Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-13 01:27 UTC

ekzhu / datasketch

MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble and HNSW

PythonMIT★ 2,951 stars⑂ 319 forkssince Mar 2015View on GitHub ↗
KindLibraryhow this is determined
#python#lsh-forest#jaccard-similarity#hyperloglog#lsh#minhash#weighted-quantiles#top-k#search#data-sketches#data-summary#lsh-ensemble#locality-sensitive-hashing#hnsw

ekzhu/datasketch holds a health index of 87 out of 100, placing it in the Excellent band. It scores highest on Vitality (79/100) and lowest on AI Readiness (62/100). It was last updated 3 days ago. A single contributor accounts for most of its recent work.

87
overall / 100
Excellent

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

87
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 73 is calibrated to 87 on the published index scale (record calibration 2026-08-02).

Ownership

Eric ZhuPersonal account
769 followers128 public repossince Jul 2010

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIdatasketch2.0.06,770,5618838 days agodatabasedatamining

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

79Good · 21% of overall

Development activity

71Good
How it's scored
36/36Push recency — last push 3 days ago
11.1/36Commit cadence — 16/52 weeks with commits
15.9/18Commit volume — 58 commits in the last year
8/10OpenSSF Scorecard: Maintained — 9 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 8
Inputs used
commits_last_year58
human_commit_share0.8
days_since_last_push3
active_weeks_last_year16

Release discipline

92Excellent
How it's scored
27/27Ships releases — 37 releases published
36/36Release recency — latest release 38 days ago
19.8/27Release cadence — a release every ~114.8 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count37
latest_release_tagv2.0.0
releases_from_tagsno
days_since_latest_release38
mean_days_between_releases114.8
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

79Good · 17% of overall

Popularity & adoption

86Excellent
How it's scored
56.3/60Stars — 2,951 stars
20.9/25Forks — 319 forks
9.1/15Watchers — 45 watchers
Inputs used
forks319
stars2,951
watchers45
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

57Moderate
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges1
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicescodecov.io
has_pull_request_templateyes

Ecosystem adoption (downloads)

100Exceptional
How it's scored
80/80Monthly downloads — 6,770,561 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesdatasketch
dependents—
ecosystemspypi
total_downloads—
monthly_downloads6,770,561
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

67Good · 23% of overall

Maintainer resilience (bus factor)

38Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
5.7/22.5Commit distribution — top contributor authored 75% of commits
13.5/13.5Contributor breadth — 31 contributors
10/10OpenSSF Scorecard: Contributors — project has 5 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled31
top_contributor_share0.746

Issue & PR responsiveness

74Good
How it's scored
30.2/42Issue resolution — 72% of issues closed
25.4/30PR acceptance — 123/145 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
9/15OpenSSF Scorecard: Code-Review — Found 13/20 approved changesets -- score normalized to 6
Inputs used
merged_prs123
open_issues50
closed_issues127
prs_merged_7d0
prs_decided_7d0
prs_merged_30d2
prs_decided_30d2
issue_closed_ratio0.718
closed_unmerged_prs22
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

70Good
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
20.8/25Owner reach — 769 followers of ekzhu
25/25Track record — 128 public repos, account ~16 yr old
Inputs used
followers769
owner_typeUser
is_verified—
owner_loginekzhu
public_repos128
account_age_days5,886
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 38 days ago
20/20Version history — 88 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesdatasketch
ecosystemspypi
any_deprecatedno
min_days_since_publish38

Engineering Quality

Are baseline engineering and documentation practices in place?

76Good · 19% of overall

Engineering practices

66Good
How it's scored
24/24CI workflows — 8 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
18/20OpenSSF Scorecard: CI-Tests — 29 out of 30 merged PRs checked by a CI test -- score normalized to 9
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://ekzhu.github.io/datasketch
10/10Repository description
10/10Topics — 14 topics
0/10Wiki
Inputs used
topicspython, lsh-forest, jaccard-similarity, hyperloglog, lsh, minhash, weighted-quantiles, top-k, search, data-sketches, data-summary, lsh-ensemble, locality-sensitive-hashing, hnsw
has_wikino
homepagehttps://ekzhu.github.io/datasketch
docs_sitehttps://ekzhu.github.io/datasketch
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

64Moderate · 16% of overall

Security posture

55Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
4.5/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.2/2.5CI-Tests — 29 out of 30 merged PRs checked by a CI test -- score normalized to 9
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
4.5/7.5Code-Review — Found 13/20 approved changesets -- score normalized to 6
2.5/2.5Contributors — project has 5 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
6/7.5Maintained — 9 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 8
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
1.5/5SAST — SAST tool is not run on all commits -- score normalized to 3
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 18 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate5.5
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages2
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:datasketch@2.0.0 runtime dependency closure — what installing the published package pulls in — 2 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

62Moderate · 4% of overall

Agent context & guidance

40Weak
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 65 of 80 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.812
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

