def test_adaptive_sampler_default_values():
adaptive_sampler = AdaptiveSampler({}, 2)
assert '%s' % adaptive_sampler == 'AdaptiveSampler(0.001000, 0.001667, 2)', 'sampler should use default values'
sampled, tags = adaptive_sampler.is_sampled(0, 'op')
assert sampled
assert tags == get_tags('probabilistic', 0.001), 'should use default probability'
assert '%s' % adaptive_sampler.samplers['op'] == 'GuaranteedThroughputProbabilisticSampler(op, 0.001000, 0.001667)'
adaptive_sampler.update(strategies = {
"defaultLowerBoundTracesPerSecond":4,
"perOperationStrategies":
[
{
"operation":"new_op",
"probabilisticSampling":{
"samplingRate":0.002
}
}
]
})
assert '%s' % adaptive_sampler == 'AdaptiveSampler(0.001000, 4.000000, 2)'
sampled, tags = adaptive_sampler.is_sampled(0, 'new_op')
assert sampled
assert tags == get_tags('probabilistic', 0.002)
assert '%s' % adaptive_sampler.samplers['new_op'] == 'GuaranteedThroughputProbabilisticSampler(new_op, 0.002000, 4.000000)'
sampled, tags = adaptive_sampler.is_sampled(0, 'op')
assert sampled
assert tags == get_tags('probabilistic', 0.001)
# TODO ruh roh, the lowerbound isn't changed if the operation isn't included in perOperationStrategies
assert '%s' % adaptive_sampler.samplers['op'] == 'GuaranteedThroughputProbabilisticSampler(op, 0.001000, 0.001667)'
def test_adaptive_sampler_default_values():
adaptive_sampler = AdaptiveSampler({}, 2)
assert '%s' % adaptive_sampler == \
'AdaptiveSampler(0.001000, 0.001667, 2)', 'sampler should use default values'
sampled, tags = adaptive_sampler.is_sampled(0, 'op')
assert sampled
assert tags == \
get_tags('probabilistic', 0.001), 'should use default probability'
assert '%s' % adaptive_sampler.samplers['op'] == \
'GuaranteedThroughputProbabilisticSampler(op, 0.001000, 0.001667)'
adaptive_sampler.update(strategies={
'defaultLowerBoundTracesPerSecond': 4,
'perOperationStrategies':
[
{
'operation': 'new_op',
'probabilisticSampling': {
'samplingRate': 0.002
}
}
]
})
assert '%s' % adaptive_sampler == 'AdaptiveSampler(0.001000, 4.000000, 2)'
sampled, tags = adaptive_sampler.is_sampled(0, 'new_op')
assert sampled
assert tags == get_tags('probabilistic', 0.002)
assert '%s' % adaptive_sampler.samplers['new_op'] == \
'GuaranteedThroughputProbabilisticSampler(new_op, 0.002000, 4.000000)'
sampled, tags = adaptive_sampler.is_sampled(0, 'op')
assert sampled
assert tags == get_tags('probabilistic', 0.001)
# TODO ruh roh, the lowerbound isn't changed
# if the operation isn't included in perOperationStrategies
assert '%s' % adaptive_sampler.samplers['op'] == \
'GuaranteedThroughputProbabilisticSampler(op, 0.001000, 0.001667)'
def test_adaptive_sampler():
strategies = {
"defaultSamplingProbability":0.51,
"defaultLowerBoundTracesPerSecond":3,
"perOperationStrategies":
[
{
"operation":"op",
"probabilisticSampling":{
"samplingRate":0.5
}
}
]
}
sampler = AdaptiveSampler(strategies, 2)
sampled, tags = sampler.is_sampled(MAX_INT-10, 'op')
assert sampled
assert tags == get_tags('probabilistic', 0.5)
# This operation is seen for the first time by the sampler
sampled, tags = sampler.is_sampled(MAX_INT-10, "new_op")
assert sampled
assert tags == get_tags('probabilistic', 0.51)
ts = time.time()
with mock.patch('jaeger_client.rate_limiter.RateLimiter.timestamp') \
as mock_time:
# Move time forward by a second to guarantee the rate limiter has enough credits
mock_time.side_effect = lambda: ts + 1
sampled, tags = sampler.is_sampled(MAX_INT+(MAX_INT/4), "new_op")
assert sampled
assert tags == get_tags('lowerbound', 0.51)
# This operation is seen for the first time by the sampler but surpasses
# max_operations of 2. The default probabilistic sampler will be used
sampled, tags = sampler.is_sampled(MAX_INT-10, "new_op_2")
assert sampled
assert tags == get_tags('probabilistic', 0.51)
sampled, _ = sampler.is_sampled(MAX_INT+(MAX_INT/4), "new_op_2")
assert not sampled
assert '%s' % sampler == 'AdaptiveSampler(0.510000, 3.000000, 2)'
# Update the strategies
strategies = {
"defaultSamplingProbability":0.52,
"defaultLowerBoundTracesPerSecond":4,
"perOperationStrategies":
[
{
"operation":"op",
"probabilisticSampling":{
"samplingRate":0.52
}
},
{
"operation":"new_op_3",
"probabilisticSampling":{
"samplingRate":0.53
}
}
]
}
sampler.update(strategies)
