Sketches are probabilistic data structures used in computing and data engineering to approximate answers to queries over large data streams with controlled error bounds and dramatically reduced memory requirements. Common sketches include Count-Min Sketch (frequency estimation), HyperLogLog (cardinality estimation), Bloom Filter (membership testing), and T-Digest (quantile estimation).
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- Approximate Query Processing, Big Data, Data Structures, Probabilistic Algorithms, Real-Time Analytics, Streaming Analytics
- Created: 2025
- Modified: 2026-05-02
Apache DataSketches is the leading open-source library of production-quality sketch implementations (Theta, HLL, Quantiles, Frequency) widely integrated into Apache Druid, Amazon Redshift, and Spark.
Human URL: https://datasketches.apache.org
- Analytics, Apache, Data Structures, Open Source, Probabilistic Algorithms
Redis Stack (RedisBloom) provides native Bloom Filter, Cuckoo Filter, Count-Min Sketch, Top-K, and HyperLogLog implementations as server-side Redis commands.
Human URL: https://redis.io/docs/data-types/probabilistic/
- In-Memory, Probabilistic Data Structures, Real-Time, Redis
Amazon Redshift provides native HyperLogLog SQL functions for fast cardinality estimation on billions of rows with controlled error bounds.
Human URL: https://docs.aws.amazon.com/redshift/latest/dg/r_HLL_function.html
- Analytics, AWS, HyperLogLog, Redshift, SQL
- sketches-sketch-schema.json — Probabilistic sketch configuration and result schema
- sketches-structure.json — Sketch types and algorithm documentation
- sketches-context.jsonld — Linked data context
- sketches-vocabulary.yml — Domain terminology for probabilistic data structures
FN: API Evangelist
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