def test_hll_example(self):
k = 12 # 2^k = 4096 rows in the table
n = 1 << 18 # ~256k unique values
# create a couple sketches and inject some values
# we'll have 1/4 of the values overlap
hll = hll_sketch(k, tgt_hll_type.HLL_8)
hll2 = hll_sketch(k, tgt_hll_type.HLL_6)
offset = int(3 * n / 4) # it's a float w/o cast
# because we hash on the bits, not an abstract numeric value,
# hll.update(1) and hll.update(1.0) give different results.
for i in range(0, n):
hll.update(i)
hll2.update(i + offset)
# although we provide get_composite_estimate() and get_estimate(),
# the latter will always give the best available estimate. we
# recommend using get_estimate().
# we can check that the upper and lower bounds bracket the
# estimate, without needing to know the exact value.
self.assertLessEqual(hll.get_lower_bound(1), hll.get_estimate())
self.assertGreaterEqual(hll.get_upper_bound(1), hll.get_estimate())
# unioning uses a separate class, and we can either get a result
# sketch or query the union object directly
union = hll_union(k)
union.update(hll)
union.update(hll2)
result = union.get_result()
self.assertEqual(result.get_estimate(), union.get_estimate())
# since our process here (including post-union HLL) is
# deterministic, we have checked and know the exact
# answer is within one standard deviation of the estimate
self.assertLessEqual(union.get_lower_bound(1), 7 * n / 4)
self.assertGreaterEqual(union.get_upper_bound(1), 7 * n / 4)
# serialize for storage and reconstruct
sk_bytes = result.serialize_compact()
self.assertEqual(len(sk_bytes), result.get_compact_serialization_bytes())
new_hll = hll_sketch.deserialize(sk_bytes)
# the sketch can self-report its configuation and status
self.assertEqual(new_hll.lg_config_k, k)
self.assertEqual(new_hll.tgt_type, tgt_hll_type.HLL_4)
self.assertFalse(new_hll.is_empty())
# if we want to reduce some object overhead, we can also reset
new_hll.reset()
self.assertTrue(new_hll.is_empty())
def calculate_sketch_statistics(data):
columns = list(data.columns)
types = list(data.dtypes)
stats_dict = {}
for column, type in zip(columns, types):
if type in [np.int32, np.int64, np.float64]:
data_col = data[column].to_numpy()
if data[column].dtype in [np.int32, np.int64]:
kll = kll_ints_sketch(2048)
elif data[column].dtype == np.float64:
kll = kll_floats_sketch(2048)
kll.update(data_col)
stat_values = kll.get_quantiles([0.05, 0.25, 0.5, 0.75, 0.95])
stat_names = ["0.05", "Q1", "Median", "Q3", "0.95"]
hll = hll_sketch(DEFAULT_HLL_K, DEFAULT_HLL_TYPE)
hll.update(data_col) #works with local fork (np.array extension)
approx_distinct_count = hll.get_estimate()
stat_values.append(round(approx_distinct_count))
stat_names.append("Distinct Count")
stat_pairs = [list(i) for i in zip(stat_names, stat_values)]
stats_dict[column] = stat_pairs
return stats_dict
def __init__(self, lg_k=None, sketch=None):
if sketch is None:
if lg_k is None:
lg_k = DEFAULT_LG_K
sketch = datasketches.hll_sketch(lg_k)
assert isinstance(sketch, datasketches.hll_sketch)
self.sketch = sketch
self.lg_k = lg_k
def compute_metrics(self, properties: Set[Property],
repo: MetadataRepository):
quantile_properties = [
property for property in properties
if isinstance(property, Quantile)
]
quantile_metrics: Dict[Property, Metric] = {}
for quantile_property in quantile_properties:
data_col = self.data[quantile_property.column].to_numpy()
sketch_type = ""
if self.data[quantile_property.column].dtype == np.int64:
kll = kll_ints_sketch(DEFAULT_SKETCH_SIZE)
sketch_type = "ints"
elif self.data[quantile_property.column].dtype == np.float64:
kll = kll_floats_sketch(DEFAULT_SKETCH_SIZE)
sketch_type = "floats"
else:
raise NotImplementedError(
f"Data Type {self.data[quantile_property.column].dtype} is not supported for sketches!"
)
kll.update(data_col)
quantile = kll.get_quantiles([quantile_property.quantile])[0]
serialized_kll = kll.serialize().hex() #bytes.fromhex()
quantile_state = QuantileState(
quantile_property.property_identifier(), serialized_kll,
quantile, sketch_type)
repo.register_state(quantile_state)
quantile_metric = metric_from_value(quantile,
quantile_property.name,
quantile_property.instance,
quantile_property.entity)
quantile_metrics[quantile_property] = quantile_metric
approx_distinct_properties = [
property for property in properties
if isinstance(property, ApproxDistinctness)
]
approx_distinct_metrics: Dict[Property, Metric] = {}
for approx_distinct_property in approx_distinct_properties:
data_col = self.data[approx_distinct_property.column].to_numpy()
hll = hll_sketch(DEFAULT_HLL_K, DEFAULT_HLL_TYPE)
#for v in data_col: #slow
# hll.update(v)
hll.update(data_col) #works with local fork (np.array extension)
approx_distinct_count = hll.get_estimate()
num_rows = len(data_col)
serialized_hll = hll.serialize_updatable().hex() #bytes.fromhex()
approx_distinct_state = ApproxDistinctState(
approx_distinct_property.property_identifier(), serialized_hll,
approx_distinct_count, num_rows)
repo.register_state(approx_distinct_state)
approx_distinctness = min(approx_distinct_count / num_rows, 1.00)
approx_distinct_metric = metric_from_value(
approx_distinctness, approx_distinct_property.name,
approx_distinct_property.instance,
approx_distinct_property.entity)
approx_distinct_metrics[
approx_distinct_property] = approx_distinct_metric
other_properties = [
property for property in properties
if (not isinstance(property, Quantile)
and not isinstance(property, ApproxDistinctness))
]
metrics = self.engine.compute_metrics(other_properties, repo)
metrics.update(quantile_metrics)
metrics.update(approx_distinct_metrics)
return metrics
def generate_sketch(self, n, k, sk_type=tgt_hll_type.HLL_4, st_idx=0):
sk = hll_sketch(k, sk_type)
for i in range(st_idx, st_idx + n):
sk.update(i)
return sk