# The probability for op has been updated
sampled, tags = sampler.is_sampled(MAX_INT-10, 'op')
assert sampled
assert tags == get_tags('probabilistic', 0.52)
# A new operation has been added
sampled, tags = sampler.is_sampled(MAX_INT-10, 'new_op_3')
assert sampled
assert tags == get_tags('probabilistic', 0.53)
assert '%s' % sampler == 'AdaptiveSampler(0.520000, 4.000000, 2)'
sampler.close()
def test_adaptive_sampler():
strategies = {
"defaultSamplingProbability":
0.51,
"defaultLowerBoundTracesPerSecond":
3,
"perOperationStrategies": [{
"operation": "op",
"probabilisticSampling": {
"samplingRate": 0.5
}
}]
}
sampler = AdaptiveSampler(strategies, 2)
sampled, tags = sampler.is_sampled(MAX_INT - 10, 'op')
assert sampled
assert tags == get_tags('probabilistic', 0.5)
# This operation is seen for the first time by the sampler
sampled, tags = sampler.is_sampled(MAX_INT - 10, "new_op")
assert sampled
assert tags == get_tags('probabilistic', 0.51)
sampled, tags = sampler.is_sampled(MAX_INT + (MAX_INT / 4), "new_op")
assert sampled
assert tags == get_tags('lowerbound', 0.51)
# This operation is seen for the first time by the sampler but surpasses
# max_operations of 2. The default probabilistic sampler will be used
sampled, tags = sampler.is_sampled(MAX_INT - 10, "new_op_2")
assert sampled
assert tags == get_tags('probabilistic', 0.51)
sampled, _ = sampler.is_sampled(MAX_INT + (MAX_INT / 4), "new_op_2")
assert not sampled
assert '%s' % sampler == 'AdaptiveSampler(0.510000, 3.000000, 2)'
# Update the strategies
strategies = {
"defaultSamplingProbability":
0.52,
"defaultLowerBoundTracesPerSecond":
4,
"perOperationStrategies": [{
"operation": "op",
"probabilisticSampling": {
"samplingRate": 0.52
}
}, {
"operation": "new_op_3",
"probabilisticSampling": {
"samplingRate": 0.53
}
}]
}
sampler.update(strategies)
# The probability for op has been updated
sampled, tags = sampler.is_sampled(MAX_INT - 10, 'op')
assert sampled
assert tags == get_tags('probabilistic', 0.52)
# A new operation has been added
sampled, tags = sampler.is_sampled(MAX_INT - 10, 'new_op_3')
assert sampled
assert tags == get_tags('probabilistic', 0.53)
assert '%s' % sampler == 'AdaptiveSampler(0.520000, 4.000000, 2)'
sampler.close()
def test_adaptive_sampler():
strategies = {
'defaultSamplingProbability': 0.51,
'defaultLowerBoundTracesPerSecond': 3,
'perOperationStrategies':
[
{
'operation': 'op',
'probabilisticSampling': {
'samplingRate': 0.5
}
}
]
}
sampler = AdaptiveSampler(strategies, 2)
sampled, tags = sampler.is_sampled(MAX_INT - 10, 'op')
assert sampled
assert tags == get_tags('probabilistic', 0.5)
# This operation is seen for the first time by the sampler
sampled, tags = sampler.is_sampled(MAX_INT - 10, 'new_op')
assert sampled
assert tags == get_tags('probabilistic', 0.51)
ts = time.time()
with mock.patch('jaeger_client.rate_limiter.RateLimiter.timestamp') \
as mock_time:
# Move time forward by a second to guarantee the rate limiter has enough credits
mock_time.side_effect = lambda: ts + 1
sampled, tags = sampler.is_sampled(int(MAX_INT + (MAX_INT / 4)), 'new_op')
assert sampled
assert tags == get_tags('lowerbound', 0.51)
# This operation is seen for the first time by the sampler but surpasses
# max_operations of 2. The default probabilistic sampler will be used
sampled, tags = sampler.is_sampled(MAX_INT - 10, 'new_op_2')
assert sampled
assert tags == get_tags('probabilistic', 0.51)
sampled, _ = sampler.is_sampled(int(MAX_INT + (MAX_INT / 4)), 'new_op_2')
assert not sampled
assert '%s' % sampler == 'AdaptiveSampler(0.510000, 3.000000, 2)'
# Update the strategies
strategies = {
'defaultSamplingProbability': 0.52,
'defaultLowerBoundTracesPerSecond': 4,
'perOperationStrategies':
[
{
'operation': 'op',
'probabilisticSampling': {
'samplingRate': 0.52
}
},
{
'operation': 'new_op_3',
'probabilisticSampling': {
'samplingRate': 0.53
}
}
]
}
sampler.update(strategies)
# The probability for op has been updated
sampled, tags = sampler.is_sampled(MAX_INT - 10, 'op')
assert sampled
assert tags == get_tags('probabilistic', 0.52)
# A new operation has been added
sampled, tags = sampler.is_sampled(MAX_INT - 10, 'new_op_3')
assert sampled
assert tags == get_tags('probabilistic', 0.53)
assert '%s' % sampler == 'AdaptiveSampler(0.520000, 4.000000, 2)'
sampler.close()