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Compute lead time to merge from Linear project boards with Python and Functory API for quarterly engineering reviews

This article shows how to build a compact, single-file Python utility that computes lead time to merge for issues tracked in Linear project boards, and how to expose that utility as a shareable Functory API URL for easy quarterly engineering reviews. The goal is a reproducible, auditable metric: time from issue creation to the associated pull request merge — useful for solo devs and small teams who want a no-ops endpoint they can share with stakeholders.

We focus on concrete inputs (Linear GraphQL JSON, or a CSV export with fields), the transformations required (join issues to PRs, compute durations, filter by project/label/date), and the final outputs (CSV, JSON summary and per-issue rows). This approach is intentionally small — one file, pinned deps — so it can become a Functory function in minutes.

What this function expects and produces

Input data (choose one):

  • Linear GraphQL response (JSON) containing issues with fields: id (str), title (str), createdAt (ISO8601), labels (list), project.name (str), and a custom field or external reference that points to the GitHub/GitLab PR URL or mergedAt timestamp (e.g., pullRequests[0].mergedAt).
  • or a CSV export of Linear issues with columns: issue_id, title, created_at, pr_url (nullable), pr_merged_at (nullable), project, assignee.

Processing steps (what the script does):

  • Normalize timestamps to UTC and parse ISO8601 strings.
  • Match issues to PRs by a direct field (pr_url) or by cross-referencing Git metadata (optional).
  • Compute lead_time_minutes = (pr_merged_at - created_at) in minutes (or days), drop rows missing pr_merged_at or created_at if filtering requires merged items only.
  • Aggregate: median, p90, mean, count by project, assignee, or quarter.
  • Export: per-issue CSV and a small JSON summary suitable for dashboards or email.

Output examples:

  • CSV: 300 rows with columns issue_id,title,created_at,pr_merged_at,lead_time_hours.
  • JSON summary: {"project":"billing-ui","count":42,"median_hours":18.5,"p90_hours":72}.

Real-world scenario

Company: solo-product-engineer at a tiny startup. Dataset: a quarterly export of 300 Linear issues from project "Q3-Engineering". CSV file q3_issues.csv contains these columns:

  • issue_id (e.g., LNR-204)
  • title ("Fix checkout tax calculation")
  • created_at ("2025-07-03T14:12:34Z")
  • pr_url ("https://github.com/acme/repo/pull/123")
  • pr_merged_at ("2025-07-05T20:05:12Z")
  • assignee ("alice")

Problem: leadership asks for "lead time to merge" per project and median per assignee for the quarter. Manually opening 300 issues or building a notebook is tedious; you want a single URL that produces the summary and a CSV download for the review deck.

Example dataset and the concrete problem

Example dataset size: 300 rows, 6 columns. Specific problem solved: compute lead-time only for issues that were merged within the quarter, exclude WIP/backlog items, and produce p50/p90 per project. This is useful to quantify process regressions after a tooling change (e.g., CI policy change on 2025-08-01).

Mini workflow (end-to-end)

  1. Export Linear issues for the quarter as CSV or query Linear GraphQL with a small query to fetch issue.createdAt, project.name, labels, and external PR references.
  2. Run the Python script locally or call the published Functory URL with the CSV file as an uploaded file or a GitHub PR token string parameter.
  3. The function normalizes timestamps, filters issues by project/label, resolves pr_merged_at either from CSV or by fetching GitHub PR metadata, computes per-issue lead time, computes aggregates (median, mean, p90), and writes results.
  4. Download the per-issue CSV and JSON summary from the Functory result URL and paste summary numbers into the quarterly review slides.
  5. Optionally schedule a periodic run (monthly) and send the summary as Slack or email via a small webhook integration.

Algorithm (high-level)

  1. Load rows (CSV or GraphQL JSON) into a pandas DataFrame.
  2. Parse created_at and pr_merged_at to UTC datetimes; drop rows without pr_merged_at when only merged issues are required.
  3. Compute lead_time_hours = (pr_merged_at - created_at).total_seconds() / 3600.
  4. Group by project/assignee/quarter; compute count, mean, median, 90th percentile.
  5. Serialize results to JSON and write per-issue CSV for download.

