Sketches

aid: sketches
url: https://raw.githubusercontent.com/api-evangelist/sketches/refs/heads/main/apis.yml
name: Sketches
description: 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). APIs in this domain include sketch-native databases like Apache DataSketches, Redis probabilistic
  data structures, and cloud analytics services that implement sketch algorithms for real-time analytics, approximate query
  processing, and streaming analytics.
tags:
- Data Structures
- Probabilistic Algorithms
- Streaming Analytics
- Approximate Query Processing
- Big Data
- Real-Time Analytics
created: '2025'
modified: '2026-05-02'
apis:
- aid: sketches:apache-datasketches-api
  name: Apache DataSketches API
  description: Apache DataSketches is the open-source library providing production-quality implementations of sketch algorithms
    including Theta Sketches (set operations), Quantiles Sketches (percentile estimation), HLL (HyperLogLog for cardinality),
    CPC, Frequency, and Tuple sketches. It is widely used in data warehouses and OLAP systems including Apache Druid, Apache
    Spark, and Amazon Redshift. The library provides Java, C++, and Python APIs.
  humanURL: https://datasketches.apache.org
  baseURL: https://datasketches.apache.org
  tags:
  - Open Source
  - Apache
  - Data Structures
  - Probabilistic Algorithms
  - Analytics
  properties:
  - url: https://datasketches.apache.org
    type: Documentation
  - url: https://github.com/apache/datasketches-java
    type: GitHubOrg
- aid: sketches:redis-probabilistic-api
  name: Redis Probabilistic Data Structures API
  description: Redis provides native probabilistic data structure commands through the Redis Stack (RedisBloom module), offering
    server-side implementations of Bloom Filter, Cuckoo Filter, Count-Min Sketch, Top-K, and HyperLogLog. These are accessible
    via the Redis command interface and all official Redis client libraries, enabling high-throughput approximate data processing
    without external dependencies.
  humanURL: https://redis.io/docs/data-types/probabilistic/
  baseURL: https://redis.io
  tags:
  - Redis
  - Probabilistic Data Structures
  - Real-Time
  - In-Memory
  properties:
  - url: https://redis.io/docs/data-types/probabilistic/
    type: Documentation
  - url: https://redis.io
    type: Website
- aid: sketches:amazon-redshift-sketches-api
  name: Amazon Redshift Approximate Query API
  description: Amazon Redshift supports approximate query processing using HyperLogLog sketch functions (HLL_CREATE_SKETCH,
    HLL_COMBINE, HLL_CARDINALITY) for fast cardinality estimation on large datasets. These native SQL functions enable analytics
    teams to run near-instantaneous approximate COUNT DISTINCT queries on billions of rows with controlled error bounds.
  humanURL: https://docs.aws.amazon.com/redshift/latest/dg/r_HLL_function.html
  baseURL: https://aws.amazon.com
  tags:
  - AWS
  - Redshift
  - Analytics
  - HyperLogLog
  - SQL
  properties:
  - url: https://docs.aws.amazon.com/redshift/latest/dg/r_HLL_function.html
    type: Documentation
common:
- type: VulnerabilityDisclosure
  url: security/sketches-vulnerability-disclosure.yml
- type: DomainSecurity
  url: security/sketches-domain-security.yml
- type: Website
  url: https://datasketches.apache.org
- type: JSONLD
  url: json-ld/sketches-context.jsonld
- type: Vocabulary
  url: vocabulary/sketches-vocabulary.yml