Package: whylogs-datasketches
The Apache DataSketches Library for PythonThis is the official version of theApache DataSketchesPython library.In the analysis of big data there are often problem queries that don’t scale because they require huge compute resources and time to generate exact results. Examples include count distinct, quantiles, most-frequent items, joins, matrix computations, and graph analysis.If approximate results are acceptable, there is a class of specialized algorithms, called streaming algorithms, or sketches that can produce results orders-of magnitude faster and with mathematically proven error bounds. For interactive queries there may not be other viable alternatives, and in the case of real-time analysis, sketches are the only known solution.This package provides a variety of sketches as described below. Wherever a specific type of sketch exists in Apache DataSketches packages for other languages, the sketches will be portable between languages (for platforms with the same endianness).Building and InstallationOnce cloned, the library can be installed by runningpython -m pip install .in the project root directory, which will also install the necessary dependencies, namely numpy andpybind11[global].If you prefer to call thesetup.pybuild script directly, you must first installpybind11[global], as well as any other dependencies listed under the build-system section inpyproject.toml.The library is also available from PyPI viapython -m pip install datasketches.UsageHaving installed the library, loading the Apache Datasketches Library in Python is simple:import datasketches.Available Sketch ClassesKLL (Absolute Error Quantiles)kll_ints_sketchkll_floats_sketchREQ (Relative Error Quantiles)req_ints_sketchreq_floats_sketchFrequent Itemsfrequent_strings_sketchError types arefrequent_items_error_type.{NO_FALSE_NEGATIVES | NO_FALSE_POSITIVES}Thetaupdate_theta_sketchcompact_theta_sketch(cannot be instantiated directly)theta_uniontheta_intersectiontheta_a_not_bHLLhll_sketchhll_unionTarget HLL types aretgt_hll_type.{HLL_4 | HLL_6 | HLL_8}CPCcpc_sketchcpc_unionVarOpt Samplingvar_opt_sketchvar_opt_unionVector of KLLvector_of_kll_ints_sketchesvector_of_kll_floats_sketchesKnown Differences from C++The Python API largely mirrors the C++ API, with a few minor exceptions: The primary known differences are that Python on modern platforms does not support unsigned integer values or numeric values with fewer than 64 bits. As a result, you may not be able to produce identical sketches from within Python as you can with Java and C++. Loading those sketches after they have been serialized from another language will work as expected.The Vector of KLL object is currently exclusive to python, and holds an array of independent KLL sketches. This is useful for creating a set of KLL sketches over a vector and has been designed to allow input as either a vector or a matrix of multiple vectors.We have also removed reliance on a builder class for theta sketches as Python allows named arguments to the constructor, not strictly positional arguments.Developer InstructionsThe only developer-specific instructions relate to running unit tests.Unit testsThe Python unit tests are run withtox. To ensure you have all the needed package, from the package base directory run:python-mpipinstall--upgradetox
toxLicenseThe Apache DataSketches Library is distrubted under an Apache 2.0 License.There may be precompiled binaries provided as a convenience and distributed through PyPI via [https://pypi.org/project/datasketches/] contain compiled code frompybind11, which is distributed under a BSD license.