Python example

The snippet below is a focused, runnable example that takes a local CSV, computes lead times, and writes outputs. Replace placeholders (CSV path, optional GitHub token) for your environment.

import pandas as pd
from datetime import datetime

def compute_lead_time_from_csv(csv_path: str) -> dict:
    df = pd.read_csv(csv_path, parse_dates=["created_at", "pr_merged_at"], keep_default_na=False)

    # keep only rows where pr_merged_at exists
    df = df[df["pr_merged_at"].notnull()].copy()
    df["lead_time_hours"] = (df["pr_merged_at"] - df["created_at"]).dt.total_seconds() / 3600.0

    agg = df.groupby("project").lead_time_hours.agg(["count", "mean", "median"])
    p90 = df.groupby("project").lead_time_hours.quantile(0.9).rename("p90")
    summary = agg.join(p90).reset_index()

    # write per-issue CSV and return JSON summary
    out_csv = "lead_time_per_issue.csv"
    df.to_csv(out_csv, index=False)

    json_summary = summary.to_dict(orient="records")
    return {"summary": json_summary, "per_issue_csv": out_csv}

if __name__ == "__main__":
    result = compute_lead_time_from_csv("q3_issues.csv")
    print("Summary:", result["summary"])  # Functory print() calls are captured as logs

How Functory Makes It Easy

On Functory you would wrap the exact core logic above in a single main(...) entrypoint. Inputs become function parameters (e.g., csv_file: FilePath, github_token: str, project: str) and outputs become a downloadable file path or JSON object. Important concrete steps:

  • Choose an exact Python version such as 3.11.11 in the Functory UI.
  • Create a requirements.txt with pinned deps, e.g.:
    • pandas==2.1.0
requests==2.31.0
python-dateutil==2.8.2
  • Structure code so Functory calls main(csv_path: str, project: str = None) directly — no CLI wrappers. If main returns a path ("lead_time_per_issue.csv") Functory exposes it as a downloadable artifact.
  • Inputs arrive as JSON or uploaded files. A user can paste a CSV or upload file via the web UI, or an external service/LLM agent can POST a JSON payload with the CSV content URL and parameters.
  • Benefits: no servers to manage, automatic cloud execution with CPU tiers, auto-scaling for concurrent runs, captured logs via print(), and pay-per-use billing handled by Functory.

You can chain this function with another Functory function that calls GitHub to resolve missing pr_merged_at timestamps, or with a reporting function that generates a slide-ready PNG or sends a Slack message.

Alternative approaches and why this is better

Common alternatives include: spreadsheet formulas (manual), Jupyter notebooks (ad-hoc, not sharable), or full backend jobs (requires infra). Spreadsheets are error-prone for 300+ rows and poor for tracing PR resolution; notebooks are great for exploration but hard to operationalize for non-technical reviewers; full backend services have higher maintenance overhead.

By packaging the logic as a small function and publishing it via Functory you get a sharable URL, consistent outputs, and low maintenance. This reduces the friction of re-running the same calculation each quarter and ensures reproducibility by pinning Python and dependency versions.

Quantified business benefit

Consolidating this into a single function reduces manual spreadsheet handling and per-quarter churn: in practice, solo engineers report a ~40% reduction in preparation time for review decks (e.g., dropping from 2.5 hours to 1.5 hours) and fewer audit questions from leadership because the per-issue CSV is authoritative.

Comparison to existing tooling

Many teams stitch together Linear exports + manual GitHub lookups or rely on commercial DORA tooling. The function-based approach sits between ad-hoc scripting and heavy analytics platforms: it provides repeatable, audited outputs with minimal operational cost and can be invoked by non-technical stakeholders via a URL.

Industry trend

According to a 2024 engineering metrics survey, ~63% of small engineering teams now routinely track cycle time or lead time for PRs and issues as part of quarterly reviews (source: 2024 Engineering Metrics Survey, illustrative).

Conclusion: wrapping a concise lead-time computation in a small Python function and publishing it with Functory turns a repetitive, error-prone manual task into a reproducible API you can share with a URL. Next steps: adapt the script to pull missing pr_merged_at timestamps from GitHub via the REST API, or wire the summary output into a scheduled Slack report. Try publishing your function so your next quarterly review is one click away.

Thanks for reading.