From 98db478b7ba46f9e44446e893cd7acd14f74679f Mon Sep 17 00:00:00 2001
From: Karma Riuk <riukkarma@gmail.com>
Date: Sat, 17 May 2025 09:42:02 +0200
Subject: [PATCH] first draft of parallelization (NOT TESTED YET)

---
 pull_requests.py | 62 +++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 61 insertions(+), 1 deletion(-)

diff --git a/pull_requests.py b/pull_requests.py
index a7e3f36..05c406d 100644
--- a/pull_requests.py
+++ b/pull_requests.py
@@ -1,5 +1,5 @@
 from collections import defaultdict
-import argparse, os, subprocess, docker, uuid
+import argparse, os, subprocess, docker, uuid, concurrent.futures
 from github.Commit import Commit
 from github.ContentFile import ContentFile
 from github.PullRequest import PullRequest
@@ -386,6 +386,66 @@ def process_repo(
                 pbar.update(1)
 
 
+# Wrapper to run in each worker process
+def process_repo_worker(
+    repo_name: str, repos_dir: str, archive_destination: str, cache: dict
+) -> list:
+    # Initialize GitHub and Docker clients in each process
+    token = os.environ.get("GITHUB_AUTH_TOKEN_CRAB")
+    g_worker = Github(token, seconds_between_requests=0)
+    docker_client_worker = docker.from_env()
+
+    # Local dataset to collect entries for this repo
+    local_dataset = Dataset()
+
+    # Call the existing process_repo, but passing the local GitHub and Docker clients
+    # You may need to modify process_repo to accept g and docker_client as parameters
+    process_repo(repo_name, local_dataset, repos_dir, archive_destination, cache)
+    return local_dataset.entries
+
+
+def process_repos_parallel(
+    df: pd.DataFrame,
+    dataset: Dataset,
+    repos_dir: str,
+    archive_destination: str,
+    cache: dict[str, dict[int, DatasetEntry]] = {},
+):
+    """
+    Parallel processing of repos using ProcessPoolExecutor.
+
+    Parameters:
+        df: DataFrame with a 'name' column of repos to process
+        dataset: Shared Dataset to collect all entries
+        repos_dir: Directory root for cloned repos
+        archive_destination: Directory for archives
+        cache: Optional cache of previously processed PR entries
+    """
+    repo_names = df["name"]
+
+    # Use all CPUs; adjust max_workers as needed
+    with concurrent.futures.ProcessPoolExecutor(max_workers=os.cpu_count()) as executor:
+        # Map each repo to a future
+        future_to_repo = {
+            executor.submit(process_repo_worker, name, repos_dir, archive_destination, cache): name
+            for name in repo_names
+        }
+
+        # Iterate as repos complete
+        for future in tqdm(
+            concurrent.futures.as_completed(future_to_repo),
+            total=len(future_to_repo),
+            desc="Processing repos",
+        ):
+            repo_name = future_to_repo[future]
+            try:
+                entries = future.result()
+                dataset.entries.extend(entries)
+                dataset.to_json(args.output)
+            except Exception as e:
+                tqdm.write(f"[ERROR] Repo {repo_name}: {e}")
+
+
 def process_repos(
     df: pd.DataFrame,
     dataset: Dataset,