def testMaxNodes(self):
"""
Set the scaler to be very aggressive, give it a ton of jobs, and
make sure it doesn't go over maxNodes.
"""
self.config.targetTime = 1
self.config.betaInertia = 0.0
self.config.maxNodes = [2, 3]
scaler = ClusterScaler(self.provisioner, self.leader, self.config)
jobShapes = [
Shape(wallTime=3600,
cores=2,
memory=h2b('1G'),
disk=h2b('2G'),
preemptable=True)
] * 1000
jobShapes.extend([
Shape(wallTime=3600,
cores=2,
memory=h2b('1G'),
disk=h2b('2G'),
preemptable=False)
] * 1000)
estimatedNodeCounts = scaler.getEstimatedNodeCounts(
jobShapes, defaultdict(int))
self.assertEqual(estimatedNodeCounts[r3_8xlarge], 2)
self.assertEqual(estimatedNodeCounts[c4_8xlarge_preemptable], 3)
def testMinNodes(self):
"""
Without any jobs queued, the scaler should still estimate "minNodes" nodes.
"""
self.config.betaInertia = 0.0
self.config.minNodes = [2, 3]
scaler = ClusterScaler(self.provisioner, self.leader, self.config)
jobShapes = []
estimatedNodeCounts = scaler.getEstimatedNodeCounts(jobShapes, defaultdict(int))
self.assertEqual(estimatedNodeCounts[r3_8xlarge], 2)
self.assertEqual(estimatedNodeCounts[c4_8xlarge_preemptable], 3)
def testPreemptableDeficitResponse(self):
"""
When a preemptable deficit was detected by a previous run of the
loop, the scaler should add non-preemptable nodes to
compensate in proportion to preemptableCompensation.
"""
self.config.targetTime = 1
self.config.betaInertia = 0.0
self.config.maxNodes = [10, 10]
# This should mean that one non-preemptable node is launched
# for every two preemptable nodes "missing".
self.config.preemptableCompensation = 0.5
# In this case, we want to explicitly set up the config so
# that we can have preemptable and non-preemptable nodes of
# the same type. That is the only situation where
# preemptableCompensation applies.
self.config.nodeTypes = [c4_8xlarge_preemptable, c4_8xlarge]
self.provisioner.setAutoscaledNodeTypes([
({t}, None) for t in self.config.nodeTypes
])
scaler = ClusterScaler(self.provisioner, self.leader, self.config)
# Simulate a situation where a previous run caused a
# "deficit" of 5 preemptable nodes (e.g. a spot bid was lost)
scaler.preemptableNodeDeficit[c4_8xlarge] = 5
# Add a bunch of preemptable jobs (so the bin-packing
# estimate for the non-preemptable node should still be 0)
jobShapes = [
Shape(wallTime=3600,
cores=2,
memory=h2b('1G'),
disk=h2b('2G'),
preemptable=True)
] * 1000
estimatedNodeCounts = scaler.getEstimatedNodeCounts(
jobShapes, defaultdict(int))
# We don't care about the estimated size of the preemptable
# nodes. All we want to know is if we responded to the deficit
# properly: 0.5 * 5 (preemptableCompensation * the deficit) = 3 (rounded up).
self.assertEqual(
estimatedNodeCounts[self.provisioner.node_shapes_for_testing[1]],
3)