The DataSketches Library is organized into the following repository groups:
This repository has the core-java sketching classes, which are leveraged by some of the other repositories.
This repository has no external dependencies outside of the DataSketches/memory repository, Java and TestNG for unit tests. This code is versioned and the latest release can be obtained from Downloads.
High-level Repositories Structure
|Common functions and utilities
|New Unique Counting Sketch with better accuracy per size than HLL
|Frequent Distinct Tuples Sketch.
|Frequent Item Sketches, for both longs and generics
|The 128-bit MurmurHash3 and adaptors
|Unique counting HLL sketches for both heap and off-heap.
|The (HLL) Unique Count Map Sketch
|Quantiles sketch with better accuracy per size than the standard quantiles sketch. Includes PMF, CDF functions, for floats, doubles. On-heap & off-heap.
|Standard Quantiles sketch, plus PMF and CDF functions, for doubles and generics. On-heap & off-heap.
|Relative Error Quantiles (REQ) sketch, plus PMF and CDF functions for floats, on-heap. Extremely high accuracy for very high ranks (e.g., 99.999%ile), or very low ranks (e.g., .00001%ile.
|Weighted and uniform reservoir sampling with generics
|Unique counting Theta Sketches for both on-heap & off-heap
|Tuple sketches for both primitives and generics
|A Tuple sketch with a Summary of a single double
|A Tuple sketch with a Summary of a single integer
|A Tuple sketch with a Summary of an array of Strings
This code is versioned and the latest release can be obtained from Downloads.
|Low level, high-performance Memory data-structure management primarily for off-heap.
This repository contains Hive UDFs and UDAFs for use within Hadoop grid enviornments. This code has dependencies on sketches-core as well as Hadoop and Hive. Users of this code are advised to use Maven to bring in all the required dependencies. This code is versioned and the latest release can be obtained from Downloads.
|Hive UDF and UDAFs for CPC sketches
|Hive UDF and UDAFs for Frequent Items sketches
|Hive UDF and UDAFs for HLL sketches
|Hive UDF and UDAFs for KLL sketches
|Hive UDF and UDAFs for Quantiles sketches
|Hive UDF and UDAFs for Theta sketches
|Hive UDF and UDAFs for Tuple sketches
This repository contains Pig User Defined Functions (UDF) for use within Hadoop grid environments. This code has dependencies on sketches-core as well as Hadoop and Pig. Users of this code are advised to use Maven to bring in all the required dependencies. This code is versioned and the latest release can be obtained from Downloads.
|Pig UDFs for CPC sketches
|Pig UDFs for Frequent Items sketches
|Pig UDFs for MurmerHash3
|Pig UDFs for HLL sketches
|Pig UDFs for KLL sketches
|Pig UDFs for Quantiles sketches
|Pig UDFs for Sampling sketches
|Pig UDFs for Theta sketches
|Pig UDFs for Tuple sketches
This relatively new repository is for Java and C++ code that we use to characterize the accuracy and speed performance of the sketches in the library and is constantly being updated. Examples of the job command files used for various tests can be found in the src/main/resources directory. Some of these tests can run for hours depending on its configuration. This component is not formally released and code must be obtained from the GitHub site.
|Common functions and utilities
|Concurrent Theta Sketch
|Compressed Probabilistic Counting Sketch
|Frequent Distinct Tuples Sketch
|Frequent Items Sketches
|Hash function performance
|Base Profiles for Unique Counting Sketches
This is a new repository for our experimental docker/container server that enables easy access to the core sketches in the library via HTTP. This component is not formally released and code must be obtained from the GitHub site.
This component implements the Frequent Directions Algorithm [GLP16]. It is still experimental in that the theoretical work has not yet supplied a suitable measure of error for production work. It can be used as is, but it will not go through a formal Apache Release until we can find a way to provide better error properties. It has a dependence on the Memory component. This component is not formally released and code must be obtained from the GitHub site.
This is the evolving C++ implementations of the same sketches that are available in Java. These implementations are binary compatible with their counterparts in Java. In other words, a sketch created and stored in C++ can be opened and read in Java and visa-versa. This code is versioned and the latest release can be obtained from Downloads.
This site also has our Python adaptors that basically wrap the C++ implementations, making the high performance C++ implementations available from Python.
This site provides the postgres-specific adaptors that wrap the C++ implementations making them available to the PostgreSQL database users. PostgreSQL users should download the PostgreSQL extension from pgxn.org. For examples refer to the README on the component site. This code is versioned and the latest release can be obtained from Downloads.