68Good
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
0/11Lint / format config
0/11Static type checking
10/10Reproducible environment — lockfile
10/10Demonstrated agent practice — 5 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 15 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configno
typecheck_configs—
agent_commit_share0.05
toolchain_manifests—
dependency_bot_commit_share0.15

Code legibility for models

54Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
54.2/55Manageable file sizes — 1/73 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes72,545
source_files_sampled73
oversized_source_files1

Machine-readable interfaces

100Exceptional
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — examples
Inputs used
example_dirsexamples
has_mcp_signalno
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

2,951GitHub stars
31contributors
58commits, last 12 months
3days since last push
37releases
1bus factor
50open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 319 ⇿
0Stars
319Forks
37Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

012525037531552015-042020-112026-07
Major 2Minor 8Patch 26

Each point covers 11 days.

OpenSSF Scorecard 5.5 / 10
5.5aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-08-13 01:27 UTC

10Binary-Artifactsno binaries found in the repo
6Branch-Protectionbranch protection is not maximal on development and all release branches
9CI-Tests29 out of 30 merged PRs checked by a CI test -- score normalized to 9
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
6Code-ReviewFound 13/20 approved changesets -- score normalized to 6
10Contributorsproject has 5 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
8Maintained9 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 8
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
3SASTSAST tool is not run on all commits -- score normalized to 3
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities18 existing vulnerabilities detected
Direct dependencies 2
RegistryPackageVersion constraintManifest
PyPInumpy>=1.11pyproject.toml
PyPIscipy>=1.0.0pyproject.toml
All dependencies 76

Full resolved dependency set from the GitHub dependency graph: 7 direct and 69 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPInumpy2.0.2direct
PyPInumpy2.2.6direct
PyPInumpy2.3.4direct
PyPIscipy—direct
PyPIscipy1.13.1direct
PyPIscipy1.15.3direct
PyPIscipy1.16.3direct
PyPIaiounittest1.5.0indirect
PyPIasync-timeout5.0.1indirect
PyPIbackports-asyncio-runner1.2.0indirect
PyPIcassandra-driver3.29.3indirect
PyPIclick8.1.8indirect
PyPIclick8.3.0indirect
PyPIcolorama0.4.6indirect
PyPIcontourpy1.3.2indirect
PyPIcontourpy1.3.3indirect
PyPIcoverage7.10.7indirect
PyPIcoverage7.11.0indirect
PyPIcycler0.12.1indirect
PyPIdatasketch2.0.0indirect
PyPIdnspython2.7.0indirect
PyPIdnspython2.8.0indirect
PyPIexceptiongroup1.3.0indirect
PyPIfarmhash—indirect
PyPIfonttools4.60.2indirect
PyPIgeomet1.1.0indirect
PyPIiniconfig2.1.0indirect
PyPIiniconfig2.3.0indirect
PyPIjoblib1.5.2indirect
PyPIkiwisolver1.4.7indirect
PyPIkiwisolver1.4.9indirect
PyPImatplotlib—indirect
PyPImatplotlib3.10.7indirect
PyPImatplotlib3.2.2indirect
PyPImock5.2.0indirect
PyPImockredispy2.9.3indirect
PyPImotor3.7.1indirect
PyPInltk3.9.2indirect
PyPInltk3.9.4indirect
PyPInmslib—indirect
PyPInose1.3.7indirect
PyPInose-exclude0.5.0indirect
PyPIpackaging25.0indirect
PyPIpandas—indirect
PyPIpandas2.3.3indirect
PyPIpillow12.3.0indirect
PyPIpluggy1.6.0indirect
PyPIpybloomfilter30.7.3indirect
PyPIpyfarmhash—indirect
PyPIpyfarmhash0.4.0indirect
PyPIpygments2.20.0indirect
PyPIpyhash0.9.3indirect
PyPIpymongo4.15.3indirect
PyPIpyparsing3.2.5indirect
PyPIpytest8.4.2indirect
PyPIpytest9.0.3indirect
PyPIpytest-asyncio1.2.0indirect
PyPIpytest-asyncio1.3.0indirect
PyPIpytest-cov7.0.0indirect
PyPIpytest-rerunfailures16.0.1indirect
PyPIpytest-rerunfailures16.1indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpytz2025.2indirect
PyPIredis7.0.1indirect
PyPIregex2025.11.3indirect
PyPIscikit-learn1.6.1indirect
PyPIscikit-learn1.7.2indirect
PyPIsetsimilaritysearch—indirect
PyPIsetsimilaritysearch1.0.1indirect
PyPIsix1.17.0indirect
PyPIthreadpoolctl3.6.0indirect
PyPItomli2.3.0indirect
PyPItqdm4.67.1indirect
PyPItyping-extensions4.15.0indirect
PyPItzdata2025.2indirect
PyPIwrapt2.0.1indirect
Dependency advisories 0

Installing pypi:datasketch@2.0.0 pulls in 2 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

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Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.31.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.