with square root of number of nodes? Or
# just with a parameter indicating how unbalanced you're willing to
# accept...
c = Cluster()
for i in range(10):
c.add_node(Node([addresses1, addresses2, addresses3, addresses4]))
c.recover()
c.random_iterations()
