Source code for apache_beam.utils.histogram

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import logging
import math
import threading
from collections import Counter

_LOGGER = logging.getLogger(__name__)

[docs]class Histogram(object): """A histogram that supports estimated percentile with linear interpolation. This class is considered experimental and may break or receive backwards- incompatible changes in future versions of the Apache Beam SDK. """ def __init__(self, bucket_type): self._lock = threading.Lock() self._bucket_type = bucket_type self._buckets = Counter() self._num_records = 0 self._num_top_records = 0 self._num_bot_records = 0
[docs] def clear(self): with self._lock: self._buckets = Counter() self._num_records = 0 self._num_top_records = 0 self._num_bot_records = 0
[docs] def copy(self): with self._lock: histogram = Histogram(self._bucket_type) histogram._num_records = self._num_records histogram._num_top_records = self._num_top_records histogram._num_bot_records = self._num_bot_records histogram._buckets = self._buckets.copy() return histogram
[docs] def combine(self, other): if not isinstance(other, Histogram) or self._bucket_type != other._bucket_type: raise RuntimeError('failed to combine histogram.') other_histogram = other.copy() with self._lock: histogram = Histogram(self._bucket_type) histogram._num_records = self._num_records + other_histogram._num_records histogram._num_top_records = ( self._num_top_records + other_histogram._num_top_records) histogram._num_bot_records = ( self._num_bot_records + other_histogram._num_bot_records) histogram._buckets = self._buckets + other_histogram._buckets return histogram
[docs] def record(self, *args): for arg in args: self._record(arg)
def _record(self, value): range_from = self._bucket_type.range_from() range_to = self._bucket_type.range_to() with self._lock: if value >= range_to: _LOGGER.warning('record is out of upper bound %s: %s', range_to, value) self._num_top_records += 1 elif value < range_from: _LOGGER.warning( 'record is out of lower bound %s: %s', range_from, value) self._num_bot_records += 1 else: index = self._bucket_type.bucket_index(value) self._buckets[index] = self._buckets.get(index, 0) + 1 self._num_records += 1
[docs] def total_count(self): return self._num_records + self._num_top_records + self._num_bot_records
[docs] def p99(self): return self.get_linear_interpolation(0.99)
[docs] def p90(self): return self.get_linear_interpolation(0.90)
[docs] def p50(self): return self.get_linear_interpolation(0.50)
[docs] def get_percentile_info(self): def _format(f): if f == float('-inf'): return '<%s' % self._bucket_type.range_from() elif f == float('inf'): return '>=%s' % self._bucket_type.range_to() else: return str(int(round(f))) # pylint: disable=round-builtin with self._lock: return ( 'Total count: %s, ' 'P99: %s, P90: %s, P50: %s' % ( self.total_count(), _format(self._get_linear_interpolation(0.99)), _format(self._get_linear_interpolation(0.90)), _format(self._get_linear_interpolation(0.50))))
[docs] def get_linear_interpolation(self, percentile): """Calculate percentile estimation based on linear interpolation. It first finds the bucket which includes the target percentile and projects the estimated point in the bucket by assuming all the elements in the bucket are uniformly distributed. Args: percentile: The target percentile of the value returning from this method. Should be a floating point number greater than 0 and less than 1. """ with self._lock: return self._get_linear_interpolation(percentile)
def _get_linear_interpolation(self, percentile): total_num_records = self.total_count() if total_num_records == 0: raise RuntimeError('histogram has no record.') index = 0 record_sum = self._num_bot_records if record_sum / total_num_records >= percentile: return float('-inf') while index < self._bucket_type.num_buckets(): record_sum += self._buckets.get(index, 0) if record_sum / total_num_records >= percentile: break index += 1 if index == self._bucket_type.num_buckets(): return float('inf') frac_percentile = percentile - ( record_sum - self._buckets[index]) / total_num_records bucket_percentile = self._buckets[index] / total_num_records frac_bucket_size = frac_percentile * self._bucket_type.bucket_size( index) / bucket_percentile return ( self._bucket_type.range_from() + self._bucket_type.accumulated_bucket_size(index) + frac_bucket_size) def __eq__(self, other): if not isinstance(other, Histogram): return False return ( self._bucket_type == other._bucket_type and self._num_records == other._num_records and self._num_top_records == other._num_top_records and self._num_bot_records == other._num_bot_records and self._buckets == other._buckets) def __hash__(self): return hash(( self._bucket_type, self._num_records, self._num_top_records, self._num_bot_records, frozenset(self._buckets.items())))
[docs]class BucketType(object):
[docs] def range_from(self): """Lower bound of a starting bucket.""" raise NotImplementedError
[docs] def range_to(self): """Upper bound of an ending bucket.""" raise NotImplementedError
[docs] def num_buckets(self): """The number of buckets.""" raise NotImplementedError
[docs] def bucket_index(self, value): """Get the bucket array index for the given value.""" raise NotImplementedError
[docs] def bucket_size(self, index): """Get the bucket size for the given bucket array index.""" raise NotImplementedError
[docs] def accumulated_bucket_size(self, end_index): """Get the accumulated bucket size from bucket index 0 until endIndex. Generally, this can be calculated as `sigma(0 <= i < endIndex) getBucketSize(i)`. However, a child class could provide better optimized calculation. """ raise NotImplementedError
[docs]class LinearBucket(BucketType): def __init__(self, start, width, num_buckets): """Create a histogram with linear buckets. Args: start: Lower bound of a starting bucket. width: Bucket width. Smaller width implies a better resolution for percentile estimation. num_buckets: The number of buckets. Upper bound of an ending bucket is defined by start + width * numBuckets. """ self._start = start self._width = width self._num_buckets = num_buckets
[docs] def range_from(self): return self._start
[docs] def range_to(self): return self._start + self._width * self._num_buckets
[docs] def num_buckets(self): return self._num_buckets
[docs] def bucket_index(self, value): return math.floor((value - self._start) / self._width)
[docs] def bucket_size(self, index): return self._width
[docs] def accumulated_bucket_size(self, end_index): return self._width * end_index
def __eq__(self, other): if not isinstance(other, LinearBucket): return False return ( self._start == other._start and self._width == other._width and self._num_buckets == other._num_buckets) def __hash__(self): return hash((self._start, self._width, self._num_buckets))