docs/source/hyper_log_log.rst - datasketches-python - Git at Google

 HyperLogLog (HLL)
 -----------------
 This is a high performance implementation of Phillipe Flajolet's HLL sketch but with significantly improved error behavior.

 If the ONLY use case for sketching is counting uniques and merging, the HLL sketch is a reasonable choice, although the highest performing in terms of accuracy for storage space consumed is CPC (Compressed Probabilistic Counting). For large enough counts, this HLL version (with HLL_4) can be 2 to 16 times smaller than the Theta sketch family for the same accuracy.

 This implementation offers three different types of HLL sketch, each with different trade-offs with accuracy, space and performance.
 These types are specified with the target_hll_type parameter.

 In terms of accuracy, all three types, for the same lg_config_k, have the same error distribution as a function of n, the number of unique values fed to the sketch.
 The configuration parameter `lg_config_k` is the log-base-2 of `K`, where `K` is the number of buckets or slots for the sketch.

 During warmup, when the sketch has only received a small number of unique items (up to about 10% of `K`), this implementation leverages a new class of estimator algorithms with significantly better accuracy.

 This sketch also offers the capability of operating off-heap.
 Given a WritableMemory object created by the user, the sketch will perform all of its updates and internal phase transitions in that object, which can actually reside either on-heap or off-heap based on how it is configured.
 In large systems that must update and merge many millions of sketches, having the sketch operate off-heap avoids the serialization and deserialization costs of moving sketches to and from off-heap memory-mapped files, for example, and eliminates big garbage collection delays.

 .. autoclass:: _datasketches.hll_sketch
     :members:
     :undoc-members:
     :exclude-members: deserialize, get_max_updatable_serialization_bytes, get_rel_err

     :member-order: groupwise

     .. rubric:: Static Methods:

     .. automethod:: deserialize
     .. automethod:: get_max_updatable_serialization_bytes
     .. automethod:: get_rel_err

     .. rubric:: Non-static Methods:
	HyperLogLog (HLL)
	-----------------
	This is a high performance implementation of Phillipe Flajolet's HLL sketch but with significantly improved error behavior.

	If the ONLY use case for sketching is counting uniques and merging, the HLL sketch is a reasonable choice, although the highest performing in terms of accuracy for storage space consumed is CPC (Compressed Probabilistic Counting). For large enough counts, this HLL version (with HLL_4) can be 2 to 16 times smaller than the Theta sketch family for the same accuracy.

	This implementation offers three different types of HLL sketch, each with different trade-offs with accuracy, space and performance.
	These types are specified with the target_hll_type parameter.

	In terms of accuracy, all three types, for the same lg_config_k, have the same error distribution as a function of n, the number of unique values fed to the sketch.
	The configuration parameter `lg_config_k` is the log-base-2 of `K`, where `K` is the number of buckets or slots for the sketch.

	During warmup, when the sketch has only received a small number of unique items (up to about 10% of `K`), this implementation leverages a new class of estimator algorithms with significantly better accuracy.

	This sketch also offers the capability of operating off-heap.
	Given a WritableMemory object created by the user, the sketch will perform all of its updates and internal phase transitions in that object, which can actually reside either on-heap or off-heap based on how it is configured.
	In large systems that must update and merge many millions of sketches, having the sketch operate off-heap avoids the serialization and deserialization costs of moving sketches to and from off-heap memory-mapped files, for example, and eliminates big garbage collection delays.

	.. autoclass:: _datasketches.hll_sketch
	:members:
	:undoc-members:
	:exclude-members: deserialize, get_max_updatable_serialization_bytes, get_rel_err

	:member-order: groupwise

	.. rubric:: Static Methods:

	.. automethod:: deserialize
	.. automethod:: get_max_updatable_serialization_bytes
	.. automethod:: get_rel_err

	.. rubric:: Non-static Methods: