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. .. autoclass:: _datasketches.tgt_hll_type .. autoattribute:: HLL_4 :annotation: : 4 bits per entry .. autoattribute:: HLL_6 :annotation: : 6 bits per entry .. autoattribute:: HLL_8 :annotation: : 8 bits per entry .. autoclass:: _datasketches.hll_sketch :members: :undoc-members: :exclude-members: deserialize, get_max_updatable_serialization_bytes, get_rel_err .. rubric:: Static Methods: .. automethod:: deserialize .. automethod:: get_max_updatable_serialization_bytes .. automethod:: get_rel_err .. rubric:: Non-static Methods: .. automethod:: __init__ .. autoclass:: _datasketches.hll_union :members: :undoc-members: :exclude-members: get_rel_err .. rubric:: Static Methods: .. automethod:: get_rel_err .. rubric:: Non-static Methods: .. automethod:: __init__